Erasmus Mundus Joint Doctoral Programme in Interactive … · Erasmus Mundus Joint Doctoral...

Erasmus Mundus Joint Doctoral Programme in Interactive and Cognitive Environments (EMJD ICE)

Syllabus and Academic Calendar 2012

Management Board

Erasmus Mundus Joint Doctorate in

Interactive and Cognitive Environments

(EMJD ICE)

2

QMUL Courses 2012-2013

Code Hours Credits Title Teacher(s)

QMUL

01 22+22 5 ICE_QMUL_ELEM006: Multimedia Systems Prof. Andrea Cavallaro

02 30+4 5 ICE_QMUL_ELEM021: Music And Speech Processing

Prof. Elaine Chew

03 22+22 5 ICE_QMUL_ ELEM023: Image and Video Processing

Prof. Shaogang Gong

04 24+12 5 ICE_QMUL_ ELEM041: Machine Learning Dr. Ioannis Patras



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TU/e

01 40 5 ICE_TU/e_DBD05: Complex Sensors Prof.dr. ir. J.B.O.S. Martens

02 40 5 ICE_TU/e_DB305: Persuasive Technology dr. H.A. van Essen, dr. M.M. Bekker

03 40 5 ICE_TU/e_ DB 617: Sensual Dynamics ir. E.J.L. Deckers, dr.ir. P.D. Levy

04 40 5 ICE_TU/e_DBB03: Qualitative Research

Methods for Interaction Design

Prof. Dr. ir. P. Markopoulos

05 40 5 DB214 Sense your heart Prof.dr. S. Bambang Oetomo, Prof. dr.ir. L.M.G. Feijs, dr. W.

Chen, dr. J. Hu, dr. ir. G.R. Langereis, G.J.A. van den

Boomen

TU/e Courses 2012-2013




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UNIGE Courses 2012-2013


UNIGE

01 25 5 Data Fusion and Bayesian Interaction Modeling for Cognitive Ambient Intelligence

Prof. Carlo Regazzoni

02 20 5 Theory and Practice of Learning from Data Prof. Davide Anguita

03 20 5 Modeling, Simulation and Games Prof. Francesco Bellotti

04 20 5 Green Networking Prof. Raffaele Bolla



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UNIKLU

01 30 5 Mobile and Wireless Communications

Prof. Christian Bettstetter

02

30 5 Pervasive Computing Prof. Bernhard Rinner

03

30

5 Mobile and Wireless Networking Prof. Christian Bettstetter

04 30 5 Sensor Networks Prof. Bernhard Rinner

05 30 5 Embedded Communications Prof. Mario Huemer

06 30 5 Interactive Systems Prof. Martin Hitz

07 30 5 Signal Processing Architectures Prof. Mario Huemer

08 30 5 Adaptive and Statistical Signal Processing Prof. Mario Huemer

09 30 5 Novel Topics in Digital Signal Processing for PhD Students

Prof. Mario Huemer

UNIKLU Courses 2012-2013




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10 45 5 Special Doctorate Seminar NES Prof. Christian Bettstetter, Prof. Bernhard Rinner,

Prof. Martin Hitz, Prof. Mario Huemer

11 30 5 Research Paradigms in Computer Science Prof. Johann Eder





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UPC

UPC Courses 2012-2013


01 125 5 Artificial Intelligence applied to Robotics

Prof. Andreu Català Prof.Cecilio Angulo

Prof. Francisco J. Ruiz

02 125 5 Artificial intelligence Applied to the Control and the Identification of Systems

Prof. Andreu Català Prof.Cecilio Angulo

Prof. Francisco J. Ruiz

03 30 1,2 Information Skills in Information Technologies

Prof. Conrado Martínez

04 150 5 Advanced Techniques in Machine Learning

Prof. Javier Bejar

05 150 5 Artificial Intelligence Aplications

Prof. Ulises Cortés

06 75 3 Artificial Intelligence Seminar

Prof. Miquel Sánchez Marré



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QMUL Courses 2012-2013

ICE_QMUL_ELEM006: Multimedia Systems

Hours: 22 (lecture) + 22 (lab - optional) Credits: 5

Teacher(s): Andrea Cavallaro

Abstract:

The course aims to provide PhD Candidates with general knowledge about digital media, their integration and basic processing techniques used to generate multimedia systems. The course also aims to discuss important topics related to the creation of content description interfaces for large multimedia databases and inherent security and copyright aspects. This course will provide an understanding of multimedia, the creation, integration and processing of multimedia data and give a good overview about how state-of-the art multimedia systems are built and work. The course also gives a quick view on the most important standards for compression and coding of multimedia, as well as content description interfaces. The course begins by giving an overview of cutting-edge multimedia applications. This is followed by a detailed treatment of fundamental tasks involved in creating and processing multimedia information. Special underlying design requirements and coding aspects are then covered. In the following lectures some important areas of multimedia systems in the context of intellectual property protection and management are covered. Each student is required to complete a coursework consisting of the implementation of a specific multimedia application. To conduct this work, one supervised laboratory hour will be provided every week. The project will conclude with a demonstration of the implemented application.

Program:

Introduction to multimedia systems:

Integration of Natural Media (video and audio) into Synthetic Environments - Selected Applications. Augmented

and Virtual Reality. Immersive Telepresence. Teleshopping. 3D Videoconferencing. Medical Imaging.

Fundamentals of multimedia data:

Text, Audio, Images, Analog to Digital Conversion - Quantisation and Sampling, Noise Reduction, Filtering - low

and high pass filtering, nonlinear filtering, Feature enhancement and detection. Convulsion. Grey-scale images.

Colour spaces. Geometric transformations. Fourier Analysis

Multimedia content description:

Multimedia Content Description. Indexing and cataloguing. Video descriptors. Metrics in a descriptor space.

Population & Querying. Feature-based portals. The MPEG7 multimedia content description interface.

Coding, compression technologies and standards:

Coding fundamentals. Huffman and Arithmetic coding. The JPEG Standard. Motion compensated coding. Basic

segmentation techniques. Object based coding. Video Coding Standards - MPEG 1/2/4, H.26x.



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Intellectual property and multimedia protection:

Traditional methods of data protection - analogue and digital media. Copyright properties of digital multimedia.

Digital watermarking. Specific applications - example: distribution from a Library. Side information properties and

information theoretical aspects.

Bibliography:

Course notes

Further reading: 1. Handbook of Image and Video Processing by A. Bovik; Academic Press 2000; ISBN 0121197905

2. Computer Vision a Modern Approach by D. Forsythe and J. Ponce; Prentice Hall 2003; ISBN 0130851981

3. Video Coding: An Introduction to Standard Codecs, M. Ghanbari; IEE Publishing 1999; ISBN 0852967624

4. Digital Multimedia by N. Chapman and J. Chapman; Wiley 2000; ISBN 0471983861

5. Computer Vision by L.G. Shapiro and G.C. Stockman; Prentice Hall 2000; ISBN 0130307963

Contact: [email protected] Assessment: Position paper: the candidate should write a short position paper on how the topics dealt with during the course can be of interest for his/her research and defend it through an oral presentation.



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ICE_QMUL_ELEM021: Music And Speech Processing


Teacher(s): Elaine Chew

Abstract:

This course aims to introduce PhD Candidates to the application of Digital Signal Processing to Music and to Speech. By the end of the module the student will be able to:

Describe the physiology and physics involved in sound production and perception. Demonstrate how various fundamental concepts in Digital Signal Processing can be combined into systems, like a Digital Power Amplifier. Demonstrate how higher level processing components are constructed from lower level ones. Discuss how compressions of speech and of music, though similar, have different requirements. Propose specific compressors for specific applications. Identify latest innovations in this area involving delivery formats such as Internet and DVD. Position their acquired knowledge in a commercial context.

Program:

Human hearing, speech system, psychoacoustics, masking, Acoustics of musical instruments, Audio and Speech Standards, Wordlengths.

Speech technologies, Pitch estimation (voice), Source-filter model and LPC coding, Cepstrum, CELP, RELP, Waveform coders ADPCM, Delta Mod and others.

Music signal compression, MPEG1 (MP3) AAC.

Digital Audio Power Amplification, noise shaping, sigma-delta modulation, oversampled ADCs and DACs.

Digital Music in the 21st Century.

Bibliography:

Course notes Further reading:

1. Speech and Audio Signal Processing: Processing and Perception of Speech and Music by B.Gold and N.Morgan; John Wiley and Sons 1999; ISBN 0471351547

2. Speech Communications: Human and Machine by D.O'Shaughnessy; John Wiley & Sons Inc. 1999; ISBN 0780334493

3. Applications of Digital Signal Processing to Audio and Acoustics by M.Kahrs and K.Brandenburg; KAP 1998; ISBN 0792381300

Contact: elaine.chew/[email protected] Assessment: Position paper: the candidate should write a short position paper on how the topics dealt with during the course can be of interest for his/her research and defend it through an oral presentation.



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ICE_QMUL_ELEM023: Image and Video Processing


Teacher(s): Shaogang Gong

Abstract: This course aims to provide the PhD Candidates with knowledge of the most relevant image and video processing methods. The course also aims to discuss important topics related to the creation and manipulation of digital image and video content. Important aspects of standard transforms, compression and coding are also considered. This course provides fundamental knowledge about well established techniques for digital image and video processing. It covers mathematical models used to analyze still images, technology and standards for image and video compression and the basic methods used to design and develop a wide range of imaging solutions. Such solutions relate to the fields of machine vision, imaging graphics, pattern recognition, medical imaging, image and video coding.

