Common Syl Lab Us

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Subject Name: Compiler DesignSubject Code: CE 701Teaching Scheme (Credits and Hours)

Teaching scheme

Total

Credit

Evaluation Scheme

L T P Total TheoryMid Sem

ExamCIA Pract. Total

Hrs Hrs Hrs Hrs Hrs Marks Marks Marks Marks Marks

04 00 02 06 5 3 70 30 20 30 150

Learning Objectives:The objective of this course is to introduce students to the following concepts underlying thedesign and implementation of compilers.

Describe the steps and algorithms used by compilers. Recognize the underlying formal models such as finite state automata, push-down

automata and their connection to language definition through regular expressions andgrammars.

Discuss the effectiveness of optimization. Explain the impact of a separate compilation facility and the existence of program

libraries on the compilation process.

Outline of the Course:

Sr. No Title of the Unit MinimumHours1 Introduction to Compiling 62 Lexical Analyzer 63 Parsing Theory

Syntax Analyzer Syntax Directed Translation

18

4 Error Recovery 35 Type Checking 46 Run Time Environments 67 Intermediate Code Generation 58 Code Generation 79 Code Optimization 5

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Total hours (Theory): 60Total hours (Practical): 30Total hours: 90

Detailed Syllabus:

Sr.No

Topic LectureHours

Weightage(%)

1 Introduction to Compiling Overview of the Translation Process- A Simple Compiler,

Difference between interpreter, assembler and compiler Overview and use of linker and loader , types of Compiler, Analysis of the Source Program, The Phases of a Compiler, Cousins of the Compiler, The Grouping of Phases, Front-end and Back-end of compiler, Pass structure A simple one-pass compiler: overview

06 10

2 Lexical Analyzer Introduction to Lexical Analyzer, Input Buffering, Specification of Tokens, Recognition of Tokens, A Language for Specifying Lexical Analyzers, Finite Automata From a Regular Expression, Design of a Lexical Analyzer Generator, Optimization of DFA

06 20

3.1 Parsing Theory- Syntax Analyzer The role of a parser Context free grammars Top Down and Bottom up Parsing Algorithms, Top-Down Parsing, Bottom-Up Parsing, Operator-Precedence Parsing, LR Parsers, Using Ambiguous Grammars, Parser Generators, Automatic Generation of Parsers.

12 25

3.2 Parsing Theory- Syntax Directed Translation Syntax-Directed Definitions, Construction of Syntax Trees, Bottom-Up Evaluation of S-Attributed Definitions, L-Attributed Definitions, Syntax directed definitions and translation schemes

06 5

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4 Error Recovery Error Detection & Recovery, Ad-Hoc and Systematic Methods

03 5

5 Type Checking Type systems Specification of a simple type checker Type conversions

04 5

6 Run Time Environments Source Language Issues, Storage Organization, Storage-Allocation Strategies, Parameter Passing, Symbol Tables, Language Facilities for Dynamic Storage Allocation, Dynamic Storage Allocation Techniques.

06 5

7 Intermediate Code Generation Different Intermediate Forms, Implementation of Three Address Code Intermediate code for all constructs of programming languages

(expressions, if-else, loops, switch case etc.)

05 10

8 Code Generation Issues in the Design of a Code Generator Basic Blocks and Flow Graphs A Simple Code Generator Register Allocation and Assignment The DAG Representation of Basic Blocks Peephole Optimization Dynamic Programming Code-Generation Algorithm

07 10

9 Code Optimization Global Data Flow Analysis, A Few Selected Optimizations like Command Sub Expression

Removal, Loop Invariant Code Motion, Strength Reduction Etc. Optimization of basic blocks

05 5

60 100

Instructional Method and Pedagogy: At the start of course, the course delivery pattern, prerequisite of the subject will be

discussed. Lectures will be conducted with the aid of multi-media projector, black board, OHP etc. Attendance is compulsory in lecture and laboratory which carries 10 marks in overall

evaluation. One internal exam will be conducted as a part of internal theory evaluation.

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Assignments based on the course content will be given to the students for each unit andwill be evaluated at regular interval evaluation.

Surprise tests/Quizzes/Seminar/tutorial will be conducted having a share of five marks inthe overall internal evaluation.

The course includes a laboratory, where students have an opportunity to build anappreciation for the concepts being taught in lectures.

Experiments shall be performed in the laboratory related to course contents.

STUDENTS LEARNING OUTCOMES:On successful completion of the course, the student will:

Understand how the design of a compiler requires most of the knowledge acquiredduring their study.

Develop a firm and enlightened grasp of concepts learned earlier in their study likehigher level programming, assemblers, automata theory, and formal languages.

Apply the ideas, the techniques, and the knowledge acquired for the purpose of otherlanguage processor design.

Working skills in theory and application of finite state machines, recursive descent,production rules, parsing, and language semantics.

Know about the powerful compiler generation tools, which are useful to the othernon-compiler applications

Reference Books:1. Compilers, Principles, Techniques and Tools by A.V. Aho, R. Sethi and J.D.Ullman,

Pearson2. Advanced compiler Design Implementation by Steven S. Muchnick3. The Compiler Design handbook: Optimization and Machine Code Generation by Y. N.

Shrikant and Priti Shankar, Second Edition4. Charles N. Fischer, Richard J. leBlanc, Jr.- Crafting a Compiler with C, Pearson

Education, 2008.

List of Practical:

Sr. No. Name of Experiment1 Implement a C program to identify keywords and identifiers using finite automata.2. Implementation of lex programs.

a. Write a lex program to identify numbers, words and other characters andgenerate tokens for each.

b. Write a lex program to identify all occurrences of LDRP and replace it withCOLLEGE.

3. Implementation of lex programsa. To display the length of each word.b. To change the case of the first letter of every word.c. To count the number of characters, words and lines in the given input.

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4. Implementation of lex programsa. To remove empty lines.b. Write a lex program that will replace the word Hello with ldrp if the line

starts with the letter a and with college if it starts with b.c. Write a lex program to identify words followed by punctuation marks.

5. Implementation of lex programsa. To display the comments from given input file.b. To identify all the lexemes from input file that follows the given RE. Provide

the RE and input file as command line arguments.6. Generate a lexer for C program.7. Write a C program to eliminate left recursion from a production.8. Write a C program to apply left factoring to a production.9. Implementation of Yacc programs.

a. Write a Yacc program for desktop calculator with ambiguous grammar.b. Write a Yacc program for desktop calculator with ambiguous grammar and

additional information.10. Implementation of Yacc program: Write a Yacc program for calculator with

unambiguous grammar.

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Subject Name: Next Generation NetworksSubject Code: CE 702Teaching Scheme (Credits and Hours)

Teaching scheme

Total

Credit

Evaluation Scheme




04 00 02 06 5 3 70 30 20 30 150

Learning Objectives:To learn Wireless technologies and Ad-hoc Network.


