Signal et Communications

Le département Signal & Communications est spécialisé dans le domaine des Mathématiques et Traitement de l’Information. Il s’intéresse à la fois aux aspects méthodologiques et théoriques mais également aux problèmes de mise en œuvre et des implémentations des algorithmes étudiés notamment via les techniques de radio logicielle. Les domaines d’applications concernent principalement la Mer, la Défense et les Télécommunications.


Le département Signal & Communications est composé en 2018 de quelques 54 membres dont 17 permanents.

photo_SC_2018_e.JPG

 

 

Recherche
Enseignement
Innovation
International

Les prochains événements du département

Il n'y a pas d'événement en cours ou à venir pour le département.
Les dernières publications du département
Communication dans un congrès
Lassami Nacerredine, Aïssa-El-Bey Abdeldjalil, Abed-Meraim Karim
Adaptive Blind Identification of Sparse SIMO Channels using Maximum a Posteriori Approach
Asilomar Conference on Signals, Systems, and Computers, Oct 2018, Pacific Grove, Ca, United States. Proceedings Asilomar Conference on Signals, Systems, and Computers, 2018
Bibtext :
@inproceedings{lassami:hal-01847560,
TITLE = {{Adaptive Blind Identification of Sparse SIMO Channels using Maximum a Posteriori Approach}},
AUTHOR = {Lassami, Nacerredine and A{\"i}ssa-El-Bey, Abdeldjalil and Abed-Meraim, Karim},
URL = {https://hal.archives-ouvertes.fr/hal-01847560},
BOOKTITLE = {{Asilomar Conference on Signals, Systems, and Computers}},
ADDRESS = {Pacific Grove, Ca, United States},
HAL_LOCAL_REFERENCE = {19096},
PAGES = {.},
YEAR = {2018},
MONTH = Oct,
KEYWORDS = {Adaptive algorithms ; Sparse channel ; SIMO channel ; Blind identification},
PDF = {https://hal.archives-ouvertes.fr/hal-01847560/file/adaptive-blind-identification.pdf},
HAL_ID = {hal-01847560},
HAL_VERSION = {v1},
}
Endnote :
%0 Conference Proceedings
%T Adaptive Blind Identification of Sparse SIMO Channels using Maximum a Posteriori Approach
%+ Lab-STICC_IMTA_CACS_COM
%+ Département Signal et Communications (SC)
%+ Laboratoire Pluridisciplinaire de Recherche en Ingénierie des Systèmes, Mécanique et Energétique (PRISME)
%A Lassami, Nacerredine
%A Aïssa-El-Bey, Abdeldjalil
%A Abed-Meraim, Karim
%< avec comité de lecture
%Z 19096
%( Proceedings Asilomar Conference on Signals, Systems, and Computers
%B Asilomar Conference on Signals, Systems, and Computers
%C Pacific Grove, Ca, United States
%P .
%8 2018-10-28
%D 2018
%K Adaptive algorithms
%K Sparse channel
%K SIMO channel
%K Blind identification
%Z Engineering Sciences [physics]/Signal and Image processingConference papers
%X In this paper, we are interested in adaptive blind channel identification of sparse single input multiple output (SIMO) systems. A generalized Laplacian distribution is considered to enhance the sparsity of the channel coefficients with a maximum a posteriori (MAP) approach. The resulting cost function is composed of the classical deterministic maximum likelihood (ML) term and an additive $\ell_p$ norm of the channel coefficient vector which represents the sparsity penalization. The proposed adaptive optimization algorithm is based on a simple gradient step. Simulations show that our method outperforms the existing adaptive versions of cross-relation (CR) method.
%G English
%2 https://hal.archives-ouvertes.fr/hal-01847560/document
%2 https://hal.archives-ouvertes.fr/hal-01847560/file/adaptive-blind-identification.pdf
%L hal-01847560
%U https://hal.archives-ouvertes.fr/hal-01847560
%~ INSTITUT-TELECOM
%~ TELECOM-BRETAGNE
%~ CNRS
%~ UNIV-UBS
%~ ENSI-BOURGES
%~ MSL
%~ MSL-THESE
%~ ENIB
%~ LAB-STICC
%~ UNIV-BREST
%~ UNIV-ORLEANS
%~ IMT-ATLANTIQUE
%~ IMTA_SC
%~ LAB-STICC_IMTA_CACS_COM
%~ PRISME-CVL
%~ LAB-STICC_IMTA
Communication dans un congrès
Khanduri Prashant, Pastor Dominique, Sharma Vinod Kumar, Varshney Pramod K.