Program: 1)Basic concepts Digital image representation. Sampling. Quantization. Colour spaces and colour images. Properties of digital images. Visual perception. Histograms. Image quality. Noise. 2) Global image transformations. Geometric transformations. Coordinate transformations. Histogram equalization. Sub-sampling and pixel interpolation. 3) Local image processing operators and filters. Convolution. Image smoothing and noise reduction. Low and high-pass filtering. Edge detectors. Variance based operators. Gaussian and Laplacian filters. The Canny operator. 4) Advanced filters for image processing. Median filter design. Morphological operators. Scale Space. Segmentation. Thresholding. Background detection. Chromakey techniques. Region growing. Split and merge techniques. Edge detection in scale space. Motion segmentation. 5) Video fundamentals. Video formats and standards. The optical flow. Motion estimation techniques. 6) Image and video coding, compression and standards. Coding fundamentals (FFT, DCT, Huffman and Arithmetic coding). The JPEG Standard. Wavelets and JPEG2000. Motion compensation. Object based coding

Bibliography:


1. Digital Image Processing by R.C. Gonzales and R.E.Woods; Prentice Hall; ISBN 0130946508

2. Handbook of Image Processing Operators by R. Klette and P. Zamperoni; Wiley 1996; ISBN 0471956422

3. Handbook of Image and Video Processing by A. Bovik; Academic Press 2000; ISBN 0121197905

4. Video Processing and Communications by Y. Wang, J. Ostermann and Y. Zhang; Prentice Hall; ISBN 0130175471

5. JPEG2000: Image Compression Fundamentals, Standards and Practice by D. Taubman and M. Marcellin; Kluwer 2002; ISBN 079237519X




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ICE_QMUL_ELEM041: Machine Learning


Teacher(s): Dr. Ioannis Patras

Abstract: The aim of the course is to give PhD Candidates an understanding of machine learning methods, including pattern recognition, clustering and neural networks, and to allow them to apply such methods in a range of areas. This course covers methods for machine learning from signals and data, including statistical pattern recognition methods, neural networks, and clustering. By the end of the module the student will be able to: Recall a range of machine learning techniques and algorithms, including neural networks and statistical methods Use concepts from probability theory in machine learning Derive and analyse properties of machine learning methods Discuss the relative merits of different machine learning techniques and approaches Apply machine learning methods to the analysis of signals and data

Program:

Introduction to Machine Learning

Probability and Random Variables

Neural Networks

Statistical Inference

Clustering

Hidden Markov Models

Independent Component Analysis

Applications

Bibliography:

Course notes

Further reading:

1. Pattern Classification by Duda Hart and Stork; 2nd Edition; Wiley 2001; ISBN 0471056693

2. Probability Random Variables and Stochastic Processes by A. Papoulis; McGraw-Hill 2002; ISBN 0071199810

3. Neural Networks for Pattern Recognition by Bishop; OUP 1995; ISBN 0198538499


mailto:[email protected]



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TU/e Courses 2012-2013

ICE_TU/e_DBD05: Complex Sensors

Hours: 40 Credits: 5

Teacher(s): Prof.dr. ir. J.B.O.S. Martens

Abstract: Ambient intelligence is based on the concept of context awareness, i.e., it assumes that the systems that surround us have access to information about identity, position, activity, etc. of the users that are present and of events that occur within the environment. Ambient Intelligence hence assumes complex sensing systems. Basic sensors are conceptually very easy because they are most often only required to signal the absence or presence of a single, a priori known, phenomenon (such as whether a light source is on or off). When discussing complex sensors, such as cameras with their accompanying image analysis software, or motion sensors, a more structured approach, using a mathematical framework, is needed. Complex sensors are not only general purpose, i.e., require little a priori knowledge of the phenomenon that they are monitoring, but also consist of many sub-sensors. These sub-sensors often have distinct characteristics and generate continuous (rather than on/off) responses. Making constructive use of these multiple simultaneous variations of a large set of sub-sensors requires a systematic approach. In this module we treat an approach that is based on using simple mathematical approximations to real signals to extract useful features (this approach reflects a special case of the more general mathematical theory of linear systems).

Program:

Basic insight into how mathematical models underlie complex sensing.

Insight into how the designer can influence the complexity of the sensing problem.

Experience with specific sensors and signal processing software.

Learning activities

This module is in the form of a workshop where students and lecturer together explore the basics of complex sensing, and the relevance for the design of intelligent (i.e., tangible or embodied) products. The module is run within the ConceptLab, so that theory can be practiced on real sensing data.

Bibliography:

Reader on Complex Sensors (Martens)

Contact: J.B.O.S. [email protected] Assessment: Report containing 1) description of your complex sensing problem, its relevance for design and the proposed algorithmic approach (including a discussion on feasibility, motivated by supporting material); 2) summary of your competency development within this module (target length of the report: 4-5 A4 in total).



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ICE_TU/e_DB305: Persuasive Technology


Teacher(s): dr. H.A. van Essen, dr. M.M. Bekker

Abstract: Persuasion is the act of persuading someone to do or believe something. Persuasive Technology, by definition, is

technology that has the ability to persuade. Effective persuasion results in a change to a person縮 behaviour, attitudes or beliefs. This module introduces the foundations of persuasive design, i.e. the implementation of well-known persuasive principles in interactive technological products. The goal of this module is to provide the student with an understanding of existing theories and strategies for explicitly or implicitly motivating people to behave differently. The module explores how these ideas might be employed by the design of interactive products or environments to purposely effect behaviour change. Also the intention of the designer and the ethical aspects of influencing people by means of design are subject.

Program: Learning objectives in the module are to obtain an understanding of 1. Theories and frameworks of persuasion and behaviour change, in particular the functional triad from Fogg, the influence factors from Cialdini, the requirements framework from Oinas-Kukkonen, and prominent theories from social sciences. 2. Existing devices and environments that motivate behaviour change. 3. Mechanisms for identifying and exploiting persuasive influences. 4. The ethics of designing persuasive environments and technologies. 5. Methods for evaluating the effectiveness of a particular design for a persuasive environment or technology. Learning activities Analysis: Gaining insight in process of behavioral change and persuasive mechanisms by analyzing existing examples of persuasive technology in terms of the presented framework. Synthesis: Design or redesign a specific application of persuasive technology for a specific target group and a well defined behavior change, emphasizing the optimization of certain persuasive mechanisms, supported by ample analysis. Evaluation: Evaluate a persuasive design (of another team) by means of an expert evaluation, share the outcome and use the evaluation results to improve your own design

Bibliography:

Fogg, B.J. (2003). Persuasive Technology: Using Computers to Change What We Think and Do. San Francisco: Morgan Kaufmann.

Cialdini, Robert B. (2000) Influence: science and practice - 4th ed..

Oinas-Kukkonen, 2008] Oinas-Kukkonen, Harri and Harjumaa, Marja, A Systematic Framework for Designing and Evaluating Persuasive Systems, in Proceedings Persuasive 2008, LNCS 5033, pp164-176. http://www.tcw.utwente.nl/theorieenoverzicht/Theory%20clusters http://www.uri.edu/research/cprc/TTM/detailedoverview.htm



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Contact: M.M. [email protected] Assessment: Reports and presentations.

mailto:%20M.M.%[email protected]



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ICE_TU/e_ DB 617: Sensual Dynamics


Teacher(s): ir. E.J.L. Deckers, dr.ir. P.D. Levy

Abstract:

Prior to interaction, there is perception. Perception is intrinsically rising from one縮 actions and from what one senses. These are the ways one is connected to and perceive the world. At this level, in direct contact with the world, there is no information, but energies (or forces). It is these forces that designers deal with when putting a new artefact in the world. This primacy of perception is the main focus of this module, proposing an approach to effectively taking them into consideration in the design process. For practical reasons, design should focus on qualities of senses. For example, touching is local, reciprocal, and private. Where I touch, I am touched by what I am touching, and nobody else can touch what I touch. On the other hand, smell is at a distance, possibly non-directional, and public. These are qualities of senses that can be dealt with in design. To do so, these qualities need to be explored, while taking into consideration static and dynamic situations. Finally, designers should comprehend these qualities and engage them in order to find opportunities for design how can I make something private at a distance? How can technology challenge these qualities (e.g. headphones make sound private)? What are the implications for design? and to explore them towards a concrete output.

Program: This module intends to elevate the students understanding about the senses and the use of their static and dynamic qualities in design. The main goal is to explore and to determine these qualities through experience and to transfer the understanding of them to design. Ultimately, the students will comprehend the necessity of considering their senses in design, and the richness of opportunities following this consideration. During this module, the students will also reflect on the primacy aspects of interaction design (i.e. how humans are

connected with the world in essence), on the actual place of information in interaction design. This reflection will aim the student at structuring a clear understanding of how design can sense qualities in order to influence perception and action. Learning activities - observe/explore experiences in order to determine sense qualities - map sense qualities both in static and in dynamics - translate sense qualities in movements, and explore perceptive qualities - do a design exploration of these qualities by prototyping - let guests explore final prototypes - record the results and reflect on them Planning - mapping example on senses qualities (in static and dynamics) - design exploration on a selected sense quality - prototyping on the selected sense quality



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Students will receive a feedback at the end of each step.

Bibliography: Lenay, C. (2010). Emotional value in distal contact. International Journal of Design, 4(2), 15-25. Deckers, E., Wensveen, S., Ahn, R., & Overbeeke, K. (2011) Designing for Perceptual Crossing to Improve User Involvement. In CHI 2011 - Proceedings of the29th Conference on Human Factors in Computing Systems 2011, May 7-12, Vancouver, BC, Canada, 1929.

Contact: P.D. [email protected] Assessment: - a map of sense qualities in static and in dynamics - videos of explorations - design exploration prototypes - final prototype with video of its use - final report including the entire process and the reflection

mailto:%20P.D.%[email protected]



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ICE_TU/e_DBB03: Qualitative Research Methods for Interaction Design


Teacher(s): Prof. Dr. ir. P. Markopoulos

Abstract:

The aim of this course is to familiarize PhD Candidates with methods and processes for performing qualitative

research to support interaction design.

Qualitative research methods have developed largely within the social sciences and recently have been applied to

the field of interaction design. This application is usually combined with a mix of creativity and elements of surprise

introduced in the research, but also often with a lack of the methodological knowledge needed to analyze the data

collected or to put the findings into perspective.

