S³ seminar : Extending Stationarity to Graph Signal Processing: a Model for Stochastic Graph Signals

Séminaire le 31 Mars 2017, 10h30 à CentraleSupelec (Gif-sur-Yvette) Salle du conseil du L2S - B4.40
Benjamin Girault, (University of Southern California)

During the past few years, graph signal processing has been extending the field of signal processing on Euclidean spaces to irregular spaces represented by graphs. We have seen successes ranging from the Fourier transform, to wavelets, vertex-frequency (time-frequency) decomposition, sampling theory, uncertainty principle, or convolutive filtering. One missing ingredient though are the tools to study stochastic graph signals for which the randomness introduces its own difficulties. Classical signal processing has introduced a very simple yet very rich class of stochastic signals that is at the core of the study of stochastic signals: the stationary signals. These are the signals statistically invariant through a shift of the origin of time. In this talk, we study two extensions of stationarity to graph signals, one that stems from a new translation operator for graph signals, and another one with a more sensible interpretation on the graph. In the course, we show that attempts of alternate definitions of stationarity on graphs in the recent literature are actually equivalent to our first definition. Finally, we look at a real weather dataset and show empirical evidence of stationarity.

Bio: Benjamin Girault received his License (B.Sc.) and his Master (M.Sc.) in France from École Normale Supérieure de Cachan, France, in 2009 and 2012 respectively in the field of theoretical computer science. He then received his PhD in computer science from École Normale Supérieure de Lyon, France, in December 2015. His dissertation entitled "Signal Processing on Graphs - Contributions to an Emerging Field" focused on extending the classical definition of stationary temporal signals to stationary graph signal. Currently, he is a postdoctoral scholar with Antonio Ortega and Shri Narayanan at the University of Southern California continuing his work on graph signal processing with a focus on applying these tools to understanding human behavior.

S³ seminar : Novel Algorithms for Automated Diagnosis of Neurological and Psychiatric Disorders

Séminaire le 28 Mars 2017, 10h30 à CentraleSupelec (Gif-sur-Yvette) Salle du conseil du L2S - B4.40
Hojjat ADELI, (The Ohio State University, Columbus, USA)

Novel algorithms are presented for data mining of time-series data and automated electroencephalogram (EEG)-based diagnosis of neurological and psychiatric disorders based on adroit integration of three different computing technologies and problem solving paradigms: neural networks, wavelets, and the chaos theory. Examples of the research performed by the author and his associates for automated diagnosis of epilepsy, the Alzheimer’s Disease, Attention Deficit Hyperactivity Disorder (ADHD), autism spectrum disorder (ASD), and Parkinson’s disease (PD) are reviewed.

Biography: Hojjat Adeli received his Ph.D. from Stanford University in 1976 at the age of 26. He is Professor of Civil, Environmental, and Geodetic Engineering, and by courtesy Professor of Biomedical Informatics, Biomedical Engineering, Neuroscience, and Neurology at The Ohio State University. He has authored over 550 publications including 15 books. He is the Founder and Editor-in-Chief of international research journals Computer-Aided Civil and Infrastructure, now in 32nd year of publication, and Integrated Computer-Aided Engineering, now in 25th year of publication, and the Editor-in-Chief of International Journal of Neural Systems. In 1998 he received the Distinguished Scholar Award from OSU, “in recognition of extraordinary accomplishment in research and scholarship”. In 2005, he was elected Distinguished Member, ASCE: "for wide-ranging, exceptional, and pioneering contributions to computing in civil engineering and extraordinary leadership in advancing the use of computing and information technologies in many engineering disciplines throughout the world.” In 2010 he was profiled as an Engineering Legend in the ASCE journal of Leadership and Management in Engineering, and Wiley established the Hojjat Adeli Award for Innovation in Computing. In 2011 World Scientific established the Hojjat Adeli Award for Outstanding Contributions in Neural Systems. He is a Fellow of IEEE, the American Association for the Advancement of Science, American Neurological Society, and American Institute for Medical and Biomedical Engineering. Among his numerous awards and honors are a special medal from Polish Neural Network Society, the Eduardo Renato Caianiello Award for Excellence in Scientific Research from the Italian Society of Neural Networks, the Omar Khayyam Research Excellence Award from Scientia Iranica, an Honorary Doctorate from Vilnius Gediminas Technical University, and corresponding member of the Spanish Royal Engineering Society.

