...Proceeding of Recent Trends in Computations and Mathematical Analysis in Engineering and...

Recent Trends in Computations

and Mathematical Analysis

in Engineering and Sciences-2015

“CRCMAS 2015”

Edited by-

Dr. B. B. Chattopadhyay

Principal, Govt. Polytechnic Silli

Samarjit Roy

Assistant Professor, Dept. of Computer Sc. & Engg.,

Techno India, Silli,

Organized by-

Govt. Polytechnic Silli, Ranchi, Jharkhand

2015

International E - Publication

www.isca.me , www.isca.co.in

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ISBN: 978-93-84659-04-2

Proceeding of Recent Trends in Computations and Mathematical Analysis in Engineering and Sciences-2015

Ranchi, Jharkhand, India 20th

-21st Nov 2015

International Science Congress Association iii

CONFERENCE ORGANIZATION

President

Sri A.K. Singh, IAS, Hon'ble Secretary, Dept. of Higher & Tech. Edu,

Govt. of Jharkhand.

Chief Patrons

Mr. G. Roy Choudhury, TIG

Mr. Mohit Chattopadhyay, Director, Jharkhand Project, TIG

Organizing Chair

Dr. B. B. Chattopadhyay, Principal, Govt. Polytechnic Silli

(+91-8405000384)

Email: [email protected]

Convener

Samarjit Roy

(+91-8405058525/ 8902041490)

Email: [email protected]/ [email protected]

Co-Convener

Nilanjan Sil

(+91-9905156745/ 9431355883)

mailto:[email protected]/



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International Science Congress Association iv

Advisory Committee

Prof. (Dr. Engg.) K.P. Ghatak, UEM, Kolkata

Mr. B.M. Kumar, JCST, Jharkhand

Dr. Sudipta Chattopadhyay, Jadavpur University, Kolkata

Dr. Partha Pratim Roy, GGU, Bilaspur

Ms. Sujata Ghatak, IEM, Kolkata

Program Committee

Dr. Debashis De, WBUT, Kolkata

Dr. Smita Dey, Ranchi University, Ranchi

Prof. (Dr.) S.R. Kumar, NIFFT, Ranchi

Prof. (Dr.) Bani Mukherjee, ISM Dhanbad, Jharkhand

Prof. (Dr.) M.K. Singh, Magadh University, Gaya

Dr. Chandan Koner, B.C. Roy Engineering College, Durgapur

Dr. B. B. Sarkar, Techno India Salt Lake

Mr. Sudipta Chakrabarty, Techno India Salt Lake

Dr. Kunal Das, BPPIMT, Kolkata

Mr. Soumik Das, Techno India Salt Lake

Mr. Bishaljit Paul, Govt. Polytechnic Silli

Mr. Souvik Paul, Techno India, Salt Lake

Organizing Committee

Dr. Arindam Sarkar

Rajiv Ranjan Sah



-21st Nov 2015

International Science Congress Association v

Utpal Kumar Pal

Aanabick Kolay

Somnath Nag

Chayan Chakraborty

Suprakash Jana

Samir Hazam

Sudip Mondal

Debashish Roy

Rajesh Guria

Sumit Kumar

Finance

Arun Kanti Manna

Sayan Kundu

Sayantan Ghorai

Suraj Das

Sangeet Panda (Student)

Members

Diwakar Kumar, Student

Uttam Anand, Student

Babli Kumari, Student



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International Science Congress Association vi

INDEX

Sr. No. Title & Authors Name Page No.

1 People Counting From an Image Using Image Processing Technique

Pijush Kanti Kumar1, Saurabh Singha

2, Premjit Sen

3

1

2 Big Data Analytics in Banking Sector

Yuvika Priyadarshini 5

3

Hydrodynamic Natural Convection Slip Flow of A Nanofluid in the Presence of Newtonian Heating and

Non-Linear Thermal Radiation

Goutam Kumar Mahato

9

4 A Global Prospective of Cloud Computing in Governance

Sudhanshu Maurya 10

5 A Foremost Survey on State-of-The-Art Computational Music Research

Sudipta Chakrabarty1, Samarjit Roy

2 and Debashis De

3

16

6

The Relationship between Climatic Factor with the ebola virus Disease Outbreak in Guinea, Liberia and

Sierra Leone, 2014-2015

Roshan Kumar and Smita Dey

26

7

Developing Local-Area Networks Using Pervasive Theory

Jyoti Kumari, Nisha Kumari,

Priyanka Kumari

and Arun Kanti Manna

31

8

Visualizing Local-Area Networks and E-Commerce

Dukhit Mahato, Deepak Kumar Paswan, Nabaranjan Mahato, Shivshankar Singh Munda

and Arun Kanti

Manna

37

9 Modeling in Gis with Spatial Data

Swagata Ghosh1, A.K.Upadhaya

2

42

10

Structure, Microstructure and Dielectric Properties OF(1-x)Ba0.06(Na1/2Bi1/2)0.94TiO3-xNaTaO3 Lead-Free

Ceramics

Sumit Kr. Roy1,a)

, S. Chaudhuri1, S.N.Singh

2 and K. Prasad

3

46

11 An Understanding OF 802.11 Mesh Networks

Suraj Kumar, Vishal Kumar Sharma, Sandip Kumar Mehta, Amit Mandal and Arun Kanti Manna*

51

12 Approaches to Implement Authentication and Encryption Techniques in Cloud Computing

Arun Kanti Manna1 and Chandan Koner

2

56

13 Proposed Artificial Intelligence Based Authentication of User in Remote System 1Biswajit Mondal,

2Priyanka Roy and Chandan Koner

1

61

14

Transmission Congestion Management, Pricing and Locational Marginal Pricing in the Deregulated Power

System

Bishaljit Paul

65

15 A Survey on the Generalizations of Association Scheme

Pankaj Kumar Manjhi and Arjun Kumar 70

16

A Fixed Point Theorem Satisfying Compatibility

Dhruva Narayan Singh

73

17 Dispersal of Arsenic into Damodar River: A Mathematical Model

Shafique Ahmad1 and Shibajee Singha Deo

2

76

18 A Survey of Vertical Handoff Schemes in Vehicular Ad-Hoc Networks

Sadip Midya, Koushik Majumder and Asmita Roy 82

19

Energy Gain of Signal Wave and That of Idler Wave Due to Nonlinear Parametric Interaction in

Piezosemiconducting Medium: A Numerical Approach

Pravat Kumar Mandal

90



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International Science Congress Association vii

20 Skin Lesion Analysis and Treatment Monitoring Using Image Processing Technique: A Review

Ishita Bhakta1 and Santanu Phadikar

2

99

21

An Understanding of Local-Area Networks Using Catalanelbow

Kumari Asima Mahato, Baby Kumari, Sunita Kumari, Sushma Kumari

and

Arun Kanti Manna

105

22 Affect detection from facial expression: A review

Aritra Ghosh and Saikat Basu 111

23 A Review on Facial Emotion Recognition System

Zahir Abbas Rahaman and Saikat Basu 119

24 Prediction in Stock Market Through Mathematical Modelling

Mrinalini Smita 123

25

Mathematical Model for Deteriorating Inventory - Items Under Trade Credit And Inventroy Level

Dependent Demand Rate

Dhrub Kumar Singh1 and Sahadeo Mahto

2

125

26

Theoretical Study of Spin-Hamiltonian Parameters for the four-Coordinated Nickel (II) Ion in Malonato

Complexes

Mitesh Chakraborty1, Vineet Kumar Rai

2 and Vishal Mishra

3

133



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International Science Congress Association 1

People Counting From an Image Using Image Processing Technique

Pijush Kanti Kumar1, Saurabh Singha

2, Premjit Sen

3

IT, Government College of Engineering & Textile Technology, Serampore, West Bengal, INDIA

Computer Science and Technology, Indian Institute of Engineering Science and Technology, Shibpur

IT, Government College of Engineering & Textile Technology, Serampore, West Bengal, INDIA

Abstract

Today people counting are a very useful task. In many cases we can measure the number of present people in an open environment.

This paper outlines a model which is useful for counting people on a ground plane through image processing technique. In the

literature overview, it was found that the most reliable and accurate sensor was the video camera but it was also the most expensive

and hard over counting. Several papers has been published this last ten years for counting people by using video processing with

different methods. Most of them are taking video frames as input for counting the people in an open environment and they are using

several methods or algorithms. But we are introducing a density based counting of people using image processing technique in

which input is an image. Many papers has been published on this topic but our new invention in this paper is we are cropping the

input image into several blocks of same size and then counting the number of people for each block and adding the results to get the

total number of people for the given input image more accurately.

Keywords: MATLAB; Image processing; People counting; Density estimation

Introduction

In recent years, the application of image processing techniques in people counting has been investigated in many ways by several

researchers. This is not a simple task, there are some situations difficult to solve even with today's computer speeds (the algorithm

has to operate in real-time so it makes limits for the complexity of methods for detection and tracking). And one of the most

difficult, is people occlusions. Real-time people counting can be a very useful information for several applications like security or

people management such as pedestrian traffic management or tourists flow estimation. People counting system is important for

marketing research also. When people entering or exiting of the field of view in group, it is very hard to distinguish all the humans

in this group. Thanks to all those research, many organizations propose people counting based on video camera. Their system or

models are very accurate and reliable but those are also expensive. So the goal of the entire project is to make a very cheap model

which is able to count people more accurately from an image. And may be in future it will become a reliable people counting

system.

Experimental Environment

Matlab: MATLAB is a high-performance language for technical computing. It integrates computation, visualization, and

programming in an easy-to-use environment where problems and solutions are expressed in familiar mathematical notation.

MATLAB is widely used in all areas of applied mathematics, in education and research at universities, and in the industry.

MATLAB stands for MATRIX LABORATORY and the software is built up around vectors and matrices. This makes the software

particularly useful for linear algebra but MATLAB is also a great tool for solving algebraic and differential equations and for

numerical integration. MATLAB has powerful graphic tools and can produce nice pictures in both 2D and 3D. It is also a

programming language, and is one of the easiest programming languages for writing mathematical programs. MATLAB also has

some tool boxes useful for signal processing, image processing, optimization, etc. Certainly, you can write data evaluation

programs in other programming languages such as Visual Basic, C++, or Java, but MATLAB is a language designed especially for

processing, evaluating and graphical displaying of numerical data.

Image processing: Image processing is a method to convert an image into digital form and perform some operations on it, in order

to get an enhanced image or to extract some useful information from it. It is a type of signal dispensation in which input is image,

like video frame or photograph and output may be image or characteristics associated with that image. Usually Image Processing

system includes treating images as two dimensional signals while applying already set signal processing methods to them. It is

among rapidly growing technologies today, with its applications in various aspects of a business. Image Processing forms core

research area within engineering and computer science discipline too.

Procedure to Count People: Here we are taking an input image of any size, then we will convert it into a fixed sixe image and

then cropping it into 16 blocks. So at first, take the input image and then resize it into a fixed size (800px X 800px). Resizing is

necessary because if we do not change the size of the uploaded image, we can’t crop it into 16 patches of equal size. Now after

resizing the input image crop it into blocks. Then convert the each cropped image from RGB to LAB to perform the transformation



-21st Nov 2015


and apply a threshold value. Use Otsu’s method which chooses the threshold to minimize the interclass variance of the black and

white pixels. After that convert it to the binary image which replaces all pixels in the input image with luminance greater than level

with the value 1 (white) and replaces all other pixels with the value 0 (black).Specify level in the range [0,1]. This range is relative

to the signal levels possible for the image's class. Therefore, a level value of 0.5 is midway between black and white, regardless of

class. Now draw bounding box using blob measurement and determine the density and number of people for each block.

Density is estimated from the equation given below-

Density=cc.NumObjects / (size (bw,1) * size(bw,2)) ;

Here cc is the connected component and it is used to finding the density of the people. And bw is the binary image.

Experimental Results

We have done many experiments on this project using various crowd images. Some of those results are shown below.

Figure-1

Results of people counting for cropped images



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Figure-2

Results of people counting for the overall images

18 16 12 14

12 13 7 10

11 9 9 5

11 8 11 9

Actual Result



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15 19 16 21

18 19 14 13

17 11 14 15

16 18 13 17

Estimated Result after Cropping

Conclusion

In this paper an image processing algorithm, suitable for people counting from an image has been suggested and analysed. In this

model we have made a graphical user interface (GUI) gives a value which is approximately 80% accurate. This model will be

useful to count people from an image of any public rally, procession, airport, railway station, cultural program etc. The application

model is easy to operate and also cheap model.

References

1. Hsiang-Chieh Chen; Ya-Ching Chang; Nai-Jen Li; Cheng-Feng Weng and Wen-June Wan , “Real-time people counting

method with surveillance cameras implemented on embedded system”, WCECS 2013, 23-25 October, 2013, San Francisco, USA.

2. Djamel Merad; Kheir-Eddine Aziz; Nicolas Thome, “Fast people counting using head detection from skeleton graph”,978-

0-7695-4264-5/10 $26.00 © 2010 IEEE DOI 10.1109/AVSS.2010.77

3. Muhammad Arif; Sultan Daud;SalehBasalamah, “Counting of People in the Extremely Dense Crowd”, IAES International

Journal of Artificial Intelligence (IJ-AI) Vol. 2, No. 2, June 2013, pp. 51~58 ISSN: 2252-8938.

4. Monali P. Patil; Varsha R. Ratnaparkhe; “Object Counting Based On Image Processing: FPGA Approach”, IOSR Journal

of VLSI and Signal Processing (IOSR-JVSP) Volume 4, Issue 2, Ver. I (Mar-Apr. 2014).

Books:

“Digital image processing using MATLAB” by Rafael C. Gonzalez, Richard E. Woods and Steven L. Eddins,Gatesmark

Publishing.

Websites:

[1] http://www.mathworks.com/

[2]http://imageprocessingblog.com

http://www.mathworks.com/

http://imageprocessingblog.com/



-21st Nov 2015


Big Data Analytics in Banking Sector

Yuvika Priyadarshini Jharkhand Rai University, Ranchi, Jharkhand, INDIA

Abstract

In the current business world, Data and its applications are of great importance for the organizations for achieving competing

advantage and defined as a new strategic approach to innovation and a potential element for creating larger market share.

Understanding the Data Management (DM) process in terms of banking sector will highlight how it influences organizational

performance. In a developing country, it is also showing signs of competition and improved performance through DM. In response

to this need, this research explores the key processes and technologies of DM being used in the banks in order to give an insight for

bankers and strategist to understand its importance.

Keywords:

Introduction

In information era, Data is becoming a crucial organizational resource that provides competitive advantage and giving rise to DM

initiatives. Many organizations have collected and stored vast amount of data. The computerization of financial operations,

connectivity through World Wide Web and the support of automated software hasentirely changed the fundamental concept of

business and the approach the business operations are being approved. The banking sector is not exclusion to it. It has also

witnessed a tremendous transform in the way the banking operations are carried out. Since 1990’s the intact concept of banking has

been transferred to centralized databases, online transactions and ATM’s all over the world, that has ended banking system

technically strong and more customer oriented. In the current day environment, the massive amount of electronic data is being

maintained by banks around the sphere. The huge size of these data bases constructs it impossible for the organizations to analyze

these databases and to retrieve useful information as per the requirement of the decision makers3,5

. Since 1980’s the banking sector

is incorporating the perception of Management Information System, through which banks are generating various kinds of reports,

which are then presented and analyzed for the decision making within the organization. However these reports obtainable in the

summarized structure can be used by the governing authorities2. While dealing with banking sector, which itself is an information

intensive industry, is quite cumbersome assignment. The banks at present generate reports from the periodic paper reports and the

statements submitted by various constitute units. Such reports have a high degree of error, due to data being recorded and

interpreted by various parties at various levels2. Moreover the Total Branch Computerization (TBC) software packages being used

at various branch levels are transaction oriented, as these were designed keeping day to day transactions in mind. Designing the

new MIS or restructuring the existing ones would not be possible by just replacing the existing Total Branch Computerization

packages. The solution seems to be in incorporating the concept of data warehousing and data mining. Due to the vast expansion of

the horizons of the data and its multivariate uses, the organizations and the individuals are feeling a need for some centralized data

management and retrieval system. The centralization of the data is required basically for better processing and in turn facilitating

the user access and analysis.

Today the bank is focusing on big data, but with an emphasis on an integrated approach to customers and an integrated

organizational structure. It thinks of big data in three different “buckets”—big transactional data, data about customers, and

unstructured data. The primary emphasis is on the first two categories. With a very large amount of customer data across multiple

channels and relationships, the bank historically was unable to analyze all of its customers at once, and relied on systematic

samples. With big data technology, it can increasingly process and analyze data from its full customer set.

Other than some experiments with analysis of unstructured data, the primary focus of the bank’s big data efforts is on understanding

the customer across all channels and interactions, and presenting consistent, appealing offers to well-defined customer segments.

For example, the Bank utilizes transaction and propensity models to determine which of its primary relationship customers may

have a credit card, or a mortgage loan that could benefit from refinancing at a competitor. When the customer comes online, calls a

call center, or visits a branch, that information is available to the online app, or the sales associate to present the offer. The various

sales channels can also communicate with each other, so a customer who starts an application online but doesn’t complete it, could

get a follow-up offer in the mail, or an email to set up an appointment at a physical branch location.



-21st Nov 2015


Data Mining For Analysis of Credit Card Transaction

With the advent of new technologies, people are increasingly showing their inclination towards electronic means of payment. Credit

card margins continue to be squeezed by a combination of high charge-off and rising account acquisition costs. Record levels of

delinquencies, personal bankruptcies and resulting charge-off coexists in a saturated market where offers are quickly becoming

commodities. In this environment, accurate risk prediction is of utmost important. In order to remain competitive, credit card

issuers are turning to data mining to uncover information from their massive databases. This application deals with credit card from

a nationalized bank. Mining the credit card data not only discover the customer segments but also helps extracting additional hidden

information that may guide the bank in developing models tailored for specific business goals, such as to detect fraud or accurately

target customers.

Credit card issuers are engaged in improving response rates while also identifying the best candidates in terms of profitability and

risk. Issuers that most accurately match customer risk profile and behavioral attributes with differentiable products will seize a

competitive advantage

The drivers for data mining the card transactions are: i. Determine whom to solicit as client, possibly with pre-approved credit

limits. ii. Customer retention – find and analyze which customer characteristics will help to offer services that will keep the best

credit card customers for the long term. iii. Customer attrition – discover which customers are most likely to leave for lower

interest- rate cards. iv. Fraud detection – find purchase patterns and trends to detect fraudulent behavior at the time of credit card

purchases. v. Payment or default analysis – identify specific patterns that will help predict when and why cardholders default on

their monthly payments. vi. Market segmentation – correctly segment cardholders into groups for promotional and evaluation

purposes.

Data mining makes the above possible by organizing the bank’s credit card holders into related groups and then examining the past

credit history, the purchasing profile, the payment profile of each group, merchant details, etc. It uncovers vital knowledge hidden

in the database so that the issuers can improve marketing of card products and related services, retain and attract good customers,

increase market share, reduce cost, and increase return on investment. Applications of big data analytics are risk management,

campaign analysis, customer profile analysis, loyalty analysis, customer care analysis, business performance analysis, sales analysis

and profitability analysis are some of the areas.

Data Warehousing

The development of management support systems is characterized by the cyclic up and down of buzzwords. Model based decision

support and executive information systems were always restricted by the lack of consisted data. Now-a-days data warehouse tries to

cover this gap by providing actual and decision relevant information to allow the control of critical success factors. A data

warehouse integrates large amounts of enterprise data from multiple and independent data sources consisting of operational

databases into a common repository for querying and analyzing. Data warehousing will gain critical importance in the presence of

data mining and generating several types of analytical reports which are usually not available in the original transaction processing

systems.

Banking being an information intensive industry, building a Management Information System is a gigantic a task. It is more so for

the public sector banks, which have a wide network of bank, branches spread all over the country. It becomes more difficult due to

prevalence of varying degrees of computerization. At present, banks generate MIS reports largely from periodic paper

reports/statements submitted by the branches and regional/zonal offices. Except for a few banks, which have been using technology

in a big way, MIS reports are available with a substantial time tag. Reports so generated have also a high margin of error due to data

entry being done at various levels and likelihood of varying interpretations at different levels.

Though computerization of bank branches has been going at a good pace, MIS requirements have not been fully addressed to. It is

on account of the fact that most of the Total Branch Computerization (TBC) software packages are transaction processing oriented.

In most banks large databases are in operation for normal daily transactions. In most cases, these operational databases have not

been designed to store historical data or to respond to queries but simply to support all the applications for day-to-day transactions.

The present information systems evolved from the legacy of the old. They exist as collection of separate islands of information that

have developed as response to certain operational needs. They have not been designed to meet the information requirement on real

time basis of decision-makers cutting across departments. Due to contingent nature of the developmental process, the hardware and

software platforms that have been used in these operational information systems lack compatibility. As a result whenever decision-



-21st Nov 2015


makers information requirements have to be met by pulling out data from various operational databases, special efforts are needed

to be made for collating these data. Another important consequence of the disparate nature of the existing system is the lack of

subject orientation in the system. This in turn reduces the utility of the system to the decision makers. Another major shortcoming

of the present system is its inability to provide consistent data for different variables for a reasonably long duration. This apart, the

most critical deficiency of the present information system is the lack of information about the availability of data. In this

connection, an application of Data Warehousing along with Online Analytical Processing (OLAP) and Data Mining techniques

appears to be the appropriate solution. Further data warehouses provide a central repository for large amounts of diverse and

valuable information.

Conclusion

Banks can use technology to improve their performance and they can get the sustainable competitive advantage. It’s important to

remember that the primary value from big data comes not from the data in its raw form, but from the processing and analysis of it

and the insights, products, and services that emerge from analysis. The sweeping changes in big data technologies and management

approaches need to be accompanied by similarly dramatic shifts in how data supports decisions and product/service innovation.

This integration will not only facilitate the capturing and coding of knowledge but also enhances the retrieval and sharing of

knowledge across the bank to gain strategic advantage and sustain in competitive market.

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6. Alavi M. and Leidner D.R., ‘Review: Knowledge Management and Knowledge Management Systems: Conceptual

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Paper, 1-10 (2002)



-21st Nov 2015


Hydrodynamic Natural Convection Slip Flow of A Nanofluid in the Presence

of Newtonian Heating and Non-Linear Thermal Radiation

Goutam Kumar Mahato Department of Mathematics, Centurion University of Technology and Management (CUTM), Bhubaneswar, Odisha, INDIA

Abstract

Natural convection flow of a viscous, incompressible, and electrically conducting nanofluidin the presence of nonlinearradiative

heat transfer, hydrodynamic slip and Newtonian heating is studied. The Brownian diffusion and thermophoresiseffects are taken

into consideration to describe the nanofluidmodel. The governing nonlinear partial differential equations are transformed to a set of

nonlinear ordinary differential equations which are then solved using spectral local linearization method (SLLM). Numerical values

of fluid velocity, fluid temperature and species concentration are displayed graphically versus boundary layer coordinatefor various

values of pertinent flow parameters whereas those of skin friction, rate of heat transfer and rate of mass transfer at the plate are

presented in tabular form for various values of pertinent flow parameters. Such fluid flow finds applications in many engineering

devices including geothermal heat source pump and in cooling of electronic devices and stretched wires.

Keywords: Nanofluid flow; natural convection; Newtonian heating; non-linear thermal radiation; velocity slip.



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A Global Prospective of Cloud Computing in Governance

Sudhanshu Maurya Department of Computer Science and Engineering, Jharkhand Rai University, Ranchi, Jharkhand, INDIA

Abstract

Cloud Computing is an actual accepted appellation in this avant-garde and computer apple in IT band-aid which is provided as an

account over the web instead of chump attributable and affairs the solution. It is an ample accumulation of accord of computers.

Over a decade of analysis it achieves in virtualization, broadcast computing, account accretion and networking. It creates account

aggressive architectonics by accoutrements software and platforms as services. It bargain advice technology for end –user on appeal

casework and abounding of the added things accompanying to it. Technologies like filigree, cluster and Cloud accretion has all

aimed for accoutrements admission to ample amount of computer in a virtualized manner like invisible, by accession assets and

alms individual arrangement examination and added over in accession to that one of the capital aim of these technologies is

Delivering accretion as a Utility.

Keywords: Cloud; Computing; Virtualization; Networking; Software, Utility.

Introduction

Cloud computing is the approaching of advice technology. It embodies all the big trends in the design and use of computer

architectures. And it ties carefully to added trends such as big data and the “Internet of things”. It is an aggregation of technologies

and trends that are authoritative IT infrastructures and applications added dynamic, added modular, and added consumable. It lets

organizations ramp up new casework and reallocate accretion assets rapidly, based on business needs. It gives users self-service

admission to accretion resources, while advancement appropriate levels of control. And, done right, it accommodate the agency to

administer beyond amalgam accretion environments, both on- and off-premise, based on cost, accommodation requirements, and

added factors.

When you store your photographs online rather than on your home machine, or utilization webmail or a person to person

communication webpage, you are utilizing a "distributed computing" administration. In the event that you are an association, and

you need to use, for instance, an online invoicing account instead of after light the centralized one you accept been application for

abounding years, that online invoicing account is a “cloud computing” service.

Distributed computing alludes to the conveyance of registering assets over the Internet. As opposed to keeping information all alone

hard drive or overhauling applications for your needs, you utilize an administration over the Internet, at an alternate area, to store

your data or utilize its applications. Doing so may offer climb to certain protection suggestions.

Figure-1

Cloud (A graphical view)

Today computers are acclimated by the government sectors, industries, military, railway everyone. An accumulation of computers

works as an individual computer to accommodate and abstracts and added applications to user on the internet. An arrangement

which is already accessible in the Billow of computer that works as the IP abode in the server that connects the several systems.

These accommodate an all-inclusive accumulator adequacy and ample calibration accumulation of collaboration. In adjustment to



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analytic the problems like allegory accident in medical accessories and banking sectors, even in computer amateur the users may

allurement through web. The ample networking accumulation of servers uses alone bargain chump PC technology. It includes

specialized abstracts access that candy affairs of them. Our capital albatross accepting authoritative abiding that all our agent accept

actual and appropriate software and accouterments for their jobs. Anybody can buy the computer but it is not enough-Whenever

you are accepting a new befalling you accept to buy software which is accepting altered versions or accomplish abiding your

accepted software authorization allows to added user.

Figure-2

Advances of cloud computing

Web-based account which entertains all the programs that the user charge for his job. It could be alleged billow accretion and it can

change the absolute computer industry. Local computers accept to do actual abundant jobs if it comes to active applications .Instead

of that the arrangement can handles them both accouterments and software users , which can as simple as web browser and the

server will yield affliction of it by active all the programs. The software and accumulator does not abide on your computer for aegis

reasons. It’s on the casework billow computing.

Deployment Models of Cloud

There are different models of cloud i.e.: public cloud, community cloud, private cloud and hybrid cloud1. A public cloud is a cloud

computing model in which resources like application and storage is available to general public over the internet. Community cloud

shares infrastructure between different organizations from a specific community with common concerns like compliance,

jurisdiction, security etc. Private cloud is basically an enterprise computing architecture, also called as internal cloud in which they

provide service to a limited number of users. Hybrid cloud is a combination of more than one clouds. It manages a heterogeneous

set of resources wherever they are located2.

Cloud Computing in Government

The development in cloud computing are leading many outside and inside of the public sector to ask, “If it works for business, why

not for government?”3. In an era of virtualisation, any time anywhere services and on-demand network, the phenomenon of cloud

computing is gaining traction across governments, industries and consumers. Cloud computing helps to lower the cost and

environmental impact of government operations, create a more secure computing environment, and drive innovation within the

government by pooling IT resources across organizational boundaries. IT services and infrastructure are shared by multiple

customers, with different physical and virtual resources dynamically assigned and reassigned in real time according to customer

demand (e.g., storage, processing, network throughput, and virtual machines)

The accelerated adoption of IT in government is now uniquely positioned to gain from this growing technology. There is an

opportunity for the government and industry to partner, to drive adoption of cloud in India and build India as a major hub for

delivering cloud solution. Cloud computing has also been identified as one of the thrust areas in the national IT policy4.



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Figure-3

Deployment Models of Cloud

Figure-5

Cloud based E-governance

Figure-5 shows how the cloud concept can be used to integrate the functioning among various government agencies and

departments.



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Government of India is effectively advancing Cloud computing through the development of different test informal lodging dispatch

of various Cloud services, for example: Cloud grids, e-governance and so on. The reception of cloud computing services, approx.

one billion US dollar in 2014, which was driven by government activities like Unique Identification Authority of India (UIDAI)

venture and e-governance.

National Association of Software and Services Companies (NASSCOM) imagined and dispatched eGovReach Portal -

http://egovreach.in - an arrangements trade entryway to cultivate closer collaboration and unite between the Government and

industry. Mr. R. Chandrasekhar, the then Secretary-Information Technology, Government of India propelled the entrance, in

August 2010. It has been produced by a start-up part organization of NASSCOM, and is facilitated on the Cloud stage.

Figure-4

Architecture of Meghraj

Source : http://www.nasscom.in/government-india%E2%80%99s-cloud-initiative?fg=248518

The portal has manufactured a rich registry of administration suppliers in the eGovernance ecosystem. The portal now has day by

day reports on tenders and opportunities from the Central and State Governments, districts, local bodies, banks and few public

sector undertaking. The portal likewise gives most recent stories on e-Governance, both at the Central and State levels [09].

In order to utilise and harness the benefits of Cloud Computing, the Indian government in a major move has launched an important

initiative – “GI Cloud” which has been coined as ‘Meghraj’. Task Force was constituted by Department of Electronics and

Information Technology (DeiTY) with a focus to bring out the strategic direction and implementation roadmap of GI Cloud,

leveraging the existing or new infrastructure5.

Meghraj, the National cloud initiative, aims to accelerate delivery of e-services provided by the government and to optimise ICT

spending of the government. In the first phase of implementation, National Informatics Centre (NIC) cloud service was launched in

Delhi in December 20136. MeghRaj has encompass a set of discrete cloud computing environments spread across multiple

locations, which is built on existing or new (augmented) infrastructure. It will follow a set of common protocols, guidelines and

standards issued by the Government of India.

The National Cloud has help the departments to procure ICT services on demand in the OPEX model rather than investing upfront

on the CAPEX. The Cloud Services available are Infrastructure as a Service (IaaS), Platform as a Service (PaaS), Software as a

Service (SaaS) and Storage as a Service (STaaS).

Some of the features of the National Cloud include self-service portal, multiple Cloud solutions, secured VPN access and multi-

location Cloud7.

International Platform

http://www.nasscom.in/government-india%E2%80%99s-cloud-initiative?fg=248518



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Cloud computing is not just an Indian phenomenon. Indeed, cloud movements are taking place in governments around the world.

For instance, In the European Union presently, the European Commission and several member states are taking actions perceived

by many as leading toward the creation of a cloud-based, common infrastructure for IT in member states8. We are however already

seeing significant cloud models being used in areas around the world. In Singapore, the government started its trip to cloud as ahead

of schedule as 2005 with the execution of an entire-of-government shared infrastructure which gives processing computing resource

to government agencies on an ‘as a service’ membership model10,11

. James Kang, Assistant Chief Executive from the Infocomm

Advancement Authority of Singapore (IDA), says that, “from here, it is the part of IDA to conceptualize, characterize, and execute

a central government cloud to encourage government offices' appropriation of cloud computing." This central government cloud,

called "G-Cloud," says Kang, will turn into the core of entire of-government framework”11,16

. In the UK, the nation published its

Digital Britain report in 2009, an archive laying out that country's roadmap for expecting and keeping up an administration part in

an inexorably advanced worldwide environment12

. Result of migration to the cloud resulted in reducing cost (up to 90%), system

flexibility, improved capabilities and complete process automation. So, customer queries and requests are handled in real time and

it allows users to access data to integrate with other online solutions like website and blogs13

. Currently cloud-based solution made

upgrades to the site takes only a day, which previously took up to nine months to complete14

. Therefore, the availability of the

online solutions like website and blogs increased up to 99.99 % that is per month almost zero downtime. The assigned budget to

www.usa.gov reduced to only $ 650.000 American dollars per year 15

. In Canada, shared Services is a government organization

concentrating on recognizing and acknowledging investment funds and efficiencies over the Canadian Federal Government17

.

Declared in August 2011, the activity expects to cut the aggregate number of government server farms from more than 300 to 20,

while paring down the quantity of email services from 100 to one and only. Cloud-based procedures and innovations, says KPMG's

Cochrane, "will essentially assume a prime part”. In July 2011, the United State Office of Management and Budget included

impressive substance, responsibility, and straightforwardness to its November 2010. Cloud First policy declaration, which obliges

offices to offer need to electronic applications and services or administrations. In a discourse given by OMB's Chief Performance

Officer, it was authoritatively declared that as of spending plan year 2012, all new federal government IT arrangements must

receive cloud innovations “wherever a protected, financially savvy, reliable cloud alternative exists”.

In Japan, the government is undertaking a cloud computing initiative named “Kasumigaseki Cloud”14

. As per the Ministry of

Internal Affairs and Communications (MIC) Japan, Kasumigaseki Cloud18

will provide greater information and resource sharing

and promote more standardization and consolidation in the IT resources of government19

. This Cloud is part of the “Digital Japan

Creation Project”. It represents a governmental strength aimed at using IT investments (valued at just under 100 trillion yen) to help

spur economic recovery by producing thousands of new IT based jobs in the upcoming years and making the Japan’s IT market

double by 202020

. In Thailand, Government Information Technology Service (GITS) is building up a private cloud for use by Thai

government organizations. The GITS has effectively settled a cloud-based email administration, and it is wanting to include

Software as a Service (SaaS) very soon. GITS trusts that such solidification will enhance administration offerings for government

organizations, while at the same time cutting their general IT costs "considerably"21

.

In South Africa, while the nation "confronts an immense test in that the condition of preparation of its processing framework, of its

subjects, and its administration, isn't very cloud-prepared," says Isaac Mophatlane, Chief Executive at frameworks integrator

Business Connection Group LTD, the official does trust that state organizations are currently proceeding onward creating measures

that will help catalyse variation. Consumer appropriation and telecoms framework will likewise have influence. “South Africa is

one of the quickest developing markets for BlackBerry and for Apple”, notes Mophatlane. As citizen interest for mobile

advancements expands, framework will have a tendency to develop in lockstep. So conditions for cloud in government are

progressing8.

Conclusion

In this paper we attempted to point the innumerable advantages like cost effectiveness, adaptability, legitimate security and

integration that cloud computing gives, has changed over it to appropriate choice for use in e-government. It could be inferred that

developed and even developing nations have basic need to make e-Government to decrease expenses furthermore having

Sustainable Development in this economic and basic circumstances and the most ideal approach to finish this matter is the

utilization of green and shoddy innovation which is the cloud computing. The cooperation of nations with one another on

specialized and lawful issues is code key for accomplishing e-government in view of cloud computing as soon as possible. It is the

best choice to execute or enhance the government services in healthcare, education and social upliftment of the citizens of the

nations.

References



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1. R. Buyya, C.S. Yeo, S. Venugopal, J. Broberg, and I. Brandic, Cloud computing and emerging IT platforms: Vision, hype, and

reality for delivering computing asthe 5th utility, Future Generation Computer Systems, 25, 599À 616, (2009)

2. Cloud Computing, Wikipedia

3. Condon, Stephanie, Is Washington ready for cloud computing? CNet News, February 25, 2009. Retrieved March 2, 2009,

from news.cnet.com/ 8301-13578_3-10172259-38.html

4. https://cloud.gov.in/aboutus.php

5. http://www.iamwire.com/2013/10/indian-government-launches-national-cloud-initiative-meghraj/21503

6. http://www.nasscom.in/government-india%E2%80%99s-cloud-initiative?fg=248518

7. http://www.nextbigwhat.com/indian-government-national-cloud-meghraj-service-297/

8. Herhalt J., Cochrane K., “Exploring the Cloud : A Global Study of Governments’ Adoption of Cloud”, Available Online at:

https://www.kpmg.com/ES/es/ActualidadyNovedades/ArticulosyPublicaciones/Documents/Exploring-the-Cloud.pdf

9. NASSCOM Annual Report 2010-2011, URL: http://www.nasscom.in/sites/default/files/NASSCOM_Annual_Report_2010-

11.pdf

10. Cloud Computing for Singapore Government, http://www.egov.gov.sg/egov-programmes/programmes-by-government/cloud-

computing-forgovernment

11. Malini Nathan, Cloud Computing for Singapore Government, IDA Singapore,

https://www.ida.gov.sg/~/media/Files/Archive/News%20and%20Events/News_and_Events_Level2/20120508123036/CloudC

omputingFactsheet.pdf

12. D.C. Wyld, Moving to the cloud: An introduction to cloud computing in government. Washington, DC: IBM Center for the

Business of Government, November (2009)

13. Toby Velte, Anthony Velte, Toby J. Velte, Robert C. Elsenpeter. Cloud Computing: A Practical Approach. New York:

McGraw Hill Professional, 274 (2010)

14. David C. Wyld, The Cloudy Future of Government IT: Cloud Computing and the Public Sector around the World,

International Journal of Web and Semantic Technology (IJWesT), 1(1), 1-20 (2010)

15. Kundra V., State of public sector cloud computing. Federal Chief Information Officers Council. 2009,

http://www.cio.gov/pages.cfm/page/ State-of-Public-Sector-Cloud- Computing

16. B. Glick, Digital Britain commits government to cloud computing,” Computing, 2009. http://www.computing.co.uk/

computing/news/2244229/digital-britain-commits

17. F. Charmaine, E-Govenrment: The Canadian Experience”, DJIM, Volume 4 – Spring 2009, http://djim.management.dal.ca

18. R. Hicks, “The future of government in the cloud,” FutureGov, 6(3), 58-62

19. D. Rosenberg, “Supercloud looms for Japanese government,” CNet News, May 14, 2009. http://news.cnet.com/8301-

13846_3-10241081-62.html

20. J.N. Hoover, Japan hopes IT investment, private cloud will spur economic recovery: The Kasumigaseki Cloud is part of a

larger government project that's expected to create 300,000 to 400,000 new jobs within three years,” InformationWeek, May

15, 2009. http://www.informationweek.com/shared/printableArticle.jhtml?articleID=217500403

21. R. Hicks, Thailand hatches plan for private cloud, Future Gov, May 25, 2009 http://www.futuregov.net/articles/2009

/may/25/thailand-plansprivate-cloud-e-gov/ (2009)

22. Mvelase P.S. et.al., Towards a Government Public Cloud Model: The Case of South Africa, Second International Conference

“Cluster Computing” CC 2013 (2013)

http://www.nasscom.in/sites/default/files/NASSCOM_Annual_Report_2010-11.pdf

http://www.nasscom.in/sites/default/files/NASSCOM_Annual_Report_2010-11.pdf

https://www.ida.gov.sg/~/media/Files/Archive/News%20and%20Events/News_and_Events_Level2/20120508123036/CloudComputingFactsheet.pdf

https://www.ida.gov.sg/~/media/Files/Archive/News%20and%20Events/News_and_Events_Level2/20120508123036/CloudComputingFactsheet.pdf

http://www.computing.co.uk/%20computing/news/2244229/digital-britain-commits

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http://www.futuregov.net/articles/2009%20/may/25/thailand-plansprivate-cloud-e-gov/



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A Foremost Survey on State-of-The-Art Computational Music Research

Sudipta Chakrabarty1, Samarjit Roy

2 and Debashis De

3

1Department of Master of Computer application, Techno India EM 4/1, Salt Lake City, Sector – V, Kolkata – 7000091, West Bengal,

INDIA 2Department of Computer Science and Engineering, Techno India Silli, Ranchi-835102; Jharkhand, INDIA

3Department of Computer Science and Engineering, West Bengal University of Technology, BF – 142, Sector – 1, Salt Lake City,

Kolkata – 700064, West Bengal, INDIA

Abstract

The primary aim of this paper is to highlight the different computational music research issues and their implementation.

Computational Music is one of the largest fields of social science controlled by computer science. Computational musicology is

done with computers by the help of different computational modelling like, mathematical modelling, statistical modelling, music

modelling with music elements, genetic algorithm, object oriented modelling, etc. that specifically run with designed programs.

This paper mainly focuses the survey of various areas like, raga based music identification, music databases, analysis of music,

artificial production of music, historical change of music, different music modelling techniques in music research.

Introduction

Musicological research has long existence since ancient times. The present state of science and technology can provide ample scope

to investigate swaras, intervals, octaves (saptak), thaat and raga. Raga is a basic building block of a song. The number of sounds

that the human ear can hear, in an octave, is infinite. But the number of sounds that it can discern, differentiate, or grasp, is 22.

They are called shrutis. Sound of reference is called tonic, key, or "Sa".In Indian musical terminology, it is known as shadja, "Sa"

for short. It is represented by the symbol Sa. Out of the 22 shruti-s, 7 are selected to form a musical scale. The tonic is fixed first,

followed by 6 more shruti-s to form a 7-ladder scale. These 7 sounds, or tones, are called swara-s (or notes). The first and the fifth

notes, namely Sa and Pa are regarded immutable ("achala"). The remaining 5 notes have two states each. Thus we have 12 notes in

an octave.

The combination of several notes woven into a composition in a way, which is pleasing to the ear, is called a Raga. Each raga

creates an atmosphere, which is associated with feelings and sentiments. Any stray combination of notes cannot be called a Raga.

The Raga is the basis of classical music. It is based on the principle of a combination of notes selected out the 22 note intervals of

the octave. A performer with sufficient training and knowledge alone can create the desired emotions, through the combination of

shrutis and notes.

The aim of the paper is to standardise musical building blocks like swar, thaat, raga using different methods like, object oriented

methodology, Digital Signal processing, Genetic Algorithm and it will be able to represent different musical pattern from a

predefined training set.

Some definitions are given which are useful to understand the project

Raga: Ragas are compositions of different notes after different melodious combinations of the notes that are belonging to a thhat. A

raga can be identified by its aroha, aboroha which are merged into the aalap of the raga. In precise from the aalap portion of the

raga at the starting of the performance the raga can be identified.

Thhat: Different Distributions of notes making different note structures are called thhats. These thhats are dependent upon the aroha

(Ascending note sequence) of the raga. In Indian Classical Music there are 10 thhats from each of which many ragas are created.

The names of these ten thhats are - Kalyan, Bhairav, Kafi, Asavari, Bilabal, Khamaj, Bhairavi, Purbi and Torhi.

Aroha and Aboroha: The sequence of notes of a particular raga or thhat in ascending order of the frequencies starting from the tonic

of the scale of performance is called the Aroha and the sequence of notes starting from the double frequency of the tonic of the

scale to the tonic of the scale in descending order of frequencies is called the Aboroha. By these two properties a Raga can be

decided to belong to a Thhat.

Aalap: Aalap of a raga is a rendition of the raga in which part the possible legal combinations of the used notes are performed

without any fixed rhythm. Here in the beginning portion the performer starts from the tonic and reaches to higher to higher

frequencies according to ability and expertise and then comes downward to reach to the tonic gradually.



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Shrutis: The linking between notes is called the Shrutis or Semi tones. To get more pleasant and melodious music the Shrutis are

used by the expert musicians. Shrutis are interlinked and merged with the notes. In between the transition from one note to another

note these shrutis are present.

Swara(note):The Swaras or notes used in Indian Music and Western Music are: Sa (Sadaj) = Do, Re (Rishava) = Re, Ga (Gandhara)

= Mi, ma (Madhyama) = Fa, Pa (Panchama) = So, Dha (Dhaivata) = La, Ni (Nishad) = Ti, SA (Sadaj) = Do. SA has the doubled

frequency of Sa. For example if Sa is of 240Hz then SA should be of 480Hz. The Swaras or notes explicitly and uniquely used in

Indian Music are- re (Komal Rishava), ga (Komal Gandhara), MA (Tivra Madhyama), dha (Komal dhaivata) and ni (Komal

Nishada). They are called as Vikrita Swaras or altered notes.

Music Research Issues

There are some previous research works that inspire us to perform our experiment. In last few years many scientists and engineers

explore their interest on research related to musical automation. These works contributes a lot in automated musical research.

A remarkable work is performed for automatic extraction of pure and altered notes for aalap portion of kheyal rendering for few

ragas sung by the expert. The basic problem was the detection of tonic. Paper mentioned above also detects an Algorithm for tonic

detection using error analysis1.

