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Start Over Topic signal processing algorithms Publication Year Range Last 50 years Publisher hal ccsd
36 results

1. Blind Source Separation in Nonlinear Mixtures: Separability and a Basic Algorithm

Author

Christian Jutten, Massoud Babaie-Zadeh, Bertrand Rivet, Bahram Ehsandoust, GIPSA - Vision and Brain Signal Processing (GIPSA-VIBS), Département Images et Signal (GIPSA-DIS), Grenoble Images Parole Signal Automatique (GIPSA-lab ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Grenoble Images Parole Signal Automatique (GIPSA-lab ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Department of Electrical Engineering [Tehran] (EE department of SUT [Tehran]), Sharif University of Technology [Tehran] (SUT), and ERC CHESS Project - 2012-ERC-Adv-320684

Subjects
Local linear, Stochastic process, Nonlinear Mixtures, 020206 networking & telecommunications, 02 engineering and technology, Blind signal separation, Independent component analysis, Independent Component Analysis, Nonlinear system, Blind Source Separation, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, Signal Processing, 0202 electrical engineering, electronic engineering, information engineering, Signal processing algorithms, 020201 artificial intelligence & image processing, Nonlinear Regression, Electrical and Electronic Engineering, Algorithm, Nonlinear regression, Random variable, Computer Science::Databases, Mathematics
Abstract

International audience; In this paper, a novel approach for performing Blind Source Separation (BSS) in nonlinear mixtures is proposed, and their separability is studied. It is shown that this problem can be solved under a few assumptions, which are satisfied in most practical applications. The main idea can be considered as transforming a time-invariant nonlinear BSS problem to local linear ones varying along the time, using the derivatives of both sources and observations. Taking into account the proposed idea, numerous algorithms can be developed performing the separation. In this regard, an algorithm, supported by simulation results, is also proposed in this paper. It can be seen that the algorithm well separates the mixed sources, however, as the conventional linear BSS methods, the nonlinear BSS suffers from ambiguities, which are discussed in the paper.

Published
2017

2. A comparison of cost construction methods onto a C6678 platform for stereo matching

Author

Jean-Francois Nezan, Luce Morin, Guillaume Gautier, Judicael Menant, Muriel Pressigout, Institut d'Électronique et des Technologies du numéRique (IETR), Université de Nantes (UN)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS), Université de Nantes (UN)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS), and Nantes Université (NU)-Université de Rennes 1 (UR1)

Subjects
Stereo cameras, Computer science, business.industry, Template matching, 010401 analytical chemistry, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, Stereo matching, [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], 02 engineering and technology, Mutual information, 01 natural sciences, 0104 chemical sciences, Very long instruction word, 0202 electrical engineering, electronic engineering, information engineering, Signal processing algorithms, 020201 artificial intelligence & image processing, Computer vision, [INFO.INFO-ES]Computer Science [cs]/Embedded Systems, Artificial intelligence, business, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, Computer stereo vision, Digital signal processing, ComputingMethodologies_COMPUTERGRAPHICS
Abstract

International audience; Stereo matching techniques aim at reconstructing depth information from a pair of images. The use of stereo matching algorithms in embedded systems is very challenging due to the complexity of state-of-the-art algorithms. Most stereo matching algorithms are made of three different parts; the cost construction, the cost aggregation and the disparity selection. This paper focuses on comparing the efficiency of different cost construction methods implemented on a Digital Signal Processor (DSP) C6678 platform. Three cost construction algorithms based on census, Sum of Absolute Differences (SAD) and Mutual Information (MI) have been compared in terms of output quality and execution time. Each method has its own specificity discussed in this paper. The SAD is the simplest one and is used as reference in this paper. The census has a good output quality, and the MI is faster.

Published
2016

3. Moving sources detection system

Author

Vladimir Kondrasovs, Romain Coulon, Karim Boudergui, Stephane Normand, Laboratoire Capteurs et Architectures Electroniques (LCAE), Département Métrologie Instrumentation & Information (DM2I), Laboratoire d'Intégration des Systèmes et des Technologies (LIST), Direction de Recherche Technologique (CEA) (DRT (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Direction de Recherche Technologique (CEA) (DRT (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Laboratoire d'Intégration des Systèmes et des Technologies (LIST), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay, Laboratoire d'Intégration des Systèmes et des Technologies (LIST (CEA)), and Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Laboratoire d'Intégration des Systèmes et des Technologies (LIST (CEA))

Subjects
Signal processing, radiation detection system, Radioactive - Safeguard, Computer science, Real-time computing, 02 engineering and technology, [PHYS.NEXP]Physics [physics]/Nuclear Experiment [nucl-ex], 01 natural sciences, Signal, Particle detector, radioactivity measurement, Reduction (complexity), Set (abstract data type), ALARM, standalone sensor, 0103 physical sciences, Source - Time series analysis, nuclear materials safeguards, 0202 electrical engineering, electronic engineering, information engineering, Algorithm design and analysis, Nuclear, [PHYS.PHYS.PHYS-INS-DET]Physics [physics]/Physics [physics]/Instrumentation and Detectors [physics.ins-det], Time series, Measurement, Radiation, temporal periodicity, 010308 nuclear & particles physics, detection method, Detector, 020206 networking & telecommunications, Detectors, Filter - Instrument, Monitor, temporal series analysis, Correlation, Algorithm, Detection, Statistical analysis, radioactivity, source vector, Container (abstract data type), Moving, natural background signal, Signal processing algorithms, Estimation, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, counting statistics analysis
Abstract

Conference of 2013 3rd International Conference on Advancements in Nuclear Instrumentation, Measurement Methods and Their Applications, ANIMMA 2013 ; Conference Code:102802; International audience; To monitor radioactivity passing through a pipe or in a given container such as a train or a truck, radiation detection systems are commonly employed. These detectors could be used in a network set along the source track to increase the overall detection efficiency. However detection methods are based on counting statistics analysis. The method usually implemented consists in trigging an alarm when an individual signal rises over a threshold initially estimated in regards to the natural background signal. The detection efficiency is then proportional to the number of detectors in use, due to the fact that each sensor is taken as a standalone sensor. A new approach is presented in this paper taking into account the temporal periodicity of the signals taken by all distributed sensors as a whole. This detection method is not based only on counting statistics but also on the temporal series analysis aspect. Therefore, a specific algorithm is then developed in our lab for this kind of applications and shows a significant improvement, especially in terms of detection efficiency and false alarms reduction. We also plan on extracting information from the source vector. This paper presents the theoretical approach and some preliminary results obtain in our laboratory.

Published
2013

4. Multimodal Approach for Emotion Recognition Using a Formal Computational Model

Author

Chokri Ben Amar, Imen Tayari Meftah, Nhan Le Thanh, Laboratoire d'Informatique, Signaux, et Systèmes de Sophia Antipolis (I3S), Université Nice Sophia Antipolis (... - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA), Web-Instrumented Man-Machine Interactions, Communities and Semantics (WIMMICS), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Scalable and Pervasive softwARe and Knowledge Systems (Laboratoire I3S - SPARKS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA)-Université Nice Sophia Antipolis (... - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA)-Laboratoire d'Informatique, Signaux, et Systèmes de Sophia Antipolis (I3S), Département de Génie Électrique de Sfax [ENIS] (CEM Lab - ENIS), École Nationale d'Ingénieurs de Sfax | National School of Engineers of Sfax (ENIS), Université Nice Sophia Antipolis (1965 - 2019) (UNS), and COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA)-Université Nice Sophia Antipolis (1965 - 2019) (UNS)

Subjects
Computer science, Speech recognition, component-Emotion recognition, multidimensional model, 02 engineering and technology, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, ComputerApplications_MISCELLANEOUS, Credibility, 0202 electrical engineering, electronic engineering, information engineering, multimodal approach, [INFO]Computer Science [cs], Emotion recognition, Facial expression, Modalities, business.industry, 020206 networking & telecommunications, Multimodal therapy, Multidimensional model, State of art, Signal processing algorithms, 020201 artificial intelligence & image processing, Artificial intelligence, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], business, algebraic representation
Abstract

Emotions play a crucial role in human-computer interaction. They are generally expressed and perceived through multiple modalities such as speech, facial expressions, physiological signals. Indeed, the complexity of emotions makes the acquisition very difficult and makes unimodal systems (i.e., the observation of only one source of emotion) unreliable and often unfeasible in applications of high complexity. Moreover the lack of a standard in human emotions modeling hinders the sharing of affective information between applications. In this paper, the authors present a multimodal approach for the emotion recognition from many sources of information. This paper aims to provide a multi-modal system for emotion recognition and exchange that will facilitate inter-systems exchanges and improve the credibility of emotional interaction between users and computers. The authors elaborate a multimodal emotion recognition method from Physiological Data based on signal processing algorithms. The authors’ method permits to recognize emotion composed of several aspects like simulated and masked emotions. This method uses a new multidimensional model to represent emotional states based on an algebraic representation. The experimental results show that the proposed multimodal emotion recognition method improves the recognition rates in comparison to the unimodal approach. Compared to the state of art multimodal techniques, the proposed method gives a good results with 72% of correct.

