Yannick Deville

发表

Hyperspectral image unmixing by non-negative matrix factorization initialized with modified independent component analysis

Djaouad Benachir, Yannick Deville, Shahram Hosseini, 2013, 2013 5th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS).

ALTERNATIVE STRUCTURES AND POWER SPECTRUM CRITERIA FOR BLIND SEGMENTATION AND SEPARATION OF CONVOLUTIVE SPEECH MIXTURES

Yannick Deville, Benoit Albouy, Y. Deville, 2003 .

A new spatial sparsity-based method for extracting endmember spectra from hyperspectral data with some pure pixels

Yannick Deville, Shahram Hosseini, Moussa Sofiane Karoui, 2012, 2012 IEEE International Geoscience and Remote Sensing Symposium.

Blind spatial unmixing of multispectral images: An approach based on two-source sparsity and geometrical properties

Djaouad Benachir, Yannick Deville, Shahram Hosseini, 2014, 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

A convolutive source separation method with self-optimizing non-linearities

Yannick Deville, Nabil Charkani, 1999, 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. ICASSP99 (Cat. No.99CH36258).

Iterative-Shift Cluster-Based Time-Frequency BSS for Fractional-Time-Delay Mixtures

Yannick Deville, Matthieu Puigt, 2009, ICA.

Matrix factorization for bilinear blind source separation: Methods, separability and conditioning

Yannick Deville, Y. Deville, 2015, 2015 23rd European Signal Processing Conference (EUSIPCO).

A Blind Identification and Source Separation Method Based on Subspace Intersections for Hyperspectral Astrophysical Data

Yannick Deville, Axel Boulais, Olivier Berné, 2017, LVA/ICA.

Mapping Benthic Habitats by Extending Non-Negative Matrix Factorization to Address the Water Column and Seabed Adjacency Effects

Mireille Guillaume, Yannick Deville, Xavier Briottet, 2020, Remote. Sens..

New self‐normalized blind source separation networks for instantaneous and convolutive mixtures

Yannick Deville, Nabil Charkani, Ovidiu Albu, 2002 .

Stable Higher-Order Recurrent Neural Network Structures for Nonlinear Blind Source Separation

Yannick Deville, Shahram Hosseini, Y. Deville, 2007, ICA.

Positivity-based separation of stellar spectra using a parametric mixing model

Yannick Deville, Shahram Hosseini, Ines Meganem, 2013, 21st European Signal Processing Conference (EUSIPCO 2013).

GaAs active structures with applications to microwave wideband 90° phase shifters and bandstop filters

Yannick Deville, 1991 .

Optimum piecewise-linear transcoders Part 2. Application to automatic TV tuner alignment and to electronic tuning

Yannick Deville, 1994 .

ICA Separability of Nonlinear Models with References: General Properties and Application to Heisenberg-Coupled Quantum States (Qubits)

Yannick Deville, 2010, LVA/ICA.

Impact of the initialisation of a blind unmixing method dealing with intra-class variability

Yannick Deville, Xavier Briottet, Véronique Achard, 2017, ESANN.

Blind Separation of Sparse Signals based on Deflation and using the Differential Evolution Algorithm

João Marcos Travassos Romano, Yannick Deville, Leonardo Tomazeli Duarte, 2016 .

Using a Panchromatic Image to Improve Hyperspectral Unmixing

Yannick Deville, Xavier Briottet, Véronique Achard, 2020, Remote. Sens..

Differential Fast Fixed-Point Algorithms for Underdetermined Instantaneous and Convolutive Partial Blind Source Separation

Yannick Deville, Shahram Hosseini, Johan Thomas, 2007, IEEE Transactions on Signal Processing.

Cumulant-based estimation of quadratic mixture parameters for blind source separation

Yannick Deville, Shahram Hosseini, Chahinez Chaouchi, 2010, 2010 18th European Signal Processing Conference.

Fast Disentanglement-Based Blind Quantum Source Separation and Process Tomography: A Closed-Form Solution Using a Feedback Classical Adapting Structure

Yannick Deville, Alain Deville, Y. Deville, 2017, LVA/ICA.

Extending NMF to blindly separate linear-quadratic mixtures of uncorrelated sources

Yannick Deville, Shahram Hosseini, Leonardo Tomazeli Duarte, 2016, 2016 IEEE 26th International Workshop on Machine Learning for Signal Processing (MLSP).

