Diffraction électromagnétique par des laminés plans renforcés par des fibres cylindriques arrangées périodiquement.

Changyou Li
Thesis defended on September 28, 2015, 2:00 PM at CentraleSupelec (Gif-sur-Yvette) Amphi Ampère

La thèse porte sur la modélisation électromagnétique et la simulation de composites stratifiés plans (laminés), renforcés par des fibres organisées périodiquement. L'objectif est d'acquérir une bonne compréhension du comportement électromagnétique de telles structures, en première et étape de ce que pourrait ultérieurement être la production d'images mettant en évidence la localisation de zones éventuellement endommagées, et fournissant une certaine quantification de celles-ci.

La thèse proprement dite se concentre donc sur la construction et l'évaluation de modèles de la diffraction électromagnétique par des composites multicouches tels que chaque couche est renforcée par des fibres disposées périodiquement. Est d'abord investiguée la diffraction par une plaque diélectrique (mono-couche) au sein de laquelle des fibres cylindriques de section circulaire de même rayon sont incorporées périodiquement, ces fibres ayant la même orientation de leurs axes et la même distance de centre à centre.

Un cas bidimensionnel impliquant des ondes planes E ou H-polarisées, ainsi que des faisceaux gaussiens, normalement ou obliquement incidents, est d'abord pris en considération afin de mieux comprendre principes et philosophies des méthodes de choix, le couplage de mode et l'expansion multipolaire. Puis le travail est étendu, la diffraction de la plaque sous un éclairement tridimensionnel (conique) étant alors traitée en détail, ce qui montre aussi le potentiel de la méthodologie mise en œuvre si l'on souhaite obtenir la réponse électromagnétique de la structure à une source ponctuelle.

Un composite multicouche, plus courant, mais plus complexe, qui est fait d'un empilement de plaques l'une sur l'autre, est alors étudié. Deux différentes espèces de composites sont ici prises en compte. Pour étudier la première, dont les fibres dans les différentes couches possèdent les mêmes orientations, des méthodes à base de matrices dites S ou dites T sont introduites, impliquant entre autre de s'intéresser à une résolution convenable du système linéaire produit selon le couplage de mode à la transition entre deux couches adjacentes. Une investigation de la deuxième espèce de composites suit alors, pour lequel les fibres au sein des différentes couches sont orientées dans des directions différentes quelconques, ce que permet une extension précautionneuse des approches précédentes.

Une certaine attention est également portée au problème de l'homogénéisation des composites, de manière à lier les démarches à petite échelle telles que développées dans la thèse à celles à grande échelle souvent les seules prises en compte dans le contrôle non destructif et l'imagerie des composites stratifiés.

De nombreux résultats de simulations numériques sont proposés et validés autant que possible par des résultats de référence de la littérature (notamment dans le cas de cristaux photoniques) et l'utilisation de solveurs «brute-force». L'accent est aussi mis sur des cas particuliers de matériaux composites (ceux à base de fibres de verre et ceux à base de fibres de carbone) qui sont le plus souvent rencontrés dans les applications pratiques, avec des bandes de fréquences appropriées choisies en accord avec le comportement des fibres, principalement diélectrique ou principalement conducteur.

 

Composition du jury :

O. Dazel, Professeur, Université du Maine, Le Mans, rapporteur,
A. Nicolet, Professeur, Aix-Marseille Université, Marseille, rapporteur,
J.-J. Greffet, Professeur, Laboratoire Charles Fabry de l'Institut d'Optique, Palaiseau, examinateur,
P. Joly, Directeur de recherche INRIA, Palaiseau, examinateur,
C. Reboud, Ingénieur-chercheur, CEA LIST, Département Imagerie Simulation pour le Contrôle, Saclay, examinateur,
D. Lesselier, Directeur de recherche CNRS, L2S, Gif-sur-Yvette, Directeur de thèse.

Caractérisation des performances minimales d’estimation pour des modèles d’observation non-standards

Chengfang Ren
Thesis defended on September 28, 2015, 2:00 PM at CentraleSupelec (Gif-sur-Yvette) Amphi Janet

Dans le contexte de l'estimation paramétrique, les performances d'un estimateur peuvent être caractérisées, entre autre, par son erreur quadratique moyenne (EQM) et sa résolution limite. La première quantifie la précision des valeurs estimées et la seconde définit la capacité de l'estimateur à séparer plusieurs paramètres. Cette thèse s'intéresse d'abord à la prédiction de l'EQM "optimale" à l'aide des bornes inférieures pour des problèmes d'estimation simultanée de paramètres aléatoires et non-aléatoires (estimation hybride), puis à l'extension des bornes de Cramér-Rao pour des modèles d'observation moins standards. Enfin, la caractérisation des estimateurs en termes de résolution limite est également étudiée. Cette thèse est donc divisée en trois parties :

- Premièrement, nous complétons les résultats de littérature sur les bornes hybrides en utilisant deux bornes bayésiennes : la borne de Weiss-Weinstein et une forme particulière de la famille de bornes de Ziv-Zakaï. Nous montrons que ces bornes "étendues" sont plus précises pour la prédiction de l'EQM optimale par rapport à celles existantes dans la littérature.

