In addition to the regular program, participants to WOSSPA 2013 will have the opportunity to attend, for free, six tutorials:

Tutorial 1

T1: Applications of Information Theory to Secure Wireless Communications

Professors Jean-Yves Chouinard and Abdellah Berdai, (Université Laval, Québec-Canada, Canada)

 Topic overview:

Wireless channels and networks are particularly vulnerable to attacks and treats. This tutorial introduces information-theoretical channel models and presents methods to ensure the integrity of transmitted information and prevent information leaks.

Target audience:

The intended audience for this tutorial includes students, researchers, and engineers, involved or with an interest in secure wireless systems.

Content details:

Since its introduction by Shannon,the ubiquitous principles information theory have been successfully applied to large variety of scientific domains including communications, error control coding, cryptography, source coding, mathematics, physics, biology, etc. In this tutorial, applications of information theory to practical problems of secrecy and authentication over wireless channels and networks will be presented.First, the context of secure and robust communications over physical wireless channels will be exposed. This will be followed by an overviewof the relevant principles ofinformation theory leading to thedefinition of information-theoretic measures in wiretap channels. Concepts of secret capacity and rate-equivocation will be presented first for simple two-way Gaussian channels and then extended to multi-user channels. Encryption and authentication methods will be revisited in the context of wireless channels along with error control coding schemes, to ensure the legitimacy and protect the confidentiality of information transmitted over channels vulnerable to passive and active threats. Protection against jamming and secure network coding will be also addressed in this tutorial.

 Presenters' expertise:

Jean-Yves Chouinard is a professor with the Department of Electrical and Computer Engineering at Laval University, Canada. He is the author/co-author of close to200 journal, conference papers and technical reports. He is co-recipient of the 1999 Neal Shepherd Best Propagation Paper Award (IEEE Vehicular Society) and the 2004 Signal Processing Best Paper Award (European Journal of Signal Processing). He is co-author of book chapters on software reconfigurable MIMO wireless communication systems and on OFDM-based mobile broadcasting and co-editor of a book on information theory. He is an Associate Editor for the IEEE Transactions on Vehicular Technology and for the IEEE Transactions on Broadcasting. He has served as a Technical Program Co-chair for the 2012 Vehicular Technology Conference (VTC’2012 Fall), Publications Chair for the IEEE International Symposium on Information Theory (ISIT'2008) and General Co-chair for the Canadian Workshop on Information Theory (CWIT'95). His research interests are signal processing, communications theory and applications, broadband wireless systems, and secure communication networks.

Abdellah Berdai received the Ph.D. degrees from Laval University, Quebec city, in 2011. Since 2012 is professor at the Department of Electrical and Computer Engineering at EMPL, Alger, Algeria.  


The tutorial will be presented as a series of PowerPoint and/or Beamer-style LaTeX slides.


Tutorial 2

Date: Sunday, 12th May 2013

Time: 09:00 – 12:15

Medical image analysis: principles and methods

A/Professor Abdelkrim Seghouane, (University of Melbourne, Australia) This e-mail address is being protected from spambots. You need JavaScript enabled to view it "> This e-mail address is being protected from spambots. You need JavaScript enabled to view it


Topic overview


The tutorial will be on Medical image analysis: principles and methods

Medical image analysis is an important and significant subject for the systems and signal processing community since the extraction of critical medical information involves many different techniques from the signal processing field among them one can cite: multidimensional signal processing, pattern recognition, time series and system modelling, digital filter and machine learning.

Target audience:


This tutorial is intended to both the signal processing and medical community. We require basic physic and mathematical knowledge from the audience.

The participant will learn about the physical principles underlying medical imaging, the modelling problems encountered and the signal processing methods used to extract information and reconstruct the images.

A wide audience is expected as this talk target both people from the signal processing field as well as people from the medical field.

