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.

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

Séminaire le 9 Juin 2015, 10h00 à 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.

Novel Microstereolithography (MSL) and Bulk Lithography (BL) technologies for polymer/ceramic 3D MEMS

Séminaire le 3 Juin 2015, 16h00 à CentraleSupelec (Gif-sur-Yvette) Salle des séminaires du L2S
Prof. Prasanna S. Gandhi


Polymers are increasingly being used for fabrication of MEMS for their advantages of low cost, easy disposability, high flexibility, and so on. With the area of printable organic electronics developing fast there are enormous possibilities with polymer MEMS, especially 3D. This talk will present two indigenously developed technologies for fabrication of polymer 3D MEMS.
The proposed MSL technology has demonstrated resolution of 6micron and a large overall size of fabricated component at the same time: a feature seldom found in other such systems in the literature. The core opto-mechanical scanner (patent pending) and mechatronic system built around it would be presented. This system is demonstrated to have positioning accuracy within 100 nm. Several cases of micro-component fabrication will be demonstrated. Main drawback of MSL is stair-stepping effects on slanted walls. To overcome this limitation, recently "Bulk Lithography" technology has been proposed by our group. The main principle used here is to impose spatial variation of laser energy dose while scanning. The method gives remarkable accuracy in getting free-form surface features desired for microlenses, tapered micro cantilevers, tapered diaphragm micromirrors and so on, which are otherwise not feasible to fabricate. Finally, ceramic microstereolithography and preliminary fabrication of ceramic microcomponents would be demonstrated. The proposed technologies hold potential for direct prototyping and also mass production of MEMS devices along with packaging.

Biography: P.S. Gandhi received the B.Eng. degree in from the University of Bombay, Mumbai in 1994 and the M.Tech degree from the Indian Institute of Technology, Bombay, Mumbai in 1996, both in mechanical engineering. He received the Ph.D. degree in mechanical engineering from the Rice University, Houston in 2001. Since 2001, he has been faculty member, currently Professor, in the Department of Mechanical Engineering at Indian Institute of Technology, Bombay, Mumbai. His research interests are in the areas of MEMS and Microsystems, Mechatronics, and Nonlinear Dynamical Systems and Control. He has been a recipient of 2006 BOYSCAST fellowship of Govt of India, 2007 Best faculty award (ME department) and Prof J.R.Issac fellowship. He has authored several patents and over 85 peer reviewed conference and journal papers. He has coordinated setup of a new laboratory Suman Mashruwala Microengineering Laboratory for research in Microdomain and has successfully developed technologies of Bulk Lithography and Microstereolithography for 3D MEMS fabrication in this laboratory. He has been a qualified teacher of stress relieving, life enhancing techniques of Art of Living foundation by Sri Sri Ravishankar

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

Séminaire le 26 Mai 2015, 14h00 à 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

An introduction to neural fields

Séminaire le 12 Mai 2015, 14h00 à CentraleSupelec (Gif-sur-Yvette) Salle du conseil du L2S - B4.40
Georgios Detorakis, post-doc L2S


The main objective of the field of Computational Neuroscience is to understand how the brain works through mathematical models (numerical and analytical works). Some of these models are described by neural fields equations. Neural fields are integro-differential equations that describe the spatiotemporal dynamics of the activity of a piece of neocortical tissue. Neural fields equations emerge when one assimilates the high number of neurons of the selected piece of brain tissue in its continuum limit. Neural fields have been studied both analytically and numerically by many researchers. They can model many different and interesting biological phenomena such as attention, working memory, self-organization, or synaptic depression. The seminar consists of two parts. The first part is a brief introduction to neuroscience and the second part is dedicated to neural fields. We will review how neural fields equations can be derived, how the steady-state solution can be computed, and its stability can be insured. Finally, some cognitive models that are based on neural fields will be presented.

