Presentation

Confirmed speakers

• Dr Karim Abed-Meraim, PRISME / University of Orléans, INSA CVL - FR

  

  Dr Karim Abed-Meraim

   

  PRISME / University of Orléans, INSA CVL - FR

  He was born in 1967. He received the State Engineering Degree from the École Polytechnique, Palaiseau, France, in 1990, the State Engineering Degree from the École Nationale Supérieure des Télécommunications (ENST), Paris, France, in 1992, the M.Sc. degree from Paris XI University, Orsay, France, in 1992, and the Ph.D. degree in the field of signal processing and communications from ENST, in 1995. From 1995 to 1998, he was a Research Staff with the Electrical Engineering Department, The University of Melbourne, where he worked on several research project related to “Blind System Identification for Wireless Communications”, “Blind Source Separation,” and “Array Processing for Communications.” From 1998 to 2012, he has been an Assistant Professor, then  an Associate Professor with the Signal and Image Processing Department, Télécom ParisTech. In September 2012, he joined the University of Orléans, France (PRISME Laboratory), as a Full Professor. His research interests include signal processing for communications, adaptive filtering and tracking, array processing, and statistical performance analysis. He is the author of about 500 scientific publications, including book chapters, international journal and conference papers, and patents. Dr. Abed-Meraim is currently a member of the IEEE SAM-TC and a Senior Area Editor of the IEEE Transactions on Signal Processing.

  Tensor methods: Concepts, Algorithms & Applications

  The era of “Big Data”, which deals with massive datasets, has brought new analysis techniques for discovering new valuable information hidden in the data. Among these techniques is multilinear low-rank approximation (LRA) of matrices and tensors, which has recently attracted a lot of attention from engineers and researchers in the signal processing and machine learning communities. A tensor is a multidimensional array and provides a natural representation of high-dimensional data. Low-rank approximation of tensors (t-LRA) can be considered as a multiway extension of LRA of matrices (which are two-way) to higher dimensions. Generally, t-LRA is referred to as tensor decomposition which allows factorizing a tensor into a sequence of basic components. As a result, t-LRA provides a useful tool for dealing with several large-scale multidimensional problems in modern data analysis which would be, otherwise, intractable by classical methods.

  This lecture is a brief review of different tensor concepts and different tensor decomposition algorithms with illustrative application examples in biomedical signal processing. It is addressed to a wide audience with general background in signal processing.

• Dr Olivier Buttelli, PRISME / University of Orléans, INSA CVL - FR

  

  Dr Olivier Buttelli

  He received the PhD degree in biomechanics and human movement physiology from the University of Orsay, France, in 1996. He joined in 1997 the University of Orléans, France, as assistant professor at the faculty of sport sciences and physical education. He joined in 2012 the Multidisciplinary Research laboratory in Systems Engineering, Mechanics and Energetics (PRISME, Orléans University). He is in charge of the master in ergonomics and physical activity engineering. His main work concerns the studies of human activity in ecological conditions, the fatigue assessment and the development of non-stationary and cyclo-stationary bioelectrical (electrocardiography, electromyography) signal processing methods with application in ergonomics, sport and medical.

  Practical workshops on measurement and calculation of HD-EMG signals

  The practical session will illustrate the theoretical knowledge and concepts presented by Prof. Aleš Holobar. It will therefore be dedicated to the acquisition of surface electromyographic (sEMG) signals by means of a high-density multichannel EMG sensor (HD-EMG) and to the blind source separation analysis. In order to understand the problem posed by the sEMG acquisition and its analysis, a brief presentation on the structure and function of the neuromuscular system will be provided. Learners will then be able to apply decomposition algorithms to identify elementary motor unit activities.

  > Several laptops will be available for the data processing part allowing a work in small groups.