Sr.No Title of the Unit

MinimumHours

1 Basic history of Mobile Computing 11

2 Overview of Wireless n/w. and Technologies 13

3 General packet radio service(GPRS) 10

4 Infrastructure and ad-hoc network 13

5 Wireless Application Protocol(WAP) WAP,MMS,GPRS applicationCDMA and 3G

13

Total hours (Theory): 60Total hours (Lab): 30Total hours: 90

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Detailed SyllabusSr.No Topic

LectureHours

Weightage(%)

1. Basic history of Mobile ComputingArchitecture for mobile computing, Three tier architecture,design considerations for mobile computing, mobilecomputing through internet, Wireless network architecture,Applications, Security, Concerns and Standards, Benefits,Future. Evolution of mobile computing.

11 10

2. Overview of Wireless n/w. and TechnologiesIntroduction, Different generations. Introduction to 1G, 2G,3G and 4G, Bluetooth, Radio frequencyidentification(Rfid),Wireless Broadband, Mobile IP:Introduction, Advertisement, Registration, TCP connections,two level addressing, abstract mobility management model,performance issue, routing in mobile host, Adhoc networks,Mobile transport layer: Indirect TCP, SnoopingTCP, Mobile TCP, Time out freezing, Selectiveretransmission, transaction oriented TCP. ,IPv6Wireless network topologies, Cell fundamentals andtopologies, Global system for mobile communication, Globalsystem for mobile communication, GSM architecture, GSMentities, call routing in GSM, PLMN interface, GSMaddresses and identifiers, network aspects in GSM,GSMfrequency allocation, authentication and security, Shortmessage services, Mobile computing over SMS,SMS, valueadded services through SMS, accessing the SMS bearer,Security in wireless networks.

13 30

3. General packet radio service(GPRS)GPRS and packet data network, GPRS network architecture,GPRS network operation, data services in GPRS,Applications of GPRS, Billing and charging in GPRS.

10 20

4. Infrastructure and ad-hoc networkSystem Architecture, Protocol Architecture, Medium AccessControl layer, MAC Management, Wireless LAN advantages,IEEE 802.11a, 802.11b standards ,Wireless LAN architecture,Mobility in Wireless LAN, Deploying Wireless LAN, Mobilead hoc networks and sensor networks, wireless LAN security

13 20

5. Wireless Application Protocol(WAP), MMS, GPRSapplication CDMA and 3GSpread-spectrum Technology, FHSS, DSSS, CDMA versus

13 20

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GSM, Wireless data, third generation networks, applicationsin 3G Wireless LAN, WiFi v/s 3G Voice over Internetprotocol and convergence, Voice over IP,H.323 frameworkfor voice over IP, SIP, comparison between H.323 ad SIP,Real time protocols, convergence technologies, call routing,call routing, voice over IP applications, IMS, Mobile VoIP,Security issues in mobile Information security, securitytechniques and algorithms, security framework for mobileenvironment.

Total 60 100



evaluation. One internal exam will be conducted as a part of internal theory evaluation. Assignments based on the course content will be given to the students for each unit and

will be evaluated at regular interval evaluation. Surprise tests/Quizzes/Seminar/tutorial will be conducted having a share of five marks in

the overall internal evaluation. The course includes a laboratory, where students have an opportunity to build an

appreciation for the concepts being taught in lectures. Experiments shall be performed in the laboratory related to course contents.

Reference Books:1. Mobile Computing , Asoke K Telukder, Roopa R Yavagal, TMH2. Mobile Communications, Jochen Schiller, Pearson3. Wireless Communications and Networks, 3G and beyond, ITI Saha Misra, TMH.4. Principle of wireless Networks by Kaveh Pahlavan and Prashant Krishnamurthy,

Pearson 2002.

List of experiments:

Name of Experiment1 What is Mobile Computing? Explain the three tier architecture of mobile computing

with diagram.2 Write a WML program to create a card.3 Write a WML program to create a deck that contain two cards and provide the

Functionality of calling two cards from one another.

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4

Write a WML program to display list of following card and provide the functionality toload a particular card,

a. Salesb. Productc. Services

5 Write a WML program for usage of template tag.

6 Write a WML program to display the text in the following format.Bold, Underlined, Emphasized, Big font, Small font, Strong font

8 Write a WML program to create the following table.Honda Suzuki YamahaMitsubishi Ford Maruti

9 Write a WML program to implement the functionality of Login by username.

10 Write a WML program to display special characters on the screen.

11 Write a WML program to create following selection list.a. Redb. Greenc. Yellowd. Blue

12

Write a WML program to create following option group.1. Honda

1.1 CD 1001.2 CD Dawn

2. Suzuki2.1 Max 1002.2 Samurai

13 Write a WML program to display the image on the screen after 5 seconds.

14 Write a WML program to develop the calculator.15 Write a program in J2ME to perform the following tasks:

A] Draw a text box on the device screen.B] Change the background color of the device screen.C] Change the color of the text.D] Change the font style and font size of the displayed text.

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16 Write a program in J2ME to perform the simple calculator operations such asa. Additionb. Subtractionc. Multiplicationd. Division

17 Write a program in J2ME to create a simple Quiz which contains 3 to 4 questions andalso display the score.

18 Write a program in J2ME to create a currency converter and also display the result.19 Write a program in J2ME to generate a calendar.20 Implement the concepts of wired LAN in NS-221 Implement the concept of Wireless LAN in NS-2

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Subject Name : Service Oriented ComputingSubject Code : CE 703-1

Teaching Scheme (Credits and Hours)Teaching scheme

Total

Credit

Evaluation Scheme




04 00 04 08 6 3 70 30 20 30 150

Learning Objectives: To gain understanding of the web services architectures and motivation for composition. To learn service basic concept of SOAP,WSDL and UDDI. To learn technology underlying the service design To learn advanced concepts such as service composition, orchestration and

Choreography. To learn about collaboration, Agents, Multi agents system, Agent communication.



MinimumHours

1 Introduction to distributed Computing and Web servicesarchitectures and standards.

5

2 Directory services, SOAP, WSDL, UDDI and Integration versusInteroperation.

5

3 Principles of Service-Oriented Architecture 54 XML primer, Conceptual modeling, RDF, RDFS and OWL 255 Execution Models, Transaction over Composed Services, Business

process management, Relevant standards BPEL4WS,WSCI,WS-C.15

6 Collaboration 5


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Detailed Syllabus:Sr.No

Topic LectureHours

Weightage(%)

1 Brief history of information technology, Challenges for composition, WebServices Architectures and Standards. Computing with services, Visionsfor web, Semantic web, Peer to Peer Computing, Processes and Protocols.Pragmatic web, Open environments.

5 10

2 Directory services, SOAP, REST WSDL, UDDI 5 103 Enterprise architectures and Service Oriented Computing

Integration versus interoperation, J2EE, .NET, Model Driven Architecture,Legacy systems.Use cases: Intra-enterprise and Inter-enterprise Interoperation, Application,Configuration, Dynamic Selection, Software Fault Tolerance, Grid, and,Utility Computing, Elements of Service-Oriented Architectures, RPCversus Document, Orientation, Composing Services.