Published
2013

5. Positive Semidefinite Matrix Factorization: A Connection with Phase Retrieval and Affine Rank Minimization

Author

Yanbin Lang, Dana Lahat, Vincent Y. F. Tan, Cédric Févotte, Signal et Communications (IRIT-SC), Institut de recherche en informatique de Toulouse (IRIT), Université Toulouse 1 Capitole (UT1), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse - Jean Jaurès (UT2J)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse 1 Capitole (UT1), Université Fédérale Toulouse Midi-Pyrénées, National University of Singapore (NUS), and ANR-19-P3IA-0004,ANITI,Artificial and Natural Intelligence Toulouse Institute(2019)

Subjects
Signal Processing (eess.SP), FOS: Computer and information sciences, Optimization, Computer Science - Machine Learning, Optimization problem, Computer science, Diagonal, Machine Learning (stat.ML), 02 engineering and technology, Positive-definite matrix, Tensors, Statistics - Computation, Machine Learning (cs.LG), Non-negative matrix factorization, Symmetric matrices, Matrix (mathematics), Factorization, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, [STAT.ML]Statistics [stat]/Machine Learning [stat.ML], Statistics - Machine Learning, Linear programming, FOS: Electrical engineering, electronic engineering, information engineering, 0202 electrical engineering, electronic engineering, information engineering, Symmetric matrix, Nonnegative matrix, Electrical Engineering and Systems Science - Signal Processing, Electrical and Electronic Engineering, Computation (stat.CO), Prediction algorithms, Approximation algorithm, 020206 networking & telecommunications, Approximation algorithms, Signal Processing, Combinatorial optimization, Affine transformation, Phase retrieval, Signal processing algorithms, Algorithm
Abstract

Positive semidefinite matrix factorization (PSDMF) expresses each entry of a nonnegative matrix as the inner product of two positive semidefinite (psd) matrices. When all these psd matrices are constrained to be diagonal, this model is equivalent to nonnegative matrix factorization. Applications include combinatorial optimization, quantum-based statistical models, and recommender systems, among others. However, despite the increasing interest in PSDMF, only a few PSDMF algorithms were proposed in the literature. In this work, we provide a collection of tools for PSDMF, by showing that PSDMF algorithms can be designed based on phase retrieval (PR) and affine rank minimization (ARM) algorithms. This procedure allows a shortcut in designing new PSDMF algorithms, as it allows to leverage some of the useful numerical properties of existing PR and ARM methods to the PSDMF framework. Motivated by this idea, we introduce a new family of PSDMF algorithms based on iterative hard thresholding (IHT). This family subsumes previously-proposed projected gradient PSDMF methods. We show that there is high variability among PSDMF optimization problems that makes it beneficial to try a number of methods based on different principles to tackle difficult problems. In certain cases, our proposed methods are the only algorithms able to find a solution. In certain other cases, they converge faster. Our results support our claim that the PSDMF framework can inherit desired numerical properties from PR and ARM algorithms, leading to more efficient PSDMF algorithms, and motivate further study of the links between these models., Comment: 18 pages (16 paper + 2 supplementary material), 9 figures, accepted for publication in the IEEE Transactions on Signal Processing. This is a revised version: there is a new additional PSDMF algorithm based on CGIHT, more numerical experiments, and some background material moved to Supplementary Material (pages 17 and 18 in this document). Supplementary Material also contains some extra figures

Published
2021

6. Measurement and Analysis of Supraharmonic Emissions in Smart Grids

Author

Deepak Amaripadath, Loic Joseph-Auguste, Fei Gao, Daniela Istrate, Jean-Pierre Braun, Dominique Fortune, Robin Roche, Franche-Comté Électronique Mécanique, Thermique et Optique - Sciences et Technologies (UMR 6174) (FEMTO-ST), Université de Technologie de Belfort-Montbeliard (UTBM)-Ecole Nationale Supérieure de Mécanique et des Microtechniques (ENSMM)-Université de Franche-Comté (UFC), Université Bourgogne Franche-Comté [COMUE] (UBFC)-Université Bourgogne Franche-Comté [COMUE] (UBFC)-Centre National de la Recherche Scientifique (CNRS), Laboratoire National de Métrologie et d'Essais [Trappes] (LNE ), and Laboratoire National de Métrologie et d’Essais (LNE)

Subjects
020209 energy, 02 engineering and technology, 01 natural sciences, 7. Clean energy, Automotive engineering, Discrete Fourier transform, [SPI.AUTO]Engineering Sciences [physics]/Automatic, [SPI]Engineering Sciences [physics], 0202 electrical engineering, electronic engineering, information engineering, Range (statistics), [PHYS.MECA.MEFL]Physics [physics]/Mechanics [physics]/Fluid mechanics [physics.class-ph], Analysis of variance, Photovoltaic systems, Sensors, Design of experiments, System of measurement, 010401 analytical chemistry, Critical factors, Photovoltaic system, [SPI.NRJ]Engineering Sciences [physics]/Electric power, Smart grids, Grid, 0104 chemical sciences, Smart grid, 13. Climate action, Power quality, [PHYS.MECA.THER]Physics [physics]/Mechanics [physics]/Thermics [physics.class-ph], Environmental science, Signal processing algorithms
Abstract

International audience; This paper describes the design of a measurement system for supraharmonic emissions in the frequency range of 2 to 150 kHz, and analyzes the measurements obtained in real grid scenarios. The measurement system is first characterized in the laboratory. The Design of Experiment approach then uses an adequate number of experiments to identify the effects and interactions of factors responsible for supraharmonic emissions. For each of these experiments, the measured supraharmonic emissions are analyzed and quantified using the discrete Fourier transform. Next, this data is studied using the Analysis of Variance method, which enables identifying the critical factors that generate supraharmonic emissions in the network. The measurement and analysis results show the individual effects and interactions between these factors.

Published
2019

7. Rapid in-memory matrix multiplication using associative processor

Author

Ahmed M. Eltawil, Fadi J. Kurdahi, Mohamed Ayoub Neggaz, Hasan Erdem Yantir, Smail Niar, Laboratoire d'Automatique, de Mécanique et d'Informatique industrielles et Humaines - UMR 8201 (LAMIH), Université de Valenciennes et du Hainaut-Cambrésis (UVHC)-Centre National de la Recherche Scientifique (CNRS)-INSA Institut National des Sciences Appliquées Hauts-de-France (INSA Hauts-De-France), Center for Embedded Computer Systems [Irvine] (CECS), University of California [Irvine] (UCI), and University of California-University of California

Subjects
Memory hierarchy, Computer science, Computation, Field programmable gate arrays, 0102 computer and information sciences, 02 engineering and technology, Parallel computing, Memristor, 01 natural sciences, Matrix multiplication, 020202 computer hardware & architecture, law.invention, Table lookup, symbols.namesake, 010201 computation theory & mathematics, law, 0202 electrical engineering, electronic engineering, information engineering, symbols, Parallel processing, [INFO]Computer Science [cs], Field-programmable gate array, Memristors, Signal processing algorithms, Von Neumann architecture
Abstract

International audience; Memory hierarchy latency is one of the main problems that prevents processors from achieving high performance. To eliminate the need of loading/storing large sets of data, Resistive Associative Processors (ReAP) have been proposed as a solution to the von Neumann bottleneck. In ReAPs, logic and memory structures are combined together to allow inmemory computations. In this paper, we propose a new algorithm to compute the matrix multiplication inside the memory that exploits the benefits of ReAP. The proposed approach is based on the Cannon algorithm and uses a series of rotations without duplicating the data. It runs in O(n), where n is the dimension of the matrix. The method also applies to a large set of row by column matrix-based applications. Experimental results show several orders of magnitude increase in performance and reduction in energy and area when compared to the latest FPGA and CPU implementations.

Published
2018

8. SATIN: A persistent musical database for music information retrieval and a supporting deep learning experiment on song instrumental classification

Author

Matthias Robine, Yann Bayle, Pierre Hanna, Centre National de la Recherche Scientifique (CNRS), Studio de Création et de Recherche en Informatique et Musique Électroacoustique (SCRIME), Université Sciences et Technologies - Bordeaux 1, Université de Bordeaux (UB), Laboratoire Bordelais de Recherche en Informatique (LaBRI), and Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)

Subjects
Computer Networks and Communications, Computer science, Music information retrieval, 02 engineering and technology, Musical, Machine learning algorithms, computer.software_genre, Music autotagging, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], Set (abstract data type), Database, Consistency (database systems), [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, 0202 electrical engineering, electronic engineering, information engineering, Media Technology, Classification of instrumentals and songs, Music recommendation, [INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB], business.industry, Deep learning, [INFO.INFO-MM]Computer Science [cs]/Multimedia [cs.MM], Content-based audio retrieval, 020207 software engineering, Reproducibility, Metadata, Signal analysis, Hardware and Architecture, [INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR], Playlist generation, [INFO.INFO-SD]Computer Science [cs]/Sound [cs.SD], Artificial intelligence, business, Signal processing algorithms, Host (network), computer, Software, Acoustic signal processing
Abstract

International audience; This paper introduces SATIN, the Set of Audio Tags and Identifiers Normalized. SATIN is a database of 400k audio-related metadata and identifiers that aims at facilitating reproducibility and comparisons among the Music Information Retrieval (MIR) algorithms. The idea is to take advantage of partnerships between scientists and private companies that host millions of tracks. Scientists can send their feature extraction algorithm to companies along SATIN identifiers and retrieve the corresponding features. This procedure allows the MIR community to have access to more tracks for classification purposes. Afterwards, scientists can provide to the MIR community the classification result for each track, which can then be compared with other algorithms results. SATIN thus resolves the major problems of accessing more tracks, managing copyrights locks, saving computation time, and guaranteeing consistency over research databases. We introduce SOFT1, the first Set Of FeaTures extracted by a company thanks to SATIN. We propose a supporting experiment classifying instrumentals and songs to detail a possible use of SATIN. We compare a deep learning approach —that has emerged in recent years in MIR— with a knowledge-based approach.