S³ seminar : Stochastic proximal algorithms with applications to online image recovery

Séminaire le 24 Mars 2017, 11h00 à CentraleSupelec (Gif-sur-Yvette) Salle du conseil du L2S - B4.40
Jean-Christophe PESQUET, CVN, CentraleSupélec

Stochastic approximation techniques have been used in various contexts in machine learning and adaptive filtering. We investigate the asymptotic behavior of a stochastic version of the forward-backward splitting algorithm for finding a zero of the sum of a maximally monotone set-valued operator and a cocoercive operator in a Hilbert space. In our general setting, stochastic approximations of the cocoercive operator and perturbations in the evaluation of the resolvents of the set-valued operator are possible. In addition, relaxations and not necessarily vanishing proximal parameters are allowed. Weak almost sure convergence properties of the iterates are established under mild conditions on the underlying stochastic processes. Leveraging on these results, we propose a stochastic version of a popular primal-dual proximal optimization algorithm, and establish its convergence. We finally show the interest of these results in an online image restoration problem.

S³ seminar : On Electromagnetic Modeling and Imaging of Defects in Periodic Fibered Laminates

Séminaire le 10 Mars 2017, 12h30 à CentraleSupelec (Gif-sur-Yvette) Salle du conseil du L2S - B4.40
Zicheng LIU, (Inverse problems Group, Signals and Statistics Division, L2S Laboratory)

Composite laminates are commonly utilized in industry due to advantages as high stiffness, light weight, versatility, etc. Multiple layers, each one involving periodically-positioned circular-cylindrical fibers in a given homogeneous matrix, are usually involved. However, defects can affect the structure and thereupon impact security and efficiency, and they call for nondestructive testing. By electromagnetic (EM) means, it requires fast and reliable computational modeling of both sound and damaged laminates if one wishes to better understand pluses and minuses of the testing, and derive efficient imaging algorithms for the end user. Both direct modeling and inverse imaging will be introduced in this presentation.  For the former, since the periodicity of the structure is destroyed due to defects, methods based on the Floquet theorem are inapplicable. Two modeling approaches are then utilized: one is with supercell methodology where a fictitious periodic structure is fabricated, so as the EM field solution everywhere in space can be well approximately modeled, provided the supercell be large enough; the other is based on fictitious source superposition (FSS) where defects are treated as equivalent sources and the field solution is a summation of responses to the exterior source and equivalent ones. For imaging, with MUSIC and sparsity-based algorithm, missing fibers could be accurately located.

Biography: Zicheng LIU was born in Puyang, China, in October 1988. He received the M.S. degree in circuit and system from Xidian University, Xi’an, China in March 2014 and is currently pursuing the Ph.D. degree with the benefit of a Chinese Scholarship Council (CSC) grant at the Laboratoire des Signaux et Systèmes, jointly Centre National de la Recherche Scientifique (CNRS), CentraleSupélec, and Université Paris-Sud, Université Paris-Saclay, Paris, France. He will defend his Université Paris-Saclay Ph.D. early Fall 2017. His present work is on the electromagnetic modeling of damaged periodic fiber-based laminates and corresponding imaging algorithms and inversion. His research interests include computational electromagnetics, scattering theory on periodic structures, non-destructive testing, sparsity theory, and array signal processing.

S³ seminar : On Imaging Methods of Material Structures with Different Boundary Conditions

Séminaire le 10 Mars 2017, 10h30 à CentraleSupelec (Gif-sur-Yvette) Salle du conseil du L2S - B4.40
Xiuzhu YE, (Beihang University, Beijing, China)

This talk is about the two-dimensional inverse scattering problems for different kinds of boundary conditions. Firstly, we propose a perfect electric conductor (PEC) inverse scattering approach, which is able to reconstruct PEC objects of arbitrary number and shape without requiring prior information on the approximate locations or the number of the unknown scatterers. Secondly, the modeling scheme of the T-matrix method is introduced to solve the challenging problem of reconstructing a mixture of both PEC and dielectric scatterers together. Then the method is further extended to the case of scatterers with four boundary conditions together. Last, we propose a method to solve the dielectric and mixed boundary through-wall imaging problem. Various numerical simulations and experiments are carried out to validate the proposed methods.