5 10

4 Description: Modeling and representationXML primer, Conceptual modeling, Ontology and knowledge sharing,Relevant standards: RDF, RDFS, and OWL, Differencing and tools,Matchmaking.Engagement:Execution Models: Messaging, CORBA, Peer to peer computing, Jini, GridComputing, Transactions: ACID Properties, Schedules, Locking,Distributed Transactions, Transactions over Composed Services:Architecture, Properties, Compositional Serializability, Processspecification: Processes, Workflows.

25 35

5 Business Process Management:Introduction of Business process Management, Process SpecificationLanguage, Relevant standards: BPEL4WS, WSCI,WS-C, ebXML, Relaxed transactions, Exception handling.

15 25

6 CollaborationDescribing collaborations, Agents, Multiagent systems, Agentcommunication, languages, Protocols, Commitments and contracts,Planning, Consistency maintenance, Relevant standards: FIPA, OWL-S,Economic models, Organizational models.

5 10

Total 60 100



evaluation. One internal exam will be conducted as a part of internal theory evaluation.

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Assignments based on the course content will be given to the students for each unit andwill be evaluated at regular interval evaluation.

Surprise tests/Quizzes/Seminar/tutorial will be conducted having a share of five marks inthe overall internal evaluation.

The course includes a laboratory, where students have an opportunity to build anappreciation for the concepts being taught in lectures.

Experiments shall be performed in the laboratory related to course contents.

Learning Outcome:

After the completion of this course student will be able to Understand primary conceptsof SOA

Know the integration of SOA technological points with Web Services. Implement of SOA in development cycle of Web Services.

Reference Books:1. Sandeep Chatterjee, James Webber, Developing Enterprise Web Services, An Architects

Guide, Pearson Education.2. Newcomer, Lomow, Understanding SOA with Web Services, Pearson Education.3. Thomas Erl, Service-Oriented Architecture: Concepts, Technology, and Design,Pearson

Education.4. Dan Woods and Thomas Mattern, Enterprise SOA Designing IT for Business Innovation

OREILLY.

List of Experiment:1. Prepare the documents of SOA terms: UDDI, SOAP, XQuery, XPath, Web Service

(JAX-WS & .net ), WSDL, BPEL, SAML, REST and Apache ANT.2. Create DTD file for student information and create a valid well-formed XML

document to store student information against this DTD file.3. Using XSL, Display student information in tabular format.4. Create .XSL file for library book information and also create well formed XML

document to store this information against .XSL.5. Create .XSD file for mobile information and also create well formed XML

document to store this information against .XSD.6. Create web calculator service in .NET and create client to consume this service7. Write a program to create a web service with the use of .net platform to send mail.

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Subject Name: Mobile Application Development with AndroidSubject Code: CE 703-2Teaching Scheme (Credits and Hours)

Teaching scheme

Total

Credit

Evaluation Scheme




04 00 04 08 6 3 70 30 20 30 150

Learning Objectives:An Android technology is generally used in mobile system, where android is an open sourcetechnology. This technology is used for mobile application development. Using androidtechnology, student can make own mobile applications and upload easily on mobile devices.



MinimumHours

1 Introduction to Android 62 Android Application Design and Resources 63 Exploring User Interfaces screen elements 44 Designing User Interfaces with Layouts 55 Drawing and working with Animation 36 Android Storage APIs 47 Sharing Data Between Applications with Content Providers 68 Using Android Network, Web and Multimedia APIs 119 Telephony API and Notifications 9

10 Introduction to application development for windows phone 6

Total hours (Theory): 60

Total hours (Lab): 60

Total hours: 120

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Detailed Syllabus:

Sr.No

Topic LectureHours

Weightage(%)

1

Introduction of Android:Android Operating System, History of Mobile SoftwareDevelopment, Open Handset Alliance (OHA), The AndroidPlatform, Downloading and Installing Eclipse, ExploringAndroid SDK, Using the Command-Line Tools and the AndroidEmulator, Build the First Android application, AndroidTerminologies, Application Context, Application Tasks withActivities, Intents, and Closer Look at Android Activities.

6 10

2

Android Application Design and Resources:Anatomy of an Android Application, Android Manifest file,Editing the Android Manifest File, Managing ApplicationsIdentity, Enforcing Application System Requirements,Registering Activities and other Application Components,Working with Permissions.

6 10

3

Exploring User Interface Screen Elements:Introducing Android Views and Layouts, Displaying Text withTextView, Retrieving Data From Users, Using Buttons, CheckBoxes and Radio Groups, Getting Dates and Times from Users,Using Indicators to Display and Data to Users, AdjustingProgress with SeekBar, Providing Users with Options andContext Menus, Handling User Events, Working with Dialogs,Working with Styles, Working with Themes.

4 7

4

Designing User Interfaces with Layouts:Creating User Interfaces in Android, View versus ViewGroup,Using Built-In Layout Classes such as FameLayout,LinearLayout, RelativeLayout, TableLayout , Multiple Layouts ona Screen, Data-Driven Containers, Organizing Screens with Tabs,Adding Scrolling Support.

5 8

5Drawing and Working with Animation:Working with Canvases and Paints, Working with Text, Workingwith Bitmaps, Working with Shapes, Working with Animation.

3 5

6

Android Storage APIs:Working with Application Preferences such as Creating Privateand Shared Preferences, Adding, Updating, and DeletingPreferences. Working with Files and Directories, Storing SQLiteDatabase such as Creating an SQLite Database, Creating,Updating, and Deleting Database Records, Closing and Deleting aSQLite Database.

4 7

7

Sharing Data Between Applications with Content Providers:Exploring Androids Content Providers, Modifying ContentProviders Data, Enhancing Applications using ContentProviders, Acting as a Content Provider, Working with Folders.

6 10

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8

Using Android Networking APIs:Understanding Mobile Networking Fundamentals, Accessing theInternet (HTTP).Using Android Web APIs:Browsing the Web with WebView, Building Web Extensionsusing WebKit, Working with Flash.Using Android Multimedia APIs:Working with Multimedia, Working with Still Images, Workingwith Video, Working with Audio.

11 18

9

Using Android Telephony APIs:Working with Telephony Utilities, Using SMS, Making andReceiving Phone Calls.Working with Notifications:Notifying a User, Notifying with Status Bar, Vibrating thePhone, Blinking the Lights, Making Noise, Customizing theNotification, Designing Useful Notification.

9 15

10

Introduction to application development for windows phoneApplication life cycle, syntax and semantics of visual studio 2013,design and build windows phone app, integrating map and locationin app, advanced topics

6 10

Instructional Method and Pedagogy:

At the start of course, the course delivery pattern, prerequisite of the subject will bediscussed.

Lectures will be conducted with the aid of multi-media projector, black board, OHP etc. Attendance is compulsory in lecture and laboratory which carries 10 marks in overall





Reference Books:

1. Android Wireless Application Development By Lauren Darcey and Shane Conder,Pearson Education, 2nd Edition.

2. Unlocking Android Developers Guide By Frank Ableson and Charlie Collins and RobiSen, Manning Publication Co.

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Sr. No Name of Experiment

1 Create First Android Application , that will display LDRP - ITR in the middle of thescreen in the Blue color with White background.