- Deuxièmement, nous proposons des bornes de type Cramér-Rao pour des contextes d'estimation moins usuels, c'est-à-dire : (i) Lorsque les paramètres non-aléatoires sont soumis à des contraintes d'égalité linéaires ou non-linéaires (estimation sous contraintes). (ii) Pour des problèmes de filtrage à temps discret où l'évolution des états (paramètres) est régit par une chaîne de Markov. (iii) Lorsque la loi des observations est différente de la distribution réelle des données.

- Enfin, nous étudions la résolution et la précision des estimateurs en proposant un critère basé directement sur la distribution des estimées. Cette approche est une extension des travaux de Oh et Kashyap et de Clark pour des problèmes d'estimation de paramètres multidimensionnels.

 

Membres du jury :
M. Jean-Yves Tourneret  Professeur à l’INP-ENSEEIHT Toulouse  (Rapporteur)
M. Philippe Forster  Professeur à l’Université Paris-Ouest  (Rapporteur)
M. Cédric Richard  Professeur à l’Université Nice Sophia-Antipolis  (Examinateur)
M. Karim Abed-Meraim  Professeur à l’Université d’Orléans  (Examinateur)
M. Éric Chaumette  Professeur à l’ISAE  (Encadrant de thèse)
M. Jérôme Galy   Maître de conférences à l’Université de Montpellier  (Encadrant de thèse)
M. Alexandre Renaux  Maître de conférences à l’Université Paris-Sud  (Directeur de thèse)


Mots clés : Estimation paramétrique, estimateurs au sens du maximum de vraisemblance, estimateurs au sens du maximum a posteriori, estimation hybride, analyse de performance, bornes inférieures de l'erreur quadratique moyenne, résolution limite statistique.

S³: Bayesian Tomography

Seminar on September 25, 2015, 10:30 AM at CentraleSupelec (Gif-sur-Yvette) Salle du conseil du L2S - B4.40
John Skilling, Maximum Entropy Data Consultants Ltd, UK

Abstract: Click here

Bio: John Skilling was awarded his PhD in radio astronomy in 1969.  Through the 1970s and 1980s he was a lecturer in applied mathematics at Cambridge University, specialising in data analysis.  He left to concentrate on consultancy work, originally using maximum entropy methods but moving to Bayesian methodology when algorithms became sufficiently powerful.  John has been a prominent contributor to the “MaxEnt” conferences since their beginning in 1981.  He is the discoverer of the nested sampling algorithm which performs integration over spaces of arbitrary dimension, which is the basic operation dictated by the sum rule of Bayesian calculus.

S³: Is the Gaussian distribution "Normal"? Signal processing with alpha-stable distributions

Seminar on September 18, 2015, 10:30 AM at CentraleSupelec (Gif-sur-Yvette) Salle des séminaires du L2S
Ecran E. Kuruoglu, Istituto di Scienza e Tecnologie dell'Informazione, Italy

There are solid reasons for the popularity of Gaussian models. They are easy to deal with, lead to linear equations, and they have a strong theoretical justification given by the Central Limit theorem. However, many data, manmade or natural, exhibit characteristics too impulsive or skewed to be successfully accommodated by the Gaussian model. The wide spread power laws in the nature, in internet, in linguistics, biology are very well known. In this talk we will challengethe "Normality" of the Gaussian distribution and will discuss the alpha‐stable distribution family which satisfies the generalised Central Limit Theorem. Alpha‐Stable distributions have received wide interest in the signal processing community and became state of the art models for impulsive noise and internet traffic in the last 20 years since the influential paper of Nikias and Shao in 1993. We will provide the fundamental theory and discuss the rich class of statistics this family enables us to work with including fractional order statistics, log statistics and extreme value statistics. We will present some application areas where alpha‐stable distributions had important success such as internet traffic modelling, SAR imaging, computational biology, astronomy, etc. We will also present recent research results on generalisation of source separation algorithms by maximizing non-alpha stability and also multivariate analysis using alpha-stable Bayesian networks. We will identify open problems which we hope will lead to fruitful discussion on further research on this family of distributions.