• Dr Aleš Holobar, University of Maribor - SI

  

  Dr Aleš Holobar

   

  University of Maribor - SI

  He received his BS and PhD degree in Computer Science from the Faculty of Electrical Engineering and Computer Science (FEECS), University of Maribor (UM), Slovenia, in 2000 and 2004, respectively. In 1997, he joined the System Software Laboratory (SSL) at FEECS, where he was employed as a researcher and teaching assistant. From 2005 to 2009, he was with Laboratory of Engineering of Neuromuscular System and Motor Rehabilitation at Politechnico di Torino, Italy, with support provided by Cassa di Risparmio di Torino and Institute for Scientific Interchange Foundations (from 2005 to 2006), and by a Marie Curie Intra-European Fellowship within the 6th European Community Framework Programme (from 2006 to 2009). In 2009 he returned to FEECS, University of Maribor, where he currently holds the position of full professor.  He is the head of the System Software Laboratory and the head of the Institute of Computer.

  Practical workshops on measurement and calculation of HD-EMG signals

  The practical session will illustrate the theoretical knowledge and concepts presented by Prof. Aleš Holobar. It will therefore be dedicated to the acquisition of surface electromyographic (sEMG) signals by means of a high-density multichannel EMG sensor (HD-EMG) and to the blind source separation analysis. In order to understand the problem posed by the sEMG acquisition and its analysis, a brief presentation on the structure and function of the neuromuscular system will be provided. Learners will then be able to apply decomposition algorithms to identify elementary motor unit activities.

  > Several laptops will be available for the data processing part allowing a work in small groups.

• Dr Anne Humeau-Heurtier, University of Angers - FR

  

  Dr Anne Humeau-Heurtier

   

  University of Angers - FR

  She received the PhD degree in Biomedical Engineering in France. She is currently a full professor in Engineering with the University of Angers, France. Her research interests include signal and image processing, mainly multiscale and entropy-based analyses, and data-driven methods. Her main applications are related to the biomedical field. She is associate editor for IEEE Transactions on Biomedical Circuits and Systems, for Frontiers in Network Physiology - Information Theory, Causality & Control, and for the Engineering Medicine and Biological Society Conference. She is member of the editorial board for the journal Entropy and area editor on Signal Processing for the IEEE Open Journal of Engineering in Medicine and Biology. She is also member of the IEEE-EMBS Technical Community on Cardiopulmonary Systems and Physiology-based Engineering. She has been guest editor for special issues in journals as Entropy, Complexity, and Computational and Mathematical Methods in Medicine.

  Review of some entropy methods with biomedical applications

  In this presentation, we will first introduce the concept of irregularity and complexity for time series and images. Then, an overview of the most recent entropy algorithms proposed to assess irregularity of signals will be given. A general view of the entropy-based measures for texture analysis of images will also be proposed. Afterwards, the algorithms of some of these entropy measures for time series will be detailed on synthetic data. An extension to bi-dimensional and tri-dimensional data will also be proposed. For each case, we will see examples of application in the biomedical field.

• Dr Meryem Jabloun, PRISME / University of Orléans, INSA CVL - FR

  

  Dr Meryem Jabloun

   

  PRISME / University of Orléans, INSA CVL - FR

  She is a researcher in biomedical signal processing. She received her Eng. and M.Sc. degrees from the Ecole Nationale Superieure de l'Electronique et de ses Applications (ENSEA) and the University of Cergy Pontoise, France, in 2003. She obtained her Ph.D. degree at the Institut National Polytechnique de Grenoble, France, in 2007. Since 2009, Dr. Jabloun has been working as a lecturer at Orleans University and PRISME laboratory. Her research interests include the analysis and interpretation of nonstationary biomedical signals, as well as ordinal pattern methods and entropy. She has published numerous research papers and has presented her work at international conferences.

  Practical workshops on measurement and calculation of HD-EMG signals

  The practical session will illustrate the theoretical knowledge and concepts presented by Prof. Aleš Holobar. It will therefore be dedicated to the acquisition of surface electromyographic (sEMG) signals by means of a high-density multichannel EMG sensor (HD-EMG) and to the blind source separation analysis. In order to understand the problem posed by the sEMG acquisition and its analysis, a brief presentation on the structure and function of the neuromuscular system will be provided. Learners will then be able to apply decomposition algorithms to identify elementary motor unit activities.