2 Create sample application with Check username and password only. On successful login, goto the next screen and on failing login, alert user using Toast. Also pass username to nextscreen.

3 Create login application where you will have to validate EmailID (UserName). Till theusername and password is not validated, login button should remain disabled.

4 Create and Login application as above. On successful login , open browser with anyURL.

5 Create an application that will change color of the screen, based on selected optionsfrom the menu.

6 Create an application that will display toast (Message) on specific interval of Time.7 Create a background application that will open activity on specific Time.

8 Create an UI such that, one screen have list of all the types of Books. On selecting of anybook name, next screen should show Book details like: B o o k name , Author Name,Publication name, images(using gallery) if available, show different colors in which it isavailable.

9 Using content providers and permissions, Read phonebook contacts using content providersand display in list.

10 Read Messages from the Mobile Devices and Display it on the screen.

11 Create an application that will play a media file from the memory card.

12 Create an application to make Insert, Update, Delete and Retrieve operation on thedatabase.

13 Create an application to send message between two emulators.

14 Create an application to pick up any image from the native application gallery anddisplay it on the screen.

15 Create simple app for windows phone.

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Subject Name: Mobile Application Development with iOSSubject Code: CE 703 - 3Teaching Scheme (Credits and Hours)

Teaching scheme

Total

Credit

Evaluation Scheme




04 00 04 08 6 3 70 30 20 30 150

Learning Objectives:The main objectives to give the subject Mobile Application Development in iOS are:

To introduce basic concepts of Objective C Programming To introduce iOS To building Mobile Application With iOS



MinimumHours

1 Fundamentals of programming 52 Learning Objective C 93 Introduction to iPhone OS 94 Application Development in iPhone 165 Database integration with SqLite 106 Deploying your iOS app 57 Introduction to application development for windows phone 6


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Detailed Syllabus:Sr.No Topic

LectureHours

Weightage(%)

1 Fundamentals of programmingOOP concepts and SQL Queries, Basics of Designing, Overview ofMAC OS and X-Code

5 10

2 Learning Objective CData Types, NSInteger, NSNumber, Operators, Loop,Introduction to .H and .M, Files Inheritance, MethodOverloading, Mutable and Immutable Strings, Mutable andImmutable Arrays, File Management

9 17

3 Introduction to iPhone OSIntroduction to iPhone Architecture, Essential COCOA TouchClasses, Interface Builder, Nib File, COCOA and MVCFramework, Overview of features of latest ios

9 17

4 Application Development in iPhoneControls and Gestures, Controllers and Memory Management,Using Application Delegate, Connecting Outlets, ManagingApplication Memory, Advance Controllers Programming,Views (Alert View, Table Views, Picker, Date and Time,Image), Navigation Based Application Development, Tab Bar andTool Bar, Audio and Video, Releasing Memory, Reading PDF Filein iPhone Simulator, Animation, Accelerometer, Location Servicesand 2-D Graphics, Email Sending, XML Parsing, JSONParsing, Web Services Integration, Exploring maps and localsearch

16 26

5 Database integration with SqLiteSqLite, Creating Outlets and Actions, Parsing Data with SqLite,Overview of Networking- SCNetwork, CFHTTP, CFFTP,CFSocket, Berkeley Sockets, Web Server

10 15

6 Deploying your iOS appDeploying the app to Beta Tester, Registering Beta device,Generating digital certificates, Submitting app to Apple byregistering Apple Id, Validating and submitting App

5 5

7 Introduction to application development for windows phoneApplication life cycle, syntax and semantics of visual studio 2013,design and build windows phone app, integrating map and locationin app, advanced topics

6 10

60 100



evaluation.

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One internal exam will be conducted as a part of internal theory evaluation. Assignments based on the course content will be given to the students for each unit and




Reference Books:1. Building iPhone and iPad Electronic Projects - MikeWesterfield - O'Reilly Media

Pub.2 . Head First iPhone and iPad Development, 2nd Edition - Dan Pilone, Tracey Pilone

O'Reilly Media3. Beginning iPhone and iPad Web Apps - ChrisApers, Daniel Paterson - Apress Pub4. Beginning iOS Programming Building and deploying iOS application, Nick Harris, Wrox

Publication

List of Practical:

Sr. No Title1 Print Hello World in iOS2 Handling button events / actions in iOS3 Implement UI elements like TextFields, Label, Toolbar, Statusbar, Tabbar4 Handling image in iOS using ImageView5 Implement UI elements like ScrollView, TableView, Pickers, Switches6 Implement UI elements like Sliders, Alerts, Icons7 Handling Accelerometer to manage change in position8 Managing camera in iOS9 Make Registration page using UI elements and SQLite Database

10 Handling audio, video and file in iOS11 Deploying iOS app on app store12 Create simple app for windows phone

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Subject Name : Image ProcessingSubject Code : CE 704-1Teaching Scheme (Credits and Hours)

Teaching scheme

Total

Credit

Evaluation Scheme




04 00 02 06 5 3 70 30 20 30 150

Learning Objectives: To understand the sensing, acquisition and storage of digital images. To study the image fundamentals and mathematical transforms necessary for image

processing. To understand the digital processing systems and corresponding terminology. To understand the base image transformation domains and methods. To have an understanding of colour models, type of image representations and related

statistics. To study the image enhancement techniques. To study image compression procedures. To study image segmentation and representation techniques. To study image restoration.



MinimumHours

1 Introduction to computer graphics 42 Image processing fundamentals 63 Image Enhancement 104 Image Restoration 105 Colour Image Processing 126 Image Compression 107 Morphological Image Processing Overview 8


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Topic LectureHours

Weightage(%)

1 Introduction to Computer Graphics: Introduction of Coordinate representation and Pixel Raster Scan & Random Scan systems Video controller and raster scan display processor.

4 8

2 Introduction to image processing: Fundamentals Applications Image processing system components Image sensing and acquisition Sampling and quantization Neighbors of pixel adjacency connectivity regions and boundaries Distance measures.

6 12

3 Image Enhancement: Frequency and Spatial Domain Contrast Stretching Histogram Equalization Low pass and High pass filtering.

10 16

4 Image Restoration: Noise models mean, orderstatistics adaptive filters Band reject, Band pass and notch filters

10 16

5 Colour Image Processing: Colour models Pseudo colour Image processing Colour transformation and segmentation.

12 20

6 Image Compression: Models Error free and lossy compression Standards.

10 16

7Morphological Image Processing: Overview

Boundary extraction Region filtering Connected component extraction Convex hull Thinning; Thickening; skeletons; pruning; Image

segmentation.

8 12

Total 60 100

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Learning Outcome:

On successful completion of the course, the student will: Be able to understand basic concepts image processing, image storage and types of

transformations that can be applied to images. Be able to compare the domains and methods of image processing. Be able to check the correctness of algorithms using inductive proofs and loop invariants. Learn Image Restoration & Enhancement techniques, colour image processing. Be able to make proper use of image processing tools. Familiar with morphological image processing.