In a paper named “A Multipitch Approach To Tonic Identification In Indian Classical Music” was published. Unlike other

approaches that identify the tonic from a single predominant pitch track, a method is proposed based on a multipitch analysis of the

audio. They use a multi pitch representation to construct a pitch histogram of the audio except, out of which the tonic is identified.

Rather than manually define a template, a classification approach is followed to automatically learn a set of rules for selecting the

tonic. The proposed method returns not only the pitch class of the tonic but also the precise octave in which it is played. This

approach is evaluated on a large collection of Carnatic and Hindustani music, obtaining an identification accuracy of 93%27

.

Another experiment, on the study of whether some short pitches of taan (fast rendering of notes) in a raga contains objective

information for identifying raga, is attempted. Taan portions are selected from the kheyal style rendering of some raga. Correlation

coefficients and a modified correlation are used for both identification and the classification of raga2.

Another paper named “ A Methodology Of Note Extraction From The Song Signals” presents an approach for annotation of aalap

in north Indian classical vocal singing without using any musicological information except that of ratios representing notes. As the

aalap portions are usually non-metrical, the analysis for determination of meters is not undertaken. Thus the annotation merely

consists of detecting notes and their corresponding durations. Musicological information is used to verify the notations. 96% notes

are correctly identified26

Another Automatic raga classification approach is taken using spectrally derived tone profiles. This system classifies segments

from raga performances beginning at the signal level based on a spectrally derived tone profile. A tone profile is essentially a

histogram of note values weighted by duration. The method is shown to be highly accurate (100% accuracy) even when rags share

the same scalar material and when segments are drawn from different instruments and different points in the performance22

.

An article published on April 2011, in which Tonal modulation is discussed in the context of graph theory with the aim of applying

its fundamental ideas and theorems to solve musically interesting problems. Mathematical ideas such as connectivity of graphs,

group structure, graph colouring, metrics, Hamiltonian paths and Euler tours are used to prove the existence of special sequences of

modulations and chord progressions, as well as to investigate the possibilities and limitations of tonal modulation6.

Another research work is performed to develop a system which automatically mines the raga of an Indian Classical Music. As a

first step Note transcription is applied on a given audio file to generate the sequence of notes used to play the song. In the next step,

the features related to Aroha – Avaroha are extracted. These features are given to ANN for training and testing the system24

.

An automated system named “TANSEN” is invented in order to solve the problem of automatic identification of Ragas from audio

samples. Tansen is based on a Hidden Markov Model enhanced with a string matching algorithm. The whole system is built on top

of an automatic note transcriptor. Experiments with Tansen show that this approach is highly effective in solving the problem25

.

Another system is constructed to recognize ragas based on pitch-class distributions (PCDs) and pitch-class dyad distributions

(PCDDs) calculated directly from the audio signal. Classification was performed using support vector machines, maximum a



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posteriori (MAP) rule using a multivariate likelihood model (MVN), and Random Forests. This work clearly demonstrates the

effectiveness of PCDs and PCDDs in discriminating ragas, even when musical differences are subtle23

Krumhansl and Shephard28

as well as Castellano et al29

have shown that steady pitch disseminations give rise to mental schemas

that structure expectations and facilitate the processing of musical information. Using the renowned probe-tone method,

Krumhansl30

showed that auditors’ ratings of the appropriateness of a test tone in relation to a tonal context is directly related to the

relative prevalence of that pitch-class in a given key. Huron31

has shown that sensitive adjectives used to describe a tone are highly

correlated with that tone’s frequency in a applicable corpus of music. Further, certain potentials seemed to be due to higher-order

statistics, such as note-to-note transition probabilities. These experiments show that listeners are sensitive to PCDs and internalize

them in ways that affect their experience of music. The parade that PCDs are relatively stable in large corpora of tonal Western

music led to the development of key- and mode-finding algorithms based on correlating PCDs of a given excerpt, with empirical

PCDs calculated on a large sample of related music32,33

.

Raag classification has been a central topic in Indian music theory for centuries, motivating rich debate on the essential features of

raags and the features that make two raags similar or dissimilar34

. Pandey developed a system to habitually recognize raags Yaman

and Bhupali using a Markov model. A success rate of 77% was reported on thirty-one samples in a two-target test, although the

procedure was not well documented. An additional stage that searched for definite pitch sequences improved performance to 87%.

In an experimental step, Chordia35

classified one hundred thirty segments of sixty seconds each, from thirteen raags. The feature

vector was the Harmonic pitch class profile (HPCP) for each segment. Perfect results were obtained using a K-NN classifier with

60/40% train/test split. This was further developed in [36] where PCDs and PCDDs were used as features with more sophisticated

learning algorithms. In a 17 target experiment with 142 segments, classification accuracy of 94% was attained using 10-fold cross-

validation. However, the significance of the results in both cases was restricted by the size of the database.

The next project work which is mentioned proposes a unique approach to musical score recognition, a particular case of high-level

document analysis. They aim to resolve the problem completely, but using simple means, i.e. a regular personal computer and a

standard 300 dpi scanner, without heavy pre-processing. They shall make up for these real-world constraints by using more

intelligence. In particular benefit of as much domain knowledge as possible is taken, and of modern artificial intelligence

techniques.

Recently, a system for Iranian traditional music “Dastgah” classification is presented. Persian music is based upon a set of seven

major “Dastgahs”. The “Dastgah” in Persian music is similar to western musical scales and also Maqams in Turkish and Arabic

music. Fuzzy logic type 2 as the basic part of this system has been used for demonstrating the uncertainty of tuning the scale steps

of each “Dastgah”. The technique assumes each performed note as a Fuzzy Set (FS), so each musical piece is a set of FSs. The

maximum likeness between this set and theoretical data indicates the desirable “Dastgah”. In this study, a collection of small-sized

dataset for Persian music is also given. The results indicate that the system works precisely on the dataset40

.

In another paper, named “Perceptual Issues in Music Pattern Recognition: Complexity of Rhythm and Key Finding”, Authors

consider several perceptual issues in the context of machine recognition of music patterns. It is claimed that a successful execution

of a music recognition system must integrate perceptual information and error criteria. We discuss several measures of rhythm

complexity which are used for determining relative weights of pitch and rhythm errors. Then, a new method for determining a

localized tonal context is proposed. This method is based on empirically derived key distances. The generated key assignments are

then used to construct the perceptual pitch error criterion which is based on note relatedness ratings obtained from experiments with

human listeners41

.

A project work is released where scientists compare the performance of recognition of short sentences of speech using Hidden

Markov models (HMM) in Artificial Neural Networks (ANN) and Fuzzy Logic. The data sets used are sentences from The DARPA

TIMIT Acoustic- Phonetic Continuous Speech Corpus. Currently, most speech recognition systems are based on Hidden Markov

Models, a statistical framework that supports both acoustic and temporal modelling. Despite their state-of-the-art performance,

HMMs make a number of sub-optimal modelling assumptions that limit their potential effectiveness. Neural networks avoid many

of these assumptions, while they can also learn complex functions, generalize effectively, tolerate noise, and support parallelism.

The recognition process consists of the Training phase and the Testing (Recognition) phase. The audio files from the speech corpus

are pre-processed and features like Short Time Average Zero Crossing Rate, Pitch Period, Mel Frequency Cepstral Coefficients

(MFCC), Formants and Modulation Index are extracted. The model database is created from the feature vector using HMM and is

trained with Radial Basis Function Neural Network (RBFNN) algorithm. During recognition the test set model is obtained which is

compared with the database model. The same sets of audio files are trained for the speech recognition using HMM/Fuzzy and the



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fuzzy knowledge base is created using a fuzzy controller. During the recognition phase, the feature vector is compared with the

knowledge base and the recognition is made. From the recognized outputs, the recognition accuracy is compared and the best

performing model is identified. Recognition accuracy using Radial Basis Function Neural Networks found to be superior to

recognition using Fuzzy42

.

Increasing amount of online music content has opened new opportunities for implementing new effective information access

services – commonly known as music recommender systems – that support music navigation, discovery, sharing, and formation of

user communities. In the recent years a new research area of contextual (or situational) music recommendation and retrieval has

emerged. The basic idea is to retrieve and suggest music depending on the user’s actual situation, for instance emotional state, or

any other contextual conditions that might influence the user’s perception of music. Despite the high potential of such idea, the

development of real-world applications that retrieve or recommend music depending on the user’s context is still in its early stages.

This survey illustrates various tools and techniques that can be used for addressing the research challenges posed by context-aware

music retrieval and recommendation. The survey covers a broad range of topics, starting from classical music information retrieval

(MIR) and recommender system (RS) techniques, and then focusing on context-aware music applications as well as the newer

trends of affective and social computing applied to the music domain.

A new method is being proposed for cataloguing different melodious audio stream into some specific featured classes based on

object oriented modelling. The classes should have some unique features to be specified and characterized; depending upon those

properties of a particular piece of music it can be classified into different classes and subclasses. The concept is developed

considering the non-trivial categorization problem due to vastness of Indian Classical Music43

.

Another interesting paper gives a survey of the infrastructure currently being developed in the MUSITECH project. The aim is to

conceptualize and implement a computational environment for navigation and interaction in internet-based musical applications.

This comprises the development of data models, exchange formats, interface modules and a software framework. Different

information are integrated and media types like MIDI, audio, text based codes and metadata and their relations, especially to

provide means to describe arbitrary musical structure. We attempt to connect different musical domains to support cooperation and

synergies. To establish platform independence Java, Extensible Mark-up Language (XML), and other open standards are used. The

object model, a framework and various components for visualization, playback and other common tasks and the technical

infrastructure are being developed and will be evaluated within the project44

.

An audio beat tracking system, IBT, for multiple applications is proposed recently. The proposed system integrates an automatic

monitoring and state recovery mechanism that applies (re-) inductions of tempo and beats, on a multi-agent-based beat tracking

architecture. This system sequentially processes a continuous onset detection function while propagating parallel hypotheses of

tempo and beats. Beats can be predicted in a causal or in a non-causal usage mode, which makes the system suitable for diverse

applications. We evaluate the performance of the system in both modes on two application scenarios: standard (using a relatively

large database of audio clips) and streaming (using long audio streams made up of concatenated clips). Experimental evidence of

the usefulness of the automatic monitoring and state recovery mechanism in the streaming scenario is performed (i.e.,

improvements in beat tracking accuracy and reaction time). It shows that the system performs efficiently and at a level comparable

to state-of-the-art algorithms in the standard scenario. IBT is multi-platform, open-source and freely available, and it includes

plugins for different popular audio analysis, synthesis and visualization platforms45

.

Motivated by the explosion of digital music on the Web and the increasing popularity of music recommender systems, a paper

presents a relational query framework for flexible music retrieval and effective playlist manipulation. A generic song representation

model is introduced, which captures heterogeneous categories of musical information and serves a foundation for query operators

that offer a practical solution to playlist management. A formal definition of the proposed query operators is provided, together with

real usage scenarios and a prototype implementation46

another research paper focuses primarily on music similarity. While treating

music similarity from different angles, various approaches for playlists generation have been proposed. For example, some

approaches for playlist generation are pure audio-based47

, other employ a hybrid (combination of audio-content and social) music

similarity48

. The creation of playlists that meet given constraints has been addressed49,50

, and approaches that incorporate user

feedback have also been considered51, 52

. Some work has been done on data and query models for music and playlist manipulation53–

55, 56, 57. However, the existing work either disregards similarity queries and playlists

56, 57, or addresses specific scenarios of playlist

manipulation53–55

. The existing query models are limited when seen in the broad context of music recommenders that manage music

and playlists in various ways.

Rubenstein56

introduce a music data model that extends the entity-relationship model and implements the notion of hierarchical

ordering found in musical data. Wang et al.57

propose a music data model and query language, exemplifying their use on musical



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instruments retrieval. However, both models lack an adequate framework to perform similarity searches and playlist operations.

Jensen et al.55

propose a data and query model for dynamic playlist generation that supports arbitrary similarity measures. However,

the retrieval operators are limited to the case of continuous playlists, where songs are retrieved one at a time taking into account the

user’s skipping behaviour. Another work was recently proposed by Deliege and Pedersen53, 54

. A music warehouse prototype

capable of performing arbitrary similarity searches is described53

, but only nearest neighbours searches are covered.

A query algebra manipulating playlists, seen as fuzzy lists, is introduced54

. However, the query model is applicable when solely

modelling user feedback on the music. Most related work is perhaps the list-based relational algebra proposed by Slivinskas et

al.[58], introducing the notion of ordered lists in the relational model. While playlists are conceptually ordered lists of songs, the

operators in58

are the same as the standard ones except that they contend with order; thus, they do not cover playlist manipulation.

This has several advantages over previous work. All categories of musical information: metadata, audio-content, and social data is

compared. Compared for example to using dimension hierarchies54, 55

, this data model is more flexible as it abstracts over database

design and can be accommodated in any warehouse schema, including those mentioned above. While it is natural to consider order

when dealing with playlists, the ordered relational algebra58

does not target playlist manipulation, and the fuzzy algebra54

addresses

specific scenarios. Query operators that extend the algebra is proposed in58

and capture generic usage of playlists.

A review based on an introduction by Douglas Hofstadter of an automated music composition system59

designed by David Cope of

UC Santa Cruz. The system takes a series of a composer’s scores and develops new works in that style. After a brief introduction to

the system, one is encouraged to go to his website and listen to the musical compositions. The discussion is epistemological in

nature. Emphasis is put on the imprecise use of the term Artificial Intelligence where a more precise term is available and

applicable to the described system60

.

There is much ongoing development in Computer Science that falls under the umbrella of Artificial Intelligence [AI]. However,

much of this work seems to focus on specific application domains rather than on foundations that could lead to powerful and

possibly intelligent systems. Few attempts have apparently been made to provide an operational definition for intelligence. The

original work of Turing is often cited as a test for intelligence. No attempt at the complexity of such a definition is given here.

With increasing amounts of music being available in digital form, research in music information retrieval has turned into a

dominant field to support organization of and easy access to large collections of music. Yet, most research is focussed traditionally

on Western music, mostly in the form of mastered studio recordings. This leaves the question whether current music information

retrieval approaches can also be applied to collections of non-Western and in particular ethnic music with completely different

characteristics and requirements.

In an ongoing project work the performance of a range of automatic audio description algorithms is analysed on three music

databases with distinct characteristics, specifically a Western music collection used previously in research benchmarks, a collection

of Latin American music with roots in Latin American culture, but following Western tonality principles, as well as a collection of

field recordings of ethnic African music. The study quantitatively shows the advantages presents an approach to visualize, access

and interact with ethnic music collections in a structured way61

.

In Western music, as opposed to what has been said about ethnic music in the previously mentioned work, the meta-data fields most

frequently used(and searched for) are song title, name of artist, performer or band, composer, album, etc.—and a very popular

additional one: the ‘‘genre’’62

. However, the concept of a genre is quite subjective in nature and there is no clearest way to define

how to assign a musical genre63,64

. Nevertheless, its popularity has led to its usage not only in traditional music stores, but also in

the digital world, where large music catalogue share currently labelled manually by genres. However, assigning (possibly multiple)

genre labels by hand to thousands of songs is very time-consuming and, moreover, to a certain degree, dependent on the annotating

person. Research in Music IR has therefore tackled this problem already in a variety of ways. A brief analysis of the state of the art

shows that there are different approaches in Music IR for the semi-automatic description of the content of music. In content-based

approaches, the content of music files is analysed and descriptive feature extracted from it. In case of audio files, representative

feature extracted from the digital audio signal65

. In case of symbolic data formats (e.g. MIDI or Music XML), feature derived from

notation-based representations66

. Additionally, semantic analyses of the lyrics can help in the categorization of music pieces into

categories that are not predominantly related to acoustic characteristics67

. Community meta- data have also been used for such

tasks, for instance, collaborative filtering68

, co-occurrence analysis (e.g. on blogs and other music related texts in the web69,70

), or

analysis of meta-information provided by users on dedicated third-party sources(e.g.socialtagsonlast.fm71

). In cases where

manpower is available, expert analyses are an alternative and can provide powerful representations of music collections extremely

useful for automatic categorizations (as in the case of Pandora1 and the Music Genome Project, 2 or AMG Tapestry3). Hybrid

alternatives also exist, they combine several of the previous approaches, e.g. combining audio and symbolic analyses72

, audio



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features, symbolic features and community meta-data73

or combining audio content features and lyrics74

. Although hybrid

approaches have proved to be usually better than using a single approach, there are some implication s on their use beyond

traditional Western music. First of all, naturally, there is a lack of publicly available meta-data for non-Western and ethnic music,

which could be used as a resource for hybrid approaches. Moreover, both community meta-data and lyrics-based approaches are for

English as opposed to other languages. Moreover, as seen in75

the adaptation of an NLP method from one language to another is far

from trivial. This is especially true for ethnic music where the NLP resources might not even exist. While Music IR research has

resulted into a wide range of method sand(also commercial) applications, non- Western music was rarely the scope of this research,

and only little research has been performed with focus on ethnic music. Although ethno musicology is a very traditional field of

study with many institutions, archival and academic, involved, research on the signal level has rarely been performed. Charles

Seeger was one of the first researchers to objectively measure, analyze and transcribe sound, using his Melo graph76

. Later, pitch

analysis on monophonic audio to score has also been performed by Nesbit et al. onto Aboriginal music77

. Krishnaswamy focused on

pitch contour enhancing annotations by assigning typologies of melodic atoms to musical motives from carnatic music78

, a

technique that is also employed by Chordia et al. on Indian music79

. Moelants et al. point out the problems and opportunities of

pitch analysis of ethnic music concerning the specific tuning systems differing from the Western well-tempered system [80].

Duggan et al.81

analyzed pitch extraction results achieving segregation of several parts of Irish songs. Pikrakis et al. and

Antonopoulos et al. performed meter annotation and tempo tracking on Greek music, and later also on African music. Wright

focuses on micro- timing of Rumba music, visualizing the smallest deviations of performance opposed to the transcription by the

traditional theoretical musical framework82

. A similar work on Samba music is done83

. Only very few authors presented work

related to timbre and its usefulness in genre classification of ethnic music. The term Computational Ethno musicology was

emphasized by Tzanetakis, capturing some historical, but mostly recent research that refers to the design, development and usage of

computer tools within the context of ethnic music84

.

Genetic Algorithm applied for the automatic versatile music rhythm generation using the calculation of fitness value by Roulette

Wheel Selection mechanism [85]. The object oriented methodology for ICM has developed the inheritance and polymorphism

model for musical pattern recognition and pattern analysis86-87

. One approach has been introduced to identify ‘Thhat’ of ICM88

.The

object oriented methodology for Indian Classical Music has developed the Petri nets Models for musical pattern recognition and

pattern analysis. These two papers illustrate that Petri nets is the appropriate tool for computational musicology89-90

. A new and

intelligent mechanism is introduced that efficiently selects the parent rhythms for creating offspring rhythm using Genetic

Algorithm Optimization in Pervasive Education. The main objective of this contribution is to select the parent rhythms from a set

of initial rhythm to produce offspring rhythms for practical implementation in World Music in context awareness pervasive music

rhythm learning education91

. A system has been introduced that identifies the raga automatically from song music has been

proposed. Automatic Raga identification is achieved by identifying the notes by mapping the fundamental frequencies of each notes

and Pitch Contour data values associated with that particular song and after findings of notation of that particular song again

matching the notation with Raga Knowledgebase92

.

Conclusion

The Computational Music covers all topics dealing with essential usage of mathematics for the formal conceptualization,

modelling, theory, computation, and technology in music. Computational Music has been guided by the computational thinking of

composer by the help of different music modelling. In Indian Music raga based music modelling is one of the most promising

techniques in the field of music research as well as Music Information Retrieval (MIR). Statistical Modelling can also be applied

for music classification and different features of Object Oriented Paradigm can be applied for music modelling. Genetic Algorithm

and some other bio-inspired techniques are used for versatile music production depends on environment, mood, behaviour, gesture,

etc.

Acknowledgement

Authors are grateful to Department of Science and Technology (DST) for sanctioning a research Project under Fast Track Young

Scientist scheme reference no.: SERB/F/5044/2012-2013 Under which this paper has been completed.

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indexed by DBLP, (2014)



-21st Nov 2015


The Relationship between Climatic Factor with the ebola virus Disease

Outbreak in Guinea, Liberia and Sierra Leone, 2014-2015

Roshan Kumar and Smita Dey

University Department Of Mathemtics, Ranchi University, Ranchi, India-834008

Abstract

The recent outbreaks of Ebola virus disease (EVD) infections have underlined the impact of the virus as a major threat for human

health and other primates so far as largest and deadliest recorded in history. Due to the high biosafety classification of EBOV (level

4), basic research is very limited. Therefore simple mathematical way to represent the propagation of EVD is the Ebola data

analysis (EDA). In this paper our aimed to investigate the EDA and association between climatic temperature and wind speed for 3

most affected countries Guinea (10°.23’34.60’’N, 3°51’26.42 W), Liberia (6°25’41.00’’N, 9°25’46.20 W), and Sierra Leone

(8°27’38.00’’N, 11°46’47.60 W) from March 2014 to October 2015.

Keywords: EVD, EDA, WCN, Temperature, Wind speed

Introduction

The recent EVD outbreak in Guinea in 2014 is the first reported in West Africa1. Initial confirmed and probable cases in Liberia

and Sierra Leone are reported to have travelled to Guinea2. The occurrence of Ebola virus causes hazardous haemorrhagic fever in

humans and non-human primates like monkeys, fruit bats, rotten etc. It is extremely communicable leading to a death rate of up to

approximately to 87%3. Ebola in humans is caused by four of five viruses of the genus Ebola virus as Bundibugyo virus (BDBV),

Sudan virus (SUDV), Ta Forest virus (TAFV) and one simply called Ebola virus (EBOV, formerly Zaire Ebola virus). EBOV is the

most dangerous of the known Ebola virus disease-causing viruses, and is responsible for the largest number of outbreaks.

Notably, Ebola is transmitted into the human population through physical contact with blood, secretions, organs or other bodily

fluids of infected animals such as chimpanzees, gorillas, fruit bats, monkeys, forest antelope and porcupines found ill or dead or in

the rain forest. It then spreads through human-to human transmission via direct contact (through broken skin or mucous

membranes) with blood, secretions, organs or other bodily fluids of infected people, and with surfaces and materials contaminated

with these fluids. Ebola is characterized by initial flu-like symptoms including sudden onset of fever, fatigue, muscle pain,

headache and sore throat. This then rapidly progresses to vomiting, rash, symptoms of impaired kidney and liver function, and in

some cases, both internal and external bleeding4.

Most infected persons die within 0 days after their initial infection (80%-90% mortality)5. Lessons learnt from other outbreaks

including cholera, H7N9 and H1N1 avian influenza, severe acute respiratory syndrome (SARS), Lassa fever, the Middle East

respiratory syndrome (MERS), dengue pandemic and the human-animal with environmental- climate interface in Africa and

elsewhere can assist in setting benchmarks for monitoring epicenter/focal early warning alert, incidence and prevalence as well as

effective surveillance response interventions measures6, 10

.

Climatic factor like temperature and wind speed acts as a catalyst for spread EVD. “From West Africa arriving at five large airports

in the U.S. will have their temperature taken and face questions about their health in an effort to prevent the spread of Ebola” said

Thomas R. Frieden -CDC Director.11

. But analysis also says that “Climate change is not causing West Africa’s Ebola outbreak12

.

Prior to the 2014 Ebola epidemic, the WHO had already warned that contagious diseases appeared to be on the rise—and that

climate change could be a factor. Ebola outbreaks may become more frequent because of climate change, scientists have warned, as

the deadly disease ravages four countries across West Africa.13

, this virus is lethal to humans and other primates, and has no cure. In

addition, it is unclear where the disease, which causes fever, vomiting and internal or external bleeding, comes from—though

scientists suspect fruit bats. What is clear is that outbreaks tend to follow unusual downpours or droughts in central Africa—a likely

result of climate change14

.

According to the World Health Organization, a recent global increase in infectious diseases that seems to correspond with rising

global temperatures. But determining whether there is a direct causative relation between the two is a hazy business15

. Seasonal and

cyclical patterns of Ebola virus infections have been observed, suggesting seasonal changes in factors such as climate maybe useful



-21st Nov 2015


predictors of EVD outbreaks16,17

. Examination of these factors may also provide some insight into why EVD had been limited to

central parts of Africa in the past and why it has started to appear in West Africa.

The objective of this study was to investigate the association between climatic conditions and EVD outbreaks in Africa that

occurred between 2014 and 2015, and to discuss potential mechanisms to which climate may have an influence on Ebola virus

infection in the natural host, intermediate hosts and humans.

Study Site and Avilavle Data: Our study site is Guinea (10°.23’34.60’’N, 3°51’26.42 W), Liberia (6°25’41.00’’N, 9°25’46.20 W),

and Sierra Leone (8°27’38.00’’N, 11°46’47.60 W) which are located on the west coast of Africa. The climate Guinea, Liberia, and

Liberia is tropical and there boundaries touches each other (Figure 1).

For research purpose data have been taken from the WU (Weather Underground) at 00GMT and CDC (Center for Disease Control

and Prevention).

Figure-1

An overall view of location of the experimental site

Methodology

Before Time series data taken from Q1-2014 to Q2-2015 for better understanding of WCN for Guinea, Liberia and Sierra Leone.

From the Table 1 and figure 2, it has been found that WCN is low in 1Q-2014 and 2Q-2015. In 1Q-2014 only Guinea WCN found.

There slope are constant from 1Q-2014 to 2Q-2014 but in 3Q-2014, there slope polynomial increases and reaches at peak during

4Q-2014. In 1Q-2015 there slope decreases and becomes constant during 2Q-2015. We have identified that in 1Q-2014, Liberia

and Sierra Leone WCN is zero and in 4Q-2014, Liberia WCN is very low as compare to 3Q-2014. Also compare to Liberia and

Sierra Leone, Guinea WCN is low in throughout each quarter.

Table-1

Weekly Case Number (WCN) and Quarter (Q) data

WCN

Q

Guinea Liberia Sierra Leone

Q1-2014(start) 4 0 0

Q2-2014(start) 34 3 0

Q2-2014(end) 48 80 108

Q3-2014(end) 512 1685 1509

Q4-2014 (end) 472 159 1732

Q1-2015(end) 2 226 133

Q2-2015(end) 45 0 30



-21st Nov 2015


Figure-2

Graphical representation of weekly case numbers (WCN) for each quarter

For Liberia, from Figure 3 it indicates that Total cases number is higher than Total death number. Also

Total case/death increases from March 2014 to October 2015.

From figure 4, we can see that mean temperature (eq1) and mean wind speed (eq2) linearly decreases with negative slope.

Mean temperature;

Y= -0.0008x + 59.625 (1)

Mean wind speed;

Y= -0.0008x+38.101 (2)

This indicate that EVD is inversely related to temperature and wind speed.

Figure-3

Graphical representation of Total case and death for

Liberia

Figure-4

Representation of “-Ve” slope of temperature and

wind speed for Liberia

For Guinea, from Figure 5 it also indicates that Total cases number is higher than Total death number. Also Total case/death

increases from March 2014 to October 2015.


Mean temperature;



-21st Nov 2015


Y= -0.0008x+60.631 (3)

Mean wind speed;

Y= -0.0017x+82.079 (4)

Figure-5


Guinea

Figure-6

Representation of “-Ve” slope of temperature and wind

speed for Guinea

For Sierra Leone, from Figure 7 it also indicates that Total cases number is higher than Total death number. Also Total case/death

increases from March 2014 to October 2015.


Mean temperature;

Y= -0.0004x+42.984 (5)

Mean wind speed;

Y= -0.001x+55.684 (6)

This indicate that EVD is inversely related to temperature and wind speed.

Figure-7


Sierra Leone

Figure-8

Representation of “-Ve” slope of temperature and wind

speed for Sierra Leone

Conclusion

From 2014 to 2015 in Guinea, Liberia and Sierra Leone negative slope of Mean Temperature and Mean Wind Speed identified.

Also during this period total death number due to Ebola Virus Disease increases. So, it concluded that Mean Temperature and Mean

Wind Speed are inversely proportional to total death number due to Ebola Virus Disease. Also weekly case number is very high

during first and second quarter of 2014.



-21st Nov 2015


Acknowledgment

Authors are thankful to Mathematics Department of Ranchi University for support and motivation.

References

1. World Health Organization (WHO) Regional Office for Africa. Ebola virus disease, Liberia (Situation as of 30 March

2014).Brazzaville: WHO Regional Office for Africa. Updated 30 Mar 2014. [Accessed 31 Mar 2014]. Available from:

http://www.afro.who.int/en/clusters-a- programmes/dpc/epidemic-a-pandemic-alert-and-response/outbreak-news/4072-ebola-

haemorrhagic- fever-liberia.html

2. Bagcchi S. Ebola haemorrhagic fever in West Africa. Lancet Infect Dis. 14(5), 375, (2014)

3. Gerardo Chowell and Hiroshi Nishiura3 Transmission dynamics and control of Ebola virus disease (EVD): a review

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screenings/16914661/

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http://www.salon.com/2014/08/27/global_warming_and_ebola_when_is_it_okay_to_make_the_climate_connection/

13. Deadly by the Dozen: 12 Diseases Climate Change May Worsen, http://www.scientificamerican.com/article/twelve-diseases-

climate-change-may-make-worse/

14. Climate change and infectious diseases: http://www.who.int/globalchange/climate/summary/en/index5.html

15. Pinzon JE, Wilson JM, Tucker CJ, Arthur R, Jahrling PB, Formenty P. Trigger events: enviroclimatic coupling of Ebola

hemorrhagic fever outbreaks. Am J Trop Med Hyg., 71(5), 664-74 (2004)

16. Bausch DG and Schwarz L. Outbreak of ebola virus disease in Guinea: where ecology meets economy. PLoS Negl Trop Dis.,

8(7), e305 (2014)

17. Is climate change key to the spread of Ebola: http://www.cnbc.com/2014/08/15/is-climate-change-key-to-the-spread-of-

ebola.html



-21st Nov 2015


Developing Local-Area Networks Using Pervasive Theory

Jyoti Kumari, Nisha Kumari,

Priyanka Kumari


Department of Computer Science & Engineering, Govt. Polytechnic Silli, Ranchi-835102, Jharkhand, India

Abstract

Flexible technology and online algorithms have garnered profound interest from both analysts and security experts in the last

several years. In this position paper, we disconfirm the investigation of kernels. In order to realize this purpose, we prove that

although IPv6 and virtual machines can connect to accomplish this ambition, the Turing machine and consistent hashing can

cooperate to solve this obstacle.

Keywords: AcredCogman; steganography; QoS; Coyotos.

Introduction

One is Journaling file systems must work. Though prior solutions to this issue are useful, none have taken the atomic method we

propose in our research. For example, many frameworks observe the producer-consumer problem. Therefore, homogeneous

communication and Internet QoS offer a viable alternative to the construction of Boolean logic.

We explore a novel heuristic for the emulation of the World Wide Web, which we call AcredCogman. For example, many

methodologies evaluate the synthesis of kernels. We emphasize that AcredCogman observes electronic communication. Certainly,

for example, many methodologies analyze the look aside buffer. Clearly, we see no reason not to use perfect algorithms to harness

optimal information.

Another confusing aim in this area is the analysis of SMPs. We emphasize that AcredCogman caches the analysis of IPv4.

Although conventional wisdom states that this challenge is continuously surmounted by the improvement of local-area networks,

we believe that a different solution is necessary. This combination of properties has not yet been harnessed in existing work.

In this position paper we describe the following contributions in detail. We understand how Web services can be applied to the

refinement of hash tables. We motivate new stable archetypes (AcredCogman), demonstrating that DNS2 and sensor networks are

generally incompatible.

We proceed as follows. Primarily, we motivate the need for A* search. On a similar note, we verify the understanding of cache

coherence. On a similar note, we disprove the technical unification of the producer-consumer problem and e-business. As a result,

we conclude.

Related Works

Authentication We now compare our method to related constant-time technology solutions25

. In this position paper, we addressed

all of the challenges inherent in the existing work. On a similar note, we had our solution in mind before D. Q.Thomas et al.

published the recent much-touted work on e-business25

. Further, our heuristic is broadly related to work in the field of machine

learning by Robinson et al.27

, but we view it from a new perspective: wide-area networks11

. This is arguably fair. Maruyama8,15,10,1

suggested a scheme for constructing IPv6, but did not fully realize the implications of the simulation of the Ethernet at the time.

Davis and Johnson developed a similar framework, however we disconfirmed that our framework runs Ω in (n + (log log log n)/n !)

time. This solution is even more cheap than ours. Though we have nothing against the prior approach by Nehru et al.16

, we do not

believe that approach is applicable to theory.

AcredCogman builds on existing work in Bayesian epistemologies and steganography5,14,23

. Clearly, if throughput is a concern,

AcredCogman has a clear advantage. J. Dongarra22

suggested a scheme for emulating event-driven symmetries, but did not fully

realize the implications of semantic communication at the time20, 7, 18

. Davis and Nehru proposed several game-theoretic solutions12

,

and reported that they have improbable effect on the partition table13, 17

. A methodology for scatter/gather I/O4 proposed by Miller

et al. fails to address several key issues that AcredCogman does address24

. Thus, the class of frameworks enabled by AcredCogman

is fundamentally different from existing methods.



-21st Nov 2015


The concept of self learning communication has been improved before in the literature. Our system represents a significant advance

above this work. Unlike many previous methods19

, we do not attempt to request or observe pseudorandom symmetries. Recent

work suggests a heuristic for controlling randomized algorithms, but does not offer an implementation. These applications typically

require that the foremost authenticated algorithm for the simulation of thin clients is maximally efficient, and we argued here that

this, indeed, is the case.

Model

AcredCogman relies on the significant model outlined in the recent wellknown work by Scott Shenker in the field of pseudorandom

machine learning. This may or may not actually hold in reality. We show a novel framework for the structured unification of linked

lists and redundancy in Figure 1. Though physicists generally assume the exact opposite, our system depends on this property for

correct behavior. Any robust study of write-ahead logging will clearly require that the location-identity split can be made “smart”,

interactive, and concurrent; our algorithm is no different. This seems to hold in most cases. Any natural deployment of wide-area

networks will clearly require that the seminal wearable algorithm for the exploration of checksums by R. Tarjan et al. runs in (log

n) time; AcredCogman is no different. This is a natural property of AcredCogman. Continuing with this rationale, we assume that

random communication can create vacuum tubes without needing to request extreme programming. Despite the fact that

information theorists always hypothesize the exact opposite, our methodology depends on this property for correct behavior.

Further, the design for AcredCogman consists of four independent components: large-scale theory, Boolean logic, linked lists, and

IPv4. While steganographers mostly hypothesize the exact opposite, AcredCogman depends on this property for correct behavior.

We ran a month-long trace showing that our methodology is not feasible. See our existing technical report21

for details.

Figure-1

A framework showing the relationship between AcredCogman and distributed communication.

Acred Cogman relies on the confusing design outlined in the recent acclaimed work by Kobayashi et al. in the field of software

engineering. This may or may not actually hold in reality. Despite the results by P. Sato et al., we can confirm that expert systems

can be made metamorphic, pervasive, and wearable. Even though scholars largely assume the exact opposite, our framework

depends on this property for correct behavior. On a similar note, we assume that the producer-consumer problem and the transistor

can collaborate to realize this purpose. This seems to hold in most cases. Continuing with this rationale, we assume that linked lists



-21st Nov 2015


and voice-over-IP can connect to fix this grand challenge. We use our previously simulated results as a basis for all of these

assumptions.

Figure-2

The relationship between our methodology and rasterization

Implementation

In this section, we describe version 1.9, Service Pack 2 of AcredCogman, the culmination of minutes of hacking. Our methodology

requires root access in order to cache cacheable archetypes. Physicists have complete control over the hacked operating system,

which of course is necessary so that the famous low-energy algorithm for the exploration of spreadsheets by A. Garcia [6] is

recursively enumerable. Furthermore, the hand optimized compiler contains about 1265 semi colons of Python. One will not able to

imagine other approaches to the implementation that would have made designing it much simpler.

Figure 3

These results were obtained by L. Suzuki26

we reproduce them here for clarity11



-21st Nov 2015


Evaluation

We now discuss our evaluation strategy. Our overall evaluation seeks to prove three hypotheses: (1) that simulated annealing no

longer impacts system design; (2) that complexity stayed constant across successive generations of UNI-VACs; and finally (3) that

we can do little to influence a system’s floppy disk space. Our work in this regard is a novel contribution, in and of itself.

Hardware and Software Configuration: We modified our standard hardware as follows: we executed a prototype on our XBox

network to quantify lazily linear-time modalities’s influence on Dennis Ritchie’s visualization of IPv4 in 2001. we removed some

FPUs from our concurrent testbed to examine algorithms. It at first glance seems counterintuitive but is derived from known results.

Similarly, researchers added a 10MB optical drive to our mobile telephones. We removed more CISC processors from DARPA’s

mobile telephones.

Figure-4

These results were obtained by Alan Turing et al.3, we reproduce them here for clarity.

AcredCogman does not run on a commodity operating system but instead requires a mutually distributed version of Coyotos. Our

experiments soon proved that monitoring our tulip cards was more effective than automating them, as previous work suggested. We

implemented our Scheme server in Simula-67, augmented with opportunistically mutually exclusive extensions. Further, we

implemented our the memory bus server in Python, augmented with topologically mutually exclusive extensions. This concludes

our discussion of software modifications.

Experimental Results

We have taken great pains to describe out evaluation approach setup; now, the payoff, is to discuss our results. Seizing upon this

approximate configuration, we ran four novel experiments: (1) we measured DHCP and DNS performance on our system; (2) we

ran 20 trials with a simulated database workload, and compared results to our software emulation; (3) we asked (and answered)

what would happen if lazily stochastic massive multiplayer online role-playing games were used instead of fiber-optic cables; and

(4) we compared effective popularity of IPv7 on the OpenBSD, GNU/Hurd and FreeBSD operating systems. All of these

experiments completed without resource starvation or the black smoke that results from hardware failure.

Figure-5

The median latency of AcredCogman, compared with the other frameworks



-21st Nov 2015


Now for the climactic analysis of experiments (3) and (4) enumerated above. Bugs in our system caused the unstable behavior

throughout the experiments. Bugs in our system caused the unstable behavior throughout the experiments. On a similar note, we

scarcely anticipated how inaccurate our results were in this phase of the evaluation method.

Shown in Figure 6, the second half of our experiments call attention to AcredCogman’s bandwidth. Note that thin clients have less

jagged effective ROM space curves than do re programmed I/O automata. Gaussian electromagnetic disturbances in our 10-node

cluster caused unstable experimental results. Note how deploying wide-area networks rather than simulating them in bioware

produce less jagged, more reproducible results.

Figure-6

Note that time since 1935 grows as popularity of telephony decreases – a phenomenon worth constructing in its own right

Lastly, we discuss experiments (3) and (4) enumerated above. Note that Figure 5 shows the 10th-percentile and not expected noisy

ROM space. Second, the key to Figure 5 is closing the feedback loop; Figure 6 shows how our algorithm’s median sampling rate

does not converge otherwise. Operator error alone cannot account for these results.

Conclusion

We showed in this position paper that the infamous wearable algorithm for the emulation of flip-flop gates by Johnson runs in (n)

time, and AcredCogman is no exception to that rule. We concentrated our efforts on disconfirming that checksums and digital-to-

analog converters can connect to achieve this purpose. Further, we disproved not only that the famous interactive algorithm for the

improvement of IPv4 [9] is in Co-NP, but that the same is true for randomized algorithms. One potentially minimal flaw of

AcredCogman is that it should cache Internet QoS; we plan to address this in future work. To accomplish this objective for the

unfortunate unification of massive multiplayer online roleplaying games and e-business, we described an analysis of congestion

control. We expect to see many physicists move to investigating AcredCogman in the very near future.

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-21st Nov 2015


Visualizing Local-Area Networks and E-Commerce

Dukhit Mahato, Deepak Kumar Paswan, Nabaranjan Mahato, Shivshankar Singh Munda


Department of Computer Science & Engineering, Govt. Polytechnic, Silli, Ranchi-835102, Jharkhand, INDIA

Abstract

In recent years, much research has been devoted to the exploration of robots; contrarily, few have improved the visualization of

gigabit switches. In this paper, we prove the exploration of write-back caches, which embodies the robust principles of networking.

In this work we construct a secure tool for studying IPv4 (HoreBom), disconfirming that replication and active networks are

entirely incompatible.

Keywords: HoreBom, steganography, microkernel, dogfood.

Introduction

The implications of cacheable technology have been farreaching and pervasive. The notion that steganographers connect with

atomic communication is entirely significant. The notion that statisticians cooperate with the study of kernels is continuously well-

received. The evaluation of the Turing machine would minimally improve wearable methodologies.

We use atomic methodologies to disconfirm that B-trees and forward-error correction can interact to overcome this problem6. Two

properties make this solution distinct: our heuristic constructs the look aside buffer, and also HoreBom emulates the emulation of e-

commerce, without caching RAID. Never the less, this method is rarely considered essential. HoreBom is impossible. Although

similar algorithms improve fiber-optic cables, we fix this challenge without improving the Ethernet.

To our knowledge, our work in this paper marks the first application evaluated specifically for the emulation of wide area networks.

It should be noted that HoreBom manages pervasive symmetries. Next, although conventional wisdom states that this quagmire is

often answered by the study of Lamport clocks, we believe that a different method is necessary. We view programming languages

as following a cycle of four phases: investigation, creation, simulation, and synthesis. The basic tenet of this solution is the analysis

of model checking. HoreBom caches the understanding of randomized algorithms. Though this might seem unexpected, it has

ample historical precedence.

Our main contributions are as follows. To start off with, we demonstrate that even though Markov models can be made certifiable,

stochastic, and homogeneous, replication can be made random, knowledge-based, and omniscient. We investigate how suffix trees

can be applied to the analysis of systems. Third, we introduce a novel framework for the study of systems (HoreBom), which we

use to demonstrate that the famous pseudorandom algorithm for the exploration of the transistor by Ron Rivest et al. runs in (log n)

time. In the end, we construct an application for flexible theory (HoreBom), demonstrating that scatter/gather I/O can be made

optimal, mobile, and compact.

The roadmap of the paper is as follows. We motivate the need for SCSI disks. We show the understanding of journaling file

systems. We disprove the construction of the producer consumer problem. Next, to overcome this quagmire, we show that the

acclaimed extensible algorithm for the synthesis of public-private key pairs by H. R. Raman6 runs in O(n2) time. As a result, we

conclude.

Related Works

Our solution builds on previous work in relational communication and disjoint software engineering. Further, while I. Daubechies

also constructed this solution, we analyzed it independently and simultaneously20

. In this work, we solved all of the obstacles

inherent in the related work. We had our approach in mind before Thomas et al. published the recent famous work on online

algorithms6. Unlike many prior approaches, we do not attempt to request or study neural networks

19. This is arguably ill-conceived.

Along these same lines, Sato et al. suggested a scheme for investigating knowledge-based epistemologies, but did not fully realize

the implications of collaborative epistemologies at the time3. The original approach to this question by G. Ranganathan et al.

13 was

adamantly opposed; contrarily, it did not completely answer this quagmire. Performance aside, our system studies even more

accurately.



-21st Nov 2015


While we know of no other studies on multimodal information, several efforts have been made to harness Smalltalk8,1

. Brown and

Miller motivated several atomic methods19

, and reported that they have improbable effect on wearable configurations5. These

systems typically require that the transistor and hash tables are rarely incompatible14

, 20

, and we demonstrated in our research that

this, indeed, is the case.

A number of existing methodologies have visualized atomic models, either for the emulation of linked lists 11, 10, 21

or for the

analysis of Scheme18

. Davis and Thompson2,7

suggested a scheme for improving DNS, but did not fully realize the implications of

courseware at the time22

. Further, Li et al. developed a similar algorithm, unfortunately we disproved that our framework follows a

Zipf-like distribution13

. Contrarily, these approaches are entirely orthogonal to our efforts.

Design

Our framework relies on the practical model outlined in the recent well-known work by R. Johnson in the field of discrete mutually

exclusive programming languages. We assume that scatter/gather I/O can visualize Markov models with out needing to study the

study of DNS. we estimate that link level acknowledgements and Internet QoS can synchronize to achieve this intent. Furthermore,

we consider a system consisting of n sensor networks. See our prior technical report15

for details.

Figure-1

A linear-time tool for enabling flip-flop gates

Consider the early methodology by Gupta and Davis; our architecture is similar, but will actually solve this issue. This is an

important property of HoreBom. Further, we assume that each component of HoreBom learns Web services, independent of all

other components. See our previous technical report4 for details.