Published
2018

9. Sparsity Analysis using a Mixed Approach with Greedy and LS Algorithms on Channel Estimation

Author

Elaine Crespo Marques, Lirida Alves de Barros Naviner, Nilson Maciel de Paiva, Secure and Safe Hardware (SSH), Laboratoire Traitement et Communication de l'Information (LTCI), Institut Mines-Télécom [Paris] (IMT)-Télécom Paris-Institut Mines-Télécom [Paris] (IMT)-Télécom Paris, Département Communications & Electronique (COMELEC), Télécom ParisTech, and HAL, TelecomParis

Subjects
computer.internet_protocol, Computer science, [SPI.NANO] Engineering Sciences [physics]/Micro and nanotechnologies/Microelectronics, Multiprotocol Label Switching, 010103 numerical & computational mathematics, 02 engineering and technology, 01 natural sciences, Mixed approach, 0202 electrical engineering, electronic engineering, information engineering, Probabilistic analysis of algorithms, 0101 mathematics, [SPI.NANO]Engineering Sciences [physics]/Micro and nanotechnologies/Microelectronics, Computer Science::Databases, ComputingMilieux_MISCELLANEOUS, Computer Science::Information Theory, Sequence, business.industry, 020206 networking & telecommunications, Pattern recognition, [SPI.TRON] Engineering Sciences [physics]/Electronics, [SPI.TRON]Engineering Sciences [physics]/Electronics, Compressed sensing, Signal processing algorithms, Algorithm design, Artificial intelligence, business, computer, Algorithm, Communication channel
Abstract

Various channels can be denoted by sparse channels and many algorithms have been proposed to exploit their sparsity. In this paper, we propose a mixed algorithm based on Greedy and LS algorithms for sparse channel estimation. Analyses of the proposed and commonly used algorithms in terms of performance and complexity are performed considering the channel's sparsity, the length of training sequence and the stopping criterion. Our results show that a suitable trade-off can be found and effective channel estimations can be obtained with a low-cost algorithm.

Published
2017

10. Cyclostationary Analysis of ECG Signals Acquired Inside an Ultra-High Field MRI Scanner

Author

Michael Friebe, Asoke K. Nandi, Johannes W Krug, Michel Haritopoulos, Alfredo Illanes, Laboratoire Pluridisciplinaire de Recherche en Ingénierie des Systèmes, Mécanique et Energétique (PRISME), Université d'Orléans (UO)-Ecole Nationale Supérieure d'Ingénieurs de Bourges (ENSI Bourges), INKA - Intelligente Katheter, Otto-von-Guericke University [Magdeburg] (OVGU), Electronic and Computer Engineering, University of Jyväskylä (JYU)-Brunel University London [Uxbridge], and Haritopoulos, Michel

Subjects
Scanner, [INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing, Cyclostationary process, Computer science, 02 engineering and technology, Signal, 030218 nuclear medicine & medical imaging, Electrocardiography, Magnetohydrodynamics, 03 medical and health sciences, 0302 clinical medicine, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, Ultra high field, 0202 electrical engineering, electronic engineering, information engineering, medicine, Coherence (signal processing), Spectral analysis, Computer vision, ComputingMilieux_MISCELLANEOUS, medicine.diagnostic_test, business.industry, 020206 networking & telecommunications, Magnetic resonance imaging, Correlation, Lead, Artificial intelligence, Ecg signal, Signal processing algorithms, business, Telecommunications, Coherence, Coherence (physics)
Abstract

In this paper, a strategy is proposed to estimate the R-peaks in ECG signals recorded inside a 7 T magnetic resonance imaging (MRI) scanner in order to reduce the disturbances due to the magnetohydrodynamic (MHD) effect and to finally obtain high quality cardiovascular magnetic resonance (CMR) images. We first show that the cyclostationarity property of the ECG signal disturbed by the MHD effect can be quantified by means of cyclic spectral analysis. Then, this information is forwarded as input to a cyclostationary source extraction algorithm applied to a set of ECG recordings acquired inside the MRI scanner in a Feet first (Ff) and a Head first (Hf) positions. Finally, detection of the R-peaks in the estimated cyclostationary signal completes the proposed procedure. Validation of the method is performed by comparing the estimated with clinical R-peaks annotations provided with the real world dataset. The obtained results are promising and future research directions are discussed.

Published
2017

11. A post-nonlinear mixture model approach to binary matrix factorization

Author

David Brie, Anthony Larue, Sebastian Miron, Mamadou Diop, CEA Tech Grand-Est (DGDE), CEA Tech en régions (CEA-TECH-Reg), Direction de Recherche Technologique (CEA) (DRT (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Direction de Recherche Technologique (CEA) (DRT (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA), Centre de Recherche en Automatique de Nancy (CRAN), Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), Laboratoire Outils d'Analyse des Données (LOAD), Département Métrologie Instrumentation & Information (DM2I), Laboratoire d'Intégration des Systèmes et des Technologies (LIST (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Laboratoire d'Intégration des Systèmes et des Technologies (LIST (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay, CEA Tech Alsace Champagne-Ardenne Lorraine, Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL), Laboratoire d'analyse des données et d'intelligence des systèmes (LADIS), Laboratoire d'Intégration des Systèmes et des Technologies (LIST), and Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Laboratoire d'Intégration des Systèmes et des Technologies (LIST)

Subjects
Inverse problems, Signal processing, Mathematical optimization, binary matrix product, sufficient condition, post-nonlinear mixture model approach, 0102 computer and information sciences, 02 engineering and technology, 01 natural sciences, Matrix decomposition, Non-negative matrix factorization, BFM model, Factorization, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, 020204 information systems, 0202 electrical engineering, electronic engineering, information engineering, Logical matrix, NMF problem, Mathematics, necessary condition, nonnegative matrix factorization, Inverse problem, Binary Matrix Factorization problem, Mixture model, nonlinear function, Europe, Nonlinear system, constrained NMF, Cost function, 010201 computation theory & mathematics, Identifiability, Mixture models, Signal processing algorithms, Algorithm, [MATH.MATH-NA]Mathematics [math]/Numerical Analysis [math.NA]
Abstract

International audience; In this paper, we address the Binary Matrix Factori-zation (BMF) problem which is the restriction of the nonnegative matrix factorization (NMF) to the binary matrix case. A necessary and sufficient condition for the identifiability for the BFM model is given. We propose to approach the BMF problem by the NMF problem using a nonlinear function which guarantees the binarity of the reconstructed data. Two new algorithms are introduced and compared in simulations with the state of art BMF algorithms.

Published
2017

12. Robust hyperspectral unmixing accounting for residual components

Author

Jose M. Bioucas-Dias, Abderrahim Halimi, Paul Honeine, Heriot-Watt University [Edinburgh] (HWU), Equipe Apprentissage (DocApp - LITIS), Laboratoire d'Informatique, de Traitement de l'Information et des Systèmes (LITIS), Institut national des sciences appliquées Rouen Normandie (INSA Rouen Normandie), Institut National des Sciences Appliquées (INSA)-Normandie Université (NU)-Institut National des Sciences Appliquées (INSA)-Normandie Université (NU)-Université de Rouen Normandie (UNIROUEN), Normandie Université (NU)-Université Le Havre Normandie (ULH), Normandie Université (NU)-Institut national des sciences appliquées Rouen Normandie (INSA Rouen Normandie), Normandie Université (NU), Instituto de Telecomunicações [Lisboa, Portugal], Université Le Havre Normandie (ULH), Normandie Université (NU)-Normandie Université (NU)-Université de Rouen Normandie (UNIROUEN), Institut National des Sciences Appliquées (INSA)-Normandie Université (NU)-Institut National des Sciences Appliquées (INSA)-Université Le Havre Normandie (ULH), and Institut National des Sciences Appliquées (INSA)-Normandie Université (NU)-Institut National des Sciences Appliquées (INSA)

Subjects
Endmember, Hyperspectral imaging, Bayesian inference, posterior distribution, 02 engineering and technology, residual component analysis model, Residual, gamma Markov random field, signal processing chain, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, ME formulation, robust unmixing, robust supervised hyperspectral unmixing, 0202 electrical engineering, electronic engineering, information engineering, Inference algorithms, Coordinate descent, Mathematics, Bayes estimator, Linear model, Computational modeling, Bayes methods, Hyperspectral imagery, 020201 artificial intelligence & image processing, Mixture models, maximum a posteriori estimator, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, coordinate descent algorithm, Signal processing, Posterior probability, maximum likelihood estimation, source separation problem, Bayesian algorithm, endmember variability, belief networks, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], Gaussian process, business.industry, [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], 020206 networking & telecommunications, Pattern recognition, Bayesian estimation, Mixture model, source separation, learning (artificial intelligence), mismodelling effects, Artificial intelligence, business, Signal processing algorithms, EV
Abstract

International audience; This paper presents a new hyperspectral mixture model jointly with a Bayesian algorithm for supervised hyperspectral unmixing. Based on the residual component analysis model, the proposed formulation assumes the linear model to be corrupted by an additive term that accounts for mismodelling effects (ME). The ME formulation takes into account the effect of outliers, the propagated errors in the signal processing chain and copes with some types of endmember variability (EV) or nonlinearity (NL). The known constraints on the model parameters are modeled via suitable priors. The resulting posterior distribution is optimized using a coordinate descent algorithm which allows us to compute the maximum a posteriori estimator of the unknown model parameters. The proposed model and estimation algorithm are validated on both synthetic and real images showing competitive results regarding the quality of the inferences and the computational complexity when compared to the state-of-the-art algorithms.