Bio:
Georgios Detorakis has studied Applied Mathematics and Neuroscience. He did his PhD on cortical plasticity, self-organization and neural fields. During his PhD, he studied the formation of topographic maps in area 3b of the primary somatosensory cortex and the multimodal problem of “Touch and the body”. He is now a postdoc fellow at L2S working with Antoine Chaillet on Parkinson’s disease in the ANR project "SynchNeuro". He uses delayed neural fields in order to model some brain areas that play a crucial role in Parkinson’s disease motor symptoms.

Stability theory, as told by its developers

Séminaire le 5 Mai 2015, 10h00 à CentraleSupelec (Gif-sur-Yvette) Salle du conseil du L2S - B4.40
Antonio Loria, Elena Panteley (L2S)


We present a brief but detailed historical review on the development of stability theory, from its early starts out of the minds of Lagrangia and Dirichlet. The survey carries on to focus on Lyapunov stability. Through exact citations from the works of the developers of stability, including many Soviet texts from the 20th century, we revise the fundamental definitions and theorems; making emphasis on several misfortunate translations which have led to wrong interpretations and ambiguous statements. We favour depth and sacrifice generality: on technical grounds, we focus on the most elementary (yet not so) well-known forms of Lyapunov stability and common but crucial qualifiers that go with it: uniform, asymptotic, global. We revise the origin of the wrongly known invariance principle ...

S³ Working memory in random neural networks

Séminaire le 3 Avril 2015, 10h30 à 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.

Séminaire le 30 Mars 2015, 13h30 à 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

Séminaire le 27 Mars 2015, 10h30 à 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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http://scube.lss.supelec.fr
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PDE-­‐based inversion method with no forward solver for inverse medium scattering problems

Séminaire le 20 Mars 2015, 14h00 à 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.

S³ - Analysis of remote sensing multi-sensor heterogeneous images

Séminaire le 20 Mars 2015, 10h30 à CentraleSupelec (Gif-sur-Yvette) Salle des séminaires du L2S
Jorge Prendes, IRIT Toulouse and SONDRA CentraleSupelec


Remote sensing images are images of the Earth acquired from planes or satellites. In recent years the technology enabling this kind of images has been evolving really fast. Many different sensors have been developed to measure different properties of the earth surface, including optical images, SAR images and hyperspectral images. One of the interest of this images is the detection of changes on datasets of multitemporal images. Change detection has been thoroughly studied on the case where the dataset consist of images acquired by the same sensor. However, having to deal with datasets containing images acquired from different sensors (heterogeneous images) is becoming very common nowadays.
In order to deal with heterogeneous images, we proposed a statistical model which describe the joint distribution of the pixel intensity of the images, more precisely a mixture model. On unchanged areas, we expect the parameter vector of the model to belong to a manifold related to the physical properties of the objects present on the image, while on areas presenting changes this constraint is relaxed. The distance of the model parameter to the manifold can be thus be used as a similarity measure, and the manifold can be learned using ground truth images where no changes are present. The model parameters are estimated through a collapsed Gibbs sampler using a Bayesian non parametric approach combined with a Markov random field.
In this talk I will present the proposed statistical model, its parameter estimation, and the manifold learning approach. The results obtained with this method will be compared with those of other classical similarity measures.

Bio: Jorge Prendes was born in Santa Fe, Argentina in 1987. He received the 5 years Eng. degree in Electronics Engineering with honours from the Buenos Aires Institute of Technology (ITBA), Buenos Aires, Argentina in July 2010. He worked on Signal Processing at ITBA within the Applied Digital Electronics Group (GEDA) from July 2010 to September 2012. Currently he is a Ph.D. student in Signal Processing in SONDRA laboratory at Supélec, within the cooperative laboratory TéSA and the Signal and Communication Group of the Institut de Recherche en Informatique de Toulouse (IRIT). His main research interest include image processing, applied mathematics and pattern recognition.