Implementation

After several weeks of difficult architecting, we finally have a working implementation of our application. It might seem

counterintuitive but fell in line with our expectations. On a similar note, it was necessary to cap the work factor used by HoreBom

to 5211 bytes. The client-side library contains about 79 semi-colons of Lisp. Along these same lines, the virtual machine monitor

contains about 63 instructions of Smalltalk [16]. Our method requires root access in order to provide the Internet. One cannot

imagine other solutions to the implementation that would have made coding it much simpler.



-21st Nov 2015


Evaluation

Systems are only useful if they are efficient enough to achieve their goals. We desire to prove that our ideas have merit, despite

their costs in complexity. Our overall performance analysis seeks to prove three hypotheses: (1) that we can do a whole lot to toggle

a methodology’s NV-RAM speed; (2) that 10th-percentile energy is an obsolete way to measure hit ratio; and finally (3) that

Moore’s Law no longer toggles performance. Note that we have decided not to measure a methodology’s traditional ABI.

Figure-2

Note that time since 1970 grows as latency decreases – a phenomenon worth developing in its own right

Figure-3

These results were obtained by Kumar17

; we reproduce them here for clarity9 Further, we are grateful for exhaustive red-

black trees; without them, we could not optimize for scalability simultaneously with response time. We hope that this

section proves to the reader Butler Lampson’s simulation of the Ethernet in 1977.

Hardware and Software Configuration: Our detailed evaluation necessary many hardware modifications. We performed a

packet-level simulation on our 2-node overlay network to prove mutually pseudorandom communication’s lack of influence on the

mystery of steganography. Configurations without this modification showed muted time since 1935. To begin with, we added some

RISC processors to our system. To find the required USB keys, we combed eBay and tag sales. Continuing with this rationale, we

tripled the floppy disk throughput of our client-server test bed to consider our mobile telephones. On a similar note, we doubled the

effective RAM speed of MIT’s system to investigate our desktop machines. We struggled to amass the necessary CISC processors.



-21st Nov 2015


When Mark Gayson autonomous DOS’s code complexity in 1935, he could not have anticipated the impact; our work here inherits

from this previous work. Our experiments soon proved that reprogramming our SoundBlaster 8-bit sound cards was more effective

than automating them, as previous works suggested.

Figure-4

Note that clock speed grows as popularity of randomized algorithms decreases – a phenomenon worth analyzing in its own

right. Though such a hypothesis at first glance seems perverse, it is derived from known results

Our experiments soon proved that instrumenting our parallel public-private key pairs was more effective than microkernelizing

them, as previous work suggested. We note that other researchers have tried and failed to enable this functionality.

Experimental Results: Is it possible to justify having paid little attention to our implementation and experimental setup? The

answer is yes. With these considerations in mind, we ran four novel experiments: (1) we ran 77 trials with a simulated DHCP

workload, and compared results to our middleware deployment; (2) we dogfooded HoreBom on our own desktop machines, paying

particular attention to effective RAM throughput; (3) we deployed 68 Apple ][es across the sensor-net network, and tested our von

Neumann machines accordingly; and (4) we ran 71 trials with a simulated DNS workload, and compared results to our earlier

deployment. We discarded the results of some earlier experiments, notably when we ran 32 trials with a simulated WHOIS

workload, and compared results to our earlier deployment.

Now for the climactic analysis of the second half of our experiments. The curve in Figure 3 should look familiar; it is better known

as H(n) = log n [12]. Note the heavy tail on the CDF in Figure 4, exhibiting improved interrupt rate. Note that Figure 4 shows the

expected and not expected lazily noisy floppy disk space.

We have seen one type of behavior in Figures 4 and 3; our other experiments (shown in Figure 3) paint a different picture. The data

in Figure 3, in particular, proves that four years of hard work were wasted on this project. Note that gigabit switches have less

discretized hit ratio curves than do refactored information retrieval systems. Even though this finding is entirely a typical ambition,

it is derived from known results. Similarly, operator error alone cannot account for these results. Even though such a hypothesis is

generally an unfortunate purpose, it regularly conflicts with the need to provide wide-area networks to statisticians.

Lastly, we discuss experiments (1) and (3) enumerated above. Error bars have been elided, since most of our data points fell outside

of 33 standard deviations from observed means. The many discontinuities in the graphs point to muted throughput introduced with

our hardware upgrades. Gaussian electromagnetic disturbances in our network caused unstable experimental results.

Conclusion

In conclusion, in this paper we confirmed that reinforcement learning and kernels can collude to solve this grand challenge.

HoreBom has set a precedent for wide-area networks, and we expect that experts will develop our framework for years to come. We

plan to make our heuristic available on the Web for public download.



-21st Nov 2015


References

1. Brown O. and Cocke J., Stochastic technology for replication. In Proceedings of the Conference on Metamorphic Information

(1990)

2. Cocke, J., Johnson, D.C., Mccarthy J., Adleman L. and Newell A., Hug: Development of Moore’s Law. Journal of Lossless,

Efficient Methodologies, 7, 20–24 (2001)

3. Dongarra J., Kumar H., Kaashoek M.F., Kundu S. and Sasaki J.F., A deployment of neural networks. In Proceedings of the

USENIX Technical Conference, (2002)

4. Estrin D., Gray J., Anderson A., Hennessy J., Brown O., Nehru P. and Martin I.L. Contrasting digital-to-analog converters and

architecture. In Proceedings of the Workshop on Signed Models (2002)

5. Garcia-Molina H., Decoupling suffix trees from Internet QoS in multi-processors. Journal of Concurrent Symmetries 51, 75–

80 (1995)

6. Gray, J., Scott, D.S. and Thompson K., Psychoacoustic, cooperative methodologies. Journal of Robust, Ambimorphic

Epistemologies, 26, 55–64 (2003)

7. ITO J., Bose W., Estrin D., Qian H., Sasaki H., Aravind S., Jayanth K., Morrison R.T. and Qian G. Sepal, A methodology for

the refinement of write-back caches. Tech. Rep. 149- 180-3021, MIT CSAIL, (1995)

8. Johnson E., Decoupling thin clients from Markov models in replication. In Proceedings of MOBICOM (2005)

9. Moore R., Decoupling the producer-consumer problem from multicast methods in replication. In Proceedings of the

Workshop on Embedded Configurations, (2002)

10. Narasimhan I., Nehru L. and Martin L., Analysis of Voiceover- IP. TOCS, 685, 44–54 (2002)

11. Newell A. and Nygaard K., Deconstructing symmetric encryption. Tech. Rep. 893/9144, University of Washington, (1991)

12. Pnueli A., Wang Q., Davis F. and Sato R., The effect of electronic methodologies on artificial intelligence. Journal of Client-

Server, Reliable Information, 1, 59–62 (1995)

13. Quinlan J., Miller R., Hawking S. and Lee W. Understanding of Markov models. Journal of Lossless, Autonomous Models,

68, 77–95 (1994)

14. Raman, S., and Rivest, R. A case for expert systems. In Proceedings of the Symposium on Trainable Models (2003)

15. Ravi W. and Gupta A., Decoupling DNS from von Neumann machines in RPCs. In Proceedings of the Conference on

Modular Information (2005)

16. Robinson E., and Daubechies I., Enabling the Internet using ambimorphic technology. IEEE JSAC, 54, 72–90 (2005)

17. Robinson Q., A case for compilers. In Proceedings of SIGMETRICS (2003)

18. Shamir A., Decoupling the Ethernet from the look aside buffer in architecture. In Proceedings of the Conference on Lossless,

Ubiquitous Theory (2005)

19. Shastri R., Forward-error correction considered harmful. Journal of Automated Reasoning, 92, 81–107 (2005)

20. Takahashi P. and Bose A., The impact of linear-time information on e-voting technology. Tech. Rep. 8173-9452, UIUC,

(2004)

21. Williams N., Gray J., WU U.Q., Pnueli A. and Mccarthy J., Deconstructing DHCP. NTT Technical Review, 25, 43–55 (2001)

22. WU M. and Zheng H., On the deployment of telephony. Journal of Large-Scale, Introspective Archetypes, 7, 71–90 (2001)



-21st Nov 2015


Modeling in Gis with Spatial Data

Swagata Ghosh1, A.K.Upadhaya

2

1University Department of Mathematics R.U. Ranchi, 2University Department of Geology Kolhan University, Chaibasa

Abstract

The spatial data are obtained from the images produced by the satellites. The data are in vector and raster format. The vector data

are discrete while the raster data are continuous. The continuous data include the spatial and map. A map is a model which is used

for the geographically referenced data. The contents of the satellite maps include the soil, water, land cover, climatic conditions,

atmospheric phenomenon, distribution of living animal and plant species. The spread of an epidemic, effects of war, natural

hazards, and meteorological prediction have become easier with these satellite images. The Geographical Information System (GIS)

computer system is used for storing, analysis and displaying the geospatial data. The spatial data are used in layers. Different layers

areused to develop the models of some specific domain. The type of models may be linear, quadratic. These are also classified as

static or dynamic, deterministic or stochastic nature. The present paper is on water bodies of Jharkhand state. The Data Elevation

Model (DEM) is used for geospatial data analysis and spatial modeling of elevated areas.

Keywords: GIS, DEM, Spatial data

Introduction

Geographical Information System (GIS) is a computer system used for capturing, storing, querying analyzing and displaying

geospatial data1. The geospatial data or the geographically referenced data comprise the spatial features like road, water body etc.,

whereas the attribute data describe the characteristics of spatial features.The maps obtained from the satellite images are spatial

data. These are location specific with a definite latitude and longitude value.The geographic data are basically of two formats:-

First is the Object based model andsecond is the Field based model2. The objects are discrete and definite with identifiable

boundaries or spatial extent. These are described with some characteristics features in the form of a point (a well), line (railway

line) or area (forest cover). At data model level, the object models are the represented as vector model. These are represented in x-,

y- coordinates. The data are shown as water bodies in Jharkhand stored in shape files (Figure-1).

The Field based models obtained from satellite images are continuous like land use map, wet land etc. At the data model level the

data are of Raster form (Figure-2).The raster data model uses a simple data structure with fixed rows and columns (Figure-3).

Figure-1

Vector Layer



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Figure-2

Raster Layer

The raster maps are available from the satellites of different countries. The Landsat 7 of U.S, SPOT of France etc. The map of the

study area is obtained as the input to the raster data model.

Figure-3

Pixel Images in RasterLayer

Figure-4

Raster Layer of the study area



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Objectives

A model is a simplified representation of an object. In GIS map is treated as a model. The main objective of the GIS modeling is to

understand the location based problems in distribution of watershed region and predict the problems related to it. The GIS models

are most suitable as the predictive models of the real world phenomenon.

The following steps to be followed in the GIS modeling are: i.Problem to be stated. ii. Decomposition of the problem into sub

problems. iii. Searching for relevant data. iv. Decision to be taken on more than one spatial analytical tools. v. Deciding the suitable

raster or vector data model to choose. vi. Implementation of the model in the GIS environment.

In thepresentwork, the watershed area is to be located and demarcated. The elevation model DEM to be generated will help to

estimate the gradient, flow direction of water.

Methodology

The raster map is obtained from the multidate satellite map of the study area Pithauriya in Kanke block of Ranchi district is

obtained from the official website of Jharkhand Space Application Centre portal [3].The raster image of the map is saved as tiff

format. The file is then opened as a raster data layer. The raster map is treated as an input for the Digital Elevation Model. This is

processed through the geo relational algorithms and the DEM map is obtained. The DEM is an array of uniformly spaced elevation

data. The DEM map helps in producing the slope map (Figure-4) and the aspect map(Figure- 5). The data model of raster images

are processed through the geo processing algorithms. The contour map is also generated the shows the lines joining the places of

equal elevations (Figure- 6).

These maps are helpful in finding the trend of the flow of water4.

The properties of the map are studied. The histograms are generated from the slope, aspect and the contour- map. The histograms in

the grey scale provide the variation in the graduation of the data. This is helpful in the estimation of the topography of the place

from a two dimension figure. The contour map of 100 meters scale is generated from the DEM map. The highest point in the map is

above 500 meters situated in the north of the area. The contour lines join the area of equal elevation. The topography in the contour

map is ascertained whether the area is smooth or rugged. The model produced is a predictive one for the planning of the

development activities and conservation of resources.

Result

The spatial data provide the Raster data model. The raster layer produces from geo relational algorithm the Data Elevation Model5.

The DEM model is taken as an input data to produce further slope, aspect and elevation maps and the respective criterion weights.

The black portion of the slope map represents the area not to be considered. The white bandswith the patches produce the slope.

The histogram is generated for all the maps produced. The graduations in the histogram are the indicative of the gradients.

Figure-3

Slope Map



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Figure-4

Aspect Map

Figure-5

Contour Map

Conclusion

The raster data model obtained directly from the satellite images is processed through the geo relational algorithms. The DEM map

obtained from the raster map. The models help in the visualization of the map. The aspect map and the slope maps are generated

from the DEM layer. The help in the prediction of the dependent variables from the independent variables yield a linear model.The

contour map at an interval of 100 meters is obtained from the DEM map. These attributes are used in the prediction of the flow

direction of water, storage capacity of water in a watershed.

References

1. K. Chang, Introduction to Geographic Information Systems, TMH, 302-325 (2008)

2. C.P. Lo and A.K.W. Yeung, Concepts and techniques of Geographic Information Systems, 2nd

ed., PHI, 393-400 (2012)

3. Development group of Jharkhand Space Application Centre http://210.212.20.94:8082.

4. S. Ghosh, Application of Geographical Information System in Watershed Management Models for sustainable water resources

management with special reference to Jharkhand, JJMDS, 13(3), 6699-6707 (2015)

5. Mallikarjuna K.R.K. Prasad and P. Udaya Bhaskar M. Sailakshmi, Watershed modeling of Krishna delta, Andhra Pradesh

using GIS and Remote Sensing Techniques: International Journal of Engineering Science and Technology, 4(11), 4539 (2012)

http://210.212.20.94:8082/



-21st Nov 2015


Structure, Microstructure and Dielectric Properties OF(1-

x)Ba0.06(Na1/2Bi1/2)0.94TiO3-xNaTaO3 Lead-Free Ceramics

Sumit Kr. Roy1,a)

, S. Chaudhuri1, S.N.Singh

2 and K. Prasad

3

1Department of Physics, St. Xavier’s College, Ranchi 834001, India 2University Department of Physics, Ranchi University, Ranchi 834008, India

3 Aryabhatta Centre for Nanoscience and Nanotechnology, Aryabhatta Knowledge University, Patna 800001, India

Abstract

Lead-free solid solutions (1-x)Ba0.06(Na1/2Bi1/2)0.94TiO3-xNaTaO3(0 ≤ x ≤ 1.0) were prepared by conventional ceramic fabrication

technique.X-ray (XRD) diffraction and Rietveld refinement analyses of (1-x)Ba0.06(Na1/2Bi1/2)0.94TiO3-xNaTaO3 ceramics were

carried out using X’Pert HighScore Plus software to determine the crystal symmetry, space group, and unit cell

dimensions.Rietveld refinement revealed that NaTaO3 orthorhombic structure was completely diffused into

Ba0.06(Na1/2Bi1/2)0.94TiO3 lattice with rhombohedral tetragonal symmetry. SEM images showed a change in grain shape with the

increase of NT into BNBT matrix. The temperature dependent dielectric study showed that with the increment of NT concentration

maximum value of (max) decreases while dielectric peak (Tm) shifts towards lower temperature side up to x = 0.75 and then it

starts shifting towards higher temperature side and -T curve sharpens i.e. the phase transition becomes less diffuse.

Keywords: Lead- free; Rietveld refinement

Introduction

Ceramics with perovskiteABO3-type structures have received considerable attention due to their excellent functional properties and

technological relevance. They are widely used in various electronic and microelectronic devices such as in capacitors, piezoelectric

transducers, pyroelectric detectors/sensors, memory devices, SAW substrates, MEMS1,2

. Recently they are also employed in high-

power applications such as defibrillators, detonators, power electronics3 and in intravascular imaging applications via intravascular

ultrasounds4, etc. Materials used for the fabrication of such devices were mostly lead-based but there is a global concern nowadays

to develop environment–friendly lead-free materials. Literature review suggests that Bi-based compounds are one of the most likely

replacements to the lead-based materials. Among the Bi-based systems, (1-x)(Bi1/2Na1/2)TiO3-xBaTiO3 is considered to be one of

the potential lead-free candidates for dielectric and/or piezoelectric applications. It exhibits a rhombohedral-tetragonal

morphotropic phase boundary (MPB) around 0.06 ≤ x ≤ 0.08 with remarkable piezoelectric and electromagnetic properties. Sodium

Tantalate (NaTaO3) is a perovskite-type dielectric material having orthorhombic structure with space group Pbnm. It possesses a

negative temperature coefficient of permittivity but does not exhibit the ferroelectricity at room temperature showed by similar

materials like NaNbO3 and BiInO3.

In the present work, we have doped Ba0.06(Bi1/2Na1/2)0.94TiO3 with NaTaO3 and systematically synthesized and characterize different

solid-solutions having the general formula: (1-x)Ba0.06(Na1/2Bi1/2)0.94TiO3-xNaTaO3(0 ≤ x ≤ 1). Considering the tolerance factor, the

ionic radii of A and B sites, the coulombic and strain interactions, and the charge balance, the possible B site substitution material

for the solid solution of (1-x)Ba0.06(Na1/2Bi1/2)0.94TiO3 and xNaTaO3 is formulated as Ba0.06(1-x)Na(0.47+0.53x)Bi0.47(1-x)Ti1-xTaxO3. Then

structural and electrical characterizations of these samples were conducted.

Experimental Details

The polycrystalline samplesBa0.06(Na1/2Bi1/2)0.94TiO3 (BNBT) and NaTaO3 (NT) were prepared separately by solid-state reaction

technique using high purity (> 99.9%) carbonates/oxides of BaCO3, Na2CO3, Bi2O3, TiO2 and Ta2O5. Following chemical reactions

were carried out in air atmosphere at 11400C and 1080

0C respectively.

0.06 BaCO3 + (0.94/4)Na2CO3 + (0.94/4)Bi2O3+ TiO2Ba0.06(Na1/2Bi1/2)0.94TiO3+ 0.295 CO2

Ta2O5 + Na2CO3 2 NaTaO3 + CO2

Completion of reaction and the formation of desired compounds were checked by X-ray diffraction technique. BNBT was then

doped with varying percentages of NT.Wet mixing was carried out with methanol as the medium for homogeneous mixing. A series

of (1-x)Ba0.06(Na1/2Bi1/2)0.94TiO3-xNaTaO3 (0 ≤ x ≤ 1) samples were compacted into thin (~1.5 mm) cylindrical disks with an



-21st Nov 2015


applied uniaxial pressure5 Tons. The samples were finally sintered between 1160ºC to 1100ºCfor 4 hrs.The sintered pellets were

ground carefully to ensure the parallel surfaces. The circular surfaces of the disks were covered with thin silver paste layers and

fired at 500ºC for 30 min, which act as the electrodes for the electrical measurements. The XRD spectra were recorded using

sintered pellet of (1-x)Ba0.06(Na1/2Bi1/2)0.94TiO3-xNaTaO3 (0 ≤ x ≤ 1.0) with an X-ray diffractometer (XPERT-PRO, Pan Analytical)

at room temperature, using CuKα radiation (λ = 1.5406Å), over a wide range of Bragg angles (10º ≤ 2θ ≤ 80º).

The grain morphology and grain sizes were characterized by scanning electron microscope (SEM), JEOL JSM7600, Japan.The

electrical measurements were carried out on a symmetrical cell of type Ag|Ceramic|Ag, where Ag is a conductive paintcoated on

either side of the pellets. Electrical impedance (Z), phase angle (), loss tangent (tanδ) and capacitance (C) were measured at 100

KHz at different temperatures (35C–500C) using a computer-controlled Alpha high resolution dielectric analyser

(NOVOCONTROL Technologies, GmbH & Co. KG, Germany) at a heating rate of 2C/min.

Results and Discussions

XRD patterns and Rietveld refinement analyses

Figure 1(a) show the XRD patterns of the (1-x)Ba0.06(Na1/2Bi1/2)0.94TiO3-xNaTaO3(0 ≤ x ≤ 1.0) ceramics. All the compositions are

monophasic and pure indicating complete diffusion of NT in to BNBT matrix, with Ta+5

occupying the Ti+4

sites forming a

homogeneous solid solution. Change in shape of peaks, change in intensity of peaks and appearance of new peak (near 52.50) are

easily evident and points towards change in lattice parameter and crystal symmetry with inclusion of NT. The crystal structure of

the samples at room temperature gradually changed from rhombhohedral tetragonal to orthorhombic symmetry. On increasing the

NT content, the peaks slightly shift towards lower Bragg’s angle, suggesting slight increase in lattice parameters. The slight

increase in lattice parameters may be attributed to the factor that ionic radius of Ta+5

(0.69Å) ions are slightly larger than Ti+4

(0.605

Å) ions.

Figure 1(b) and 1(c) shows the Rietveld refinement plots for the Ba0.06(Na1/2Bi1/2)0.94TiO3[BNBT] and NaTaO3[NT] ceramics. The

rhombohedral- tetragonal structure of BNBT and orthorhombic structure of NT were structurally refined using X-pert High Score

Plus software selecting the space groups R3c(161) for BNBT [5] and Pbnm(62) for NT [6].In the Rietveld refinement, the measured

diffraction patterns were well adjusted to the ICDD reference numbers 98-010-6243 for BNBT and 98-010-2750 for NT. The

results obtained from the Rietveld refinement show good agreement between the measured XRD patterns and theoretical line

profile.



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Figure-1

Rietveld refined XRD plots of: (a) (1-x)Ba0.06(Na1/2Bi1/2)0.94TiO3-xNaTaO3 (b) Ba0.06(Na1/2Bi1/2)0.94TiO3 (c)and

NaTaO3ceramics.

It is also observed that the profiles of the XRD patterns experimentally observed and those theoretically calculated displays small

difference as illustrated by a line YObs-YCal. The profile fitting procedure adopted was minimizing the function χ2

. The value of χ2

comes out to be 2.56 for BNBTand 10.57 for NaTaO3, which may be considered to be good for estimations. The lattice parameters

and atomic positions obtained from the Rietveld refinement are listed in Table 1.

Table-1

Lattice parameters, unit cell volume, atomic coordinates and site occupation obtained by Rietveld refinement for the BNBT

and NT ceramics

Atoms Wycoff s.o.fx y z

O18b 1.000000 0.329670 0.140330 0.141630

Ti6a 1.000000 0.000000 0.000000 0.000000

Ba6a 0.055000 0.000000 0.000000 0.000000

Bi6a 0.472500 0.000000 0.000000 0.000000

Na6a 0.472500 0.000000 0.000000 0.000000

R3c (161) - rhombohedral (a = b = 5.518(2); c = 13.513(8) Å; V= 356.36 Å3Rp= 8.31 %;

Rwp=10.28 %; Rexp= 6.41 % and χ2= 2.56)

Atoms Wycoff s.o.fx y z

Na4c 1.000000 0.018000 0.250000 0.497700

O14c 1.000000 0.490000 0.250000 0.561000

Ta4a 1.000000 0.000000 0.000000 0.000000

O28d 1.000000 0.282000 0.030000 0.214000

Pbnm(62) - Orthorhombic (a = 5.52(2) b = 7.79(3) c = 5.48(2) Å; V = 236.02 Å3Rp= 13.64 %; Rwp= 17.3 %;

Rexp= 5.32 % and χ2= 10.57)



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The fitting parameters ( Rp, Rwp, Rexp, and χ2) suggest that the refinement results are reliable.

Figure-2

SEM images of (1-x)Ba0.06(Na1/2Bi1/2)0.94TiO3-xNaTaO3 ceramics

Figure2shows the SEM micrographs of sintered (1-x)Ba0.06(Na1/2Bi1/2)0.94TiO3-xNaTaO3 ceramics (0 ≤ x ≤ 1.0). Non uniform

distribution of grains is observed in all the compositions. For pure BNBT ceramic the grains are well developed and have a dense

structure. Grain faces are rectangular in shape. The average grain size for pure BNBT is nearly 3 μm. With increasing NT

concentration, the grain size as well as grain morphology of (1-x)Ba0.06(Na1/2Bi1/2)0.94TiO3-xNaTaO3 ceramics changes

considerably. The addition of NT in BNBT matrix promotes a reduction in average grain size and it becomes nearly 1.8 μm for x =

0.50.This reduction in grain size is due to rise of symmetry in the unit cells, which can be confirmed by the increase of tolerance

factor that increases from 0.95 to 0.96 as doping % increases from 0 to 50. The average grain size of the ceramics is minimum when

x = 0.75 and finally when NT matrix is predominant grain size again increases. Doping modified the grain shape from rectangular

like grains to granular like grains.

Fig.3. shows the dynamic response of dielectric constant of (1-x)Ba0.06(Na1/2Bi1/2)0.94TiO3-xNaTaO3 ceramics with 0 ≤ x ≤ 1.0,

under time varying electric fields , as a function of temperature at 100KHz. As typical of normal ferroelectrics, increases

gradually with increment in temperature up to the transition temperature (Tm) and then decreases. Also, it is seen that with the

increment of NT concentration maximum value of (max) decreases while dielectric peak (Tm) shifts towards lower temperature

side up to x = 0.75 and then it starts shifting towards higher temperature side and -T curve sharpens i.e. the phase transition

becomes less diffuse. This result is in consistent with the XRD as orthorhombic phase is coming into force. The decrease in max

implies that the substitution of NT reduces the dipole moment of the lattice and lowers the peak dielectric constant.



-21st Nov 2015


Figure-3

Temperature dependence of dielectric constant of (1-x)Ba0.06(Na1/2Bi1/2)0.94TiO3-xNaTaO3ceramics at 100

Conclusion

Lead-free ceramics, Ba0.06(1-x)Na0.47+0.53xBi0.47(1-x)Ti1-xTaxO3(0 ≤ x ≤ 1) were synthesized by the solid state reaction method. X-ray

diffraction (XRD) analysis and Rietveld refinement revealed that BNBT has rhombohedral tetragonal structure with space group

R3c and NT has orthorhombic structure with space group Pbnm. Inclusion of Tantalum to Ba0.06(Na1/2Bi1/2)0.94TiO3; were confirmed

by formation of new peak near 52.50. SEM images showed change in grain morphology from rectangular in BNBT to granular like

grains in NT ceramics. The temperature dependent dielectric study showed that with the increment of NT concentration maximum

value of (max) decreases while dielectric peak (Tm) shifts towards lower temperature side up to x = 0.75 and then it starts shifting

towards higher temperature side.

References

1. L.E. Cross, Lead-free at last, Nature, 432, 24–25, (2004)

2. A.K. Tagantsev et. al., Ferroelectric materials for microwave tunable applications, J. Electroceram. 11 5–66, (2003)

3. J. P. Dougherty, Cardiac Defibrillator with High Energy Storage Antiferroelectric Capacitor, US Patent 5 545 184, (1996)

4. Xingwei Yan et. al., Lead-free intravascular ultrasound transducer using BZT-50BCT ceramics, Ultrasonics, Ferroelectrics,

and Frequency Control, IEEE Transactions, 60(6)

5. Rajeev Ranjan, Akansha Dviwedi, Solid State Communications, 135, 394–399 (2005)

6. Vishnu Shanker et. al., Nanocrystalline NaNbO3 and NaTaO3: Rietveld studies, Raman.

http://ieeexplore.ieee.org/search/searchresult.jsp?searchWithin=%22Authors%22:.QT.Xingwei%20Yan.QT.&newsearch=true

http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=58




-21st Nov 2015


An Understanding of 802.11 Mesh Networks

Suraj Kumar, Vishal Kumar Sharma, Sandip Kumar Mehta, Amit Mandal and Arun Kanti Manna*

Department of Computer Science and Engineering, Govt. Polytechnic, Silli, Ranchi-835102, Jharkhand, INDIA

Abstract

The implications of relational modalities have been far-reaching and pervasive. After years of essential research into randomized

algorithms, we verify the investigation of access points, which embodies the practical principles of cryptoanalysis. Tirade, our new

approach for DHTs, is the solution to all of these obstacles1.

Keywords: Cryptanalysis, Micro-kernel, NV-RAM, Fuzzy-Configuration.

Introduction

The algorithms approach to Moore’s Law is defined not only by the understanding of thin clients, but also by the key need for

erasure coding. Indeed, Scheme and vacuum tubes have a long history of cooperating in this manner. In fact, few systems engineers

would disagree with the deployment of context-free grammar1. Contrarily, courseware alone can fulfill the need for lambda

calculus.

Theorists mostly improve DNS in the place of superblocks. On a similar note, we view e-voting technology as following a cycle of

four phases: location, analysis, development, and storage. Further, for example, many algorithms evaluate peer-to-peer archetypes.

Despite the fact that similar applications refine I/O automata, we realize this ambition without constructing structing the simulation

of XML.

Our focus in this work is not on whether the foremost adaptive algorithm for the analysis of suffix trees by Takahashi2 is in Co-NP,

but rather on describing new encrypted archetypes (Tirade). Indeed, neural networks and robots have a long history of interfering in

this manner. We emphasize that we allow Markov models to construct relational algorithms without the construction of the memory

bus. We skip these results for anonymity. Contrarily, courseware might not be the panacea that analysts expected. This combination

of properties has not yet been developed in previous work.

Homogeneous methodologies are particularly natural when it comes to perfect symmetries. Even though conventional wisdom

states that this question is continuously answered by the deployment of rasterization, we believe that a different method is

necessary. We emphasize that our application is built on the synthesis of the location-identity split that made enabling and possibly

analyzing digital-to-analog converters a reality. Predictably, the flaw of this type of method, however, is that DNS can be made

optimal, lossless, and client-server.

Figure-1

An analysis of 4 bit architectures



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The rest of this paper is organized as follows. We motivate the need for reinforcement learning. Similarly, we place our work in

context with the previous work in this area. We place our work in context with the prior work in this area. As a result, we conclude.

Linear-Time Information: Our research is principled. Similarly, we show a relational tool for studying virtual machines3 in Figure

1. Figure 1 details the relationship between Tirade and event-driven theory. Despite the results by Raman and Martinez, we can

prove that the infamous relational algorithm for the investigation of redundancy by Bhabha et al. is in Co-NP. This may or may not

actually hold in reality.

Continuing with this rationale, we performed a trace, over the course of several weeks, disconfirming that our design is not feasible.

The design for our approach consists of four independent components: “fuzzy” configurations, wire less methodologies, IPv7, and

peer-to-peer communication. The methodology for Tirade consists of four independent components: signed configurations,

pseudorandom algorithms, heterogeneous methodologies, and the visualization of RPCs. We use our previously studied results as a

basis for all of these assumptions.

The model for our framework consists of four independent components: rasterization, Smalltalk, decentralized algorithms, and

semaphores. This is a structured property of Tirade. Continuing with this rationale, consider the early framework by Douglas

Engelbart et al.; our methodology is similar, but will actually overcome this challenge. Similarly, we estimate that each component

of our solution explores Boolean logic, independent of all other components. Similarly, we believe that digital-to-analog

converters1,1,4,5,6,7,1

can be made “fuzzy”, extensible, and amphibious. This is a robust property of our system. Figure 1 details

Tirade’s client-server refinement. This may or may not actually hold in reality.

Omniscient Information: Our implementation of our application is flexible, authenticated, and reliable. Continuing with this

rationale, the centralized logging facility contains about 36 instructions of SQL. We have not yet implemented the server daemon,

as this is the least natural component of our algorithm.

Figure-2

The mean distance of Tirade, as a function of latency

Our framework requires root access in order to locate multimodal epistemologies. The code base of 42 Perl files and the client-side

library must run on the same node. Overall, Tirade adds only modest overhead and complexity to related cooperative

methodologies.

Results and Analysis

Evaluating complex systems is difficult. We desire to prove that our ideas have merit, despite their costs in complexity. Our overall

evaluation seeks to prove three hypotheses: (1) that RAID has actually shown exaggerated distance over time; (2) that response

time is an obsolete way to measure expected clock speed; and finally (3) that Moore’s Law no longer impacts system design. Our

evaluation strategy holds suprising results for patient reader.



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Hardware and Software Configuration: Though many elide important experimental details, we provide them here in gory detail.

We instrumented a deployment on our mobile telephones to measure atomic communication’s inability to effect the uncertainty of

artificial intelligence.

Figure-3

The 10th-percentile block size of Tirade, compared with the other heuristics

We only noted these results when emulating it in courseware. We quadrupled the tape drive speed of UC Berkeley’s read write

cluster. Second, we quadrupled the ROM throughput of our decommissioned Apple ][es to discover the NV-RAM speed of our

Internet test bed. Along these same lines, we doubled the effective RAM space of Intel’s mobile telephones to examine modalities.

On a similar note, we removed 3GB/s of Wi-Fi throughput from our event-driven test bed. In the end, we removed 10MB/s of Wi-

Fi throughput from our mobile telephones. Tirade does not run on a commodity operating system but instead requires a provably

micro-kernelized version of Sprite Version 1c. all software components were hand assembled using GCC 5c, Service Pack 9 built

on the Italian toolkit for opportunistically emulating block size. All software was hand assembled using GCC 4.2.7 linked against

homogeneous libraries for architecting sensor networks.

Figure-4

The average instruction rate of our method, as a function of instruction rate

Similarly, all software components were hand assembled using GCC 6.4 built on D. Kobayashi’s toolkit for computationally

controlling lazily Bayesian latency. We made all of our software is available under an Old Plan 9 License license.

Experimental Results: Given these trivial configurations, we achieved non-trivial results. That being said, we ran four novel

experiments: (1) we ran local-area networks on 37 nodes spread throughout the sensor-net network, and compared them against 32

bit architectures running locally; (2) we ran 70 trials with a simulated instant messenger workload, and compared results to our

software deployment; (3) we measured RAM speed as a function of tape drive space on an Apple][e; and (4) we ran Web services

on 19 nodes spread throughout the 100-node network, and compared them against superblocks running locally. All of these

experiments completed with without unusual heat dissipation or paging.



-21st Nov 2015


Now for the climactic analysis of the first two. Note the heavy tail on the CDF in Figure 4, exhibiting duplicated median instruction

rate. Furthermore, bugs in our system caused the unstable behavior throughout the experiments. Note that Figure 4 shows the mean

and not expected parallel hard disk throughput.

We next turn to experiments (3) and (4) enumerated above, shown in Figure 3. Bugs in our system caused the unstable behavior

throughout the experiments. The results come from only 3 trial runs, and were not reproducible. We scarcely anticipated how

precise our results were in this phase of the evaluation.

Lastly, we discuss all four experiments. The key to Figure 2 is closing the feedback loop; Figure 2 shows how Tirade’s RAM speed

does not converge otherwise. Continuing with this rationale, Gaussian electromagnetic disturbances in our distributed testbed

caused unstable experimental results. Further, the many discontinuities in the graphs point to improved energy introduced with our

hardware upgrades.

Related Works: Our approach is related to research into adaptive modalities, vacuum tubes, and the partition table. Along these

same lines, Thompson et al. developed a similar system, nevertheless we disconfirmed that Tirade runs in O(log n) time.

Furthermore, Gupta et al. originally articulated the need for encrypted archetypes8. While we have nothing against the existing

approach9, we do not believe that approach is applicable to software engineering

10,4,9,11,11. Several constant-time and “fuzzy” frame

works have been proposed in the literature. Security aside, our solution emulates more accurately. Furthermore, unlike many related

approaches, we do not attempt to observe or allow rasterization1. Instead of controlling the simulation of hierarchical

databases12,13,14,15

, we accomplish this goal simply by analyzing the structured unification of digital-to analog converters and

congestion control. In the end, note that our system improves cacheable technology; as a result, Tirade runs in O(2n) time16

. In our

research, we fixed all of the grand challenges inherent in the existing work.

We now compare our method to prior event driven symmetries solutions. Continuing with this rationale, the choice of von

Neumann machines in17

differs from ours in that we analyze only appropriate methodologies in Tirade.

Next, recent work by Harris and Sato suggests a framework for constructing the construction of e-commerce, but does not offer an

implementation. However, these solutions are entirely orthogonal to our efforts.

Conclusion

We demonstrated in this position paper that the Ethernet18

can be made real-time, cacheable, and ubiquitous, and Tirade is no

exception to that rule. Continuing with this rationale, we disproved that complexity in Tirade is not a quandary. Similarly, the

characteristics of Tirade, in relation to those of more much-touted frameworks, are dubiously more typical. We demonstrated that

performance in our framework is not a riddle. We see no reason not to use our application for preventing thin clients.

References

1. E. Schroedinger, A. Shamir and M. Raman, Boaster: Amethodology for the evaluation of fiberoptic cables, in Proceedings of

SIGGRAPH, (2001)

2. R. Brooks, The impact of unstable epistemologies on programming languages,” in Proceedings of IN- FOCOM, Oct. (1996)

3. Utpal K. Lakshminarayanan, D. Clark, W. Kobayashi and P. Erd˝OS, “A methodology for the construction of spreadsheets,”

in Proceedings of the Conference on Electronic, Unstable Symmetries, (1995)

4. D. Clark, Deploying DNS using authenticated configurations, in Proceedings of the Symposium on Compact, Encrypted

Epistemologies, (2005)

5. E. Taylor, J. Wilkinson, L. Adleman, A. Tanenbaum and C. Darwin, Homogeneous, adaptive symmetries for interrupts,

Journal of Permutable, Scalable, Game-Theoretic Modalities, 1, 84–108, (1996)

6. I. Newton and K. Thompson, Towards the emulation of robots, in Proceedings of the USENIX Technical Conference, (1998)

7. X.I. Davis, Efficient, inter active modalities for erasure coding,” Journal of Peer-to-Peer Methodologies, 1, 82–103, (1995)

8. A. Turing, B. Lampson and R. Tarjan, Deploying Markov models and a* search with Fluke,” Journal of Scalable, Efficient

Symmetries, 9, 77–84, (1999)

9. M. Sato, R.M. Wilson, J. Fredrick P. Brooks, J. Sivashankar, E. Harris, J. Taylor, O.C. Harris and K. Nygaard, Visualization

of SCSI disks, in Proceedings of the Workshop on Self-Learning, Lossless Archetypes, (2000)



-21st Nov 2015


10. Manna and R. Hamming, The effect of autonomous communication on artificial intelligence, Journal of Unstable, Modular

Models, 80, 77–91, (2005)

11. C. Darwin, R. Brown and A. Turing, Exploring replication using constant-time information,” in Proceedings of JAIR, (2001)

12. R. Hamming and J. Hennessy, Collaborative theory, in Proceedings of the Conference on Classical Technology, (1992)

13. N. Jackson, Deconstructing journaling file systems using Eld, in Proceedings of the Conference on Co-operative Algorithms,

(2005)

14. M. Welsh, F. Corbato and J. Smith, Decoupling B-Trees from consistent hashing in congestion control, in Proceedings of the

Workshop on Constant Time, Unstable, Amphibious Epistemologies, (1994)

15. O. Dahl and G. Anderson, Linear-time, highly available epistemologies for Moore’s Law,” UCSD, Tech. Rep. 9642/717,

(1998)

16. S. Floyd, The producer-consumer problem no longer considered harmful,” in Proceedings of the Workshop on Distributed,

Peer-to-Peer Theory, (1993)

17. F. Corbato and A. Pnueli, Harnessing architecture using trainable models,” Journal of Ambimorphic, Random Algorithms, 37,

1–14, (2003)

18. D. Ritchie, S. Vikram and H. Wu, Comparing interrupts and Markov models using Laud, Journal of Robust, Efficient Models,

0, 71–90, (2004)



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Approaches to Implement Authentication and Encryption Techniques in

Cloud Computing

Arun Kanti Manna1 and Chandan Koner

2

1Department of Computer Science and Engineering, Govt. Polytechnic, Silli, Jharkhand, INDIA 2Department of Computer Science and Engineering, Dr. B. C. Roy Engineering College, Durgapur, W.B, INDIA

Abstract

Cloud computing is the freedom of computing services over the networks of networks i.e. “Internet”. Cloud services permit

individual subscriber or organization as a hole to use software and hardware that are controlled by third-party service providers at

distant locations. This computing technology provides a shared pool of resources, including data storage space, networks, computer

processing power, and specialized commercial and user applications. The cloud computing model allows access to information and

computer resources from anywhere in network. With the application of this technology, the cost of computation, application

hosting, data storage and delivery is reduced significantly. Information security in cloud computing is a challenging issue to the

future scientists and technologists due to increasing security threats and attacks with the emerging growth of cloud computing

applications. In recent years, several authentication and encryption techniques have been developed and installed but it is found that

no technique is suitable for all applications neither any technique is suitable invariably. In this paper, we have proposed several

authentication and encryption techniques in cloud computing.

Keywords: Cloud Computing; Information Security; Mutual Authentication; TVA; Key Refreshment

Introduction

In today, cloud computing1-6

is the most prevalent term among enterprises and bulletins. It is observed that the generation of

revenues for IT companies by public cloud computing are increasing promptly in every year. The growth of cloud infrastructure

continues to outperform the overall market of IT infrastructure. According to the latest forecast by Allied Market Research5, the

global personal cloud market is expected to top almost $90 billion (£58.5bn) in revenue by 2020. Cloud computing customers or

subscribers do not own the physical infrastructure; rather they rent the usage from a third-party provider. This helps them to avoid

huge. They consume resources as a service and pay only for resources that they use. Most cloud computing infrastructures consist

of services delivered through common centers and built on servers. Cloud computing references two essential perceptions, One is it

abstracts the details of system implementation from users and developers. Applications run on physical systems that are not

specified, data is stored in locations that are unknown, administration of systems are outsourced to others, and access by users is

ubiquitous. Another is it virtualizes systems by pooling and sharing resources systems and storage can be provisioned as needed

from a centralized infrastructure, costs are assessed on a metered basis, multi-tenancy is enabled, and resources are accessible with

agility. Information security8-12

deals with the protection of data and / or information against intentional and / or unintentional

modification, loss or damage and fabrication of data, and / or deliberate disclosure of data to unauthorized persons or miscreants.

Usually the core concepts of information security had been dealing with providing Confidentiality, Integrity and Availability.

Afterwards, some other elements like Possession, Authenticity and Utility were proposed. Furthermore, the techniques to achieve

information security include cryptography, especially when transferring data / information. Information would be encoded

(encrypted) in such a way that it would be usable only to the authorized ones.

Review of Related Works

Authentication is a process by which a cloud server (service provider) gains confidence about the identity of the communication

partner. It ensures that a legitimate subscriber is accessing the services provided by a cloud server. In recent times, Identification

based cloud computing security models and frameworks have been developed by Li. et al.13

in 2009. But only identifying the actual

user does not all the time prevent data hacking or intruding in the database of cloud environment. Yao’s Garbled Circuit is

identification based work which is used for secure data saving in cloud servers14

. But, this work does not ensure security in whole

cloud computing platform. Tang et al. and Vaquero et al. used AES based file encryption system in some of cloud computing

models15,16

. But these models keep both the encryption key and encrypted file in one database server. In 2009 and 2010, some other

models and secured architectures are developed for ensuring security in cloud computing17,18

by Wang et al. and Nguyen et al.

respectively. Although these models ensures secured communication between users and servers, but the loaded information is not

encrypted. For best security ensuring process, the uploaded information needs to be encrypted so that none can know about the

information and its location. Recently some other data security models for cloud computing environment are also being planned19,20

in 2009 and 2011. But, these models also fail to ensure all criteria of cloud computing security issues21

. In 2010, Chow et al.22



-21st Nov 2015


proposed a framework of authentication to the mobile users in cloud. This approach is based on a flexible framework for supporting

authentication decisions and on a behavioral authentication approach referred to as implicit authentication. Another framework for

User Authentication Framework in Cloud Computing was researched by Chaudhury et al. in 201123

. In this framework user

legitimacy is strongly verified before enter into the cloud. And it provides identity management, mutual authentication, session key

establishment between the users and the cloud server. A user can also change his password when he wants. Password based two-

factor authentication scheme24

was developed by Yassin et al. in 2012. In this scheme, authentication is verified by Schnorr digital

signature and fingerprint of subscriber/user. In the same year, Zhang et al. proposed an identity-based authentication scheme25

for

many applications scenarios of e-business based on cloud computing.