Content details: 


The increasing use of different imaging modalities and modern imaging methods in clinical medicine has gradually changed traditional medical diagnosis. Modern medical imaging has moved away from the production of qualitative static photographs of anatomy towards the generation and use of critical medical information extracted from digitized images and signals. This tutorial introduces the principles of medical imaging technologies such as X-ray, ultrasound, MRI and PET. It presents mathematical and statistical techniques used in the field of medical image analysis with a focus on computer implementation. Algorithms and strategies based on the use of various models to solve the following medical imaging problems: image enhancement (for improving the visibility of significant structures), image segmentation and pattern recognition (for localization and identification of target structures), image reconstruction (for three-dimensional image formation) and image registration (to determine the correspondence of multiple images of the same anatomical structure); will be studied.  

The tutorial is divided into two parts. In the first part we will present the principles underlying X-ray, PET and SPECT, NMR and MRI and ultrasound imaging. We will also discuss the problems encounter in each of these imaging modalities to extract the medical information.

The second part of the tutorial is devoted to the description of some methods used to resolves the problems of image enhancement, image segmentation and pattern recognition, image reconstruction and image registration.


As indicated above this tutorial is made of two oral presentations with a break in between. The presentation includes videos.

Presenter expertise:


Abd-krim Seghouane is a Senior Researcher in National ICT Australia (NICTA), Canberra Research Laboratory. He received a Ph.D. in Control and Signal Processing from Universite Paris sud in 2003. He has been with NICTA since 2004, prior to that he was a postdoctoral fellow at INRIA Rocquencourt. His main research interest is in statistical signal processing with application to biomedical engineering.

Selected Publications:

- J. Ong and A. K. Seghouane, “Polyp Detection in CT Colonography using Principal Curvature and Orientation Estimation of Geodesic Ring Neighbourhoods ”, IEEE Transactions on Image Processing, Vol. 20, pp. 1000-1010, 2011.

- A. K. Seghouane and A. Shah, “HRF Estimation in fMRI Data with an Unknown Drift Matrix by Iterative Minimization of the Kullback-Leibler Divergence ”, IEEE Transactions on Medical Imaging, Vol. 31, pp. 192-206, 2012.


Tutorial 3

Date: Sunday, 12th May 2013

Time: 09:00 – 12:15

Bayesian inference with hierarchical prior models for inverse problems in imaging systems

Professor Ali Mohammad-Djafari, (Orsay University, France)


Target audience

I think that all those who have heard about the Bayesian estimation will be interested to this topic. They will learn about the state of the art prior modeling and the corresponding Bayesian computation algorithms. I expect at least 40% of the participants.


Simple prior laws (Gaussian, Generalized Gaussian, Gauss-Markov and more general Markovian priors) are nowadays common in modeling and in their use in Bayesian inference methods. But, we need still more appropriate prior models which can account for the presence of the contours and homogeneous regions.

Recently, we proposed a family of hierarchical prior models, called Gauss-Markov-Potts, which seems to be more appropriate for many applications in Imaging systems such as X ray Computed Tomography (CT) or Microwave imaging in Non Destructive Testing (NDT). In this presentation, first I will present this family of prior models, then show how to use them in practical CT or other imaging systems and show some results in 2D and 3D.


- A Mohammad-Djafari (2008) Gauss-Markov-Potts Priors for Images in Computer Tomography Resulting to Joint Optimal Reconstruction and segmentation International Journal of Tomography & Statistics 11: W09. 76-92

- Ali Mohammad-Djafari (2010) Inverse Problems in Vision and 3D Tomography. Edited by: Ali Mohammad-Djafari. ISTE-WILEY isbn:9781848211728

- Ali Mohammad-Djafari (2009) Problèmes inverses en imagerie et en vision en deux volumes inséparables. Edited by:Ali Mohammad-Djafari. Traité Signal et Image, IC2  isbn:2-7462-0348-0

- A Mohammad-Djafari (2008) Super-Resolution : A short review, a new method based on hidden Markov modeling of HR image and future challenges. The Computer Journal doi:10,1093/comjnl/bxn005:

- Hacheme Ayasso, Ali Mohammad-Djafari (2009) Joint image restoration and segmentation using Gauss-Markov-Potts prior models and variational Bayesian computation, IEEE Trans. on Image Processing 1297-1300


Ali Mohammad-Djafari received the B.Sc. degree in electrical engineering from Polytechnique of Teheran, in 1975, the diploma degree (M.Sc.)  from Ecole Supérieure d'Electricit(SUPELEC), Gif  sur Yvette, France, in 1977 and the "Docteur-Ingénieur"   (Ph.D.) degree and "Doctorat d'Etat" in Physics, from the University of Paris Sud 11 (UPS), Orsay, France, respectively in 1981 and 1987. He was Associate Professor at UPS for two years (1981-1983).

Since1984, he has a permanent position at "Centre National de la Recherche Scientifique (CNRS)" and works at "Laboratoire des signaux et systèmes (L2S)" at SUPELEC. He was a visiting Associate Professor at University of Notre Dame, Indiana, USA during 1997-1998. From 1998 to 2002, he has been at the head of Signal and Image Processing division at this laboratory.

Presently, he is "Directeur de recherche" and his main scientific interests are in developing new probabilistic methods based on Bayesian inference, Information Theory and Maximum Entropy approaches for Inverse Problems in general in all aspects of data processing, and more specifically in imaging and vision: image reconstruction, signal and image deconvolution, blind source separation, sources localization, data fusion, multi and hyper spectral image segmentation. The main application domains of his interests are Computed Tomography (X rays, PET, SPECT, MRI, microwave, ultrasound and eddy current imaging) either for medical imaging or for non destructive testing (NDT) in industry, multivariate and multi dimensional data, signal and image processing, data mining, clustering, classification and machine learning methods for biological or medical applications.

He has supervised more than fifty M.Sc. research projects, more than 15 Ph.D.  thesis and more than 10 Post-doc research activities. In 2012, he was supervising 6 Ph.D. thesis. He has more than 40 full journal papers and more than 200 papers in national and international conferences. He has organized or co-organized about 10 international workshops and conferences. He has been expert for a great number of French national and international projects. Since 1988 he has many teaching activities in M.Sc. and Ph.D. Level in SUPELEC and University of Paris sud.

He also participated and managed many industrial contracts with many French national industries such as EDF and Thales or R & D great institutions such as CEA and INSERM as well as the regional (such as Digiteo), national (such as ANR) and European projects (such as ERASYSBIO).

Tutorial 4

Date: Sunday, 12th May 2013

Time: 13:45 – 17:00

LTE and beyond LTE.

Professor Merouane Debbah, (SUPELEC, France)


Summary: Next generation wireless communication systems (LTE and LTE advanced) are based on a new multiple access technique called OFDMA and a multiple antenna architecture which increases drastically the rate. Many deployments of this technology are actually taking place around the world with a  complementary co-existence with other technologies such as 3G, 2G or Wi-Fi. As a consequence,, it is of major importance to understand the pros and cons of LTE as well as its impact on the different services provided to users (voice as well as data). The goal of this tutorial is to provide the fundamentals of LTE with a clear understanding of its impacts in heterogeneous networks (Femto-cells, small cells and macrocells).


Biography: Mérouane Debbah  entered the Ecole Normale Supérieure de Cachan (France)  in 1996 where he received his M.Sc and Ph.D. degrees respectively. He worked for Motorola Labs (Saclay, France) from 1999-2002 and the Vienna Research Center for Telecommunications  (Vienna, Austria) until 2003. He then joined the Mobile Communications department of the Institut Eurecom (Sophia Antipolis, France) as an Assistant Professor until 2007. He is now a Full Professor at Supelec (Gif-sur-Yvette, France), holder of the Alcatel-Lucent Chair on Flexible Radio and a recipient of the ERC starting grant MORE (Advanced Mathematical Tools for Complex Network Engineering). His research interests are in information theory, signal processing and wireless communications. He is a senior area editor for IEEE Transactions on Signal Processing. Mérouane Debbah is the recipient of the "Mario Boella"  award in 2005, the 2007 General Symposium IEEE GLOBECOM best paper award,  the Wi-Opt 2009 best paper award, the 2010 Newcom++ best paper award as well as the Valuetools 2007, Valuetools 2008, Valuetools 2012 and CrownCom2009 best student paper awards. He is a WWRF fellow. In 2011, he received the IEEE Glavieux Prize Award.