This course aims to provide interaction designers with an awareness of processes for analysing non numerical data,

but also related caveats and opportunities. It aims also to provide them with a reference framework so that the

numerous combinations and variations of known methods that they may apply in a particular project can be related

to each other. Most of all, the objective is to break mental barriers between research and design and to show how a

scientific approach to studying humans may be revealing and rewarding for interaction designers.

Program:

Lecture: Introductory overview of Qualitative Research Methods

Exercise: revisiting the data collection and data analysis of an earlier project

Study Case: 'Handling Interruptions at the Office'

Method Card assignment

Bibliography:

Brenda Laurel, Design Research, MIT Press, 2003.

Berg, B.L., Qualitative Research Methods for Social Sciences, 2nd edition, 1995.

In addition a selection of papers shall be given to PhD Candidates for use in the study case.

Contact:

[email protected]

Assessment:

- Report 'How would I do this study again and how would I

analyze the data'

- Presentation describing the particular qualitative method

chosen for study case and the analysis process, including

reflection on limitations and strengths of the method used.

- Presentation and report on process and result of study

case.

- Hand in an extended method card.



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ICE_TU/e_DB214: Sense your heart


Teacher(s): Prof.dr. S. Bambang Oetomo, Prof. dr.ir. L.M.G. Feijs, dr. W. Chen, dr. J. Hu, dr. ir. G.R. Langereis, G.J.A.

van den Boomen

Abstract: Some of you, who have followed biology classes at high school, may already know that the human body uses electricity to feel, move and think. Where this electricity originates from and what the possibilities of measuring bio-electricity are, will be discussed. Common applications are ECG (electric measurements on the heart) and EEG (measurements on the brain). These signals can be used to develop various applications, for example, health monitoring at hospital and home, sport applications, etc. This course aims to help PhD Candidates gain essential knowledge about the principles of physiological signal (e.g. ECG) measurements on the human body. PhD Candidates will learn how nerve cells interact and how the heart and the brain functions, understand the measurement principles of ECG, and gain skills to integrate the ECG frontend designed by ID.

Program:

Lecture “Monitoring heart function”

Lecture “Einthoven’s Triangle”

Lecture “ECG amplifier”

Workshop on “ECG measurement system”

Lecture “Heart rate variability”

Lecture “Filters for ECG”

Design an application involving ECG measurements

Bibliography:


Wilson FN, Johnston FD, Rosenbaum FF, Erlanger H, Kossmann CE, Hecht H, Cotrim N, Menezes de Olivieira R, Scarsi R, Barker PS (1944): The precordial electrocardiogram. Am. Heart J. 27: 19-85.

Lederer W.J in Medical Physiology by Boron and Boulpaep eds. 2003.

Park MK, Guntheroth WG: How to read pediatric ECGs, ed 3, St Louis, 1992, Mosby

W. Chen, S. T. Nguyen, R. Coops, S. Bambang Oetomo, L. Feijs, “Wireless Transmission Design for Health Monitoring at Neonatal Intensive Care Units”, IEEE the 2nd international symposium on applied sciences in biomedical and communication technologies (ISABEL 2009),ISBN 978-80-227-3216-1, Bratislava, Slovakia, Nov. 2009.

S. Bouwstra, W. Chen, L. Feijs, S. Bambang Oetomo, “Smart jacket design for neonatal monitoring with wearable sensors”, Proceedings of Body Sensor Networks (BSN), 2009, Berkeley, USA, pp. 162 - 167.

Contact: [email protected]

Assessment: - Development of a prototype of your own application which uses ECG. - A report on the system design, implementation (including



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schematics, diagrams, photo of the ECG circuit board and coding), results (including screenshots from oscilloscope, ECG signals shown on the interface, heart rate calculation, or HRV or filtering) and possible application idea. - Short presentation / demonstration of the application.



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ICE_TU/e_DB411: Intercultural Markers in the Design Process


Teacher(s): ir. M. Bruns, dr. O. Tomico Plasencia, dr. J.M.L. Kint

Abstract: The main goal of this course is to analyze tradition and culture as a way for differentiating and positioning design in a global market, creating value in terms of process and in terms of content. To do so it will explore the use of traditional disciplines as catalysers/platforms to bring the creative industries and technological companies together to find new design opportunities specifically tied to the country of origin. The approach to follow is research in context in order to develop a methodology that supports innovation based on cultural differentiation through value creation. The design process basically looks for cultural traces (archetypes) in traditional disciplines, like ceramics, jewellery, furniture, and cooking in order to valorize and differentiate innovation. The implementation of this process consists of different phases: identification, creation, nurturing and supporting a cultural driven value ecosystem. The expected output is an interactive product designed by means of a culturally inspired process.

Program:

Learning objectives: PhD Candidates will learn how to analyze cultural activities and how to use them as a source of inspiration to generate ideas and concepts. Furthermore they will learn how to attentively observe a process and abstract it to a level, which they can apply in the development of their designs. Finally, the main objective is to understand how culture can steer the design process in such a way that it can be used for the benefit for local industries.

Learning activities: The current module will focus on developing culturally inspired designs by analyzing the ways of cooking and eating of two distinct cultures. The preparation and consumption of food is a process that is very much influenced by culture and tradition. The goal of this module is to analyze the cooking process to determine markers that identify the specific culture and to apply those markers in the design of an interactive product, unrelated to cooking. It will include the following steps:

Observation: PhD Candidates will be divided into two equally mixed groups and assigned a specific culture. Each group will prepare a designated traditional meal and while preparing the dinner they will describe their process by speaking aloud, while the other PhD Candidates observe and analyze (video tape and note) the process. PhD Candidates may ask questions that will clarify the process. However, they are not allowed to make suggestive comments that could interfere with the process.

Analysis: Both groups will make a detailed description of the preparation process separately. This starts by studying the recipe, acquiring the materials, preparing, cooking, serving, eating the food and finally cleaning the table and washing the dishes. Continuously the PhD Candidates will discuss the separate process descriptions together and culturally different reflections will be pointed out. These culturally different markers should be discussed in the broader context of tradition.

Presentation: Each group will present the cultural markers while reflecting on their cooking process and describe how they differ from their assigned culture by observing the differences in cooking.

Implementation: The culturally different markers will continuously be used to inspire the design of an interactive product. PhD Candidates will only be allowed to adapt form and interaction of their designs



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based on the cultural markers, while using pre-defined materials.

Evaluation: The products developed inspired by the two different cultures will be evaluated at the end of the module as to their level of cultural expression.

Bibliography:


Koichi Miyase (Ed.). (2008) Think globally act locally. Think locally act globally: 01 Designers. Movement. Culture. From JAPAN to the WORLD. Japan Issue. Tokyo, Japan: Bueno! Books.

Further reading will be handed out in class.


Assessment: Report including: Detailed description of the process both visual and written, Sketchbook, Reflection, Prototype of a culturally inspired design of an interactive product




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ICE_TU/e_DBB03: Qualitative Research Methods for Interaction Design


Teacher(s): Prof. Dr. ir. P. Markopoulos

Abstract:

The aim of this course is to familiarize PhD Candidates with methods and processes for performing qualitative

research to support interaction design.

Qualitative research methods have developed largely within the social sciences and recently have been applied to

the field of interaction design. This application is usually combined with a mix of creativity and elements of surprise

introduced in the research, but also often with a lack of the methodological knowledge needed to analyze the data

collected or to put the findings into perspective.

This course aims to provide interaction designers with an awareness of processes for analysing non numerical data,

but also related caveats and opportunities. It aims also to provide them with a reference framework so that the

numerous combinations and variations of known methods that they may apply in a particular project can be related

to each other. Most of all, the objective is to break mental barriers between research and design and to show how a

scientific approach to studying humans may be revealing and rewarding for interaction designers.

Program:

Lecture: Introductory overview of Qualitative Research Methods

Exercise: revisiting the data collection and data analysis of an earlier project

Study Case: 'Handling Interruptions at the Office'

Method Card assignment

Bibliography:

Brenda Laurel, Design Research, MIT Press, 2003.

Berg, B.L., Qualitative Research Methods for Social Sciences, 2nd edition, 1995.

In addition a selection of papers shall be given to PhD Candidates for use in the study case.

Contact: [email protected] Assessment:

- Report 'How would I do this study again and how would I

analyze the data'

- Presentation describing the particular qualitative method

chosen for study case and the analysis process, including

reflection on limitations and strengths of the method used.

- Presentation and report on process and result of study

case.

- Hand in an extended method card.



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ICE_TU/e_DB214: Sense your heart


Teacher(s): Prof.dr. S. Bambang Oetomo, Prof. dr.ir. L.M.G. Feijs, dr. W. Chen, dr. J. Hu, dr. ir. G.R. Langereis, G.J.A.

van den Boomen

Abstract: Some of you, who have followed biology classes at high school, may already know that the human body uses electricity to feel, move and think. Where this electricity originates from and what the possibilities of measuring bio-electricity are, will be discussed. Common applications are ECG (electric measurements on the heart) and EEG (measurements on the brain). These signals can be used to develop various applications, for example, health monitoring at hospital and home, sport applications, etc. This course aims to help PhD Candidates gain essential knowledge about the principles of physiological signal (e.g. ECG) measurements on the human body. PhD Candidates will learn how nerve cells interact and how the heart and the brain functions, understand the measurement principles of ECG, and gain skills to integrate the ECG frontend designed by ID.

Program:

Lecture “Monitoring heart function”

Lecture “Einthoven’s Triangle”

Lecture “ECG amplifier”

Workshop on “ECG measurement system”

Lecture “Heart rate variability”

Lecture “Filters for ECG”

Design an application involving ECG measurements

Bibliography:


Wilson FN, Johnston FD, Rosenbaum FF, Erlanger H, Kossmann CE, Hecht H, Cotrim N, Menezes de Olivieira R, Scarsi R, Barker PS (1944): The precordial electrocardiogram. Am. Heart J. 27: 19-85.

Lederer W.J in Medical Physiology by Boron and Boulpaep eds. 2003.