Figure-1

An example of cloud computing

Multi-factor authentication (MFA) is an approach to user validation that requires the presentation of two or more authentication

factors. In 2014, Liu et al. researched a multi-factor cloud authentication system (MACA)26

utilizing big data. In MACA, the first

factor is a password while the second factor is a hybrid profile of user behaviour. The hybrid profile is based on users' integrated

behaviour, which includes both host-based characteristics and network flow-based features. This is the first MFA that considers

both user privacy and usability combining big data features. They take up fuzzy hashing and fully homomorphic encryption (FHE)

to protect users' sensitive profiles and to handle the varying nature of the user profiles. In most recent27

, a shared authority based

privacy-preserving authentication protocol (SAPA) was proposed by Liu et al. for cloud storage. In SAPA, shared access authority

is achieved by anonymous access request matching mechanism with security and privacy considerations, attribute based access

control is adopted to realize that the user can only access its own data fields; proxy re-encryption is applied to provide data sharing

among the multiple users.

Proposed Authentication and Encryption Techniques for Cloud Computing

In the thesis work we propose to develop a few new authentication and encryption techniques/algorithms. All the techniques will be

a collection of different phases, namely, Enrollment Phase, Authentication Phase, Network Authentication Phase, Encryption Phase

etc.

The proposed techniques will be address in following directions for investigation –

Two-way or Mutual Authentication: It means that both communicating pair authenticates each other. The entire authentication

techniques and improvements provide only one-way authentication i.e. only server in cloud computing environment can check the

authenticity of a subscriber by the entities (e.g. user id, password etc) of subscriber. The server can check the authenticity of a

http://ieeexplore.ieee.org/search/searchresult.jsp?searchWithin=p_Authors:.QT.Zhi-Hua%20Zhang.QT.&searchWithin=p_Author_Ids:38489941200&newsearch=true



-21st Nov 2015


subscriber but the subscriber cannot check whether he is communicating with a correct server or not. It is a vital gap where a

potential adversary can spoof the server and get valuable user information. This motivate to construct an authentication technique

for cloud computing environment that provides user and server authentication, and in this approach, server in cloud examines the

authenticity of subscriber and as well as subscriber verified whether he is connecting correct server in cloud or not.

Figure-2

Two-way or Mutual Authentication Technique

Time Variant Authentication (TVA): When a subscriber wants to access a server, then the subscriber sends a login request to the

server. Then the server checks the authenticity of subscriber by the entities (e.g. user id, password etc) of subscriber. If the

subscriber is authentic, the sever gives permission to access. After getting the permission, the user starts to access the resources of

the sever. During the time of accessing, authenticity of subscriber is further not checked by the server. The processes as of today in

one time check only. Thus the subscriber submits his entities in the login time and can access the server for unlimited time.

TVA is technique where the server checks the authenticity of user time to time throughout the accessing of the server. This gives

better confidence of security as per Shannon. Thus user or user system has to enter his entity/s after a regular interval throughout

the communication with the server and server has to verify the authenticity of user at every interval. At any instant, if the server

senses that the entity/s are wrong, server stops the communication.

In the entire authentication technique the challenge of the designer is to make entity/s unbreakable whereas the challenger threats to

break the entity/s. The entity/s would be impossible to break if the entity/s are made automatic variable. The automatic variable

entity can be implemented by changing the entity/s from session to session.

Automatic variable entity/s can be implemented in TVA, by entering different (modified) entity/s at every regular interval. So,

whenever user or user system will enter entity/s to the server, firstly entity/s is modified by selective logical operation and the

modified entity/s is submitted to the server. After receiving the modified entity/s, server applies reverse logical operation on

modified password to obtain the original entity/s. Superiority of AVP is that the users entity/s (which user enters at the login time)

are changed time to time by an intelligent technique to make the entitiy/s is unbreakable.

Figure-3

Time Variant Authentication



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Key Refreshment: Key based encryption systems for ensuring message integrity with key refreshment techniques (using different

keys in each and every session). In all cryptosystem the challenge of the designer is to make key unbreakable whereas the

challenger threats to break the key. Vernum proposed that key would be impossible to break if the key is made time variant. In this

approach, keys will be changed in each and every session by producing new keys time to time.

Figure-4

Key Refreshment

Conclusion

Cloud computing network is completely described above with the present authentication and encryption schemes. Our future work

is to invent new efficient authentication and encryption techniques for cloud computing network.

References

1. A. Doss, R. Nanda, Cloud Computing: A Practitioner's Guide, McGraw Hill Education, (2013)

2. Dr. Kumar Saurabh, Cloud Computing, Wiley Publication, (2012)

3. Thomas Erl, Ricardo Puttini, Zaigham Mahmood, Cloud Computing: Concepts Technology and Architecture, Pearson, (2013)

4. Buyya, Vecchiola, Selvi, “Mastering Cloud Computing”, McGraw Hill Education, (2013)

5. By James Bourne Personal cloud market to hit $90bn by 2020, research study claims

6. Forrester Research. EGEE '08. Istanbul, September, (2008)

7. NIST cloud definition, version 15 http://csrc.nist.gov/groups/SNS/cloud-computing/

8. Yashpal Kadam, Security Issues in Cloud Computing A Transparent View, International Journal of Computer Science

Emerging Technology, 2(5), 316-322 (2011)

9. Z. Wang, Security and Privacy Issues within Cloud Computing” IEEE Int. conference on computational information sciences,

Chengdu, China, (2011)

10. Mathisen, Security Challenges and Solutions in Cloud Computing 5th IEEE International Conference on Digital Ecosystems

and Technologies (IEEE DEST2011) , Daejeon, Korea, (2011)

11. Greveler U, Justus b et al., A Privacy Preserving System for Cloud Computing, 11th IEEE International Conference on

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12. John Harauz, Lorti M. Kaufinan. Bruce Potter, Data Security in the World of Cloud Computing, IEEE Security and Privacy,

Co published by the IEEE Computer and Reliability Societies, July/August 2009. (2011)

13. Hongwei Li, Yuanshun Dai, Ling Tian and Haomiao Yang, “Identity-Based Authentication for Cloud Computing”, CloudCom

2009, LNCS 5931, 157–166, (2009)

14. Sven Bugiel, Stefan Nurnberger, Ahmad-Reza Sadeghi, Thomas Schneider, “Twin Clouds: Secure Cloud Computing with

Low Latency”, CASED, Germany, (2011)

15. Yang Tang, Patrick P.C. Lee, John C.S. Lui and Radia Perlman, FADE: Secure Overlay Cloud Storage with File Assured

Deletion, (2010)

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16. Luis M. Vaquero, Luis Rodero-Merino, Daniel Morán, “Locking the sky: a survey on IaaS cloud security”, Computing, 91,

93–118, (2011)

17. Cong Wang, Qian Wang, and Kui Ren, Wenjing Lou, Ensuring Data Storage Security in Cloud Computing”, US National

Science Foundation under grant CNS-0831963, CNS-0626601, CNS-0716306, and CNS-0831628, (2009)

18. Thuy D. Nguyen, Mark A. Gondree, David J. Shifflett, Jean Khosalim, Timothy E. Levin, Cynthia E. Irvine, “A Cloud-

Oriented Cross-Domain Security Architecture”, The 2010 Military Communications Conference, U.S. Govt.

19. Vaibhav Khadilkar, Anuj Gupta, Murat Kantarcioglu, Latifur Khan, Bhavani Thuraisingham, Secure Data Storage and

Retrieval in the Cloud, University of Texas, (2011)

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Application to Mobile Users” CCSW’10, Chicago, Illinois, USA October 8, (2010)

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-21st Nov 2015


Proposed Artificial Intelligence Based Authentication of User in Remote

System 1Biswajit Mondal,

2Priyanka Roy and Chandan Koner

1

1Department of Computer Science and Engineering Dr. B. C. Roy Engineering College, Durgapur, West Bengal 2Department of Information Technology, Dr. B. C. Roy Engineering College, Durgapur, West Bengal

Abstract

Authentication of remote user’s message is a research challenge for future scientists and researchers due to increasing security

threats and attacks with the increasing volume of wireless traffic. Next generation remote system has been developed for

introducing several new messaging systems having increased volume of data. In the entire popular remote user authentication, the

authenticity of a user is checked by the server at the starting time up of communication. These authentication techniques are based

on application of cryptographic algorithms and functions for user’s authentication, but do not provide any message authentication

method. In this paper, we propose an artificial intelligence based user message authentication scheme. This paper also reports how

human intelligence can be efficiently introduced to a message server for checking authenticity of the users.

Introduction

Remote system authentication is a process by which a remote system gains confidence about the identity of the communication

partner. Remote user authentication ensures that a legitimate user is accessing the services provided by a remote server. Remote

server checks the authenticity of remote user when the user wants to access the resources of remote server. For the last few decades,

several remote user authentication schemes have been developed and installed due to increasing security threats and attacks with

the emerging growth of wired and wireless traffic. But the communications are still violated by security threats and attacks.

The basic remote user password authentication was first designed by Lamport in 19811. However, Lamport’s scheme suffers from

high hash overhead and the necessity for password resetting problem decreases its suitability for practical use. In addition, the

scheme is vulnerable to the replay attack. Haller2 proposed secret key based one time password scheme (a modified version of

Lamport’s scheme) that eliminates hash function chaining, but the scheme is also vulnerable to the replay attack. Furthermore, in

both the schemes, it is required to maintain user password database in remote server. Thus, an attacker can hack and change the

password of users. To overcome the above drawback, many researchers have planed the use of cryptographic mechanisms to

prevent the intruder from acquiring the secret password. But all of these mechanisms needed the involvement of remote server for

change of user password. To solve the problem authentication token by the means of smart card is introduced for storing the user

information (Password, Identity, Biometric property, Secret key etc). Remote user password authentication scheme with smart card

was developed by Chang and Wu in 1993. After that, there is history of advancement of remote user password authentication

technology with smart card. Several new remote user password authentication schemes with smart card have been planed and

developed. In 1995 a new remote login authentication scheme, proposed by Wu3, is based on simple geometric Euclidean plane. His

scheme allows users to freely choose passwords themselves. However, Hwang4 has showed that the weakness of Wu’s scheme lies

in the security. Wang and Chang invented a smart card based password authentication scheme5 to eliminate the security problems in

the traditional password authentication scheme. The cryptographic technique used in their scheme is combined application of

ElGamal’s6 public-key scheme and Shamir’s ID-based signature scheme

7. But in 2001, Chan-Cheng

8 pointed out that Wang and

Chang’s scheme is vulnerable to a replay attack. After that remote user authentication using smart card, introduced by Hwang and

Li in 20009, is an application of ElGamal’s

6 scheme to authenticate a user. In that year

10, Chen and Cheng pointed out that the

Hwang-Li’s scheme is vulnerable to the masquerade attack. In 2003, Shen, Lin, and Hwang11

presented a different attack on the

Hwang-Li’s scheme that cannot withstand a masquerade attack and a enhance scheme to solve the problem of Hwang-Li’s scheme.

In the same year, Chang and Hwang12

proposed at extended attack to solve the problem of Chan and Cheng’s attack on Hwang-Li’s

authentication scheme. Das et al. developed a dynamic-ID13

based remote user authentication scheme which allows the users to

choose and change their passwords freely in 2004. Subsequently a few public-key based authentication schemes have been invented

and improved. But all of the schemes check only the authenticity of user but cannot check the authenticity of server. In 2006, Das et

al.14

invented a flexible remote user authentication scheme using smart card that authenticates user as well as remote sever.

Recently in 200815

, Das and Narasimhan planed a two factor entity authentication scheme for remote systems, which provides

strong authentication, greater flexibility and requires less computational cost. The remote server authentication is necessary by

which the user can check whether he is communicating with the intended server or not. User fingerprint-based authentication

scheme first proposed by Lee et al. in 200216

, is a biometric authentication to check the validity of a user by user biometric

property. In 2006, Khan et al.17

introduced modified version of Lee’s scheme that requires only a secret key but in 2008, Xu et al.18

showed that it is vulnerable to the parallel session attack and impersonation attack.



-21st Nov 2015


Artificial Intelligence (AI) is a theory based on uncertainty. Fuzzy operations19

can be performed for taking decision in AI based

application. AI can be applied in such applications which are based on uncertainties like vagueness, ambiguity and imprecision.

User message writing characteristics is also based on uncertainties. This provides a research challenge for application of AI on user

authentication. We propose to use parameter of user’s writing habit. It means which sentences, idioms, phrasal verbs are used with

most frequently, more frequently, less frequently by the user. By the theory of AI, different relative grade can be assigned for most

frequently; more frequently, less frequently used word groups or sentences considering the frequency of those word groups or

sentences in messages. Fuzzy sets may then be derived by relative grade and number of occurrence of those sentences, idioms,

phrasal verbs in a message. Then applying fuzzy operations on fuzzy sets, authenticity of user can be verified.

Proposed Technique

Artificial Intelligence (AI) based user message authentication technique that will check the authenticity of a user by fuzzy operation

on fuzzy sets which are derived from user’s earlier messages. The earlier or past messages of the user are stored in database of the

remote server, which ultimately indicates to measure this authentication technique. Remote server performs a feasibility study of

user writing characteristics i.e. writing habit or style from stored messages. It assigns different relative grades according to the

appearance in the past messages i.e. frequency of sentences, idioms with salutations and phrasal verbs appearing like most

frequently, more frequently, less frequently used sentences, idioms with salutations and phrasal verbs in those messages. Thus it

ascertains the theory of artificial intelligence and thereof derives fuzzy sets from the relative grades which are obtained from

number of occurrence of those sentences or idioms or phrasal verbs in a message. Now applying fuzzy operations on fuzzy sets, the

server validates authenticity of the users.

User Message Authentication Technique

Proposed AI based user message authentication technique is a collection of two different phases, namely, User Enrollment Phase

and User Authentication Phase. These two phases are explained below.

User Enrollment Phase

In user enrollment phase, the user is enrolled to a remote server. This phase is executed only once for one user.

UEP1: The user sends an application request to the authority concerned for new smart card.

UEP 2: After receiving the request, the authority asks to submit his twenty different past messages.

UEP 3: The user sends his twenty different past messages to the authority.

UEP 4: After receiving the messages, authority examines those messages thoroughly and performs a feasibility study of writing

habit of a user from those messages. The authority records the followings,

(i) Which sentences (including proverbs) are most frequently, more frequently and less frequently are used by the user for writing a

message?

(ii) Which idioms (including salutation and subscription) are most frequently, more frequently and less frequently are used by the

user for writing a message?

(iii) Which phrasal verbs are most frequently, more frequently and less frequently are used by the user for writing a message?

UEP 5: The authority uses three databases in server for storing the above user writing habit. The first database, DS stores the user

most frequently, more frequently and less frequently used sentences (including proverbs) and theirs corresponding relative grades.

The first row, DSR1 of DS, stores the most frequently used sentences and their relative grade which is assigned by 0.99. The second

row, DSR2 of DS, stores the more frequently used sentences and their relative grade which is assigned by 0.66. The third row, DSR3 of

DS, stores the less frequently used sentences and their relative grade which is assigned by 0.33.

The second database, DI stores the most frequently, more frequently and less frequently used idioms (including salutation and

subscription) and theirs corresponding relative grades. The first row, DIR1 of DI, stores the most frequently used idioms and their

relative grade which is assigned by 0.99. The second row, DIR2 of DI, stores the more frequently used idioms and their relative grade

which is assigned by 0.66. The third row, DIR3 of DI, stores the less frequently used idioms and their relative grade which is assigned

by 0.33.

The third database, DP stores the most frequently, more frequently and less frequently used phrasal verbs and theirs corresponding

relative grades. The first row, DPR1 of DP, stores the most frequently used phrasal verbs and their relative grade which is assigned by



-21st Nov 2015


0.99. The second row, DPR2 of DP, stores the more frequently used phrasal verbs and their relative grade which is assigned by 0.66.

The third row, DPR3 of DP, stores the less frequently used phrasal verbs and their relative grade which is assigned by 0.33.

MSE6: If the authority does not get sufficient information, it sends a request to the user for sending other different past messages.

Then the authority executes the above steps again to create a novel database.

User Authentication Phase

When a user requests for sending a message, M, the server receives the M and counts number of sentences (including salutation and

subscription), n in the M. Then server scans M and executes the following operations,

UAP 1: Finds the matched sentences within the rows DSR1, DSR2, DSR3 of DS and M. Let the number of matched sentences in DSR1,

DSR2 and DSR3 are x1, y1and z1 respectively.

UAP 1.1: Calculates, a1= (0.99 × x1)/n, b1= (0.66 × y1)/n and c1 = (0.33 × z1)/n.

The membership functions of a fuzzy set F1 can be defined as follows,

µF1 (a1) = (0.99 × x1)/n, µF1 (b1) = (0.66 × y1)/n, µF1 (c1) = (0.33 × z1)/n

Hence, F1 = (a1, (0.99 × x1)/n), (b1, (0.66 × y1)/n), (c1, (0.33 × z1)/n)

UAP 2: Finds the matched idioms within the rows DIR1, DIR2, DIR3 of DI and M. Let the number of matched idioms in DIR1, DIR2 and

DIR3 are x2, y2 and z2 respectively.

UAP 2.1: Calculates, a2= (0.99 × x2)/n, b2= (0.66 × y2)/n and c2 = (0.33 × z2)/n


µF2 (a2) = (0.99 × x2)/n, µF2 (b2) = (0.66 × y2)/n,

µF2 (c2) = (0.33 × z2)/n


UAP 3: Finds the matched phrasal verbs within the rows DPR1, DPR2, DPR3 of DP and M. Let the number of matched phrasal verbs in

DPR1, DIR2, DIR3 are x3, y3 and z3 respectively.

UAP 3.1: Calculates, a3= (0.99 × x3)/n, b3= (0.66 × y3)/n and c3 = (0.33 × z3)/n


µF3 (a3) = (0.99 × x3)/n, µF3 (b3) = (0.66 × y3)/n,

µF3 (c3) = (0.33 × z3)/n


UAP 4: Computes,

UAP 4.1: µF1

F2

F3 (a) = min µF1 (a1), µF2 (a2), µF3 (a3)

UAP 4.2: µF1 F2

F3 (a) = max µF1 (a1), µF2 (a2), µF3 (a3)

UAP 4.3: If µF1

F2

F3 (a) ≥ 0.09 and

µF1 F2

F3 (a) ≥ 0.18, server ensures that the user is authentic. Else executes the next steps.

UAP 4.4: µF1

F2

F3 (b) = min µF1 (b1), µF2 (b2), µF3 (b3)

UAP 4.5: µF1 F2

F3 (b) = max µF1 (b1), µF2 (b2), µF3 (b3)

UAP 4.6: If µF1

F2

F3 (b) ≥ 0.045 and

µF1 F2

F3 (b) ≥ 0.09, then server ensures that the user is authentic. Else executes the next steps.

UAP 4.7: µF1

F2

F3 (c) = min µF1 (c1), µF2 (c2), µF3 (c3)

UAP 4.8: µF1 F2

F3 (c) = max µF1 (c1), µF2 (c2), µF3 (c3)

UAP 4.9: If µF1

F2

F3 (c) ≥ 0.03 and

µF1 F2

F3 (c) ≥ 0.06, then server ensures that the user is authentic.

If no conditions are matched, then remote server ensures that the user is unauthentic and ignores the message of user. Server sends

an authentication failure message to the user.

Conclusion

In our proposed artificial intelligence based technique, message authentication scheme is developed for the user of any remote

system. A novel artificial intelligence is introduced to the server for this authentication purpose. This technique enables to work

within a real time basis for the present as well as the next generation remote system networks.

References



-21st Nov 2015


1. L. Lamport, “Password authentication with insecure communication”.Communication. ACM, 24(11), 770-772, (1981)

2. N.M. Haller, A one-time password system, RFC 1704, (1994)

3. T.C. Wu, Remote login authentication scheme based on a geometric approach Computer Communications, 18(12), 959-963,

(1995)

4. M.S. Hwang, Cryptanalysis of remote login authentication scheme” Computer Communications, vol. 22(8), 742-744, (1999)

5. S.J. Wang, J.F. Chan, Smart card based secure password authentication scheme”, Computers and Security, 15(3), 231-237,

(1996)

6. T. ElGamal, A public key based cryptosystem and a signature scheme based on discrete algorithms”, IEEE Transactions on

Information Theory, 31(4), 469-472, (1985)

7. A. Shamir, Identity-based cryptosystems and signature schemes, Proc. CRYPTO’ 84, Lecturer notes in Computer Science,

vol. 196, Springer, Berlin, 47-53, (1985)

8. C.K. Chan and L.M Cheng, Remarks on Wang-Chang’s password authentication scheme”, Electronics Letters, 37(1), 22-23

(2001)

9. M.S. Hwang and L.H. Li, A new remote user authentication scheme using smart cards”, IEEE Transactions on Consumer

Electronics, 46(1), 28-30, (2000)

10. C.K. Chan and L.M Cheng, Cryptanalysis of a remote user authentication scheme using smart cards”, IEEE Transactions on

Consumer Electronics, 46(4), 992-993, (2000)

11. J.J. Shen, C.W. Lin, and M.S. Hwang, A modified remote user authentication scheme using smart cards”, IEEE Transactions

on Consumer Electronics, 49(2), 414-416, (2003)

12. C.C. Chang and K.F. Hawng, Some forgery attacks on a remote user authentication scheme using smart cards”, Informatics,

14(3), 289-294, (2003)

13. M.L. Das, A. Saxsena and V.P. Gulati, A dynamic ID-based remote user authentication scheme, IEEE Transactions on

Consumer Electronics, 50(2), 629-631, (2004)

14. M.L. Das, Flexible and Secure Remote Systems Authentication Scheme Using Smart Cards”. HIT Transaction on ECCN,

1(2), 78-82, (2006)

15. M.L. Das and V.L. Narasimhan, EARS: Efficient Entity Authentication in Remote Systems, Proc. ITNG08, USA, 603-608,

(2008)

16. J.K. Lee, S.R. Ryu and K.Y. Yoo, Fingerprint-based remote user authentication scheme using smart cards”, Electronics

Letters, 38(2), 597-600, (2002)

17. M. K. Khan and J. Zhang, An efficient and practical fingerprint-based remote user authentication scheme with smart cards”,

IPSEC 2006, Lecturer Notes in Compueter Science 3903, 260-269, (2006)

18. Jing Xu, Wen-Tao Zhu, Deng-Guo Feng, Improvement of a Fingerprint-Based Remote User Authentication Scheme, ISA

2008, 87-92, (2008)



-21st Nov 2015


Transmission Congestion Management, Pricing and Locational Marginal

Pricing in the Deregulated Power System

Bishaljit Paul

Department of Electrical Engineering, Techno India, Silli, Ranchi, Jharkhand, INDIA

Introduction

Around the whole world there is a very large impact on power transmission in almost all the power systems due to the deregulation

and privatization of electricity markets. Among the market participants in a competitive market environment there is an obstacle for

perfect competition due to the bottlenecks in the transmission line. Planning should be done appropriately for the operation of the

transmission systems. Many participants who buy and sell electricity make the competitive electricity market which is very

complex in nature. As the supply and demand must be balanced at all times, the complexity arises due to the limitations of the

available transmission systems. A system is said to be congested, when the producers and consumers of electric energy desire to

produce and consume the amounts that would cause the transmission system beyond their transfer capabilities. ‘Locational

Marginal Pricing’ approach is chosen to locate the spots of congestion in the utility system. The results are found efficient in

minimizing the congestion due to transmission line outage, increase in loads and generation failure.

Under deregulated market operation, electric power utilities are undergoing major restructuring process1-3

. Due to power system

deregulation, the benefit of lower electricity cost, better consumer service, improved system efficiency are gained. Electric Supply

Industry (ESI) through out the world is undergoing restructuring for better utilization of the resources and to provide quality ser-

vices to the consumers at competitive prices. Introducing competition at various levels, the monopoly in the generation and trading

sectors is being abolished due to the restructuring of the power industry. As different parties compete with each other to win their

market share and remain in business, which promotes technical growth, improves customer satisfaction, increases efficiency, the

electricity sector restructuring is done which is popularly known as deregulation.

Electricity market throughout the whole world needs competitive forces which makes the market more efficient and the price is

determined by the supply and demand functions. Due to the increased volatility of the electricity market, a market participant can

make trading contracts with other parties to discard possible risks and get better returns. Congestion which is due to overloading of

the transmission lines or transformers prevents the system operators from dispatching additional power from a specific generator.

When congestion occurs on a bulk power grid, Locational Marginal Pricing (LMP), a market pricing approach is used to manage

the efficient use of the transmission system. One or more restrictions on the transmission system prevent the economic or least

expensive supply of energy from serving the demand, it is a case when a congestion arises. A transmission constraint is that when

transmission lines may not have enough capacity to carry all the electicity to meet the demand in a certain location. LMP includes

the cost of congestion i.e. it includes the cost of supplying the more expensive electricity in those locations, thus providing a

precise, market based method for pricing energy. At every location on the grid, a clear and accurate signal of the price of electricity

is provided by the LMP to the market participants.

Consumers are charged more than the average cost of production of electricity due to the nonlinear nature of the power flow and the

constraints imposed by the Optimal Power Flow (OPF) when LMPs are used for settlement of transactions. The difference is

accumulated as network rental by the Independent System Operator (ISO). Rental is of two components i.e. loss rental and con-

straint rental. The difference in average losses and marginal losses is loss rental which is due to nonlinear nature of losses.

Transmission Congestion: To have a market based solution with economic efficiency, congestion management in a multi-

buyer/multi-seller system is one of the most involved tasks. Generation, transmission and distribution are within direct control of a

central agency or a single utility in a vertically integrated utility structure. To achieve the system least cost, generation is dispatched

accordingly10

. The possible occurence of congestion is eliminated by the optimal dispatch solution using security constrained

economic dispatch. Thus the generations are dispatched in such a way that the power flow limits on the transmission lines are not

exceeded. Congestion management is a mechanism in which the transactions are prioritized and committed to work in such a

schedule which would not overload the network. In a deregulated power environment the things are not as simple. Irrespective of

relative geographical location of buyer and seller, every buyer wants to buy power from the cheapest generator available in a

deregulated environment. As a result of this, the transmission corridors evacuating the power of cheaper generators would get

overloaded if all such transactions are approved. Congestion thus occurs and the system operator finds all the transactions cannot be

allowed due to overlad of the transmission network. The system operator handles this situation by means of real time congestion



-21st Nov 2015


management which involves precautionary as well as remedial action on system operator’s part as follows –

(i) That allow only that set of transactions which taken together, keeps the transmission system within limits.

(ii) Remedial action in real time is to be taken as the transmission corridors may get overloaded due to unscheduled flowA set of

rules are to be ensured in the transmission congestion management to ensure control over generation and loads to maintain

acceptable level of system security and reliability. Under open market structure the rules ensure market efficiency maximization

and a set of players will always be looking for loopholes in the mechanism to exploit it.

The market must be modelled in a deregulated environment so that the participants (buyers and sellers) [16] engage freely in

transactions in a manner that does notthreaten the security of the power system. Congestion management schemes has become an

important activity of power system operators and its objective is to minimize the interference of the transmission network in the

market to ensure the secured operation of the power system.

Whenever physical or operational constraints in a transmission network become active, the system is said to be in a state of

congestion. The possible limits that may hit in case of congestion are -

(i) Line thermal limits (ii) Transformer emergency ratings (iii) Bus voltage limits (iv) Transient or oscillatory stability.

Effects of Transmission Congestion

(i) Market inefficiency - The effect of transmission congestion is to create market inefficienct. Market efficiency refers to a market

outcome that maximizes the social welfare which is the sum of producer surplus and consumer surplus. Market efficiency results

with respect to generation when the most cost effective generation resources are used to serve the load. The difference of social

welfare between a perfect market and a real market is a measure of efficiency of the real market.

(ii) Market power - If the generator can successfully increase its profits by strategic bidding19

or by any means other than lowering

its costs, a market power exits. In a two area system with cheaper generation in area 1 and relatively costlier generation in area 2,

buyers in both the areas will prefer generation in area 1 and the tie-lines between the two areas will start operating at full capacity

such that no further power transfer from area 1 to area 2 is possible. The sellers in area 2 are then said to possess market power

since these sellers can charge higher price to buyers if the loads are inelastic. So congestion leads to market power which results in

market inefficiency.

In a centralized dispatch structure the system operator changes schedules of generators by raising generation of some while

decreasing the others. The operator compensates the parties who were asked to generate more by paying them for their additional

power production and giving lost opportunity payments to parties who were ordered to step down.

Congestion Management in Electricity Markets: In a competitive electricity market, congestion occurs in transmission system

due to overloading of lines or transformers for market settlement. In the deregulated market the chances of congestion is quite high

since the customers would like to purchase electricity from cheapest available sources. For secure operation of the power system,

the congestion which is undesirable should be alleviated. Congestion management use optimal power flow techniques11

for

rescheduling output of sources, compensating devices and curtailment of loads. In a restructured electricity market, the transmission

network operate at or beyond transfer limits, when the producers and consumers of electric energy desire to produce and consume

in amounts. If the congestion in the system persist for a long time, it can cause sudden rise in the electricity price and threaten

system security and reliabity. Congestion management is one of the most challenging tasks of the system operator (SO) in the

deregulated environment. There are three different ways to overcome the network congestion - (i) Price Area Congestion

Management (ii) ATC based Congestion Management (iii) OPF based Congestion Management.

(i) Price Area Congestion Management - In Norway, Sweden, Finland and Den- mark when congestion is predicted, the system

operator declares it and system is split into price areas at the congestion areas. Spot market bidders submit separate bids for each

price area in which they have generation and loads. In case of no congestion market will settle at one price and in case of

congestion, the price areas are separated and settled at prices that satisfy transmission constraints. Lower prices exist in excess

generation and higher prices for excess loads.

(ii) ATC based Congestion Management - Federal Energy Regulatory Com- mission (FERC) established a system where each SO

would be responsible for monitoring its own regional transmission system and calculating its ATC for congested paths entering,

leaving and inside its network. The ATC values [12] for the next hour are placed in a website Open Access same-time Information

System (OASIS), operated by SO.



-21st Nov 2015


(iii) Optimal power Flow based Congestion Management - OPF is performed to minimize generators’ operating cost subject to a set

of constraints that represent the transmission system within which the generators operate. Customers willing to purchase power

send a bid function to the SO. OPF solution gives cost/MW at each node of the system. Zonal pricing method is followed in which

the system is divided into various zones on geographical basis. The zone prices obtained from OPF are used in the manner that

generators are paid zone price of energy and the loads pay the zone price of energy.

Electricity markets have been established as part of this industry restructuring. Two schemes of electricity pricing have been at the

core of currently operating electricity markets. These are Nodal Pricing and Zonal Pricing. Nodal pricing is quite prevalent in the

US electricity markets (PJM, New York ISO), Zonal pricing is in some European countries. Nodal pricing scheme is more complex

and intensive than zonal pricing because nodal pricing uses the pricing scheme known as Locational Marginal Price (LMP), where

electricity price is determined on a marginal basis at the bus level. Under nodal pricing, LMP is composed of three components -

System Marginal energy Price (SMP), Marginal Congestion Component (MCC), Marginal Loss Component (MLC).

Locational Marginal Price

There are many different methods for congestion management with varying levels of economic signal. Locational Marginal Pricing

(LMP) is the most effective mechanism as it provides the strongest economic signal to market participants. Locational Marginal

Pricing (LMP) 4

are the incremental prices of energy at each node on the power system. On a lossless system, LMP comprise the

marginal price for generation and a transmission congestion component. In the absence of congestion, the LMPs are the same

everywhere since the incremental load at each node can be met by incremental generation from the marginal unit. However, when a

transmission constraint becomes prominent, the system is said to be congested and the marginal prices will, in general be different

at each node. The deregulation of electricity market27

gives rise to competitive market which are complex systems with many

participants who buy and sell electricity. Due to the limitations of the transmission systems and the fact that supply and demand

must be in balance at all times, the complexity arises. LMP solution is done by Linear Programming Technique. Through the

historical operational information, loss factors for the network losses are set. LMP reflects not only the marginal cost of energy

production but also its delivery because of the effects of transmission losses and transmission system congestion. LMP6 vary

significantly from one location to another. Decomposition of LMP into three components Marginal Energy Price (MEP), Marginal

Loss Price (MLP), and Marginal Congestion Price (MCP) is not unique and there is a large level of arbitariness in any

decomposition.

LMP based clearance scheme23

is used to calculate the amount of money earned from ISO by the energy sellers and paid to ISO by

the energy consumers. Linearized DC OPF problems are usually applied for the approximation of nonlinear AC OPF problems in

order to find real power solutions for restructured wholesale power markets. LMP is defined as a change in production cost to

optimally deliver an increment of load at the locations while satisfying all the constraints. Cost of transmission services20

is

accounted together with LMP which represents energy price, network losses cost and transmission congestion cost.

When the producers and consumers of electric energy19

desire to produce and consume energy that would cause the transmission

system to operate beyond one or more transfer limits, LMP approach is preferred to locate the spots of congestion under various

critical conditions. Incidence Matrix approach18

for calculating LMP is an effective tool for short-term and long-term economic

analysis of restructered power system. LMP components17

are evaluated for an important role played by the nodes with generators

having free capacity.LMPs of nodes without generation or with generation at their limits are a function of the LMPs at the marginal

nodes and the impacts of the network constraints, including losses in the network.

LMP components5, 28

produced by loss modeling by introducing loss distribution factors to balance the consumed losses in the

lossless d.c power system model, achieves dependable and predictable market-clearing results. LMP can be higher than the highest

generation bidding price and LMP can be lower than the lowest generation bidding price.LMP sensitivities8 are calculated with

respect to changes in demand throughout an electric power network. Not only prices but their sensitivities with respect to demands

constitute fundamental information now a days in matured electricity markets. The changes in LMP9 as parameters vary and

provide insight on the functioning and behaviour of the electric energy system. To assess the degree of competitiveness in the

electricity markets, producers and consumers establish their bidding strategies by the sensitivity information. For short term15

market based operation and planning, the information of congestion and price versus load is obtained. It helps the planners to

identify the possible congestion as the system load grows. Generation companies get useful information for possible congestions

and price change as the system load grows as most of them use OPF model for congestion and price forecasting to achieve better

economic benefits.



-21st Nov 2015


An AC OPF based formulation for procuring, pricing and settling energy and ancillary services by integrated market systems

provides LMP for energy and Ancillary Service Marginal Prices (ASMP) where the characteristics of the prices are analyzed when

economic substitution among ancillary services is required.

Accordingly, LMP is stated as follows :-

LMP=LM Penergy + LM Ploss + LM Pcongestion

That is, LMP is the summation of the costs of marginal energy, marginal loss, and congestion. Two general methods are applied for

calculating LMP. One is to determine the three components separately and then sum them up. A second method is to first calculate

LMPs based on full ac network model and then identify individual components as necessary. The LMP difference between any two

locations represents the cost of transmission from the injection to the withdrawal, including congestion and losses. When

congestion exists in the transmission system, this method first uses loss factor (LF) to determine the portion of LMP that represents

losses. Then, by subtracting the sum of the marginal energy and the marginal loss costs from the LMP at the location of interest, we

can get the transmission congestion cost.

Conclusion

The fundamental concepts of LMP have been discussed. The LMP difference between two adjacent buses is the congestion cost

which arises when the energy is transfered from one location to the other location. Transmission losses may impact LMP

differences as well. LMP at a certain bus can be higher, lower or even negative than the highest offer price. It is possible that we

have to reduce output of cheaper units and increase outputs of more expensive units in order to supply an additional MW load at a

specific bus. DC load flow is the linearized model of AC load flow and is capable of giving acceptable results in many systems. So

it is preferred for LMP calculations in power market operations. As the LMP implementation is very efficient for restructed power

markets in bidding strategies, the electricity market through out the world employs LMP as one of the most popular approaches and

more research is required.

Future Work

As we can calculate the price at all locations, LMP for IEEE 30 bus is to be calculated and compared with the LMP values under

cost minimization and loss minimization in a secured constrained power system as these data are helpful for bidding strategy. LMP

values under Unit-committed cases for hourly load cases are to be calculated and reflected in the website.

References

1. M. Shahidehpour, H. Yamin and Z.Y. Li, Market operations in electric power system. John Wiley and Sons,, Inc., New York,

(2002)

2. Website http://www.pjm.com: Sponsored by Pennsylvania-New Jersey- Maryland Interconnection, (2001)

3. Z. Li and H. Daneshi, Some observations on market clearing price and locational marginal price. IEEE Power Engineering

Society General Meeting, 2005. 2702-2709, (2005)

4. T.J. Overbye, X. Cheng and Y. Sun, A comparison of the AC and DC power flow models for LMP calculations. Proceedings

of the 37th Hawaii international conference on system sciences, (2004)

5. E. Litvinov, T. Zheng, G. Rosenwald and P. Shamsollahi, Marginal loss modeling in LMP calculation. IEEE Transactions on

Power Systems, 19(2), 880-888, (2004)

6. Young Fu, Member, IEEE, Zuyi Li, Member, IEEE Different Models and Properties on LMP Calculations.

7. Nicholas Steffan, Student Member, IEEE, Gerald T. Heydt, Life Fellow, IEEE Quadratic Programming and Related

Techniques for the Calculation of Locational Marginal Prices in Distribution Systems.

8. Antonio J. Conejo, Fellow IEEE, Enrique Castillo, Roberto Minguez and Federico Milano, Member IEEE Locational

Marginal Price Sensitivities.

9. Liu Yang, Chunlin Deng, China A united method for Sensitivity Analysis of the Locational Marginal Price based on the

Optimal Power Flow.

10. Richard D. Christie, Member IEEE, B.F. Wollenberg, Fellow IEEE, Ivar Wangensteen. Transmission Management in the

http://www.pjm.com/



-21st Nov 2015


Deregulated Environment.

11. Ashwani Kumar, S.C Srivastava, IIT Kanpur, India. AC Power Transfer Distribution Factors for Allocating Power

Transactions in a Deregulated Market.

12. S.C Srivastava, IIT Kanpur, India. Transmission System Management in Restructured Electricity Markets.

13. A. Kumar and Punit Kumar, NIT Kurukshetra. Locational Marginal Prices with SVC in Indian Electricity Market.

14. Fungxing Li, Senior Member, IEEE, Rui Bo, Student Member, IEEE. DCOPF based LMP Simulation: Algorithm,

Comparison with ACOPF, and Sensitivity.

15. Ignacio J. Perez-Arriaga, Luis Olmos and Michel Rivier. Transmission Pric- ing.

16. Fangxing Li, Senior Member IEEE, Rui Bo, Student Member, IEEE. Con-gestion and Price Prediction under Load Variation.

17. Tina Orfanogianni, George Gross, Fellow IEEE. A General Formulation for LMP Evaluation.

18. Mohammad Sadegh Javadi. Incidence Matrix- Based LMP Calculations: Algorithm and Applications. Islamic Azad

University, Fass, Iran.

19. P.Ramachandran, R. Senthil. Locational Marginal Pricing approach to minimize Congestion in Restructured Power Markets.

Anna University, Chen- nai, India.

20. Muhammad Bachtiar Nappu. Locational Marginal Prices scheme considering Transmission Congestion and Network Losses.

Hasanuddin University, Sulaweri, Indonesia.

21. Avinash Swami. Transmission Congestion Impacts on Electricity Market: An Overview.

22. A.J. Wood and B.F. Wollenberg, Power Generation, Operation and Control. New York: John Wiley and Sons, (1996)

23. Md. Irfan Ahmed, Saket Saurabh, NIT Patna. DC-OPF for LMP Calculations in wholesale Electricity Market.

24. Tong Wu, Mark Rothleder, Ziad Alaywan, Alex D. Papalexopoulos, Fellow IEEE. Pricing Energy and Ancillary Services in

Integrated Market Systems by an Optimal Power Flow.

25. B.B. Chakrabarti, Member IEEE, D. Godwin, N.K.C Nair, Member IEEE. Power System Congestion - In search of an Index

from LP Basis Matrices.

26. S.M.H Nabavi, A. Kazemi, Tehran, Iran M.A.S. Masoum, Perth Australia. Congestion Management using Genetic Algorithm

in Deregulated Power Environments.

27. Basant Kumar Panigrahi, IIT, Roorke. LMP in Deregulated Electricity Markets.

28. Ashikur Bhuiya, Alberta Electric System Operator, N. Chowdhury, Univ Saskatchewan. Determination of Loss Factors in a

Deregulated System.

29. K. Kamaldass, P. Kankaraj, M. Prabavathi, Madurai, Tamil Nadu. Locational Marginal Pricing in Restructured Power Market.

30. James Momoh, Lamine Mili. Economic Design and Planning for Electric Power Systems.

31. D.S. Kircchen, Goran Strabac. Power System Economics.



-21st Nov 2015


A Survey on the Generalizations of Association Scheme

Pankaj Kumar Manjhi and Arjun Kumar University Department of Mathematics, Vinoba Bhave University, Hazaribag – 825301, INDIA

Abstract

In this paper a survey on the generalization of Association scheme is given by weakening its conditions. These generalizations are

Coherent Configuration, Generalized Directed Association Scheme and Frobenius Generalized Association Scheme. In this paper

we have only studied the relationship of these generalizations with Association Scheme on the basis of the conditions of

Association Scheme.

Keywords: Association Scheme; Coherent Configuration; AMS (2010): 05E30; 05CXX.

Association Scheme (AS)

Bose and Simamoto [5] have been defined Association Scheme is as a set of non-empty relations C=c0, c1, c2, …, cm on a finite

set X, which satisfies the following conditions:

(i) i

i=0

C X X;m

(ii) Co = Diag(X) = diagonal relation on X ( , ) : Xx x x

;

(iii) Ci is symmetric for i = 1,2,…, m;

(i) For all i, j, k, in 0, 1, 2 …., m there is an integer

k

ijp such that, for all (x, y) in Ck.

| z X : (x,z) Ci and (z, y) Cj|=

k

ijp .

Association schemes are also defined by the adjacency matrices Ao, A1,…, Am of their associate classes which have the following

properties:

(i) None of the Ai is equal to Ox and 0

A Jm

i X

i

.

(ii) A0 = Ix ;

(iii) Ai is symmetric for i = 1,2,…..m;

(i) For all i, j in 1,2, …, m the product Ai Aj is a linear combination of Ao, A1, …., Am;

(Vide [2] , [3],[4] and [9])

Coherent Configuration (CC)

In 1967 D.G.Higman defines Coherent Configuration as a set of non-empty relations C=C1, C2, …, Cm on a finite set X, which

satisfies the following conditions:

(i) C is a partition of X x X

that is, 1

Cm

i

i = X x X ;

(ii) There exist a subset Co of C which is a partition of the diagonal relation = (x,x) : x X ) ;

(iii) for every relation Ci C, its converse

C’i = (x,y) : (y,x) Ci is in C,

That is

'

*C C Ci i

(iv) there exist integer

k

ijp for 1 i, j, k m such that for any (x,y) Ck the number of points z X such that (x,z) Ci

and (z,y) Cj is equal to

k

ijp(and, in particular, is independent of the choice of (x, y) Ck).



-21st Nov 2015


CC is also defined by adjacency matrices of classes of C. If A1 A2 , …, Am are adjacency matrices of C1, C2, Cm respectively then

the axioms take the following form:

(i) A1 + A2 + … + Am = J;

(ii) There exist a subset of A1, A2,…., Am with sum I= identity matrix;

(iii) Each element of the set A1, A2,…, Am is closed under transposition

(iv) Ai Aj =1

Am

k

ij k

i

p

where

k

ijp are non-negative integers.

(Vide [1],[7] and [8])

Generalized Directed Association Scheme (Gdas)

In 2012 Singh and Manjhi [10] introduced Generalized Directed Association Scheme (GDAS) as a set C = C0, C1, C2, ….., Cm of

binary relations on a finite set X (subsets of X X) satisfying the following two conditions:

(i) C is a partition of X x X

that is, = X x X ;

(ii) For all i, j, k, in 0, 1, 2 …., m there is an integer

k

ijp such that, for all (x, y) in Ck.

z X : (x,z) Ci and (z, y) Cj|=

k

ijp .