on Random Distortion testing based sequential Non parametric Hypothesis Testing
56th annual Allerton Conference on communication, control and computing, Oct 2018, Urbana-Champaign, United States. Proceedings 56th annual Allerton Conference on communication, control and computing, 2018
Bibtext :
@inproceedings{khanduri:hal-01867116,
TITLE = {{on Random Distortion testing based sequential Non parametric Hypothesis Testing}},
AUTHOR = {Khanduri, Prashant and Pastor, Dominique and Sharma, Vinod Kumar and Varshney, Pramod K.},
URL = {https://hal.archives-ouvertes.fr/hal-01867116},
BOOKTITLE = {{56th annual Allerton Conference on communication, control and computing}},
ADDRESS = {Urbana-Champaign, United States},
HAL_LOCAL_REFERENCE = {19176},
PAGES = {.},
YEAR = {2018},
MONTH = Oct,
KEYWORDS = {Random Distortion ; testing ; sequential testing ; SPRT},
HAL_ID = {hal-01867116},
HAL_VERSION = {v1},
}
Endnote :
%0 Conference Proceedings
%T on Random Distortion testing based sequential Non parametric Hypothesis Testing
%+ School of Information Studies (Syracuse University) (iSchool)
%+ Lab-STICC_IMTA_CID_TOMS
%+ Département Signal et Communications (SC)
%+ Indian Institue of Technology Guwahati ()
%A Khanduri, Prashant
%A Pastor, Dominique
%A Sharma, Vinod Kumar
%A Varshney, Pramod K.
%< avec comité de lecture
%Z 19176
%( Proceedings 56th annual Allerton Conference on communication, control and computing
%B 56th annual Allerton Conference on communication, control and computing
%C Urbana-Champaign, United States
%P .
%8 2018-10-02
%D 2018
%K Random Distortion
%K testing
%K sequential testing
%K SPRT
%Z Engineering Sciences [physics]/Signal and Image processingConference papers
%X In this work, we propose a new method for sequential binary hypothesis testing. The approach is non-parametric in the sense that it does not assume any knowledge of signal distributions under each hypothesis. The proposed framework is based on Random distortion testing (RDT) which addresses the problem of testing whether or not a random signal, Ξ, deviates by more than a specified tolerance, τ, from a fixed value, ξ 0 . We first state the problem setup and then discuss earlier approaches to solve the problem. We then propose a new sequential algorithm, T-SeqRDT, which is shown to control the probabilities of error while reducing the number of samples required to make a decision compared to the fixed-sample-size version of RDT. Finally, via simulations we compare T-SeqRDT to other algorithms and show its robustness compared to standard likelihood ratio based approaches.
%G English
%L hal-01867116
%U https://hal.archives-ouvertes.fr/hal-01867116
%~ CNRS
%~ UNIV-UBS
%~ INSTITUT-TELECOM
%~ ENIB
%~ LAB-STICC
%~ UNIV-BREST
%~ IMT-ATLANTIQUE
%~ LAB-STICC_IMTA_CID_TOMS
%~ IMTA_SC
%~ LAB-STICC_IMTA
Communication dans un congrès
Nguyen Van Duong, Vadaine Rodolphe, Hajduch Guillaume, Garello René, Fablet Ronan
A Multi-task Deep Learning Architecture for Maritime Surveillance using AIS Data Streams
International Conference on Data Science and Advanced Analytics (DSAA), Oct 2018, Turin, Italy
Bibtext :
@inproceedings{nguyen:hal-01808176,
TITLE = {{A Multi-task Deep Learning Architecture for Maritime Surveillance using AIS Data Streams}},
AUTHOR = {Nguyen, Van Duong and Vadaine, Rodolphe and Hajduch, Guillaume and Garello, Ren{\'e} and FABLET, Ronan},
URL = {https://hal-imt-atlantique.archives-ouvertes.fr/hal-01808176},
BOOKTITLE = {{International Conference on Data Science and Advanced Analytics (DSAA)}},
ADDRESS = {Turin, Italy},
YEAR = {2018},
MONTH = Oct,
KEYWORDS = { anomaly detection ; transfer learning ; deep learning ; maritime surveillance ; AIS ; variational recurrent neural networks ; trajectory reconstruction ; vessel type identification},
PDF = {https://hal-imt-atlantique.archives-ouvertes.fr/hal-01808176/file/A%20multitask%20deep%20learning%20model%20for%20vessel%20monitoring%20using%20AIS%20streams.pdf},
HAL_ID = {hal-01808176},
HAL_VERSION = {v3},
}
Endnote :
%0 Conference Proceedings
%T A Multi-task Deep Learning Architecture for Maritime Surveillance using AIS Data Streams
%+ Lab-STICC_IMTA_CID_TOMS
%+ Collecte Localisation Satellites (Entreprise) (CLS)
%+ Département Signal et Communications (SC)
%A Nguyen, Van Duong
%A Vadaine, Rodolphe
%A Hajduch, Guillaume
%A Garello, René
%A FABLET, Ronan
%< avec comité de lecture
%B International Conference on Data Science and Advanced Analytics (DSAA)
%C Turin, Italy
%8 2018-10
%D 2018
%K anomaly detection
%K transfer learning
%K deep learning
%K maritime surveillance
%K AIS
%K variational recurrent neural networks
%K trajectory reconstruction
%K vessel type identification
%Z Computer Science [cs]/Machine Learning [cs.LG]
%Z Sciences of the Universe [physics]/Ocean, Atmosphere
%Z Computer Science [cs]/Artificial Intelligence [cs.AI]Conference papers
%X In a world of global trading, maritime safety, security and efficiency are crucial issues. We propose a multi-task deep learning framework for vessel monitoring using Automatic Identification System (AIS) data streams. We combine recurrent neural networks with latent variable modeling and an embedding of AIS messages to a new representation space to jointly address key issues to be dealt with when considering AIS data streams: massive amount of streaming data, noisy data and irregular time-sampling. We demonstrate the relevance of the proposed deep learning framework on real AIS datasets for a three-task setting, namely trajectory reconstruction, anomaly detection and vessel type identification.