Bio: Ercan E. Kuruoglu was born in Ankara, Turkey in 1969. He obtained his BSc and MSc degrees both in Electrical and Electronics  Engineering  at  Bilkent  University  in  1991  and  1993  and  the  MPhil  and  PhD  degrees  in  Information  Engineering  at  the Cambridge University, in the Signal Processing Laboratory, in 1995 and 1998 respectively. Upon graduation from Cambridge, he joined the Xerox Research Center in Cambridge as a permanent member of theCollaborative Multimedia Systems Group. In 2000, he was in INRIA‐Sophia Antipolis as an ERCIM fellow. In 2002, he joined ISTI‐CNR, Pisa as a permanent member. Since 2006, he  is  an Associate Professor  and  Senior Researcher. He was  a  visiting professor  in Georgia Institute of Technology graduate  program  in  Shanghai  in  2007  and  2011. He was a 111 Project (Bringing Foreign Experts to China Program) Fellow and was a frequent visitor to Shanghai Jiao Tong University, China (2007‐2011). He was an Visiting Professor in Hong Kong, in August 2012 as a guest of the HK IEEE Chapter. He is a recipient of the Alexander von Humboldt Foundation Fellowship (2012‐2014) which allowed him to work in as a visiting scientist at Max‐Planck Institute for Molecular Biology. He was an Associate Editor for IEEE Transactions on Signal Processing in 2002‐2006 and for IEEE Transactions on Image Processing in 2005‐2009. He is currently the Editor in Chief of Digital Signal Processing: a Review Journal and also is in the editorial board of EURASIP Journal on Advances in Signal Processing. He was the Technical co‐Chair for EUSIPCO 2006, special sessions chair of EUSIPCO 2005 and tutorials  co‐chair of ICASSP 2014. He served  as  an  elected member of the IEEE  Technical Committee on  Signal Processing Theory and Methods (2004‐2010), was a member of IEEE Ethics committee in 2012 and is a Senior Member of IEEE. He was a plenary speaker at Data Analysis for Cosmology (DAC 2007) and ISSPA 2010 and tutorial speaker at ICSPCC 2012 and Bioinformatiha 2013 and 2014 . He is the author of more than 100 peer reviewed publications and holds 5 US, European and Japanese patents. His research interests  are  in  statistical  signal  processing  and  information  and  coding  theory  with  applications  in  image  processing, computational biology, telecommunications, astronomy and geophysics.

 

Access information are available on the website http://www.lss.supelec.fr/scube/

 

A sparsity-promoting reconstruction algorithm for diffuse optical tomography based on a transport model

Seminar on September 08, 2015, 11:30 AM at CentraleSupelec (Gif-sur-Yvette) Salle du conseil du L2S - B4.40
K. Prieto (formerly University of Manchester, since Aug 2015 Hokkaido University) and O. Dorn (University of Manchester) (presenting author)

Non-linear inverse problems with sparsity

Seminar on September 08, 2015, 10:30 AM at CentraleSupelec (Gif-sur-Yvette) Salle du conseil du L2S - B4.40
P. Maass (University of Bremen)

Compressive Sensing as a tool for exploiting sparsity and incoherence in computational electromagnetics

Seminar on September 08, 2015, 9:30 AM at CentraleSupelec (Gif-sur-Yvette) Salle du conseil du L2S - B4.40
A. Massa (presenting author), G. Oliveri, M. Salucci, N. Anselmi (all at ELEDIA, University of Trento, A. Massa as DIGITEO Chair holder L2S & CEA LIST)

S³: Bayesian Cyclic Networks, Mutual Information and Reduced-Order Bayesian Inference

Seminar on July 17, 2015, 10:30 AM at CentraleSupelec (Gif-sur-Yvette) Salle des séminaires du L2S
Robert Niven, University of New South Wales, Canberra, Australia

A branch of Bayesian inference involves the analysis of so-called "Bayesian networks", defined as directed acyclic networks composed of probabilistic connections. We extend this class of networks to consider cyclic Bayesian networks, which incorporate every pair of inverse conditional probabilities or probability density functions, thereby enabling the application of Bayesian updating around the network. The networks are assumed Markovian, although this assumption can be relaxed when necessary. The analysis of probabilistic cycles reveals a deep connection to the mutual information between pairs of variables on the network. Analysis of a four-parameter network - of the form of a commutative diagram - is shown to enable thedevelopment of a new branch of Bayesian inference using a reduced order model (coarse-graining) framework.

Bio: https://research.unsw.edu.au/people/dr-robert-niven

S³: The method of brackets

Seminar on July 03, 2015, 10:30 AM at CentraleSupelec (Gif-sur-Yvette) Salle du conseil du L2S - B4.40
Victor H. Moll, Department of Mathematics, Tulane University, New Orleans, USA

A new heuristic method for the evaluation of denite integrals is presented. This method of brackets has its origin in methods developed for the evaluation of Feynman diagrams. We describe the operational rules and illustrate the method with several examples. The method of brackets reduces the evaluation of a large class of denite integrals to the solution of a linear system of equations.