Improvement of remote sensing multispectral image classification by using Independent Component Analysis

Yannick Deville, Shahram Hosseini, Moussa Sofiane Karoui, 2009, 2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing.

Semi-blind separation of galaxy spectra from a mixture obtained by slitless spectroscopy

Yannick Deville, Shahram Hosseini, Ahmed Selloum, 2015, 2015 23rd European Signal Processing Conference (EUSIPCO).

A New Separation Method for Galaxy Spectra Based on Data Fusion between Two Grism Orders in Slitless Spectroscopy

Yannick Deville, Shahram Hosseini, Thierry Contini, 2019, ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

STOCHASTIC QUANTUM INFORMATION PROCESSING, WITH APPLICATIONS TO BLIND QUANTUM SYSTEM IDENTIFICATION AND SOURCE SEPARATION

Yannick Deville, Alain Deville, Y. Deville, 2018, 2018 IEEE 28th International Workshop on Machine Learning for Signal Processing (MLSP).

Self-adaptive separation of convolutively mixed signals with a recursive structure. Part II: Theoretical extensions and application to synthetic and real signals

Yannick Deville, Nabil Charkani, Y. Deville, 1999, Signal Process..

Latent Variable Analysis and Signal Separation: 14th International Conference, LVA/ICA 2018, Guildford, UK, July 2–5, 2018, Proceedings

Mark D. Plumbley, Sharon Gannot, Yannick Deville, 2018 .

A class of replacement policies for medium and high-associativity structures

Yannick Deville, Jean Gobert, 1992, CARN.

Blind Separation of Linear-Quadratic Mixtures of Real Sources Using a Recurrent Structure

Yannick Deville, Shahram Hosseini, Y. Deville, 2009, IWANN.

Blind Separation of Quantum States: Estimating Two Qubits from an Isotropic Heisenberg Spin Coupling Model

Yannick Deville, Alain Deville, Y. Deville, 2007, ICA.

Blind separation of piecewise stationary non-Gaussian sources

Yannick Deville, Shahram Hosseini, Zbynek Koldovský, 2009, Signal Process..

Analysis of the stability of time-domain source separation algorithms for convolutively mixed signals

Yannick Deville, Nabil Charkani, Y. Deville, 1997, 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing.

Separation of galaxy spectra measured with slitless spectroscopy

Yannick Deville, Ahmed Selloum, Shahram Hosseini, 2020, Digit. Signal Process..

Differential source separation: Concept and application to a criterion based on differential normalized kurtosis

Yannick Deville, Mohammed Benali, Y. Deville, 2000, 2000 10th European Signal Processing Conference.

Using Taylor Series Expansions and Second-Order Statistics for Blind Source Separation in Post-Nonlinear Mixtures

Christian Jutten, Yannick Deville, Romis de Faissol Attux, 2018, LVA/ICA.

Separation aveugle de sources Markoviennes et no n-stationnaires

Yannick Deville, Rima Guidara, Shahram Hosseini, 2007 .

Effect of indirect dependencies on maximum likelihood and information theoretic blind source separation for nonlinear mixtures

Yannick Deville, Shahram Hosseini, Alain Deville, 2011, Signal Process..

neural digital processor operating with an approximation of an activation function nonlinear.

Yannick Deville, 1991 .

A Quantum-feedforward and classical-feedback separating structure adapted with monodirectional measurements; Blind qubit uncoupling capability and links with ICA

Yannick Deville, Alain Deville, Y. Deville, 2013, 2013 IEEE International Workshop on Machine Learning for Signal Processing (MLSP).

Fast disentanglement-based blind quantum source separation and process tomography using a feedforward quantum-classical adapting structure

Yannick Deville, Alain Deville, Y. Deville, 2016, 2016 24th European Signal Processing Conference (EUSIPCO).

Méthode temps-fréquence de séparation aveugle de sources basée sur la fonction de cohérence segmentée

Yannick Deville, Benoit Albouy, 2003 .

Blind Maximum Likelihood Separation of a Linear-Quadratic Mixture

Yannick Deville, Shahram Hosseini, Y. Deville, 2004, ICA.

A second-order differential approach for underdetermined convolutive source separation

Yannick Deville, Stephane P. Savoldelli, Y. Deville, 2001, 2001 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.01CH37221).