%G English
%2 https://hal-imt-atlantique.archives-ouvertes.fr/hal-01808176v3/document
%2 https://hal-imt-atlantique.archives-ouvertes.fr/hal-01808176/file/A%20multitask%20deep%20learning%20model%20for%20vessel%20monitoring%20using%20AIS%20streams.pdf
%L hal-01808176
%U https://hal-imt-atlantique.archives-ouvertes.fr/hal-01808176
%~ IMT-ATLANTIQUE
%~ CNRS
%~ UNIV-UBS
%~ INSTITUT-TELECOM
%~ ENIB
%~ LAB-STICC
%~ GIP-BE
%~ UNIV-BREST
%~ LAB-STICC_IMTA_CID_TOMS
%~ LAB-STICC_IMTA
%~ IMTA_SC
Pré-publication, Document de travail
Tandeo Pierre, Ailliot Pierre, Bocquet Marc, Carrassi Alberto, Miyoshi Takemasa, Pulido Manuel, Zhen Yicun
Joint Estimation of Model and Observation Error Covariance Matrices in Data Assimilation: a Review
2018
Bibtext :
@unpublished{tandeo:hal-01867958,
TITLE = {{Joint Estimation of Model and Observation Error Covariance Matrices in Data Assimilation: a Review}},
AUTHOR = {TANDEO, Pierre and Ailliot, Pierre and Bocquet, Marc and Carrassi, Alberto and Miyoshi, Takemasa and PULIDO, Manuel and Zhen, Yicun},
URL = {https://hal-imt-atlantique.archives-ouvertes.fr/hal-01867958},
NOTE = {working paper or preprint},
YEAR = {2018},
MONTH = Sep,
PDF = {https://hal-imt-atlantique.archives-ouvertes.fr/hal-01867958/file/tandeo_2018.pdf},
HAL_ID = {hal-01867958},
HAL_VERSION = {v1},
}
Endnote :
%0 Unpublished work
%T Joint Estimation of Model and Observation Error Covariance Matrices in Data Assimilation: a Review
%+ Lab-STICC_TB_CID_TOMS
%+ Département Signal et Communications (SC)
%+ Laboratoire de Mathématiques (LM-Brest)
%+ Centre d'Enseignement et de Recherche en Environnement Atmosphérique (CEREA)
%+ Nansen Environmental and Remote Sensing Center - Norway (NERSC)
%+ RIKEN Advanced Institute for Computational Science (AICS)
%+ Department of Physics (Universidad Nacional del Nordeste) (GICA)
%+ Département Signal et Communications (SC)
%A TANDEO, Pierre
%A Ailliot, Pierre
%A Bocquet, Marc
%A Carrassi, Alberto
%A Miyoshi, Takemasa
%A PULIDO, Manuel
%A Zhen, Yicun
%8 2018-09-04
%D 2018
%Z Statistics [stat]/Methodology [stat.ME]
%Z Sciences of the Universe [physics]/Ocean, AtmospherePreprints, Working Papers, ...
%X This paper is a review of a crucial topic in data assimilation: the joint estimation of model Q and observation R matrices. These covariances define the observational and model errors via additive Gaussian white noises in state-space models, the most common way of formulating data assimilation problems. They are crucial because they control the relative weights of the model forecasts and observations in reconstructing the state, and several methods have been proposed since the 90's for their estimation. Some of them are based on the moments of various innovations, including those in the observation space or lag-innovations. Alternatively, other methods use likelihood functions and maximum likelihood estimators or Bayesian approaches. This review aims at providing a comprehensive summary of the proposed methodologies and factually describing them as they appear in the literature. We also discuss (i) remaining challenges for the different estimation methods, (ii) some suggestions for possible improvements and combinations of the approaches and (iii) perspectives for future works, in particular numerical comparisons using toy-experiments and practical implementations in data assimilation systems.