Tutorial 5

Date: Sunday, 12th May 2013

Time: 13:45 – 17:00

A review of wavelet denoising in medical imaging

Professor Abdeldjalil Ouahabi, (Polytechnique-Tours, France)


Abstract Medical images, e.g. obtained from MRI, are the most common tool for diagnosis in medical field. These images are often affected by random noise arising in the image acquisition process, measurement and transmission. The resolution of this problem may lead to improved diagnosis and surgical procedures. Noise removal is essential in medical imaging applications in order to enhance and recover fine details that may be hidden in the data.

A common approach for image denoising is to convert the noisy image into a transform domain such as the wavelet (and/or contourlet) domain, and then compare the transform coefficients with a fixed or adapted threshold. The wavelet representation naturally compresses the essential information in a signal into relatively few, large coefficients, which represent image details at different resolution scales.

In general, image denoising using wavelet-based multiresolution analysis imposes a compromise between noise reduction and preserving significant image details.

In this tutorial, we review recent wavelet denoising techniques for medical ultrasound and for magnetic resonance images, evaluate their implementation via MATLAB package and discuss their performances in termes of SNR (signal-to- noise ratio) or PSNR (peak signal-to-noise ratio) and visual aspects of image quality. However, image denoising using wavelet-based multirésolution analysis requires a delicate compromise between noise reduction and preserving significant image details. Hence, some subtleties associated with these denoising techniques will be explained in detail.


He has a strong interest in sampling theories multiresolution algorithms, optimal filtering, spectral analysis, wavelets, and the use of fractals for image processing. He is the author of over 120 published papers in these areas.

He was the Head of the Electrical Engineering Institute at USTHB-Algiers (1990-1994) and Head of the International Relations at Polytech’Tours (2004-2010). He is also the founder of Systems and Signals Laboratory at Algeriers in 1992. He is Member of the Board of Polytech’Tours (2002-2012) and was Member of the Board of Studies and University Life (CEVU) of the University of Tours (2009-2012).

Prof. Abdeldjalil Ouahabi has acted as expert and scientific advisor to the EU, the UNESCO, the Ministry of Higher Education and Research and the Ministry of Foreign and European Affairs of France, the Ministry of Higher Education and Research of Algeria...

He has organised and Chaired a large number of International Conferences including the International Conference on Signals and Systems, ICSS'94, held in Algiers in 1994 and the Club EEA Congress which is held in Tours (France) in 2009. He also served as Editor or Guest Editor for several journals or books including « Analog Integrated Circuit and Signal Processing » (Springer 2010-2011), Hermès-Lavoisier editions (2012) and Iste-Wiley editions (2012).

Tutorial 6

Date: Sunday, 12th May 2013

Time: 13:45 – 17:00

Machine Learning and Pattern Recognition

Dr. Djamel Bouchaffra (Director of Research, CDTA)


Topic Overview

This tutorial provides an in‐depth analysis of some important issues within the field of Machine Learning and Pattern Recognition. It reflects recent developments while providing a comprehensive introduction to some fundamental issues pertaining to the fields machine learning and pattern recognition. It targets advanced undergraduates or first year Ph.D. students as well as researchers and practitioners. It focuses on deepening current understanding of the underneath mathematical models when applied to real world applications.


Target audience

The tutorial aims to provide an attractive opportunity for young researchers and PhD students. The participants will benefit from direct interaction and discussions with the speaker. The researchers will also have the opportunity to exchange on their scientific research, and interact with their colleagues in a friendly and constructive atmosphere.