 

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S³: Rare event simulation: a Point Process interpretation with application in probability and quantile estimati

Séminaire le 13 Mars 2015, 10h00 à CentraleSupelec (Gif-sur-Yvette) Salle des séminaires du L2S
Clément Walter, CEA and University Paris Diderot


Clément Walter, 25, graduated from Mines ParisTech in 2013. Beforehand he attended preparatory classes in Lycée Sainte-Geneviève (branch Maths and Physics). For his master degree he specialised in Geostatistics and started working with CEA as an intern on emulation of complex computer codes (especially kriging) for rare event simulation and estimation. He has then pursued his work in a PhD under the direction of Pr. Josselin Garnier, focusing on multilevel splitting methods.

S³ L0 optimization for DOA and sparse channel estimation

Séminaire le 6 Mars 2015, 10h00 à CentraleSupelec (Gif-sur-Yvette) Salle des séminaires du L2S
Adilson Chinatto, Univeristy of Campinas, BR and Ecole Normale Supérieure de Cachan, FR


Bio: Adilson Chinatto received a degree in Electrical Engineering in 1997 and Masters in 2011, both from the University of Campinas (Unicamp), Brazil. He worked as hardware, software and firmware development engineer for optical transmission equipment in the companies AsGa and CPqD in Brazil. He is a co-founder of Espectro Ltd., a Brazilian design house for hardware and software, focused in signal processing. Nowadays he is coordinator of a High Performance GPS Receiver Project at Espectro Ltd. funded by the Brazilian National Counsel of Technological and Scientific Development (CNPq). He has experience in electrical engineering with emphasis on telecommunication systems, digital signal processing and smart antennas, working mainly with development and implementation of programmable logic devices (FPGA). He is currently finishing his Ph.D. at Unicamp, working with sparse and compressive sensing signal processing.

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S³ Bayesian Tomography

Séminaire le 6 Mars 2015, 10h00 à CentraleSupelec (Gif-sur-Yvette) Salle des séminaires du L2S
John Skilling, Maximum Entropy Data Consultants Ltd, UK


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.

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http://www.lss.supelec.fr/scube/
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FREEDM Concept: Power Distribution System Modernization with Solid State Electronics and Integrated Renewables

Séminaire le 5 Mars 2015, 11h00 à CentraleSupelec (Gif-sur-Yvette) Salle des séminaires du L2S
Prof. Iqbal Husain


This FREEDM concept provides an architecture for a future electric power distribution system that is suitable for plug-and-play of distributed renewable energy and distributed energy storage devices. Motivated by the success of the (information) Internet, the FREEDM concept envisions the “Energy Internet,” a system that enables flexible energy sharing for consumers in a residential distribution system. One of the core technologies for the FREEDM system is the solid-state transformer (SST) which is a power electronic device, but makes use of high frequency transformer for isolated AC-AC power conversion. The SST not only dramatically decreases the volume and weight compared with the conventional power transformer, but also behave like a smart transformer with the advantage of power flow control, reactive power compensation capability, and potential fault current limitation. Besides, the regulated low voltage DC/AC bus of SST could be used as an interface to distributed renewable resources and storage devices such as wind, solar, charging stations, and DC micro-grid. The SST is an "Energy Router" proposed to provide such a compatible and flexible interface for energy routing. In addition to the role of a traditional ac transformer, the SST can also provide a low voltage (LV) DC bus. The bi-directional power flow capability of the SST provides possibilities to feed locally generated power back to the grid. Islanding can also be achieved when the HV side is disconnected from grid. The SST has been designed and built based on 13kV SiC MOSFET and JBS diode and is to be connected to medium voltage (MV) distribution network up to 7.2kV and output both a 380V low voltage (LV) dc and 120/240V ac.   

This talk will present the hardware research activities and results aimed at demonstrating the FREEDM concept followed by the power electronic controls for the SST. The experimental results of the developed DC/AC hybrid microgrid built to demonstrate the SST's ability to integrate different renewable energy sources and to improve grid quality will also be discussed.