GDAS is also defined by adjacency matrices of classes of C. If A0, A1, …, Am are adjacency matrices of C0, C1, …, Cm respectively

then the axioms take the following form:

(i) A0+A1 + A2 + , …, + Am = J;

(ii) Ai Aj = 1

Am

k

ij k

i

p

where

k

ijp are non-negative integers

Frobenius Generalized Association Scheme

In 2012 Singh and Manjhi [11] introduced Frobenius Generalized Association Scheme (FGAS) as follows:

Let G be a Frobenius group of order n+1 with permutational representation Gp= A0, A1, A2,…, An, where Ai (i=0,1,2, …, n) are

permutation matrices. Since AiAj = Ak for some k0,1,2,…,n ,Gp constitute a GAS called Frobenius Generalized Association

Scheme (FGAS).

Example: G = S3 = I,(12), (23), (13), (123), (132)

Then A0 = I3

1

0 1 0

1 0 0

0 0 1

A

,

2

1 0 0

0 0 1

0 1 0

A

,

3

0 0 1

0 1 0

1 0 0

A

,

4

0 0 1

1 0 0

0 1 0

A

,

5

0 1 0

0 0 1

1 0 0

A



-21st Nov 2015


We see that:

(i) A0Ai = AiA0 for all i = 1, 2, 3, 4, 5.

And = A0

(ii) A1A2 = A4, = A0, A2A1 = A5, A1A3=A5, A3A1 = A4, A1A4=A2 , A4A1=A3, A1A5 = A3, A5A1 = A2

(iii) A2A3 = A4, = A0, A3A2 = A5, A2A4=A3, A4A2 = A1, A2A5=A1, A5A2=A3.

(iv) = A0, A3A4 = A1, A4A3=A2, A3A5 = A2, A5A3=A1

(v) = A5, A4A5 = A0, A5A4=A0

(vi) = A4

Therefore A0, A1, A2, A3, A4, A5 constitutes a FGAS.

Relationship of Generalizations With Association Scheme

We see that Coherent Configuration is obtained from Association scheme by weakening conditions (ii) and (iii), Generalized

Directed Association Scheme is obtained by removal of conditions (ii) and (iii) of Association Scheme and Frobenius Generalized

Association Scheme is obtained from Association Scheme by removing conditions (i),(ii) and (iii).In future we are looking for

more generalization of Association Schemes and their applications.

References

1. P.P. Alejandro, R.A. Bailey and P.J. Cameron, Association schemes and permutation groups, Discrete Mathematics 266, 47-

67 (2003)

2. R.A. Bailey, Generalised wreath product of association schemes, preprint, (2003)

3. R.A. Bailey, Association schemes designed experiments, algebra and combinatorics,Cambridge University Press, (2004)

4. R.A. Bailey and P.J. Cameron, Crested product of Association Schemes, submitted exclusively to the London Mathematical

Society.

5. R.C. Bose and T. Shimamoto, Classi_cation of analysis of partially balanced incomplete block designs with two associate

classes, Journal of the American statistical association, 47, 151-184 (1952)

6. D.G. Higman, Intersection matrices for _nite permutation groups, Journal of Algebra, 6, 22-42 (1967)

7. D.G. Higman, Coherent Con_guration I, Geometriac Dedicata, 4, 1-32 (1952)

8. D.G. Higman, Coherent Con_guration II, Geometriac Dedicata, 5, 413-426 (1967)

9. J.A. Nelder, The analysis of randomized experiments with orthogonal block structure I. Block structure and the null analysis

of variance, Proceeding of the Royal Society of London, Series A 283, 147-162 (1965)

10. M.K. Singh and P.K. Manjhi, Generalized Directed Association Scheme and its Applications, International J. of Math. Sci.

and Engg. Appls., 6(III), 99-113 (2012)

11. M.K. Singh and P.K. Manjhi, Algebra of Generalized Association Scheme and its Applications, Ph.D. thesis, Ranchi

University Ranchi, India.



-21st Nov 2015


A Fixed Point Theorem Satisfying Compatibility

Dhruva Narayan Singh Dept. of Mathematics, Chas College, Chas, Jharkhand, INDIA

Abstract

In this paper we have established fixed point theorem satisfying the notion of compatibility which was introduced by Naidu and

Prasad (1986) under Gregus type and ψ type contractive conditions.

Introduction

In this Paper we shall prove a common fixed point theorem in 2-metric space using the notion of compatibility introduced by Naidu

and Prasad. Our theorems extend and improve the result of Fisher and Murthy (1987), Naidu and Prasad (1986) and Singh and

Singh (2010).

Fisher and Murthy (1987) proved the following result on metric space.

Theorem: Let f be a self-map on a complete metric space (M,ρ)such that:

ρ2(fx,fy)≤ αρ(x,f(x)).ρ(y,fy)+βρ(x,fy).ρ(y,f(x))

for all x,y in M for some non-negative constants α,β with α<1. Then f has a fixed point. Further if β<1, then f has a unique fixed poi

nt in M.

Naidu and Prasad (1986) also proved the following result in a complete 2-metric space.

Theorem: Suppose (X,d) is a complete 2-metric space and

d2(fx,fy,a)≤αd(x,fx,a).d(y,fy,a)+βd(x,fy,a)d(fx,y,a)

for all x,y,a in X and for same non-negative constants α and β with α<1, then f has a fixed point in X.

Preliminaries

Now we give some basic definitions and well known results that are needed in the sequel.

Definition (2.1): Let x be a non-empty set and d: XxXxX→R+. If for all x, y, z, and u in X. We have

(d1) d(x, y, z) = 0 if at least two of x, y, z are equal.

(d2) for all x ≠ y, there exists a point z in x such that d(x, y, z) ≠ 0.

(d3) d(x, y, z) = d(x, z, y) = d(y, z, x) =............... and so on

(d4) d(x, y, z) ≤ d(x, y, u) + d(x, u, z) + d(u, y, z)

Then d is called a 2-metric on X and the pair (X, d) is called 2-metric space.

Definition (2.2): A sequence xnn ϵ N in a 2-metric space (X,d) is said to be a cauchy sequence

if for all a ϵ X.

Definition (2.3): A sequence xnn ϵ N in a 2-metric space (X,d) is said to be a convergent if = 0 for all a ϵ X.

The point x is called the limit of the sequence.

Definition (2.4): A 2-metric space (X,d) is said to be complete if every cauchy sequence in X is convergent.

Definition (2.5): A pair f1,f2 of self-map on a 2- metric space (X,d) is said to be a compatible pair if

1 2 n 2 1 nnf x , f f x ,alim d f

= 0 for all aϵX, whenever xnnϵNϵX such that

1 n 2 nn nx xlim limf f

= t for some tϵX.

Results

Theorem (3.1): Let E,F and T be three self-maps of a complete 2-metric space (X,d)s.t.

(i) T is continuous

(ii) E,T and F,T are compatible pairs.



-21st Nov 2015


(iii) E(X)⊆T(X) : F(X)⊆T(X)

(iv) d2(Ex,Fy,a)≤αd(Tx,Ex,a).d(Ty,Fy,a)+βd(Tx,Fy,a)d(Ex,Ty,a)+γd(Tx,Ty,a).d(Ex,Fy,a)

for all x,y,a in X and for some non-negative constants α,β,γ with 0≤α,β,γ<1.

Then E,F and T have a common fixed point in X. Further if β+γ≤1 then E,F and T have a unique common fixed point in X.

Proof: Let x0be any arbitrary point of X. Since E(X)⊆T(X), F(X)⊆T(X), we can choose a point x1 in X such that Tx1=Ex0 and x2 in

X such that Tx2=Fx1. In general

Tx2p+1=Ex2p and Tx2p+2=Fx2p+1 for p=0,1,2,……….

Now we first prove that d(Tx2p,Tx2p+1,Tx2p+2)=0.

d2(Tx2p,Tx2p+1,Tx2p+2) = d(Ex2p,Fx2p+1,Tx2p)

≤ αd(Tx2p,Ex2p,Tx2p).d(Tx2p+1,Fx2p+1,Tx2p)

+ βd(Tx2p,Fx2p+1,Tx2p).d(Ex2p,Tx2p+2,Tx2p)

+ γd(Tx2p,Fx2p+1,Tx2p).d(Ex2p,Fx2p+1,Tx2p)

= 0

or d2(Tx2p,Tx2p+1,Tx2p+2) = 0

i.e. d(Tx2p,Tx2p+1,Tx2p+2) = 0

Now we consider

d2(Tx2p,Tx2p+1,a) = d

2(Fx2p-1,Ex2p,a)

= d2(Ex2p,Fx2p-1,a)

≤ αd(Tx2p,Ex2p,a).d(Tx2p-1,Fx2p-1,a)

+ βd(Tx2p,Fx2p-1,a).d(Ex2p,Tx2p-1,a)

+ γd(Tx2p,Fx2p-1,a).d(Ex2p,Fx2p-1,a)

= αd(Tx2p,Tx2p+1,a).d(Tx2p-1,Tx2p,a)

+ βd(Tx2p,Tx2p,a).d(Tx2p,Tx2p-1,a)

+ γd(Tx2p,Tx2p-1,a).d(Tx2p+1,Tx2p,a)

= (α+γ) d(Tx2p,Tx2p+1,a).d(Tx2p-1,Tx2p,a)

i.e. d(Tx2p,Tx2p+1,a)≤ (α+γ)d(Tx2p-1,Tx2p,a)

Again,

d2(Tx2p+1,Tx2p+2,a) = d

2(Ex2p,Fx2p+1,a)

≤ αd(Tx2p,Ex2p,a).d(Tx2p-1,Fx2p+1,a)

+ βd(Tx2p,Fx2p+1,a).d(Ex2p,Tx2p+1,a)

+ γd(Tx2p,Tx2p+1,a).d(Ex2p,Fx2p+1,a)

= αd(Tx2p,Tx2p+1,a).d(Tx2p+1,Tx2p+2,a)

+ βd(Tx2p,Tx2p+2,a).d(Tx2p+1,Tx2p+1,a)

+ γd(Tx2p,Tx2p+1,a).d(Tx2p+1,Tx2p+2,a)

= (α+γ) d(Tx2p,Tx2p+1,a).d(Tx2p+1,Tx2p+2,a)

i.e. d2(Tx2p+1,Tx2p+2,a) ≤ (α+γ) d(Tx2p,Tx2p+1,a)

= hd(Tx2p,Tx2p+1,a) where h=(α+γ)

≤ h2d(Tx2p-1,Tx2p,a)

⋮ ≤ h

2pd(Tx0,Tx1,a)

Hence Tx2p is convergent. Let x0 be the limit point of this sequence. As E,T and F,T are compatible pairs then

d(ETx2p,TEx2p,a)→0 and d2(FTx2p+1,TFx2p+1,a)→0

Again, E,T and F,T are compatible pairs and Tx2p∊X is a sequence then d(ETx2p,TEx2p,a)→0 as Ex2p and Tx2p

converges to the same limit, so:

2 p 2 pp p

lim lim.ETx .TEx

2 p 2 pp p

lim limE .Tx T .Ex

Eu=Tu, Similarly Tu=Fu.

Also Eu=u=Tu=Fu as follows:

d2(Eu,u,a) ≤ [d(Eu,u,Tx2p+2)+d(Eu,Tx2p+2,a)+d(Tx2p+2,u,a)]

2

= d2(Eu,u,Tx2p+2)+d

2(Tx2p+2,u,a)+d

2(Eu,Fx2p+1,a)+.......

≤ d2(Eu,u,Tx2p+2)+d

2(Tx2p+2,u,a)

+αd(Tu,Eu,a)d(Tx2p+1,Fx2p+1,a)

+βd(Tu,Fx2p+1,a)d(Eu,Tx2p+1,a)



-21st Nov 2015


+γd(Tu,Tx2p+1,a)d(Eu,Tx2p+1,a)

whenp→∞, Tx2p+2→u, Tx2p+1→u, Fx2p+1→u as Eu=Tu we have:

d2(Eu,u,a) ≤ βd(Tu,u,a).d(Eu,u,a)+γd(Tu,u,a).d(Eu,u,a)

= (β+γ)(Tu,u,a).d(Eu,u,a)

= (β+γ)d2(Eu,u,a)asEu=Tu

i.e. d(Eu,u,a)≤(β+γ)d(Eu,u,a), which is a contradiction.

Hence, Eu=u, similarly we can show that Fu=u.

Also Eu=Tu=uSinceEu=Tu. Therefor Tu=u.

Thus we get Eu=Fu=Tu=u. i.e. u is a common fixed point for E,F and T.

Uniqueness: If possible, Letv be another fixed point, then:

d2(u,v,a) ≤ d

2(Eu,Fv,a)

≤ αd(Tu,Eu,a)d(Tv,Fv,a)

+βd(Tu,Fv,a)d(Eu,Tv,a)

+γd(Tu,Tv,a)d(Eu,Tv,a)

= αd(u,u,a)d(v,v,a)+βd(u,v,a)d(u,v,a)

+γd(u,v,a)d(u,v,a)

= (β+γ)d2(u,v,a)

i.e. d(u,v,a)≤(β+γ)d(u,v,a), which is a contradiction.

Hence, d(u,v,a)=0. i.e. u is a unique common fixed point of E,F. and T.

References

1. S. Gahler, 2-metrische Raume and ihretopologische structure, Math Nach, 26, 115-148 (1963)

2. L. Gajic and M. Stojakovic, On Compatible mappings in Fixed point theory, Univer. U. Navom Sadu Zb. Rad Prirod - Mat.

Fak. Ser. mat. 24(2), 3951 (1994)

3. L. Shambhu Singh and L. Sharmeshwar Singh, Some fixed point theorems in 2-metric space, International transactions in

Mathematical Sciences and Computer, 3(1), H. 121-129, (2010)

4. P.P. Murthy, S.S. Chang, T.J. Cho and B.K. Sharma, Compatitble mappings of type (A) and common fixed point theorems,

Kyungpook Malt J. 322003-216 (1992)

5. M.A. Ahmed, Some Common Fixed Point Therorems for weakly Compatible Mappings in Metric Spaces, Fixed point Theory

and applications, Volume 2009, Article ID804734, (2009)

6. Naidu S.V.R. and Rajendra Prasad, Fixed point theorems in 2- metric space, Indian. J. Pure Appl. Math, 17(8) 974-993 (1986)



-21st Nov 2015


Dispersal of Arsenic into Damodar River: A Mathematical Model

Shafique Ahmad1 and Shibajee Singha Deo

2

1Dept. of Mathematics, B.D.A. College, Pichari, Bokaro, Jharkhand, INDIA 2Dept. of Mathematics, A.M. College, Jhalda, West Bengal, INDIA

Abstract

Damodarriver, in her course through highly populated and highly industrialized area of coalfield has become a repository of many

types of wastes produced by various human activities like industrial, agricultural and domestic. The polluted plight of the river is

enhanced with passing time due to continuous and uncontrolled discharge of toxic and hazardous effluents into it by over 46

industries located on its banks or in its vicinity particularly in Bokaro - Dhanbad areas of Jharkhand. The mathematical model is

employed as it captures the essential physic of arsenic disposition .It gives detail distribution of arsenic contamination, especially in

the high – dosage region near the point of dispersion.

Keywords: Damodarriver; Wedgemodel; arsenic; arsenolized; downstream; Dhanbad.

Introduction

Historically arsenic is known as a poison. It does not often present in its elemental state but is more common in sulfides and

sulfosalt such as Arsenopyrite. In some countries, arsenic is the most important chemical pollutant in groundwater and drinking

water. The Bengal delta region is particular affected as an estimated people have been drinking arsenic rich water for the past 20 -

30 years. According to Chowdhury et al.1 examination for arsenical dermatologic symptoms in 29 thousand people showed that

15% had skin lesions. Arsenic removal from groundwater by household sand studied by Berg et al.2. Buschmann et al.

3 describes

arsenic and Manganese pollution in upper Mekong delta.Newman et al.4 also indicated that microorganisms play critical roles in

both the reduction andremoval of As(V) from groundwater. Poyla et al.5 described that natural contamination of groundwater by

arsenic is also an emerging issue in some countries of Southeast Asia, including Vietnam, Thailand, Cambodia and Myanmar.

Vulnerable areas for arsenic contamination are typically young Quaternary deltaic and alluvial sediments comprising highly

reducing aquifers. The development of symptoms of chronic arsenic poisoning is strongly dependent on exposure time and resulting

accumulation in the body. The various stages of arsenic sis are characterized by skin pigmentation, keratosis, skin cancer, effects on

the cardiovascular and nervous system, and increased risk of lung, kidney and bladder cancer. The European Union allows a

maximum arsenic concentration of 10 µg/L in drinking water, and the World Health Organization (WHO) recommends the same

value. In contrast, developing countries are struggling to establish and implement measures to reach standards of 50 µg/L in arsenic

affected areas. Drinking water supplies in Cambodia and Vietnam are dependent on groundwater resources.

The Mekong and the Red River deltas are the most productive agricultural regions of South East Asia. Both deltas have young

sedimentary deposits of Holocene and Pleistocene age. The ground waters are usually strongly reducing with high concentrations of

iron, manganese, and ammonium. The Mekong and Red River deltas are currently exploited for drinking water supply using

installations of various sizes. In the last 7-10 years a rapidly growing rural population has stopped using surface water or water

from shallow dug wells because they are prone to contamination by harmful bacteria. Instead, it has become popular to pump

groundwater using individual private tube-wells, which is relatively free of pathogens. The Vietnamese capital Hanoi is situated in

the upper part of the 11,000 km. Red River delta, which is inhabited by 11million people and is one of the most populous areas in

the world. Due to naturally anoxic condition in the aquifers, the ground waters contain large amounts of iron and manganese that

are removed in Hanoi drinking water plants by aeration and sand filtration discussed by Duong et al.6. Understanding the mobility

of arsenic in subsurface environments is important for evaluating its possible environmental and economic effects studied by

Williams et al.7. Islam et al.

8 suggestedthat arsenic adsorbed onto sediment surfaces could bemobilized into groundwater by

anaerobic respiration of Fe(III)reducing bacteria. According to Smedley and Kinniburgh9 arsenic iscarcinogenic, and can also cause

other human health effects, such as black disease and diabetes. According to Feldman and Rosenboom10

the upper and lower

Quaternary aquifers were investigated by analyzing ground waters from small-scale tube-the Cambodian Mekong delta area in

2000, and has since been investigated and addressed through close collaboration of local authorities and NGOs. In this paper, the

arsenic levels in Damodar river of the Dhanbad region are presented, which is reported for the first time. In addition to an overview

of the magnitude of arsenic poisoning in this region, the limited information available in the literature on the geology and genesis of

Damodarriver is summarised.

Nomenclature

α : Wedge angle at the point of disposal



-21st Nov 2015


i : Average distance travelled by arsenic particle before its gets fully deposited

t : time

u : water velocity

r : distance =u.t

: Deposition velocity of arsenic

d(r) : radial spread (thickness)

C(r) : arsenic concentration

b( /s) : water – intake rate

MO : Total mass of arsenic that become arsenoid into intake form

m(r) : Amount of arsenic problem in the plume as a dispersal (i.e. after time t = r/a)

h : depth of mixing layer of river

=

Let the point of dispersal of arsenic be at the point C in the Damodarriver of city Dhanbad. All of the arsenic is oxidized into

arsenic oxide. The arsenic oxide mix with water and spread .The flow of river will transport it to considerable distance. Upon

disposal, the arsenic oxide will rapidly mix up. This the mixing layer of the water let h (= CD) be the depth of mixing layer. We

assume that this depth remains constant as the flow moves at a constant speed α.

Let the velocity of water in the river be u and will be centered after time t at a distance r (= ut) with same radial (downstream) speed

d(r). The cross flow spread (that is perpendicular to the flow direction) is taken to be an arc. Let this arc subtend a wedge angle α at

the disposal point C. The annular segment gives the horizontal section of the flow at some instant of time t.

Figure-1

Schematic representation of arsenic disposal at point C of Damodar River.

Development of Mathematical Model

Let the arsenic concentration C(r) be uniform at any time t. Let M(r) be the amount of aerosol present in the flow at distance r

(=DE). Assume that the amount deposited on the ground is proportional to the amount present in the river.

So we have M(r) = (1)

Where is the total mass of arsenic that became arsenolized into drinking form and is the average distance traversed by an

arsenic particle before it gets deposited.

So = (2)

Where is deposition velocity of aerosol. If the amount deposited per unit area is denoted by ,

This gives

= `

= (3)

The volume of water at a specific point said to be plume after arsenic deposition be .



-21st Nov 2015


If the concentration is C(r, α, z) then

C(r, α, z) = (4)

Now consider a person who is about to be reversed is the river .Since the plume has thickness d(r), therefore it will pass the person

is time

(5)

If the water intake rate is b /s, the person reversed in river will intake water in a volume b , containing an amount of arsenic

given (in mg) by

=

= (6)

This shows that is independent of the thickness d(r).

Let the population density per unit area within the specified area of theDamodarriver where arsenic deposition is available be

P(r, ). If the total amount of arsenic taken (through water)by the population is denoted by then

=

= (7)

If the population density is uniform say then this reduces to

=

= from (2) (8)

Figure-2

Schematic representation of intake of arsenic (through river water) by the Population in the city Dhanbad



-21st Nov 2015


Assume that a city Dhanbad D of width and length lies downstream of the flow of Damodar river within the wedge angle

at a distance from the place C where the deposition of arsenic occurs.(figure 2) .

Further assume that the city is an annular piece at a distance from the disposal and subtend an angle .

= . The city D we can assume the population density uniform equal to .

Thus from equation (6) we have

=

= dr

= (8)

Results from equation (7) & (8) can be applied to the arsenic intake by the population from original contaminated plume from

dispersal as it moves downstream for numerical calculations we take the following admissible values of some parameters which are

as follows:

mg , =population density /sqmt

b = water intake rate of arsenic =3.3×

Range of aerosol disposition velocity is 0.001≤ v≤ m/sec

U = downstream velocity =m/sec

H = mixing depth =500cm

= 50 km

= 10 km

=

=50

For these values (7) becomes

= (9)

& equation (8) gives

= (10)

Where 0.1 ≤ α ≤ 0.3

Taking these values the estimation for & is made for different values of.

The results of calculations are entered in tables (1) to (4)

Table-1

Values of for different values of v& using equation (10)

↓ .5× .75× 1× 1.5× 2×

0.001 1650 2475 3300 4950 6600

0.005 330 495 660 990 1320

0.01 165 24.75 330 495 660

0.05 33 49.5 66 99 132

0.1 16.5 24.75 33 49.5 66



-21st Nov 2015


Table-2

Variation of against α for u=.01m/s

↓α

.75× / 1.5× / 2× /

0.1 33.0091 66.018 88.0240

0.2 16.5045 33.009 44.0120

0.3 11.0030 22.006 29.3413

Table-3

Variation of against α for u=0.05m/s

↓α

.75× / 1.5× / 2× /

0.1 6.6018 13.3036 17.6048

0.2 3.3040 6.6018 8.8024

0.3 2.2006 4.4012 5.8682

Table-4

Variation of against α for u = 0.1m/s

↓α

.75× / 1.5× / 2× /

0.1 3.3009 6.6018 8.8024

0.2 1.6504 3.3009 4.4012

0.3 1.1003 2.2006 2.9341

Conclusion

The mathematical model is employed as it captures the essential physic of arsenic disposition. It gives detail distribution of arsenic

contamination, especially in the high – dosage region near the point of dispersion. Calculation for (i.e. the total amount of

arsenic depostel water taken when population density is uniform) & (= arsenic intake in city’s population during plume) have

been made by taking empirical data for the parameter involved.

By numerical analysis of the entries made in tables (1) to (4) we arrive at the following calculation.

(i) The effect of arsenic intake ( ) during the plume increases as population size increases for a given values of arsenic.

Disposal velocity v but for a fixed population size,the effect of arsenic intake during the passage of plume decreases as the

downstream velocity u increases (Table 1) .

(ii)Arsenic absorption by between the people in the city at which deposition from factory occurs and the people in the adjoining

region in obtained by computing . For a fixed population size decreases as the wedge angle increases .For a fixed value of

wedge angle , increases as population size increases (see table 2) to (4))

(iii) As deposition velocity v of arsenic increases, decreases for all values of α and (see tables (2) to (4)).

References

1. U.K. Chowdhury, B.K. Biswas, G. Samanta and B.K. Mandal, Groundwater arsenic contamination in Bangladesh and West

Bengal”, Environ. Health Perspect, India, 1089(5), 393-7, (2000)

2. M. Berg, S Trang, Luzi P.K.T. and S. Stuben, Arsenic removal from groundwater by household sand filters- comparative field

study, model calculations, and health benefits, Environ. Sci. Technol., 40(55), 67-73, (2006)



-21st Nov 2015


3. J. Buschmann, M. Berg, C. Stengel and M. L. Sampson, Arsenic and Manganese pollution in upper Mekong delta, Cambodia:

comprehensive groundwater survey”, Environment Sci. Tech., 37(212), 26-34, (2011)

4. D.K. Newman, D. Ahmann, F. M. M. Morel, A brief review of microbial arsenate respiration, Geomicrobiol, J., 15, 255–268,

(1998)

5. D.A. Polya, A.G. Gault, N. Diebe, Arsenic hazard in shallow Cambodian groundwaters, mineral Mag, 69(5), 807-23, (2005)

6. H.A. Duong, M.H. Hoang and W. Giger, Trihalomethane formation by chlorination of ammonium and bromide-containing

groundwater in water supplies of Hanoi, Vietnam, Water Res, 37(42), 4 -52, (2003)

7. L.E. Williams, M.O. Barnett, T.A. Kramer and J.G. Melville, Adsorption and transport of arsenic(V) in experimental

subsurface systems, J. Environ. Qual, 32, 841–850, (2003)

8. F. Islam, A. Gault, C. Boothman, D. Polya, J. Charnock, D. Chatterjee and J. Lyond, Role of metal-reducing bacteria in

arsenic release from Bengal delta sediments, Nature, 430, 68–71, (2004)

9. P.L. Smedley, D.G. Kinniburgh, A review of the source, behaviour and distribution of arsenic in natural waters”. Appl.

Geochem, 17, 517–568, (2002)

10. P.R. Feldman and J.W. Rosenboom, Cambodia drinking water quality assessment Phnom Penh, Cambodian Ministry of

Industry, Mines and Energy, (2001)



-21st Nov 2015


A Survey of Vertical Handoff Schemes in Vehicular Ad-Hoc Networks

Sadip Midya, Koushik Majumder and Asmita Roy Department of CSE, WBUT, Kolkata, India

Abstract

With the growth in population in recent times, numbers of vehicles on road also have increased drastically. And with this increase

in vehicle numbers, accident rates, traffic congestion also have increased proportionately. Having a way of establishing

communication between vehicles can reduce accident rates and traffic congestion significantly. VANET (vehicular ad-hoc

networks) is the study of connection and communication between vehicles on road and the infrastructure that supports it.

Establishing a connection between two static vehicles or nodes is a concern, but establishing connection between two moving nodes

is a challenge. A vehicle at a point can leave its home network and move to a new network. Then it is required to perform handoff

procedure between the two networks. Handoff mechanism can be divided into two classes, Horizontal handoff and Vertical

handoff. Here, a survey is done on various vertical handoff schemes and a detailed comparison is done between them based on

different layers involved in hand off and various technologies used by different handoff mechanisms etc. Some improvements over

the present handoff mechanisms are proposed leading to a wide area of research on this field.

Keywords: VANET, Vertical Handoff, MAG, MHVA, GVMM, VHDS, NEMO, DHCP, CoA

Introduction

The drastic development of technologies involving vehicles requires the safety of vehicles in traffic. So, nowadays, the car-

manufacturers are working with government agencies to increase on-road safety and ease in traffic1. In Vehicular Ad-hoc Networks

(VANET) maintaining a continuous connection is very challenging, as a VANET network needs to maintain two types of

communication V2V (Vehicle-to-Vehicle communication) and V2I (Vehicle-to Infrastructure communication).Some of the

technologies that are useful in vehicle-to-vehicle communication are DSRC (Dedicated Short Range Communication) and WAVE

(Wireless Access for Vehicular Environment), while vehicle-to-infrastructure uses GPRS, WiFi or WiMax. Any Internet

Application needs a continuous connectivity to function properly. While in stationary objects maintaining a continuous connection

may not be of major concern but in VANET where a vehicle moves between various networking environments it needs to switch

between various networks in different areas. A frequent switch in networks may cause degradation in performance of the system.

This switch between various networks is known as handoff or sometimes handover. Handoff can be defined as a process which is

triggered when a vehicle switches network areas without interruption or loss of service2.

Handoff Classification: Handoffs can be classified in two ways according to network technology involved:

Horizontal Handoff: This is a traditional handoff mechanism, also known as intra-system handoff. Horizontal handoff occurs when

the MS (Mobile Station) switches between different BS’s(Base Stations) or AP(Access points) of the same radio access network.

Vertical Handoff: In modern times where there is a variety of network technologies, we deal with networks with great diversity and

heterogeneity. So, we require a handoff mechanism that not only deals with switching in between networks, but also heterogeneous

networks having different wireless access network or technologies. Vertical handoff or inter-network handoff scheme supports

handoff between two heterogeneous networks.

The rest of the Paper is organized as follows Section II provides with a Review and Survey of various vertical handoff schemes in

VANET. Section III presents a Comparison table of the various schemes reviewed in section II. And Finally, The Conclusion and

Future Scope are given in Section IV

Survey on Various Vertical Handoff Schemes

A Mobility Handover Scheme for IPv6-Based Vehicular Ad Hoc Networks[3]

A new scheme MHVA is described in this section. In MHVA, the vehicles are uniquely identified by its home address; hence no

CoA(Care Of Address) is required during mobility. So, the mobility HO(Handoff) cost is reduced and HO delay is shortened. Here

the handover in the network layer is completed before HO in link layer thus a vehicle can continue to keep connection with its AP

in the link layer and continue receiving packets from the AP(Access Point).

In MHVA when a vehicle joins VANET It acquires a home address (IPV6) and is identified by the home address throughout its

lifetime. The vehicles Home Agent stores the local vehicle table. A local vehicle table has two entries namely: - i) IPv6 address of a

vehicle ii) IPv6 address of an AR. Between the Home Agents the AR’s identify the subnet where the vehicle is located. The AR



-21st Nov 2015


stores vehicle routing table which also has two entries namely: - i) IPv6 address of a vehicle ii) IPv6 address of a AP The AP here is

the associated AP of the vehicle. Each AP stores the neighbor AP table also having two entries: - i) IPv6 address of neighbor AP ii)

Relative orientation of neighbor AP. Even if Omni-directional antennae is used, it is possible to obtain angle between two AP’s by

sending beacon frames using AOA method

An AP can get the relative orientation with respect to its neighboring AP using beacon frames sent from the neighboring AP using

AoA (Angle of Arrival)[4] method. In the same way an AP can determine whether a vehicle is leaving its communication area or

not, by measuring the RSS of the vehicle. It can also get the relative orientation of vehicles with the help of beacon frames and AoA

method.

When the AP of the vehicle detects that a vehicle is going to leave its communication area then the AP can determine the next

associated AP (NAAP) of the vehicle. According to the information collected on the basis of relative orientation of vehicle and

relative orientation of NAAP. It selects the neighbor AP whose relative orientation is equal to the relative orientation of the

Neighbor AP.

This scheme can be applicable in two scenarios:- a)Handoff within a Subnet, b)Handoff in between Subnets .When a vehicle is

inside a subnet the AP within its one-hop scope is responsible for the mobility HO process while when a vehicle is moving inter-

subnet then the neighbor vehicle within its one-hop scope is responsible for mobility HO process. The respective vehicle doesn’t

take part in the HO process.

Advantages and Disadvantages: The MHVA scheme is advantageous because it completes its handover in the network layer

before completing the handoverin the link layer. So the devices are still connected in the link layer while handover takes place in

the network layer. It does not use CoA to identify each vehicle but identifies each vehicle using their home addresses, that reduces

the cost of calculating CoA for each vehicle entering a new network area The only disadvantage in this scheme is that if a large

amount of vehicles enters the subnet it increases the load of the VANET network and calculation throughput.

Figure-1

(a) Mobility Handoff Within a Subnet (b) Message Flow diagram

(a)

(b)



-21st Nov 2015


Figure-2

(a) Mobility Handoff in Between Subnets (b) Message Flow Diagram

A Cross Layer Fast Handover Scheme in VANET [5]

This scheme introduces a fast handover scheme for VANET known as VFHS. The most powerful and efficient way to access the

internet in a transportation environment is the Multi-hop technique. For example we can take IEEE 802.16j Worldwide

Interoperability for Microwave Access Mobile Multi-hop Relay (WiMAX MMR).

This scheme VFHS improves L2 handover process to solve dis-connectivity problem between sedan and coaches or access points.

In [7] VFHS (Vehicular fast handoff scheme) improves layer 2 handover performance by utilizing topology information

broadcasted by oncoming small sized vehicles

The vehicles on the freeway move with unlimited speed as long as their relative speeds are supported by MMR WiMax.. There are

mainly 3 kinds of vehicles in this model RV (Relay vehicle) BV (Broken Vehicles) and OSV (Oncoming way small vehicle).

RV (Relay vehicle) –The relay vehicle RV is a large vehicular coach which has the capabilities of relaying and mobility

management of MMR WiMAX network to its neighboring vehicles. An RV along with the vehicles in its transmission range forms

a cluster.

Broken Vehicle (BV) – A small vehicle which is outside the transmission range of any RV and is willing to connect to an RV is

known as a broken vehicle.

Oncoming small size vehicle (OSV) – An OSV or an oncoming small size vehicle is a vehicle that is moving in the opposite

direction than the RV and BV, which has no packets to transmit. An OSV collects physical layer information of the RV and

provides the information to the BV or broken vehicle in the opposite lane with the help of a cross layer network topology message

NTM(Network Topology Message).

(a)

(b)



-21st Nov 2015


VFHS adapts a dynamic approach in handoff, where it uses cross layer design to send messages across devices. It sends an NTM,

which is a cross-layer message, comprising of physical and MAC layer. The Physical layer comprises of information like position

and channel whereas the Mac layer comprises of information on WiMAX. The position and channel information of RV are

accumulated and abstracted by OSV. Now the OSV along with the information of RV also inserts its position information and

broadcasts the NTM to oncoming BV’s. ON receiving the NTM the BV’s adjust the channel frequency of its WiMAX adapter to

the channel frequency of the RV in front, by comparing its location. So, the BV, instead of searching all channel frequency in the

physical layer, just searches for the RV in front.

Advantages and Disadvantages: The Advantage of this VFHS scheme is that it transmits cross-layer message named as NTM

which contains topology information like position and channel, which are physical layer information as well as MAC layer

information. By receiving physical layer information along with MAC layer it can perform L2 handoff very smoothly. A vehicle on

receiving an NTM can set its frequency to a desired channel and perform handoff. Secondly, the concept of Relay Vehicles which

are large vehicles having the capability of relaying and transmitting packets to and from smaller vehicles. A base station can only

have direct connection with the Relay vehicles instead of all vehicles, which lowers handoff latency.

The Disadvantage of this scheme is that if no relay vehicles are around the smaller vehicles has no provision to connect to the base

station. And the handoff procedure of relay vehicles is not clearly demonstrated.

Figure-3

VFHS architecture

A New Scheme of Global Mobility Management for Inter VANETs Handover of Vehicles in V2V/V2I Network

Environments[6]

The architecture of GMM is shown is Figure 4. It consists of GVMM (Global vehicle mobility management) and LVMM (Local

vehicle mobility management). The GVMM stores the MAC address, care-of-address (CoA) and permanent IP address (PoA),

Local VANET ID (VID), Id of V2V group (GID) and the IP of the LVMM. The MAC addresses of the vehicles that are entering

the network area are forwarded to the LVMM by the local AP.

When a vehicle VC#1 enters a new AP area, the AP retrieves the vehicles MAC address and sends it to the new LVMM in the form

of AR (Association Report) message. The new LVMM then makes an entry of the new vehicle in its L-ABT (Local Address

Binding Table).Then it sends a LR (Location Reply) message to the GVMM, where the GVMM updates its C-ABT (Central

Address Binding table) with the entry of the new vehicle. The GVMM then sends a Location update message to the old LVMM,

and sends a Location Reply Acknowledge message to the new LVMM which currently is associated with the vehicle. The old

LVMM then sends a Location Update Acknowledge to the GVMM. This completes the Handover Procedure

Advantages and Disadvantages: It performs fast handoff using L2 triggering is its advantage. Its Disadvantage is that it requires

CoA configuration which delays the handoff latency.



-21st Nov 2015


(a)

(b)

Figure-4

(a) VFHS architecture (b) Message Flow Diagram showing handoff

A Proxy MIPv6 Handover Scheme for VehicularAd-hoc Networks [7]

In this scheme, the road has two lanes on each direction. The technologies used at points of attachments are WiMAX, 3G/4G on

RSU’s. The LMA (Local Mobility Anchor) stores the vehicles new location and the correspondent Node acts as an FTP server. For

providing seamless mobility we can consider MIPV6, FMIPV6 and HMIPV6 as host-based mobility protocol. A protocol namely

PMIPV6 is used for network based mobility management.

Here there are two entities Mobile Access Gateway, whose job is to create a tunnel with the HA. The MAG does the mobility

management signaling with HA for the MN attached to the network. The local mobility anchor (LMA) acts as an anchor for MN by

providing a Home network prefix, when the mobile node is outside its Home network.

It provides with an Early Binding Registration prior to the Handoff procedure. Each vehicle is firstly provided with a GPS to

forward its current coordinates to its respective points of attachments. This helps in detecting that the vehicle is leaving its coverage

area or not. Its current position is compared with a pre-configured threshold, which varies with velocity of the vehicle, i.e it

decreases with the increase of velocity. So, by using this GPS coordinate, it is easier to detect in which direction the car is moving

and also the next Point of attachment.

An information request (IR) message is sent by the current PoA (Point of attachment) to the P-MAG (Previous Mobile Access

Gateway), to retrieve the information of the next PoA and the vehicles Home address. Each MAG contains information of their

neighboring MAG’s maintained in a specific table. It also maintains the pool IP address within a specific range being assigned to

each MAG.



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This helps the MAGs to send the IR message to the specific N-MAG (New Mobile Access Gateway) directly. After receiving the

IR message, the N-MAG selects an IP address from the IP address pool and sends an IR acknowledgement with the newly selected

CoA of the vehicle back to the P-MAG. At the same time it also sends to the LMA, a request for binding cache entry (RBCE)

message. On receiving the RBCE the LMA updates its BCE table. And it replies to the N-MAG with a proxy binding

acknowledgement (PBA), containing the HNP of the vehicle, and then it establishes a bi-directional tunnel with the N-MAG. In the

meantime, the IRA messages arrives the vehicle via the currently associated point of attachment.

Figure-5

(a) EBR-PMIPv6 Scheme (b)Message Flow Diagram

So, the relevant information about handoff is configured in advance while still in connection with its new PoA.

Advantages and Disadvantages: The advantage of this algorithm is that it uses a gateway to produce a tunnel with the Home

agent, which allows packet to be transmitted from pMAG to nMAG i.e from the network where the vehicle was associated

previously to the network where the vehicle is associated at present.

(a)

(b)



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Comparison Table

Handoff Execution Algorithms

Table-1

Scheme Addressing Layers Involved in

Handover

Subnet /Network

Area

detection

Technology used

A Mobility Handover

Scheme (MHVA)

Vehicles are identified

by its home IPV6

address instead of CoA

The handover takes

place in the network

layer while it is still

connected in the link

layer.

Uses AoA method to

detect the target subnet

to which the vehicle is

to be handed over.

AoA to determine

NAAP(Next associated

AP) ,Access routers

(AR),Access points (AP)

Cross Layer Fast

Handover

Scheme(VFHS)

Does not use any CoA,

vehicles are identified

by their physical

addresses or MAC

addresses

The handover scheme

uses only physical and

MAC layer to perform

the handover

procedure.

Uses message

broadcast from OSV

(other side vehicle) to

detect to which RV the

BV is to be associated

with.

Uses NTM (Network

topology message) ,a

cross layer message

comprising of information

of the physical as well as

MAC layer, WiMAX

MMR (Mobile Multi-Hop

Relay),.3 types of

vehicles RV(relay

vehicles),BV(Broken

Vehicles), OSV(Other

Side Vehicles)

Global Mobility

Management for Inter-

VANET Handover

(GMM)

Uses CoA (Care of

Address) to identify

vehicles in a subnet.

When a vehicle enters

a new subnet it is

identified by a new

CoA that is assigned to

it.

This scheme uses

Mobile IPV6 thus takes

place in the network

layer.

Handover is performed

after the vehicle enters

the subnet

GVMM(Global Vehicular

Mobility Management),

LVMM (Local Vehicular

Mobility Management

A Proxy MIPv6

Handover Scheme for

Vehicular network

Uses CoA to identify

vehicles in a network

area. IP address

obtained from vehicles

in the opposite

direction.

This involves IP

addressing thus

handover takes place in

layer 2 (Data link

layer)

And layer 3(Network

layer)

Handover takes place

when a vehicle enters a

network area covered

by PoA which

periodically broadcasts

Link-up triggers.

Handover process

starts when a vehicle

picks up these link-up

triggers.

MAG’s (Mobile access

Gateway), LMA (Local

mobility

anchor)Correspondent

node (CN) acting as FTP

server.

Conclusion and Future Scope

The above algorithms studied shows us 4 different handoff execution techniques where some scheme uses a care-of-Address and

some uses its own home address. The problem with CoA is that it requires a DAD (Duplicate Address Detection) phase for

detecting whether two vehicles has the same IP address or not. The DAD phase causes almost 70% of the delay in handoff. So, to

obtain a time efficient handoff process, we require omitting the DAD phase.

A technique that doesn’t use any CoA is also known as MHVA. In Addition, detecting the vehicle movement is necessary, and also

assuming the next associated access point to initiate the handoff. A method is mentioned in MHVA which uses AoA (Angle of

Arrival) to detect vehicle orientation and orientation of next associated access point. This causes far efficient handoffs. Also in

MHVA, handoff occurs in network layer while the data link layer remains connected. This two layer handoff proves to be much

efficient for seamless handoff. Its disadvantageis that when the number of vehicles increases then the handoff throughput becomes



-21st Nov 2015


quite high. A solution to this problem is that we could decrease the number of vehicles by allowing only a selective number of

vehicles to connect with the base station.

The technique VFHS lacks a proper stationary infrastructure. VFHS uses Relay vehicle to which all the small scale vehicles are

connected. But if a vehicle gets detached from a relay vehicle or there is no relay vehicle around, there is no provision for those

small vehicles to connect to the VANET network. This can be overcome by introducing a proper Infrastructure which is stationary

like a base station to which the smaller vehicles can connect to when no Relay Vehicles are around. A new architecture can be

formed using the infrastructure of MHVA [3] and the concept of Relay vehicles from VFHS [5], using the Access points, AR, AoA

techniques from MHVA while the Relay vehicle will be introduced where smaller vehicles will connect to the relay vehicles instead

of the Access Points. QoS can also be introduced in order to take handoff decision, some parameters included and discarded based

on the handoff scenarios. This reduces packet drop probability and thus manages power of mobile nodes.

References

1. Kang J., Chen Y., Yu R., Zhang X., Chen H. and Zhang L., Vertical handoff in vehicular heterogeneous networks using

optimal stopping approach. In Communications and Networking in China (CHINACOM), 2013 8th International ICST

Conference on (pp. 534-539). IEEE (2013)

2. Kumaran U. and Shaji R.S.,. Vertical handover in Vehicular ad-hoc network using multiple parameters. In Control,

Instrumentation, Communication and Computational Technologies (ICCICCT), 2014 International Conference on (pp. 1059-

1064). IEEE (2014)

3. Wang X. and Qian H., A mobility handover scheme for IPv6-based vehicular ad hoc networks.Wireless personal

communications, 70(4), 1841-1857 (2013)

4. Seow C.K. and Tan S.Y., Localization of omni-directional mobile device in multipath environments. Progress In

Electromagnetics Research, 85, 323-348 (2008)

5. Chiu K.L., Hwang R.H. and Chen Y.S., A cross layer fast handover scheme in VANET. In Communications, 2009.ICC'09.

IEEE International Conference on, 1-5 IEEE. (2008)

6. Lee J.M., Yu M.J., Yoo Y.H. and Choi S.G., A new scheme of global mobility management for inter-vanets handover of

vehicles in v2v/v2i network environments. In Networked Computing and Advanced Information Management, 2008.