%G English
%2 https://hal-imt-atlantique.archives-ouvertes.fr/hal-01867958/document
%2 https://hal-imt-atlantique.archives-ouvertes.fr/hal-01867958/file/tandeo_2018.pdf
%L hal-01867958
%U https://hal-imt-atlantique.archives-ouvertes.fr/hal-01867958
%~ IMT-ATLANTIQUE
%~ CNRS
%~ UNIV-BREST
%~ UNIV-UBS
%~ MATHBREST
%~ INSTITUT-TELECOM
%~ LMBA
%~ ENIB
%~ LAB-STICC_ENIB
%~ LAB-STICC
%~ GIP-BE
%~ LAB-STICC_TB
%~ ENPC
%~ LAB-STICC_IMTA_CID_TOMS
%~ IMTA_SC
%~ LAB-STICC_IMTA
%~ EDF
Article dans une revue
Gripon Vincent, Pasdeloup Bastien, Mercier Grégoire, Pastor Dominique, Rabbat Michael
Characterization and Inference of Graph Diffusion Processes From Observations of Stationary Signals
IEEE transactions on Signal and Information Processing over Networks, IEEE, 2018, 4 (3), pp.481 - 496. 〈10.1109/TSIPN.2017.2742940〉
Bibtext :
@article{gripon:hal-01875916,
TITLE = {{Characterization and Inference of Graph Diffusion Processes From Observations of Stationary Signals}},
AUTHOR = {Gripon, Vincent and Pasdeloup, Bastien and MERCIER, Gr{\'e}goire and Pastor, Dominique and Rabbat, Michael},
URL = {https://hal.archives-ouvertes.fr/hal-01875916},
JOURNAL = {{IEEE transactions on Signal and Information Processing over Networks}},
PUBLISHER = {{IEEE}},
VOLUME = {4},
NUMBER = {3},
PAGES = {481 - 496},
YEAR = {2018},
MONTH = Sep,
DOI = {10.1109/TSIPN.2017.2742940},
HAL_ID = {hal-01875916},
HAL_VERSION = {v1},
}
Endnote :
%0 Journal Article
%T Characterization and Inference of Graph Diffusion Processes From Observations of Stationary Signals
%+ Département Electronique (ELEC)
%+ Lab-STICC_IMTA_CACS_IAS
%+ Département Image et Traitement Information (ITI)
%+ Département Signal et Communications (SC)
%+ Department of Electrical and Computer Engineering [Montreal]
%A Gripon, Vincent
%A Pasdeloup, Bastien
%A MERCIER, Grégoire
%A Pastor, Dominique
%A Rabbat, Michael
%< avec comité de lecture
%@ 2373-776X
%J IEEE transactions on Signal and Information Processing over Networks
%I IEEE
%V 4
%N 3
%P 481 - 496
%8 2018-09
%D 2018
%R 10.1109/TSIPN.2017.2742940
%Z Computer Science [cs]/Machine Learning [cs.LG]
%Z Computer Science [cs]/Artificial Intelligence [cs.AI]
%Z Computer Science [cs]/Signal and Image Processing
%Z Computer Science [cs]/Computer Science and Game Theory [cs.GT]Journal articles
%G English
%L hal-01875916
%U https://hal.archives-ouvertes.fr/hal-01875916
%~ CNRS
%~ UNIV-BREST
%~ UNIV-UBS
%~ INSTITUT-TELECOM
%~ ENIB
%~ LAB-STICC
%~ IMT-ATLANTIQUE
%~ IMTA_ELEC
%~ IMTA_SC
%~ IMTA_ITI
%~ LAB-STICC_IMTA_CACS_IAS
Article dans une revue
Hajji Zahran, Aïssa-El-Bey Abdeldjalil, Amis Cavalec Karine
Simplicity-based recovery of finite-alphabet signals for large-scale MIMO systems
Digital Signal Processing, Elsevier, 2018, 〈10.1016/j.dsp.2018.05.012〉
Bibtext :
@article{hajji:hal-01811519,
TITLE = {{Simplicity-based recovery of finite-alphabet signals for large-scale MIMO systems}},
AUTHOR = {Hajji, Zahran and A{\"i}ssa-El-Bey, Abdeldjalil and Amis Cavalec, Karine},
URL = {https://hal.archives-ouvertes.fr/hal-01811519},
JOURNAL = {{Digital Signal Processing}},
HAL_LOCAL_REFERENCE = {18997},
PUBLISHER = {{Elsevier}},
PAGES = {.},
YEAR = {2018},
MONTH = Sep,
DOI = {10.1016/j.dsp.2018.05.012},
KEYWORDS = {Compressed sensing ; Source separation ; Underdetermined systems ; Sparsity ; Simplicity ; Massive MIMO},
PDF = {https://hal.archives-ouvertes.fr/hal-01811519/file/simplicity-based-recovery.pdf},
HAL_ID = {hal-01811519},
HAL_VERSION = {v1},
}
Endnote :
%0 Journal Article
%T Simplicity-based recovery of finite-alphabet signals for large-scale MIMO systems
%+ Lab-STICC_IMTA_CACS_COM
%+ Département Signal et Communications (SC)
%A Hajji, Zahran
%A Aïssa-El-Bey, Abdeldjalil
%A Amis Cavalec, Karine
%< avec comité de lecture
%Z 18997
%@ 1051-2004
%J Digital Signal Processing
%I Elsevier
%P .