Text Book:1. Digital Image Processing, Second Edition by Rafel C. Gonzalez and Richard E.

Woods, Pearson Education

Reference books:1. Digital Image Processing by Bhabatosh Chanda and Dwijesh Majumder, PHI2. Fundamentals of Digital Image Processing by Anil K Jain, PHI3. Digital Image Processing Using Matlab, Rafel C. Gonzalez and Richard E.

Woods, Pearson Education

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Sr. No Name of Experiment1 Image Printing Program Based on Halftoning.

2 Reducing the Number of Intensity Levels in an Image.

3 Zooming and Shrinking Images by Pixel Replication.

4 Zooming and Shrinking Images by Bilinear Interpolation.

5 Arithmetic Operations.

6 Image Enhancement Using Intensity Transformations.

7 Histogram Equalization.

8 Spatial Filtering.

9 Enhancement Using the Laplacian.

10 Unsharp Masking.

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Subject Name: Embedded SystemsSubject Code: CE 704-2Teaching Scheme (Credits and Hours)

Teaching scheme

Total

Credit

Evaluation Scheme




04 00 02 06 5 3 70 30 20 30 150

Learning Objectives:To learn the concepts of Embedded System and implement these concepts into practice.



MinimumHours

1 Introduction 10

2 Memory and Input Management 15

3 Processes and Operating System 15

4 Embedded Software 20


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Detailed Syllabus:Sr.No Topic

LectureHours

Weightage(%)

1 IntroductionChallenges of Embedded Systems, Embedded system designprocess, Embedded System processors & Micro controllers,ARM, PIC architecture

10 20

2 Memory and Input ManagementCommon memory types, Memory hierarchy, Cache Memory,Memory system mechanisms, Memory and I/Odevices and interfacing, Interrupts handling

15 25

3 Processes and Operating SystemMultiple tasks and processes, Context switching, Schedulingpolicies, Inter process communication mechanisms, Performanceissues, Introduction to RTOS, Process management & memorymanagement in RTOS along with Real time scheduling

15 25

4 Embedded SoftwareProgramming embedded systems in assembly and C, Meeting realtime constraints, Arduino Uno and its programming, Embedded CProgramming, Introduction to Raspberry Pi and programming

20 30

Total 60 100







Reference Books:1. Computers as Components: Principles of Embedded Computing System Design, Wayne

Wolf, 2nd Edition, Morgan Kaufmann Publishers2. Embedded System Design: A Unified Hardware Software Approach, Frank Vahid and

Tony Givargis3. Michael J. Pont, Embedded C, Pearson Education , 2007

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List of Practical:

Sr. No Title1 To print Hello World using Embedded C.2 To implement operators in Embedded C.3 To implement conditional statements and loop in Embedded C.4 To implement the concept of port programming using Embedded C.5 To display decimal numbers from 0-9 in the seven segment display.6 To blink an LED.7 To prepare digital clock.8 To implement functions of Arduino board.9 Controlling home appliances using Arduino board.

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Subject Name : Semantic WebSubject Code : CE 704-3Teaching Scheme (Credits and Hours)

Teaching scheme

Total

Credit

Evaluation Scheme




04 00 02 06 5 3 70 30 20 30 150

Learning Objectives: To Introduce Semantic Web Vision Understanding about XML,RDF,RDFS,OWL Querying Ontology Ontology Reasoning Migration from Document to Data Web LOD Cloud



MinimumHours

1 Foundation of Semantic Web Technologies 32 Basic Description Logics 183 Structured Web Documents in XML 54 Describing Web Resources in RDF 105 Web Ontology Language: OWL 86 SPARQL 67 Linked Open data 10


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Topic LectureHours

Weightage(%)

1 Foundation of Semantic Web Technologies Introduction Current web vs Semantic Web Semantic Web Technologies A layered approach

3 5

2 Descriptive Logic Introduction Definition of the basic formalism Reasoning algorithms Language extensions

18 30

3 Structured Web Documents in XML Introduction XML Structuring Namespaces Addressing and querying XML document Processing

5 8

4 Describing Web Resources: RDF Introduction RDF: Basic Ideas RDF: XML-Based Syntax RDF serialization RDF Schema: Basic Ideas RDF Schema: The Language RDF and RDF Schema in RDF Schema

10 15

5 Web Ontology Language: OWL Introduction OWL and RDF/RDFS Three Sublanguages of OWL Description of the OWL Language Layering of OWL Examples OWL in OWL

8 12

6 SPARQL SPARQL simple Graph Patterns, Complex Graph Patterns,

Group Patterns, Queries with Data Values, Filters OWL Formal Semantics,

6 10

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7 Linked Open data Introduction Principles of Linked Data Web of Data LOD Cloud Linked Data Source : Dbpedia, Freebase

10 20

Total 60 100







Learning Outcome:

Understand the semantic web Vision and technologies Understand about ontology Understanding about Data Web(Linked open data Cloud)

Text Book: A Semantic Web Primer by Grigoris Antoniou Frank van Harmelen, The MIT Press

Cambridge Foundation of Semantic Web Technologies, Pascal Hitzler, Markus and Sebastian Linked Data : Evolving the Web into a Global Data space by Tom Heath, Christian Bizer

, Morgan & Claypool publication Basic Description Logic by Franz Baader, Warner Nutt

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Sr. No Name of Experiment1 Working with XML

2 Working with XML Schema, DTD

3 Design Of Ontology using RDF

4 Design RDF document with different Serialization format (e.g. tutle,N-triple)

5 Design Of Ontology using RDFS

6 Design Of Ontology using OWL

7 Case study : Pizza Ontology

8 Querying Ontology using SPARQL

8 Design of any domain specific Ontology in Protg

9 Case Study : Dbpedia

10 Case study : LOD Cloud

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Subject Name: Internet of ThingsSubject Code: CE 704-4Teaching Scheme (Credits and Hours)

Teaching scheme

Total

Credit

Evaluation Scheme




04 00 02 06 5 3 70 30 20 30 150

Learning Objectives:Students will understand the concepts of Internet of Things and can able to build IoTapplications.


