S³: Robust spectral estimators for long-memory processes: Time and frequency domain approaches.

Séminaire le 29 Janvier 2016, 10h30 à CentraleSupelec (Gif-sur-Yvette) Salle du conseil du L2S - B4.40
Valderio Anselmo Reisen

This paper discusses the outlier effects on the estimation of a spectral estimator for long memory process under additive outliers and proposes robust spectral estimators. Some asymptotic properties of the proposed robust methods are derived and Monte Carlo simulations investigate their empirical properties.  Pollution series, such as, PM (Particulate matter), SO2 (Sulfur dioxide), are the applied examples investigated here to show the usefulness of the proposed  robust methods in real applications.  These pollutants present, in general, observations with high levels of pollutant concentrations which may produce sample densities with heavy tails  and these high levels of concentrations can be identified as outliers which can destroy the statistical properties of sample functions such as the standard mean,  covariance and the periodogram.

Bio: Valderio Anselmo Reisen is full Professor of Statistics at the Federal University of Espirito Santo (UFES), Vitoria, Brazil. His main interests are time series analysis, forecasting, econometric modeling, bootstrap, robustness in time series, unit root processes, counting processes,  environmental and economic data analysis, periodically correlated processes, and multivariate time series.

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Séminaire S³
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S³ seminar: A Two-Round Interactive Receiver Cooperation Scheme for Multicast Channels

Séminaire le 8 Janvier 2016, 16h30 à CentraleSupelec (Gif-sur-Yvette) Salle du conseil du L2S - B4.40
Victor Exposito

We consider the problem of transmitting a common message from a transmitter to two receivers over a broadcast channel, which is also called multicast channel in this case. The two receivers are allowed to cooperate with each other in full-duplex over non-orthogonal channels. We investigate the information-theoretic upper and lower bounds on the achievable rate of such channels. In particular, we propose a two-round cooperation scheme in which the receivers interactively perform compress-forward (CF) and then decode-forward (DF) to improve the achievable rate. Numerical results comparing the proposed scheme to existing schemes and the cutset upper bound are provided. We show that the proposed scheme outperforms the non-interactive DF and CF schemes as well as the noisy network coding. The gain over the DF scheme becomes larger when the channel becomes symmetric, while the gain over the CF scheme becomes larger when the channel becomes asymmetric.
 
Bio: Victor Exposito received the Engineering and M.Sc. degree (valedictorian) in communication systems and networks from the Institut National des Sciences Appliquées de Rennes (INSA-Rennes), Rennes, France, in 2014. He is currently working at Mitsubishi Electric R&D Centre Europe (MERCE-France), Rennes, France and Ecole Supérieure d’Electricité (CentraleSupélec), Gif-sur-Yvette, France, toward the Ph.D. degree. His current research interests mainly lie in the area of network information theory.
 

Structured data analysis with Regularized Generalized Canonical Correlation Analysis

A. Tenenhaus
Habilitation à Diriger des Recherches (HDR) le 5 Janvier 2016, 09h30 à

The challenges related to the use of massive amounts of data (e.g omics data, imaging-genetic data, etc)
include identifying the relevant variables, reducing dimensionality, summarizing information in a comprehensible way
and displaying it for interpretation purposes. Often, these data are intrinsically structured in blocks of variables, in groups
of individuals or in tensor. Classical statistical tools cannot be applied without altering their structure leading to the risk of
information loss. The need to analyze the data by taking into account their natural structure appears to be essential but
requires the development of new statistical techniques that constitutes the core of my research for many years.
In particular, I am interested in multiblock, multigroup and multiway structures. In that context a general framework
for structured data analysis based on Regularized Generalized Canonical Correlation Analysis (RGCCA) is defined.


Membres du Jury :

* Hervé Abdi, Professeur, Université of Texas, Rapporteur
* Florence d’Alché-Buc, Professeur, Telecom ParisTech, Rapporteur
* Jean-Philippe Vert, Directeur de Recherche, Mines ParisTech, Rapporteur
* Mohamed Hanafi, Ingénieur-Chercheur, ONIRIS, Examinateur
* Jean-Michel Poggi, Professeur, Paris V, Examinateur
* Gilbert Saporta, Professeur, CNAM, Examinateur

S³: Gegenbauer polynomials and positive definiteness

Séminaire le 27 Novembre 2015, 10h30 à CentraleSupelec (Gif-sur-Yvette) Salle des séminaires du L2S
Christian Berg, University of Copenhagen, Denmark