Blind Separation of Noisy Mixtures of Non-stationary Sources Using Spectral Decorrelation

Yannick Deville, Shahram Hosseini, Hicham Saylani, 2009, ICA.

Exact and Approximate Quantum Independent Component Analysis for Qubit Uncoupling

Yannick Deville, Alain Deville, Y. Deville, 2012, LVA/ICA.

A time-scale correlation-based blind separation method applicable to correlated sources

Hervé Carfantan, Yannick Deville, Shahram Hosseini, 2006, ESANN.

Maximum Likelihood Blind Image Separation Using Nonsymmetrical Half-Plane Markov Random Fields

Yannick Deville, Shahram Hosseini, Rima Guidara, 2009, IEEE Transactions on Image Processing.

Overview of source separation applications

Christian Jutten, Ricardo Vigário, Yannick Deville, 2010 .

Time-domain fast fixed-point algorithms for convolutive ICA

Yannick Deville, Shahram Hosseini, Johan Thomas, 2006, IEEE Signal Processing Letters.

From Blind Quantum Source Separation to Blind Quantum Process Tomography

Yannick Deville, Alain Deville, Y. Deville, 2015, LVA/ICA.

A unified stability analysis of the Hérault-Jutten source separation neural network

Yannick Deville, Y. Deville, 1996, Signal Process..

A process-dependent partitioning strategy for cache memories

Yannick Deville, 1993, CARN.

A map-based NMF approach to hyperspectral image unmixing using a linear-quadratic mixture model

Yannick Deville, Shahram Hosseini, Ahmed Ben Hamida, 2016, 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

AN NMF-BASED UNMIXING METHOD WITH KNOWN SPECTRA OF PHOTOVOLTAIC PANELS FOR THEIR DETECTION AND AREA ESTIMATION FROM URBAN HYPERSPECTRAL REMOTE SENSING DATA

Yannick Deville, Moussa Sofiane Karoui, Xavier Briottet, 2018 .

Differential source separation for underdetermined instantaneous or convolutive mixtures: concept and algorithms

Yannick Deville, Mohammed Benali, Frédéric Abrard, 2004, Signal Process..

Temporal and time-frequency correlation-based blind source separation methods. Part I: Determined and underdetermined linear instantaneous mixtures

Yannick Deville, Matthieu Puigt, Y. Deville, 2007, Signal Process..

Panorama des applications biomédicales des méthodes de séparation aveugle de sources

Yannick Deville, Y. Deville, 2003 .

Application and Extension of PCA Concepts to Blind Unmixing of Hyperspectral Data with Intra-class Variability

Yannick Deville, Xavier Briottet, Charlotte Revel, 2018 .

Nonlinear Blind Source Separation Applied to a Simple Bijective Model

Yannick Deville, Shahram Hosseini, Hicham Saylani, 2012, ICISP.

New Classes of Blind Quantum Source Separation and Process Tomography Methods Based on Spin Component Measurements Along Two Directions

Yannick Deville, Alain Deville, Y. Deville, 2018, LVA/ICA.

A second-order statistics method for blind source separation in post-nonlinear mixtures

Christian Jutten, Yannick Deville, Romis de Faissol Attux, 2019, Signal Process..

Blind Source Separation of Overdetermined Linear-Quadratic Mixtures

João Marcos Travassos Romano, Christian Jutten, Yannick Deville, 2010, LVA/ICA.

Markovian Blind Image Separation

Christian Jutten, Yannick Deville, Shahram Hosseini, 2006, ICA.

A necessary and sufficient condition for the blind extraction of the sparsest source in convolutive mixtures

João Marcos Travassos Romano, Yannick Deville, Leonardo Tomazeli Duarte, 2016, 2016 24th European Signal Processing Conference (EUSIPCO).

A Blind Source Separation Method Based on Output Nonlinear Correlation for Bilinear Mixtures

Yannick Deville, Shahram Hosseini, Andréa Guerrero, 2018, LVA/ICA.

Fast Blind Separation of Long Mixture Recordings Using Multivariate Polynomial Identification

Yannick Deville, Shahram Hosseini, Johan Thomas, 2008, IEEE Transactions on Signal Processing.

Differential Fast Fixed-Point BSS for Underdetermined Linear Instantaneous Mixtures

Yannick Deville, Shahram Hosseini, Johan Thomas, 2006, ICA.

Characterization of hyperspectral images prior to unmixing, based on eigendecompositions and sum-to-one condition

Yannick Deville, Xavier Briottet, Véronique Achard, 2014, 2014 6th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS).