Published
2016

13. Joint Eigenvalue Decomposition of Non-Defective Matrices Based on the LU Factorization With Application to ICA

Author

Xavier Luciani, Laurent Albera, Laboratoire d'Informatique et Systèmes (LIS), Aix Marseille Université (AMU)-Université de Toulon (UTLN)-Centre National de la Recherche Scientifique (CNRS), Signal et Image (SIIM), Aix Marseille Université (AMU)-Université de Toulon (UTLN)-Centre National de la Recherche Scientifique (CNRS)-Aix Marseille Université (AMU)-Université de Toulon (UTLN)-Centre National de la Recherche Scientifique (CNRS), Parcimonie et Nouveaux Algorithmes pour le Signal et la Modélisation Audio (PANAMA), Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-SIGNAUX ET IMAGES NUMÉRIQUES, ROBOTIQUE (IRISA-D5), Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS), Laboratoire Traitement du Signal et de l'Image (LTSI), Université de Rennes (UR)-Institut National de la Santé et de la Recherche Médicale (INSERM), Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS), and Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National de la Santé et de la Recherche Médicale (INSERM)

Subjects
Mathematical optimization, Eigenvalues and eigenfunctions, Complexity theory, Context, Context (language use), LU decomposition, law.invention, Matrix decomposition, Matrix (mathematics), Factorization, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, law, Signal Processing, Identifiability, Jacobian matrices, Joints, Electrical and Electronic Engineering, Signal processing algorithms, Algorithm, Eigendecomposition of a matrix, Eigenvalues and eigenvectors, Mathematics
Abstract

International audience; In this paper we propose a fast and efficient Jacobi-like approach named JET (Joint Eigenvalue decomposition based on Triangular matrices) for the Joint EigenValue Decomposition (JEVD) of a set of real or complex non-defective matrices based on the LU factorization of the matrix of eigenvectors. Contrarily to classical Jacobi-like JEVD methods, the iterative procedure of the JET approach can be reduced to the search for only one of the two triangular matrices involved in the factorization of the matrix of eigenvectors, hence decreasing the numerical complexity. Two variants of the JET technique, namely JET-U and JET-O, which correspond to the optimization of two different cost functions are described in detail and these are extended to the complex case. Numerical simulations show that in many practical cases the JET approach provides more accurate estimation of the matrix of eigenvectors than its competitors and that the lowest numerical complexity is consistently achieved by the JET-U algorithm. In addition, we illustrate in the ICA context the interest of being able to solve efficiently the (non-orthogonal) JEVD problem. More particularly, based on our JET-U algorithm, we propose a more robust version of an existing ICA method, named MICAR-U. The identifiability of the latter is studied and proved under some conditions. Computer results given in the context of brain interfaces show the better ability of MICAR-U to denoise simulated electrocortical data compared to classical ICA techniques for low signal to noise ratio values.

Published
2015

14. More efficient sparsity-inducing algorithms using inexact gradient

Author

Liva Ralaivola, Sokol Koço, Alain Rakotomamonjy, Equipe Apprentissage (DocApp - LITIS), Laboratoire d'Informatique, de Traitement de l'Information et des Systèmes (LITIS), Université Le Havre Normandie (ULH), Normandie Université (NU)-Normandie Université (NU)-Université de Rouen Normandie (UNIROUEN), Normandie Université (NU)-Institut national des sciences appliquées Rouen Normandie (INSA Rouen Normandie), Institut National des Sciences Appliquées (INSA)-Normandie Université (NU)-Institut National des Sciences Appliquées (INSA)-Université Le Havre Normandie (ULH), Institut National des Sciences Appliquées (INSA)-Normandie Université (NU)-Institut National des Sciences Appliquées (INSA), Laboratoire d'informatique Fondamentale de Marseille - UMR 6166 (LIF), and Université de la Méditerranée - Aix-Marseille 2-Université de Provence - Aix-Marseille 1-Centre National de la Recherche Scientifique (CNRS)

Subjects
Mathematical optimization, Signal processing, Linear programming, Matching pursuit algorithms, [SCCO.COMP]Cognitive science/Computer science, 020206 networking & telecommunications, 0102 computer and information sciences, 02 engineering and technology, 01 natural sciences, Matching pursuit, Set (abstract data type), 010201 computation theory & mathematics, 0202 electrical engineering, electronic engineering, information engineering, Signal processing algorithms, Algorithm, ComputingMilieux_MISCELLANEOUS, Mathematics
Abstract

In this paper, we tackle the problem of adapting a set of classic sparsity-inducing methods to cases when the gradient of the objective function is either difficult or very expensive to compute. Our contributions are two-fold: first, we propose methodologies for computing fair estimations of inexact gradients, second we propose novel stopping criteria for computing these gradients. For each contribution we provide theoretical backgrounds and justifications. In the experimental part, we study the impact of the proposed methods for two well-known algorithms, Frank-Wolfe and Orthogonal Matching Pursuit. Results on toy datasets show that inexact gradients can be as useful as exact ones provided the appropriate stopping criterion is used.

Published
2015

15. A perturbed linear mixing model accounting for spectral variability

Author

Pierre-Antoine Thouvenin, Nicolas Dobigeon, Jean-Yves Tourneret, Signal et Communications (IRIT-SC), Institut de recherche en informatique de Toulouse (IRIT), Université Toulouse 1 Capitole (UT1), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse - Jean Jaurès (UT2J)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse 1 Capitole (UT1), Université Fédérale Toulouse Midi-Pyrénées, Institut National Polytechnique (Toulouse) (Toulouse INP), CoMputational imagINg anD viSion (IRIT-MINDS), Institut National Polytechnique de Toulouse - INPT (FRANCE), Centre National de la Recherche Scientifique - CNRS (FRANCE), Université Toulouse III - Paul Sabatier - UT3 (FRANCE), Université Toulouse - Jean Jaurès - UT2J (FRANCE), Université Toulouse 1 Capitole - UT1 (FRANCE), and Institut National Polytechnique de Toulouse - Toulouse INP (FRANCE)

Subjects
0211 other engineering and technologies, Accounting, 02 engineering and technology, Image (mathematics), [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], Traitement des images, Statistics::Machine Learning, Endmember variability, (ADMM ), [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, 0202 electrical engineering, electronic engineering, information engineering, Traitement du signal et de l'image, Synthèse d'image et réalité virtuelle, Mixing (physics), 021101 geological & geomatics engineering, Mathematics, Abundance estimation, Signal processing, Spectral signature, Alternating Direction Method of Multipliers, Optimization algorithm, business.industry, Hyperspectral imaging, [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], Vision par ordinateur et reconnaissance de formes, Intelligence artificielle, [INFO.INFO-GR]Computer Science [cs]/Graphics [cs.GR], Computer Science::Computer Vision and Pattern Recognition, Hyperspectral imagery, [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV], Signal processing algorithms, 020201 artificial intelligence & image processing, business, Linear unmixin
Abstract

International audience; Hyperspectral unmixing aims at determining the reference spectral signatures composing a hyperspectral image, their abundance fractions and their number. In practice, the spectral variability of the identified signatures induces significant abundance estimation errors. To address this issue, this paper introduces a new linear mixing model explicitly accounting for this phenomenon. In this setting, the extracted endmembers are interpreted as possibly corrupted versions of the true endmembers. The parameters of this model can be estimated using an optimization algorithm based on the alternating direction method of multipliers. The performance of the proposed unmixing method is evaluated on synthetic and real data.

Published
2015

16. Group sparse LMS for multiple system identification

Author

Chen Wei, Lei Yu, Gang Zheng, Wuhan University [China], Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 (CRIStAL), Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS), Non-Asymptotic estimation for online systems (NON-A), Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Inria Lille - Nord Europe, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Inria Lille - Nord Europe, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 (CRIStAL), and Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS)

Subjects
0209 industrial biotechnology, Mathematical optimization, Multiple system identification, System identification, 020206 networking & telecommunications, 02 engineering and technology, Impulse (physics), Proximal splitting method, 020901 industrial engineering & automation, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, Index Terms— LMS, 0202 electrical engineering, electronic engineering, information engineering, Signal processing algorithms, Algorithm design, Online algorithm, Algorithm, Impulse response, Group sparsity, Mathematics
Abstract

International audience; Armed with structures, group sparsity can be exploited to extraordinarily improve the performance of adaptive estimation. In this paper, a group sparse regularized least-mean-square (LMS) algorithm is proposed to cope with the identification problems for multiple/multi-channel systems. In particular, the coefficients of impulse response function for each system are assumed to be sparse. Then, the dependencies between multiple systems are considered, where the coefficients of impulse responses of each system share the same pattern. An iterative online algorithm is proposed via proximal splitting method. At the end, simulations are carried out to verify the superiority of our proposed algorithm to the state-of-the-art algorithms.

Published
2015

17. Gossip algorithms for principal component analysis in networks

Author

Farah Mourad-Chehade, Joumana Farah, Nisrine Ghadban, Paul Honeine, Clovis Francis, Université Libanaise, Laboratoire Modélisation et Sûreté des Systèmes (LM2S), Institut Charles Delaunay (ICD), Université de Technologie de Troyes (UTT)-Centre National de la Recherche Scientifique (CNRS)-Université de Technologie de Troyes (UTT)-Centre National de la Recherche Scientifique (CNRS), Holy-Spirit University of Kaslik [Jounieh], Université Libanaise (UL), and This work is supported by Universit´e de Technologie de Troyes, Universit´e Libanaise, and R´egion Champagne-Ardenne (grant 'WiDiD')

Subjects
Signal processing, Theoretical computer science, Computational complexity theory, Computer science, Iterative method, Principal component analysis, time series acquisition, 010103 numerical & computational mathematics, 02 engineering and technology, 01 natural sciences, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, Gossip, 0202 electrical engineering, electronic engineering, information engineering, Algorithm design and analysis, 0101 mathematics, wireless sensor networks, Data mining, asynchronous nodes, Routing, iterative rule, computational complexity, Dimensionality reduction, gossip algorithms, [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], 020206 networking & telecommunications, in-network processing, adaptive learning, Gossip averaging, Cost function, Asynchronous communication, distributed processing, iterative methods, Algorithm design, Oja, time series, Signal processing algorithms, Wireless sensor network, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, large-scale networks, Principal axis theorem
Abstract

International audience; This paper deals with the issues of the dimensionality reduction and the extraction of the structure of data using principal component analysis for the multivariable data in large-scale networks. In order to overcome the high computational complexity of this technique, we derive several in-network strategies to estimate the principal axes without the need for computing the sample covariance matrix. To this aim, we propose to combine Oja's iterative rule with average gossiping algorithms. Gossiping is used as a solution for communication between asynchronous nodes. The performance of the proposed approach is illustrated on time series acquisition in wireless sensor networks.