 
Biography:

Dr. Iqbal Husain is the Director of the Future Renewable Electric Energy Delivery and Management (FREEDM) Engineering Research Center and the ABB Distinguished Professor at North Carolina State University where he joined in Fall 2011. Prior to coming to NC State he was serving as a faculty member at the University of Akron, Ohio for seventeen years. He was a visiting Professor at Oregon State University in 2001, and also lectured at Texas A&M University. Prof. Husain also serves as the Director of the Advanced Transportation Energy Center (ATEC).He received the B.Sc. degree from Bangladesh University of Engineering and Technology, Bangladesh, and the M.S. and Ph.D. degrees from Texas A&M University, College Station, Texas. 

Prof. Husain’s research interests are in the areas of control and modeling of electrical drives, design of electric machines, development of power conditioning circuits, microinverters for distributed power generation, inverter controls for grid synchronization, and modeling and control of electric and hybrid vehicle systems. The primary application of Prof. Husain’s work is in the transportation, automotive, aerospace, power system and renewable energy industries.

Prof. Husain was the General Co-Chair for Energy Conversion Congress & Expo (ECCE) 2012 in Raleigh, NC, the chairman of the IEEE-IAS Transportation Systems Committee, and the chairman of the IEEE-IAS Electric Machines Committee. Prof. Husain received the 2006 SAE Vincent Bendix Automotive Electronics Engineering Award, the 2004 College of Engineering Outstanding Researcher Award, the 2000 IEEE Third Millennium Medal, the 1998 IEEE-IAS Outstanding Young Member award, and several IEEE-IAS prize paper awards. He was elected to be an IEEE Fellow in 2009.

 

 

Online adaptive optimal control of a hybrid electric vehicle

Séminaire le 5 Février 2015, 14h30 à CentraleSupelec (Gif-sur-Yvette) Salle du conseil du L2S - B4.40
Catalin Stefan Teodorescu


Par Doctor Catalin Stefan Teodorescu Project engineer, Flanders' Mechatronics Technology Centre (FMTC), www.fmtc.be A division of Flanders Make, www.flandersmake.be In general, the hybrid driveline concept consists of using simultaneously 2 power sources: the internal combustion engine (ICE) on the one hand, and an electric power source, e.g., a supercapacitor (SC), on the other hand. In the above figure, the red arrows represent power flow of the two illustrated power sources: specifically, it can be generative or regenerative. The latter translates the ability of the SC to store some energy, which can potentially be used in order to achieve significant fuel consumption reduction: e.g. by avoiding wasting energy when the driver pushes the break pedal and instead storing it. In this presentation we will investigate the following problem. In order to be able to cope with the actual demand of the driver expressed in terms of demanded power load (see the above figure), we have an infinite number of choices/combinations concerning the power split ICE versus SC. However, only one (or a few) of these trajectories are optimal in terms of fuel consumption and this is what is being investigated. Two control solutions are presented: Dynamic Programming, mainly used for benchmarking purpose and Equivalent Consumption Minimization Strategy. Results show the latter to be a strong candidate towards online, real-time implementation.

S³ Correlation mining in high dimension with limited samples

Séminaire le 30 Janvier 2015, 10h30 à CentraleSupelec (Gif-sur-Yvette) Salle des séminaires du L2S
Alfred Hero


Alfred Hero:
Title: Correlation mining in high dimension with limited samples 

Abstract: Correlation mining arises in many areas of engineering, social sciences, and natural sciences. Correlation mining discovers columns of a random matrix that are highly correlated with other columns of the matrix and can be used to construct a dependency network over columns. However, when the number n of samples is finite and the number p of columns increases such exploration becomes futile due to a phase transition phenomenon: spurious discoveries will eventually dominate. In this presentation I will present theory for predicting these phase transitions and present Poisson limit theorems that can be used to determine finite sample behavior of correlation structure. The theory has application to areas including gene expression analysis, network security, remote sensing, and portfolio selection. 