Bio: Victor H. Moll studied under Henry McKean at the Courant Institute, graduated in 1984 with a thesis on the Stabilization of the standing wave in a caricature for nerve conduction. This so-called caricature had been proposed by McKean as a simpler model from the classical Nagumo and Hodgkin-Huxley models. After graduation, he spent two years as a Lawton instructor at Temple University. In 1986 he moved to Tulane University, New Orleans, where he is now a Professor of Mathematics.
He is interested in all aspects of the mathematics coming from the evaluation of integrals. The subject is full of interesting problems that he shares with colleagues, graduate and undergraduate students. Among the variety of results that have come out of this work, one should mention the theory of Landen transformations that are the rational version of the well-known transformations of Landen and Gauss for
elliptic integrals. His long term project is to provide proofs, automatic and human of all entries
in the classical table of Integrals by I. S. Gradshteyn and I. M. Ryzhik. Most of his work comes from exploring, via symbolic languages, unexpected relations among classical objects. Some of his work has been written in the book Numbers and Functions published in the Student Mathematical Library series from AMS. He is actively involved with bringing undergraduates into Mathematics. He has guided undergraduate research at Tulane University and also was the research leader at the REU programs SIMU (at the University of Puerto Rico at Humacao 2000 and 2002) and at MSRI-UP, Berkeley (2008 and 2014). A large number of his students have continued to graduate school in Mathematics.

S³: Un modèle stochastique de la transcription d’un gène

Seminar on June 26, 2015, 11:00 AM at CentraleSupelec (Gif-sur-Yvette) Salle des séminaires du L2S
Marc Roussel, University of Lethbridge, Alberta, Canada

Nous étudions depuis quelques années des modèles stochastiques de la transcription, c’est-à-dire de la synthèse de l’ARN à partir de la séquence de l’ADN par une machine moléculaire, l’ARN polymérase. Pour le cas d’une seule polymérase, il est possible de solutionner exactement nos modèles. Lorsque les interactions entre les polymérases sont importantes, il faut par contre utiliser (pour le moment) des méthodes numériques. En forme d’introduction au sujet, je présenterai un de nos modèles les plus simples, et je démontrerai comment on peut obtenir tous les moments voulus de la distribution du temps de transcription, c’est-à-dire comment on peut solutionner ce modèle. Cette distribution pourra être utilisée dans des modèles d’expression génétique, où elle apparaitra comme distribution de retards de la production de l’ARN.


Bio: Marc R. Roussel is Professor at Alberta RNA Research and Training Institute, Department of Chemistry and Biochemistry, University of Lethbridge. More details can be found at his homepage http://people.uleth.ca/~roussel/

S³: High dimensional minimum risk portfolio optimization

Seminar on June 26, 2015, 10:00 AM at CentraleSupelec (Gif-sur-Yvette) Salle des séminaires du L2S
Liusha Yang, Department of Electronic and Computer Engineering, Hong Kong University of Science and Technology

The performance of the global minimum risk portfolio (GMVP) relies on the accuracy of the estimated covariance matrix of the portfolio asset returns. For large portfolios, the number of available market returns is often of similar order to the number of assets, making the sample covariance matrix performs poorly. In this talk, we discuss two newly-developed GMVP optimization strategies under high dimensional analysis. The first approach is based on the shrinkage Tyler’s robust M-estimation with a risk-minimizing shrinkage parameter. It not only deals with the problem of sample insufficiency, but also the impulsiveness of financial data. The second approach is built upon a spiked covariance model, by assuming the population covariance matrix follows the spiked covariance model, in which several eigenvalues are significantly larger than all the others, which all equal one. The performances of our strategies will be demonstrated through synthetic and real data simulations.

Bio: Liusha Yang received the B.S. in Communication Engineering from the Beijing University of Posts and Telecommunications in 2012. Currently, she is a Ph.D. student in the Department of Electronic and Computer Engineering at the Hong Kong University of Science and Technology. Her research interests include random matrix theory and signal processing, with applications in financial engineering.

Wireless devices and services for distributed sensing, monitoring, and decision support

Seminar on June 25, 2015, 10:00 AM at CentraleSupelec (Gif-sur-Yvette) Salle du conseil du L2S - B4.40
F. Viani: "Research Associate" et membre du Centre de Recherche ELEDIA de l'université TRENTO

Low-power and compact wireless devices, like smart sensors, embedded systems, smartphones, tablets are more and more becoming everyday life tools, bringing advantages not limited to the mobile communications but also referred to improved context awareness. The potentialities of such wireless technologies are enriched by the integration of dedicated real-time processing techniques which enable not only the distributed sensing of heterogeneous parameters, but also the improved management, understanding, and forecasting of complex processes. The output of such analysis is also exploited to support operators in decision making. Representative application examples are in the field of smart cities and communities, where distributed wireless sensors and mobile devices are largely applied both in indoor (e.g., in smart buildings, smart museums, etc.) and outdoor (smart lighting, road security, fleet management, etc.) scenarios.