Published
2015

18. Variants of non-negative least-mean-square algorithm and convergence analysis

Author

Paul Honeine, Jose C. M. Bermudez, Jie Chen, Cedric Richard, Laboratoire Modélisation et Sûreté des Systèmes (LM2S), Institut Charles Delaunay (ICD), Université de Technologie de Troyes (UTT)-Centre National de la Recherche Scientifique (CNRS)-Université de Technologie de Troyes (UTT)-Centre National de la Recherche Scientifique (CNRS), Joseph Louis LAGRANGE (LAGRANGE), Université Nice Sophia Antipolis (1965 - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire de la Côte d'Azur, COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Université Côte d'Azur (UCA)-Université Côte d'Azur (UCA)-Centre National de la Recherche Scientifique (CNRS), Universidade Federal de Santa Catarina = Federal University of Santa Catarina [Florianópolis] (UFSC), Université Côte d'Azur (UCA)-Université Nice Sophia Antipolis (... - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Observatoire de la Côte d'Azur, and Université Côte d'Azur (UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)

Subjects
Mathematical optimization, Adaptive signal processing, Equations, Gaussian, least mean squares methods, nonnegative least-mean-square algorithm, nonstationary environments, non-negativity constraints, 02 engineering and technology, non-negativity, convergence analysis, symbols.namesake, Wiener filtering problem, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, NNLMS, adaptive weights, normalized algorithm, Convergence (routing), adaptive filtering, 0202 electrical engineering, electronic engineering, information engineering, Algorithm design and analysis, least-mean-square algorithms, Sensitivity (control systems), Electrical and Electronic Engineering, computational cost, Prediction algorithms, Mathematics, Wiener filters, Gaussian inputs, sign-sign algorithm, Estimation theory, Wiener filter, 020206 networking & telecommunications, Vectors, stochastic behavior, Adaptive filter, Moment (mathematics), exponential algorithm, Signal Processing, symbols, 020201 artificial intelligence & image processing, Algorithm design, stochastic processes, parameter estimation, Signal processing algorithms, Convergence, Estimation, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing
Abstract

International audience; Due to the inherent physical characteristics of systems under investigation, non-negativity is one of the most interesting constraints that can usually be imposed on the parameters to estimate. The Non-Negative Least-Mean-Square algorithm (NNLMS) was proposed to adaptively find solutions of a typical Wiener filtering problem but with the side constraint that the resulting weights need to be non-negative. It has been shown to have good convergence properties. Nevertheless, certain practical applications may benefit from the use of modified versions of this algorithm. In this paper, we derive three variants of NNLMS. Each variant aims at improving the NNLMS performance regarding one of the following aspects: sensitivity of input power, unbalance of convergence rates for different weights and computational cost. We study the stochastic behavior of the adaptive weights for these three new algorithms for non-stationary environments. This study leads to analytical models to predict the first and second order moment behaviors of the weights for Gaussian inputs. Simulation results are presented to illustrate the performance of the new algorithms and the accuracy of the derived models.

Published
2014

19. Performance of ADONIS-LYNX system for burn-up measurement applications at AREVA NC la Hague reprocessing plant

Author

Michael Shen, Stephane Normand, Gabriele Grassi, Eric Barat, Cedric Herman, N. Menaa, Jeremie Lefevre, Thomas Dautremer, Laboratoire Capteurs et Architectures Electroniques (LCAE), Département Métrologie Instrumentation & Information (DM2I), Laboratoire d'Intégration des Systèmes et des Technologies (LIST), Direction de Recherche Technologique (CEA) (DRT (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Direction de Recherche Technologique (CEA) (DRT (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Laboratoire d'Intégration des Systèmes et des Technologies (LIST), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay, Groupe AREVA, Canberra Inc., CANBERRA France, Joint research program CEA – AREVA NC P3C, Laboratoire d'Intégration des Systèmes et des Technologies (LIST (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Laboratoire d'Intégration des Systèmes et des Technologies (LIST (CEA)), and IEEE

Subjects
Adonis, Computer science, HPGe spectrometry, digital signal processing, Real-time computing, Collimators, Assembly, ADONIS-LYNX system, Fuels, radioactive waste processing, [PHYS.NEXP]Physics [physics]/Nuclear Experiment [nucl-ex], 01 natural sciences, 030218 nuclear medicine & medical imaging, ADONIS bimodal Kalman smoother, 03 medical and health sciences, 0302 clinical medicine, 0103 physical sciences, Digital signal processing, Spectroscopy, biology, 010308 nuclear & particles physics, business.industry, Kalman smoother, fission reactor fuel reprocessing, burn-up measurement applications, measurement campaign, gamma passive assay, biology.organism_classification, Current measurement, AREVA NC La Hague reprocessing plant, ADONIS system, Signal processing algorithms, business, Cooling
Abstract

Conference of 2013 3rd International Conference on Advancements in Nuclear Instrumentation, Measurement Methods and Their Applications, ANIMMA 2013 ; Conference Date: 23 June 2013 Through 27 June 2013; Conference Code:102802; International audience; This work deals with the last measurement campaign done at the AREVA NC La Hague reprocessing plant with the new industrial ADONIS system called ADONIS LYNX. In this paper we briefly explain the ADONIS bimodal Kalman smoother. Next, we present the experimental set-up as well as the industrial approach for the ADONIS system. Results from measurement campaign are then discussed. Some ways of improvement are also explained.

Published
2013

20. Kernel-based localization using fingerprinting in wireless sensor networks

Author

Hichem Snoussi, Joumana Farah, Sandy Mahfouz, Farah Mourad-Chehade, Paul Honeine, Laboratoire Modélisation et Sûreté des Systèmes (LM2S), Institut Charles Delaunay (ICD), Université de Technologie de Troyes (UTT)-Centre National de la Recherche Scientifique (CNRS)-Université de Technologie de Troyes (UTT)-Centre National de la Recherche Scientifique (CNRS), and Holy-Spirit University of Kaslik [Jounieh]

Subjects
Signal processing, Engineering, Wi-Fi array, Real-time computing, interference, Wireless communication, 02 engineering and technology, indoor environment, localization, radio-location fingerprinting, kernel methods, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, kernel-based localization, 0202 electrical engineering, electronic engineering, information engineering, Electronic engineering, Computer Science::Networking and Internet Architecture, Wireless, RSSI, ComputerSystemsOrganization_SPECIAL-PURPOSEANDAPPLICATION-BASEDSYSTEMS, Real-time systems, Noise (signal processing), business.industry, ComputerSystemsOrganization_COMPUTER-COMMUNICATIONNETWORKS, Conferences, [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], 020206 networking & telecommunications, radiofrequency interference, Wireless sensor networks, received signal strength indicator, Key distribution in wireless sensor networks, indoor localization, Kernel, Kernel method, fingerprinting, Kernel (statistics), telecommunication signalling, 020201 artificial intelligence & image processing, business, Signal processing algorithms, Wireless sensor network, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, indoor radio
Abstract

International audience; Indoor localization is an important issue in wireless sensor networks for a very large number of applications. Recently, localization techniques based on the received signal strength indicator (RSSI) have been widely used due to their simple and low cost implementation. In this paper, we propose an algorithm for localization in wireless sensor networks based on radio-location fingerprinting and kernel methods. The proposed method is compared to another well-known localization algorithm in the case of real data collected in an indoor environment where RSSI measures are affected by noise and other interferences.

Published
2013

21. Online One-Class Machines Based on the Coherence Criterion

Author

Noumir, Zineb, Honeine, Paul, Richard, Cédric, Laboratoire Modélisation et Sûreté des Systèmes (LM2S), Institut Charles Delaunay (ICD), Université de Technologie de Troyes (UTT)-Centre National de la Recherche Scientifique (CNRS)-Université de Technologie de Troyes (UTT)-Centre National de la Recherche Scientifique (CNRS), Joseph Louis LAGRANGE (LAGRANGE), Université Côte d'Azur (UCA)-Université Nice Sophia Antipolis (... - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Observatoire de la Côte d'Azur, Université Côte d'Azur (UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS), ANR-08-SECU-0013,VIGIRES'EAU,Surveillance en temps réel de la qualité de l'eau potable d'un réseau de distribution en vue de la détection d'intrusions(2008), Université Nice Sophia Antipolis (1965 - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire de la Côte d'Azur, and COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Université Côte d'Azur (UCA)-Université Côte d'Azur (UCA)-Centre National de la Recherche Scientifique (CNRS)

Subjects
Optimization, cybersecurity, online one-class machines, optimisation, low computational demanding algorithm, online learning, Time series analysis, least squares approximations, one-class, support vector machines, kernel methods, pattern classification, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, adaptive filtering, coherence parameter, sparsity, [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], one-class classification, least-squares optimization problem, Kernel, machine learning, Dictionaries, learning (artificial intelligence), online one-class classification method, Signal processing algorithms, Coherence, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing
Abstract

International audience; In this paper, we investigate a novel online one-class classification method. We consider a least-squares optimization problem, where the model complexity is controlled by the coherence criterion as a sparsification rule. This criterion is coupled with a simple updating rule for online learning, which yields a low computational demanding algorithm. Experiments conducted on time series illustrate the relevance of our approach to existing methods.