Hyperspectral Imagery for Environmental Urban Planning

Walid Ouerghemmi, Yannick Deville, Moussa Sofiane Karoui, 2018, IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium.

NMF-Based spectral unmixingof herschel observations of the horsehead nebula

Yannick Deville, J. Pety, Axel Boulais, 2013, 2013 5th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS).

Blind identification and separation methods for Linear-Quadratic mixtures and/or linearly independent non-stationary signals

Yannick Deville, Shahram Hosseini, 2007, 2007 9th International Symposium on Signal Processing and Its Applications.

Extraction of stellar spectra from dense fields in hyperspectral muse data cubes using non-negative matrix factorization

Hervé Carfantan, Yannick Deville, Shahram Hosseini, 2011, 2011 3rd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS).

Effects of audio coding on ICA performance: An experimental study

Athanasios Mouchtaris, Emmanuel Vincent, Yannick Deville, 2013, 2013 IEEE 11th International Workshop of Electronics, Control, Measurement, Signals and their application to Mechatronics.

A control for a memory buffer reconfigurable partitioning.

Yannick Deville, 1990 .

Noisy cyclo-stationary BSS using frequency-domain pseudo-correlation

Yannick Deville, Shahram Hosseini, Hicham Saylani, 2010, 2010 18th European Signal Processing Conference.

Bilinear Matrix Factorization using a Gradient Method for Unmixing Hyperspectral Images Combinedc with Multispectral Data

Yannick Deville, Shahram Hosseini, Moussa Sofiane Karoui, 2018, 2018 9th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS).

Separation of Stellar Spectra Based on Non-negativity and Parametric Modelling of Mixing Operator

Yannick Deville, Shahram Hosseini, Inès Meganem, 2016 .

An NMF-Based Approach for Hyperspectral Unmixing Using a New Multiplicative-tuning Linear Mixing Model to Address Spectral Variability

Yannick Deville, Moussa Sofiane Karoui, Fatima Zohra Benhalouche, 2019, 2019 27th European Signal Processing Conference (EUSIPCO).

Blind quantum source separation: Quantum-processing qubit uncoupling systems based on disentanglement

Yannick Deville, Alain Deville, Y. Deville, 2017, Digit. Signal Process..

A method based on nonnegative matrix factorization dealing with intra-class variability for unsupervised hyperspectral unmixing

Yannick Deville, Xavier Briottet, Véronique Achard, 2015, 2015 7th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS).

Blind separation of dependent sources using the "time-frequency ratio of mixtures" approach

Yannick Deville, Frédéric Abrard, F. Abrard, 2003, Seventh International Symposium on Signal Processing and Its Applications, 2003. Proceedings..

Classical-processing and quantum-processing signal separation methods for qubit uncoupling

Yannick Deville, Alain Deville, Y. Deville, 2012, Quantum Inf. Process..

Mixing model in slitless spectroscopy and resulting blind methods for separating galaxy spectra

Yannick Deville, Shahram Hosseini, Ahmed Selloum, 2016, 2016 IEEE 26th International Workshop on Machine Learning for Signal Processing (MLSP).

Blind Separation of Nonstationary Markovian Sources Using an Equivariant Newton–Raphson Algorithm

Yannick Deville, Shahram Hosseini, Rima Guidara, 2009, IEEE Signal Processing Letters.

Validity of the Independence Assumption for the Separation of Instantaneous and Convolutive Mixtures of Speech and Music Sources

Emmanuel Vincent, Yannick Deville, Matthieu Puigt, 2009, ICA.

Hyperspectral data multi-sharpening based on linear-quadratic nonnegative matrix factorization

Yannick Deville, Moussa Sofiane Karoui, Abdelaziz Ouamri, 2015, 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS).

Optimization of the asymptotic performance of time-domain convolutive source separation algorithms

Yannick Deville, Nabil Charkani, Y. Deville, 1997, ESANN.

Modified nonnegative matrix factorization for endmember spectra extraction from highly mixed hyperspectral images combined with multispectral data

Yannick Deville, Shahram Hosseini, Moussa Sofiane Karoui, 2017, 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

Markovian blind separation of non-stationary temporally correlated sources

Yannick Deville, Shahram Hosseini, Rima Guidara, 2007, ESANN.