Park MK, Guntheroth WG: How to read pediatric ECGs, ed 3, St Louis, 1992, Mosby

W. Chen, S. T. Nguyen, R. Coops, S. Bambang Oetomo, L. Feijs, “Wireless Transmission Design for Health Monitoring at Neonatal Intensive Care Units”, IEEE the 2nd international symposium on applied sciences in biomedical and communication technologies (ISABEL 2009),ISBN 978-80-227-3216-1, Bratislava, Slovakia, Nov. 2009.

S. Bouwstra, W. Chen, L. Feijs, S. Bambang Oetomo, “Smart jacket design for neonatal monitoring with wearable sensors”, Proceedings of Body Sensor Networks (BSN), 2009, Berkeley, USA, pp. 162 - 167.


Assessment: - Development of a prototype of your own application which uses ECG. - A report on the system design, implementation (including



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schematics, diagrams, photo of the ECG circuit board and coding), results (including screenshots from oscilloscope, ECG signals shown on the interface, heart rate calculation, or HRV or filtering) and possible application idea. - Short presentation / demonstration of the application.



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ICE_UNIGE_01: Data Fusion and Bayesian Interaction Modeling for Cognitive Ambient Intelligence


Teacher(s): Carlo Regazzoni

Abstract: The course aims at providing PhD PhD Candidates knowledge on basic tools in data fusion domain together with more advanced theories for representing, modelling and automatically interpreting interactions occurring between users, between users and artificial systems etc. within a smart cognitive environment. A Bayesian approach is used as a main methodological track in the course. In particular this module aims at :

providing a common framework to identify and to describe methodologies and techniques for integrating

multisensorial contextual data by using Data Fusion paradigms and techniques

providing a common framework for defining behavioural artificial models for context based, adaptive and

personalized decision steps used by cognitive system to address and react with respect to different

contextual working situations.

Showing examples and applications of specific techniques within cognitive telecommunication systems by

means of description of two main case studies: cognitive radio and multisensor/multimodal cognitive

human-machine interfaces in smart spaces.

Program:

Data Fusion methodologies and techniques for integrating multisensorial contextual data Data Fusion

models: the JDL model and its extensions: signals, objects, situations, threats, processes and cognitive

refinement. Alignment, association, state extimations steps in data fusion levels. Alignment techniques:

Space, Time, Frequency calibration techniques in video and radio based systems. Multisensor data

association techniques: nearest neighbour, PDAF and JPDAF. State estimation techniques: from Kalman

filter to non linear and non Gaussian state estimation techniques (extended Kalman Filter, Unscented

Kalman Filter, Mean Shift, Particle Filters). Bayesian Networks for scene interpretation. Distributed Data

Fusion (DDF): models and techniques. Distributed decision theory.

Interaction Modeling. Bio-inspired behavioral cognitive artificial models for context based, adaptive and

personalized decision. Neural basis of consciousness: the Damasio model (core-self, protoself,

autobiographical memory and autobiographical self). The brain, memory and prediction. Adaptive and

UNIGE Courses 2012-2013



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personalized embodied decision models for analyzing situations and driving actions and re-actions within

cognitive systems. Decision space representation. Autobiographical memories and their representation and

estimation through Bayesian learning techniques.

Applications and case studies: Cognitive radio: Behavioral models for interactions between base stations

and mobile terminals. Cognitive safety and physical security systems (smart patrolling in cooperative

environments, preventive automotive vehicles, smart buidings, etc.)

Bibliography:

Course slides will be provided and made available at www.icephd.org

Further reading:

David L. Hall, James Llinas, “Handbook of Multisensor Data Fusion”, CRC Press, 2001;

Y.Bar-Shalom, W.D.Blair, “Multitarget-Multisensor Tracking: Applications and Advances”, second edition,

Artech House, 2000;

Pramod Varshney, “Distributed Dtection and Data Fusion”, Springer, 1997

Joseph Mitola III, “Aware, Adaptive and Cognitive Radio: The Engineering Foundations of Radio XML”

Wiley-Interscience, 2006

Anthonio Damasio, “The feeling of what happens: Body and Emotion in the Making of Consciousness”

(1999)”, Harcourt Brace & Company,


http://www.icephd.org/

http://www.amazon.com/exec/obidos/ISBN=0151003696/thecalvinbooksheA/

http://www.amazon.com/exec/obidos/ISBN=0151003696/thecalvinbooksheA/



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ICE_UNIGE_02: Theory and Practice of Learning from Data


Teacher(s): Davide Anguita

Abstract: This course aims at unifying the different views of model building from experimental data, as addressed by many research fields like Computational Intelligence, Pattern Recognition, Data Mining, Machine Learning and Statistics. The problem of building a model for understanding a physical phenomenon is traditionally approached by creating a reasonable mathematical representation and subsequently tuning and validating it through experimental data. Then, the obtained model can be used to better understand and make effective predictions about the events under observation. A more modern approach, instead, which has been named the “learning” approach, tries to automate this procedure, starting from the available data and building the optimal model, according to some quality measures, limiting or avoiding any user intervention. The course will present the theoretical background of learning from data as well as practical applications in several areas including industrial (e.g. automotive, robotics, etc.), scientific (e.g. cognitive science, medical- and bio-informatics) and economic (e.g. time series prediction, market analysis) fields.

Program:

Data, information and models: induction, deduction and transduction

Statistical inference: Bayesians vs. Frequentists

Simple models: Associations rules, Classification trees, Naïve Bayes

Classification and Regression

Nonlinear models: Neural Networks and Kernel methods

Model selection and error estimation

Clustering, Novelty Detection and Ranking

Bibliography:

Course notes will be provided and made available at www.icephd.org Further readings:

V.Cherkassky, F.Mulier, “Learning from Data: Concepts, Theory and Methods”, 1998.

T.Hastie, R.Tibshirani, J.Friedman, “The Elements of Statistical Learning: Data Mining, Inference and Prediction”, 2009.





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ICE_UniGe_03: Modelling, simulations and games

Hours: 20

Credits: 5

Teacher(s): Francesco Bellotti

Abstract: The course aims at providing PhD students with knowledge on principles, design theories and development tools for modelling and simulation. Applications in the Serious Gaming field is a major focus of the course.

Program: Modeling and simulation

Simulation examples

Concepts in Discrete-Event Simulation

List Processing

Simulation software

Statistical models in simulation

Queing models

Input modeling

Verification and calibration of simulation models

Serious Game Applications.

State of the art of Serious Games for educational domains such as: safety, crisis management, cultural heritage

o Targets, achievements and research perspectives

Requirements from target users and stakeholders o Gathering requirements and defining specifications

Pedagogical and psychological foundations

Architectures o Game engines and additional Artificial Intelligence modules o Programming languages o Interoperability and semantics

Contents o Authoring tools

Deployment and testing o Integration of SGs in educational contexts and in corporate training o Qualitative and quantitative methods for impact assessment

Bibliography:

Course notes will be provided and made available at www.icephd.org




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Further reading:

Banks, Carson II, Nelson, Nicol, Discrete-event system simulation, Pearson

Buckland, Programming Game AI by Example, Wordware

Akenine-Moeller, Haines, Hoffman, Real-Time Rendering, A K Peters

Zeigler, Praehofer, Kim, Theory of Modeling and Simulation, Academic Press

Nitschke , Professional XNA Game Programming: For Xbox 360 and Windows , John Wiley & Sons

Prensky, Teaching Digital Natives---Partnering for Real Learning, Corwin

Prensky, Digital Game-Based Learning, McGraw-Hill

Contact: [email protected]; [email protected] Assessment: Position paper: the candidate should write a short position paper on how the topics dealt with during the course can be of interest for his/her research and defend it through an oral presentation.

http://www.amazon.co.uk/Professional-XNA-Game-Programming-Windows/dp/0470126779/ref=sr_1_14?ie=UTF8&s=books&qid=1274341178&sr=1-14





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ICE_UNIGE_04: Green Netwoking


Teacher(s): Raffaele Bolla

Abstract: The concept of energy-efficient networking has begun to spread in the past few years, gaining increasing popularity. Besides the widespread sensitivity to ecological issues, such interest also stems from economic needs, since both energy costs and electrical requirements of Telecom Operators' and Internet Service Providers' infrastructures around the world show a continuously growing trend. In this respect, a common opinion among networking researchers is that the sole introduction of low consumption silicon technologies may not be enough to effectively curb energy requirements. Thus, for disruptively boosting the network energy efficiency, these hardware enhancements must be integrated with ad-hoc mechanisms that explicitly manage energy saving, by exploiting network-specific features. This tutorial aims at providing a twofold contribution to green networking. At first, we explore current perspectives in power consumption for next generation networks. Energy consumption trends from word-wide telecom operators will be analyzed, and decomposed in contributions representing different the network segments (e.g., wireless and wire-line, access and core networks, data centers, etc.). Then, the focus will be moved on wire-line networks, and, in particular, on single network devices with the aim of deeply understanding how different functionalities and device building blocks weight on the overall energy requirements. Secondly, we provide a detailed survey on emerging technologies, projects, and work-in-progress standards, which can be adopted in networks and related infrastructures in order to reduce their carbon footprint. From a general point of view, the largest part of undertaken approaches is founded on few base concepts, which have been generally inspired by energy-saving mechanisms and power management criteria that are already partially available in computing systems. These base concepts can be classified as follows: i) re-engineering, ii) dynamic adaptation, iii) sleeping/standby..

Program:

Modern Telecommunication Networks – Status and Trends o The Internet and the Future Internet o Reasons for going green o Carbon footprint o Does the fixed network matter (in terms of consumption and OPEX)?

Energy Consumption Breakdown in Network Segments and Network Devices

A Wrap-up on the Structure and Functionalities of Fixed Network Devices

Taxonomy of Green Networking Approaches

Potential Savings (or, is there room for network energy optimization?)