Published
2012

22. Hyperspectral image unmixing using manifold learning: methods derivations and comparative tests

Author

Cedric Richard, Celine Theys, Nguyen Hoang Nguyen, Paul Honeine, Joseph Louis LAGRANGE (LAGRANGE), Université Côte d'Azur (UCA)-Université Nice Sophia Antipolis (... - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Observatoire de la Côte d'Azur, Université Côte d'Azur (UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS), Laboratoire Modélisation et Sûreté des Systèmes (LM2S), Institut Charles Delaunay (ICD), Université de Technologie de Troyes (UTT)-Centre National de la Recherche Scientifique (CNRS)-Université de Technologie de Troyes (UTT)-Centre National de la Recherche Scientifique (CNRS), Université Nice Sophia Antipolis (1965 - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire de la Côte d'Azur, and COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Université Côte d'Azur (UCA)-Université Côte d'Azur (UCA)-Centre National de la Recherche Scientifique (CNRS)

Subjects
Endmember, Geodesic, Equations, Hyperspectral imaging, dimensionality-reduction step, 0211 other engineering and technologies, 02 engineering and technology, spectral component mixture, abundance estimation step, nonlinear estimation, Mathematical model, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, 0202 electrical engineering, electronic engineering, information engineering, linear mixture model, differential geometry, Manifolds, Materials, geodesic distance, 021101 geological & geomatics engineering, Mathematics, Abundance estimation, Pixel, business.industry, Nonlinear dimensionality reduction, [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], 020206 networking & telecommunications, Pattern recognition, hyperspectral image unmixing analysis, Mixture model, nonlinear technique, Nonlinear system, endmember extraction, geophysical image processing, manifold learning method, comparative testing, Computer Science::Computer Vision and Pattern Recognition, learning (artificial intelligence), Artificial intelligence, business, Signal processing algorithms, Estimation, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing
Abstract

International audience; In hyperspectral image analysis, pixels are mixtures of spectral components associated to pure materials. Although the linear mixture model is the mostly studied case, nonlinear techniques have been proposed to overcome its limitations. In this paper, a manifold learning approach is used as a dimensionality-reduction step to deal with non-linearities beforehand, or is integrated directly in the endmember extraction and abundance estimation steps using geodesic distances. Simulation results show that these methods improve the precision of estimation in severely nonlinear cases.

Published
2012

23. Blind digital modulation identification for spatially-correlated MIMO systems

Author

Marion Berbineau, Iyad Dayoub, Kais Hassan, Walaa Hamouda, C. N. Nzeza, Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 (IEMN), Centrale Lille-Institut supérieur de l'électronique et du numérique (ISEN)-Université de Valenciennes et du Hainaut-Cambrésis (UVHC)-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF), Institut d’Électronique, de Microélectronique et de Nanotechnologie - Département Opto-Acousto-Électronique - UMR 8520 (IEMN-DOAE), Centrale Lille-Institut supérieur de l'électronique et du numérique (ISEN)-Université de Valenciennes et du Hainaut-Cambrésis (UVHC)-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-Centrale Lille-Institut supérieur de l'électronique et du numérique (ISEN)-Université de Valenciennes et du Hainaut-Cambrésis (UVHC)-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF), Concordia University [Montreal], Laboratoire Electronique, Ondes et Signaux pour les Transports (INRETS/LEOST), and Institut National de Recherche sur les Transports et leur Sécurité (INRETS)

Subjects
0209 industrial biotechnology, Spatial correlation, MIMO, Feature extraction, Higher-order statistics, 02 engineering and technology, [SPI]Engineering Sciences [physics], 020901 industrial engineering & automation, Control theory, 0202 electrical engineering, electronic engineering, information engineering, Waveform, Electrical and Electronic Engineering, Computer Science::Information Theory, Mathematics, Modulation, Signal to noise ratio, Applied Mathematics, 020206 networking & telecommunications, Keying, Vectors, Correlation, Computer Science Applications, Channel state information, Signal processing algorithms, Algorithm, Quadrature amplitude modulation
Abstract

International audience; Modulation type is one of the most important characteristics used in signal waveform identification and classification. Spatial correlation is a crucial factor for practical multiple-input multiple-output (MIMO) systems. This paper addresses the problem of blind digital modulation identification in spatially-correlated MIMO systems. The proposed algorithm is verified using higher order statistical moments and cumulants of the received signal. The purpose is to discriminate among different M-ary shift keying linear modulation schemes without any priori signal information. This study employs several MIMO techniques to identify the modulation with and without channel state information (CSI). The proposed classifier shows a high identification performance in acceptable signal-to-noise ratio (SNR) range.

Published
2012

24. A wavelet-based filtering approach to functional bipartite ranking

Author

Stéphan Clémençon, M. Depecker, Laboratoire Traitement et Communication de l'Information (LTCI), Institut Mines-Télécom [Paris] (IMT)-Télécom Paris, Département Images, Données, Signal (IDS), Télécom ParisTech, Laboratoire Information, Modèles, Apprentissage [Gif-sur-Yvette] (LIMA), Département Métrologie Instrumentation & Information (DM2I), Laboratoire d'Intégration des Systèmes et des Technologies (LIST), Direction de Recherche Technologique (CEA) (DRT (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Direction de Recherche Technologique (CEA) (DRT (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Laboratoire d'Intégration des Systèmes et des Technologies (LIST), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay, Télécom ParisTech-Institut Mines-Télécom [Paris] (IMT), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Direction de Recherche Technologique (CEA) (DRT (CEA)), Laboratoire d'Intégration des Systèmes et des Technologies (LIST (CEA)), and Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Laboratoire d'Intégration des Systèmes et des Technologies (LIST (CEA))

Subjects
input curves, Function space, Binary number, 02 engineering and technology, 01 natural sciences, 010104 statistics & probability, multivariate data, Wavelet, [STAT.ML]Statistics [stat]/Machine Learning [stat.ML], 0202 electrical engineering, electronic engineering, information engineering, nonlinear wavelet-based approximation, ROC optimization, Mathematics, functional data analysis, functional bipartite ranking, wavelet analysis, Approximation algorithm, Wavelet transform, Functional data analysis, artificial intelligence, Approximation algorithms, binary feedback, machine learning, wavelet-based filtering approach, filtering theory, Filtering, bipartite ranking, [STAT.ME]Statistics [stat]/Methodology [stat.ME], FDA, Signal processing, Approximation methods, supervised learning, Ranking (information retrieval), functional analysis, Wavelet transforms, Stochastic processes, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], 0101 mathematics, bipartite ranking task, approximation theory, Discrete mathematics, Approximation theory, business.industry, 020206 networking & telecommunications, Pattern recognition, AUC maximization, ROC curve, [MATH.MATH-PR]Mathematics [math]/Probability [math.PR], Artificial intelligence, business, Signal processing algorithms
Abstract

ISBN 978-1-4577-0569-4; International audience; It is the purpose of this paper to investigate the bipartite ranking task from the perspective of functional data analysis (FDA). Precisely, given a collection of independent copies of a (possibly sampled) random curve X = (X(t))t∊[0,1] taking its values in a function space X, with a locally smooth autocorrelation structure and to which a binary label Y ∊ {−1, +1} is randomly assigned, the goal is to learn a scoring functions: X → R with optimal ROC curve. Based on nonlinear wavelet-based approximation, it is shown how to select compact finite dimensional representations of the input curves in order to build accurate ranking rules, using recent advances in the ranking problem for multivariate data with binary feedback

Published
2011

25. A novel kernel-based nonlinear unmixing scheme of hyperspectral images

Author

Paul Honeine, Jie Chen, Cedric Richard, Laboratoire Hippolyte Fizeau (FIZEAU), Université Nice Sophia Antipolis (... - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire de la Côte d'Azur, Université Côte d'Azur (UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Université Côte d'Azur (UCA)-Centre National de la Recherche Scientifique (CNRS), Laboratoire Modélisation et Sûreté des Systèmes (LM2S), Institut Charles Delaunay (ICD), Université de Technologie de Troyes (UTT)-Centre National de la Recherche Scientifique (CNRS)-Université de Technologie de Troyes (UTT)-Centre National de la Recherche Scientifique (CNRS), Université Nice Sophia Antipolis (1965 - 2019) (UNS), and COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Université Côte d'Azur (UCA)-Université Côte d'Azur (UCA)-Centre National de la Recherche Scientifique (CNRS)

Subjects
hyperspectral images, Endmember, Hyperspectral imaging, pixels, Feature vector, 0211 other engineering and technologies, 02 engineering and technology, nonlinear hyperspectral unmixing problem, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, 0202 electrical engineering, electronic engineering, information engineering, Algorithm design and analysis, photons, linear mixture model, Materials, 021101 geological & geomatics engineering, Mathematics, kernel-based nonlinear unmixing scheme, business.industry, Linear model, [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], 020206 networking & telecommunications, Pattern recognition, Vectors, Mixture model, Real image, Nonlinear system, geophysical image processing, Kernel, Kernel (statistics), Computer Science::Computer Vision and Pattern Recognition, spectral components, kernel-based learning theory, learning (artificial intelligence), endmember components, Artificial intelligence, business, Signal processing algorithms, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing
Abstract

International audience; In hyperspectral images, pixels are mixtures of spectral components associated to pure materials. Although the linear mixture model is the most studied case, nonlinear models have been taken into consideration to overcome some limitations of the linear model. In this paper, nonlinear hyperspectral unmixing problem is studied through kernel-based learning theory. Endmember components at each band are mapped implicitly in a high feature space, in order to address the nonlinear interaction of photons. Experiment results with both synthetic and real images illustrate the effectiveness of the proposed scheme.