Biography: Xiuzhu YE was born in Heilongjiang, China, in December 1986. She received the Bachelor degree of Communication Engineering from Harbin Institute of Technology, China, in July 2008 and the Ph.D. degree from the National University of Singapore, Singapore, in April 2012. From February 2012 to January 2013, she worked in the Department E.C.E., National University of Singapore, as a Research Fellow. Currently, she is Assistant Professor in the School of Electronic and Information Engineering of the Beihang University. She has been and is engaged under various guises with Ecole Centrale de Pékin (ECPK) also. She is presently benefiting from an invited professorship position at University Paris-Sud and later this Summer 2017 she will be benefiting from an invited professorship position at CentraleSupélec, both within the Laboratoire des Signaux et Systèmes, jointly Centre National de la Recherche Scientifique (CNRS), CentraleSupélec, and Université Paris-Sud, Université Paris-Saclay, Gif-sur-Yvette, France. Her current research interest mainly includes fast algorithms in solving inverse scattering problems, near field imaging, biomedical imaging, and antenna designing.

S³ seminar : FastText: A library for efficient learning of word representations and sentence classification

Séminaire le 24 Février 2017, 10h30 à CentraleSupelec (Gif-sur-Yvette) Salle du conseil du L2S - B4.40
Piotr Bojanowski, (Facebook AI Research)

In this talk, I will describe FastText, an open-source library that can be used to train word representations or text classifiers. This library is based on our generalization of the famous word2vec model, allowing to adapt it easily to various applications. I will go over the formulation of the skipgram and cbow models of word2vec and how these were extended to meet the needs of our model. I will describe in details the two applications of our model, namely document classification and building morphologically-rich word representations. In both applications, our model achieves very competitive performance while being very simple and fast.

S³ seminar : Stochastic Quasi-Newton Langevin Monte Carlo

Séminaire le 10 Février 2017, 10h30 à CentraleSupelec (Gif-sur-Yvette) Salle du conseil du L2S - B4.40
Umut Şimşekli, (LTCI, Télécom ParisTech)

Recently, Stochastic Gradient Markov Chain Monte Carlo (SG-MCMC) methods have been proposed for scaling up Monte Carlo computations to large data problems. Whilst these approaches have proven useful in many applications, vanilla SG-MCMC might suffer from poor mixing rates when random variables exhibit strong couplings under the target densities or big scale differences. In this talk, I will present a novel SG-MCMC method that takes the local geometry into account by using ideas from Quasi-Newton optimization methods. These second order methods directly approximate the inverse Hessian by using a limited history of samples and their gradients. Our method uses dense approximations of the inverse Hessian while keeping the time and memory complexities linear with the dimension of the problem. I will provide formal theoretical analysis where it is shown that the proposed method is asymptotically unbiased and consistent with the posterior expectations. I will finally illustrate the effectiveness of the approach on both synthetic and real datasets. This is a joint work with Roland Badeau, Taylan Cemgil and Gaël Richard. arXiv: https://arxiv.org/abs/1602.03442

S³-PASADENA seminar : Detecting confounding in multivariate linear models via spectral analysis

Séminaire le 31 Janvier 2017, 10h30 à CentraleSupelec (Gif-sur-Yvette) Salle du conseil du L2S - B4.40
Dominik Janzing, Max Planck Institute for Intelligent Systems, Tuebingen, Germany

We study a model where one target variable Y is correlated
with  a vector X:=(X_1,...,X_d) of predictor variables  being potential causes of Y.
We describe  a method that infers to what extent the statistical dependences between X and Y
are due to the influence of X on Y and to what extent due to a hidden common cause
(confounder) of X and Y. The method is based on an independence assumption stating that, in the absence of confounding,
the vector of regression coefficients describing the influence of each X on Y has 'generic orientation'
relative to the eigenspaces  of the covariance matrix of X. For the special case of a scalar confounder we show that confounding typically spoils this generic orientation in a characteristic way that can be used to quantitatively estimate the amount of confounding.
I also show some encouraging experiments with real data, but the method is work in progress and critical comments are highly appreciated.

Postulating 'generic orientation' is inspired by a more general postulate stating that
P(cause) and P(effect|cause) are independent objects of Nature and therefore don't contain information about each other [1,2,3],
an idea that inspired several causal inference methods already, e.g. [4,5].

[1] Janzing, Schoelkopf: Causal inference using the algorithmic Markov condition, IEEE TIT 2010.
[2] Lemeire, Janzing: Replacing causal faithfulness with the algorithmic independence of conditionals, Minds and Machines, 2012.
[3] Schoelkopf et al: On causal and anticausal learning, ICML 2012.
[4] Janzing et al: Telling cause frome effect based on high-dimensional observations, ICML 2010.
[5] Shajarisales et al: Telling cause from effect in deterministic linear dynamical systems, ICML 2015.