%8 2018-09
%D 2018
%R 10.1016/j.dsp.2018.05.012
%K Compressed sensing
%K Source separation
%K Underdetermined systems
%K Sparsity
%K Simplicity
%K Massive MIMO
%Z Engineering Sciences [physics]/Signal and Image processingJournal articles
%X In this paper, we consider the problem of finite-alphabet source separation in both determined and underdetermined large-scale systems. First, we address the noiseless case and we propose a linear criterion based on ℓ1-minimization combined with box constraints. We investigate also the system conditions that ensure successful recovery. Next, we apply the approach to the noisy massive MIMO transmission and we propose a quadratic criterion-based detector. Simulation results show the efficiency of the proposed detection methods for various QAM modulations and MIMO configurations. We mention that there is no change in the computational complexity when the constellation size increases. Moreover, the proposed method outperforms the classical Minimum Mean Square Error (MMSE)-based detection algorithms.
%G English
%2 https://hal.archives-ouvertes.fr/hal-01811519/document
%2 https://hal.archives-ouvertes.fr/hal-01811519/file/simplicity-based-recovery.pdf
%L hal-01811519
%U https://hal.archives-ouvertes.fr/hal-01811519
%~ CNRS
%~ UNIV-UBS
%~ INSTITUT-TELECOM
%~ ENIB
%~ LAB-STICC
%~ UNIV-BREST
%~ LAB-STICC_IMTA_CACS_COM
%~ IMTA_SC
%~ IMT-ATLANTIQUE
%~ LAB-STICC_IMTA
Article dans une revue
Poda Pasteur, Saoudi Samir, Chonavel Thierry, Guilloud Frédéric, Tapsoba Théodore
Non-parametric kernel-based bit error probability estimation in digital communication systems: An estimator for soft coded QAM BER computation
Revue Africaine de la Recherche en Informatique et Mathématiques Appliquées, INRIA, 2018, Volume 27 - 2017 - Special issue CARI 2016
Bibtext :
@article{poda:hal-01449035,
TITLE = {{Non-parametric kernel-based bit error probability estimation in digital communication systems: An estimator for soft coded QAM BER computation}},
AUTHOR = {Poda, Pasteur and Saoudi, Samir and Chonavel, Thierry and GUILLOUD, Fr{\'e}d{\'e}ric and Tapsoba, Th{\'e}odore Marie-Yves},
URL = {https://hal.archives-ouvertes.fr/hal-01449035},
JOURNAL = {{Revue Africaine de la Recherche en Informatique et Math{\'e}matiques Appliqu{\'e}es}},
PUBLISHER = {{INRIA}},
VOLUME = {Volume 27 - 2017 - Special issue CARI 2016},
YEAR = {2018},
MONTH = Aug,
KEYWORDS = {Kernel estimator ; Bit error rate ; Probability density function ; Monte Carlo method ; M{\'e}thode Monte Carlo ; Probabilit{\'e} d'erreur binaire ; Taux d'erreur binaire ; Estimateur {\`a} noyau ; Fonction de densit{\'e} de probabilit{\'e} ; Bit error probability},
PDF = {https://hal.archives-ouvertes.fr/hal-01449035/file/ARIMA-Vol27-101-120.pdf},
HAL_ID = {hal-01449035},
HAL_VERSION = {v3},
}
Endnote :
%0 Journal Article
%T Non-parametric kernel-based bit error probability estimation in digital communication systems: An estimator for soft coded QAM BER computation
%+ Université Polytechnique Nazi Boni Bobo-Dioulasso (UNB)
%+ Département Signal et Communications (SC)
%+ Lab-STICC_IMTA_CACS_COM
%+ Lab-STICC_IMTA_CID_TOMS
%A Poda, Pasteur
%A Saoudi, Samir
%A Chonavel, Thierry
%A GUILLOUD, Frédéric
%A Tapsoba, Théodore, Marie-Yves
%Z Projet RESEAU SCAC-Ambassade de France au Burkina Faso , FP7 project ICT-317669 METIS
%< avec comité de lecture
%@ 1638-5713
%J Revue Africaine de la Recherche en Informatique et Mathématiques Appliquées
%I INRIA
%V Volume 27 - 2017 - Special issue CARI 2016
%8 2018-08-03
%D 2018
%K Kernel estimator
%K Bit error rate
%K Probability density function
%K Monte Carlo method
%K Méthode Monte Carlo
%K Probabilité d’erreur binaire
%K Taux d’erreur binaire
%K Estimateur à noyau
%K Fonction de densité de probabilité
%K Bit error probability
%Z Engineering Sciences [physics]/Signal and Image processing
%Z Engineering Sciences [physics]/OtherJournal articles
%X The standard Monte Carlo estimations of rare events probabilities suffer from too much computational time. To make estimations faster, kernel-based estimators proved to be more efficient for binary systems whilst appearing to be more suitable in situations where the probability density function of the samples is unknown. We propose a kernel-based Bit Error Probability (BEP) estimator for coded M-ary Quadrature Amplitude Modulation (QAM) systems. We defined soft real bits upon which an Epanechnikov kernel-based estimator is designed. Simulation results showed, compared to the standard Monte Carlo simulation technique, accurate, reliable and efficient BEP estimates for 4-QAM and 16-QAM symbols transmissions over the additive white Gaussian noise channel and over a frequency-selective Rayleigh fading channel.