Abstract file

Bio: Professor Christian Berg graduated from Næstved Gymnasium 1963 and studied mathematics at the University of Copenhagen. He became cand.scient. in 1968, lic.scient. (ph.d.) in 1971, and dr. phil. in 1976. Christian Berg received the gold medal of the University of Copenhagen in 1969 for a paper about Potential Theory.
He became assistant professor at University of Copenhagen in 1971, associated professor in 1972 and professor since 1978. Christian Berg had several research visits abroad, in USA, France, Spain, Sweden and Poland.
He became member of The Royal Danish Academy of Sciences and Letters 1982, vice-president 1999-2005. Member of The Danish Natural Sciences Research Council 1985-1992. President of the Danish Mathematical Society 1994-98. Member of the editorial board of Journal of Theoretical Probability (1988-1999) and Expositiones Mathematicae since 1993. Member of the advisory board of Arab Journal of Mathematical Sciences since 1995.
At the Department of Mathematics of the University of Copenhagen, he was Member of the Study Board 1972-74, member of the Board 1977-1984, 1993-1995, chairman 1996-97, and Director of the Institute for Mathematical Sciences 1997-2002.
Christian Berg  has so far published app. 110 scientific papers in international journals, mainly about potential theory, harmonic analysis and moment problems.

S³:Bayesian Fusion of Multiple Images - Beyond Pansharpening

Séminaire le 13 Novembre 2015, 10h30 à CentraleSupelec (Gif-sur-Yvette) Salle des séminaires du L2S
Jean-Yves Tourneret, University of Toulouse, FR

This presentation will discuss new methods for fusing high spectral resolution images (such as hyperspectral images) and high spatial resolution images (such as panchromatic images) in order to provide images with improved spectral and spatial resolutions. These methods are based on Bayesian estimators exploiting prior information about the target image to be recovered, constructed by interpolation or by using dictionary learning techniques. Different implementations based on MCMC methods, optimization strategies or on the resolution of Sylvester equations will be explored

Bio: Jean-Yves TOURNERET (SM08) received the ingenieur degree in electrical engineering from the Ecole Nationale Supérieure d'Electronique, d'Electrotechnique, d'Informatique, d'Hydraulique et des Télécommunications (ENSEEIHT) de Toulouse in 1989 and the Ph.D. degree from the National Polytechnic Institute from Toulouse in 1992. He is currently a professor in the university of Toulouse (ENSEEIHT) and a member of the IRIT laboratory (UMR 5505 of the CNRS). His research activities are centered around statistical signal and image processing with a particular interest to Bayesian and Markov chain Monte Carlo (MCMC) methods. He has been involved in the organization of several conferences including the European conference on signal processing EUSIPCO'02 (program chair), the international conference ICASSP'06 (plenaries), the statistical signal processing workshop SSP'12 (international liaisons), the International Workshop on Computational Advances in Multi-Sensor Adaptive Processing CAMSAP 2013 (local arrangements), the statistical signal processing workshop SSP'2014 (special sessions), the workshop on machine learning for signal processing MLSP'2014 (special sessions). He has been the general chair of the CIMI workshop on optimization and statistics in image processing hold in Toulouse in 2013 (with F. Malgouyres and D. Kouamé) and of the International Workshop on Computational Advances in Multi-Sensor Adaptive Processing CAMSAP 2015 (with P. Djuric). He has been a member of different technical committees including the Signal Processing Theory and Methods (SPTM) committee of the IEEE Signal Processing Society (2001-2007, 2010-present). He has been serving as an associate editor for the IEEE Transactions on Signal Processing (2008-2011, 2015-present) and for the EURASIP journal on Signal Processing (2013-present).

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

S³: Algorithmes d’Estimation et de Détection en contexte Hétérogène Rang Faible

Séminaire le 6 Novembre 2015, 10h30 à CentraleSupelec (Gif-sur-Yvette) Salle des séminaires du L2S
A. Breloy, Ecole Normale Supérieure de Cachan, FR

Covariance Matrix (CM) estimation is an ubiquitous problem in statistical signal processing. In terms of application purposes, the accuracy of the CM estimate directly impacts the performance of the considered adaptive process. In the context of modern data-sets, two major problems are currently at stake:

- Samples are often drawn from heterogeneous (non gaussian) distributions.
- Only a low sample support is available.

To respond to these problems, one has to develop new estimation tools that are based on an appropriate modeling of the data.

Bio: Arnaud Breloy graduated from Ecole Centrale Marseille and recived a Master's degree of Signal and Image Processing from university of Aix-Marseille in 2012-13. Formerly Ph.D student at the SATIE and SONDRA laboratories, he is currently lecturer at University Institute of Technology of Ville d’Avray. His research interests focuses on statistical signal processing, array and radar signal processing, robust estimation methods and low rank methods.

Panneaux complexes anisotropes et imagerie électromagnétique rapide.