Recurrent Source Separation Structures as Iterative Methods for Solving Nonlinear Equation Systems

Yannick Deville, Ricardo Suyama, Leonardo Tomazeli Duarte, 2012 .

Blind partial separation of underdetermined convolutive mixtures of complex sources based on differential normalized kurtosis

Yannick Deville, Johan Thomas, Frédéric Abrard, 2008, Neurocomputing.

Hypersharpening by Joint-Criterion Nonnegative Matrix Factorization

Yannick Deville, Moussa Sofiane Karoui, Issam Boukerch, 2017, IEEE Transactions on Geoscience and Remote Sensing.

Recurrent networks for separating extractable-target nonlinear mixtures. Part I: Non-blind configurations

Yannick Deville, Shahram Hosseini, Y. Deville, 2009, Signal Process..

Linear–Quadratic Mixing Model for Reflectances in Urban Environments

Yannick Deville, Shahram Hosseini, Xavier Briottet, 2014, IEEE Transactions on Geoscience and Remote Sensing.

Analytical performance analysis for blind quantum source separation with time-varying coupling

Yannick Deville, Ali Mansour, Simon Rebeyrol, 2017, 2017 23rd Asia-Pacific Conference on Communications (APCC).

A new unsupervised method for hyperspectral image unmixing using a linear-quadratic model

Yannick Deville, Shahram Hosseini, Ahmed Ben Hamida, 2014, 2014 1st International Conference on Advanced Technologies for Signal and Image Processing (ATSIP).

Blind qubit state disentanglement with Quantum processing: Principle, criterion and algorithm using measurements along two directions

Yannick Deville, Alain Deville, Y. Deville, 2014, 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

Quantum-Source Independent Component Analysis and Related Statistical Blind Qubit Uncoupling Methods

Yannick Deville, Alain Deville, Y. Deville, 2014 .

Linear-quadratic and polynomial Non-Negative Matrix Factorization; application to spectral unmixing

Yannick Deville, Shahram Hosseini, Xavier Briottet, 2011, 2011 19th European Signal Processing Conference.

A Time-Frequency CORRelation-Based Blind Source Separation Method for Time-Delayed Mixtures

Yannick Deville, Matthieu Puigt, 2006, 2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings.

Digital Neural Networks For High-Speed Divisions And Root Extractions

Yannick Deville, 1993, Int. J. Neural Syst..

Maximum likelihood blind separation of two quantum states (qubits) with cylindrical-symmetry Heisenberg spin coupling

Yannick Deville, Alain Deville, 2008, 2008 IEEE International Conference on Acoustics, Speech and Signal Processing.

Recurrent networks for separating extractable-target nonlinear mixtures. Part II. Blind configurations

Yannick Deville, Shahram Hosseini, Y. Deville, 2013, Signal Process..

Bilinear matrix factorization using a gradient method for hyperspectral endmember spectra extraction

Yannick Deville, Moussa Sofiane Karoui, Abdelaziz Ouamri, 2016, 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS).

Blind Separation of Parametric Nonlinear Mixtures of Possibly Autocorrelated and Non-Stationary Sources

Yannick Deville, Shahram Hosseini, Y. Deville, 2014, IEEE Transactions on Signal Processing.

Blind Separation of Non-stationary Images Using Markov Models

Yannick Deville, Shahram Hosseini, Rima Guidara, 2007, ICA.

Numerical and analytical solutions to the differential source separation problem

Yannick Deville, Mohammed Benali, Frédéric Abrard, 2000, 2000 10th European Signal Processing Conference.

Source separation algorithms for the analysis of hyper-spectral observations of very small interstellar dust particles

Yannick Deville, Shahram Hosseini, Matthieu Puigt, 2009, 2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing.

Inertia-Constrained Pixel-by-Pixel Nonnegative Matrix Factorisation: a Hyperspectral Unmixing Method Dealing with Intra-class Variability pdf

Yannick Deville, Xavier Briottet, Charlotte Revel, 2017, Remote. Sens..

A distributed adaptive block matching algorithm: DIS-ABMA

Benoit M. Macq, Yannick Deville, Xavier Marichal, 1998, 9th European Signal Processing Conference (EUSIPCO 1998).

Multi-tag radio-frequency identification systems based on new blind source separation neural networks

Yannick Deville, Nabil Charkani, Jacques Damour, 2002, Neurocomputing.

