Professor Pierre Comon, Universite' de Nice Sophia Antipolis, France

Title: TENSORS, USEFULNESS AND UNEXPECTED PROPERTIES

Date and Time: 02/09/2009, 9:00 am

Abstract: Since the nineties, tensors are increasingly used in Signal Processing and Data Analysis. Various definitions and properties are emphasized. In particular, striking differences between tensors and matrices include: (1) the rank of a tensor generally exceeds its dimension, (2) there exist tensors having a rank larger than generic, (3) the maximal achievable rank of a tensor is not clearly known, (4) the rank can differ in the real and complex fields, (5) it is difficult to assess the rank of a tensor and to compute its decomposition into a sum of rank-1 terms, (6) subtracting the best rank-1 approximate generally increases tensor rank, (7) the best low rank approximate may not exist... Despite all these odd properties, which reveal our ignorance in the domain, tensors still remain attractive because they allow to restore identifiability in several problems, and in particular for blindly identifying underdetermined linear mixtures.

Biography: Pierre Comon graduated in 1982, and received the Doctorate degree in 1985, both from the University of Grenoble, France. He later received the Habilitation to Lead Researches in 1995, from the University of Nice, France. He has been for nearly 13 years in industry, first with Crouzet-Sextant, Valence, France, between 1982 and 1985, and then with Thomson Marconi, Sophia-Antipolis, France, between 1988 and 1997. He spent 1987 with the ISL laboratory, Stanford University, CA. He joined in 1997 the Eurecom Institute, Sophia-Antipolis, France, and left during the fall of 1998. He is now research director with CNRS since 1998 at laboratory I3S, Sophia-Antipolis, France, where he heads both the STIC Doctoral School, and the SIS (Signal Images Systems) department of the I3S lab. His research interests include High-Order Statistics (HOS), Blind Deconvolution and Equalization, tensor-based Factor Analysis and Statistical Signal and Array Processing. Dr. Comon was Associate Editor of the IEEE Transactions on Signal Processing from 1995 to 1998, and a member of the French National Committee of Scientific Research from 1995 to 2000. He was the coordinator of ATHOS, the European Basic Research Working Group on HOS, from 1992 to 1995. Between 1992 and 1998, he was a member of the Technical and Scientific Council of the Thomson multinational company. Between 2001 and 2004 he acted as launching Associate Editor with the IEEE Transactions on Circuits and Systems I, in the area of Blind Techniques. He is currently Fellow member of the IEEE, and Associate Editor of the Elsevier journal Signal Processing.

Professor Lajos Hanzo, University of Southampton, UK

Title: THE MULTIPLE ACCESS SAGA AND WIRELESS FUTURES: A LIGHT-HEARTED RECITAL ON FDMA, TDMA, CDMA, OFDMA, SDMA, DMA, CCMA AND ’ALL THAT’...

Date and Time: 01/09/2009, 2:00 pm

Abstract: This overview is rguing that virtually any sufficiently unique feature of a tetherlessly communicating user may be employed for providing a multiple access capability. Naturally, depending on the specific statistical characteristics of these different unique, user-specific features, such as a unique frequency- or time-slot, they are differently affected by the hostile wireless transmission medium and hence they require a different level of transmitter versus receiver signal processing complexity. More specifically, it is possible to appropriately split the overall system complexity, so that we simplify either the transmitter or the receiver, at the expense of the other, as exemplified by employing a multiuser detector, or  sophisticated multiuser transmitter combined with a single-user atched-filter receiver.
   Commencing with a brief historical perspective on the cross-pollination of signal processing and the family of multiple access techniques across the wireless generations, a whistle-stop tour of FDMA, TDMA, CDMA, OFDMA, SDMA, IDMA, CCMA and 'all that' will be offered. We will be 'reminiscing those, which are no longer with us and looking to the future generations' - hence multi-standard operation is an important requirement... Further attention will be devoted to SDMA and to introducing the younger members of the family, such IDMA and CCMA - my hope is that you, valued colleagues forgive me for the suspense of withholding the meaning of the acronyms.  The overview will be concluded by a roadmap of both classic and unorthodox transceiver signal processing techniques applicable to both single and multiple-antenna-aided solutions, reaching out to the challenging topic of overall system efficiency improvements using low-power cross-layer signal processing. Just to mention a few, SVD-based multi-user transmission cobined with single-user matched filtered reception versus Sphere Decoding (SD) aided, as well as random guided genetic algorithm assisted, particle swarm optimised and ant-colony based near-maximum likelihood detectors will be featured in challenging rank-deficient reception scenarios.