Short CV: Federico Viani received the B.S. and M.S. degrees in Telecommunication Engineering and  the PhD degree in Information and Communication Technology from the University of Trento, Italy, in 2004, 2007, and 2010, respectively. Since 2011, Dr. Viani is a Research Associate (Post-Doc) at the Department of Information Engineering and Computer Science (DISI) of the University of Trento, Italy, and a member of the ELEDIA Research Center.
Since 2007, Dr. Viani has been the co-advisor of 18 M.S/B.S Thesis. Since 2010 he has been the official teacher of the Bachelor degree course "Design Techniques for Wireless Communications", and since 2007 he has been a teaching assistant of Bachelor degree and Master degree courses in Telecommunication Engineering offered by the University of Trento, including "Electromagnetic Propagation", "Project Course on Wireless Technologies", "Antennas for Wireless Communications", "Biomedical Diagnostic Techniques", "Mobile Communications".
Dr. Viani is author/co-author of over 77 peer reviewed papers on international journals and conferences, including 28 contributions on peer-reviewed international journals, 49 in international conferences. Moreover, Dr. Viani has been cited 574 times and his H-Index is equal to 14 in the Scopus Database. He has been invited to submit papers to International Journals and to present contributions to Scientific Sessions in International Conferences. He has organized and/or chaired 3 Special Sessions in International Conferences. Since 2007, he has attended 7 national and international conferences, presenting as a speaker 15 contributions.
Since 2007, Dr. Viani has been a Participant in 17 Research Projects, funded by EU, Industries, and National Agencies.
The research activities of Dr. Viani are oriented to the development of methodological strategies and applications in the framework of Electromagnetic Fields (S.S.D. ING‐INF/02, S.C. 09/F1), with main emphasis on applied electromagnetics. He has been involved in activities concerning the design of multiband, wideband, and ultra-wideband antennas, the study and development of optimization techniques as well as learning-by-example methodologies for the solution of complex electromagnetic problems including inverse problems and active/passive wireless localization. He is also involved in the design and development of distributed and pervasive monitoring by means of wireless sensor networks (WSNs) and robot swarms, and in the application of decision support systems (DSS) to fleet management and emergency-related applications.
Dr. Viani is a Reviewer for international Journals, including IEEE Transactions on Antennas and Propagation, IEEE Antennas and Wireless Propagation Letters, Progress in Electromagnetic Research/Journal of Electromagnetic Waves and Applications, IEEE Transactions on Vehicular Technologies.
Dr. Viani is a Senior Member of the IEEE, member of the IEEE Antennas and Propagation Society, and of the European Microwave Association (EuMA).

S³: Stability of continuous-time quantum filters

Seminar on June 19, 2015, 10:00 AM at CentraleSupelec (Gif-sur-Yvette) Salle des séminaires du L2S
Nina H. Amini, CNRS, Laboratory of Signals and Systems, France

In this talk, we study quantum filtering and its stability problem. Indeed, we show that the fidelity between the state of a continuously observed quantum system and the state of its associated quantum filter, is always a sub-martingale. The observed system could be governed by a continuous-time Stochastic Master Equation (SME), driven simultaneously by Wiener and Poisson processes which takes into account incompleteness and errors in measurements. This stability result is the continuous-time counterpart of a similar stability result already established for discrete-time quantum systems. This result implies the stability of such filtering process but does not necessarily ensure the asymptotic convergence of such quantum filters.

Bio: Nina H. Amini is a CNRS researcher at Laboratory L2S at CentraleSupelec since October 2014. She did her first postdoc from June 2012 for six months at ANU, College of Engineering and Computer Science and her second postdoc at Edward L. Ginzton Laboratory, Stanford University since December 2012. She received her Ph.D. in Mathematics and Control Engineering from Mines-ParisTech (Ecole des Mines de Paris), in September 2012. Prior to her Ph.D., she earned a Master in Financial Mathematics and Statistics at ENSAE and the Engineering Diploma of l’Ecole Polytechnique, in 2009. Her research interests include stochastic control, quantum control, (quantum) filtering theory, (quantum) probability, and (quantum) information theory.