%X Les estimations de probabilités d'événements rares par la méthode de Monte Carlo classique souffrent de trop de temps de calculs. Des estimateurs à noyau se sont montrés plus efficaces sur des systèmes binaires en même temps qu'ils paraissent mieux adaptés aux situations où la fonction de densité de probabilité est inconnue. Nous proposons un estimateur de Probabilité d'Erreur Bit (PEB) à noyau pour les systèmes M-aires codés de Modulations d'Amplitude en Quadrature (MAQ). Nous avons défini des bits souples à valeurs réelles à partir desquels un estimateur à noyau d'Epanechnikov est conçu. Les simulations ont montré, par rapport à la méthode Monte Carlo, des estimées de PEB précises, fiables et efficaces pour des transmissions MAQ-4 et MAQ-16 sur canaux à bruit additif blanc Gaussien et à évanouïssements de Rayleigh sélectif en fréquence.
%G English
%2 https://hal.archives-ouvertes.fr/hal-01449035v3/document
%2 https://hal.archives-ouvertes.fr/hal-01449035/file/ARIMA-Vol27-101-120.pdf
%L hal-01449035
%U https://hal.archives-ouvertes.fr/hal-01449035
%~ EPISCIENCES
%~ ARIMA
%~ CNRS
%~ UNIV-UBS
%~ INSTITUT-TELECOM
%~ ENIB
%~ LAB-STICC
%~ UNIV-BREST
%~ IMT-ATLANTIQUE
%~ LAB-STICC_IMTA_CID_TOMS
%~ IMTA_SC
%~ LAB-STICC_IMTA
%~ LAB-STICC_IMTA_CACS_COM
Article dans une revue
Mai Van Khanh, Pastor Dominique, Aïssa-El-Bey Abdeldjalil, Le Bidan Raphaël
Semi-Parametric Joint Detection and Estimation for Speech Enhancement based on Minimum Mean Square Error
Speech Communication, Elsevier : North-Holland, 2018, 〈10.1016/j.specom.2018.05.005〉
Bibtext :
@article{mai:hal-01817262,
TITLE = {{Semi-Parametric Joint Detection and Estimation for Speech Enhancement based on Minimum Mean Square Error}},
AUTHOR = {Mai, Van Khanh and Pastor, Dominique and A{\"i}ssa-El-Bey, Abdeldjalil and Le Bidan, Rapha{\"e}l},
URL = {https://hal.archives-ouvertes.fr/hal-01817262},
JOURNAL = {{Speech Communication}},
HAL_LOCAL_REFERENCE = {19037},
PUBLISHER = {{Elsevier : North-Holland}},
PAGES = {.},
YEAR = {2018},
MONTH = Aug,
DOI = {10.1016/j.specom.2018.05.005},
KEYWORDS = {Speech enhancement ; Noise reduction ; Bayesian estimation ; Non-parametric estimation ; Smoothed sigmoid-based shrinkage},
HAL_ID = {hal-01817262},
HAL_VERSION = {v1},
}
Endnote :
%0 Journal Article
%T Semi-Parametric Joint Detection and Estimation for Speech Enhancement based on Minimum Mean Square Error
%+ Département Signal et Communications (SC)
%+ Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance (Lab-STICC)
%+ Lab-STICC_IMTA_CID_TOMS
%+ Lab-STICC_IMTA_CACS_COM
%A Mai, Van Khanh
%A Pastor, Dominique
%A Aïssa-El-Bey, Abdeldjalil
%A Le Bidan, Raphaël
%< avec comité de lecture
%Z 19037
%@ 0167-6393
%J Speech Communication
%I Elsevier : North-Holland
%P .