Published
2011

26. A Modified Non-Negative LMS Algorithm and its Stochastic Behavior Analysis

Author

Cedric Richard, Jose C. M. Bermudez, Paul Honeine, Jie Chen, Laboratoire Hippolyte Fizeau (FIZEAU), Université Nice Sophia Antipolis (... - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire de la Côte d'Azur, Université Côte d'Azur (UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Université Côte d'Azur (UCA)-Centre National de la Recherche Scientifique (CNRS), Laboratoire Modélisation et Sûreté des Systèmes (LM2S), Institut Charles Delaunay (ICD), Université de Technologie de Troyes (UTT)-Centre National de la Recherche Scientifique (CNRS)-Université de Technologie de Troyes (UTT)-Centre National de la Recherche Scientifique (CNRS), Universidade Federal de Santa Catarina = Federal University of Santa Catarina [Florianópolis] (UFSC), Université Nice Sophia Antipolis (1965 - 2019) (UNS), and COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Université Côte d'Azur (UCA)-Université Côte d'Azur (UCA)-Centre National de la Recherche Scientifique (CNRS)

Subjects
Endmember, hyperspectral image, Equations, nonlinear interaction, Feature vector, least mean squares methods, Feature extraction, real images, 02 engineering and technology, Approximation methods, non-negativity, 01 natural sciences, feature space, synthetic images, end member components, 010104 statistics & probability, nonlinear hyperspectral unmixing problem, Mathematical model, Stochastic processes, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, adaptive filtering, 0202 electrical engineering, electronic engineering, information engineering, 0101 mathematics, stochastic behavior analysis, Mathematics, modified nonnegative LMS algorithm, business.industry, feature extraction, Linear model, Hyperspectral imaging, [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], 020206 networking & telecommunications, Pattern recognition, Vectors, Mixture model, Nonlinear system, Kernel (statistics), Computer Science::Computer Vision and Pattern Recognition, spectral components, kernel-based learning theory, learning (artificial intelligence), Artificial intelligence, business, Signal processing algorithms, Convergence, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing
Abstract

International audience; In hyperspectral images, pixels are mixtures of spectral components associated to pure materials. Although the linear mixture model is the most studied case, nonlinear models have been taken into consideration to overcome some limitations of the linear model. In this paper, nonlinear hyperspectral unmixing problem is studied through kernel-based learning theory. Endmember components at each band are mapped implicitly in a high feature space, in order to address the nonlinear interaction of photons. Experiment results with both synthetic and real images illustrate the effectiveness of the proposed scheme.

Published
2011

27. Error correction via smoothed L0-norm recovery

Author

Massoud Babaie-Zadeh, Saman Ashkiani, Christian Jutten, Jutten, Christian, A. Ferrari, P. Djuric, C. Richard, Department of Electrical Engineering [Tehran] (EE department of SUT [Tehran]), Sharif University of Technology [Tehran] (SUT), GIPSA - Vision and Brain Signal Processing (GIPSA-VIBS), Département Images et Signal (GIPSA-DIS), Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Grenoble Images Parole Signal Automatique (GIPSA-lab), and Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)

Subjects
Channel code, Computer science, [INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing, Speech recognition, 020206 networking & telecommunications, 02 engineering and technology, 01 natural sciences, 010309 optics, Real-field coding, Compressed sensing, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, Compressed Sensing, Norm (mathematics), 0103 physical sciences, 0202 electrical engineering, electronic engineering, information engineering, Signal processing algorithms, Detection theory, Minification, Sparse Signal Processing, Error detection and correction, Algorithm, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, Decoding methods, [SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing
Abstract

International audience; Channel coding has been considered as a classical approach to overcome corruptions occurring in some elements of input signal which may lead to loss of some information. Proper redundancies are added to the input signal to improve the capability of detecting or even correcting the corrupted signal. A similar scenario may happen dealing with real-field numbers rather than finite-fields. This paper considers a way to reconstruct an exact version of a corrupted signal by using an encoded signal with proper number of redundancies. The proposed algorithm uses Graduated Non-Convexity method beside using a smoothed function instead of 0-norm to correct all the corrupted elements. Simulations show that our proposed algorithm substantially improves the probability of exact recovery in comparison to previous algorithms.

Published
2011

28. Comparison of the Time-Reversal and SEABED imaging algorithms applied on ultra-wideband experimental data

Author

Cresp, A., Yedlin, M., Sakamoto, T., Aliferis, Iannis, Sato, T., Dauvignac, Jean-Yves, Pichot, Christian, Laboratoire d'Electronique, Antennes et Télécommunications (LEAT), Université Nice Sophia Antipolis (... - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS), Kyoto University [Kyoto], and IEEE

Subjects
ISSN={}, frequency bandwidth, time-reversal algorithm, ultra-wideband experimental SPR data, number={}, surface-penetrating radar, author={A. Cresp and M. J. Yedlin and T. Sakamoto and I. Aliferis and T. Sato and J. -. Dauvignac and C. Pichot}, volume={}, title={Comparison of the time-reversal and SEABED imaging algorithms applied on ultra-wideband experimental SPR data}, ultra wideband radar, Antenna measurements, year={2010}, Imaging, Antenna arrays}, eight port vector network analyzer, }, keywords={electromagnetic wave scattering, linear antenna arrays, network analysers, ultra wideband antennas, Arrays, month={Sept}, BST, @INPROCEEDINGS{5615099, SEABED imaging algorithm, doi={}, microwave antenna arrays, pages={360-363}, frequency 1.5 GHz to 8 GHz, Radar imaging, [SPI.ELEC]Engineering Sciences [physics]/Electromagnetism, directly scattered wave extraction, ultra-wideband antenna linear array, shape estimation algorithm, Signal processing algorithms, ground penetrating radar, UHF antennas, booktitle={The 7th European Radar Conference}
Abstract

International audience; This paper presents a comparison study between a simple time-reversal algorithm (designed at LEAT) and the SEABED algorithm (designed at Kyoto University) with their application to multiple-target experiments. Data are collected with an eight element ultra-wideband antenna linear array connected to an eight port vector network analyzer, working in a frequency bandwidth starting from 1.5 GHz up to 8 GHz. Several target configurations demonstrate the advantages and disadvantages of both algorithms.

Published
2010

29. Real-Time Pitch Tracking using the eCate Algorithm

Author

El Hassane Ibn Elhaj, Joseph Di Martino, Fadoua Bahja, Laboratoire de Recherche en Informatique et Télécommunications [Rabat] (GSCM-LRIT), Université Mohammed V de Rabat [Agdal] (UM5), Analysis, perception and recognition of speech (PAROLE), INRIA Lorraine, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université Henri Poincaré - Nancy 1 (UHP)-Université Nancy 2-Institut National Polytechnique de Lorraine (INPL)-Centre National de la Recherche Scientifique (CNRS)-Université Henri Poincaré - Nancy 1 (UHP)-Université Nancy 2-Institut National Polytechnique de Lorraine (INPL)-Centre National de la Recherche Scientifique (CNRS), Institut National des Postes et Télécommunications [Rabat] (INPT), and University of Mohammed V

Subjects
Computer science, Speech recognition, pitch tracking, [INFO.INFO-OH]Computer Science [cs]/Other [cs.OH], Speech technology, real-time, 020206 networking & telecommunications, Pitch detection algorithm, CATE signal, 02 engineering and technology, Fundamental frequency, Speech processing, 030507 speech-language pathology & audiology, 03 medical and health sciences, 0202 electrical engineering, electronic engineering, information engineering, Signal processing algorithms, temporal excitation signal, 0305 other medical science, Pitch tracking, Algorithm, circular autocorrelation
Abstract

ISBN : 978-1-4244-5996-4; International audience; This paper deals with the determination in real-time of the fundamental frequency F0 of speech signals using an enhanced version of the CATE algorithm. The voiced / unvoiced decision as well as pitch determination is realized in real-time. Our approach was tested with the Bagshaw database of the Center for Speech Technology Research at Edinburgh. The presented algorithm, besides being able to work in real time, provides comparable or better results than those obtained by eSRPD, CATE or ALS algorithms.

Published
2010

30. Non-negative Distributed Regression for Data Inference in Wireless Sensor Networks

Author

Jie Chen, Cedric Richard, Jose C. M. Bermudez, Paul Honeine, Laboratoire Hippolyte Fizeau (FIZEAU), Université Nice Sophia Antipolis (... - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire de la Côte d'Azur, Université Côte d'Azur (UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Université Côte d'Azur (UCA)-Centre National de la Recherche Scientifique (CNRS), Laboratoire Modélisation et Sûreté des Systèmes (LM2S), Institut Charles Delaunay (ICD), Université de Technologie de Troyes (UTT)-Centre National de la Recherche Scientifique (CNRS)-Université de Technologie de Troyes (UTT)-Centre National de la Recherche Scientifique (CNRS), Universidade Federal de Santa Catarina = Federal University of Santa Catarina [Florianópolis] (UFSC), Université Nice Sophia Antipolis (1965 - 2019) (UNS), and COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Université Côte d'Azur (UCA)-Université Côte d'Azur (UCA)-Centre National de la Recherche Scientifique (CNRS)

Subjects
Theoretical computer science, Computer science, Distributed computing, non-negative distributed regression, Inference, 02 engineering and technology, non-negativity, regression analysis, Heating, heat diffusion equations, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, 0202 electrical engineering, electronic engineering, information engineering, Inference algorithms, distributed algorithm, data inference, sparsity, operating system kernels, [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], 020206 networking & telecommunications, Regression analysis, synthetic data, Wireless sensor networks, Key distribution in wireless sensor networks, Kernel, Cost function, Distributed algorithm, kernel-based machine learning strategy, 020201 artificial intelligence & image processing, learning (artificial intelligence), Convergence, Signal processing algorithms, Wireless sensor network, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing
Abstract

International audience; Wireless sensor networks are designed to perform on inference the environment that they are sensing. Due to the inherent physical characteristics of systems under investigation, non-negativity is a desired constraint that must be imposed on the system parameters in some real-life phenomena sensing tasks. In this paper, we propose a kernel-based machine learning strategy to deal with regression problems. Multiplicative update rules are derived in this context to ensure the non-negativity constraints to be satisfied. Considering the tight energy and bandwidth resource, a distributed algorithm which requires only communication between neighbors is presented. Synthetic data managed by heat diffusion equations are used to test the algorithms and illustrate their tracking capacity.