S³ Seminar: Adapting to unknown noise level in super-resolution

Séminaire le 20 Janvier 2017, 10h30 à CentraleSupelec (Gif-sur-Yvette) Salle du conseil du L2S - B4.40
Claire Boyer (LSTA, UPMC)

We study sparse spikes deconvolution over the space of complex-valued measures when the input measure is a finite sum of Dirac masses. We introduce a new procedure to handle the spike deconvolution when the noise level is unknown. Prediction and localization results will be presented for this approach. An insight on the probabilistic tools used in the proofs could be briefly given as well.

S³ seminar : Inverse problems for speech production

Séminaire le 20 Janvier 2017, 10h30 à CentraleSupelec (Gif-sur-Yvette) Salle du conseil du L2S - B4.40
Benjamin Elie (LORIA, IADI)

Studies on speech production are based on the extraction and the analysis of the acoustic features of human speech, and also on their relationships with the articulatory and phonatory configurations realized by the speaker. An interesting tool, which will be the topic of the talk, to make such researches is the articulatory synthesis, which consists in the numerical simulation of the mechanical and acoustical phenomena that are involved in speech production. The aim is to numerically reproduce a speech signal that contains the observed acoustic features with regards to the actual articulatory and phonatory gestures of the speaker. Using the articulatory approach may lead to a few problems that will be tackled in this talk, and to which possible solutions will be discussed. Firstly, the different articulatory gestures realized in natural speech should be precisely observed. For that purpose, the first part of the talk focuses on methods to acquire articulatory films of the vocal tract by MRI techniques with a fast acquisition rate via sparse techniques (Compressed Sensing). The aim is, in fine, to build an articulatory and a coarticulation model. The investigation of the acoustical phenomena involved in natural speech require to separate the contributions of the different acoustic sources in the speech signal. The periodic/aperiodic decomposition of the speech signal is the subject of the second part of the talk. The challenge is to be able to study the acoustic properties of the frication noise that is generated during the production of fricatives, and also to quantify the amount of voicing produced during fricatives. Finally, in order to directly use the analysis by synthesis methods, it is interesting to estimate the articulatory configurations of the speaker from the acoustic signal. This is the aim of the acoustic-articulatory inversion for copy synthesis, which is the third part of the talk. Direct applications of these problems for the study of speech production and phonetics will be presented.

S³ seminar: High dimensional sampling with the Unadjusted Langevin Algorithm

Séminaire le 23 Novembre 2016, 10h30 à CentraleSupelec (Gif-sur-Yvette) Salle du conseil du L2S - B4.40
Alain Durmus (LTCI, Telecom ParisTech)

Recently, the problem of designing MCMC sampler adapted to high-dimensional distributions and with sensible theoretical guarantees has received a lot of interest. The applications are numerous, including large-scale inference in machine learning,  Bayesian nonparametrics, Bayesian inverse problem, aggregation of experts among others. When the density is L-smooth (the log-density is continuously differentiable and its derivative is Lipshitz), we will advocate the use of a “rejection-free” algorithm, based on the discretization of the  Euler diffusion with either constant or decreasing stepsizes. We will present several new results allowing convergence to stationarity under different conditions for the log-density (from the  weakest, bounded oscillations on a compact set and super-exponential in the tails to the log concave).
When the density is strongly log-concave, the convergence of an appropriately weighted empirical measure is also investigated and bounds for the mean square error and exponential deviation inequality for Lipschitz functions will be reported.
Finally, based on optimzation techniques we will propose new methods to sample from high dimensional distributions. In particular, we will be interested  in densities which are not continuously differentiable. Some Monte Carlo experiments will be presented to support our findings.

Gaussian Channels: I-MMSE at Every SNR

Séminaire le 20 Octobre 2016, 14h00 à CentraleSupelec (Gif-sur-Yvette) Salle du conseil du L2S - B4.40
Prof. Shlomo Shamai, The Andrew and Erna Viterbi Faculty of Electrical Engineering at the Technion-Israel Institute of Technology

Multi-user information theory presents many open problems, even in the simple Gaussian regime. One such prominent problem is the two-user Gaussian interference channel which has been a long standing open problem for over 30 years. We distinguish between two families of multi-user scalar Gaussian settings; a single transmitter (one dimension) and two transmitters (two dimensions), not restricting the number and nature of the receivers. Our first goal is to fully depict the behavior of asymptotically optimal, capacity
achieving, codes in one dimensional settings for every SNR. Such an understanding provides important insight to capacity achieving schemes and also gives an exact measure of the disturbance such codes have on unintended receivers.