NCM'08.Fourth International Conference on (Vol. 2, pp. 114-119).IEEE (2008)

7. Moravejosharieh A. and Modares H., A Proxy MIPv6 Handover Scheme for Vehicular Ad-hoc Networks. Wireless personal

communications, 75(1), 609-626 (2014)



-21st Nov 2015


Energy Gain of Signal Wave and That of Idler Wave Due to Nonlinear

Parametric Interaction in Piezosemiconducting Medium: A Numerical

Approach

Pravat Kumar Mandal Dept. of Maths., A.M. College, Purulia, W.B., INDIA

Abstract

The present paper is an attempt to investigate the nonlinear parametric interaction of longitudinal acoustic waves in n-type

degenerate piezosemiconducting media. The nonlinearity arises due to nonlinear transport property of electrons in these media. The

equation of mechanical motion and of electricity supplemented by basic equations of piezosemiconducting media are used to solve

the problem. The numerical analysis presented in this paper, is facilitated by assumption of the equality of drift velocity with sound

velocity. The gain of energy of signal wave and that of idler wave have been computed by numerical approach for different

propagation distances.

Keywords: Parametric amplification, Piezosemiconductor, Acoustic Wave.

Introduction

Since the discovery of helicon wave propagation in semiconductors, the interaction between it and other modes-acoustic as well as

electromagnetic of the propagating medium have received considerable attention. There exists a vast literature on acoustic wave

propagation, including review articles and monographs on the propagation characteristics, experimental properties and applications

of these waves1-6

. Most of the above mentioned works with the linear behavior of these waves. However in the last few decades,

nonlinear propagation characteristics of helicons and other electromagnetic modes as well as acoustic modes have become a lively

subject of investigation in semiconductor as well as piezosemiconductors. One of the most important areas of studies in acoustics is

to investigate wave propagation in piezosemiconductors mainly on account of electromechanical coupling in these media.

Thepossibility of acoustic wave amplification in both magnetisedandunmagnetised piezoelectric semiconductor have been studied

during last few decades7-11

. The cause of nonlinearity is attributed to the heating of the carriers by the pump.Kinetic theory of

nonlinear wave interaction has been studied by M.Lazar et al12

.Modifiedinteractions of longitudinal Phonon-Plasmon in magnetized

piezoelectric semiconductor has been studied by M. Salimullah, et. al13

.Shukla et al14

and Brodin et al15

have studied nonlinear

wave interactions in quantum magnetoplasma medium. Parametric interactions in Ion-implanted magnetized piezoelectric

semiconductor plasma is analytically investigated by using hydrodynamic model of semiconductor plasmas and a coupled mode

theory of interacting waves16

. In recent years different aspects of linear and nonlinear wave propagation of electron plasma waves in

quantum plasma field have been studied17

. Mandal18

has studied the amplification of acoustohelicon waves in

magnetisedpiezosemiconducting medium. Ultrasonic wave instability in a n-type degenerate thermopiezo-semicoducting medium

has been studied by P.Mandal19

.

Most of these studies are analytical in nature. Motivated by these and the intense interest in the field of energy gain or loss of

nonlinear acoustic waves in piezosemiconductors, in the present paper we have attempted to study the three-wave nonlinear

parametric interaction in these media by numerical simulation. The gain of energy of signal wave and that of idler wave have been

computed for different propagation distances in n-type degenerate piezosemiconducting medium.

Statement of the Problem and Basic Equations

Weconsider the longitudinal wave propagation through an-type degeneratepiezosemi-conducting medium which is subjected to d.c

electric field. Our problem is to investigate gain or loss of energy of waves in the case of three-wave nonlinear parametric

interaction of acoustic waves by numerical computation. The basic equations of the problem are the equations of mechanical

motion and of electricity supplemented by constitutive equation, vide, Sinha and Gupta9.

The constitutive relations and basic equations used in this analysis are:

T = C1S - epz E (2.1)

D = εE + epz S (2.2)

senx

D

(2.3)



-21st Nov 2015


e

0x

j

t

ns

(2.4)

j = |e|(no + fns)

E- eDn x

(no + fns) (2.5)

x

=2

2 )(

t

x

(2.6)

The notations used are as follows :

where

E Electric field

T Mechanical Stress

D Electric Displacement

S Strain

epz Piezoelectric constant

ε Dielectric permittivity at constant strain

C1 Elastic constant at constant electric field

Material density

x Mechanical displacement

n0 Mean carrier density

ns Perturbed carrier

f Fraction of the space charge

sen Space charge contributing to conduction process

Using equations (2.2) to (2.5) we get

4

)(4

3

`~3

(

)~

(

3

)(3

2

`~2

(

`~

2

)(2

`~

`~

2

)(3

`~2

x

xpze

x

EfnD

x

EoE

x

xpze

x

Ef

x

E

x

xpze

x

Ef

x

E

tx

xpze

tx

E

(2.7)

where we have E =

~

EEo , oE being the applied electric field such that the drift velocity

dV (≡-

oE) is equal to the sound velocity su

. Again substituting (2.1) in (2.6), we get

.)()(

2

2`~

2

2

1t

x

x

Ee

x

xC pz

(2.8)

Numerical Solution and Discussion

As usual, for the method of parametric interaction, we treat the interaction of waves by taking displacement x and perturbed

electric field

~

E for three wave interaction as in9

x = u1(x)e i(

1t-k

1x)

+ u2(x)e i(

2t-k

2x)

+ u3(x)e i(

3t-k

3x)

+ c.c ~

E = E1(x)e i(

1t-k

1x)

+ E2(x)e i(

2t-k

2x)

+ E3 (x) e i(

3t-k

3x)

+ c.c ] (3.1)

wherec.c is the conjugate complex parts and 1,2and 3 are the frequencies of the signal wave, pump wave and idler wave

respectively and they are related by the equation



-21st Nov 2015


3 = 1+ 2 (3.2)

and phase matching condition is

k3 = k1+ k2 (3.3)

Substituting (3.1) in (2.7), (2.8) and equating the terms of equal frequencies, we get the following amplitude equations

x

E

kC

eiE

C

e

x

u pzpz

1

11

1

1

1 .22

(3.4)

x

E

kC

eiE

C

e

x

u pzpz2

2

21

2

1

.22

(3.5)

x

E

kC

eiE

C

e

x

u pzpz

3

31

3

1

3 .22

(3.6)

*

231

*

2313

*

21111

1111 EESEuREuQEP

x

ENuM

x

uL1

(3.7)

*

132

*

1323

*

12222

2222

2 EESEuREuQEPx

ENuM

x

uL

(3.8)

3L033

3333

3

EP

x

ENuM

x

u

(3.9)

where

ikeDkeL pznpz1

3

111 4

1M=

4

1keD pzn

1N= 3

3

1kDn + σ - i 1

1P =- σi 1k

- i

3

1k

1Q= μ pze

i( 1k2

2k-

3

2k)

1R= -μ pze

i( 2k2

3k-

2

3k)

1S=2 μ

2

232 kkk 2

3k

ikeDkeL pznpz2

3

222 4

2M=

4

2keD pzn

2N= 3

2

2kDn + σ - i 2

2P = - σi 2k

-

3

2kDn i

2Q= μ pze

i

3

1k

2R= - μ pze

i( 1k2

3k-

3

3k)

2S= 2μ

2

331 kkk 2

3k

33 keL pz2



-21st Nov 2015


33

2

33 keikieM pzpz3

3N= σ - i 3

3P = - σi 3k

f ≈ 1

In deducing the amplitude equations, we have assumed that the amplitudes are taken to be varying slowly with x so that we could

neglect the second and higher order terms. We have also assumed the amplitude of initial pump wave to be very much large

compared to that of signal wave so that the pump energy loss due to nonlinear interaction is negligible and shift in energy is caused

solely due to linear interaction with the medium. Again, since the drift velocity is exactly equal to the sound velocity and since

there is no linear gain or loss when drift velocity is equal to sound velocityvide, Sinha and Gupta[9]we can take the amplitude of

pump wave 3u(x) as

3u(x) = 3u

(0)eiνx

………. (3.10)

where ν (a real quantity)can be easilyobtainedin the form as, given by

ν = 2

3k

K2 D

C

D

3

3

3

……….. (3.11)

Now by the equation (3.6), we can express 3E(x) in terms of 3u

(0) as

3E(x) = ...................................)0(

)(2

23

3

31 xi

pz

euke

kiC

(3.12)

To facilitate the solution we take

………. (3.13)

wherea, b, c, d, p, q, r, s are functions of x only.

Substituting (3.10), (3.12), (3.13) in (3.4), (3.5), (3.7),(3.8) and equating real and imaginary parts, ultimately we geteight first-order

nonlinear differential equations involving a, b, c, d, p, q, r, s are

x

a

= a- b - c- (-P Cosνx+ Q Sinνx)p - (P Sinνx +QCosνx)q +

(-R Sinνx+ SCosνx)r + (S Cosνx+ R Sinνx) (3.14a)

x

b

= a + b + c- (P Cosνx+ Q Sinνx)q - (P Sinνx +Q Cosνx)p +

(S Sinνx+ R Cosνx)r + (-S Cosνx+ R Sinνx) (3.14b)

x

c

= a + b + c - (P1 Cosνx+ Q1 Sinνx)p - (P1Sinνx +Q1Cosνx)q +

(R1Sinνx+ S1Cosνx)r + (-R1Cosνx+S1Sinνx) (3.14c)



-21st Nov 2015


x

d

= a + b + c + (P1Sinνx + Q1Cosνx)p + (P1 Cosνx +Q1 Sinνx)q +

(R1 Cosνx + S1Sinνx)r + (S1Cosνx+ R1Sinνx)s (3.14d)

x

p

= (A2 Cosνx –B2 Sinνx)a - (A2 Sinνx +B2 Cosνx)b +(C2 Sinνx – D2Cosνx)c+

(C2 Cosνx –D2 Sinνx)d –P2p –Q2q–R2r (3.14e)

x

q

= - (A2Sinνx - B2Cosνx)a + (A2Cosνx - B2 Sinνx) b + (C2Cosνx - D2 Sinνx) c–

(C2 Sinνx –D2 Cosνx) d - Q2 p -P2q –R2s (3.14f)

x

r

= - (A3 Sinνx – B3 Cosνx)a + (A3Cosνx – B3 Sinνx) b + (C3 Cosνx – D3Sinνx) c –

(C3 Sinνx – D3 Cosνx) d – P3 p + Q3 q –R3 s (3.14g)

x

s

= - (A3Cosνx –B3 Sinνx)a - (A3 Sinνx + B3 Cosνx)b - (C3 Sinνx – D3Cosνx)c -

(C3Cosνx –D3 Sinνx)d +Q3 p + P3 q + R3 r (3.14h)

where

= 0.870418

= 0.3207161

0.890922

P= 0.281840

Q= 0.1796191

R = 0.78493401

S = 0.5031092

= 0.5087721

= 0.6047290

= 0.4850473

= 0.463357

P1= 0.302290

Q1= 0.890466

R1= 0.514904

S1= 0.8701092

B2= 0.7911076

C2= 0.18717948

D2= 0.5460331

P2 = 0.890466

Q2= 0.8479031

R2= 0.467033

A3= 0.2119437

B3= 0.6560930

C3 = 0.8710622

D3= 0.5330725



-21st Nov 2015


Q3= 0.6747302

P3 = 0.2112307

R3= 0.890741

The values of physical constants for (Cds material) used to deduce the above system of equations (3.14a) to (3.14h) are given below

:

= 3 10-2

m2 / v-sec

C1 = 9.36 1010

N/m2

= 4.8 10-11

C/v-m

n = 1019

m-3

us = 1.1992 103 m/sec

= 4.8 10-2

C/m – v sec

1 = 1 104 sec

-1

2 = 1.1 104 sec

-1

3 = 2.1 10

4 sec

-1

k1 = 8.33333m-1

k2 = 9.1666663m-1

k3 = 17.499999m-1

D= 1.8580645 10

9 sec

-1

T = 300oK

epz = 0.44 A-sec/m2

Dn = 0.775 10-3

m2/sec

c= 10

9 sec

-1

K2 = 0.4309116 10

-1

ν = 0.385622310-11

m-1

To solve the above mentioned system of differential equations (3.14a) to (3.14h) numerically

we use Runge-Kutta method and develop the program in Fortran – 77 and run in PC, taking

a(0) =0.00001, b(0) =0.0, c(0)=0.0, d(0)=0.0, p(0) =0.0, q(0) =0.0, r(0) =0.0, s(0) =0.0,

u3(0) =0.001.

The expression of energy of the component , is given by

=

where strain amplitude of the wave at frequency is given by

= 2

So the relative change in energy of the signal wave over a propagation path (x) ix given by

= =

The expression of energy of the idler wave at frequency is given by

= = 2

The expression of energy of the signal wave at frequency is given by

= = 2

The ratio of energy of idler wave to that of signal wave is

= 1.21



-21st Nov 2015


The energy of the signal wave andthat of idler wave and the ratio of energies have been computed over a propagation path

0.05 step length of 0.001 . But these values are shown at an interval 0.005 in the following table.

Table-1

Propagation path(x) in meter Relative change in energy of

the signal wave Energy of the idler wave

Ratio of energy of the idler

wave to that of signal wave

1.00E-03 1.69E-12 1.25E-14 9.56E-10

5.00E-03 2.39E-11 3.01E-09 2.32E-08

1.00E-02 1.32E-09 1.24E-08 9.54E-08

1.50E-02 1.46E-08 2.79E-08 2.09E-07

2.00E-02 8.76E-08 4.98E-08 3.69E-07

2.50E-02 3.27E-07 7.46E-08 5.98E-07

3.00E-02 1.12E-06 1.18E-07 8.79E-07

3.50E-02 2.65E-06 1.49E-07 1.09E-06

4.00E-02 6.13E-06 1.98E-07 1.37E-06

4.50E-02 1.21E-05 2.42E-07 1.89E-06

5.00E-02 2.38E-05 3.05E-07 2.38E-06

Figure-1

Relative change in energy of the signal wave



-21st Nov 2015


Figure-2

Energy of the idler wave

Figure-3

Ratio of energy of the idler wave to that of signal wave



-21st Nov 2015


Conclusion

In the present analysis, the gain of energy of signal wave and that of idler wave have been studied by numerical computation in

presence of strong pump wave due to nonlinear parametric interaction of acoustic waves in piezosemiconducting medium. For

numerical simulation we reduce the coupled mode equations into eight simultaneous 1st order ordinary differential equations (3.14a)

to (3.14h) and then solve by Runge-Kutta Algorithm which is shown in Table-1. We use MATLAB software to construct the

figures 1,2,3. From Fig.1,2 it is clear that the energy of the signal wave as well as the idler are increasing with the increase of

propagation distances. It is also evident from Fig1. to Fig3. that the increase is more rapid in the latter stage of propagation in both

the cases.

Sometimes it is very difficult to study the nonlinear interaction through analytical approach due to complicated form of the

nonlinear differential equations and analytical results can only be obtained after imposing some conditions. But this short of

stipulation is not required when we study those above by numerical approach.

References

1. J. Pozhela, Plasma and Current Instabilities in Semiconductors, Oxford/London Pergamon Press (1981)

2. S. Ghosh and R.B. Saxena, Raman instability in n-type piezoelectric semiconducting plasmas, Jr. Appl. Phys 58, 3133-40

(1985)

3. S. Ghoshand S. Khan, “Parametric instability in n-type piezoelectric semiconductor plasmas.” Ultrasonics 24, 93-99 (1986)

4. K.D. Misra and M.K. Pandey, “Acoustohelicon Amplification in a Piezoelectric Semiconducting Plasma”, Phys. Status Solidi

(b) 182, 153 (1994)

5. W. Huang and W.X. Ding, “Estimation of Lyapunov-exponent spectrum of plasma chaos” Phys. Rev. E50, 1062 (1994)

6. H.A Shah, I.U.R Durrani and T. Abdullah, “Nonlinear helicon-wave propagation in a layered medium” Phys. Rev. B.V.47,

1980 (1993)

7. S. Ghosh and D.K Sinha, Amplification of acoustic waves in piezoelectric semiconductors: Effect of nonlinearity in electron

effective mass and collision frequency” J. Appl. Phys. 60, 267 (1986)

8. K.K Ghosh and S.N Paul, Acousto-helicon interaction in narrow-gap semiconductors” Phys. Status Solidi(b), V.197, 441

(1996)

9. D.K. Sinha and M. Gupta, Nonlinear parametric interaction in a piezosemiconductingmedium”Phys. Stat. Sol (b), 107, 469

(1981)

10. S. Ghoshand S. Khan, Parametric instability in n-type piezoelectric semiconductor plasmas, Ultrasonics, 24, 63 (1986)

11. K.L Jat, A. Neogi and S. Ghosh, Parametric amplification in a magnetized non degenerate plasmas” Acta Phys. Polonica A.79

(1991)

12. M. LazarandI. Merches, “Kinetic theory of nonlinear waves interaction in relativistic plasmas, Phys. Letters A 313 (2003)

13. M. Salimullahet. al., Modified interactions of longitudinal phonon-plasmon in magnetized piezoelectric semiconductor

plasmas, Physica B, 351, 163-170, (2004)

14. P.K Shukla, S. Ali and M. Stenflo, Nonlinear wave interactions in quantum magnetoplasmas, Phys. of Plasmas 13: 11, (2006)

15. G. Brodin, M. Marklund, L. Stelnflo and P.K. Shukla, “Dispersion relation for electromagnetic wave propagation in a strongly

magnetized plasma” New Jr. of Physics 8 (2006)

16. N. Jadav, S. Ghosh, P. Thakur, M. Jamil, and M. Salimullah, Parametric interactions in ion-implanted piezoelectric

semiconductor plasmas” A.J.S.E., 231-240 (2010)

17. S. Chandra, S. N.Paul, B. Ghosh, Linear and non-linear propagation of electron plasma in quantum plasma”Ind. Jr. of Pure

and Appl. Phys, 50, 314-319, (2012)

18. P.K. Mandal, Amplification of Acousto helicon waves in Magnetised Nondegenerate Piezoelectric Semiconductor: A

Numerical Approach” Int. Jr. of Inf. and Comp. Sc. (IJICS) ISSN 0972-1347, 16(2), 13-20, (2013)

19. P.K. Mandal, Ultrasonic wave Instability in a n-type degenerate thermopiezo semiconducting medium - A numerical

approach, Acta Ciencia Indica, Mathematics , ISSN 0970-0455, XL.M(3), 299-307, (2014)



-21st Nov 2015


Skin Lesion Analysis and Treatment Monitoring Using Image Processing

Technique: A Review

Ishita Bhakta1 and Santanu Phadikar

2

1Department of Information Technology, West Bengal University of Technology 2Department of Computer Science, West Bengal University of Technology

Abstract

An important application of digital image processing technology is found in the field of medical environment. Now a day’s a new

technology called medical imaging gains popularity as a combination of digital image processing and its medical applications.

Medical imaging helps doctors in disease diagnosis by seeing inside of human body without surgery. It also provides medical

treatment to the patients of remote area where doctors are not available. It combines biology and image processing with enhanced

technology and improve healthcare monitoring. Skin disease is very common in today’s polluted world. With the help of medical

imaging technology automated skin disease detection system can be designed to help patient and doctors for quality service. This

paper provides an overview of different image processing technique which are already been used to diagnose specific skin disease

automatically.

Keywords: Image segmentation, hypopigmented skin lesion, hyperpigmented skin lesion, Feature extraction.

Introduction

Medical imaging is a technique and process to create visual representations of the internal structures of a body for clinical analysis

and medical intervention to diagnose and treat disease. The technical field where epiluminescence microscope is used to view skin

lesion in magnification in-vivo is called dermoscopy. It is mainly useful in the early detection of melanomas. Dermoscopic images

can be taken with digital camera and examined to extract information from that image which is a part of digital imaging and tele-

dermatology. Automation of skin disease diagnosis is an interesting field of medical imaging technology. Because a lot of

difficulties is faced by individual in accessing health care - Shortage of specialists, Uneven geographical distribution of doctors and

Long waiting times. Automated treatment procedure helps health professional and patient to take early steps about disease.

However, noise, variability of biological tissues, imaging system anisotropy etc. make the difficulties in automated analysis of

medical images. So far, medical imaging has contributed immensely towards advancing medical procedures. The fact that

interpretation and analysis of medical imaging results are still heavily dependent on medical experts (whose availability are low or

non-existence) is a serious concern for developing and underserved regions (especially rural settings). An approach is needed to

minimize this dependency and also to limit probable bias of medical personnel in the analysis of a medical image result.

This paper presents a review of research work done in the field of computerized analysis of dermatological images with computer-

aided systems. Diagnostic accuracy of dermoscopy may be lower in the hands of inexperienced dermatologists. This subjective

variation of visual interpretation can be minimized by computerized image analysis techniques. The steps involve in an automated

skin disease recognition system is described in Figure-1.

Figure-1

Steps of automated skin disease recognition system

Image

Acquisition

Image

Segmentation

Feature

Extraction

Classification

Image

Enhancement



-21st Nov 2015


There is a strong possibility of certain degree of ambiguities regarding terminology in interdisciplinary subject like dermatology

and computer vision. So to overcome these ambiguities a detailed guidance in the relevant medical material and computer vision is

required. This paper provides a detailed study, necessary information and relevant references on the specific parts of image

processing techniques to detect skin disease.

This paper is structured as follows. Section 1 illustrates normal human skin anatomy and Skin Diseases. Section 2 gives an

overview of methods for skin disease detection using medical imaging technique. Finally section 3concludes the paper.

Normal Human Skin Anatomy and Skin Diseases

The human skin is the outer portion of the body which covers the inner part. Human skin composed of multiple layers of

ectodermic tissue which guards internal organs.

Figure-2

Anatomy of the skin, showing the epidermis, the dermis, and subcutaneous (hypodermic) tissue

(copyright 2008 by Terese Winslow)

There are two main layers in human skin. These are epidermis and dermis (Figure-2). Epidermis provides protection against any

external aggressions like injuries, ultraviolet radiation, infections and water loss. It is a layered stratified squamous epithelium like

tissue. It consists of four different types of cells1. These are –

Keratinocytes –In epidermis major portion (95%) of cells are represented by Keratinocytes. These are responsible for continuous

renewal of human skin. They divide and differentiate basal layer to the stratum corneum (the horny layer). Keratinocytes are

produced by division in the basal layer and move to the next layers transforming their morphology and biochemistry called

differentiation. The outer most layer of the epidermis is called Corneocytes. This layer is created as result of this differentiation and

transformation. These are the flattened cells filled with keratin without nuclei. Atthe end of this differentiation process, the

corneocytes lose its cohesion. As a result they separate from the inner surface by the process called desquamation.

Melanocytes: It is the dendritic cells which found in the basal layer of the epidermis. They are filled with melanin pigment,

surrounding keratinocytes and it gives the color to skin and hair.

Langerhans cells: Its function is to identify foreign antigens that have entered into the epidermis and destroy it by phagocytosis.

Merkel cells: These are probably originated from keratinocytes and act as touch receptors. The dermis is the inner part of skin

layer composed of collagen and elastic tissue. It has two sub-layers -the reticular dermis (thick layer) and the papillary dermis (thin

layer). The papillary dermis serves as “glue” that holds the dermis and the epidermis together. The reticular dermis contains blood

vessels, lymphatic channel, nerve endings, hair follicles and sweat glands. It supplies nutrition and energy to the epidermis. It also

plays an important role in thermoregulation, healing process, touch, pain, and pressure and temperature sensation.



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Figure-3

Hypo-pigmented skin

Figure-4

Hyper-pigmented skin lesion

There are two broad categories of skin diseases – Hypo pigmented and Hyper pigmented skin disease. In pathology we use the term

skin lesion instead of skin disease. Hypo pigmented skin lesion means loss of skin color and hyper pigmented skin lesion means

darkening of an area of skin. Some examples of hypo pigmented skin lesion are given in Figure-2. Some examples of hyper

pigmented skin lesion are given in Figure-3.

Methods for Skin Disease Detection

This section gives an overview of different medical imaging technology that had been used to detect different skin disease so far.

Image acquisition: It is method of transforming illumination energy into a voltage waveform by combination of input electrical

power and sensor material. After that digitization is done using sampling and quantization of the voltage waveform to generate

digital image. Image acquisition can be done in following three ways -

1. Using single sensor – used in photodiode.

2. Using sensor strips or line sensors – used in scanners.

3. Using array sensor – used in digital camera.

Image Enhancement: Different illumination source and devices are used to acquire image which creates difficulties during

segmentation. Low contrast images make accurate border detection difficult. That’s why some pre-processing steps are required to

enhance the color information and contrast of the image. This enhancement procedure improves the performance of lesion

segmentation algorithms. The most important image enhancement operation for lesion diagnosis is color correction or calibration.

This operation recovers real colors of a photographed lesion. It is a reliable operation to extract color information in manual and

automated system. Recent studies give special emphasis on color correction of JPEG images obtained using low-cost digital

cameras. Other image enhancement operations are illumination correction, contrast and edge enhancement. Another image

enhancement operation is Karhunen-Loève Transform (KLT) which is also known as Hoteling Transform or Principal Component

Analysis (PCA). An adaptive image enhancement method for contrast stretching of dermatological images in the wavelet domain is



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proposed in2. An automatic color equalization method is used for low contrast images in

3. A segmentation accuracy of 86.07% is

achieved for RGB images if images are normalized with adaptive light compensation technique4.

Image Segmentation: Image segmentation plays important role in automated skin disease detection system. Segmentation is

required to detect skin lesion border accurately and separate lesion area from normal skin area. Feature extraction is done only after

segmentation. There are mainly three types of segmentation methods in image processing.

Edge based segmentation – boundary detection based on local discontinuities in intensity. This can be done by different edge

detection algorithm. Gradient vector based on first order derivative is used for edge detection. Laplacian detector based on second

order derivative is also used for edge detection.

Gradient vector flow (GVF) and adaptive snake (AS) are two edge based segmentation technique used for melanoma skin disease

detection. But among these two methods AS provides better performance5 for melanoma skin disease analysis.

Region based segmentation – partitioning an image into regions that are similar according to a set of predefined criteria. Region

growing, Region split and merge are two procedure of region based segmentation.

Three region based algorithms - level set method of Chan et al. (C-LS), expectation-maximization level set (EM-LS), fuzzy-based

split-and-merge algorithm (FBSM) are compared on a data set of 100 melanoma skin disease images5.

Celebi et al. used a modified unsupervised region based segmentation algorithm (JSEG) for melanoma skin disease6. The method

requires less than 1 min. on a Pentium IV 1.8 Ghz. computer to segment an image of size 768× 512 pixels. This border detection

method may not perform well on images with significant amount of hair. The detection of regions inside the lesion with significant

coloring is not done here.

K-means clustering is used for segmentation of cancerous skin diseases7. Fuzzy C-Means (FCM), Improved Fuzzy C-Means

(IFCM) are also used for segmentation of brain tumor cell, breast cancer cell. FCM is very sensitive to noise. To overcome this

drawback of FCM algorithm IFCM is proposed.

In8 an evolutionary strategy (ES) based segmentation algorithm was used to identify the lesion area. This method is useful to detect

malignant melanomas. The lesion was segmented by an optimized ellipsoid. Optimization is done by ES algorithm with respect to

the defined objective function.

Segmentation using thresholding – Dividing an image based on a threshold. There are various method of thresholding – otshu

thresholding, global thresholding, optimum thresholding, entropy base thresholding etc.

Segmentation based on laplacian and otshu thresholding is used to segment nucleus of skin segment9. This method is used to

differentiate malignant and benign skin tumor. This segmentation method does not work well for indistinct nuclei edges, low

chromatin nuclei edges.

Another threshold based segmentation technique used for melanoma skin disease diagnosis is adaptive thresholding (AT)5.

Features extraction: Feature extraction is an important and crucial step in automated skin disease analysis system in image

processing. Most of the research work concentrates on segmentation technique to detect melanoma skin diseases. Accuracy of an

automated skin disease detection system can be achieved from feature extraction and classification technique. But there is very little

work on feature extraction technique for skin disease detection. Some of these feature extraction technique used to extract specific

global and local features of skin diseases are discussed below.

In10

color and texture features are extracted from lesion area. Four color features - Mean, Variance, Standard deviation and

skewness is extracted from RGB,HSV and Y,Cb,Cr color spaces. Texture features are extracted from gray level co-occurrence

matrix (GLCM). GLCM is an effective method to evaluate similarity of rock texture but is not effective when texture is

heterogeneous. Principle Component Analysis (PCA) is a commonly used dataset acts as a cluster analysis tool in micro array

research. PCA is used to extract RGB color features of psoriasis image lesions12

.PCA can be used successfully to discriminate

plaque clearly along with other psoriasis group diseases. The Independent Component Analysis (ICA) is recommended to use in

future work for psoriasis group disease analysis. Since Independent Component Analysis (ICA) is a most recent topic in medical

signal processing. Border irregularity of a skin lesion is measured using Compact index, Edge Abruptness, Fractal Dimension13

.

Border irregularity is used for melanoma skin disease detection. An ellipse-fitting algorithm is used to extract and measure the



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characteristics of melanoma skin disease according to ABCDE rule14

.Psoriasis is an immune-mediated skin disease. It cannot be

cured completely but its growth can be controlled under medication. In15

psoriasis skin disease is analyzed by extracting color

features. There are mainly four types of psoriasis skin disease - Guttate, Nail, Plaque and Pustular. This paper diagnoses the type of

psoriasis skin lesion based on color histogram features. At first RGB color space of an image is converted to HSV color space then

feature extraction process is applied.

Classification: The final step of an automated skin disease recognition system is classification. The output of lesion classification

can be binary, ternary or n-ary depending on the system which identifies several skin diseases. After feature extraction

classification of skin lesion is done depending on feature descriptors. Performance evaluation depends on the extracted feature

descriptors, used data set and chosen classifier. So comparison of classification approaches must be performed on same dataset and

same set of descriptors to obtain optimal results. Mostly used classifiers are – ANN, SVM, Decision trees, k-NN, Baysian classifier

and fuzzy logic. Among all these classifier Decision trees are not suitable for an automated system since it required user

intervention.

In10

performance of SVM and k-NN classifier is compared on a dataset of 726 samples collected from 141 images with 5 different

types of diseases. About 46.71% accuracy is achieved using SVM classifier and 34% accuracy using k-NN classifier. Multilayer

perceptron classifier is tested on 180 images of different types of Dermatitis, Eczema, and Urticaria11

and 96.6% accuracy is

achieved.

Conclusion

Currently there is no system for detection and monitoring all types of hypo and hyper pigmented skin lesion. Such a system is very

useful to patient in remote area and health professional. It makes skin disease treatment procedure easier. Only border detection,

segmentation and feature extraction for benign and malignant tumor is done in the literature. Very few works are available in the

literature on feature extraction of skin diseases. This paper carried out a detailed study on different medical imaging technique used

in literature for skin disease automated system.

References

1. Korotkov, Konstantin and Rafael Garcia, Computerized analysis of pigmented skin lesions: a review." Artificial intelligence in

medicine, 56.2, 69-90, (2012)

2. Jung, Cláudio R. and Jacob Scharcanski, Sharpening dermatological color images in the wavelet domain." Selected Topics in

Signal Processing, IEEE Journal of 3.1, 4-13, (2009)

3. Schaefer, Gerald, et al. "Colour and contrast enhancement for improved skin lesion segmentation." Computerized Medical

Imaging and Graphics 35.2, 99-104 (2011)

4. Ch'ng, Yau Kwang, et al. "A two level k-means segmentation technique for eczema skin lesion segmentation using class

specific criteria." Biomedical Engineering and Sciences (IECBES), 2014 IEEE Conference on. IEEE, (2014)

5. Silveira, Margarida, et al. "Comparison of segmentation methods for melanoma diagnosis in dermoscopy images." Selected

Topics in Signal Processing, IEEE Journal of 3.1, 35-45 (2009)

6. Celebi, M. Emre, Y. Alp Aslandogan and Paul R., Bergstresser, Unsupervised border detection of skin lesion

images." Information Technology: Coding and Computing, 2005. ITCC 2005. International Conference on. 2. IEEE, (2005)

7. Trabelsi Olfa, et al., Skin disease analysis and tracking based on image segmentation." Electrical Engineering and Software

Applications (ICEESA), 2013 International Conference on. IEEE, Shortage of specialists, (2013)

8. Situ Ning, et al., Automatic segmentation of skin lesion images using evolutionary strategy." Image Processing, 2007. ICIP

2007. IEEE International Conference on. 6. IEEE, (2007)

9. Tanaka, Toshiyuki, Tomoo Joke and Teruaki Oka, Cell nucleus segmentation of skin tumor using image

processing." Engineering in Medicine and Biology Society, 2001. Proceedings of the 23rd Annual International Conference of

the IEEE. 3, IEEE, (2001)

10. Sumithra R., Mahamad Suhil and D. S. Guru, Segmentation and Classification of Skin Lesions for Disease Diagnosis."

Procedia Computer Science, 45, 76-85 (2015)

11. Mittra Anal Kumar and R. Parekh, Automated detection of skin diseases using texture features, International Journal of

Engineering Science and Technology (IJEST) 3.6 (2011)



-21st Nov 2015


12. Hashim Hadzli, et al., A study on RGB color extraction of psoriasis lesion using principle component analysis (PCA)."

Research and Development, 2006. SCOReD 2006. 4th Student Conference on. IEEE, (2006)

13. Clawson K., Morrow P., Scotney B., McKenna J. and Dolan O., Analysis of pigmented skin lesion border irregularity using

the harmonic wavelet transform, In Machine Vision and Image Processing Conference, 2009. IMVIP'09. 13th International

(18-23). IEEE (2009)

14. Ganzeli H.S., et al. "SKAN: Skin Scanner-System for Skin Cancer Detection Using Adaptive Techniques." Latin America

Transactions, IEEE (Revista IEEE America Latina) 9.2, 206-212, (2011)

15. Dhandra B.V., et al., Color Histogram Approach for Analysis of Psoriasis Skin Disease, (2013)



-21st Nov 2015


An Understanding of Local-Area Networks Using Catalanelbow

Kumari Asima Mahato, Baby Kumari, Sunita Kumari, Sushma Kumari

and

Arun Kanti Manna Department of Computer Science and Engineering,Govt. Polytechnic Silli, Ranchi-835102, Jharkhand, INDIA

Abstract

Scholars agree that virtual modalities are an interesting new topic in the field of robotics, and end-users concur. In fact, few

biologists would disagree with the development of redundancy. We introduce a Bayesian tool for visualizing fiber-optic cables,

which we call Catalan Elbow.

Keywords: Catalan Elbow, Dogfooding, Fiber-optic cable, Robotics

Introduction

The programming languages solution to active networks22

is defined not only by the evaluation of rasterization, but also by the

essential need for forward-error correction. However, a confirmed challenge in hardware and architecture is the theoretical

unification of forward-error correction and the analysis of B-trees. On a similar note, contrarily, an important question in

networking is the exploration of Scheme. However, replication alone is not able to ful fill the need for the analysis of reinforcement

learning. CatalanElbow, our new application for atomic archetypes, is the solution to all of these obstacles. Without a doubt, the

shortcoming of this type of method, however, is that the fore most replicated algorithm for the improvement of massive multiplayer

online role-playing games by V. Moore et al.14

is in Co-NP. Our method is in Co-NP. This combination of properties has not yet

been analyzed in prior work.

In this paper, we make two main contributions. We use large-scale technology to argue that extreme programming can be made loss

less, ubiquitous, and interposable. We verify not only that extreme programming and DHCP are continuously incompatible, but that

the same is true for interrupts.

The rest of this paper is organized as follows. We motivate the need for replication. Continuing with this rationale, to fix this

question, we demonstrate that though IPv6 and write-ahead logging can agree to accomplish this ambition, RPCs can be made

ubiquitous, metamorphic, and authenticated. We place our work in context with the prior work in this area. Next, to achieve this

intent, we verify that while journaling file systems can be made lossless, client-server, and wireless, simulated annealing and

802.11b are regularly incompatible. As a result, we conclude.

Framework

Our research is principled. We executed a 9-year-long trace arguing that our framework is not feasible. This is an appropriate

property of our algorithm. We show CatalanElbow’s efficient creation in Figure 1. Similarly, despite the results by Takahashi , we

can validate that multi-processors and architecture can interfere to realize this objective. The question is, will CatalanElbow satisfy

all of these assumptions? Exactly so14

.

We consider a system consisting of n virtual machines. Though it at first glance seems perverse, it has ample historical precedence.

Further more, Figure 1 details the relationship between CatalanElbow and classical algorithms. This seems to hold in most cases.

We scripted a minute-long trace validating that our design holds for most cases. This seems to hold in most cases.

We believe that the seminal empathic algorithm for the emulation of courseware that would allow for further study into thin clients

by Kobayashi et al. is optimal. Furthermore, we assume that vacuum tubes can be made low-energy, stable, and real-time. We

consider a methodology consisting of n online algorithms. Next, any robust refinement of homogeneous methodologies will clearly

require that 802.11b18

and systems can synchronize to surmount this problem; Catalan Elbow is no different.



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Figure-1

Our methodology develops hash tables in the manner detailed above.

Figure-2

CatalanElbow’s semantic improvement.

Implementation

After several minutes of difficult architecting, we finally have a working implementation of Cata-lanElbow. On a similar note,

despite the fact that we have not yet optimized for security, this should be simple once we finish implementing the homegrown

database. Even though this result is rarely a practical mission, it is derived from known results. Hackers worldwide have complete

control over the centralized logging facility, which of course is necessary so that Boolean logic and replication can collaborate to

address this obstacle. CatalanElbow is composed of a server daemon, a server daemon, and a virtual machine monitor.

Results

As we will soon see, the goals of this section are manifold. Our overall evaluation seeks to prove three hypotheses: (1) that ROM

throughput behaves fundamentally differently on our desktop machines; (2) that NV-RAM throughput behaves fundamentally

differently on our planetary-scale overlay network; and finally (3) that popularity of the memory bus is more important than an

application’s historical ABI when minimizing 10th-percentile time since 2004. our logic follows a new model: performance matters

only as long as performance takes a back seat to usability22

. Note that we have decided not to emulate a heuristic’s wireless user-

kernel boundary. Despite the fact that such a claim at first glance seems unexpected, it is supported by prior work in the field.



-21st Nov 2015


Third, only with the benefit of our system’s flash-memory throughput might we optimize for complexity at the cost of complexity

constraints. Our evaluation will show that doubling the USB key throughput of opportunistically cooperative models is crucial to

our results.

Figure-3

The average hit ratio of Catalan Elbow, compared with the other heuristics.

Hardware and Software Configuration: Our detailed evaluation mandated many hardware modifications. We performed an

emulation on our system to disprove the computationally scalable behavior of mutually exclusive models. This step flies in the face

of conventional wisdom, but is crucial to our results. To begin with, we added 200Gb/s of Wi-Fi through-put to our

decommissioned Atari 2600s to better understand the effective ROM throughput of our introspective overlay network. On a similar

note, we added more 10GHz Pentium IIs to our linear-time overlay network to probe the effective floppy disk space of our XBox

network. Further, we tripled the effective ROM throughput of our embedded cluster to discover algorithms13

. Along these same

lines, we removed some tape drive space from our network to measure the mutually ubiquitous behavior of partitioned models.

Similarly, we added 8MB of flash-memory to our decommissioned Motorola bag telephones to disprove the work of British gifted

hacker I. V. Davis. Lastly, we removed 300MB/s of Ethernet access from our mobile telephones.

CatalanElbow does not run on a commodity operating system but instead requires a mutually hardened version of FreeBSD. All

software was hand hex-editted using a standard toolchain built on the American toolkit for independently improving wireless

Nintendo Gameboys. We implemented our simulated annealing server in Smalltalk, augmented with mutually wired extensions10,22

.

Next, we note that other researchers have tried and failed to enable this functionality.

Figure-4

The expected block size of CatalanElbow, as a function of popularity of hierarchical databases.

Dogfooding Methodology

Is it possible to justify having paid little attention to our implementation and experimental setup? No. Seizing upon this

approximate configuration, we ran four novel experiments: (1) we measured WHOIS and DNS performance on our mobile

telephones; (2) we asked (and answered) what would happen if independently wireless superblocks were used instead of



-21st Nov 2015


superpages; (3) we dogfooded CatalanElbow on our own desktop machines, paying particular attention to ROM throughput; and (4)

we ran 85 trials with a simulated RAID array workload, and compared results to our earlier deployment. Now for the climactic

analysis of all four experiments. Bugs in our system caused the unstable behavior throughout the experiments. On a similar note,

the many discontinuities in the graphs point to exaggerated interrupt rate introduced with our hardware upgrades. On a similar note,

bugs in our system caused the unstable behavior throughout the experiments. Shown in Figure-7, the first two experiments call

attention to our system’s response time. The results come from only 0 trial runs, and were not reproducible. Second, note that

Figure-7 shows the average and not expected parallel expected response time. Next, note how deploying semaphores rather than

deploying them in a chaotic spatiotemporal environment produce smoother, more reproducible results. Our objective here is to set

the record straight.

Figure-5

The mean signal-to-noise ratio of our algorithm, compared with the other solutions.

Lastly, we discuss all four experiments. Gaussian electromagnetic disturbances in our Internet-2 overlay network caused unstable

experimental results. Next, note how simulating access points rather than simulating them in software produce more jagged, more

reproducible results. Error bars have been elided, since most of our data points fell outside of 25 standard deviations from observed

means.

Related Works

While we know of no other studies on efficient communication, several efforts have been made to deploy hierarchical databases12

.

The fore most framework does not store the refinement of 802.11 mesh networks as well as our solution.

Further, although Sato and Kumar also explored this method, we emulated it independently and simultaneously 4. Although we

have nothing against the prior approach by Johnson et al.19

, we do not believe that method is applicable to homogeneous robotics11,

7.

Figure-6

Note that distance grows as distance decreases – a phenomenon worth improving in its own right.



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The improvement of perfect models has been widely studied5. We had our solution in mind before Sasaki et al. published the recent

acclaimed work on the synthesis of von Neumann machines. Charles Darwin et al. suggested a scheme for synthesizing multi-

processors, but did not fully realize the implications of congestion control at the time3. Contrarily, these solutions are entirely

orthogonal to our efforts. Although we are the first to explore the study of red-black trees in this light, much related work has been

devoted to the deployment of architecture20

. A recent unpublished undergraduate dissertation16,5,1

proposed a similar idea for XML6.

Unlike many previous approaches23

, we do not attempt to harness or locate flexible technology15

. On a similar note, instead of

visualizing psychoacoustic algorithms8, we solve this grand challenge simply by exploring the investigation of XML

9,17. As a result,

the framework of Moore et al.2 is a technical choice for “fuzzy” models. Here, we fixed all of the challenges inherent in the

previous work.

Figure-7

These results were obtained by Harris et al.21

; we reproduce them here for clarity.

Conclusion

Our algorithm will answer many of the problems faced by today’s hackers worldwide. Further, our framework for analyzing

reinforcement learning is famously useful. On a similar note, to surmount this problem for the construction of link-level

acknowledgements, we proposed new embedded symmetries. The characteristics of our methodology, in relation to those of more

much-touted solutions, are predictably more appropriate. The exploration of evolutionary programming is more compelling than

ever, and our heuristic helps physicists do just that.