Sr. No Title of the Unit MinimumHours1 Introduction to IoT 62 IoT & M2M 63 Network & Communication aspects 164 Challenges in IoT 105 Domain specific applications of IoT 66 Developing IoTs 16


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Detailed Syllabus:

Sr.No

Topic LectureHours

Weightage(%)

1 Introduction to IoTDefining IoT, Characteristics of IoT, Physical design of IoT, Logicaldesign of IoT, Functional blocks of IoT, Communication models &APIs

6 10

2 IoT & M2MMachine to Machine, Difference between Iot and M2M, Softwaredefine Network

6 10

3 Network & Communication aspectsWireless medium access issues, MAC protocol survey, Surveyrouting protocols, Sensor deployment & Node discovery, Dataaggregation & dissemination

16 30

4 Challenges in IoTDesign challenges, Development challenges, Security challenges,Other challenges

10 15

5 Domain specific applications of IoTHome automation, Industry applications, Surveillance applications,Other IoT applications

6 10

6 Developing IoTsIntroduction to Python, Introduction to different IoT tools,Developing applications through IoT tools, Developing sensor basedapplication through embedded system platform, Implementing IoTconcepts with python

16 25

60 100







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Understand the concepts of Internet of Things Analyze basic protocols in wireless sensor network Design IoT applications in different domain and be able to analyze their performance Implement basic IoT applications on embedded platform

Reference Books:1. Vijay Madisetti, Arshdeep Bahga, Internet of Things: A Hands-On Approach2. Waltenegus Dargie,Christian Poellabauer, "Fundamentals of Wireless Sensor Networks:

Theory and Practice"

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Subject Name: Mini ProjectSubject Code: CE 705Teaching Scheme (Credits and Hours)

Teaching scheme

Total

Credit

Evaluation Scheme




00 00 06 6 3 0 0 0 50 100 150

Learning Objectives:Student will be developing a project to integrate knowledge and skills acquired during theirstudies of various courses and focus on all aspects of Software Development Life Cycle.Students inventiveness, degree of effort and documentation skills will be enhanced.

Instructional Method and Pedagogy: A student is required to prepare project independently or in a team. The project involves analytical, numerical or system analysis and design, system

development and testing, study - research project or combination of these. Two internal presentations will be conducted. The student is required to demonstrate their inventiveness, degree of effort and

documentation skills. The students are also required to submit the report and defend the same.

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Internal Project evaluation based on following criteria:Sr.No Criteria Weightage Examiners

1. Presentation 1 35% Panel consisting of minimum twomembers2. Presentation 2 35%

3. Documentation 30% Internal Guide

Evaluation Criteria of Presentation 1

Criteria

InnovativeProject

Definitionand

literaturesurvey

DatabaseDesign

SystemDiagrams

PresentationSkill

QuestionAnswer

Interactionwith

InternalGuide

Weightage 20% 20% 20% 10% 15% 15%


Criteria PracticalImplementation

Input /OutputDesign

TechnicalKnowledgeof Project

PresentationSkill

QuestionAnswer

Interactionwith

InternalGuide

Weightage 30% 10% 20% 10% 15% 15%

External Project evaluation based on following criteria:

CriteriaInnovative

ProjectDefinition

LiteratureSurvey

DatabaseDesign

PracticalImplementation

& Testing

PresentationSkill

QuestionAnswer

ProjectReport

Weightage 10% 10% 15% 30% 10% 15% 10%

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Subject Name: Big Data AnalyticsSubject Code: CE 801Teaching Scheme (Credits and Hours)

Teaching scheme

Total

Credit

Evaluation Scheme




04 00 02 06 5 3 70 30 20 30 150

Learning Objectives: To introduce students to basic applications, concepts, and techniques of Data

Warehousing & mining. Understand the fundamental processes, concepts and techniques of data mining and

develop an appreciation for the inherent complexity of the data- mining task. To develop skills for using recent data mining software to solve practical problems

in a variety of disciplines. To introduce students to bigdata and its implementation.



MinimumHours

1 Introduction to Data Warehousing 62 Concepts and techniques in Data Warehousing 53 Introduction to data mining (DM) 54 Data Preprocessing 65 Concept Description & Association Rule Mining 86 Classification and Prediction 87 Advance topics 88 Introduction to Big Data, Fundamental concepts, MapReduce and

Hadoop, Hadoop Implementation and Deployment 14

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Detailed SyllabusSr.No Topic

LectureHours

Weightage(%)

1 Overview and concepts Data WarehousingWhat is data warehousing - The building Blocks, DefiningFeatures Data warehouses and data marts, Overview of thecomponents, Metadata in the data warehouse, Need for datawarehousing, Basic elements of data warehousing, Trends indata warehousing

6 10

2 Concepts and techniques in Data WarehousingOLAP (Online analytical processing) definitions, Differencebetween OLAP and OLTP, Dimensional analysis - What arecubes?, Drill-down and roll-up - slice and dice or rotation,OLAP models, ROLAP versus MOLAP, defining schemas:Stars, snowflakes and fact constellations

5 10

3 Introduction to Data Mining (DM)DM Functionalities, Classification of DM Systems, Issues in DM KDD Process

5 10

4 Data PreprocessingWhy to preprocess data?, Data cleaning: Missing Values,Noisy Data, Data Integration and transformation, DataReduction: Data cube aggregation, Dimensionality Reduction,Data Compression, Numerosity Reduction, Data MiningPrimitives, Languages and System Architectures: Taskrelevant data, Kind of Knowledge to be mined, Discretizationand Concept Hierarchy

6 10

5 Concept Description and Association Rule MiningIntroduction to Concept description, Data Generalization andsummarization-based Characterization, AnalyticalCharacterization, Class Comparisons, Descriptive StatisticalMeasures, Market basket analysis- basic concepts,Association Rule Mining, The Apriori Algorithm, MiningMultilevel Association Rule Mining, MiningMultidimensional Association Rule Mining

8 12

6 Introduction to Classification and PredictionIntroduction to classification and prediction, Issues regardingClassification, Classification using Decision trees, BayesianClassification, Classification by Backpropagation, Prediction

8 12

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Classification Accuracy7 Advance topics

Introduction of Clustering, Spatial mining, Web mining, Textmining

6 10

8 Introduction to Big Data, MapReduce and Hadoop:What Is Big Data?, Driving the growth of Big Data,Differentiating between Big Data and traditional enterpriserelational data, Challenges of Bid Data, Hadoop, MapReduceWhy Is MapReduce Necessary?, How Does MapReduceWork?, Real-World MapReduce Examples

8 14

9 Hadoop Implementation and Deployment:Introducing Hadoop, Hadoop cluster components, HadoopArchitecture, Hadoop Ecosystem, Evaluation criteria fordistributed MapReduce runtimes, Enterprise-grade HadoopDeployment, Hadoop Implementation

8 12

Total 60 100







Learning Outcome:

Upon completion of this course, students will be able to do the following: Students will able to understand important of data mining and its various concepts

like data preprocessing, various classification algorithms etc. Student will be able to develop a reasonably sophisticated data mining

application. Student will be able to develop a reasonably sophisticated data mining

application. Student is able to select methods and techniques appropriate for the task Student is able to develop the methods and tools for the given task

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Text Books: J. Han, M. Kamber, Data Mining Concepts and Techniques, Morgan Kaufmann

Paulraj Ponnian, Data Warehousing Fundamentals, John Willey.

Robert D. Schneider , Hadoop for Dummies, Wiley India.

Reference Books: M. Kantardzic, Data mining: Concepts, models, methods and algorithms, John Wiley

&Sons Inc.