Biography: Lajos Hanzo, FRAEng, FIEEE, FIET, DSc has held various academic and research positions in Hungary, Germany and the UK.  Since 1986 he has been with the University of Southampton, where he holds the Chair f Telecommunications. He has co-authored 17 books on signal processing for communications and published in excess of 800 research papers. Lajos has also been awarded a number of distinctions and he is an IEEE Distinguished Lecturer. He acts as a Governor of both the Communications and the Vehicular Technology Society, as well as EIC of the IEEE Press.  For further information on research in progress and for associated papers and book chapters please refer to http://www-mobile.ecs.soton.ac.uk.

Professor Athina P. Petropulu, Drexel University, USA

Ttile: COOPERATIVE APPROACHES FOR IMPROVING PERFORMANCE IN WIRELESS COMMUNICATION NETWORKS

Date and Time: 01/09/2009, 10:00 am 

Abstract: Node cooperation in wireless communication networks can overcome several of the limitations introduced by the wireless channel, such as fading, attenuation, susceptibility to eavesdropping and collisions. This talk presents research results in the area cooperative communications. First, the talk explores cooperative beamforming for reliable transmission of information over long distances n an energy and cost efficient fashion. Second, it explores the role of cooperative transmissions in guaranteeing secure communications without use of cryptography. In particular, it presents an information theoretic framework for optimizing secrecy capacity in the presence of one or multiple eavesdroppers. Third, it explores collision resolution via cooperative retransmissions and presents some research results showing that this approach can be the basis of new cross-layer protocols that can sustain high throughput while enjoying power savings. 

Biography: Athina P. Petropulu received the Diploma in electrical engineering from the National Technical University of Athens, Greece, in 1986, and the M.Sc. and Ph.D. degrees, both in electrical and computer engineering in 1988 and 1991, respectively, from Northeastern University, Boston, MA.
In 1992, she joined the Department of Electrical and Computer Engineering, Drexel University, where she is now a Professor. She has held visiting appointments at Supelec, France, during 1999–2000 and at Princeton University, Princeton, NJ, during 2006–2007. Her research interests span the area of statistical signal processing, wireless communications and networking, and biomedical applications of signal processing. She is the coauthor (with C. L. Nikias) of the textbook Higher-Order Spectra Analysis: A Nonlinear Signal Processing Framework, (Englewood Cliffs, NJ: Prentice-Hall, 1993).
Dr. Petropulu is the recipient of the 1995 Presidential Faculty Fellow Award in Electrical Engineering given by NSF and the White House. She has served as an Associate Editor for the IEEE TRANSACTIONS ON SIGNAL PROCESSING and the IEEE SIGNAL PROCESSING LETTERS, and is a member of the editorial board of the IEEE SIGNAL PROCESSING MAGAZINE and the EURASIP Journal on Wireless Communications and Networking. She is the IEEE SPS Vice President Conferences and member of the IEEE Signal Processing Board of Governors. She was the General Chair of the 2005 International Conference on Acoustics Speech and Signal Processing (ICASSP-05), Philadelphia, PA. She is corecipient of the 2005 IEEE Signal Processing Magazine Best Paper Award.  

Professor Jean-Philippe Thiran, Swiss Federal Institute of Technology, Switzerland

Ttile: FAST ENERGY MINIMIZATION METHODS FOR 3-D BRAIN TISSUE CLASSIFICATION

Date and Time: 03/09/2009, 2:00 pm

Abstract: Brain tissue segmentation is one of the most important quantitative image analysis tools needed for early diagnosis of most of the brain degenerative diseases, including Alzheimer's disease. In this talk we present 3-D brain tissue classification schemes using three recent promising energy minimization methods for Markov Random Fields (MRF): graph cuts (GC), loopy belief propagation (LBP) and tree-reweighted message passing (TRW). We compare the results of the above methods with widely used iterative conditional modes (ICM) algorithm, using identical parameters. The evaluation is performed on a dataset containing simulated T1-weighted MR brain volumes with varying noise and intensity non-uniformities.

Biography: Jean-Philippe Thiran was born in Namur, Belgium, in 1970. He received the Elect. Eng. and Ph.D. degrees from the Universite catholique de Louvain (UCL), Louvain-la-Neuve, Belgium, in 1993 and 1997, respectively. He joined the Signal Processing Laboratory (LTS) of the Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland, in February 1998 as a Senior Lecturer. Since January 2004, he has been an Assistant Professor, responsible for the Image Analysis Group. His current scientific interests include image segmentation, prior knowledge integration in image analysis, partial differential equations and variational methods in image analysis, multimodal signal processing, medical image analysis, including multimodal image registration, segmentation, computer-assisted surgery, and diffusion MRI. He is author or co-author of two 4 book chapters, 69 journal papers, and some 130 peer-reviewed papers published in proceedings of international conferences. He holds four international patents. Dr. Thiran was Co-Editor-in-Chief of Signal Processing (published by Elsevier Science) from 2001 to 2005. He is currently an Associate Editor of the International Journal of Image and Video Processing (published by Hindawi), and member of the Editorial Board of Signal, Image and Video Processing (published by Springer). He was the General Chairman of the 2008 European Signal Processing Conference (EUSIPCO 2008). He is a senior member of the IEEE, and member of the MLSP and IVMSP technical committees of the IEEE Signal Processing Society.