References

1. Agarwal R., and Harris G. Exploring IPv6 and cache coherence with SixBattel. In Proceedings of the Symposium on Atomic,

Reliable Information (2001)

2. Anand O., Wu B. and Wilkinson J. Investigating IPv4 and thin clients. In Proceedings of FOCS (1999)

3. Anderson P.J. and Martin G.L., A deployment of Voice-over-IP. Journal of Automated Reasoning, 1-11 (1999)

4. Bhabha V., The effect of cacheable symmetries on cryptoanalysis. In Proceedings of FPCA, (2004)

5. Daubechies I. and Jackson F., A methodology for the construction of erasure coding. Journal of Secure, Autonomous

Symmetries, 34, 75–80 (1999)

6. Dongarra J., Qian H. and Zhao G., A case for virtual machines. OSR, 28, 1–11 (1999)

7. Erd˝ OS P. and Sasaki D., A case for the producer-consumer problem. In Proceedings of SIGCOMM, (1993)

8. Garcia E., Towards the evaluation of Internet QoS. In Proceedings of MICRO, (2004)

9. Garcia V. and Shenker S., Introspective, autonomous archetypes for multi-processors, In Proceedings of WMSCI, (1997)

10. Harris V. and Jackson K., Deploying reinforcement learning using modular technology. OSR, 71, 86–103 (2001)

11. KunduS., Smith M. and Kundu S., Deconstructing the Turing machine with yufts. Journal of Cacheable, Introspective

Communication 3, 157–190 (2002)



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12. Minsky M. and Dijkstra E., Emulation of superblocks. In Proceedings of the Symposium on Probabilistic Models (2003)

13. Moore T.V. and Cook S., A case for IPv6. In Proceedings of INFOCOM, (1999)

14. Nehru L.G., An improvement of IPv4. Tech. Rep. 4603, IBM Research, (2003)

15. Perlis A., Johnson R. and Adleman L., A case for wide-area networks. TOCS, 5, 70–85 (2002)

16. Quinlan J. Evaluation of RPCs, Journal of Robust, Knowledge-Based Technology 1, , 81–109 (2002)

17. Robinson F. and Leiserson C., Sensor networks considered harmful. In Proceedings of the Symposium on Replicated, Highly-

Available Modalities, (1991)

18. Seshagopalan Z., Reddy R., Leiserson C., Corbato F., Estrin D. and Knuth D., Vast: A methodology for the refinement of

write-ahead logging. Journal of Atomic, Heterogeneous Theory, 20, 57–63 (2003)

19. Smith J., Anderson J., Corbato F. and Sasaki F., ECLAT: Improvement of extreme programming. Journal of Probabilistic

Methodologies, 10, 85–107 (1997)

20. Thomas O. and Sun D., A methodology for the analysis of red-black trees. In Proceedings of the WWW Conference (1990)

21. Wilkes M.V., Decoupling expert systems from web browsers in web browsers. OSR 55, 1–16 (1998)

22. Williams F., The influence of authenticated information on artificial intelligence. Journal of Secure, Pseudorandom

Archetypes 9, 153–196 (1999)

23. Zhao Q., Operating systems no longer considered harmful. Journal of Virtual Models 4, 156–197 (1999)



-21st Nov 2015


Affect detection from facial expression: A review Aritra Ghosh and Saikat Basu

1Department of Software Engineering, West Bengal University of Technology, Kolkata, INDIA 2Department of Computer Science and Engineering, West Bengal University of Technology, Kolkata, INDIA

Abstrac

In our daily life,most primary form of attention is the face. Any person’s emotion is first reflected in their face. At first a basic idea

about human emotion is given. Various Dimensional models of emotions are described to present an idea about different types of

emotions. In this paper, our main focus is on emotion recognition or affect detection from facial expression. Different stages of

emotion recognition from facial expression is described step by step. Various facial features can be extracted from main facial

components. We use different feature extraction algorithms to obtain these features. After that classification algorithms are

employed to train the system by the available relevant features to recognize emotions.

Keywords: HCI; SVM; PCA; SVD; Stimuli; LOSO; 10 fold cross validation.

Introduction

Now a days, recognition of a person’s emotional state is very useful in various fields such as Human Computer Interaction(HCI), 1-3

physiological health services, medicalscience, education such as counselling, e-learning applications etc. Recently emotion

recognition has been used widely in HCI. The ability of computers to understand and analyze human emotions and perform

appropriate actions accordingly is one of the key focus area of Human Computer Interaction (HCI). If the state of user’s mind can

be recognized or understoodby the computers and robots then they can interact with them accordingly. It will also help the user to

use the system with a greater ease and the interactions will be more meaningful. For example: if a psychologist or psychiatrist

knows the mental state of the patient it will be more convenient for the doctor to treat the patient. Distance or e-learning would be

more meaningful and effective if the mental state of the user can be analyzed by the system. The study materials can be provided

accordingly2. Accidents may be avoided by recognizing driver’s mental state

4 etc. Facial expression and color, sound, speech,

physiological signal, gesture- Movement of different body parts these are the different approaches which are used for human

emotion recognition. In this paper our main focus is the recognition of emotion from facial expression. Use of Facial Expression

one of most commonly used and simpler approach in this field as it changes with a person’s mental state and change in emotion.

This also is most common way to express the emotion and very easy to capture and analyze.

Models of Emotion

Emotions affects human consciousness, it is a mental state or spontaneous reaction. Emotions have many types such as anger,

happiness, disgust, sorrow, fear, surprise etc.

There are various models of emotions proposed by different researchers’ like-

Discrete Emotional Model: This is one of the most applied model of emotion proposed by Paul Ekman. According to this model

there is some basic emotions among all cultures. These basic emotions are happiness, sadness, surprise, disgust, anger and fear 2.

Two Dimensional Valance Arousal Model: In this model emotions are characterized by their valance and arousal, which

represents pleasantness and activation level respectively. Valance range from positive to negative and Arousal range from low to

high. Here basically emotions is categorized based on scale. Two Dimensional model was proposed by Lang.

Plutchik's model

Plutchik's model is commonly used, in different forms or versions, in HCI (Human Computer Interaction) or sentiment analysis. A

hybrid of both basic-complex categories and dimensional theories is proposed by Robert Plutchik. It is basically a three-

dimensional model which arranges emotions in concentric circles. Here inner circles are more basic and outer circles more

complex. The inner circle emotions are bended to form outer circles. Plutchik's model emanates from a circumflex representation.

Based on similarity emotional words are plotted.



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Figure-1

Two dimensional valance arousal model.

Figure-2

Plutchik’s model [5]

Steps Of Emotion Recognition From Facial Expression

Emotion Elicitation Stimuli: In order to recognize emotion accurately gathering of high quality facial data is important. For the

purpose of recognition, all the emotions must be natural. Different techniques of emotion elicitation such as International affective

picture system, Audio visual clips and other multimodal approaches2, 7

are used. To obtain the target emotion chances of inducting

multiple emotions in the subject is eliminated.



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Pre-Processing: Raw data are always noisy and contains external artefacts. So, before processing elimination of these noise and

artefacts is necessary. For facial data various types of filtering techniques such as Low-pass filters (Butterworth, Adaptive filters

etc.), Smoothing techniques, Histogram are used for pre-process the raw data.

Feature Extraction: After the raw data is pre-processed emotion is recognized from different facial features. For this purpose

required features are extracted from the images. Here a number of different feature extraction techniques are used such as

Geometrical feature extraction, Template matching algorithms, Multimodal Information6.

Geometrical Feature Extraction

Eye and Eye-brow features: Thickness of Eye-brows, an Eye-brow’s height, gap between an Eye and an Eye-brow, height of an

Eye, Eye-brow width, horizontal difference between the left-most edge of an eyebrow and that of an eye, distance between the

centre of an eye and the left-most edge of an Eye, distance between the centre of an eye and the right-most edge of an eye, Eye

width and The farthest height between an eye and an eyebrow6.

Mouth Features: The closest distance between the upper lip andthe bottom of a nose, thickness of the upper lip, height of the

widened mouth, height of the wholemouth, the left-steepest region of the lower lip, distance between the center and the left-most

mouth, the width of the whole mouth, the right steepest region of the lower lip and the width of the widened mouth6.

Nose Feature: Height of a nose, distance between the eyebrow and the bottom of a nose, distance between the eye and the bottom of

a nose, distance between the peak and the bottom of a nose and nose width6.

Chin Feature: Height between the upper lip and the edge of theChin, height to the lower lipand c: the horizontal half width of the

chin.6

Template Matching: Here the face is represented by the template. Bi-dimensional array of intensity values represent images and

comparison done by Euclidean distance.

Feature Reduction: In feature extraction, many features are extracted amongst which some of them may not be related with the

emotion. So, the feature reduction is performed in order to eliminate the unnecessary features extracted from feature extraction as

they can degrade the performance and accuracy of the system. Different searching algorithms are implemented to recognize the

relevant features and the remaining unnecessary features are eliminated. Algorithms used for searching are sequential forward

search, sequential backward search, Fischer projection2,7

etc.

Classification: When the selection of relevant features are done the system is trained to recognize and classify different emotional

state with the help of available features. For this purpose various classifiers such as K-nearest neighbor (KNN), Support Vector

Machines (SVM), Linear Discriminant Analysis (LDA), Artificial Neural Network (ANN)2,7

etc. are used.

Figure-3

Process flow of Facial Emotion Recognition.



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Related Works

Facial expression is most commonly used technique for recognizing human emotion. Different approaches and techniques are used

by researcher for extracting facial features and recognize mental state of a person from facial expression. Table [1, 2.] below shows

recognition of different emotions by using Principle Component Analysis (PCA) and Singular Value Decomposition (SVD). Here

two different approaches are used in first approach only PCA is used in second approach PCA is used with SVD in both cases

accuracy is calculated here the main useful statistical measurements implemented are: Recognition Rate, Precision and Accuracy

Table [1, 2, 3.] [1.]. It is noticed that overall accuracy is grater when PCA is used with SVD Table [3.].

Figure-4

System Architecture.

Table-1

Accuracy Rates of Various Facial Expression1

Facial Expression Accuracy Rate PCA+SVD (%) Accuracy Rate PCA (%)

Happy 92.85 90

Disgust 92.85 90

Natural 95.71 94.29

Sad 90 82.86

Anger 95.71 97.14

Surprise 97.14 91.43

Fear 95.71 95.71

Table-2

Recognition Rates of Various Facial Expression1

Facial Expression Recognition Rate PCA+SVD (%) Recognition Rate PCA (%)

Happy 57.14 42.85

Disgust 89.47 68.42

Natural 85 70

Sad 76.19 71.43

Anger 80 75

Surprise 65 50

Fear 63.63 63.63



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Another approach used in [7]. Here audio-visual affective information of six emotions: happiness, sadness, surprise, anger, disgust

and fear is analyzed. This technique improve the recognition by modelling the cross-relation data. Here the data is treated as

asynchronous streams [7]. Binary-class classification is performed here using Support Vector Machine (SVM) for binary

classification. 10 fold cross validation where database is divided into 10 folds which contains approximately equal data and 9 folds

are tested on the remaining fold (for subject dependent analysis) or LOSO (Leave-One-Subject-Out) cross validation strategy (for

subject independent analysis). Analysis of Variance (ANOVA) is employed for statistical results. Then the Multi-Class

Classification is performed and the result of Binary-class classification is compared with this. For Multi-Class Classification SVM

is employed for classification and LOSO (Leave-One-Subject-Out) is used as cross validation strategy (for subject independent

analysis). The un-weighted accuracy is grater while using LOSO than 10 fold Cross Validation Table [4] [5] [6].

Table-3

Multi-Class Classification Accuracy (In %): Confusion Table For Randomized 10 Fold Cross Validation

REFERENCE

EMOTION

RECOGNISED EMOTION

ANGER DISGUST FEAR HAPPY SAD SURPRISE

ANGER 65.2 7.0 9.3 2.3 7.4 8.8

DISGUST 6.5 72.1 7.0 6.5 6.0 1.9

FEAR 7.9 8.4 48.8 6.5 14.4 14.0

DAPPY 0.5 3.8 5.7 82.5 2.3 5.2

SAD 9.7 4.2 16.3 2.8 53.5 13.5

SURPRISE 10.2 0.9 15.3 4.2 14.0 55.3

Table-4

Multi-Class Classification Accuracy (In %): Confusion Matrix For Leave-One-Subject-Out Cross Validation

REFERENCE

EMOTION

RECOGNISED EMOTION

ANGER DISGUST FEAR HAPPY SAD SURPRISE

ANGER 65.2 7.0 9.3 2.3 7.4 8.8

DISGUST 6.5 72.1 7.0 6.5 6.0 1.9

FEAR 7.9 8.4 48.8 6.5 14.4 14.0

DAPPY 0.5 3.8 5.7 82.5 2.3 5.2

SAD 9.7 4.2 16.3 2.8 53.5 13.5

SURPRISE 10.2 0.9 15.3 4.2 14.0 55.3

Paper in9 mainly concentrate on determining the state of a student’s mind during a class thus it can be very useful in enhancing the

education system. A student may be confused during a class may be having difficulties understanding a certain topic. So the

proposed system can analyze the mental state and can help improving the learning process. Here mental state is analyzed by

analyzing the facial features of the student and its relationship with the student’s mental state. Eyes, Eye-brows, Forehead, Mouth

and two inner Eye corners are recognized to be the most relevant features in reflecting mental state of the students. Raised Eye-

brows, widely opened eyes, shrieked lips, two inner eye corners having under natural state little wrinkle etc. Are recognized as

positive state, whereas naturally opened eyes, naturally closed mouth, twoInner eyes corners are the signs of neutral state. Dropped

Eye-brows, shrink eyes, unnaturally open or closed mouth indicates negative state. Here the image information is calculated and

extracted by using a texture description operator11

which came from the Local Binary Pattern (LBP) proposed in10

. SVM is used as

classifier. So the complexity of the training completely depends upon sample size.Paper in9 mainly concentrate on determining the

state of a student’s mind during a class thus it can be very useful in enhancing the education system. A student may be confused

during a class may be having difficulties understanding a certain topic. So the proposed system can analyze the mental state and can

help improving the learning process. Here mental state is analyzed by analyzing the facial features of the student and its relationship

with the student’s mental state. Eyes, Eye-brows, Forehead, Mouth and two inner Eye corners are recognized to be the most

relevant features in reflecting mental state of the students. Raised Eye-brows, widely opened eyes, shrieked lips, two inner eye

corners having under natural state little wrinkle etc. Are recognized as positive state, whereas naturally opened eyes, naturally

closed mouth, twoInner eyes corners are the signs of neutral state. Dropped Eye-brows, shrink eyes, unnaturally open or closed

mouth indicates negative state. Here the image information is calculated and extracted by using a texture description operator11

which came from the Local Binary Pattern (LBP) proposed in10

. SVM is used as classifier. So the complexity of the training

completely depends upon sample size.



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Table-5

Observations [9]

NO TRAIN SAMPLES TEST SAMPLES RIGHT NOS. RECOGNITION RATE (%)

1 190 23 17 73.91

2 191 22 15 68.18

3 191 22 16 72.73

4 193 20 13 65.00

5 192 21 14 66.67

6 192 21 16 76.19

7 193 21 16 80.00

8 192 21 15 71.4

9 192 21 14 66.67

10 191 22 16 73.73

Avg. Recognition Rate 71.35%

Table-6

Observations9

EXPRESSION TOTAL NO. RIGHT RECOGNIZED

NO.

WRONG RECOGNIZED

NO.

RECOGNITION

RATE (%)

ANGER 30 20 10 66.67

DISGUST 29 20 9 68.97

FEAR 32 22 10 68.75

HAPPINESS 31 25 6 80.65

NETURAL 30 22 8 73.33

SADNESS 31 22 9 70.97

SURPRISE 30 21 9 70.00

Table-7

Comparison Of different facial expression emotion recognition techniques

Ref

No.

Stimuli Used Database

Used

NO Of Subjects Classification Avg. Accuracy (%)

[1] Facial Expression

Images

JAFFE 70 PCA 67.14

[1] Facial Expression

Images

JAFFE 70 PCA+SVD 78.57

[7] Audio-Visual eNTERFACE’05 42 SVM, 10 fold cross validation,

ANOVA

47.6

[7] Audio-Visual eNTERFACE’05 42 SVM, 10 fold cross validation,

ANOVA

62.9

[9] Facial Images Student

volunteers

190 SVM+LBP 71.35

[15] Images Unknown 42 SVM+AAM 84.55

Algorithms: When recognizing human emotions different types of algorithms are used such as Machine learning algorithms,

Searching algorithms, Feature Reduction algorithms, Statistical algorithms. We employ all of above types of algorithms to design a

system which can recognize emotion. Machine learning algorithms are used to train a system where as Searching Algorithms can be

used to searchfeatures relevant and irrelevant to emotion. Statistical algorithms are employed to recognize the emotion as accurately

as possible.



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Principal Component Analysis (PCA): PCA comes under feature extraction algorithm it’s basically used for representing data.

When the data set to be processed is very large we implement PCA to construct small dimensionality feature space12

by reducing

the large dimensionality data space12

. In PCA the vector which is most suited for distribution of facial expression within the entire

image space12,13

. In PCA first a Principle Component is detected which is the largest possible variance from a large database then

the succeeding component has the largest possible value after the previous component. As the principle components are the

eigenvector of the symmetric convenience matrix thus they are orthogonal. It’s the easiest amongst eigenvector-based analysis but

PCA only extract the features and reduce a large dataset to smaller datasets.

Support Vector Machine (SVM): SVM is a non-probabilistic binary linear classifier14

used for classification and regression

analysis. This is a model of supervised learning. It analyses the features and assigns them to different categories15

. SVM classifier is

trained to recognize different emotional features and then classify the features into different categories and recognize emotions from

there. SVM is very accurate and produces robust classification results regardless of the non-linearly separable data. During test

phase SVM can be a bit slow, algorithmic complexity of SVM is high.

Analysis of Variance (ANOVA): It’s basically a statistical model by which differences among group means and their associated

procedures are analyzed to determine differences between the means of several groups. In this technique the total variance present

in the data set is divided into non-negative component due to factor of variation. Pros are its very simple and experimental error can

be reduced. ANOVA is not suitable for non-homogeneous data.

K-nearest neighbors (K-NN) algorithm: It’s a non-parametric method basically used for classification purposes and regression.

The output of K-NN classification is a member of the same class. It is one of the simplest machine learning algorithm. Here

functions are approximated locally and all the calculations are postponed till classification is done. The major drawback of K-NN is

that it simply uses the training for classification rather than learning anything from it known as lazy learning.

Future Scope: Emotion recognition from facial expression is the simplest of all the available techniques. In future an android based

emotion recognition system can be built using mobile camera. Facial expression based emotion recognition may be simplest of all

but it is not giving accurate result always. So there are many scope of improvement.

Conclusion

In this paper, main focus is affect detection from facial expression. At first a brief idea about different types of emotions is given.

Emotion recognition from facial expression has several steps. Different facial features are recognized. When those features are

analyzed, mental state of a person may be realized since the features changes with emotion. Classification algorithms plays a huge

part in emotion recognition as they are used to train the system and classify the emotional features in different categories such

algorithms are SVM, K-NN, LDA etc. There is also feature reduction algorithms like PCA for extracting key features from a large

data-set. In present days, emotions are recognized from physiological signals, speech, and gesture etc. but the widely used

technique is emotion recognition from facial expression for its simplicity. Although it not as accurate as other technique as it is very

easy to fake.

References

1. Gosavi Ajit P. and S.R. Khot, Emotion recognition using Principal Component Analysis with Singular Value Decomposition."

In Electronics and Communication Systems (ICECS), 2014 International Conference on, 1-5. IEEE, (2014)

2. Jerritta, S., M. Murugappan, R. Nagarajan and Khairunizam Wan, "Physiological signals based human emotion recognition: a

review." In Signal Processing and its Applications (CSPA), 2011 IEEE 7th International Colloquium on, 410-415. IEEE,

(2011)

3. W.R. Picard, Affective computing: challenges, International Journal of Human-Computer Studies - Application of affective

computing in human—Computer interaction, 59, 55-64, (2003)

4. Baker S., Real-time non-rigid driver head tracking for driver mental state estimation, (2004)

5. Kamińska, Dorota, and Adam Pelikant. "Recognition of human emotion from a speech signal based on Plutchik's model."

International Journal of Electronics and Telecommunications, 58(2), 165-170 (2012)

6. Byun Kwang-Sub, Chang-Hyun Park, and Kwee-Bo Sim. "Emotion recognition from facial expression using hybrid-feature

extraction, In SICE annual conference, 2483-2487 (2004)



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7. Pantic Maja and Léon JM Rothkrantz, An expert system for multiple emotional classification of facial expressions." In Tools

with Artificial Intelligence, 1999. Proceedings. 11th IEEE International Conference on, 113-120. IEEE, (1999)

8. Tawari Ashish and Mohan Manubhai Trivedi, Face expression recognition by cross modal data association." Multimedia,

IEEE Transactions on, 15(7), 1543-1552 (2013)

9. Changjun, Zhou, Peipei Shen, and Xiong Chen. "Research on algorithm of state recognition of students based on facial

expression, In Electronic and Mechanical Engineering and Information Technology (EMEIT), 2011 International Conference

on, 2, 626-630, (2011)

10. T. Ojala, M. Pietikainen and T. Maenpaa, Multiresolution Gray Scaleand Rotation Invariant Texture Classification with Local

Binary Pattern," IEEE Transaction on PAMI, 24(7), 971-987, (2002)

11. A.Hadid, M.Pietikainen, and T.Ahonen, "A discriminative feature space for detecting and recognizing faces, In CVPR, pages

797-804,

12. Washington, DC, USA, (2004)

13. Meher, Sukanya Sagarika, and Pallavi Maben, Face recognition and facial expression identification using PCA." In Advance

Computing Conference (IACC), 2014 IEEE International, 1093-1098. IEEE, (2014)

14. Kim, Kwang In, Keechul Jung, and Hang Joon Kim. "Face recognition using kernel principal component analysis." Signal

Processing Letters, IEEE 9(2), 40-42 (2002)

15. 14 Sun, Jian-ming, Xue-sheng Pei, and Shi-sheng Zhou. "Facial emotion recognition in modern distant education system using

SVM." In Machine Learning and Cybernetics, 2008 International Conference on, 6, 3545-3548. IEEE, (2008)

16. Sun, Jian-ming, Xue-sheng Pei, and Shi-sheng Zhou. "Facial emotion recognition in modern distant education system using

SVM." In Machine Learning and Cybernetics, 2008 International Conference on, 6, 3545-3548. IEEE, (2008)



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A Review on Facial Emotion Recognition System

Zahir Abbas Rahaman and Saikat Basu 1Department of Information Technology, West Bengal University of Technology, Kolkata, West Bengal, India

2Department of Computer Science and Engineering, West Bengal University of Technology

Introduction

The present review relates generally to the field of facial expression recognition. Specifically, to a method that use as an apparatus

for recognizing an emotion of an individual using facial Action. In real-time people often express emotions through facial

expressions. Facial expressions are some of the most powerful, natural, and immediate ways for humans to communicate their

emotions and intentions. The face can express an emotion sooner than people verbally or even realize their feelings. For example,

different emotions are expressed using various facial regions, mainly the mouth, the eyes, and the eyebrows. More often, emotional

expression is communicated by certain changes in one or a few discrete facial features, such as a tightening of the lips in anger or

certain way of opening the lip corners in sadness. Many computer systems are configured to recognize a small set of different type

of emotional expressions, e.g., joy, surprise, anger, sadness, fear, and disgust. A Facial unit (FU) has been developed for describing

facial expressions. The change of the facial facts are changes due to facial muscles are recorded in database. Some changes are an

atomically related to contractions of specific facial muscles, i.e., 12 different type of mussels changes are for upper face, and 18 are

for lower face, can occur either singly or in combination. When this occur in combination, they may be additive, in which the

combination does not change the appearance of the constituent to non-additive, in which the appearance of the constituents does

change.

Summary of Emotion Recognition

Accordingly, the present invention is made to address at least the above-described problems described above and to provide at least

advantages described below,

In accordance with an aspect of the present invention, a method is provided for recognizing an emotion of an individual based on

FUs. The method includes receiving an input FUS string including one or more FUs that represents a facial expression of an

individual from an FUS detector, matching the input FUs string with each of a plurality of FUs strings, wherein each of the plurality

of FUs strings includes a set of highly discriminative FUs, each representing an emotion, identifying an FUs string from the

plurality of FUs strings that best matches the input FUS string, and outputting an emotion label corresponding to the best matching

FUS string that indicates the emotion of the individual.

In accordance with another aspect of the present invention, an apparatus is provided for recognizing an emotion of an individual

based on FUs. The apparatus includes a processor; and a memory coupled to the processor. The memory includes instructions

stored therein, that when executed by the processor, causes the processor to receive an input FUs string including one or more FUs

that represents a facial expression of the individual; match the input FUs string with each of a plurality of FUs strings, wherein each

of the plurality of FUs strings includes a set of highly discriminative FUs, each representing an emotion; identify an FUs string

from the plurality of FUs strings that best matches the input FUs string; and output an emotion label corresponding to the best

matching FUs string that indicates the emotion of the individual.

Emotion Recognition System

Various methodologies is present in this review will now be described in detail With reference to the accompanying drawings. In

the following description, specific details such as detailed configuration and components are merely provided to assist the overall

understanding of these embodiments of the present invention. Therefore, it should be apparent to those skilled in the art that various

changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the

present invention. In addition, descriptions of Well-known functions and constructions are omitted for clarity and conciseness.

Herein, the terms “facial units (FUs)” and “FUs” are used interchangeably. To map FUs detected from a face of an individual to

target emotions, a relation matrix is formed based on dis criminative power of FUs With respect to each of the target emotions.

Value that helps to determine statistical relationship between each action unit and one or more emotions. For example, a high

discriminative power indicates that the action unit belongs to an emotion, more than the action unit with a low discriminative

power. The relation matrix is used for mapping an input FUs string with a number of template FUs strings selected from the relation

matrix to recognize an emotion of an individual, according to an embodiment of the present invention.



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Figure-1

Flowchart

Figure-1 is a flowchart illustrating a method of forming a relation matrix indicating statistical relationship between a set of FUs and

an emotion, according to an embodiment of the present invention. Referring to FIGURE-1, discriminative power is computed for

each FUS associated with a facial expression. Discriminative power is a value whose magnitude quantifies discriminative power of

FUs associated with facial actions for an emotion. Thus, the discriminative power enables identification of highly discriminative

facial actions for various emotions using statistical data. Statistical data relates to probabilities/ statistics of correlations between

various emotions and FUs derived from a large set of facial expressions. In accordance With an embodiment of the present

invention, the discriminative power for each FUS is computed based on Equation (1)

H= A(|Y|-lXl-)—A(Yjl)_(i)/Normal factor …….. (1) In Equation (1), A (Yj|Xl-) is the probability of action unit Yj, given that the

emotion Xi has occurred, and A(Yj|Xi) is the probability of action unit Yj, given that the emotion Xi has not occurred. Using the

values, a relation matrix is formed to represent statistical relationship between each FUS and six emotions, as illustrated in

FIGURE-2. The values in the relation matrix are then normalized to suppress learning sample size for each emotion. Herein, the

discriminative power is computed for action units, which are based on the Facial System.

Figure-2

Intensity of action units



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Referring to figure-2, positive intensity a lighter colour indicates high probability of an action unit belonging to a particular

emotion, while negative intensity (a darker colour) indicates high probability of an action unit not belonging to a particular emotion.

For example, the emotion “happy” has FU 12, FU 6, and FU 26, which are positive discriminative FUs, and FU1, FU2, and FU5,

which are negative discriminative FUs. A matrix is derived from the relation matrix based on the identified set of highly

discriminative action units for each of the target emotions. An example of a matrix including five highly discriminative action units

selected for six emotions, i.e., angry, fear, sad, happy, sur prise, and disgust is shown in Table 1 below.

Table-1

Example of Facial Expression

In Table l, for example, the emotion “angry” has FU20, FU10, FU17, FU7 , and FU10 as highly discriminative FUs, and “happy”

has FU22, FU5, FU21, FU2, and FU27 as highly discriminative action units. The matrix in Table l helps efficiently map an input

FU string to one or more FU strings corresponding to six emotions for recognizing an emotion of an individual based on detected

facial expressions

The input FU string is matched across the template FU strings in the matrix formed in a template FU string from the tem plate FU

strings that best matches the input FU string is determined using a longest common subsequence technique. The longest common

subsequence technique is an approximate string matching technique indicating a greatest amount of similarity between the input FU

string and one of the template FU strings. Thus, the longest common subsequence technique helps determine a template FU string

having mini mal matching cost against the input FU string as compared to remaining template FU strings.

In accordance With an embodiment of the present invention, a common subsequence is determined by matching the input FU string

with each of the template FU strings. The common subsequence is indicative of a distance measure of an amount of similarity

between the input FU string and the template FU strings. Based on the common subsequence, a longest common subsequence

associated With an FU string best matching the input FU string is identified. In the longest common sub-sequence, a sub-sequence

is a sequence in Which FUs appears in a same relative order but are not necessarily contiguous. Additionally, the longest common

subsequence technique allows insertion and deletion of FUs in the input FU string, but not substitution of FUs.

An emotion label corresponding to the determined template FU string is output as an emotion associated with the individual. For

example, if the input FU string is 4 6 l2 17 26, the input FU string best matches with the template FU string 12 6 26 10 23,

which corresponds to an emotion label “happy”, as shown in Table l. In this example, the input FU string includes erroneous FUs

like 4, and 17. However, because the longest common subsequence technique allows for insertion of erroneous action units in the

input FU string, the erroneous input FU string can be accurately mapped to the template FU string 7, l0, 12, 20, 26 to recognize

that the individual is happy. Likewise, deletion becomes important to map the input FU strings like 6, 6, 10, 6, 10, 17 to

happy as all of these FUs indicate happy

Compression

A method for recognizing an emotion of an individual based on facial Units (FUs), the method comprising: receiving an input FU

string including one or more FUs that represents a facial expression of an individual from an FU detector; matching the input FU

string With each of a plurality of FU strings, Wherein each of the plurality of FU strings includes a set of highly discriminative

FUs, each representing an emotion; identifying an FU string from the plurality of FU strings that best matches the input FU string;

and outputting an emotion label corresponding to the best matching FU string that indicates the emotion of the individual. The

method of claim 1, Wherein identifying the FU string from the plurality of FU strings that best matches the input FU string



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comprises: determining a common sub sequence between the input FU string and each of the plurality of FU strings; and

identifying a longest common subsequence from the determined common sub-sequences, Wherein the longest common

subsequence is indicative of a greatest amount of similarity between the input FU string and one of the plurality of FU strings.

Wherein the common sub-sequence is indicative of a distance measure of an amount of similarity between the input FU string and

each of the of FU strings. Further comprising: determining a discriminative power for each of a multicity of FUs based on statistical

data; selecting a set of FUs representing each of a plurality of emotion labels, based on the discriminative power for each of FUs;

and storing the selected set of FUs associated with each of the plurality of emotion labels as FU strings. The method of claim

wherein the selected set of FUs associated with each of the plurality of emotion labels is stored as the FU strings in a matrix.

Wherein the discriminative power for each of the string of FUs indicates a probability of each FU belonging to one of the

combination of emotion labels. An apparatus for recognizing an emotion of an individual using facial Units (FUs), the apparatus

comprising: a processor; and a memory coupled to the processor, Wherein the memory includes instructions stored therein, that

When executed by the processor, cause the processor to: receive an input FU string including one or more FUs that represents a

facial expression of the individual; match the input FU string With each of a plurality of FU strings, Wherein each of the plurality

of FU strings includes a set of highly discriminative FUs, each representing an emotion; identify an FU string from the plurality of

FU strings that best matches the input FU string; and output an emotion label corresponding to the best matching FU string that

indicates the emotion of the individual.

Conclusion

This paper about reviewing some paper regarding facial recognition with adding feature of facial feature extraction. The features

also known as facial unit(s) FUs. This FU compare with template which also a database of particular emotion. this documentation

is an overview of the methods or methodology that are used by various paper authored by various author and published in reputed

publish house like IEEE, springer etc.

References

1. Y. Amit, D. Geman and K. Wilder, Joint induction of shape features and tree classifiers, (1997)

2. Y. Freund and R. Schapire, A decision-theoretic generalization of on-line learning and an application to boosting. In

Eurocolt '95, 23-37. Springer-Verlag, (1995)

3. C. Papageorgiou, M. Oren and T. Poggio, A general framework for object detection. In ICCV, (1998)

4. D. Roth, M. Yang and N. Ahuja, A snow-based face detector. In NIPS 12,2000. H. Rowley, S. Baluja, and T. Kanade. Neural

network-based face detection. In IEEE PAMI, volume 20, (1998)

5. H. Schneiderman and T. Kanade, A statistical method for 3D object detection applied to faces and cars. In ICCV, (2000)

6. K. Sung and T. Poggio, Example-based learning for view- based face detection. In IEEE PAMI, volume 20, pages 39-5 I,

(1998)

7. K. Tieu and P. Viola, Boosting image retrieval. In ICCV, (2000)



-21st Nov 2015


Prediction in Stock Market through Mathematical Modelling

Mrinalini Smita Department of Mathematics, St. Xavier’s College, Ranchi.Ranchi, Jharkhand, INDIA

Abstract

Prediction is a very difficult art, especially when it involves the future‖ -Neils Bohr (Nobel Laureate Physicist). “Forecasting is the

process of making statements about events whose actual outcomes (typically) have not yet been observed” Wikipedia. Words such

as predicting are used to also refer to forecasting. The art of forecasting into the future is a very vital but important exercise to many

stakeholders in diverse industries. As farmers would like to know the future rainfall pattern in order to properly sow their seeds and

at the right time so do financial analyst expect to know the future performance of various market stocks to guide investment options

available to them.

It is not possible to accurately forecast the future. Forecasting into the future comes with a margin of error. The margin of error

widens especially when forecasting deep into the future. In other words , when predicting variables and their expected influence

may change (with social, economic and political change) and new variables may emerge. These errors arise as a result of the level

of inaccuracy of the base information used and the method used to forecast into the future. This makes the choice of the forecasting

method pivotal when predicting into the future. In many cases forecasting uses quantitative data rather than qualitative data which

depends on the judgment of experts.

There are several forecasting models and methods in practice. The popularity of a forecasting model as against another is solely

based on their risk metrics. Forecasting of stocks is generally believed to be a very difficult task. Common time series forecasting

models are as follows: Box – Jerkin‟s Methods, Holt Winters Exponential Smoothing and simple linear regression. The result

always favours one of the methods of forecasting and this can be ascertained by the use of error metrics.

In time- series model, the past behaviour of a time series is examined to infer something about its future behaviour instead of

searching for effect of one or more variables on the forecast variable. They put more emphasis on the data analysis for

simplification of the model.

Different patterns or trends can be seen in the time series data. The time series is influenced by several factors like random

components, seasonal components, cyclic components etc. The random component in the time series may shield the influence of

other components and make it difficult to describe the observed trends or patterns in the data. .This influence the performance and

accuracy of Time-series model . Therefore, mathematical modelling( the process of developing a method of simulating real –life

situations with mathematical equations to forecast their future behaviour ) can be the best way to break everything down and

predict how something new will play out .Having a good mathematical model will make it easier to predict how certain plans will

play out.The main idea of forecasting techniques is to minimise the difference between actual and predicted values since this

should influence the performance and reliability of models.To figure out mathematical equation we had to use the right

mathematical model that would fit our needs best and help us.

Significance of the Study

The role of research in several fields of applied economics, whether related to business or to the economy has greatly increased in

modern times. This study will help in focussing attention on increasingly complex nature of business and government and hence in

solving operational problems.

The primary advantage of forecasting is that it provides various stakeholders (a person who has interest in or investment in

something and who is impacted by and cares about how it turns out) with valuable information that can be used to make decisions

about the future.

It will go along way to help the managers of financial portfolios and to understand and appreciate the underlying factors behind the

in- sample forecasting accuracy of stocks in stock exchanges.

It will further boost the confidence of stakeholders in the financial industry to do more business with less risk.

Other beneficiaries of research are investors, directors, regulators and other financial institutions as well as researchers in academia.



-21st Nov 2015


This study will enable public to invest wisely in stock market

To make investment as ‘Social revolution’ by spreading awareness of capital investment in stock market.

To educate laymen for investment without any risk in stock market.

This study will definitely an important source providing guidelines for solving different business, governmental and social

problems.

On the whole, it is concluded that Mathematical modelling is much more useful for prediction in stock market than time series

model as the random components in the time series may shield the influence of other components and make it difficult to describe

the observed trend or patterns in the data. Through Mathematical modelling it is possible to develop mathematical equations to

forecast future behaviour in stock market including several factors affecting stock market.

Keywords: Prediction, stakeholders, time-series models, mathematical model.



-21st Nov 2015


Mathematical Model for Deteriorating Inventory - Items Under Trade Credit

And Inventroy Level Dependent Demand Rate

Dhrub Kumar Singh1 and Sahadeo Mahto

2

1University Department of Mathematics, Ranchi University, Ranchi-834001, Jharkhand, INDIA 2Associate Professor, Department of Mathematics, Ranchi University, Ranchi-834001, Jharkhand, INDIA

Abstract

This paper deals with the problem of determining the optimal selling price and order quantity simultaneously under EOQ model for

deteriorating items. It is assumed that the demand rate depends not only on the on-display stock level but also the selling price per

unit, as well as the amount of shelf/display space is limited. We formulate a mathematical model to manifest the extended EOQ

models for maximizing profits and derive the algorithms to find the optimal solution. Numerical examples are presented to illustrate

the models developed and sensitivity analysis is reported.

Keywords: Inventory control, pricing, stock-dependent demand, deterioration.

Introduction

In the classical inventory models, the demand rate is regularly assumed to be either constant or time-dependent but independent of

the stock levels. However, practically an increase in shelf space for an item induces more consumers to buy it. This occurs owing to

its visibility, popularity or variety. Conversely, low stocks of certain goods might raise the perception that they are not fresh.

Therefore, it is observed that the demand rate may be influenced by the stock levels for some certain types of inventory. In years,

marketing researchers and practitioners have recognized the phenomenon that the demand for some items could be based on the

inventory level on display. Levin et al.(1972) pointed out that large piles of consumer goods displayed in a supermarket would

attract the customer to buy more. Silver and Peterson (1985) noted that sales at the retail C., T., Chang, et. al. / Inventory Models

with Stock-and Price- Dependent Demand level tend to be proportional to stock displayed. Baker and Urban (1988) established an

EOQ model for a power-form inventory-level-dependent demand pattern. Padmanabhan and Vrat (1990) developed a multi-item

inventory model of deteriorating items with stock-dependent demand under resource constraints and solved by a non-linear goal

programming method. Datta and Pal (1990) presented an inventory model in which the demand rate is dependent on the

instantaneous inventory level until a given inventory level is achieved, after which the demand rate becomes constant. Urban (1992)

relaxed the unnecessary zero ending-inventory at the end of each order cycle as imposed in Datta and Pal (1990). Pal et al. (1993)

extended the model of Baker and Urban (1988) for perishable products that deteriorate at a constant rate. Bar-Lev et al. (1994)

developed an extension of the inventory-level-dependent demand-type EOQ model with random yield. Giri et al. (1996)

generalized Urban’s model for constant deteriorating items. Urban and Baker (1997) further deliberated the EOQ model in which

the demand is a multivariate function of price, time, and level of inventory. Giri and Chaudhuri (1998) expanded the EOQ model to

allow for a nonlinear holding cost. Roy and Maiti (1998) developed multiitem inventory models of deteriorating items with stock-

dependent demand in a fuzzy environment. Urban (1998) generalized and integrated existing inventory-control models, product

assortment models, and shelf-space allocation models. Datta and Paul (2001) analyzed a multi-period EOQ model with stock-

dependent, and price-sensitive demand rate. Kar et al. (2001) proposed an inventory model for deteriorating items sold from two

shops, under single management dealing with limitations on investment and total floorspace area. Other papers related to this area

are Pal et al. (1993), Gerchak and Wang (1994), Padmanabhan and Vrat (1995), Ray and Chaudhuri (1997), Ray et al. (1998),

Hwang and Hahn (2000), Chang (2004), and others.

As shown in Levin et al. (1972), “large piles of consumer goods displayed in a supermarket will lead customers to buy more. Yet,

too many goods piled up in everyone’s way leave a negative impression on buyers and employees \alike.” Hence, in this present

paper, we first consider a maximum inventory level in the model to reflect the facts that most retail outlets have limited shelf space

and to avoid a negative impression on customer because of excessively piled up in everyone’s way. Since the demand rate not only

is influenced by stock level, but also is associated with selling price, we also take into account the selling price and then establish an

EOQ model in which the demand rate is a function of the on-display stock level and the selling price. In Section 2, we provide the

fundamental assumptions for the proposed EOQ model and the notations used throughout this paper. In Section 3, we set up a

mathematical model. The properties of the optimal solution are discussed as well as its solution algorithm and numerical examples

are presented. An easy-to-use algorithm is developed to determine the optimal cycle time, economic order quantity and ordering

point. Finally, we draw the conclusions and address possibly future work in Section 5.

Assumptions and Notations



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A single-item deterministic inventory model for deteriorating items with price- and stock-dependent demand rate is presented under

the following assumptions and notations.

1. Shortages are not allowed to avoid lost sales.

2. The maximum allowable number of displayed stocks is B to avoid a negative impression and due to limited shelf/display

space.

3. Replenishment rate is infinite and lead time is zero.

4. The fixed purchasing cost K per order is known and constant.

5. Both the purchase cost c per unit and the holding cost h per unit per unit time are known and constant. The constant selling

price p per unit is a decision variable within the replenishment cycle, where .

6. The constant deterioration rate is only applied to on-hand inventory. There are two possible cases for the cost

of a deteriorated item s: (1) if there is a salvage value, that value is negative or zero; and (2) if there is a disposal cost, that

7. value is positive. Note that c > s (or − s ).

8. All replenishment cycles are identical. Consequently, only a typical planning cycle with T length is considered (i.e., the

planning horizon is [0, T]).

9. The demand rate is deterministic and given by the following expression:

10. ,where I(t) is the inventory level at time is a non-negative constant, and α ( p) is a non-

negative function of p with .

11. As stated in Urban (1992), “it may be desirable to order large quantities, resulting in stock remaining at the end of the cycle,

due to the potential profits resulting from the increased demand.” Consequently, the initial and ending inventory levels y are

not restricted to be zero

The order quantity Q enters into inventory at time t = 0. Consequently,

During the time interval [0, T], the inventory is depleted by the combination of demand and deterioration. At time

T, the inventory level falls to y, i.e., I(T) = y. The initial and ending inventory level y can be called ordering point. The

mathematical problem here is to determine the optimal values of T, p and y such that the average net profit in a replenishment cycle

is maximized.

Mathematical Model and Analysis

At time t = 0, the inventory level I(t) reaches the top Ī (with Ī ≤ B) due to ordering the economic order quantity Q. The inventory

level then gradually depletes to y at the end of the cycle time t = T mainly for demand and partly for deterioration. A graphical

representation of this inventory system is depicted in Figure 1. The differential equation expressing the inventory level at time t can

be written as follows:

Inventory Level

Figure-1

Graphical Representation of Inventory System



-21st Nov 2015


(1)

with the boundary condition .

Accordingly, the solution of Equation (1) is given by

(2)

Applying (2), we obtain that the total profit TP over the period [0, T] is denoted by

( 3)

Hence, the average profit per unit time is AP = TP / T

= ×

Necessary conditions for an optimal solution

Taking the first derivative of AP as defined in (4) with respect to T, we have

=

From Appendix, we show that is greater than zero. is the benefit received from a

unit of inventory and is the total cost (i.e., holding and deterioration costs) per unit inventory. Let

and based on the values of and , two distinct cases for finding the optimal T * are

discussed as follows:

Case 3.1 (Building up inventory is profitable) “ ” implies that the benefit received from a unit of inventory is

larger than the total cost (i.e., holding and deterioration costs) due to a unit of inventory. That is, it is profitable to build up

inventory. Using Appendix ,



-21st Nov 2015


If . Namely, AP is an increasing function of T with Therefore, we should pile up inventory to the maximum

allowable number B of stocks displayed in a supermarket without leaving a negative impression on

customers. So, . From , we know

, (6)

which implies that T is a function of p and y.

Substituting (6) into (4), we know that AP is a function of y and p.

The necessary conditions of AP to be maximized are and

. Hence, we have the following two conditions:

[ + (7)

and

= -K+ (8)

where T is defined as (6) and

(9)

From (7) and (8), the optimal values of p* and y* are obtained. Substituting p* and y* into (6), the optimal value T* is solved.

Since AP(y, p) is a complicated function, it is not possible to show analytically the validity of the sufficient conditions. However,

according to the following mention, we know that the optimal solution can be obtained by numerical examples. Because building

up is profitable and AP is a continuous function of y and p over the compact set where is a sufficiently large

number, so AP has a maximum value. It is clear that AP is not maximum at y = 0 (or B) and p = 0 (or L). Therefore, the optimal

solution is an inner point and must satisfy (7) and (8). If the solution from (7) and (8) is unique, then it is the optimal solution.

Otherwise, we have to substitute them into (4) and find the one with the largest values.

Case 3.2. (Building up inventory is not profitable)

First taking the partial derivative of AP with respect to y, we obtain

=

Next, we get . Substituting y* = 0 into (4), we have AP is a function of p and T. So, the necessary conditions of AP to be

maximized are and ∂AP / p = 0. Then, we get the following two conditions:

=

and

[



-21st Nov 2015


From (11) and (12), we can obtain the values for T and p. Substituting y* = 0, T and p into (2) and check whether I(0) < B or not. If

I(0) < B, then the optimal values T* = T, p* = p and Q* = I(0). If I(0) ≥ B, then set I(0) = B and obtain

T = ,

which is a function of . Substituting and into , we have is only depend on . Then, the necessary conditions

of AP to be maximized is Hence,

+ + ]T

(15)

The optimal value p* is determined by (14). Substituting p* into (13), the optimal value T* is solved.