S³: Modeling and mismodeling in radar applications: parameter estimation and bounds

Seminar on June 09, 2015, 10:00 AM at CentraleSupelec (Gif-sur-Yvette) Salle des séminaires du L2S
Maria S. Greco, Department of Information Engineering, University of Pisa

The problem of estimating a deterministic parameter vector of acquired data is ubiquitous in signal processing applications. A fundamental assumption underlying most estimation problems is that the true data model and the model assumed to derive an estimation algorithm are the same, that is, the model is correctly specified.
This lecture will focus on the general case in which, for some non-perfect knowledge of the true data model or for operative constraints on the estimation algorithm there is a mismatch between assumed and true data model.
After a short first part dedicated to explain the radar framework of the estimation problem, the lecture will be dedicated to the evaluation of lower bounds on the Mean Square Error of the estimate of a deterministic parameter vector under misspecified model with particular attention to Mismatched Maximum Likelihood estimator and Huber bounds.

Bio: Maria S. Greco graduated in Electronic Engineering in 1993 and received the Ph.D. degree in Telecommunication Engineering in 1998, from University of Pisa, Italy. From December 1997 to May 1998 she joined the Georgia Tech Research Institute, Atlanta, USA as a visiting research scholar where she carried on research activity in the field of radar detection in non-Gaussian background.
    In 1993 she joined the Department of Information Engineering of the University of Pisa, where she is Associate Professor since December 2011. She’s IEEE fellow since January 2011 and she was co-recipient of the 2001 IEEE Aerospace and Electronic Systems Society’s Barry Carlton Award for Best Paper and recipient of the 2008 Fred Nathanson Young Engineer of the Year award for contributions to signal processing, estimation, and detection theory. She has been co-general-chair of the 2007 International Waveform Diversity and Design Conference (WDD07), Pisa, Italy, in the Technical Committee of the 2006 EURASIP Signal and Image Processing Conference (EUSIPCO), Florence, Italy, in the Technical Committee of the 2008 IEEE Radar Conference, Rome, Italy, in the Organizing Committee of CAMSAP09, Technical co-chair of CIP2010 (Elba Island, Italy), General co-Chair of CAMSAP2011 (San Juan, Puerto Rico), Publication Chair of ICASSP2014, Florence, Italy, Technical Co-Chair of the CoSeRa2015, Pisa, Italy and Special Session Chair of CAMSAP2015, Cancun, Mexico. She is lead guest editor of the special issue on "Advanced Signal Processing for Radar Applications" to appear on the IEEE Journal on Special Topics of Signal Processing, December 2015, she was guest co-editor of the special issue of the Journal of the IEEE Signal Processing Society on Special Topics in Signal Processing on "Adaptive Waveform Design for Agile Sensing and Communication," published in June 2007 and lead guest editor of the special issue of International Journal of Navigation and Observation on” Modelling and Processing of Radar Signals for Earth Observation published in August 2008. She’s Associate Editor of IET Proceedings – Sonar, Radar and Navigation, Associate Editor-in-Chief of the IEEE Aerospace and Electronic Systems Magazine, member of the Editorial Board of the Springer Journal of Advances in Signal Processing (JASP), Senior Editorial board member of IEEE Journal on Selected Topics of Signal Processing (J-STSP), member of the IEEE Signal Array Processing (SAM) Technical Committees. She's also member of the IEEE AES and IEEE SP Board of Governors and Chair of the IEEE AESS Radar Panel. She's as well SP Distinguished Lecturer for the years 2014-2015, AESS Distinguished Lecturer for the years 2015-2016 and member of the IEEE Fellow Committee.
     Maria is a coauthor of the tutorials entitled “Radar Clutter Modeling”, presented at the International Radar Conference (May 2005, Arlington, USA), “Sea and Ground Radar Clutter Modeling” presented at 2008 IEEE Radar Conference (May 2008, Rome, Italy) and at 2012 IEEE Radar Conference (May 2012, Atlanta, USA), coauthor of the tutorial "RF and digital components for highly-integrated low-power radar" presented at the same conference, of the tutorial "Recent Advances in Adaptive Radar Detection" presented at the 2014 International Radar Conference (October 2014, Lille, France) and co-author of the tutorial "High Resolution Sea and Land Clutter Modeling and analysis", presented at the 2015 IEEE International Radar Conference (May 2015, Washington DC, USA).
    Her general interests are in the areas of statistical signal processing, estimation and detection theory. In particular, her research interests include clutter models, spectral analysis, coherent and incoherent detection in non-Gaussian clutter, CFAR techniques, radar waveform diversity and bistatic/mustistatic active and passive radars. She co-authored many book chapters and more than 150 journal and conference papers.

S³: The appliction of medium grazing angle sea-clutter models -- The NRL multi-aperture SAR: system description and recent results

Seminar on May 26, 2015, 2:00 PM at CentraleSupelec (Gif-sur-Yvette) Salle des séminaires du L2S
Dr. Luke Rosenberg - DSTO, Australia

Details are given in the attached file. Access information are available on the website http://www.lss.supelec.fr/scube/

Seminaire_s3-sondra-icode May 26th

Approches bayésiennes en tomographie micro-ondes. Application à l'imagerie du cancer du sein

Leila GHARSALLI
Thesis defended on April 10, 2015, 10:30 AM at CentraleSupelec (Gif-sur-Yvette) Amphi F3-05

Ce travail concerne l'imagerie micro-onde en vue d'application à l'imagerie biomédicale.  Cette technique d'imagerie a pour objectif de retrouver la distribution des propriétés diélectriques internes (permittivité diélectrique et conductivité) d'un objet inconnu illuminé par une onde interrogatrice connue à partir des mesures du champ électrique dit diffracté résultant de leur interaction.