Dynamic Adaptation I – Link Protocols

Dynamic Adaptation II – Packet Processing Engine

Standby and Virtualization

Modeling and Optimization o An Introduction to Network Queuing Models o Traffic Models and Traffic Engineering



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o Queues with Vacations o Modeling Line Card Queues o Power-Performance Tradeoff o Device- and Network-Level Optimizations

Ongoing Projects

Research Challenges

Bibliography:

Course notes will be provided and made available at www.icephd.org Further reading:

R. Bolla, R. Bruschi, F. Davoli, F. Cucchietti, “Energy Efficiency in the Future Internet: A Survey of Existing Approaches and Trends in Energy-Aware Fixed Network Infrastructures,“ IEEE Communications Surveys and Tutorials (IEEE COMST), vol. 13, no. 2, pp. 223-244, 2nd Qr. 2011 .

R. Bolla, R. Bruschi, K. Christensen, F. Cucchietti, F. Davoli, and S. Singh, “The Potential Impact of Green Technologies in Next-Generation Wireline Networks – Is There Room for Energy Saving Optimization?,” IEEE Communication Magazine (IEEE COMMAG), Special Topic in “Green Communications,” to appear.

R. Bolla, R. Bruschi, A. Carrega, F. Davoli, “An Analytical Model for Designing and Controlling New-Generation Green Devices,” Proc. 3rd IEEE Workshop on Green Communications (GreenCom), co-located with GLOBECOM 2010, Miami, FL, USA. Winner of the Best Paper Award.

R. Bolla, R. Bruschi, A. Cianfrani, M. Listanti, “Introducing Standby Capabilities into Next-generation Network Devices,” Proc. ACM SIGCOMM Workshop on Programmable Routers for Extensible Services of Tomorrow (ACM SIGCOMM PRESTO), Philadelphia, PA, USA, Nov. 2010.

R. Bolla, R. Bruschi, A. Carrega, F. Davoli, "Theoretical and technological limitations of power scaling in network devices", Proc. Australasian Telecommunication Networks and Applications Conf. 2010 (ATNAC 2010), Auckland, New Zealand, Nov. 2010.

R. Bolla, R. Bruschi, A. Ranieri “Green Support for PC-based Software Router: Performance Evaluation and Modeling,” Proc. 2009 IEEE International Conference on Communications (IEEE ICC 2009), Dresden, Germany, June 2009. Winner of the Best Paper Award.

R. Bolla, R. Bruschi and F. Davoli, “Energy-aware Performance Optimization for Next Generation Green Network Equipment”, Proc. ACM SIGCOMM 2009 Workshop on Programmable Routers for Extensible Services of Tomorrow (ACM PRESTO 2009), Barcelona, Spain, Aug. 2009, pp. 49-54.

R. Bolla, R. Bruschi, F. Davoli, “Energy-aware Resource Adaptation for Next Generation Network Equipment,” Proc. 2009 IEEE Global Communications Conference (IEEE GLOBECOM 2009), Honolulu, Hawaii, USA, Dec. 2009.

R. Bolla, R. Bruschi, A. Carrega, F. Davoli, “Green Networking Technologies and the Art of Trading-off,” Proc. INFOCOM 2011 Workshop on Green Communications and Networking, Shanghai, China, April 2011.

R. Bolla, R. Bruschi, A. Cianfrani, M. Listanti, “Putting Backbone Networks to Sleep,” IEEE Network Magazine, Special Issue on “Green Networking”, vol. 25, no. 2, pp. 26-31, March/April 2011.






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ICE_UNIKLU_01: Mobile and Wireless Communications


Teacher(s): Prof. Christian Bettstetter

Abstract: The lectures Mobile and Wireless Communications and Networking give a bottom-up introduction to the area of mobile and wireless communication systems. The main goal is to give a fundamental understanding of the principles behind wireless transmission and networking. Current technologies, such as UMTS and IEEE 802.11, are used as examples to explain these principles. Moreover, a whole chapter is dedicated to ad hoc networks. The lectures are complemented by group projects, whose results are discussed in class. A tutorial course with exercises is offered for the 1st lecture.

Program:

Introduction and Overview (2 hours). History of wireless communications; Different kinds of mobility; Overview and classification of current wireless technologies; Key challenges in mobile and wireless systems; Transmission chain.

Antennas (2 hours). Antenna types; How are radio waves generated? Energy and power aspects of radio waves; Directivity and gain; How much power is received at a certain distance?

Radio Propagation (7 hours). Path loss and shadowing (propagation in free space, generalized path loss models, shadow fading); Multipath propagation (small-scale fading models, time spread, frequency spread, time-variant behavior; frequency-variant behavior, relationships); Overview of fading mitigation techniques (diversity schemes, equalization, spread-spectrum communications, error control and interleaving, multicarrier modulation); Group project "Path loss and shadowing".

Coding, Modulation, and Duplexing (9 hours). o Representation of signals. Conversion from analog to digital. o Channel coding (overview, block coding, convolutional coding, coding gain, channel coding in

practice). o Digital modulation (overview, linear modulation, coherent demodulation, modulation in practice,

spead spectrum modulation). o Duplexing. o Group project "Convolutional coding".

Multiple Access and Cellular Concept (2.5 hours). Ideas behind TDMA, FDMA, CDMA, SDMA. Cellular concept and channel reuse. Group project "Orthogonal Frequency Division Multiplexing (OFDM)"

Bibliography:


M. Schwartz: Mobile Wireless Communications, Cambridge University Press, 2005

A. Goldsmith: Wireless Communications, Cambridge University Press, 2005



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T. S. Rappaport: Wireless Communications, Prentice Hall, 2001

A. F. Molisch: Wireless Communications, Wiley-IEEE Press, 2005


ICE_UNIKLU_02: Pervasive Computing


Teacher(s): Prof. Bernhard Rinner

Abstract: This lecture focuses on the fundamentals of pervasive computing. Pervasive computing integrates computation into the environment, rather than having computers which are distinct objects. Computation is embedded into the environment and everyday objects and would enable people to interact with information-processing devices more naturally and casually than they currently do, and in ways that suit whatever location or context they find themselves in. Most topics will be presented by the lecturer. Depending on the number of participants PhD Candidates may prepare and present special topics such as applications and case studies of pervasive computing.

Program:

Introduction

Spontaneous Networking

Localization

Identification

Context-Aware Computing

Sensor Networks

Wearable Computing

Middleware Systems

Applications

Bibliography:

Course notes https://elearning.uni-klu.ac.at/moodle/course/view.php?id=3728

Further reading:

Stefan Poslad. Ubiquitous Computing. Wiley 2009

Hansmann, Merk, Nicklous, Stober. Pervasive Computing. Springer 2003

Adelstein, Gupta, Richard, Schwiebert. Fundamentals of Mobile and Pervasive Computing. McGraw-Hill. 2005


https://elearning.uni-klu.ac.at/moodle/course/view.php?id=3728



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ICE_UNIKLU_03: Mobile and Wireless Networks


Teacher(s): Prof. Christian Bettstetter

Abstract: The lectures Mobile and Wireless Communications and Networking give a bottom-up introduction to the area of mobile and wireless communication systems. The main goal is to give a fundamental understanding of the principles behind wireless transmission and networking. Current technologies, such as UMTS and IEEE 802.11, are used as examples to explain these principles. Moreover, a whole chapter is dedicated to ad hoc networks. The lectures are complemented by group projects, whose results are discussed in class. A tutorial course with exercises is offered for the 1st lecture.

Program:

Medium Access Control (MAC) Protocols (3 hours). ALOHA. Slotted ALOHA. CSMA. CSMA/CA. Performance studies

Wireless LAN 802.11 (1 hour).

Network Architecture and Mobility Protocols (7 hours). o Architecture of cellular networks (General architecture; system components in GSM, UMTS, and

LTE). o Mobility in cellular networks (Addressing and location updating; Routing to mobile users; Roaming

and handover). o Mobility in the Internet (Addressing and mobility problem; Autoconfiguration; Device mobility with

Mobile IP; Service mobility). o Group project "Internet mobility protocols".

Security in Mobile Networks (2 hours). Basics. Security in UMTS.

Ad Hoc Networks (8 hours). Introduction and Applications. Routing. Relaying. Connectivity and Capacity.

Economic, Health, and Social Aspects (2 hours)

Bibliography:


M. Schwartz: Mobile Wireless Communications, Cambridge University Press, 2005

J. Eberspächer, H.-J. Vögel, C. Bettstetter, C. Hartmann: GSM: Architecture, Protocols, and Services, 3rd ed., Wiley, 2008

B. H. Walke: Mobile Radio Networks, 2nd ed., Wiley, 2002

B. H. Walke, P. Seidenberg, A.-P. Althoff: UMTS: The Fundamentals, Wiley, 2003

H. Kaaranen, A. Ahtiainen, L. Laitinen, S. Naghian, V. Niemi: UMTS Networks: Architecture, Mobility, and Services, 2nd ed., Wiley, 2005

E. Dahlman, S. Parkvall, J. Sköld, P. Beming: 3G Evolution: HSPA and LTE for Mobile Broadband, 2nd edition, Wiley, 2008

R. S. Koodli and C. E. Perkins: Mobile Inter-Networking with IPv6: Concepts, Principles, and Practices, Wiley, 2007

C. E. Perkins (Ed.): Ad Hoc Networking, Addison-Wesley Longman, 2001



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ICE_UNIKLU_04: Sensor Networks


Teacher(s): Prof. Bernhard Rinner

Abstract: Due to the advances in electronics and (wireless) communication, the development of networks of low-cost, low-power, multi-functional sensors has received increasing attention. These sensor networks are a new type of networked, embedded computing systems and are expected to become a key technology for many pervasive computing applications. This lecture covers the fundamental concepts of sensor networks, including architectures, various networking aspects, power-awareness and sensor fusion. The lecture is complemented by a lab course where PhD Candidates can get hands-on experience in developing sensor network applications.