BioAlfred O. Hero III received the B.S. (summa cum laude) from Boston University (1980) and the Ph.D from Princeton University (1984), both in Electrical Engineering. Since 1984 he has been with the University of MichiganAnn Arbor, where he is the R. Jamison and Betty Williams Professor of Engineering. His primary appointment is in the Department of Electrical Engineering and Computer Science and he also has appointments, by courtesy, in the Department of Biomedical Engineering and the Department of Statistics. From 2008-2013 he was held the Digiteo Chaire d'Excellence at the Ecole Superieure d'Electricite, Gif-sur-Yvette, France. He is a Fellow of the Institute of Electrical and Electronics Engineers (IEEE) and several of his research articles have recieved best paper awards. Alfred Hero was awarded the University of Michigan Distinguished Faculty Achievement Award (2011). He received the IEEE Signal Processing Society Meritorious Service Award (1998), the IEEE Third Millenium Medal (2000), and the IEEE Signal Processing Society Technical Achievement Award (2014). Alfred Hero was President of the IEEE Signal Processing Society (2006-2008) and was on the Board of Directors of the IEEE (2009-2011) where he served as Director of Division IX (Signals and Applications). Alfred Hero's recent research interests are in statistical signal processing, machine learning and the analysis of high dimensional spatio-temporal data. Of particular interest are applications to networks, including social networks, multi-modal sensing and tracking, database indexing and retrieval, imaging, and genomic signal processing.
 

György Terdik:
Title: A new covariance function for spatio-temporal data analysis with application to atmospheric pollution and sensor networking

Abstract: See the attached file.

Bio: György Terdik, received the Ph.D. degree from theKossuth Lajos University, Hungary. He is a Professor  of the Department of Information Technology at the University ofDebrecen, Hungary. His research areas include nonlinear, non-Gaussian time series analysis, Lévyprocesses, and high-speed network modeling, spatio-temporal time series, Dr. Terdik is a member of the editorial board of the

quarterly Publicationes Mathematicae Debrecen.
 

Márton Ispány:
Title Poisson INAR processes with serial and seasonal correlation 

Abstract: Recently, there has been considerable interest in integer-valued time series models.  Motivation to include discrete data models comes from the need to account for the discrete nature of certain data sets, often counts of events, objects or individuals. Among the most successful integer-valued time series models proposed in the literature we mention the INteger-valued AutoRegressive model of order p (INAR(p)). However, seasonal count processes have not been investigated yet, except one of our new papers. In the talk, we study INAR processes which possess serial and seasonal structure as well. The main properties of the models will be derived such as the stationarity and the autocorrelation function. The conditional least squares and conditional maximum likelihood estimators of the model parameters will be studied and their asymptotical properties will be established. In addition, we would like to discuss the case in which the marginal distributions are Poisson in detail. Monte 
Carlo experiments will be conducted to evaluate and compare the performance of various estimators for finite sample sizes. Real data set on the area of insurance will be applied to evaluate the model performance. 

Bio: Márton Ispány (https://it.inf.unideb.hu/honlap/ispanymarton/english) received the M.Sc.(1989) and PhD (summa cum laude) in Statistics (1997) from University of Debrecen. Since 2007 he has been with the  Department of Information Technology, Faculty of Informatics, University of Debrecen. Since 2012 he has been the head of the department. Márton Ispány 's recent research interests are in branching processes (functional limit theorems, asymptotics for conditional least squares estimation, integer valued autoregression), statistical modelling(generalized SVD, contaminated statistical models, EM algorithm), data mining (decision trees, stochastic algorithms, MCMC, web mining), and applied statistics: econometrics and insurance, cross-country modelling, statistical genetics.

 

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