Un tel problème constitue un problème dit inverse par opposition au problème direct associé qui consiste à calculer le champ diffracté, l'onde interrogatrice et l'objet étant alors connus.

La résolution du problème inverse nécessite la construction préalable du modèle direct associé. Celui-ci est ici basé sur une représentation intégrale de domaine des champs électriques donnant naissance à deux équations intégrales couplées dont les contreparties discrètes sont obtenues à l'aide de la méthode des moments.

En ce qui concerne le problème inverse, hormis le fait que les équations physiques qui interviennent dans sa modélisation directe le rendent non-linéaire, il est également mathématiquement mal posé au sens de Hadamard, ce qui signifie que les conditions d'existence, d'unicité et de stabilité de la solution ne sont pas simultanément garanties. La résolution d'un tel problème nécessite sa régularisation préalable qui consiste généralement en l'introduction d'information a priori sur la solution recherchée. Cette résolution est effectuée, ici, dans un cadre probabiliste bayésien où l'on introduit une connaissance a priori adaptée à l'objet sous test et qui consiste à considérer ce dernier comme étant composé d'un nombre fini de matériaux homogènes distribués dans des régions compactes. Cet information est introduite par le biais d'un modèle de « Gauss-Markov-Potts ». Le calcul bayésien nous donne la loi a posteriori de toutes les inconnues à partir de laquelle on peut définir les estimateurs ponctuels. On s'attache ensuite à déterminer les estimateurs a posteriori via des méthodes d'approximation variationnelles et à reconstruire ainsi l'image de l'objet recherché.

Les principales contributions de ce travail sont d'ordre méthodologique et algorithmique. Elles sont illustrées par une application de l'imagerie micro-onde à l'imagerie du cancer du sein. Cette dernière constitue en soi un point très important et original de la thèse. En effet, l'imagerie du cancer du sein par la technique micro-onde est une alternative très intéressante à la mammographie par rayons X, mais n'en est encore qu'à un stade exploratoire.

Membres du jury:

Directeur de thèse   Mr Duchêne Bernard  Chargé de recherche, CNRS
Co-directeur de thèse   Mr Mohammad-Djafari Ali   Directeur de recherche, CNRS
Encadrant   Mr Ayasso Hacheme  Maître de conférences à l'Université de Grenoble
Rapporteurs  Mme Litman Amélie  Maître de conférences à l'Université d'Aix-Marseille
                    Mr Massa Andréa  Professeur à l'Université de Trento, Italie
Examinateurs  Mme Blanc-Feraud Laure  Directrice de recherche, CNRS
                      Mr Pichot du Mezeray Christian  Directeur de recherche, CNRS

S³ Working memory in random neural networks

Seminar on April 03, 2015, 10:30 AM at CentraleSupelec (Gif-sur-Yvette) Salle des séminaires du L2S
Gilles Wainrib, ENS, Computer Science Department

Numerous experimental studies investigate how neural representations of a signal depend on its past context. Although synaptic plasticity and adaptation may play a crucial role to shape this dependence, we study here the hypothesis that this dependence upon past context may be also explained by dynamical network effects, in particular due to the recurrent nature of neural networks connectivity.

Short Bio: Gilles Wainrib is assistant professor in the Computer Science Department at Ecole Normale Supérieure and his research interests range from theoretical biology to applied mathematics and artificial intelligence.

Talk 1: Level set methods for seismic full waveform inversion. Talk 2: Some inverse problems for cargo container screening.

Seminar on March 30, 2015, 1:30 PM at CentraleSupelec (Gif-sur-Yvette) Salle des séminaires du L2S
Oliver Dorn, Foreign Guest DIGITEO

Bio: Oliver Dorn is currently Lecturer at the School of Mathematics at The University of Manchester. He has obtained his PhD in Applied Mathematics from The University in Muenster, Germany, in 1997, followed by various postdoctoral research stays in the US and Canada. From 2002 until 2007 he was awarded a Ramon y Cajal fellowship at Universidad Carlos III de Madrid, where he became full professor (Profesor Titular) in 2008. He has visited Supélec and Université Paris Sud frequently for longer periods, and has published more than 50 papers in internationally competitive journals and conference proceedings. He is a professional member of SIAM (Society for Industrial and Applied Mathematics), IEEE (Institute of Electrical and Electronics Engineers) and EAGE (European Association of Geoscientists and Engineers) and is on the advisory board of the Journal 'Inverse Problems'. Right now he is at L2S as Foreign Guest DIGITEO.