Program:

Introduction

Sensor Technology

Networking Aspects (Media Access, Routing, Data dissemination)

Power- and Energy-Awareness

Synchronization

Sensor Fusion

Applications and Case Studies

Bibliography:

Course notes https://elearning.uni-klu.ac.at/moodle/course/view.php?id=4531

Further reading:

F. Zhao, L. Guibas. Wireless Sensor Networks. Morgan Kaufmann 2004

Dressler. Self-Organization in Sensor and Actor Networks. Wiley 2007

Stojmenovic. Handbook of Sensor Networks. Wiley 2005

M. Ilyas, I. Mahgoub. Handbook of Sensor Networks: Compact Wireless and Wired Sensing Systems. CRC Press 2005


Assessment:

https://elearning.uni-klu.ac.at/moodle/course/view.php?id=4531



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ICE_UNIKLU_05: Embedded Communications


Teacher(s): Prof. Mario Huemer

Abstract: This lecture deals with the architecture of modern wireless communication systems. All main functional blocks of a mobile phone platform including the baseband processor and the RF (radio frequency) transceiver are addressed. Different board level and chip level architectures will be presented. In the "Digital Baseband Transceiver" chapter important baseband algorithms (equalization, channel estimation, OFDM modulation technique, MIMO signal processing,…), possible implementation options, and corresponding complexity considerations are discussed. The "Analog Transceiver" chapter deals with the analog signal processing tasks of a wireless device. Modern transmitter and receiver architectures are discussed, and future trends on the way to Software Defined Radio (SDR) architectures are presented. In parallel a practical course will be offered (optional). Systems and algorithms presented in the lecture will be simulated and tested in the MATLAB/SIMULINK environment.

Program:

Review of Communications Engineering Basics

The Mobile Radio Channel

Abstract Hardware View on a Mobile Phone Terminal Platform (Functional Blocks, Partitioning, Technology, Power Consideration)

The Digital Baseband Transceiver (Equalization, Channel Estimation, OFDM versus Single Carrier Transmission, MIMO Concepts)

The Analog Transceiver (Receiver Architectures, Transmitter Architectures, Future Trends)

Bibliography:


Nevio Benvenuto, Giovanni Cherubini, Algorithms for Communications Systems, John Wiley & Sons Ltd., 2002.

Karl Dirk Kammeyer, Nachrichtenübertragung, Vieweg & Teubner Verlag, Wiesbaden 2008.

Behzad Razavi, RF Microelectronics, Prentice Hall Inc., Upper Saddle River, 1998.




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ICE_UNIKLU_06: Interactive Systems


Teacher(s): Prof. Martin Hitz

Abstract: The lecture provides an introduction into the field of Human-Computer-Interaction (HCI). PhD Candidates will learn the essential characteristics of human-machine communication processes (including perceptive and cognitive issues), know how to design usable interfaces (especially GUIs), understand the principle architecture of modern GUIs, be able to implement a GUI, and know the principles of usability engineering processes.

Program:

Introduction (Interaction with everyday things, interaction models, history of HCI)

Interaction styles

Classical GUIs

Interface Modeling

Perceptive and cognitive issues of interface design

Usability

Usability Engineering

Bibliography:

Dix et al., Human-Computer Interaction, Prentice-Hall, 2003.

Shneiderman & Plaisant, Designing the User Interface, 4th edition, Addison-Wesley, 2004.

Raskin, The Humane Interface, Addison-Wesley, 2000.

Norman, The Psychology of Everyday Things, BasicBooks, 1988


Assessment:



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ICE_UNIKLU_07: Signal Processing Architectures



Abstract: This lecture deals with signal processing algorithms and with appropriate implementation architectures that focus on embedded applications. As a consequence low power consumption and low chip area are of great importance for the regarded architectures. The course starts with a short repetition of important signal processing theory and algorithms (FFT, digital filters). In the following the focus lies on implementation oriented issues like fixed point effects and architectural options for various important signal processing algorithms and applications. In parallel a lab will be offered (optional), which will cover the following:

MATLAB part: the effects and architectures covered in the lecture are in-depth studied with the help of MATLAB/SIMULINK simulations

Review of VHDL programming

Implementation of signal processing architectures on FPGAs Program:

Introduction & review of digital signal processing basics

DSP arithmetic

DSP hardware: DSPs, FPGAs and ASICs

CORDIC-algorithm: theory, architectures and applications

Fixed point effects in digital filtering

Multirate signal processing (interpolation, decimation, filter banks): theory and low power - low area HW-architectures

Low cost digital filters

Sigma-delta modulation and oversampling ADCs

Numerically controlled oscillators

Bibliography:


Peter Pirsch, Architectures for Digital Signal Processing, John Wiley & Sons Ltd., 2001.

Uwe Meyer-Baese, Digital Signal Processing with Field Programmable Gate Arrays, Springer Berlin Heidelberg New York, 2007.


Assessment:



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ICE_UNIKLU_08: Adaptive and Statistical Signal Processing



Abstract: Statistical and adaptive signal processing algorithms can be found at the heart of many electronic systems designed to transmit, receive, store or extract information. These systems include radar, sonar, speech, image analysis, biomedicine, communications, control… and many more. This lecture gives an introduction to the most prominent concepts. We will adopt the theory to a large number of applications from the engineering world (mainly from communications engineering and radar). Algorithms presented in the theory part will be simulated and tested in the MATLAB/SIMULINK environment.

Program:

Random Variables and Vectors (short review)

Classical Estimation in Signal Processing (Bounds, Linear Models, ML, BLUE, LS)

Bayesian Estimation in Signal Processing (MMSE, MAP, LMMSE)

Discrete Time Stochastic Processes

Wiener Filters, LS Filters and their Applications

Adaptive Filters (LMS, RLS Algorithms) and their Applications

Bibliography:


Steven M. Kay, Fundamentals of Statistical Signal Processing: Estimation Theory, Prentice Hall PTR, Upper Saddle River, New Jersey, 1993.

Dimitris G. Manolakis, Vinay K. Ingle, Stephen M. Kogon, Statistical and Adaptive Signal Processing, Artech House, Norwood, 2005.

Paulo S. R. Diniz, Adaptive Filtering – Algorithms and Practical Implementation, Kluwer Academic Publishers, Boston /Dordrecht / London, 1997.


Assessment:



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ICE_UNIKLU_09: Novel Topics in Digital Signal Processing for PhD Students

Hours: 30

Credits: 5

Teacher(s): Mario Huemer

Abstract:

This seminar addresses PhD students that work in the field of signal processing. Novel topics appearing in the field of signal processing shall be studied, and presentations shall be given in front of an audience. In-depth discussions shall help to penetrate the topic.

Program:

E-Learning

Cooperation: e.g. online discussions, wikis, video conferences or peer-reviews etc.

Bibliography:

Scientific publications


Assessment: Presentation



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ICE_UNIKLU_10: Doctoral Seminar NES


Teacher(s): Professors Bettstetter, Huemer, Rinner, Hitz

Abstract:

In this seminar PhD PhD Candidates give presentations on their own research work. PhD Candidates learn to present, discuss and argue about a research topic within the scientific setting.

Program:

Individual presentations and discussions within a small group of professors and PhD Candidates

Bibliography:

Course notes


Assessment:



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ICE_UNIKLU_11: Research Paradigms in Computer Science


Teacher(s): Prof. Johann Eder

Abstract:

This lecture is devoted to the different research paradigms used in computer science research.

Program:

Topics:

Research Proposals

Hypothesis and Research Goals

Research Methods

Bibliography:

Further reading:

Martin S. Olivier: Information Technology Research, Van SCheik Publishers, 2007


Assessment:



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UPC Courses 2012-2013

ICE_UPC_1: Artificial Intelligence applied to Robotics


Teacher(s): Andreu Català, Cecilio Angulo, Francisco J. Ruiz

Abstract: The objective of the course is to familiarize to the student with the structures of data and the techniques of developed reasoning and learning in the scope of the Artificial intelligence that have found application in Robotics.

Program: 1. OBJECT/ACTION/REPRESENTATIÓN OF THE SPACE 1.1 Symbolic versus subsymbolic 1.2 Graphs and search 1.3 Sensorimotor Functions 1.4 Symbol grounding/anchoring 2. CONTROL ARCHITECTURES 2.1 Subsumption 2.2 Agents 3. ADAPTABILITY - NEURONAL NETWORKS 3.1 Perceptual grouping 3.2 Sensorimotor functions adaptation 3.3 Neurophysiologics models of control for the motor 3.4 Genetic algorithms and evolutionary computation 4. DECISION SYSTEMS AND PLANNING 4.1 Fuzzy control 4.2 Case based reasoning 4.3 Bayesian networks 4.4 Planners based on logics 5. LEARNING IN ROBOTS 5.1 Reinforcement learning: exploration 5.2 Interaction human - robot 5.3 Imitation and learning by demonstration 6. ADVANCED SUBJECTS 6.1 Ambient intelligence 6.2 Ubiquitous and contextual computing

Bibliography: S. Russel and P. Norving, “Artificial Intelligence: A modern Approach” (second edition), Prentice Hall, 2003.



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B. Martín del Brío, A. Sanz. Redes Neuronales y Sistemas Borrosos. RA-MA Ed. (2001) Copeland, J. (1996). "Inteligencia Artificial". Alianza Editorial. Brown, M., Harris, C.J. (1994). "Neurofuzzy Adaptive Modelling and Control". Prentice Hall. Kecman, V. (2001). "Learning and Soft Computing: Support Vector Machines, Neural Networks and Fuzzy Logic Models". MIT Press.

Contact: Andreu Català [email protected] Cecilio Angulo [email protected] [email protected]

Assessment: The evaluation fundamentally is based on the degree and level of participation of the student throughout the course in the sessions of class (contribution to the debate of the subjects, exposition and resolution of questions...), and in the development, conclusions and presentation of its works of practices. Complementarily, and based on the development of the course, partial tests of one or several of the parts are made that the subject consists. These tests are made up fundamentally of conceptual questions that they require short answers.