An NMF-Based Method For Hyperspectral Unmixing Using A Structured Additively-Tuned Linear Mixing Model To Address Spectral Variability

Yannick Deville, Moussa Sofiane Karoui, Abdelaziz Ouamri, 2020, 2020 Mediterranean and Middle-East Geoscience and Remote Sensing Symposium (M2GARSS).

Two Time-Frequency Ratio-Based Blind Source Separation Methods for Time-Delayed Mixtures

Yannick Deville, Matthieu Puigt, 2006, ICA.

A Linear NMF-Unmixing-Based Approach Addressing Spectral Variability For Shadow Compensation In Hyperspectral Remote Sensing Data

Yannick Deville, Moussa Sofiane Karoui, Khelifa Djerriri, 2020, 2020 Mediterranean and Middle-East Geoscience and Remote Sensing Symposium (M2GARSS).

Analysis and control of the stability of a differential separation method for underdetermined convolutive mixtures

Yannick Deville, 2002, 2002 11th European Signal Processing Conference.

Principles and performance of the LI-TIFROM software

Yannick Deville, Matthieu Puigt, 2009 .

Analysis of the convergence properties of a self-normalized source separation neural network

Yannick Deville, 1997, First IEEE Signal Processing Workshop on Signal Processing Advances in Wireless Communications.

Quantum process tomography with unknown single-preparation input states.

Yannick Deville, Alain Deville, 2019 .

Unmixing methods based on nonnegativity and weakly mixed pixels for astronomical hyperspectral datasets

Yannick Deville, Axel Boulais, Olivier Bern'e, 2020 .

Joint nonnegative matrix factorization for hyperspectral and multispectral remote sensing data fusion

Yannick Deville, Moussa Sofiane Karoui, Sarah Kreri, 2013, 2013 5th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS).

Blind Separation of Convolutive Mixtures of Non-stationary and Temporally Uncorrelated Sources Based on Joint Diagonalization

Yannick Deville, Shahram Hosseini, Hicham Saylani, 2012, ICISP.

A Time-Frequency Blind Source Separation Method Based on Segmented Coherence Function

Yannick Deville, Benoit Albouy, 2003, IWANN.

Separation of Sparse Signals in Overdetermined Linear-Quadratic Mixtures

Christian Jutten, Yannick Deville, Leonardo Tomazeli Duarte, 2012, LVA/ICA.

Physical modelling and non-linear unmixing method for urban hyperspectral images

Yannick Deville, Shahram Hosseini, Xavier Briottet, 2011, 2011 3rd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS).

A pixel-by-pixel NMF-based method for hyperspectral unmixing using a new linear mixing model to address additively-tuned spectral variabilty

Yannick Deville, Moussa Sofiane Karoui, Abdelaziz Ouamri, 2019, Remote Sensing.

Blind Source Separation in Convolutive Mixtures: A Hybrid Approach for Colored Sources

Yannick Deville, Frédéric Abrard, F. Abrard, 2001, IWANN.

Linear-Quadratic NMF-Based Urban Hyperspectral Data Unmixing With Some Known Endmembers

Yannick Deville, Moussa Sofiane Karoui, Fatima Zohra Benhalouche, 2018, 2018 9th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS).

FUSION OF HYPERSPECTRAL AND PANCHROMATIC DATA BY SPECTRAL UNMIXING IN THE REFLECTIVE DOMAIN

Jocelyn Chanussot, Yannick Deville, Xavier Briottet, 2020 .

Sparse Component Analysis: A General Framework for Linear and Nonlinear Blind Source Separation and Mixture Identification

Yannick Deville, Y. Deville, 2014 .

Blind Operation of a Recurrent Neural Network for Linear-Quadratic Source Separation: Fixed Points, Stabilization and Adaptation Scheme

Yannick Deville, Shahram Hosseini, 2010, LVA/ICA.

Filter apparatus for processing a plurality of channels in a cable television distribution system

Yannick Deville, Paul Friedel, Pierre Chanteau, 1996 .

A criterion for deriving the sub/super gaussianity of source signals from their mixtures

Yannick Deville, Mohammed Benali, 2000, 2000 10th European Signal Processing Conference.

N-qubit system in a pure state: a necessary and sufficient condition for unentanglement

Yannick Deville, Alain Deville, Y. Deville, 2019, Quantum Information Processing.