  > Several laptops will be available for the data processing part allowing a work in small groups.

• Dr Trung Thanh LE, PRISME / University of Orléans, INSA CVL - FR

  

  Dr Trung Thanh LE

   

  PRISME / University of Orléans, INSA CVL - FR

  He is currently a postdoctoral researcher at the University of Orléans, INSA CVL, PRISME, France. He received his B.Sc. and M.Sc. degrees in Electronics and Communications from Vietnam National University Hanoi, VNU-UET, in 2016 and 2018 respectively, and his Ph.D. degree in Computer Science and Signal Processing from the University of Orléans, INSA CVL, PRISME, France in 2022. His research interests include signal processing and its applications, e.g., subspace analysis/tracking, tensor decomposition/tracking, system identification, and biomedical signal processing.

  Tensor methods: Concepts, Algorithms & Applications

  The era of “Big Data”, which deals with massive datasets, has brought new analysis techniques for discovering new valuable information hidden in the data. Among these techniques is multilinear low-rank approximation (LRA) of matrices and tensors, which has recently attracted a lot of attention from engineers and researchers in the signal processing and machine learning communities. A tensor is a multidimensional array and provides a natural representation of high-dimensional data. Low-rank approximation of tensors (t-LRA) can be considered as a multiway extension of LRA of matrices (which are two-way) to higher dimensions. Generally, t-LRA is referred to as tensor decomposition which allows factorizing a tensor into a sequence of basic components. As a result, t-LRA provides a useful tool for dealing with several large-scale multidimensional problems in modern data analysis which would be, otherwise, intractable by classical methods.

  This lecture is a brief review of different tensor concepts and different tensor decomposition algorithms with illustrative application examples in biomedical signal processing. It is addressed to a wide audience with general background in signal processing.

• Dr Philippe Ravier, PRISME / University of Orléans, INSA CVL - FR

  

  Dr Philippe Ravier

   

  PRISME / University of Orléans, INSA CVL - FR

  He received the PhD degree in engineering science majoring in signal, image and speech, from the Polytechnic National Institute of Grenoble, Grenoble, France, in 1998. He joined in 1999 the University of Orléans, France as Assistant Professor at the Polytech Orléans Engineering School and the Multidisciplinary Research laboratory in Systems Engineering, Mechanics and Energetics (PRISME). Currently, he works as a Full Professor in the IUT Chartres, University Institute of Technology, University of Orléans. He is in charge of the signal processing team in the PRISME laboratory. His main research interests include nonstationary time-frequency, time-scale and cyclo-stationary signal processing as well as information theory tools with applications to electrophysiological, electrical device and speech signals. He (co)authored 1 patent, 56 papers in peer-reviewed journals, 3 book chapters and more than 80 conference contributions. He is a member of IEEE; INTICC and GDR-ISIS societies. He has been guest editor for special issue in journals as journal Entropy.

  Permutation entropy methods with applications

  Among the entropy-based measures, the Permutation entropy (PE) and multiscale permutation entropy (MPE) are extensively used to measure irregularity in the analysis of time series, particularly in the context of biomedical signals. The spirit of these entropies relies on samples ranking used for building ordinal pattern distributions from which entropy measures are derived. As accuracy is crucial for researchers to obtain relevant interpretations, we will present some statistical results for PE and variants of PE. The tutorial will also focus on pitfalls and recommendations when interpreting the results of these measures applied on biomedical signals.

  A practical session will illustrate both presentations by processing signals with various entropy measures. On the one hand, the participants will be invited to evaluate entropies on some typical simulated signals. On the other hand, they will have the opportunity to evaluate entropies on real data as potential indicators for assessing some sEMG characteristics. Several laptops with Matlab installed and complexity measures software will be available for data processing allowing a work in small groups.
   