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ICE_UPC_2: Artificial intelligence Applied to the Control and the Identification of Systems


Teacher(s): Cecilio Angulo, Francisco J. Ruiz, Andreu Català

Abstract: To know which are the branches of the Artificial intelligence that can contribute to the process control as well as to its modeling, understand the most excellent techniques of these branches and their foundations, and to be able to apply them in tasks of control and identification of systems.

Program: 1. INTRODUCTION TO THE ARTIFICIAL INTELLIGENCE 1.1 Definitions 1.2 Bases of the IA. Intelligent action 1.3 Representation of the Knowledge. Traditional Methods. 1.4 Search and Optimization 2. REPRESENTATION OF THE KNOWLEDGE. NEURONAL NETWORKS 2.1 Reasoning and Inference 2.2 Foundations of neuronal networks 2.3 Perceptron – Delta Rule 2.4 MultiLayer Perceptron – Backpropagation 2.5 New techniques of machine learning: RBF networks and Support Vector Machines (SVM) 3. SYSTEMS OF FUZZY LOGIC AND APPLICATIONS 3.1 Introduction 3.2 Basic elements of a fuzzy inference system 3.3 Examples of operation 3.4 Type of fuzzy logic controller 3.5 Neuro-fuzzy schemes 4. COMPLEX SYSTEMS. EVOLUTIONARY ALGORITHMS 4.1 Introduction – motivation 4.2 General scheme of an evolutionary algorithm 4.3 Evolutionary and neuro-evolutionary schemes 5. APPLICATIONS OF THE IA TO THE INTELLIGENT CONTROL OF PROCESSES 5.1 Application to the identification and the modeling of nonlinear systems 5.2 Application to the control and the supervision 6. AGENTS AND MULTIAGENT SYSTEMS IN CONTROL 6.1 Definitions 6.2 Physical agents and holonics systems



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Bibliography: Hastie, T., Tibshirani, R., Friedman, J. (2001). “The Elements of Statistical Learning. Data Mining, Inference and Prediction”. Springer Nørgaard, M., Ravn, O., Poulsen, N.K., Hansen, L.K.. (2000) “Neural Networks for Modelling and Control of Dynamic Systems: A Practitioner’s Handbook”, Springer-Verlag, Brown, M., Harris, C.J. (1994). "Neurofuzzy Adaptive Modelling and Control". Prentice Hall. Passino, K. (2004). “Biomimicry for Optimization, Control and Automation”. Springer Kecman, V. (2001). "Learning and Soft Computing: Support Vector Machines, Neural Networks and Fuzzy Logic Models". MIT Press. Dreyfus, G. (2005). “Neural Networks: Methodology and Applications” Springer Verlag.

Contact: Cecilio Angulo [email protected] [email protected] Andreu Català [email protected]

Assessment: The evaluation fundamentally is based on the degree and level of participation of the student throughout the course in the sessions of class (contribution to the debate of the subjects, exposition and resolution of questions...), and in the development, conclusions and presentation of its works of practices. Complementarily, and based on the development of the course, partial tests of one or several of the parts are made that the subject consists. These tests are made up fundamentally of conceptual questions that they require short answers.



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ICE_UPC_3: Information Skills in Information Technologies

Hours: 30 Credits: 1,2

Teacher(s): Conrado Martínez

Abstract: This subject provides the procedures, concepts and necessary values to solve problems that imply the research, selection, organization, analysis and communication of information. This means, to learn to manage documental information, to transform it into knowledge, and to communicate it.

Program:

1. The value of the information in the process of research Bibliotècnica, the digital library of UPC. Information in the 21st century 2. Typology of scientific and technical documents The Scientific Method. Basic documents: handbook, course book, magazines, etc. Advanced documents: report, patent, dissertation, journals, conference proceedings, etc. 3. The catalog of the UPC libraries Searching by author, subject, title, keyword. Journal searching, monograph searching, etc. 4. Elaboration of a search strategy Information needs. Terminological findings: dictionaries, thesaurus… Noise and Silence in information retrieval. Boolean operators, proximity commands, truncation, ... 5. The scientific publication in Internet Open Access initiatives Pre-prints, post-prints, and dissertation repositories. 6. Databases of journal articles and conference papers Scope and search options. INSPEC, IEEEXplore, Safari TechBooks, Elsevier Science Direct, ACM Digital Library, CSIC ICYT. 7. Bibliographic reference managers Bibliographic citations, reference managers, location of specific documents 8. Searching information on the Internet



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Different approaches to Search Engines and Internet directories. Advanced search options: natural speaking, delimitators, Boolean operators, etc. The invisible web 9. Oral communication Recommendations to communicate orally the results of a research in an effective way. 10. Criteria for information evaluation and selection 11. Models to elaborate, to structure and to communicate the master thesis 12. What's on? Alert services. 13. Practices and resolution of doubts about the final work of the subject

Bibliography:

Beumala, Àngel; Aragüés, Montse; Roca, Marta HITI: [BRGF], 2007


The evaluation will consist of a final work, and 4 compulsory exercises which it will be necessary to give along the course. The punctuation of 4 compulsory exercises represents 40% of the final mark. The punctuation of the final work represents 60% of the final mark



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ICE_UPC_4: Advanced Techniques in Machine Learning


Teacher(s): Javier Béjar

Abstract:

Goals: This course is an extension of the methodologies learned from the previous machine learning courses. The main subjects treated in this course include unsupervised learning from different perspectives (machine learning, intelligent data analysis, knowledge discovery) and case based reasoning

Program: Part I – Unsupervised Learning 1. Unsupervised Learning and Data Mining 2. Elements of the process of unsupervised learning Data representation Data preprocessing and transformation Distance functions 3. Unsupervised learning ouside machine learning Cognitive Psicology Numerical Taxonomy Data analysis 4. Machine learning perspective Conceptual Clustering Concept formation Evaluation of unsupervised learning Sintatic Biasing, Semantic Biasing 5. Unsupervised Learning in Knowledge Discovery/Data Mining Clustering Algorithms for Knoledge Discovery Unsupervised Learning from patterns and structures Association Rules, Time Series, Trees, Graphs Part II – Case Based Learning and Reasoning 1. Introduction 2. CBR Foundations Basic cicle of reasoning Models for expertise representation 3. CBR Applications Academic application: CHEF, CASEY, JULIA, HYPO, PROTOS Practical application to complex domains OPENCASE: A domain independent CBR system



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4. Cases representation and organization Representation structures Library structures 5. CBR system phases Case retrieval Similarity evaluation Adaptation strategies and/or methods Learning 6. Reflective reasoning in CBR 7. Applications and tools for CBR developement Industrial applicacions Software tools 8. CBR evaluation 9. Advanced Research in CBR

Bibliography: # Course notes

Contact: [email protected] Assessment: A Position paper: the candidate should write a short position paper on how the topics dealt with during the course can be of interest for his/her research and defend it through an oral presentation.



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ICE_UPC_5: Artificial Intelligence Applications


Teacher(s): Ulises Cortés

Abstract:

The objective of this subject is to complement and broaden what students learn in the compulsory subject Artificial Intelligence and the optional subjects Learning and Natural Language Processing. To achieve this goal, the subject will be redesigned and updated every semester. To enhance students' receptivity of the subject matter, this subject has an eminently practical approach and gives students a set of problems that they must solve and implement. In recent years, the subject has placed emphasis on autonomous agents and their use in e-business. Given the importance of the practical component in this subject, it has an important weight of the students' final assessment. At the end of this subject, students will have a broader vision of the methods used in artificial intelligence and their applications in the real world

Program: 1. Artificial Intelligence perspectives Introduction to fields in which AI can be applied. 2. Introduction to intelligent agents What is an agent? Agents as basic building blocks. Agent types. agent-building architectures. 3. Ontologies What is an ontology? Methods for constructing ontologies. Description logics. Ontological languages. 4. Logic systems for Artificial Intelligence Reasoning for AI applications. Modal logics. Temporal logics. Reasoning under uncertainty. 5. Communication The need for communication between agents. Speech Act Theory. Languages for establishing communication between agents. 6. Advanced search algorithms Best search algorithms. Tabu Search, meta-heuristics. Genetic algorithms. 7. Planning Description of planning problems. Planning algorithms: Linear planning, with partial order, hierarchy. 8. Co-ordination, negotiation Need for co-ordination in multi-agent systems. Negotiation between agents.

Bibliography:

Stuart J. Russell and Peter Norvig Artificial intelligence : a modern approach- third edition, Prentice Hall, 2011.

Zbigniew Michalewicz, David B. Fogel How to solve it : modern heuristics, Springer,, 2004. Asunción Gómez-Pérez, Mariano Fernández-López, and Oscar Corcho Ontological engineering : with

examples from the areas of knowledge management, e-commerce and the semantic Web, Springer-Verlag, 2004.



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Ghallab Malik, Dana Nau, Paolo Traverso Automated planning : theory and practice, Elsevier/Morgan Kaufmann, 2004.

Weiming Shen, Douglas H. Norrie and Jean-Paul A. Barthès Multi-agent systems for concurrent intelligent design and manufacturing, Taylor & Francis, 2001


Assessment: Evaluation is based on a final exam and a part exam, grading of course assignments, and a grade for lab work. The final and part exams will test the theoretical knowledge and the methodology acquired by students during the course. The grade for course assignments will be based on submissions of small problems set during the course. Lab grades will be based on students" reports and lab practical work carried out throughout the course. At about half of the 4-moth term there will be an exemptive part exam, testing the first half of the course (exemptive only if the grade is 5 or more). The final exam will test both the first and the second part of the course. The first half is compulsory for those students who didn"t pass the part exam, and optional for the rest. The maximum of both grades (or the only one for the part exam) will stand as the grade for the first part. The final grade will be calculated as follows: GPar = part exam grade GEx1 = 1st half of the final exam grade GEx2 = 2nd half of the final exam grade Total Exams grade = [max(Npar, NEx1) + NEx2]/2 Final grade= Total Exams grade * 0.5 + Exercises grade * 0.2 + lab grade * 0.3.