%8 2018-08
%D 2018
%R 10.1016/j.specom.2018.05.005
%K Speech enhancement
%K Noise reduction
%K Bayesian estimation
%K Non-parametric estimation
%K Smoothed sigmoid-based shrinkage
%Z Engineering Sciences [physics]/Signal and Image processingJournal articles
%X We propose a novel estimator for estimating the amplitude of speech coefficients in the time-frequency domain. In order to avoid a phase spectrum estimator of complex coefficients when using the Fourier transform, we consider the discrete cosine transform (DCT). This estimator aims at minimizing the mean square error of the absolute values of the speech DCT coefficients. In order to take advantage of both parametric and non-parametric approaches, the proposed method combines block shrinkage and Bayesian statistical estimation. First, the absolute value of the clean coefficient is estimated by block smoothed sigmoid-based shrinkage (Block-SSBS). The block size required by the block-SSBS is obtained by statistical optimization. This step enables us to reduce the negative impact on speech intelligibility of classical denoising methods similarly to smoothed binary masking. Second, for refining the estimation, an optimal statistical estimator is added to handle musical noise. For evaluating the performance of the proposed method, objective criteria are used. The experiments enhance the relevance of the approach, in terms of speech quality and intelligibility.
%G English
%L hal-01817262
%U https://hal.archives-ouvertes.fr/hal-01817262
%~ CNRS
%~ UNIV-UBS
%~ INSTITUT-TELECOM
%~ ENIB
%~ LAB-STICC
%~ UNIV-BREST
%~ LAB-STICC_IMTA_CID_TOMS
%~ IMTA_SC
%~ LAB-STICC_IMTA_CACS_COM
%~ IMT-ATLANTIQUE
%~ LAB-STICC_IMTA
Article dans une revue
Ouelha Samir, Aïssa-El-Bey Abdeldjalil, Boashash Boualem
An improved time-frequency noise reduction method using a psycho-acoustic Mel model
Digital Signal Processing, Elsevier, 2018, 79, pp.199 - 212. 〈10.1016/j.dsp.2018.04.005〉
Bibtext :
@article{ouelha:hal-01774898,
TITLE = {{An improved time-frequency noise reduction method using a psycho-acoustic Mel model}},
AUTHOR = {Ouelha, Samir and A{\"i}ssa-El-Bey, Abdeldjalil and Boashash, Boualem},
URL = {https://hal.archives-ouvertes.fr/hal-01774898},
JOURNAL = {{Digital Signal Processing}},
HAL_LOCAL_REFERENCE = {18892},
PUBLISHER = {{Elsevier}},
VOLUME = {79},
PAGES = {199 - 212},
YEAR = {2018},
MONTH = Aug,
DOI = {10.1016/j.dsp.2018.04.005},
KEYWORDS = {Mel filterbank ; Time-frequency analysis ; Psycho-acoustic model ; Noise reduction ; Signal enhancement ; Wavelet thresholding},
HAL_ID = {hal-01774898},
HAL_VERSION = {v1},
}
Endnote :
%0 Journal Article
%T An improved time-frequency noise reduction method using a psycho-acoustic Mel model
%+ Qatar University
%+ Lab-STICC_IMTA_CACS_COM
%+ Département Signal et Communications (SC)
%A Ouelha, Samir
%A Aïssa-El-Bey, Abdeldjalil
%A Boashash, Boualem
%< avec comité de lecture
%Z 18892
%@ 1051-2004
%J Digital Signal Processing
%I Elsevier
%V 79
%P 199 - 212
%8 2018-08
%D 2018
%R 10.1016/j.dsp.2018.04.005
%K Mel filterbank
%K Time-frequency analysis
%K Psycho-acoustic model
%K Noise reduction
%K Signal enhancement
%K Wavelet thresholding
%Z Engineering Sciences [physics]/Signal and Image processingJournal articles
%X This paper addresses the problem of noise reduction in non-stationary signals. The paper first describes a human physiology based time-frequency (TF) representation (TFHP) using Mel filterbanks. It is then used to improve a noise reduction algorithm that does not require any a priori information about the signal of interest and the noise. This algorithm is efficiently implemented using an original wavelet shrinkage method. The overall method results in an original TF denoising procedure that yields a denoised TFHP (DTFHP). From this representation one can reconstruct a denoised time-domain signal and therefore define a new improved noise reduction algorithm, whose performance is evaluated and compared with other state-of-the-art methods. The performance assessment uses several criteria: (1) signal-to-noise-ratio (SNR), (2) segmental SNR (SSNR) and (3) mean square error (MSE). The results indicate an improvement of up to 4.72 dB with respect to SNR, 2.79 dB w.r.t SSNR and 4.72 dB w.r.t. MSE for a speech database signals corrupted with four different noises. In addition, other applications such as EEG signal enhancement show promising results.