Published
2010

31. Image denoising using a new implementation of the hyperanalytic wavelet transform

Author

Ioana Firoiu, Alexandru Isar, Corina Nafornita, Jean-Marc Boucher, Département Signal et Communications (SC), Université européenne de Bretagne - European University of Brittany (UEB)-Télécom Bretagne-Institut Mines-Télécom [Paris] (IMT), Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance (UMR 3192) (Lab-STICC), Université européenne de Bretagne - European University of Brittany (UEB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-Institut Brestois du Numérique et des Mathématiques (IBNM), Université de Brest (UBO)-Télécom Bretagne-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS), Politehnica University of Timisoara (UPT), Lab-STICC_TB_CID_TOMS, Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance (Lab-STICC), École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-Télécom Bretagne-Institut Brestois du Numérique et des Mathématiques (IBNM), Université de Brest (UBO)-Université européenne de Bretagne - European University of Brittany (UEB)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS)-École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-Télécom Bretagne-Institut Brestois du Numérique et des Mathématiques (IBNM), and Université de Brest (UBO)-Université européenne de Bretagne - European University of Brittany (UEB)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS)

Subjects
business.industry, Noise reduction, Filtering theory, Wavelet transform, 020206 networking & telecommunications, Image processing, Pattern recognition, 02 engineering and technology, Filter bank, Wavelet, 0202 electrical engineering, electronic engineering, information engineering, Signal processing algorithms, 020201 artificial intelligence & image processing, Artificial intelligence, Electrical and Electronic Engineering, Image denoising, business, Instrumentation, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, Mathematics
Abstract

International audience; Shift invariance associated with good directional selectivity is important for the use of a wavelet transform (WT) in many fields of image processing. Generally, complex wavelet transforms, e.g., the double-tree complex WT (DTCWT), have these useful properties. In this paper, we propose the use of a recently introduced implementation of such a WT, namely, the hyperanalytic WT (HWT), in association with filtering techniques already used with the discrete WT (DWT). The result is a very simple and fast image denoising algorithm. Some simulation results and comparisons prove the performance obtained using the new method.

Published
2009

32. Continuous identification of gait phase for robotics and rehabilitation using microsensors

Author

Bernard Espiau, R. Heliot, R. Pissard-GiboIIet, F. Favre-Reguilion, Modelling, Simulation, Control and Optimization of Non-Smooth Dynamical Systems (BIPOP), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Commissariat à l'énergie atomique et aux énergies alternatives - Laboratoire d'Electronique et de Technologie de l'Information (CEA-LETI), Direction de Recherche Technologique (CEA) (DRT (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA), Service Expérimentation et Développement (SED [Grenoble]), Institut National de Recherche en Informatique et en Automatique (Inria), and SED [Grenoble]

Subjects
Engineering, Microsensors, steady-state sagittal walk, Accelerometer, Gait (human), [INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO], Computer vision, robotics, Signal processing, Robot kinematics, Wavelet transform, Robotics, gait phase identification, thigh, wavelet transforms, embedded microsensors, gait state, human posture, gait analysis, magnetometer, gait cycle, medical robotics, human movement, patient rehabilitation, cyclogram analysis, rehabilitation, qualitative analysis, Humans, mathematical morphology, Knee, Rehabilitation robotics, signal processing, Robustness, wavelet transform, Simulation, business.industry, reconstructed knee joint angle, Magnetic analysis, Magnetometers, robot kinematics, accelerometer, morphomathematics, Gait analysis, Couplings, Artificial intelligence, gait movement, Accelerometers, business, Signal processing algorithms, tibia
Abstract

International audience; Using microsensors for the robust and accurate analysis of human posture or gait is an interesting opportunity for rehabilitation and robotics applications. This paper describes a feasibility study in which the possibility of using a new type of embedded microsensors, based on the coupling of accelerometers and magnetometers, and developed by CEA/LETI is investigated. This study consists in identifying what part of the gait cycle is active by using a reconstruction of the knee joint angle by two microsensors fixed on tibia and thigh, during a steady- state sagittal walk. More than just an identification of a few gait states, this approach allows us to continuously extract the current position on the gait cycle. We compare the reconstructed knee joint angle with a stored reference taking into account uncertainties on the velocity and perturbations of the terrestrial magnetic field. To accurately identify the phase of the gait movement, we fuse different simple and complementary meth- ods: morphomathematics, cyclogram analysis, wavelet transform, qualitative analysis, crosscorrelation. These results encourage us to extend this work to explore the possibility of recognition of a larger set of human movements using more sensors and improved algorithms of signal processing.

Published
2005

33. Design of a variable rate algorithm for CS-ACELP coder

Author

E.H. Baghious, Stéphane Azou, Gilles Burel, G. Madre, Laboratoire d'Electronique et Systèmes de Télécommunications (LEST), and Université de Brest (UBO)-Ecole Nationale Supérieure des Télécommunications de Bretagne-Centre National de la Recherche Scientifique (CNRS)

Subjects
Digital signal processor, Computer science, Low-pass filter, Speech coding, 02 engineering and technology, Reduction (complexity), symbols.namesake, 020210 optoelectronics & photonics, 0202 electrical engineering, electronic engineering, information engineering, Algorithm design and analysis, Codec, Nonlinear filters, Digital signal processing, Voice activity detection, business.industry, 020206 networking & telecommunications, Algebraic code-excited linear prediction, Discrete Fourier transforms, Fourier transforms, Computational complexity, Fourier transform, Low pass filters, symbols, Algorithm design, Speech analysis, business, Signal processing algorithms, Algorithm, Internet telephony, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing
Abstract

International audience; This paper is about the reduction of the computational complexity of the CS-ACELP codec, described in ITU recommendation G.729, and used for the transmission of voice over IP. A voice activity detection module is proposed to replace the G.729 Annex B algorithm. The new procedure was developed to allow its implementation with number theoretic transforms. The use of Fermat number transforms can reduce the cost of variable rate algorithm implementation on digital signal processor (DSP).

Published
2003

34. A prototyping method of embedded real time systems for signal processing applications

Author

Luc Bianco, Michel Auguin, Alain Pegatoquet, Laboratoire d'Informatique, Signaux, et Systèmes de Sophia Antipolis (I3S), Université Nice Sophia Antipolis (... - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA), and IEEE

Subjects
Signal processing, [INFO.INFO-AR]Computer Science [cs]/Hardware Architecture [cs.AR], Computer science, Design space exploration, Time to market, Real-time computing, 02 engineering and technology, Embedded operating system, embedded real time systems, design space exploration, Hardware, Coprocessors, 0202 electrical engineering, electronic engineering, information engineering, Algorithm design and analysis, Prototypes, Embedded system, Hardware architecture, System of systems, Space exploration, business.industry, Time factors, prototyping, Real time systems, 020202 computer hardware & architecture, Systems architecture, Systems design, 020201 artificial intelligence & image processing, Algorithm design, embedded systems, business, hardware-software codesign, Signal processing algorithms
Abstract

International audience; Since the complexity of embedded applications is continuously increasing, system designers are faced to a more and more difficult task of selecting and interconnecting the right system components to implement a system functionality such that time and design constraints are satisfied. The lack of methods and tools able to explore various system solutions contributes to increase the time to market. In this paper we present an efficient algorithm for a semi-automatic system design space exploration to aid the designers in the determination of the specifications of a cost-efficient system architecture that satisfies the performance and design constraints

Published
1999

35. Improving 'performance vs silicon size' tradeoffs using coprocessors: A case study: G.721 on OAK and Pine DSP Cores

Author

Pegatoquet, Alain, Auguin, Michel, Gresset, Emmanuel, Laboratoire d'Informatique, Signaux, et Systèmes de Sophia Antipolis (I3S), Université Nice Sophia Antipolis (... - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA), and IEEE

Subjects
[INFO.INFO-AR]Computer Science [cs]/Hardware Architecture [cs.AR], VVF3500 DSP cores, Assembly, digital signal processing chips, Registers, VLSI technology, Digital signal processing, VVF3000 DSP cores, VLSI, Coprocessors, Hardware, G.721 adaptative differential pulse code modulation, digital signal processors, differential pulse code modulation, Oak DSP core, adaptive modulation, Signal processing algorithms, DSP Group Pine DSP core, Software
Abstract

International audience; The increase in performance and internal memory of current DSP cores allows most applications to have software only implementations. However, recent applications in telecommunications (e.g. MPEG-4) require more and more computational power. A solution to this problem is to implement a subset of the algorithm in hardware. In this paper we present a software only and a mixed hardware/software solution for the G.721 [1] Adaptative Differential Pulse Code Modulation (AD-PCM) on VLSI Technology VVF3000 and VVF3500 DSP cores which stand for DSP Group PineDSPCore and OakDSPCore™ [2]. We will show the benefits of a coprocessor based solution and describe our methodology to extract hardware blocks from a software implementation.

Published
1998

36. A path analysis based partitioning for time constrained embedded systems

Author

Luc Bianco, Michel Auguin, Guy GOGNIAT, Alain Pegatoquet, Laboratoire d'Informatique, Signaux, et Systèmes de Sophia Antipolis (I3S), Université Nice Sophia Antipolis (... - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA), and IEEE

Subjects
logic partitioning, [INFO.INFO-AR]Computer Science [cs]/Hardware Architecture [cs.AR], Silicon, 020209 energy, 02 engineering and technology, Hardware, partitioning, 0202 electrical engineering, electronic engineering, information engineering, HW/SW partitioning, path analysis, Timing, high level synthesis, mapping, Embedded system, time constrained embedded systems, Time factors, 020208 electrical & electronic engineering, timing constraints, Application specific integrated circuits, Real time systems, co-design flow, heterogeneous embedded systems, Energy consumption, real-time systems, Application software, time margin, Signal processing algorithms, software engineering
Abstract

International audience; The HW/SW partitioning problem addressed in this paper is one of the key steps in the co-design flow of heterogeneous embedded systems. Generally the aim is to provide solutions that respect timing constraints and minimize an objective function such as the total area and/or the power consumption. Minimizing the hardware area conflicts with reducing execution time. Therefore, we introduce an heuristic for synthesizing heterogeneous systems that uses a global metric to guide the mapping of tasks according to the reusability of components and the time margin induced by timing constraints.

Published
1998