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ICE_UPC_6: SEMINAR on Artificial Intelligence


Teacher(s): Miquel Sánchez Marré

Abstract: To introduce new and current research themes in Artificial Intelligence field.

Program:

Development of the theoretical content by the teachers. Each academic year the content could be changed. See the Course schedules section for detailed information of current content.

Bibliography:

Contact: [email protected] Assessment: Evaluation will normally be based in some assignments to the students

https://postgrau.upc.edu/ai/gimaster/courses/1042210-artificial-intelligence-seminar-upc-sem-upc/resolveuid/8f12533760dcc1d394f68deac77f3b41



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ICE PhD Courses - General Rules

This note contains some general rules to ensure that all the courses offered in the ICE PhD program follow high-quality standards and present to the PhD Candidates a comprehensive and harmonized view of the ICE PhD courses.

1. Language

Both the lectures and the teaching material are in English.

2. Syllabus and EMJD ICE General Regulation

The present syllabus refers and is subject to EMJD ICE Regulation, in particular to the annex document “Conditions for Obtaining the Joint/Double Doctorate”.

3. Didactical Material

Each course provides the didactical material (slides, lecture notes, software, etc.) in advance through the ICE web site and/or through the Professor(s) and/or through the Didactic Manager.

4. Assessment

Each course assesses the PhD Candidates through a final test, shortly after the completion of the lectures. The assessment is carried out by the same people who taught the course.

The assessment consists in one of the following:

Written exam: multiple choice test, free response test or exercises;

Position paper: the candidate should write a short position paper on how the topics dealt with during the course can be of interest for his/her research and defend it through a oral presentation.

The final outcome of the test is “passed”/”failed”.

5. ECTS Definition and assignation

European Credit Transfer and Accumulation System (ECTS) is a standard for comparing the study attainment and performance of PhD Candidates of higher education across the European Union and other collaborating European countries. For successfully completed studies, ECTS credits are awarded. One academic year

http://en.wikipedia.org/wiki/Higher_education

http://en.wikipedia.org/wiki/European_Union



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corresponds to 60 ECTS-credits that are equivalent to 1500–1800 hours of study in all countries irrespective of standard or qualification type and is used to facilitate transfer and progression throughout the Union.

The ECTS will be complemented by the European credit transfer system for vocational education and training (ECVET) which the ministers responsible for vocational training in 32 European countries agreed to develop in the Maastricht Communiqué of 14 December 2004.

ECTS also includes a standard ECTS grading scale, intended to be shown in addition to local (i.e. national) standard grades1.

According to the agreements settled during the Conference Call of the Management Board, that took place on 28 June 2010, the assignation of credits is based on the effort and the more advanced contents of the courses. The specific PhD Courses account for 8 ECTS; the MSc Courses or the soft skills assign 5 ECTS.

Conditions for Obtaining the ICE Joint/Double Doctorate

Joint Degree

The ICE Joint Doctorate is a three year international PhD programme with a total of 180 ECTS. The PhD Candidates must conduct the programme in at least two different institutions. The joint title shall only be awarded after the student has completed his/her studies at the selected institutions and has fulfilled the following minimum requirements:

(1) Successful application and nomination to enter the programme;

Details about the application and selection procedure are described in the “ICE Call for Application Model”.

(2) Study and perform research for at least 18 months at the primary institution and at least 12 months at the secondary institution;

The PhD Candidates will be jointly supervised by two supervisors—one at the primary institution and one at the secondary institution. A maximum period of 6 months can be spent at any institution participating in the ICE programme.

The student has to give a presentation about his research during her/his stay at the secondary institution.

(3) Pursued courses of a total of 20 ECTS;

The PhD Candidates must pursue courses of at least 10 ECTS from the ICE course modules of the primary institution and courses of at least 5 ECTS from the ICE course modules of the secondary institution. The lectures of the ICE course modules of all institutions are specified jointly by the PSC on a yearly basis.

1 For any further detailed information refer to http://en.wikipedia.org/wiki/European_Credit_Transfer_and_Accumulation_System.

http://en.wikipedia.org/wiki/ECTS_grading_scale

http://en.wikipedia.org/wiki/European_Credit_Transfer_and_Accumulation_System



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(4) Approved research skills of a total of 40 ECTS;

The PhD Candidates must demonstrate their research skills by an active participation at the ICE summer school and/or authorship of scientific papers. Each year an ICE summer school will be organized which accounts for the following credits.

- active participation at the ICE summer school: 10 ECTS

- presentation of the thesis contribution (preferably in third year): 10 ECTS

Active participation includes (i) providing a report about the scientific activities performed during the previous year, (ii) participating in the lectures and talks, and (iii) giving a presentation about the state of their own research. The PhD Candidates are required to participate in at least two ICE summer schools. The presentation of the thesis contribution is required.

The PhD Candidates can demonstrate their research skills by authorship of papers accepted in refereed journal or refereed conferences. One accepted scientific paper accounts for 5 ECTS; at most 2 papers can be taken into account.

(5) Supplementary skills of a total of 20 ECTS;

The following supplementary skills can get accounted for the PhD programme:

- Additional courses selected from any ICE course modules (max 10 ECTS)

- Soft-skills and non-technical courses (max 10 ECTS)

These courses must get approved by both supervisors at the primary and the secondary institution.

- International recognition of scientific contribution (max 10 ECTS)

An international recognition of a scientific contribution of the student such as a best paper award accounts for 5 ECTS. No credits can be accounted for an international recognition of a scientific paper, if credits for the same paper have already been taken into account under “approved research skills” (paragraph 4).

- External research collaboration (max 10 ECTS)

PhD Candidates can perform research at an external institution, i.e., the associated ICE partner institutions, during their PhD programme. An external research period of one month accounts for 5 ECTS. The external research collaboration has to be approved in advance by the supervisors at the primary and the secondary institutions.

(6) Approved dissertation (100 ECTS);

The dissertation must be approved by at least the supervisor at the primary institution and the supervisor at the secondary institution.

(7) PhD defence;

A PhD defence at the primary institution completes the PhD programme. The defence is performed in front of an international committee composed of members of at least the primary and the secondary institution. Members from other institutions of the ICE consortium may be part of the international committee as well.



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Double Degree

The ICE Double Doctorate is a three year international PhD programme with a total of 180 ECTS. The PhD Candidates

must conduct the programme in at least two different institutions. The double title shall only be awarded after the

student has completed his/her studies at the selected institutions and has fulfilled the following minimum

requirements:

(1) Successful application and nomination to enter the programme;

Details about the application and selection procedure are described in the annexed document “ICE Call for

Application Model”.

Study and perform research for at least: 18 months in the Primary Institution; 12 months in the Secundary

institution; 6 months to be spent according to Primary and Secundary Institution regulations, taking into

account candidates PhD track activities. The PhD Candidates will be jointly supervised by two supervisors—

one at the primary institution and one at the secondary institution.

The student has to give a presentation about his research during her/his stay at the secondary institution.

(2) Pursued courses of a total of 20 ECTS;

The PhD Candidates must pursue courses of at least 10 ECTS from the ICE course modules of the primary

institution and courses of at least 5 ECTS from the ICE course modules of the secondary institution. The

lectures of the ICE course modules of all institutions are specified jointly by the PSC on a yearly basis.

(3) Approved research skills of a total of 40 ECTS;

The PhD Candidates must demonstrate their research skills by an active participation at the ICE summer

school and/or authorship of scientific papers. Each year an ICE summer school will be organized which

accounts for the following credits.

- active participation at the ICE summer school: 10 ECTS

- presentation of the thesis contribution (preferably in third year): 10 ECTS

Active participation includes (i) providing a report about the scientific activities performed during the

previous year, (ii) participating in the lectures and talks, and (iii) giving a presentation about the state of their

own research. The PhD Candidates are required to participate in at least two ICE summer schools. The

presentation of the thesis contribution is required.

The PhD Candidates can demonstrate their research skills by authorship of papers accepted in refereed

journal or refereed conferences. One accepted scientific paper accounts for 5 ECTS; at most 2 papers can be

taken into account.

(4) Supplementary skills of a total of 20 ECTS;



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The following supplementary skills can get accounted for the PhD programme:

- Additional courses selected from any ICE course modules (max 10 ECTS)

- Soft-skills and non-technical courses (max 10 ECTS)

These courses must get approved by both supervisors at the primary and the secondary institution.

- International recognition of scientific contribution (max 10 ECTS)

An international recognition of a scientific contribution of the student such as a best paper award

accounts for 5 ECTS. No credits can be accounted for an international recognition of a scientific

paper, if credits for the same paper have already been taken into account under “approved research

skills” (paragraph 4).

- External research collaboration (max 10 ECTS)

PhD Candidates can perform research at an external institution, i.e., the associated ICE partner

institutions, during their PhD programme. An external research period of one month accounts for 5

ECTS. The external research collaboration has to be approved in advance by the supervisors at the

primary and the secondary institutions.

(5) Approved dissertation (100 ECTS);

The dissertation must be approved by at least the supervisor at the primary institution and the supervisor at

the secondary institution.

(6) PhD defence;

A defense at the primary institution;

A defense at the secondary institution as well;

Both defenses are performed in front of an international committee composed by at least one member from the hosting institution plus at least one member from another institution from the ICE consortium that may require their presence. Members from other institutions of the ICE consortium may be part of the international committee as well.

Credits Assignment (Example)

CRITERION ACTIVITY TYPE OF EXAM ECTS

Knowledge Following at least four teaching modules Oral presentation of a research topic related to the courses

evaluated by the lecturer 20

Skills & Capabilities Report and official recognition of

research performance. Active participation at the ICE summer school and/or authorship of

scientific papers; report about the scientific activities performed. 40

Supplementary Skills/knowledge

Achievement of complementary skills and expertise.

Additional courses, Soft-skills and non-technical courses, International recognition of scientific contribution, External research

collaboration. 20

Outcome Approved Final Dissertation

The dissertation must be approved by at least two international supervisors (preferably from the primary and the secondary

institution. 100

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