We first discuss the Gaussian point-to-point channel and enhance some known results. We then consider the Gaussian wiretap channel and the Gaussian Broadcast channel (with and without secrecy demands) and reveal MMSE properties that confirm "rules of thumb" used in the achievability proofs of the capacity region of these channels and provide insights to the design of such codes.
We also include some recent observations that give a graphical interpretation to rate and equivocation in this one dimensional setting.
Our second goal is to employ these observations to the analysis of the two dimensional setting. Specifically, we analyze the two-user Gaussian interference channel, where simultaneous transmissions from two users interfere with each other. We employ our understanding of asymptotically point-to-point optimal code sequences to the analysis of this channel. Our results also resolve the "Costa Conjecture"
(a.k.a the "missing corner points" conjecture), as has been recently proved by Polyanskiy-Wu, applying Wasserstein Continuity of Entopy.

The talk is based on joint studies with R. Bustin, H. V. Poor and R. F. Schaefer.

S³: Material-by-Design for Synthesis, Modeling, and Simulation of Innovative Systems and Devices

Séminaire le 30 Septembre 2016, 10h30 à CentraleSupelec (Gif-sur-Yvette) Salle du conseil du L2S - B4.40
Giacomo Oliveri (ELEDIA, University of Trento)

Several new devices and architectures have been proposed in the last decade to exploit the unique features of innovative artificially-engineered materials (such as metamaterials, nanomaterials, biomaterials) with important applications in science and engineering. In such a framework, a new set of techniques belonging to the Material-by-Design (MbD) framework [1]-[5] have been recently introduced to synthesize innovative devices comprising task-oriented artificial materials. MbD is an instance of the System-by-Design paradigm [6][7] defined in short as “How to deal with complexity”. More specifically, MbD considers the problem of designing artificial-material enhanced-devices from a completely new perspective, that is "The application-oriented synthesis of advanced systems comprising artificial materials whose constituent properties are driven by the device functional requirements". The aim of this seminar will be to review the fundamentals, features, and potentialities of the MbD paradigm, as well as to illustrate selected state-of-the-art applications of this design framework in sensing and communications scenarios.

Bio: Giacomo Oliveri received the B.S. and M.S. degrees in Telecommunications Engineering and the PhD degree in Space Sciences and Engineering from the University of Genoa, Italy, in 2003, 2005, and 2009 respectively. He is currently an Tenure Track Associate Professor at the Department of Information Engineering and Computer Science (University of Trento), Professor at CentraleSupélec, member of the Laboratoire des signaux et systèmes (L2S)@CentraleSupélec, and member of the ELEDIA Research Center. He has been a visiting researcher at L2S, Gif-sur-Yvette, France, in 2012, 2013, and 2015, and he has been an Invited Associate Professor at the University of Paris Sud, France, in 2014. In 2016, he has been awarded the "Jean d'Alembert" Scholarship by the IDEX Université Paris-Saclay. He is author/co-author of over 250 peer-reviewed papers on international journals and conferences, which have been cited above 2200 times, and his H-Index is 26 (source: Scopus). His research work is mainly focused on electromagnetic direct and inverse problems, system-by-design and metamaterials, compressive sensing techniques and applications to electromagnetics, and antenna array synthesis. Dr. Oliveri serves as an Associate Editor of the International Journal of Antennas and Propagation, of the Microwave Processing journal, and of the International Journal of Distributed Sensor Networks. He is the Chair of the IEEE AP/ED/MTT North Italy Chapter.

Almost Lossless Variable-Length Source Coding on Countably Infinite Alphabets

Séminaire le 8 Juillet 2016, 14h00 à CentraleSupelec (Gif-sur-Yvette) Salle du conseil du L2S - B4.40
Prof. Jorge F. Silva, University of Chile, Santiago.

Motivated from the fact that universal source coding on countably infinite alphabets is not feasible, in this talk a notion of almost lossless source coding will be introduced.  This idea —analog to the  weak variable-length source coding proposed by Han 2000— aims at relaxing the lossless block-wise assumption to allow a distortion that vanishes asymptotically as the block-length goes to infinity.  In this almost lossless coding setting, new source coding results will be presented that on one hand show that Shannon entropy characterizes the minimum achievable rate (known statistics), while on the other,  that almost lossless universal source coding becomes feasible for the family of finite entropy stationary and memoryless sources with countably infinite alphabets.