Blind spatial unmixing of multispectral images: New methods combining sparse component analysis, clustering and non-negativity constraints

Yannick Deville, Shahram Hosseini, Moussa Sofiane Karoui, 2012, Pattern Recognit..

From separability/identifiability properties of bilinear and linear-quadratic mixture matrix factorization to factorization algorithms

Yannick Deville, Y. Deville, 2019, Digit. Signal Process..

Partial Linear NMF-Based Unmixing Methods for Detection and Area Estimation of Photovoltaic Panels in Urban Hyperspectral Remote Sensing Data

Yannick Deville, Moussa Sofiane Karoui, Xavier Briottet, 2019, Remote. Sens..

An Overview of Blind Source Separation Methods for Linear-Quadratic and Post-nonlinear Mixtures

Yannick Deville, Leonardo Tomazeli Duarte, 2015, LVA/ICA.

Blind Signal Separation Methods for the Identification of Interstellar Carbonaceous Nanoparticles

Yannick Deville, Olivier Berné, Christine Joblin, 2007, ICA.

Self-adaptive separation of convolutively mixed signals with a recursive structure. Part I: Stability analysis and optimization of asymptotic behaviour

Yannick Deville, Nabil Charkani, Y. Deville, 1999, Signal Process..

A time-frequency blind signal separation method applicable to underdetermined mixtures of dependent sources

Yannick Deville, Frédéric Abrard, F. Abrard, 2005, Signal Process..

Hyperspectral endmember spectra extraction based on constrained linear-quadratic matrix factorization using a projected gradient method

Yannick Deville, Moussa Sofiane Karoui, Abdelaziz Ouamri, 2016, 2016 IEEE 26th International Workshop on Machine Learning for Signal Processing (MLSP).

ICA-based and second-order separability of nonlinear models involving reference signals: General properties and application to quantum bits

Yannick Deville, Y. Deville, 2012, Signal Process..

Blind Separation of Nonstationary Sources by Spectral Decorrelation

Yannick Deville, Shahram Hosseini, 2004, ICA.

A New Hyperspectral Unmixing Method Using Co-Registered Hyperspectral and Panchromatic Images

Yannick Deville, Xavier Briottet, Simon Rebeyrol, 2019, 2019 10th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS).

A new multiplicative nonnegative matrix factorization method for unmixing hyperspectral images combined with multispectral data

Yannick Deville, Shahram Hosseini, Moussa Sofiane Karoui, 2017, 2017 25th European Signal Processing Conference (EUSIPCO).

Blind Separation methods based on correlation for sparse possibly-correlated images

Yannick Deville, Matthieu Puigt, Ines Meganem, 2010, 2010 IEEE International Conference on Acoustics, Speech and Signal Processing.

Concepts and Criteria for Blind Quantum Source Separation and Blind Quantum Process Tomography

Yannick Deville, Alain Deville, Y. Deville, 2016, Entropy.

Extension of EFICA algorithm for blind separation of piecewise stationary non Gaussian sources

Yannick Deville, Shahram Hosseini, Zbynek Koldovský, 2008, 2008 IEEE International Conference on Acoustics, Speech and Signal Processing.

Blind separation of linear instantaneous mixtures of non-stationary signals in the frequency domain

Yannick Deville, Shahram Hosseini, Hicham Saylani, 2009, Signal Process..

Blind Unmixing of Hyperspectral Remote Sensing Data: A New Geometrical Method Based on a Two-Source Sparsity Constraint

Djaouad Benachir, Yannick Deville, Moussa Sofiane Karoui, 2020, Remote. Sens..

A new time-frequency correlation-based source separation method for attenuated and time-shifted mixtures

Yannick Deville, Matthieu Puigt, 2007 .

A linear-quadratic unsupervised hyperspectral unmixing method dealing with intra-class variability

Yannick Deville, Xavier Briottet, Véronique Achard, 2016, 2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS).

Linear-Quadratic Blind Source Separation Using NMF to Unmix Urban Hyperspectral Images

Yannick Deville, Shahram Hosseini, Xavier Briottet, 2014, IEEE Transactions on Signal Processing.

Blind Separation of Convolutive Mixtures of Non-stationary Sources Using Joint Block Diagonalization in the Frequency Domain

Yannick Deville, Shahram Hosseini, Hicham Saylani, 2010, LVA/ICA.

A low-cost usage-based replacement algorithm for cache memories

Yannick Deville, 1990, CARN.