M. Dunham, Data Mining: Introductory and Advanced Topics, Pearson

Pieter Adriaans, Dolf Zantinge , Data Mining, Pearson Education Asia


Name of Experiment1 Design and Create Cube by identifying measures and dimensions for Star Schema,

Snowflake2 Design and Create Cube by identifying measures and dimensions for Design storage

for cube using storage3 Process Cube and Browse Cube Data

1. By replacing a dimension in the grid, filtering and drilldown using cube browser2. Browse dimension data and view dimension members, member properties, member

property values3. Create calculated member using arithmetic operators and member property of

dimension Member4 Create and use Excel Pivot Table Report based on data cube5 Design and Create data mining models using Analysis Service of SQL server 20056 Design and Build targeted mailing data mining model using analysis service of SQL

server 2005 and compare their predictive capabilities using the Mining AccuracyChart View and Create predictions using Prediction Query Builder.

7 Perform various steps of Preprocessing on the given relational database / warehouse.8 To implement Data Mining Extensions (DMX) language and MDX query language9 Perform various steps of Preprocessing using WEKA software.10 Creating Data Mining Structure & Predictive Models (Neural Networks and Decision

Tree) using the Excel Add-In for SQL Server 2008.11 Case Study: To study research papers on the given topic and prepare the report on it.12 To setup Hadoop.13 To run sample program using hadoop.

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Subject Name : Information RetrievalSubject Code : CE 802-1Teaching Scheme (Credits and Hours)

Teaching scheme

Total

Credit

Evaluation Scheme




03 00 02 05 4 3 70 30 20 30 150

Learning Objectives: Learn to write code for text indexing and retrieval. Learn to evaluate information retrieval systems Learn to analyze textual and semi-structured data sets Learn to evaluate information retrieval systems Learn about text similarity measure Understanding about search engine Text Classification



MinimumHours

1 Overview of text retrieval systems 52 Retrieval models and implementation: Vector Space Models 63 Query expansion and feedback 54 Probabilistic models; statistical language models 85 Text classification & Text clustering 106 Web search basics, crawling, indexes, Link analysis 87 IR applications 3


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Topic LectureHours

Weightage(%)

1 Overview of text retrieval systems Boolean retrieval The term vocabulary and postings lists Dictionaries and tolerant retrieval Index construction and compression

5 12

2 Retrieval models and implementation: Vector SpaceModels

Vector Space Model TF-IDF Weight Evaluation in information retrieval

6 15

3 Query expansion and feedback Relevance feedback pseudo relevance feedback Query Reformulation

5 12

4 Probabilistic models; statistical language models Okapi/BM25; Language models KL-divergence Smoothing

8 15

5 Text classification & Text clustering The text classification problem Naive Bayes text classification k- nearest neighbors Support vector Machine Feature Selection Vector-space clustering; K-means algorithm Hierarchical clustering DBSCAN algorithm PAM and PAMK EM algorithm

10 22

6 Web search basics, crawling, indexes, Link analysis Web Characteristic Crawling Web As a graph Page Rank Hubs and Authorities

8 15

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7 IR applications Information extraction Question answering Opinion summarization Social Network

3 9

Total 45 100







Learning Outcome:

To Understand Document as Vector Performance evolution metric for IR To understand search Engine functionality Various Supervised and Unsupervised learning Method

Text Book:

Christopher D. Manning, Prabhakar Raghavan and Hinrich Schtze, Introduction toInformation Retrieval, Cambridge University Press. 2008. http://nlp.stanford.edu/IR-book/information-retrieval-book.html

ChengXiang Zhai, Statistical Language Models for Information Retrieval (SynthesisLectures Series on Human Language Technologies), Morgan & Claypool Publishers,2008.

http://www.morganclaypool.com/doi/abs/10.2200/S00158ED1V01Y200811HLT001

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List of Practicals:

Sr. No Name of Experiment1 Implementation of various classification algorithm on text

2 Implementation of various Clustering algorithm on text

3 Implement Domain specific Search Engine

4 Social media analytic

5 Design and development of Question/Answering System

6 IR from Micro blog

7 Various track at TREC 2015 conference (students will be encouraged to participate insuch track)

Clinical Decision Support Track Contextual Suggestion Track Microblog Track Temporal Summarization Track Tasks Track

8 Various track at CLEF 2015 Conference(students will be encouraged to participate inbelow track

Linked Data Track

Tweet Contextualization track

Relevance Feedback Track

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Subject Name: High Performance ComputingSubject Code: CE 802-2Teaching Scheme (Credits and Hours)

Teaching scheme

Total

Credit

Evaluation Scheme




03 00 02 05 4 3 70 30 20 30 150

LEARNING OBJECTIVES:The objective of this course is

To Study various computing technology architecture. To know Emerging trends in computing technology. To highlight the advantage of deploying computing technology.

OUTLINE OF THE COURSE:

Sr. No Title of the Unit MinimumHours1 Cluster Computing and its Architecture 10

2 Cluster Setup and Administration 5

3 Introduction to Grid and its Evolution 6

4 Introduction to Cloud Computing 8

5 Nature of Cloud 11

6 Cloud Elements 5


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DETAILED SYLLABUS:Sr.No

Topic LectureHours

Weight age(%)

1 Cluster Computing and its Architecture: Ease of Computing Scalable Parallel Computer Architecture Towards Low Cost Parallel Computing & Motivation Windows opportunity A Cluster Computer And Its Architecture Cluster Classification Commodity Components for Clusters Network Services/Communication SW Cluster Middleware and Single Systems Image Resource management & Scheduling (RMS)

10 20

2 Cluster Setup and Administration: Introduction Setting up the cluster Security System Monitoring System Tuning

5 14

3 Introduction to Grid and its Evolution: Introduction to Grid and its Evolution: Beginning of the Grid Building blocks of Grid Grid Application and Grid Middleware Evolution of the Grid: First, Second & Third Generation

6 14

4 Introduction to Cloud Computing: Defining Clouds Cloud Providers Consuming Cloud Services Cloud Models Iaas, Paas, SaaS Inside the cloud Administering cloud services Technical interface Cloud resources

8 18

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5 Nature of Cloud: Tradition Data Center Cost of Cloud Data Center Scaling computer systems Cloud work load Managing data on clouds Public, private and hybrid clouds

11 22

6 Cloud Elements: Infrastructure as a service Platform as a service Software as a service

5 12

INSTRUCTIONAL METHOD AND PEDAGOGY At the start of course, the course delivery pattern, prerequisite of the subject will be







On successful completion of the course, the student will be having the basic knowledgeof computing technology.

Student will be able to understand architecture of computing technology. Student will be able to know cloud computing service models. Know about emerging trends in computing technology. Student will be able to know big data and hadoop architecture.

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TEXT BOOKS:

1. High Performance Cluster Computing, Volume 1, Architecture and Systems, RajkumarBuyya, Pearson Education.

2. Berman, Fox and Hey, Grid Computing Making the Global Infrastructure a Reality,Wiley India.

3. Hurwitz, Bllor, Kaufman, Halper, Cloud Computing for Dummies, Wiley India.

REFERENCE BOOKS:

1. Ronald Krutz, Cloud Security, Wiley India.2. Cloud Computing, A Practical Approach, Anthony Velte, Toby Velte, Robert Elsenpeter,

McGrawHill.