Algorithm

The algorithm for determining an optimal selling price p*, optimal ordering point y*, optimal cycle time T*, and optimal economic

order quantity Q* is summarized as follows:

Step 1. Solving (7) and (8), we get the values for p and y.

Step 2. If , then and the optimal value T* can be obtained by substituting p and y into

(6).

Step 3. If , then re-set . By solving (11) and (12), we get the values for T and p. Substituting y* = 0, p and T into

(2) to find I(0). If I(0) < B, then the optimal values T* = T, p* = p and Q* = I(0), and stop. Otherwise, go to Step 4.

Step 4. If the simultaneous solutions T and p in (11) and (12) make I(0) > B, then the optimal value p* is determined by (14), T* is

obtained by substituting p* into (13), and Q* = I(0) by substituting p* and T* into (2).

Numerical examples

To illustrate the proposed model, we provide the numerical examples here. For simplicity, we set the function

where x and r are non-negative constants. That is, we assume that demand is a constant elasticity function of the price. C., T.,

Chang, et. al. / Inventory Models With Stock-62 And Price- Dependent Demand.

Example 3.1 Let K = $10 per cycle, x = 1000 units per unit time, h = $0.5 per unit per unit time, s = $0 per unit, r = 2.5 and

. Following through the proposed algorithm, the optimal solution can be obtained. Since (4) and (6)-(9) are nonlinear,

they are extremely difficult to solve. We use Maple 9.5 software to solve them. The computational results for the optimal values of

and AP with respect to different values of are shown in Table 3.1.

Table 3.1

Computational results for the case of

Q* p* T* AP*

0.15 100 1.5 29.7671 70.2329 6.036963 2.995380 53.8080

0.20 27.5915 72.4085 5.057843 2.228339 65.6087

0.25 21.6955 78.3045 4.4.1015 1.874682 74.6548

0.30 12.9392 87.0608 3.916335 1.700138 81.5477

0.35 1.5681 98.4319 3.542865 1.626419 86.6871

0.20 100 1.5 27.5915 72.4085 5.057843 2.228339 65.6087

110 25.7399 84.2601 4.916473 2.437927 66.5922

130 19.8247 110.1753 4.727722 2.927107 67.8135



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150 12.1859 137.8141 4.618470 3.478172 68.6228

170 3.9578 166.0422 4.552949 4.059602 69.1629

0.20 100 1.1 47.2880 52.7120 5.192483 1.538303 79.0717

1.3 38.7618 61.2382 5.099564 1.811917 72.2547

1.5 27.5915 72.4085 5.057843 2.228339 65.6087

1.7 14.7100 85.2900 5.094514 2.827599 59.3269

1.9 2.3596 97.6404 5.209061 3.598091 53.5902

Based on the computational results as shown in Table 3.1, we obtain the following managerial phenomena when building up

inventory is profitable: (1) A higher value of β causes higher values of Q* and AP*, but lower values

of y*, p* and T*. It reveals that the increase of demand rate will result in the increases of optimal economic order quantity and

average profit, but the decreases of optimal ordering point, selling price and cycle time. (2) A higher value of B causes higher

values of Q*, T* and AP*, but lower values of y*and p*. It implies that the increase of shelf space will result in the increases of

optimal economic order quantity, cycle time and average profit, but the decreases of optimal ordering point and selling price. (3) A

higher value of c causes higher values of Q* and T*, but lower values of y* and AP*. It implies that the increase of purchase cost

will result in the increases of optimal economic order quantity and cycle time, but the decreases of optimal ordering point and

average profit.

Example 3.2 Let K = $10 per cycle, x = 1000 units per unit time, h = $0.2 per unit per unit time, c = $1.0 per unit,

s = $0 per unit, r = 2.8, and B = 300. From Step 3 of the proposed algorithm, we obtain the optimal ordering point y* =

0. Using Maple 9.5 software, we solve (2), (4), (11) and (12). The computational results for the optimal values of p, Q, T and AP

with respect to different values of are shown in Table 3.2.

Table 3.2

Computational results for the case of

Q* p* T* AP*

0.10 162.6161 1.685130 0.666568 129.4149

0.12 169.2624 1.689956 0.693068 130.4691

0.15 181.2873 1.698773 0.741555 132.1537

0.17 191.2537 1.706166 0.782279 133.3611

0.20 211.0556 1.721085 0.864684 135.3406

Table 3.2 shows that a higher value of β causes in higher values of Q*, p*, T* and AP*. It indicates that the increase of demand rate

will result in the increases of optimal economic order quantity, selling price, cycle time and average profit, when building up

inventory is not profitable.

Conclusion

This article presents the inventory models for deterioration items when the demand is a function of the selling price and stock on

display. We also impose a limited maximum amount of stock displayed in a supermarket without leaving a negative impression on

customers. Under these conditions, a proposed model has been shown for maximizing profits. Then, the properties of the optimal

solution are discussed as well as its solution algorithm and numerical examples are presented to illustrate the model.

Furthermore, we discover some intuitively reasonable managerial results. For example, if the benefit received from a unit of

inventory is larger than the total cost per unit inventory, then the building up inventory is profitable and thus the beginning

inventory should reach to the maximum allowable level. Otherwise, building up inventory is not profitable and the ending inventory

should be zero. Finally, numerical examples are provided to demonstrate the applicability of the proposed model. The results also

indicate that the effect of stock dependent selling rate on the system behavior is significant, and hence should not be ignored in

developing the inventory models. The sensitivity analysis shows the influence effects of parameters on decision variables. The

proposed models can further be enriched by incorporating inflation, quantity discount, and trade credits etc. Besides, it is interested

to extend the proposed model to multi-item inventory systems based on limited shelf space or to consider the demand rate which is

a polynomial form of on-hand inventory dependent demand. Finally, we may extend the deterministic demand function to

stochastic fluctuating demand patterns.



-21st Nov 2015


If then AP is an increasing function of T

To prove we set x

, for (A.1)

Then (A.1) yields so for

(A.2)

(A.3)

References

1. Baker R.C. and Urban T.L., A deterministic inventory system with an inventory level dependent demand rate, Journal of the

Operational Research Society, 39, 823-831 (1988)

2. Bar-Lev, S.K., Parlar M. and Perry D., On the EOQ model with inventory-level-dependent demand rate and random yield”,

Operations Research Letters, 16, 167-176 (1994)

3. Chang C.T., Inventory model with stock-dependent demand and nonlinear holding costs for deteriorating items”, Asia-Pacific

Journal of Operational Research, 21, 435-446 (2004)

4. Datta T.K. and Pal A.K., A note on an inventory model with inventory level dependent demand rate”, Journal of the

Operational Research Society, 41, 971-975 (1990)

5. Datta T.K. and Paul K., An inventory system with stock-dependent, price-sensitive demand rate”, Production Planning and

Control, 12, 13-20 (2001)

6. Gerchak Y. and Wang Y., Periodic-review inventory models with inventory-level dependent demand”, Naval Research

Logistics, 41, 99-116 (1994)

7. Giri B.C. and Chaudhuri K.S., Deterministic models of perishable inventory with stock dependent demand rate and nonlinear

holding cost”, European Journal of Operational Research, 105, 467-474 (1998)

8. Giri B.C., Pal S., Goswami A. and Chaudhuri K.S., An inventory model for deteriorating items with stock-dependent demand

rate, European Journal of Operational Research, 95, 604-610 (1996)

9. Hwang H. and Hahn K.H., An optimal procurement policy for items with an inventory level-dependent demand rate and fixed

lifetime, European Journal of Operational Research, 127, 537-545 (2000)

10. Kar S., Bhunia A.K. and Maiti M., Inventory of multi-deteriorating items sold from two shops under single management with

constraints on space and investment, Computers and Operations Research, 28, 1203-1221 (2001)

11. C.T., Chang, et. al. / Inventory Models With Stock-And Price- Dependent Demand 69

12. Levin R.I., McLaughlin C.P., Lamone R.P. and Kottas J.F., Productions / Operations Management: Contemporary Policy for

Managing Operating Systems, McGraw-Hill, New York, 373, (1972)

13. Padmanabhan G. and Vrat P., Analysis of multi-item inventory systems under resource constraints: A non-linear goal

programming approach, Engineering Costs and Production Economics, 20, 121-127 (1990)

14. Padmanabhan G. and Vrat P., EOQ models for perishable items under stock dependent selling rate”, European Journal of

Operational Research, 86, 281-292 (1995)

15. Pal S., Goswami A. and Chaudhuri K.S., A deterministic inventory model for deteriorating items with stock-dependent rate,

International Journal of Production Economics, 32, 291-299 (1993)

16. Ray J. and Chaudhuri K.S., An EOQ model with stock-dependent demand, shortage, inflation and time discounting,

International Journal of Production Economics, 53, 171-180 (1997)

17. Ray J., Goswami A. and Chaudhuri K.S., On an inventory model with two levels of storage and stock-dependent demand rate,

International Journal of Systems Science, 29, 249-254 (1998)



-21st Nov 2015


18. Roy T.K. and Maiti M., Multi-objective inventory models of deteriorating items with some constraints in a fuzzy environment,

Computers and Operations Research, 25, 1085-1095 (1998)

19. Silver E.A. and Peterson R., Decision Systems for Inventory Management and Production Planning, 2nd edition, Wiley, New

York, (1985)

20. Urban T.L., An inventory model with an inventory-level-dependent demand rate and relaxed terminal conditions, Journal of

the Operational Research Society, 43, 721-724 (1992)

21. Urban T.L. and Baker R.C., Optimal ordering and pricing policies in a single-period environment with multivariate demand

and markdowns, European Journal of Operational Research, 103, 573-583 (1997)

22. Urban T.L., An inventory-theoretic approach to product assortment and shelf-space allocation”, Journal of Retailing, 74, 15-

35 (1998)



-21st Nov 2015


Theoretical Study of Spin-Hamiltonian Parameters for the four-Coordinated

Nickel (II) Ion in Malonato Complexes

Mitesh Chakraborty1, Vineet Kumar Rai

2 and Vishal Mishra

3

1Department of PhysicsSt. Xavier’s College, Ranchi, INDIA 2Laser and Spectroscopy LaboratoryDepartment of Applied Physics, Indian School of Mines, Dhanbad, INDIA

3Department of Physics and ElectronicsRajdhani College, University of Delhi, New Delhi, INDIA

Abstract

In the present paper we have evaluated the spin-Hamiltonian g and zero field splitting (ZFS) tensor from computation methods. The

ab-initio quantum chemistry program developed by Nesse et al. has been employed. All calculations are based on four-coordinated

tetragonal symmetry with distortion. The DFT calculations for ZFS and g-Tensor employed the open shell UKS functional and

Ahrichs’ valence triple-ζ basis set (TZV) for all functional in conjuction with TZV/J auxiliary basis sets.

Keywords: electronic g-Tensor, zero field splitting tensor, density functional theory, z-matrix

Introduction

The zero field splitting parameters are important factors to describe the anisotropy of any complex system. It is an important factor

to study the local site-symmetry of a dopant ion in the host. Due to the effect of spin-orbit interaction the charge distribution is

spheroidal and not spherical. The asymmetry is tied to the direction of the spin, so that a rotation of the spin direction relative to the

crystal axes changes the exchange energy and also affects the electrostatic interaction energy of the charge distribution on the pair

of atoms, hence give rise to ZFS factor [1].

Both orbital and spin motion contribute to zero field splitting (ZFS) parameters. The ZFS factor can also be due to admixture of

higher excited states into ground states. Due to its high sensitivity to the local environment, the study of ZFS parameters has

become a subject of active interest among the researchers. In EPR technology, zero field splitting factor corresponds to high spin

paramagnets’, raised from magnetic dipolar interaction between the multiple itinerant unpaired electrons in the doped system [2].

The axial and rhombic ZFS parameters are said to be more fundamental to spin-Hamiltonian theory [3]. The two most important

parameters in magnetic system are ZFS and electronic g-tensors [4-6].

The g-tensor calculations are mostly based on first principle evaluation [7]. The ZFS has a strong influence to break the degeneracy

and hence it is visible in the Electron Paramagnetic Resonance (EPR) spectra [8, 9]. The ZFS parameters of the dopant ion in one

host is different from that of the another, hence strongly ascribes the structural information. ORCA (Quantum Chemistry Program

package) developed by Prof. Dr. Frank Nesse et al. is an ab-initio quantum chemistry program package that contains modern

electronic structure methods including density functional theory, many-body perturbation, coupled cluster theories, multireference,

and semiempirical methods. Its main field of application is larger molecules, transition metal complexes, and their spectroscopic

optical properties [10]. ORCA uses standard Gaussian basis functions. Due to the user-friendly style, ORCA is considered to be a

helpful tool for computational theorists and can be extended in developing the full information content of experimental data with

the help of calculations.

Computational Details

Density functional theory (DFT) calculations of complex were performed using ORCA version 3.0.1. software package developed

by Nesse et al [10]. Atomic coordinated were performed using empirical study [11]. The dopant Manganese(II) ion was replaced

by Nickel (II) in the original host. Due to difference in the ionic radii of Manganese (II) and Nickel (II) , a variation in bond length

and angles have been made in the nearest neighbor coordinated tetrahedral symmetry. The DFT calculations for ZFS and g-Tensor

employed the open shell UKS functional and Ahrichs’ valence triple-ζ basis set (TZV) for all functional in conjuction with TZV/J

auxiliary basis sets [12-15].

The COSMO model is used for dielectric modeling of the environment. Further zero order regular approximation (ZORA) is used

for evaluation of ZFS and g-Tensor.

The Z-matrix designed from computational DFT studies is given as



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Table 1

Z-matrix of the tetrahedrally coordinated dopant Nickel (II) in the host

---------------------------------------------------------------------------------------------------- ------------

INTERNAL COORDINATES (Bond length and angles in angstrom and degrees) -------------------------------------------------------------------------------------------------------

Ni 0 0 0 0.000000 0.000 0.000

O 1 0 0 2.690000 0.000 0.000

O 1 2 0 2.510000 180.000 0.000

O 1 2 3 2.700000 90.000 0.000

O 1 2 4 2.500000 90.000 180.000

Table-2

The table shows the Self- Consistent field (SCF) iterations using the open shell Ab-initio calculations. SCF converged after

104 cycles.

--------------------------------------------------------------------------------------------------------------------

ITER Energy Delta-E Max-DP RMS-DP [F,P] Damp

------------------------------------------------------------------------------------------------

0 -1827.7316211024 0.000000000000 0.13378505 0.00308155 0.6547954 0.7000

1 -1828.5710308437 -0.839409741237 0.11169661 0.00163221 0.4066678 0.7000

2 -1828.7164462412 -0.145415397472 0.18580124 0.00134262 0.2851812 0.7000

3 -1828.8874989811 -0.171052739930 0.06097549 0.00067418 0.1961800 0.7000

4 -1829.0207544413 -0.133255460233 0.02702070 0.00037296 0.1350557 0.7000

5 -1829.1112894747 -0.090535033374 0.05641224 0.00088134 0.0953056 0.0000

6 -1829.3222423953 -0.210952920584 0.02637227 0.00023256 0.0118450 0.0000

7 -1829.3233301984 -0.001087803131 0.01551630 0.00012618 0.0143277 0.0000

8 -1829.3242352662 -0.000905067835 0.01187378 0.00009101 0.0029494 0.0000

9 -1829.3245842284 -0.000348962206 0.00972375 0.00007455 0.0022174 0.0000

10 -1829.3247938656 -0.000209637180 0.00807931 0.00006166 0.0026669 0.0000

11 -1829.3249477567 -0.000153891040 0.00563116 0.00004520 0.0011556 0.0000

12 -1829.3250417455 -0.000093988803 0.00503124 0.00003981 0.0011682 0.0000

13 -1829.3251069490 -0.000065203554 0.00395263 0.00003546 0.0011346 0.0000

14 -1829.3251585603 -0.000051611230 0.29595680 0.00240100 0.0005092 0.0000

15 -1829.0957971877 0.229361372561 0.00002037 0.00000036 0.4042369 0.7000

16 -1829.0957480312 0.000049156454 0.00013866 0.00000261 0.4042659 0.7000

17 -1829.0955259185 0.000222112760 0.00019330 0.00000345 0.4044436 0.7000

18 -1829.0952719617 0.000253956766 0.00025236 0.00000352 0.4046341 0.7000

19 -1829.0950358298 0.000236131943 0.11389973 0.00138515 0.4047896 0.7000

20 -1829.2023725033 -0.107336673572 0.03901936 0.00048045 0.2095139 0.7000

21 -1829.2254769150 -0.023104411631 0.03024494 0.00050585 0.1455774 0.7000

22 -1829.2561264232 -0.030649508272 0.05020508 0.00068281 0.0761691 0.0000

23 -1829.2604874135 -0.004360990270 0.01563454 0.00012010 0.2223787 0.7000

24 -1829.2777178865 -0.017230472992 0.02127906 0.00015977 0.1883793 0.7000

25 -1829.2955084920 -0.017790605502 0.02104471 0.00016004 0.1495359 0.7000

26 -1829.3093172433 -0.013808751277 0.01902525 0.00015208 0.1159732 0.7000

27 -1829.3202671178 -0.010949874535 0.05760737 0.00057577 0.0791928 0.0000

28 -1829.3318441113 -0.011576993443 0.01233685 0.00017706 0.0519889 0.0000

29 -1829.3335837834 -0.001739672120 0.00395263 0.00006007 0.0465996 0.0000

30 -1829.3352107758 -0.001626992400 0.00503479 0.00005162 0.0245803 0.0000

31 -1829.3359986741 -0.000787898362 0.00462537 0.00004196 0.0076423 0.0000

32 -1829.3362352514 -0.000236577276 0.00408997 0.00003688 0.0026674 0.0000

33 -1829.3363141772 -0.000078925739 0.00310360 0.00002893 0.0055805 0.0000

34 -1829.3363675030 -0.000053325818 0.00267730 0.00002539 0.0062893 0.0000

35 -1829.3364069767 -0.000039473743 0.00238852 0.00002196 0.0069301 0.0000

36 -1829.3364232152 -0.000016238494 0.00203833 0.00001948 0.0078664 0.0000

37 -1829.3363467022 0.000076512998 0.00132680 0.00001670 0.0108871 0.0000

38 -1829.3363712001 -0.000024497887 0.00125596 0.00002053 0.0082908 0.0000



-21st Nov 2015


39 -1829.3364885649 -0.000117364827 0.00172080 0.00002888 0.0080304 0.0000

40 -1829.3366000906 -0.000111525635 0.00220285 0.00003255 0.0016829 0.0000

41 -1829.3365844643 0.000015626230 0.00150708 0.00001818 0.0067642 0.0000

42 -1829.3366444523 -0.000059987922 0.00164436 0.00001672 0.0031723 0.0000

43 -1829.3366885620 -0.000044109720 0.00176584 0.00001675 0.0013417 0.0000

44 -1829.3367182871 -0.000029725117 0.01629190 0.00022680 0.0007841 0.0000

45 -1829.3367888384 -0.000070551325 0.00009746 0.00000191 0.0101076 0.0000

46 -1829.3367721872 0.000016651231 0.00008841 0.00000136 0.0106486 0.0000

47 -1829.3367606857 0.000011501470 0.00006010 0.00000096 0.0109879 0.0000

48 -1829.3367546871 0.000005998630 0.00005743 0.00000085 0.0111406 0.0000

49 -1829.3367487173 0.000005969755 0.00541460 0.00007628 0.0113600 0.0000

50 -1829.3364901707 0.000258546671 0.00129214 0.00002751 0.0223978 0.0000

51 -1829.3367692949 -0.000279124275 0.00105905 0.00002096 0.0116185 0.0000

52 -1829.3369298206 -0.000160525663 0.00132553 0.00001794 0.0059637 0.0000

53 -1829.3369909211 -0.000061100508 0.00089752 0.00001176 0.0013329 0.0000

54 -1829.3370041903 -0.000013269205 0.01035550 0.00011268 0.0006102 0.0000

55 -1829.3370077316 -0.000003541320 0.00001109 0.00000017 0.0066382 0.0000

56 -1829.3370071505 0.000000581153 0.00000811 0.00000013 0.0066742 0.0000

57 -1829.3370065253 0.000000625228 0.00001301 0.00000026 0.0067072 0.0000

58 -1829.3370064385 0.000000086713 0.00019093 0.00000349 0.0067412 0.0000

59 -1829.3370099085 -0.000003469978 0.00473097 0.00006816 0.0065288 0.0000

60 -1829.3364663129 0.000543595593 0.01414064 0.00021400 0.0248646 0.0000

61 -1829.3301438798 0.006322433101 0.00481759 0.00008343 0.0862524 0.0000

62 -1829.3351349163 -0.004991036508 0.00250263 0.00004246 0.0380003 0.0000

63 -1829.3363825350 -0.001247618686 0.00211823 0.00003001 0.0214049 0.0000

64 -1829.3368517447 -0.000469209712 0.00198063 0.00002774 0.0117594 0.0000

65 -1829.3370492523 -0.000197507602 0.00129746 0.00001755 0.0028529 0.0000

66 -1829.3370720648 -0.000022812424 0.00067114 0.00001150 0.0013330 0.0000

67 -1829.3370751998 -0.000003135009 0.00054549 0.00000735 0.0023419 0.0000

68 -1829.3370765781 -0.000001378308 0.00049611 0.00000561 0.0024687 0.0000

69 -1829.3370772548 -0.000000676707 0.00044496 0.00000454 0.0024612 0.0000

70 -1829.3370761071 0.000001147697 0.00038923 0.00000970 0.0028250 0.0000

71 -1829.3370790854 -0.000002978315 0.00061799 0.00001514 0.0019298 0.0000

72 -1829.3370825844 -0.000003498968 0.00039713 0.00000600 0.0027514 0.0000

73 -1829.3370909726 -0.000008388253 0.00042337 0.00000459 0.0015866 0.0000

74 -1829.3370951945 -0.000004221901 0.00515626 0.00005508 0.0006818 0.0000

75 -1829.3371045881 -0.000009393618 0.00008599 0.00000148 0.0018357 0.0000

76 -1829.3371047299 -0.000000141806 0.00007736 0.00000114 0.0020736 0.0000

77 -1829.3371062843 -0.000001554388 0.00007943 0.00000112 0.0018086 0.0000

78 -1829.3371079365 -0.000001652157 0.00008825 0.00000103 0.0016491 0.0000

79 -1829.3371090461 -0.000001109602 0.00043911 0.00000782 0.0015179 0.0000

80 -1829.3371190205 -0.000009974391 0.00059791 0.00001013 0.0012111 0.0000

81 -1829.3371054159 0.000013604575 0.00026189 0.00000506 0.0037932 0.0000

82 -1829.3371176862 -0.000012270313 0.00023453 0.00000337 0.0012374 0.0000

83 -1829.3371214765 -0.000003790236 0.00274034 0.00002851 0.0003335 0.0000

84 -1829.3371231765 -0.000001700003 0.00001645 0.00000040 0.0014652 0.0000

85 -1829.3371236418 -0.000000465343 0.00005020 0.00000101 0.0013839 0.0000

86 -1829.3371241503 -0.000000508465 0.00005940 0.00000093 0.0011872 0.0000

87 -1829.3371246844 -0.000000534114 0.00006525 0.00000082 0.0011054 0.0000

88 -1829.3371251177 -0.000000433338 0.00236869 0.00002635 0.0009512 0.0000

89 -1829.3371188260 0.000006291702 0.00014582 0.00000269 0.0021661 0.0000

90 -1829.3371241947 -0.000005368703 0.00008367 0.00000183 0.0017181 0.0000

91 -1829.3371270150 -0.000002820278 0.00011726 0.00000173 0.0013895 0.0000

92 -1829.3371296066 -0.000002591602 0.00085960 0.00001366 0.0007993 0.0000

93 -1829.3371294235 0.000000183110 0.00005655 0.00000087 0.0012582 0.0000

94 -1829.3371299165 -0.000000493004 0.00006900 0.00000128 0.0011936 0.0000



-21st Nov 2015


95 -1829.3371309227 -0.000001006226 0.00078234 0.00000879 0.0008757 0.0000

96 -1829.3371325696 -0.000001646905 0.00032107 0.00000358 0.0002968 0.0000

97 -1829.3371332802 -0.000000710538 0.00058458 0.00000616 0.0002282 0.0000

98 -1829.3371335114 -0.000000231197 0.00019449 0.00000337 0.0003052 0.0000

99 -1829.3371328022 0.000000709172 0.00083510 0.00000809 0.0003575 0.0000

100 -1829.3371334411 -0.000000638881 0.00022095 0.00000406 0.0001908 0.0000

101 -1829.3371337795 -0.000000338398 0.00038685 0.00000439 0.0006104 0.0000

102 -1829.3371317807 0.000001998761 0.00021765 0.00000248 0.0006292 0.0000

103 -1829.3371336950 -0.000001914257 0.00013028 0.00000193 0.0001576 0.0000

Figure 1: The figure depicts the SCF convergence .

Step N

10510095908580757065605550454035302520151050

Energy, A

.U

1.65

1.6

1.55

1.5

1.45

1.4

1.35

1.3

1.25

1.2

1.15

1.1

1.05

1

0.95

0.9

0.85

0.8

0.75

0.7

0.65

0.6

0.55

0.5

0.45

0.4

0.35

0.3

0.25

0.2

0.15

0.1

0.05

0

-0.05

Fiure-1

Energy in (A.U.) versus step N



-21st Nov 2015


Table-3

Orbital energies

SPIN UP ORBITALS

----------------------------------------------------------------------------------------------------------- -------------------------

No. Occupancy E(Eh) E(eV)

-----------------------------------------------------------------------------------------------

0 1.0000 -305.275601 -8306.9714

1 1.0000 -36.029138 -980.4027

2 1.0000 -30.891867 -840.6104

3 1.0000 -30.887779 -840.4992

4 1.0000 -30.876307 -840.1870

5 1.0000 -18.709641 -509.1152

6 1.0000 -18.701190 -508.8853

7 1.0000 -18.689708 -508.5728

8 1.0000 -18.685332 -508.4537

9 1.0000 -3.913556 -106.4933

10 1.0000 -2.448546 -66.6283

11 1.0000 -2.434243 -66.2391

12 1.0000 -2.416675 -65.7611

13 1.0000 -0.616668 -16.7804

14 1.0000 -0.607396 -16.5281

15 1.0000 -0.595240 -16.1973

16 1.0000 -0.589977 -16.0541

17 1.0000 -0.152059 -4.1377

18 1.0000 -0.137770 -3.7489

19 1.0000 -0.120324 -3.2742

20 1.0000 -0.114831 -3.1247

21 1.0000 -0.108951 -2.9647

22 1.0000 -0.108184 -2.9438

23 1.0000 -0.084186 -2.2908

24 1.0000 -0.083072 -2.2605

25 1.0000 -0.074782 -2.0349

26 1.0000 -0.074572 -2.0292

27 1.0000 -0.063747 -1.7346

28 1.0000 -0.059278 -1.6130

29 1.0000 -0.055928 -1.5219

30 1.0000 -0.054829 -1.4920

31 1.0000 -0.050204 -1.3661

32 1.0000 -0.045902 -1.2491

33 1.0000 -0.044702 -1.2164

34 0.0000 0.020726 0.5640

35 0.0000 0.144096 3.9211

36 0.0000 0.170793 4.6475

37 0.0000 0.246720 6.7136

38 0.0000 0.253631 6.9016

39 0.0000 0.290498 7.9048

40 0.0000 0.296183 8.0596

41 0.0000 0.304975 8.2988

42 0.0000 0.349789 9.5182

43 0.0000 0.354598 9.6491

44 0.0000 0.441989 12.0271

45 0.0000 0.449325 12.2267

46 0.0000 0.508918 13.8484

47 0.0000 0.518570 14.1110

48 0.0000 0.522247 14.2111

49 0.0000 0.522787 14.2258



-21st Nov 2015


50 0.0000 0.540929 14.7194

51 0.0000 0.571672 15.5560

52 0.0000 0.574266 15.6266

53 0.0000 0.580082 15.7848

54 0.0000 0.606159 16.4944

55 0.0000 0.617860 16.8128

56 0.0000 0.738635 20.0993

57 0.0000 0.745174 20.2772

58 0.0000 0.857528 23.3345

59 0.0000 0.871521 23.7153

60 0.0000 1.123914 30.5833

61 0.0000 1.142429 31.0871

62 0.0000 1.151358 31.3300

63 0.0000 1.167576 31.7714

64 0.0000 1.232824 33.5469

65 0.0000 1.614380 43.9295

66 0.0000 1.625558 44.2337

67 0.0000 1.626038 44.2467

68 0.0000 1.631251 44.3886

69 0.0000 1.631872 44.4055

70 0.0000 1.635804 44.5125

71 0.0000 1.637634 44.5623

72 0.0000 1.640780 44.6479

73 0.0000 1.642269 44.6884

74 0.0000 1.644709 44.7548

75 0.0000 1.649418 44.8829

76 0.0000 1.653399 44.9913

77 0.0000 1.664465 45.2924

78 0.0000 1.674817 45.5741

79 0.0000 1.691772 46.0355

80 0.0000 1.704035 46.3692

81 0.0000 1.705373 46.4056

82 0.0000 1.759606 47.8813

83 0.0000 1.824795 49.6552

84 0.0000 1.868875 50.8547

85 0.0000 1.918153 52.1956

86 0.0000 2.142904 58.3114

87 0.0000 2.159208 58.7550

88 0.0000 2.657241 72.3072

89 0.0000 2.660182 72.3872

90 0.0000 2.672185 72.7138

91 0.0000 2.674584 72.7791

92 0.0000 2.678693 72.8909

93 0.0000 2.681152 72.9579

94 0.0000 2.690325 73.2075

95 0.0000 2.702944 73.5508

96 0.0000 2.704890 73.6038

97 0.0000 2.710063 73.7446

98 0.0000 2.731982 74.3410

99 0.0000 2.774419 75.4958

100 0.0000 2.811496 76.5047

101 0.0000 2.880642 78.3863

102 0.0000 2.892405 78.7063

103 0.0000 3.049782 82.9888

104 0.0000 3.134255 85.2874

105 0.0000 4.155180 113.0682



-21st Nov 2015


106 0.0000 4.189692 114.0073

107 0.0000 4.218409 114.7888

108 0.0000 4.227468 115.0353

109 0.0000 4.299867 117.0053

110 0.0000 5.335909 145.1975

111 0.0000 5.341549 145.3509

112 0.0000 5.349443 145.5658

113 0.0000 5.349624 145.5707

114 0.0000 5.352739 145.6554

115 0.0000 5.354070 145.6917

116 0.0000 5.355814 145.7391

117 0.0000 5.357839 145.7942

118 0.0000 5.359042 145.8269

119 0.0000 5.360043 145.8542

120 0.0000 5.362342 145.9168

121 0.0000 5.363882 145.9587

122 0.0000 5.365173 145.9938

123 0.0000 5.366059 146.0179

124 0.0000 5.367861 146.0669

125 0.0000 5.368087 146.0731

126 0.0000 5.372969 146.2059

127 0.0000 5.374398 146.2448

128 0.0000 5.375270 146.2685

129 0.0000 5.376565 146.3038

130 0.0000 5.379700 146.3891

131 0.0000 5.380843 146.4202

132 0.0000 5.381512 146.4384

133 0.0000 5.381603 146.4409

134 0.0000 5.383668 146.4971

135 0.0000 5.384380 146.5164

136 0.0000 5.386017 146.5610

137 0.0000 5.407414 147.1432

138 0.0000 5.520806 150.2288

139 0.0000 5.528270 150.4319

140 0.0000 5.547358 150.9513

141 0.0000 5.548637 150.9861

142 0.0000 5.552496 151.0911

143 0.0000 5.558367 151.2508

144 0.0000 5.568808 151.5350

145 0.0000 6.359346 173.0466

146 0.0000 6.362020 173.1194

147 0.0000 6.363798 173.1678

148 0.0000 6.367700 173.2739

149 0.0000 6.370478 173.3495

150 0.0000 6.374323 173.4541

151 0.0000 6.377404 173.5380

152 0.0000 6.380253 173.6155

153 0.0000 6.381519 173.6499

154 0.0000 6.389407 173.8646

155 0.0000 6.389475 173.8664

156 0.0000 6.389951 173.8794

157 0.0000 6.402354 174.2169

158 0.0000 6.405097 174.2915

159 0.0000 6.420030 174.6979

160 0.0000 6.452245 175.5745

161 0.0000 6.471764 176.1057



-21st Nov 2015


162 0.0000 6.490457 176.6143

163 0.0000 6.510742 177.1663

164 0.0000 6.572458 178.8457

165 0.0000 10.133443 275.7450

166 0.0000 10.286773 279.9173

167 0.0000 10.313816 280.6532

168 0.0000 14.200271 386.4090

169 0.0000 14.220642 386.9633

170 0.0000 14.389965 391.5708

171 0.0000 14.430994 392.6873

172 0.0000 18.932579 515.1817

173 0.0000 68.693000 1869.2315

174 0.0000 77.096598 2097.9051

175 0.0000 77.115356 2098.4155

176 0.0000 77.288353 2103.1230

177 0.0000 77.338481 2104.4871

178 0.0000 176.190144 4794.3776

179 0.0000 176.584868 4805.1186

180 0.0000 176.603465 4805.6246

181 0.0000 410.212638 11162.4534

182 0.0000 2710.501264 73756.4891

--------------------------------------------------------------------------------------------------------------------------------------------

SPIN DOWN ORBITALS

------------------------------------------------------------------------------------------------------------------- ------------------------

No Occupancy E(Eh) E(eV)

----------------------------------------------------------------------------------------------------------------------------- --------------

0 1.0000 -305.275552 -8306.9701

1 1.0000 -36.002100 -979.6670

2 1.0000 -30.867588 -839.9498

3 1.0000 -30.865153 -839.8835

4 1.0000 -30.856703 -839.6536

5 1.0000 -18.702391 -508.9179

6 1.0000 -18.695155 -508.7210

7 1.0000 -18.687620 -508.5160

8 1.0000 -18.683969 -508.4167

9 1.0000 -3.847772 -104.7032

10 1.0000 -2.374823 -64.6222

11 1.0000 -2.370157 -64.4953

12 1.0000 -2.359088 -64.1941

13 1.0000 -0.600068 -16.3287

14 1.0000 -0.593637 -16.1537

15 1.0000 -0.590262 -16.0619

16 1.0000 -0.586838 -15.9687

17 1.0000 -0.108583 -2.9547

18 1.0000 -0.096461 -2.6248

19 1.0000 -0.091878 -2.5001

20 1.0000 -0.083856 -2.2818

21 1.0000 -0.072651 -1.9769

22 1.0000 -0.071437 -1.9439

23 1.0000 -0.069091 -1.8800

24 1.0000 -0.066331 -1.8050

25 1.0000 -0.065043 -1.7699

26 1.0000 -0.055259 -1.5037

27 1.0000 -0.054940 -1.4950

28 1.0000 -0.049610 -1.3499



-21st Nov 2015


29 1.0000 -0.049252 -1.3402

30 1.0000 -0.046519 -1.2658

31 1.0000 -0.042299 -1.1510

32 0.0000 -0.034679 -0.9437

33 0.0000 -0.000818 -0.0223

34 0.0000 0.030283 0.8240

35 0.0000 0.146013 3.9732

36 0.0000 0.179795 4.8925

37 0.0000 0.255159 6.9432

38 0.0000 0.257490 7.0067

39 0.0000 0.295313 8.0359

40 0.0000 0.296190 8.0597

41 0.0000 0.306643 8.3442

42 0.0000 0.353729 9.6255

43 0.0000 0.355207 9.6657

44 0.0000 0.451510 12.2862

45 0.0000 0.453205 12.3323

46 0.0000 0.516277 14.0486

47 0.0000 0.521589 14.1931

48 0.0000 0.524903 14.2833

49 0.0000 0.525631 14.3032

50 0.0000 0.547290 14.8925

51 0.0000 0.575851 15.6697

52 0.0000 0.580514 15.7966

53 0.0000 0.581585 15.8257

54 0.0000 0.607905 16.5419

55 0.0000 0.624820 17.0022

56 0.0000 0.746007 20.2999

57 0.0000 0.748251 20.3609

58 0.0000 0.862733 23.4762

59 0.0000 0.873720 23.7751

60 0.0000 1.130964 30.7751

61 0.0000 1.165822 31.7236

62 0.0000 1.175678 31.9918

63 0.0000 1.189300 32.3625

64 0.0000 1.239437 33.7268

65 0.0000 1.628959 44.3262

66 0.0000 1.634389 44.4740

67 0.0000 1.634534 44.4779

68 0.0000 1.638106 44.5751

69 0.0000 1.640297 44.6347

70 0.0000 1.641764 44.6747

71 0.0000 1.643354 44.7179

72 0.0000 1.644717 44.7550

73 0.0000 1.646171 44.7946

74 0.0000 1.646295 44.7980

75 0.0000 1.650172 44.9035

76 0.0000 1.662899 45.2498

77 0.0000 1.671716 45.4897

78 0.0000 1.683667 45.8149

79 0.0000 1.702429 46.3255

80 0.0000 1.709107 46.5072

81 0.0000 1.712546 46.6007

82 0.0000 1.771337 48.2005

83 0.0000 1.830066 49.7986

84 0.0000 1.896659 51.6107



-21st Nov 2015


85 0.0000 1.933379 52.6099

86 0.0000 2.175168 59.1893

87 0.0000 2.181992 59.3750

88 0.0000 2.666685 72.5642

89 0.0000 2.667986 72.5996

90 0.0000 2.684771 73.0563

91 0.0000 2.686481 73.1029

92 0.0000 2.687102 73.1197

93 0.0000 2.690299 73.2068

94 0.0000 2.695349 73.3442

95 0.0000 2.708452 73.7007

96 0.0000 2.713613 73.8412

97 0.0000 2.727608 74.2220

98 0.0000 2.734156 74.4002

99 0.0000 2.778863 75.6167

100 0.0000 2.838016 77.2263

101 0.0000 2.894551 78.7647

102 0.0000 2.899303 78.8940

103 0.0000 3.061172 83.2987

104 0.0000 3.152383 85.7807

105 0.0000 4.217387 114.7609

106 0.0000 4.234656 115.2309

107 0.0000 4.247536 115.5813

108 0.0000 4.251086 115.6779

109 0.0000 4.365805 118.7996

110 0.0000 5.351578 145.6239

111 0.0000 5.359625 145.8428

112 0.0000 5.362454 145.9198

113 0.0000 5.363452 145.9470

114 0.0000 5.365772 146.0101

115 0.0000 5.367816 146.0657

116 0.0000 5.367879 146.0674

117 0.0000 5.369236 146.1043

118 0.0000 5.370871 146.1488

119 0.0000 5.371963 146.1785

120 0.0000 5.373359 146.2165

121 0.0000 5.373713 146.2262

122 0.0000 5.374604 146.2504

123 0.0000 5.374923 146.2591

124 0.0000 5.376762 146.3091

125 0.0000 5.377903 146.3402

126 0.0000 5.378323 146.3516

127 0.0000 5.379256 146.3770

128 0.0000 5.381495 146.4379

129 0.0000 5.383350 146.4884

130 0.0000 5.384791 146.5276

131 0.0000 5.384853 146.5293

132 0.0000 5.385547 146.5482

133 0.0000 5.385944 146.5590

134 0.0000 5.386425 146.5721

135 0.0000 5.386953 146.5864

136 0.0000 5.390853 146.6926

137 0.0000 5.416967 147.4032

138 0.0000 5.583728 151.9410

139 0.0000 5.596180 152.2798

140 0.0000 5.600212 152.3895



-21st Nov 2015


141 0.0000 5.604790 152.5141

142 0.0000 5.612839 152.7331

143 0.0000 5.620910 152.9527

144 0.0000 5.626930 153.1165

145 0.0000 6.381833 173.6585

146 0.0000 6.382867 173.6866

147 0.0000 6.382999 173.6902

148 0.0000 6.384663 173.7355

149 0.0000 6.386109 173.7749

150 0.0000 6.387455 173.8115

151 0.0000 6.389440 173.8655

152 0.0000 6.390597 173.8970

153 0.0000 6.392155 173.9394

154 0.0000 6.392870 173.9588

155 0.0000 6.396737 174.0641

156 0.0000 6.399399 174.1365

157 0.0000 6.406067 174.3180

158 0.0000 6.423330 174.7877

159 0.0000 6.428062 174.9165

160 0.0000 6.468081 176.0054

161 0.0000 6.483256 176.4184

162 0.0000 6.502738 176.9485

163 0.0000 6.528690 177.6547

164 0.0000 6.587435 179.2532

165 0.0000 10.192697 277.3574

166 0.0000 10.360090 281.9124

167 0.0000 10.371595 282.2255

168 0.0000 14.215961 386.8360

169 0.0000 14.224957 387.0808

170 0.0000 14.402456 391.9108

171 0.0000 14.438920 392.9030

172 0.0000 19.010808 517.3104

173 0.0000 68.751376 1870.8201

174 0.0000 77.106603 2098.1773

175 0.0000 77.118069 2098.4893

176 0.0000 77.296044 2103.3323

177 0.0000 77.343780 2104.6312

178 0.0000 176.235761 4795.6189

179 0.0000 176.640260 4806.6258

180 0.0000 176.648413 4806.8477

181 0.0000 410.229388 11162.9092

182 0.0000 2710.505629 73756.6079

----------------------------------------------------------------------------------------------------------------------------- --------------



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Figure 2

The figure represent the molecular orbital (MO) energy level diagram for the occupied and unoccupied orbitals. The alpha and beta

molecular orbitals indicates the up and the down electron spin.



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Table-4

The table shows the dipole moment at the local site coordinated geometry of the Nickel (II) ion in the complex from

electronic and nuclear contribution.

------------------------------------------------------------------------------------------------

DIPOLE MOMENT

--------------------------------------------------------------------------------------------- ---

X Y Z

Electronic contribution: -0.58243 -0.70555 -0.00083

Nuclear contribution : 0.05996 0.06662 0.00000

--------------------------------------------------------------------------------------------------------------------

Total Dipole Moment : -0.52247 -0.63892 -0.00083

------------------------------------------------------------------------------------------------------- -------------

Magnitude in (a.u.): 0.82535

Magnitude in (Debye): 2.09787

Table 5: The electronic g-Tensor evaluated for the dopant Nickel (II) in the host compound

______________________________________________________________________________

2.1358577 0.0017694 0.0003577

0.0011180 2.1235246 -0.0000014

0.0002321 -0.0000066 2.1486413

______________________________________________________________________________

The principal eigen values of the g matrix evaluated from the diagonalisation properties of matrices are as follows

g-total (gxx, gyy, gzz ) : 2.1233578 2.1360177 2.1486482

Table 6: The ZFS tensor in the local site symmetry of the dopant Nickel (II) in the host matrix is as follows

______________________________________________________________________________

ZERO-FIELD-SPLITTING (ZFS) TENSOR

______________________________________________________________________________

Raw-matrix :

225.845632 0.016965 0.108113

0.016965 212.021112 0.000367

0.108113 0.000367 215.172831

Diagonalized D matrix :

212.021091 215.171736 225.846747

The axial (D) and the rhombic (E) zero field splitting (ZFS) parameters evaluated from the computational technique of Density

functional theory (DFT) are 12.25 cm-1

and 1.58 cm-1

.

Figure 3: Nickel (II) ion in the tetragonally distorted structure of the local site symmetry about the oxygen atoms.



-21st Nov 2015


Figure-3(a)

Figure-3 (a): The bond angles and lengths of the central metal nickel (II) ion with respect to the nearest neighbor oxygen atoms.

The arrow (blue colour) shows the direction of the dipole moment.



-21st Nov 2015


Figure-3(b)

Figure-3(b): The Mayer’s free valence of the various atoms is shown in the Nickel (II) dopant and oxygen atoms. The blue colour

arrow shows the direction of the dipole moment.

Conclusion

The present work deals with theoretical computational studies of the dopant Nickel(II) ion in the diaqua malonato complexes. The

dipole matrix, g-Tensor and ZFS matrix are evaluated in the four coordinated nearest neighbor interaction. The dielectric and spin-

orbital molecular operator is taken into account. The spin unrestricted self consistent (UKS) method is employed to check the

convergence of energy. A high value of zero field splitting is observed.

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