Detection And Area Estimation For Photovoltaic Panels In Urban Hyperspectral Remote Sensing Data By An Original Nmf-Based Unmixing Method

Yannick Deville, Moussa Sofiane Karoui, Xavier Briottet, 2018, IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium.

A second-order blind source separation method for bilinear mixtures

Yannick Deville, Shahram Hosseini, Ahmed Ben Hamida, 2018, Multidimens. Syst. Signal Process..

Fusion of Panchromatic and Hyperspectral Images in the Reflective Domain by a Combinatorial Approach and Application to Urban Landscape

Hervé Carfantan, Yannick Deville, Xavier Briottet, 2021, 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS.

Hyperspectral Unmixing Based on Constrained Bilinear or Linear-Quadratic Matrix Factorization

Yannick Deville, Moussa Sofiane Karoui, Abdelaziz Ouamri, 2021, Remote. Sens..

Nonlinear Blind Source Separation and Blind Mixture Identification

Yannick Deville, Shahram Hosseini, Leonardo Tomazeli Duarte, 2021 .

Two-Qubit Unitary Quantum Process Tomography by Multiple-Delay Output Measurements for One Unknown Input Pure State Value

Yannick Deville, François Verdeil, Alain Deville, 2021, 2021 IEEE Statistical Signal Processing Workshop (SSP).

Hypersharpening by a Multiplicative Joint-Criterion NMF Method Addressing Spectral Variability

Yannick Deville, Moussa Sofiane Karoui, Yasmine Kheira Benkouider, 2021, 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS.

Gradient-Based NMF Methods for Hyperspectral Unmixing Addressing Spectral Variability with a Multiplicative-Tuning Linear Mixing Model

Yannick Deville, Moussa Sofiane Karoui, Fatima Zohra Benhalouche, 2021, 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS.

A Penalization-Based NMF Approach for Hyperspectral Unmixing Addressing Spectral Variability with an Additively-Tuned Mixing Model

Yannick Deville, Moussa Sofiane Karoui, Abdelaziz Ouamri, 2021, 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS.

Hypersharpening by an NMF-Unmixing-Based Method Addressing Spectral Variability

Yannick Deville, Moussa Sofiane Karoui, Abdelaziz Ouamri, 2021, IEEE Geoscience and Remote Sensing Letters.

An Informed NMF-Based Unmixing Approach for Mineral Detection and Mapping in the Algerian Central Hoggar Using PRISMA Remote Sensing Hyperspectral Data

Yannick Deville, Moussa Sofiane Karoui, Fatima Zohra Benhalouche, 2021, 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS.

Single-preparation unsupervised quantum machine learning: concepts and applications

Yannick Deville, Alain Deville, 2021 .

Hyperspectral Pansharpening in the Reflective Domain with a Second Panchromatic Channel in the SWIR II Spectral Domain

Yannick Deville, Xavier Briottet, Vincent Crombez, 2021, Remote. Sens..

Modified independent component analysis for initializing non-negative matrix factorization: An approach to hyperspectral image unmixing

Djaouad Benachir, Yannick Deville, Shahram Hosseini, 2013, 2013 IEEE 11th International Workshop of Electronics, Control, Measurement, Signals and their application to Mechatronics.

Blind unmixing of remote sensing data with some pure pixels: Extension and comparison of spatial methods exploiting sparsity and nonnegativity properties

Yannick Deville, Shahram Hosseini, Moussa Sofiane Karoui, 2013, 2013 8th International Workshop on Systems, Signal Processing and their Applications (WoSSPA).

Analysis and quantification of seabed adjacency effects in the subsurface upward radiance in shallow waters.

Mireille Guillaume, Yannick Deville, Xavier Briottet, 2019, Optics express.

Extension of the ” TIme-Frequency Ratio Of Mixtures ” blind source separation method to more than 2 channels

Yannick Deville, Frédéric Abrard, 2003 .

Hyperspectral remote sensing of shallow waters: Considering environmental noise and bottom intra-class variability for modeling and inversion of water reflectance

Mireille Guillaume, Yannick Deville, Audrey Minghelli, 2017 .

GaAs integrated RC active filters with high-selectivity bandpass and low-selectivity bandstop responses

Yannick Deville, 1991 .

Maximum likelihood separation of spatially autocorrelated images using a Markov model

Christian Jutten, Yannick Deville, Shahram Hosseini, 2006 .