   

• Dr Steeve Zozor, GIPSA-lab, University Grenoble - Alpes / CNRS - France

  

  Dr Steeve Zozor

   

  GIPSA-lab, University Grenoble - Alpes / CNRS - France 

  He was born on August 19, 1972 in Colmar, France. He received the Dipl.-Ing. and the M.S. degrees in 1995 and the Ph.D. degree in 1999, both from the Grenoble Institute of Technology (G-INP), France, all in electrical engineering. In 2012 he received the habilitation to direct researches (Habilitation à Diriger des Recherches) from the G-INP. He joined the National Center of Scientific Research (CNRS), France, in 2001 as Full Researcher (Chargé de Recherche), working at the GIPSA-Lab (Grenoble Laboratory of Image, sPeech, Signals and Automatics), Grenoble, France. From 2011 to 2015, he was member of the GIPSA-Lab board (as elected member) and from 2015 to 2019, he was at the head of the CICS group (Communication and Information in Complex Systems) from the GIPSA-Lab.
  Dr. Zozor has regular teaching activities at the Grenoble Institute of Technology, France, at the Polytechnic Institute of Bucharest, Romania, and at the faculties of engineering, of mathematics and of physics of La Plata, Argentina. In 2000, he spent 7 months at the Swiss Federal Institute of Technology of Lausanne (EPFL). In 2012-2013, he spent 13 months as invited professor at the Institute of Physics of La Plata (faculty of Physics, National University of La Plata), Argentina and received a grant of the region Rhône-Alpes for this stay. His current researches include noise-enhanced information processing (stochastic resonance, noise-improved detection and information transmission, effect of noise in pooling networks and distributed signal sensors networks…), generalized measures of information (Lempel-Ziv complexity, classical and quantum Csiszàr divergences and Salicrú entropies, applications in biology, generalized entropic formulations of the uncertainty principle and of the Landau-Pollak inequality…), classical signal processing in special statistical contexts (estimation and detection in alpha-stable noise, elliptical distributions, interference modeling for information transmission…). Since June 2015, he is in the editorial board of IEEE Signal processing Letters as Associate Editor, and since June 2019 as Senior Editor Area. Since September 2021 he is elected member of the French National committee for the Scientific Research (in section 7 — Automatic, Robotic, Speech, Image, Signal, Statistical Learning, Communication, SoC — and in the interdisciplinary commission CID 55 — sciences and data— ), and also responsible of the speciality Signal-Image-Speech-Telecommunication of the doctoral school Electronic, Electrotechnic, Automatics and Signal Processing of Grenoble.

  Lempel-Ziv complexity and variations for multivariate data analysis

  In this course, I will provide an overview of an algorithmic complexity measure, namely the Lempel-Ziv complexity, and of associated measures, as potential tools for the analysis of multidimensional data. I will focus on this tool dealing with data with discrete states taken their values on an alphabet of finite size —context for which this complexity is defined, but also of continuous states. In the latter case, the approach is based on a quantification based on the construction of the so-called permutation vectors, the same process that underlies the permutation entropy due to Bande and Pompe (PRL 2002).
  In a first part, I will give the definition of the first version of the Lempel-Ziv complexity, due to Lempel and Ziv (IEEE TIT 1976), which I will discuss during this course. This measure will also be presented dealing with multidimensional data, with components taking their values not necessarily in the same alphabet. I will then present measures associated with this complexity measures such as mutual information-like measure. These measures will then be applied to synthetic data (random boolean networks, minority game) to illustrate their potential for data analysis.
  As real life data are rarely with discrete states, I will show in a second step how the Lempel-Ziv complexity can be used is such a context. To this end, I will recall how permutation vectors can be constructed from vectors with continuous state components. I will also show that dealing with scalar signals, this step can be preceded by an embedding step in a higher dimensional space. The quantization can be schematically seen as similar to the so-called sigma-delta quantization. I will end this part with illustrations analysing chaotic signals and a real EEG signal via the measure presented in this part.
  I will then end the course by presenting an application of the Lempel-Ziv complexity used in conjunction with the Shannon entropy, applied on permutation vectors, as a tool to contrast chaotic signals and noise.

   

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