Biography: Jorge F. Silva is Associate Professor at the Electrical Engineering Department and director of the Information and Decision Systems (IDS) Group at the University of Chile, Santiago, Chile. He received the Master of Science (2005) and Ph.D. (2008) in Electrical Engineering from the University of Southern California (USC). He is IEEE member of the Signal Processing and Information Theory Societies and he is associate editor of the  IEEE Transactions on Signal Processing.  Dr. Silva is recipient of the Viterbi Doctoral Fellowship  2007–2008 and Simon Ramo Scholarship 2007–2008 at USC.   Dr. Silva general research interests include: detection and estimation, information theory and statistics, universal source coding, sparse and compressible models and  compressed sensing.

The Appointment Scheduling Problem: The Doctor, Her Patients and The Waiting Room

Séminaire le 8 Juillet 2016, 14h00 à CentraleSupelec (Gif-sur-Yvette) Salle du conseil du L2S - B4.40
Prof. Stijn De Vuyst, Ghent University (UGent), Belgium.

We consider the appointment scheduling problem in the case of one doctor who sequentially provides service to the patients in the waiting room, in particular with respect to the incurred waiting times for both the patients and the doctor. This problem is different from a classical single-service queueing system in at least two ways: (1) the arrivals happen at pre-determined instants instead of randomly and (2) equilibrium solutions are of no use here since we need to know the waiting time of each individual patient. Given the length of the session and the consultation time distribution of each of K scheduled patient, we obtain the moments of the patient's waiting time and of the doctor's idle times. We also discuss the complicating factors such as the impact of unpunctuality, i.e. what happens if patients do not arrive exactly as appointed as usually the case in practice. A mild degree of unpunctuality can be handled by our model, but problems arise as soon as patients can overtake each other. Finally, we use the our results to construct suitable heuristics for finding optimal optimal appointment schedules.

Biography: Stijn De Vuyst is currently assistant professor at the Faculty of Engineering and Architecture of Ghent University (UGent), Belgium, in the Department of Industrial Systems Engineering and Product Design. His expertise is in operations research, in particular stochastic modelling, simulation, queueing theory and scheduling with application to the design, planning and performance evaluation of production systems as well as telecommunication systems. He obtained a master degree in Electrical Engineering and a PhD degree in Engineering Sciences at Ghent University. Prior to 2012, he was a post-doctoral researcher affiliated with the department of Telecommunication and Information Processing and for 6 months with the Informatics department at Université Libre de Bruxelles. From 2012 to 2015 he presided the faculty's educational board for the Master program Industrial Engineering and Operations Research. He currently teaches various courses on stochastic simulation, quality engineering and industrial statistics.

S³: Condition monitoring using vibration signals

Séminaire le 24 Mai 2016, 10h30 à CentraleSupelec (Gif-sur-Yvette) Salle des séminaires du L2S
Asoke K. Nandi

Condition monitoring of machines is an essential part of smooth, efficient, safe, and productive operation of machines. In this presentation, focus will be on rotating machines and in the use of vibration signals. Classification of vibration signals to different states of machines has been achieved through the developments and applications of signal processing and machine learning. This presentation will cover research efforts and some case studies carried out over many years.

Bio: Professor Asoke K. Nandi received the degree of Ph.D. in Physics from the University of Cambridge, Cambridge (UK). He held academic positions in several universities, including Oxford (UK), Imperial College London (UK), Strathclyde (UK), and Liverpool (UK) as well as Finland Distinguished Professorship in Jyvaskyla (Finland). In 2013 he moved to Brunel University London (UK), to become the Chair and Head of Electronic and Computer Engineering. Professor Nandi is a Distinguished Visiting Professor at Tongji University (China) and an Adjunct Professor at University of Calgary (Canada).
In 1983 Professor Nandi contributed to the discovery of the three fundamental particles known as W+, W− and Z0 (by the UA1 team at CERN), providing the evidence for the unification of the electromagnetic and weak forces, which was recognized by the Nobel Committee for Physics in 1984. His current research interests lie in the areas of signal processing and machine learning, with applications to communications, gene expression data, functional magnetic resonance data, and biomedical data. He has made many fundamental theoretical and algorithmic contributions to many aspects of signal processing and machine learning. He has much expertise in “Big Data”, dealing with heterogeneous data, and extracting information from multiple datasets obtained in different laboratories and different times. He has authored over 500 technical publications, including 200 journal papers as well as four books, entitled Automatic Modulation Classification: Principles, Algorithms and Applications (Wiley, 2015), Integrative Cluster Analysis in Bioinformatics (Wiley, 2015), Automatic Modulation Recognition of Communications Signals (Springer, 1996), and Blind Estimation Using Higher-Order Statistics (Springer, 1999),. Recently he published in Blood, BMC Bioinformatics, IEEE TWC, NeuroImage, PLOS ONE, Royal Society Interface, and Signal Processing. The h-index of his publications is 63 (Google Scholar).