LIST OF PRACTICALS:

Sr. No Name of Experiment1 To study the basic commands of linux.2 To establish Beowulf Cluster using MPI(Message Passing Interface) Library.3 Installation and configuration of Alchemi Grid.4 Running a sample application on Alchemi Grid and analysing it.5 To study a Grid Simulation Toolkit.6 To run two sample programs using GridSim Toolkit.7 To study a Cloud Simulation Toolkit.8 To setup Cloud.

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Subject Name : Soft ComputingSubject Code : CE 802-3Teaching Scheme (Credits and Hours)

Teaching scheme

Total

Credit

Evaluation Scheme




03 00 02 05 4 03 70 30 20 30 150

Learning Objectives:The main objective of the Soft Computing Techniques to Improve Data Analysis Solutions is tostrengthen the dialogue between the statistics and soft computing research communities in orderto cross-pollinate both fields and generate mutual improvement activities.

Soft Computing is a consortia of methodologies which collectively provide a body of conceptsand techniques for designing intelligent systems.


Sr.No

Title of the Unit Minimum Hours

1 Introduction of Soft computing and Hard computing 4

2 Neural Networks 10

3 Fuzzy Logic 8

4 Genetic Algorithm 8

5 Hybrid System 5

6 GA and Fuzzy based Backpropagation Network 10

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Total hours (Theory): 45

Total hours (Lab): 30Total hours: 75

Detailed Syllabus:

Sr.No

Topic LectureHours

Weightage(%)

1Introduction:What is Soft Computing? Difference between Hard and Softcomputing, Requirement of Soft computing, Major Areas ofSoft Computing, Applications of Soft Computing.

4 10

2

Neural Networks:What is Neural Network, Learning rules and variousactivation functions, Single layer Perceptrons , BackPropagation networks, Architecture of Backpropagation(BP)Networks, Backpropagation Learning, Variation of StandardBack propagation Neural Network, Introduction to AssociativeMemory, Adaptive Resonance theory and Self OrganizingMap, Recent Applications.

10 24

3

Fuzzy Systems:Fuzzy Set theory, Fuzzy versus Crisp set, Fuzzy Relation,Fuzzification, Minmax Composition, Defuzzification Method,Fuzzy Logic, Fuzzy Rule based systems, Predicate logic, FuzzyDecision Making, Fuzzy Control Systems, FuzzyClassification.

8 18

4

Genetic Algorithm:History of Genetic Algorithms (GA), Working Principle,Various Encoding methods, Fitness function, GAOperators- Reproduction, Crossover, Mutation, Convergenceof GA, Bit wise operation in GA, Multi-level Optimization.

8 18

5

Hybrid Systems:Sequential Hybrid Systems, Auxiliary Hybrid Systems,Embedded Hybrid Systems, Neuro-Fuzzy Hybrid Systems,Neuro-Genetic Hybrid Systems, Fuzzy-Genetic HybridSystems.

5 10

6GA based Backpropagation Networks:GA based Weight Determination, K - factor determination inColumns.

5 10

7

Fuzzy Backpropagation Networks:LR type Fuzzy numbers, Fuzzy Neuron, Fuzzy BPArchitecture, Learning in Fuzzy BP, Application of Fuzzy BPNetworks.

5 10

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Instructional Method and Pedagogy:

At the start of course, the course delivery pattern, prerequisite of the subject will bediscussed.

Lectures will be conducted with the aid of Multi-media projector, Green board, OHP etc. Attendance is compulsory in lecture and laboratory which carries 10 marks in overall





Reference Books:

Neural Networks, Fuzzy Logic and Genetic Algorithms: Synthesis & Applications,S.Rajasekaran, G. A. Vijayalakshami, PHI.

Genetic Algorithms: Search and Optimization, E. Goldberg. Neuro-Fuzzy Systems, Chin Teng Lin, C. S. George Lee, PHI. Build_Neural_Network_With_MS_Excel_sample by Joe choong.


Sr.No Name of Experiment

1Create a perceptron with appropriate no. of inputs and outputs. Train it using fixed incrementlearning algorithm until no change in weights is required. Output the final weights.

2 Create a simple ADALINE network with appropriate no. of input and output nodes. Train itusing delta learning rule until no change in weights is required. Output the final weights.

3 Train the autocorrelator by given patterns: A1=(-1,1,-1,1), A2=(1,1,1,-1), A3=(-1, -1, -1,1). Test it using patterns: Ax=(-1,1,-1,1), Ay=(1,1,1,1), Az=(-1,-1,-1,-1).

4Train the hetrocorrelator using multiple training encoding strategy for given patterns:A1=(000111001) B1=(010000111), A2=(111001110) B2=(100000001), A3=(110110101)B3(101001010). Test it using pattern A2.

5Implement Union, Intersection, Complement and Difference operations on fuzzy sets. Alsocreate fuzzy relation by Cartesian product of any two fuzzy sets and perform max-mincomposition on any two fuzzy relations.

6 Solve Greg Viots fuzzy cruise controller using MATLAB Fuzzy logic toolbox.7 Solve Air Conditioner Controller using MATLAB Fuzzy logic toolbox8 Implement TSP using GA.

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Subject Name: ProjectSubject Code: CE 803Teaching Scheme (Credits and Hours)

Teaching scheme

Total

Credit

Evaluation Scheme




00 00 34 34 17 0 0 0 300 300 600

Learning Objectives:Student will be developing a project to integrate knowledge and skills acquired during theirstudies of various courses and focus on all aspects of Software Development Life Cycle.Students inventiveness, degree of effort and documentation skills will be enhanced.

Instructional Method and Pedagogy: This is the full time project so the student will undergo sincere work under the guidance

of internal faculty members as well as external guides from industry. A student is required to prepare project independently or in a team. The project involves analytical, numerical or system analysis and design, system

development and testing, study - research project or combination of these. Two internal presentations will be conducted. One of the faculty member will visit the industry in which student is doing project to

interact with external guide and take the feedback. This feedback carries weightage in theevaluation scheme.

The student is required to demonstrate their inventiveness, degree of effort anddocumentation skills.

The students are also required to submit the report and defend the same.

of 53

Internal Project evaluation based on following criteria:Sr.No Criteria Weightage Examiners

4. Presentation 1 35% Panel consisting of minimum twomembers5. Presentation 2 35%

6. Documentation 20% Internal Guide7. Industry Feedback 10% External Guide


Criteria

InnovativeProject

Definitionand

literaturesurvey

DatabaseDesign

SystemDiagrams

PresentationSkill

QuestionAnswer

Interactionwith

InternalGuide

Weightage 20% 20% 20% 10% 15% 15%


Criteria PracticalImplementation

Input /OutputDesign

TechnicalKnowledgeof Project

PresentationSkill

QuestionAnswer

Interactionwith

InternalGuide

Weightage 30% 10% 20% 10% 15% 15%

External Project evaluation based on following criteria:

CriteriaInnovative

ProjectDefinition

LiteratureSurvey

DatabaseDesign

PracticalImplementation

& Testing

PresentationSkill

QuestionAnswer

ProjectReport

Weightage 10% 10% 15% 30% 10% 15% 10%

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