Professor Deliang Wang, Ohio State University, USA

Title: A HIDDEN MARKOV MODEL FRAMEWORK FOR MULTI-TARGET TRACKING

Date and Time: 02/09/2009, 2:00 pm

Abstract: We present a new hidden Markov model (HMM) framework for tracking multiple dynamic targets in acoustic signal processing. In this framework, a hidden node indicates the state space for each time frame, which further consists of continuous subspaces corresponding to active targets, and the transitions between hidden nodes describe the evolution of the state space in time. This broad framework naturally allows for statistical prediction and verification. We have applied the framework to the challenging problems of multipitch tracking and the tracking of simultaneously moving sound sources. For multipitch tracking, the HMM model along with a method of probability integration across different frequency channels leads to a robust algorithm that detects pitch tracks in noisy speech. For tracking moving sound sources, we observe that binaural cues are strongly correlated with source locations in time–frequency regions dominated by only one source. Based on this observation and multichannel integration for producing the likelihood function, the HMM algorithm can automatically detect the number of active sources and track individual moving talkers across time. Comparisons with related algorithms, including a Kalman filter approach, suggest that the HMM based algorithms produce superior results for multi-target tracking.

Professor Abdelhak Zoubir, Technical University of Darmsdast, Germany

Ttile: ADVANCES ON BOOTSTRAP METHODS FOR SIGNAL PROCESSING

Date and Time: 03/09/2009, 9:00 am

Abstract: The use of more accurate models in signal processing applications such as communications, radar, sonar, biomedical engineering, speech and image processing and machine learning has become a fundamental requirement. With an improved accuracy the models have become more complex and inferential statistical signal processing required in parameter estimation and signal detection and classification, for example, has become intractable. The signal processing practitioner requires a simple but accurate method for assessing errors of estimates and answering inferential questions. Asymptotic approximations are useful only when enough data is available, which is not always possible due to time constraints, the nature of the signal or the measurement setting. In place of the formulae and tables of parametric and non-parametric procedures based on complicated mathematics and asymptotic approximations, tools such as the Bootstrap have revolutionized statistics in the last decade and have become powerful for solving complex engineering problems. It is the method of an engineer's choice for solving inferential signal processing problems, such as signal detection, confidence limits estimation and model selection, to mention a few.
First, a brief history of the bootstrap metholodogy is given. We then discuss the basic principle underlying the bootstrap method for identically and independently distributed data and give several practical examples of its use. A comprehensive overview of the bootstrap for dependent data is also given with emphasis on spectral analysis. Examples with real-life measurements are given throughout the talk.

Biography: Abdelhak M Zoubir is a Fellow of the IEEE. He received his Dr.-Ing. from Ruhr-Universität Bochum, Germany. He was with Queensland University of Technology, Australia from 1992-1998 where he was Associate Professor. In 1999, he joined Curtin University of Technology, Australia as a Professor of Telecommunications and was Interim Head of the School of Electrical & Computer Engineering from 2001 until 2003. In 2003, he moved to Technische Universität Darmstadt, Germany as Professor and Head of the Signal Processing Group. His research interest lies in statistical methods for signal processing with emphasis on bootstrap techniques, robust detection and estimation and array processing applied to radar, sonar, telecommunications, car engine monitoring and biomedicine. He published extensively on these areas. Professor Zoubir was Technical Chair of the 11th IEEE Workshop on Statistical Signal Processing (SSP 2001) held in Singapore in 2001, General Co-Chair of the 3rd IEEE International Symposium on Signal Processing & Information Technology (ISSPIT 2003) held in Darmstadt, Germany in 2003 and General Co-Chair of the 5th IEEE Workshop on Sensor Array and Multi-channel Signal Processing (SAM 2008), which was held in Darmstadt, Germany in 2008. He was Member of the Technical Committee as Co-Chair for Plenary Sessions for ICASSP-08 held in Las Vegas, USA. Dr Zoubir served as an Associate Editor of the IEEE Transactions on Signal Processing from 1999-2005 and he currently serves on the Editorial Boards of the EURASIP journals Signal Processing and the Journal on Advances in Signal Processing (JASP). Since 2009 he has been a Member of the Senior Editorial Board of the IEEE Journal on Selected Topics in Signal Processing. He is Vice-Chair of the IEEE SPS Technical Committee Signal Processing Theory and Methods (SPTM) (member from 2002-2007) and Member of the IEEE SPS Technical Committee Sensor Array and Multichannel Signal Processing (SAM) (since 2007). He was a Member of the IEEE SPS Technical Committee on Signal Processing Education (SpEd) from 2006-2008. He is an elected member of AdCom for European Association for Signal and Image Processing (EURASIP).