Professor Nandi is a Fellow of the Royal Academy of Engineering and also a Fellow of seven other institutions including the IEEE and the IET. Among the many awards he received are the Institute of Electrical and Electronics Engineers (USA) Heinrich Hertz Award in 2012, the Glory of Bengal Award for his outstanding achievements in scientific research in 2010, the Water Arbitration Prize of the Institution of Mechanical Engineers (UK) in 1999, and the Mountbatten Premium, Division Award of the Electronics and Communications Division, of the Institution of Electrical Engineers (UK) in 1998.

S³: Time Frequency Array Signal Processing: Multi-Dimensional processing for non-stationary signals

Séminaire le 20 Mai 2016, 10h30 à CentraleSupelec (Gif-sur-Yvette) Salle du conseil du L2S - B4.40
Adel Belouchrani

Conventional time-frequency analysis methods are being extended to data arrays, and there is a potential for a great synergistic development of new advanced tools by exploiting the joint properties of time-frequency methods and array signal processing methods. Conventional array signal processing assumes stationary signals and mainly employs the covariance matrix of the data array. This assumption is motivated by the crucial need in practice for estimating sample statistics by resorting to temporal averaging under the additional hypothesis of ergodic signals. When the frequency content of the measured signals is time varying (i.e., nonstationary signals), this class of approaches can still be applied. However, the achievable performances in this case are reduced with respect to those that would be achieved in a stationary environment. Instead of considering the nonstationarity as a shortcoming, Time Frequency Array Processing   takes advantage of the nonstationarity by considering it as a source of information in the design of efficient algorithms in such environments. This talk deals with this  relationship between time-frequency methods and array signal processing methods. Recent results on the performance analysis of the Time Frequency MUSIC algorithm will be also presented.

Bio: Adel Belouchrani was born in Algiers, Algeria, on May 5, 1967. He received the State Engineering degree in 1991 from Ecole Nationale Polytechnique (ENP), Algiers, Algeria, the M.S. degree in signal processing from the Institut National Polytechnique de Grenoble (INPG), France, in 1992, and the Ph.D. degree in signal and image processing from Télécom Paris (ENST), France, in 1995. He was a Visiting Scholar at the Electrical Engineering and Computer Sciences Department, University of California, Berkeley, from 1995 to 1996. He was with the Department of Electrical and Computer Engineering, Villanova University, Villanova, PA, as a Research Associate from 1996 to 1997. From 1998 to 2005, he has been with the Electrical Engineering Department of ENP as Associate Professor. He is currently and since 2006 Full Professor at ENP. His research interests are in statistical signal processing, (blind) array signal processing, time-frequency analysis and time-frequency array signal processing with applications in biomedical and telecommunications. Professor Adel Belouchrani is an IEEE Senior Member and has published over 180 technical publications including 48  journal papers, 4 book chapters and 4 patents that have been cited over 5400 times according Google Scholar  and  over 2000 time according to ISI Web Of Science. He has supervised over 19 PhD students. Professor Adel Belouchrani is currently Associated Editor of the IEEE Transactions on Signal Processing and Editorial board member of the Digital signal processing Journal (Ed. Elsevier).  He has been recently nominated  as a founding member of the Algerian Academy of Science  and Technology.

Improved Millimeter-Wave Radar Concealed-Threat Person Scanning

Séminaire le 8 Avril 2016, 14h00 à CentraleSupelec (Gif-sur-Yvette) Salle du conseil du L2S - B4.40
Carey M. Rappapor

Metal-detecting airport security scanners for airline passengers are being replaced by millimeter-wave imagers. These new systems are much better at revealing concealed manmade objects, but they can be improved. At our Advanced Imaging Technology Lab at Northeastern University in Boston, we are developing a custom-designed elliptical toroid reflector antenna which allows multiple overlapping beams for focused wide-angle illumination to speed data acquisition and accurately image strongly inclined body surfaces.  We have developed the concept of the Blade Beam Reflector both as a single transmitting antenna and a multi-beam Toroidal Reflector, with multiple feeds. Each feed generates a different incident beam with different viewing angles, while still maintaining the blade beam configuration of narrow slit illumination in the vertical direction.  Having multiple transmitters provides horizontal resolution and imaging of full 120 deg. of body.  Furthermore, the reflector can simultaneously be used for receiving the scattered field, with high gain, overlapping, high vertical resolution beams for each transmitting or receiving array element. The multistatic transmitting and receiving array configuration sensing avoids dihedral artifacts from body crevices and reduces non-specular drop-outs, and will leads to a faster, higher resolution, and less expensive security system.