%G English
%L hal-01774898
%U https://hal.archives-ouvertes.fr/hal-01774898
%~ CNRS
%~ UNIV-UBS
%~ INSTITUT-TELECOM
%~ ENIB
%~ LAB-STICC
%~ UNIV-BREST
%~ LAB-STICC_IMTA_CACS_COM
%~ IMTA_SC
%~ IMT-ATLANTIQUE
%~ LAB-STICC_IMTA
Communication dans un congrès
Lopez Radcenco Manuel, Pascual Ananda, Gomez-Navarro Laura, Aïssa-El-Bey Abdeldjalil, Fablet Ronan
Analog data assimilation for along-track nadir and SWOT altimetry data in the Western Mediterranean Sea
IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Jul 2018, Valencia, Spain. 2018, Proceedings IEEE International Geoscience and Remote Sensing Symposium (IGARSS)
Bibtext :
@inproceedings{lopezradcenco:hal-01739800,
TITLE = {{Analog data assimilation for along-track nadir and SWOT altimetry data in the Western Mediterranean Sea}},
AUTHOR = {Lopez Radcenco, Manuel and Pascual, Ananda and Gomez-Navarro, Laura and A{\"i}ssa-El-Bey, Abdeldjalil and FABLET, Ronan},
URL = {https://hal.archives-ouvertes.fr/hal-01739800},
BOOKTITLE = {{IEEE International Geoscience and Remote Sensing Symposium (IGARSS)}},
ADDRESS = {Valencia, Spain},
HAL_LOCAL_REFERENCE = {18862},
SERIES = {Proceedings IEEE International Geoscience and Remote Sensing Symposium (IGARSS)},
PAGES = {.},
YEAR = {2018},
MONTH = Jul,
KEYWORDS = {Western Mediterranean Sea ; SWOT ; Altimetry data ; Sea Level Anomaly ; Spatio-temporal interpolation ; Data-driven model ; Data assimilation},
PDF = {https://hal.archives-ouvertes.fr/hal-01739800/file/lopez_radcenco_et_al_igarss_2018.pdf},
HAL_ID = {hal-01739800},
HAL_VERSION = {v1},
}
Endnote :
%0 Conference Proceedings
%T Analog data assimilation for along-track nadir and SWOT altimetry data in the Western Mediterranean Sea
%+ Lab-STICC_IMTA_CID_TOMS
%+ Département Signal et Communications (SC)
%+ Instituto Mediterráneo de Estudios Avanzados (CSIC-UIB) (IMEDEA)
%+ Institut des Géosciences de l'Environnement (UMR 5001 CNRS, Grenoble INP, IRD, Univ Grenoble Alpes,) (IGE)
%+ Lab-STICC_IMTA_CACS_COM
%A Lopez Radcenco, Manuel
%A Pascual, Ananda
%A Gomez-Navarro, Laura
%A Aïssa-El-Bey, Abdeldjalil
%A FABLET, Ronan
%< avec comité de lecture
%Z 18862
%B IEEE International Geoscience and Remote Sensing Symposium (IGARSS)
%C Valencia, Spain
%3 Proceedings IEEE International Geoscience and Remote Sensing Symposium (IGARSS)
%P .
%8 2018-07-23
%D 2018
%K Western Mediterranean Sea
%K SWOT
%K Altimetry data
%K Sea Level Anomaly
%K Spatio-temporal interpolation
%K Data-driven model
%K Data assimilation
%Z Engineering Sciences [physics]/Signal and Image processingConference papers
%X The ever increasing availability of in situ, remote sensing and simulation data supports the development of data-driven alternatives to classical model-driven methods for the interpolation of sea surface geophysical fields from partial satellite-derived observations. In this respect, we recently introduced the Analog Data Assimilation (AnDA), which exploits patch-based analog forecasting operators within a classic Kalman-based data assimilation framework. In this work, we consider the application of AnDA to the spatio-temporal interpolation of SLA (Sea Level Anomalies) from two types of satellite altimetry data, namely from along-track nadir data and data from the upcoming wide-swath SWOT mission. We report a sensitivity analysis w.r.t. the main parameters of the proposed AnDA scheme. Overall, the reported benchmarking analysis supports the relevance of the proposed AnDA scheme for an improved reconstruction of mescoscale structures for horizontal scales ranging from ~20km to ~100km, with an gain of 42% (12%) in terms of SLA RMSE (correlation) with respect to Optimal Interpolation (OI). Results suggest an additional potential improvement from the joint assimilation of SWOT and along-track nadir observations.
%G English
%2 https://hal.archives-ouvertes.fr/hal-01739800/document
%2 https://hal.archives-ouvertes.fr/hal-01739800/file/lopez_radcenco_et_al_igarss_2018.pdf
%L hal-01739800
%U https://hal.archives-ouvertes.fr/hal-01739800
%~ INSTITUT-TELECOM
%~ LAB-STICC_IMTA_CACS_COM
%~ IMTA_SC
%~ LAB-STICC_IMTA_CID_TOMS
%~ IRD
%~ UNIV-BREST
%~ LAB-STICC
%~ UNIV-UBS
%~ CNRS
%~ IMT-ATLANTIQUE
%~ TELECOM-BRETAGNE
%~ ENIB
%~ LAB-STICC_IMTA
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