S³ - Inverse problems in signal and image processing and S³ - Bayesian inference framework: from basic to advanced Bayesian computation

Seminar on March 27, 2015, 10:30 AM at CentraleSupelec (Gif-sur-Yvette) Salle des séminaires du L2S
Ali Mohammad Djafari, CNRS, L2S

In signal and image processing community, we can distinguish two categories:
- Those who start from the observed signals and images and do classical processing: filtering for denoising, change detection, contour detection, segmentation, compression, …
- The second category called “model based”, before doing any processing try first to understand from where those signals and images come from and why they are here . So, first defining what quantity has been at the origin of those observations, then modeling their link by “forward modeling” and finally doing inversion. This approach is often called “Inverse problem approach”. Then, noting the “ill-posedness” of the inverse problems, many “Regularization methods” have been proposed and applied successfully. However, deterministic regularization has a few limitations and recently the Bayesian inference approach has become the main approach for proposing unsupervised methods and effective solutions in many real applications. Interestingly, even many classical methods have found better understanding when re-stated as inverse problem. The Bayesian approach with simple prior models such as Gaussian, Generalized Gaussian, Sparsity
enforcing priors or more sophisticated Hierarchical models such as Mixture models, Gaussian Scale Mixture or Gauss-Markov-Potts models have been proposed in different applications of imaging systems with great success. However, Bayesian computation still is too costly and need more practical algorithms than MCMC. Variational Bayesian Approximation (VBA) methods have recently became a standard for computing the posterior means in unsupervized methods.
Interestingly, we show that VBA includes Joint Maximum A Posteriori (JMAP) and Expectation-Maximization (EM) as special cases. VBA is much faster than MCMC methods, but, it gives only access to the posterior means.
This talk gives an overview of these methods with examples in Deconvolution (simple or blind, signal or image) and  in Computed Tomography (CT).

Bio: http://djafari.free.fr/index.htm

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Séminaire S³
http://scube.lss.supelec.fr
seminaire.scube@l2s.centralesupelec.fr
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PDE-­‐based inversion method with no forward solver for inverse medium scattering problems

Seminar on March 20, 2015, 2:00 PM at CentraleSupelec (Gif-sur-Yvette) Salle du conseil du L2S - B4.40
Dr. Yu Zhong

Bio: Dr. Yu Zhong received his B.S. and M.S. degrees in electronic engineering from Zhejiang University, Hangzhou, China, in 2003 and 2006, respectively, and the Ph.D. degree from the National University of Singapore, Singapore, in 2010.
He is currently a Scientist in Institution of High Performance Computing (IHPC), A*STAR, Singapore.
His research interests mainly are inverse-­‐scattering problems and electromagnetic modeling on composite materials. He is a regular visitor at the Laboratoire des Signaux et Systèmes (L2S) in Gif-­sur-­‐Yvette, France as an invited senior scientific expert since 2012.
 
Talk 1: PDE-­‐based inversion method with no forward solver for inverse medium scattering problems
A new partial differential equation (PDE) based inversion method for inverse medium scattering problems is proposed in this talk, which does not need to solve any forward problem. The proposed method is the subspace-­‐based optimization method (SOM) in the differential-­‐equation frame. The finite difference scheme is used to discretized the Helmholtz equation, and the twofold subspace-­‐based regularization scheme, as in the integral equation based SOM, is applied in this PDE-­‐based inversion method to stabilize the solver. By using such a PDE-­‐based inversion method, the Green’s funciton for the domain of interests is no longer needed. Representative numerical tests are presented to verify the efficacy of the proposed method.


Talk 2: New integral equation and new partial differential equation for inverse medium scattering problems with strong scatterers 

In this talk, we propose two new equations, an integral equation (IE) and a partial differential equation (PDE), for solving inverse medium scattering problems (IMSP) with strong scatterers. First, we present a new integral equation, which could effectively reduce the globle wave contribution in estimating the contrast (the difference between permittivities of the scatterers and the known background) compared to the original Lippmann-­‐Schwinger equation. Using such a new IE in the IE-­‐based inversion method one is able to solve the highly nonlinear IMSP with strong scatterers.
Subsequently, the connection between the PDE-­‐based inversion method (in Talk 1), using the Helmholtz equation, and the conventional IE based inversion method, using the Lippmann-­‐Schwinger equation, is discussed. With such a connection and the new IE, we propose a new PDE,
using which the PDE-­‐based inversion method can also solve the highly nonlinear IMSP. At last, we discuss the pros and cons of both PDE-­‐ and IE-­‐based inversion methods.

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