Content details

The 3 hours tutorial covers theory and practice of some important issues in Machine Learning and Pattern Recognition. A tentative outline follows:


Part A: Theory and Models


Dr. Djamel Bouchaffra (Director of Research @ CDTA and Former Professor @ Oakland University, USA)


1.  Feature selection/extraction and manifold learning for dimensionality reduction (30mn)

2.  Non-parametric density estimation (kernel-density estimation-nearest neighbors) (30mn)

3.  Kernel tricks: supervised and semi-supervised support vector machines (30mn)

4.  HMMs-based models and sequential data (30mn)

Part B: Practical Demos & Programming Session (60 minutes)

Teaching Assistants:

  • Messaoud Bengherabi
  • Farid Harizi
  • Fayçal Ykhlef


MATLAB projects (or Computer Exercises) as experiments to the theory covered are provided by Teaching Assistants (TAs) with a good experience in interacting with the participants an interactive way with the participants.


Regarding Part B, Practical Demonstrations: the participants are encouraged to have their own laptops to practice the examples in MATLAB.


[1] R.O. Duda, P. E. Hart et D.G. Stork, Pattern Classification, 2nd Edition, Wiley-Interscience, 2001.

[2] C. M. Bishop, Pattern Recognition and Machine Learning, Springer 2nd Edition, 2006.

[3] M. Cheriet, N. Kharma, C.-L. Liu et C.Y. Suen, Character Recognition Systems: A Guide for Students and Practitioners, John Wiley & Sons, 1st Edition, 2007. ISBN: 978-0-471-41570-1.

[4] D. Bouchaffra, "Mapping Dynamic Bayesian Networks to Alpha-Shapes: Application to Human Faces Identification across Ages", in: IEEE Transactions On Neural Networks and Learning Systems (TNNLS), Volume 23, Issue 8, pp. 1229-1241, August 2012 (available in the author’s website).

[5] D. Bouchaffra, "Conformation-based Hidden Markov Models: Application to Human Face Identification", in: IEEE Transactions On Neural Networks (TNN), vol. 21, no. 4, pp. 597-608, 2010 (available in the author’s website).

Open source tools and samples of examples will be distributed to tutorial participants together with handouts of the slides.


Djamel Bouchaffra received the Ph.D. degree in Computer Science from Grenoble University, France. He currently holds the title of Director of Research. In 2012, he joined the Center for Development of Advanced Technology, Algeria and in January 2013 he was appointed Head of the Division "Systems Architecture and Multimedia" (aka ASM). Prior to this appointment, Dr. Bouchaffra was a Professor of Computer Science at the Department of Mathematics and Computer Science, Grambling State University, LA. He was a Senior Lead Researcher at the Center of Excellence for Document Analysis and Recognition (CEDAR) at the University of New York, Buffalo. Prior to this appointment, Dr. Bouchaffra was an Assistant Professor at Oakland University, Michigan. He is currently working on mathematical models that embed discrete structures into a Euclidean space or a Riemannian manifold and merge topology with statistics for a classification or a regression task. He introduced the structural and the topological hidden Markov models. He has written several papers in peer-reviewed conference proceedings and premier journals, such as the IEEE Transactions on Pattern Analysis and Machine Intelligence and Pattern Recognition. His current research interests include pattern recognition, machine learning, computer vision, and artificial intelligence. Dr. Bouchaffra was the lead Guest Editor of a special issue in the journal of Pattern Recognition titled “Feature Extraction and Machine Learning for Robust Multimodal Biometrics, published by Elsevier.” He is an Editorial Board Member of several journals, such as Pattern Recognition (Elsevier), and Advances in Artificial Intelligence (Hindawi). He chaired several sessions in conferences. He is on the review panels of governmental funding agencies, such as NASA (Galaxy Classification) and EPSRC, U.K. He is an IEEE senior Member and a member of the IEEE Computer Society.

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