Giacomo RODEGHIERO
Soutenance de thèse de doctorat le 29 Septembre 2015, 14h00 à CentraleSupelec (Gif-sur-Yvette) Amphi Ampère

Le Contrôle Non Destructif (CND) de matériaux composites multicouches pour des problèmes de qualité, viabilité,  sécurité et disponibilité des systèmes qui impliquent des pièces fabriquées dans les industries aéronautiques et de l'automobile est devenu une tâche essentielle aujourd'hui. L'objectif visé par cette thèse est l'imagerie électromagnétique de structures complexes multicouches anisotropes, de plus en plus utilisées dans des applications, et encore source de sérieux défis à l'étape de leur modélisation et encore plus à l'étape souvent en enfance de leur imagerie. En utilisant une vaste gamme de fréquences, qui va des courants de Foucault jusqu'aux micro-ondes, il y a un fort besoin de rendre disponibles des procédures de modélisation et d'imagerie qui sont robustes, rapides, précises et utiles à la décision des utilisateurs finaux sur des défauts potentiels, tant donc en basse fréquence (BF) (matériaux conducteurs, type fibre de carbone) qu'en haute fréquence (HF) (matériaux diélectriques, type fibre de verre). De plus, il est important d'obtenir des résultats en des temps brefs. Cependant, cela nécessite la connaissance d'une réponse précise à des sources externes aux multicouches, en considérant les couches des composites comme non endommagées ou endommagées : on parle donc de solution du problème direct, avec le cas particulier de sources élémentaires conduisant aux dyades de Green (DGF).

La modélisation et la simulation numérique du problème direct sont gérés principalement via une solution au premier ordre de la formulation intégrale de contraste de source impliquant le tenseur de dépolarisation des défauts, quand ceux-ci sont assez petits vis-à-vis de l'épaisseur de peau locale (cas BF) ou de la longueur d'onde locale (cas HF). La précision des DGF doit nécessairement être assurée alors, même si les sources se situent loin de l'origine, ce qui donne un spectre de dyades qui oscille très rapidement. La technique d'interpolation-intégration dite de Padua-Domínguez est ainsi introduite dans le but d'évaluer de façon efficace des intégrales fortement oscillantes.

Néanmoins, les matériaux composites peuvent souffrir de divers défauts, lors du processus de fabrication ou pendant leurs utilisations. Vides d'air, cavités remplies de liquide, fissures, etc., peuvent affecter le fonctionnement correct des structures composites. Il est donc indispensable de pouvoir détecter la présence des défauts. Ici, l'insistance est sur la méthode bien connue d'imagerie dite MUltiple SIgnal Classification (MUSIC), qui est basée sur la décomposition en valeurs singulières (SVD) des DGF ; celle-ci est développée afin de localiser les positions de multiples petits défauts volumiques en interaction faible enfouis dans des milieux anisotropes uniaxiaux. Le principal inconvénient de la méthode MUSIC est cependant sa sensibilité par rapport au bruit. Par conséquent, des méthodes MUSIC avec une résolution améliorée et la Recursively Applied and Projected (RAP) MUSIC sont introduites afin de surmonter un tel inconvénient de l'algorithme standard et de fournir des résultats de qualité avec une meilleure résolution. De nombreuses simulations numériques illustrent ces investigations.

Composition du jury :

H. Haddar, Directeur de recherche INRIA, DEFI-CMAP, Palaiseau, rapporteur,
A. Tamburrino, Professeur, Università degli Studi di Cassino e del Lazio Meridionale, Cassino, rapporteur,
M. Bonnet, Directeur de recherche CNRS, POems, Unité de Mathématiques Appliquées, Palaiseau, examinateur,
J.-P. Groby, Chargé de recherche CNRS, Laboratoire d'Acoustique de l'Université du Maine, Le Mans, 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.

 

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

Changyou Li
Soutenance de thèse de doctorat le 28 Septembre 2015, 14h00 à 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
Soutenance de thèse de doctorat le 28 Septembre 2015, 14h00 à 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

Séminaire le 25 Septembre 2015, 10h30 à 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

Séminaire le 18 Septembre 2015, 10h30 à 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

Séminaire le 8 Septembre 2015, 11h30 à 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

Séminaire le 8 Septembre 2015, 10h30 à 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

Séminaire le 8 Septembre 2015, 09h30 à 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

Séminaire le 17 Juillet 2015, 10h30 à 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

Séminaire le 3 Juillet 2015, 10h30 à 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

Séminaire le 26 Juin 2015, 11h00 à 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

Séminaire le 26 Juin 2015, 10h00 à 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

Séminaire le 25 Juin 2015, 10h00 à 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

Séminaire le 19 Juin 2015, 10h00 à 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.

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