Bio — Carey M. Rappaport received five degrees from the Massachusetts Institute of Technology:  the SB in Mathematics, the SB, SM, and EE in Electrical Engineering in June 1982, and the PhD in Electrical Engineering in June 1987.  He is married to Ann W. Morgenthaler, and has two children, Sarah and Brian. Prof. Rappaport joined the faculty at Northeastern University in Boston, MA in 1987.  He has been Professor of Electrical and Computer Engineering since July 2000. In 2011, he was appointed College of Engineering Distinguished Professor.  He was Principal Investigator of an ARO-sponsored Multidisciplinary University Research Initiative on Humanitarian Demining, Co-Principal Investigator of the NSF-sponsored Engineering Research Center for Subsurface Sensing and Imaging Systems (CenSSIS), and Co-Principal Investigator and Deputy Director of the DHS-sponsored Awareness and Localization of Explosive Related Threats (ALERT) Center of Excellence. Prof. Rappaport has authored over 400 technical journal and conference papers in the areas of microwave antenna design, electromagnetic wave propagation and scattering computation, and bioelectromagnetics, and has received two reflector antenna patents, two biomedical device patents and three subsurface sensing device patents.  He was awarded the IEEE Antenna and Propagation Society's H.A. Wheeler Award for best applications paper, as a student in 1986.  He is a member of Sigma Xi and Eta Kappa Nu professional honorary societies.

S³: Topological Pattern Selection in Recurrent Networks

Séminaire le 1 Avril 2016, 10h30 à CentraleSupelec (Gif-sur-Yvette) Salle du conseil du L2S - B4.40
Alireza Bahraini

One of the differences between memory function of hypocampus and  neural networks situated at neocortex is that in the  latter memory operation still reflect the topography informing synaptic connections. This means that the activity of a unit relates also to its position in the tissue.
We introduce two approaches for incorporating the information of the geometry  of the underlying neural network into its dynamics. This phenomenon is carried out based on two probability rules for selecting storing patterns. First  a Gibbs type distribution inspired by the architecture of the network is applied. We are then led to a second method to introduce topological effects on the dynamics of the network. In both approaches a significant enhancement on the capacity of the network is observed after considerable rigorous computations.

Some References:
1- Bahraini,A, .  Abbassian,A.  Topological Pattern Selection in Recurrent Networks, Journal of Neural Networks, 31, 2012, 22-32.
2- Roudi, Y, Treves, A,. An associative Network with Spatially Organized Connectivity,2004, Journal of Statistical Mechanics.
3- Gallan, R., F., On how network architecture determines the dominant patterns of spontaneous neural activity, PLoS One , 2008.

Bio: I obtained my DEA in 2001 and my PhD in 2004 at University Paris 7 under the supervision of Professor Daniel Bennequin. The title of my thesis was Super-symmetry and Complex Geometry. I joined the department of mathematical sciences of Sharif university of technology as an assistant
professor in 2004 and in 2012 I became an associate professor at the same department.

S³: Solving large-scale inverse problems using forward-backward based methods

Séminaire le 11 Mars 2016, 10h30 à CentraleSupelec (Gif-sur-Yvette) Salle du conseil du L2S - B4.40
Audrey Repett: post-doctoral researcher at the Heriot-Watt university, in Scotland

Recent developments in imaging and data analysis techniques came along with an increasing need for fast convex optimization methods for solving large scale problems.  A simple optimization strategy to minimize the sum of a Lipschitz differentiable function and a non smooth function is the forward-backward algorithm. In this presentation, several approaches to accelerate convergence speed and to reduce complexity of this algorithm will be proposed. More precisely, in a first part, preconditioning methods adapted to non convex minimization problems will be presented, and in a second part, stochastic optimization techniques will be described in the context of convex optimization. The different proposed methods will be used to solve several inverse problems in signal and image processing.

Bio: Audrey Repetti is a post-doctoral researcher at the Heriot-Watt university, in Scotland. She received her M.Sc. degree from the Université Pierre et Marie Curie (Paris VI) in applied mathematics, and her Ph.D. degree from the Université Paris-Est Marne-la-Vallée in signal and image processing. Her research interests include convex and non convex optimization, and signal and image processing.