Y. Deville

Y. Deville, Axel Boulais, Olivier Bern, 2015, 2015 IEEE International Workshop of Electronics, Control, Measurement, Signals and their Application to Mechatronics (ECMSM).

Beyond the density operator and Tr(\rho A): Exploiting the higher-order statistics of random-coefficient pure states for quantum information processing

Y. Deville, A. Deville, 2022, 2204.10031.

Cross-validation of blindly separated interstellar dust spectra

Y. Deville, M. Puigt, S. Hosseini, 2009 .

Time-domain fast fixed-point algorithms for blind separation of convolutive mixtures

Y. Deville, S. Hosseini, Johan Thomas, 2007 .

Blind decomposition of Herschel-HIFI spectral maps of the NGC 7023 nebula

Y. Deville, O. Berné, C. Joblin, 2012, 1210.3453.

Random-coefficient pure states and statistical mixtures

Y. Deville, A. Deville, 2022, 2201.03248.

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

Y. Deville, A. Deville, François Verdeil, 2021, 2021 IEEE Statistical Signal Processing Workshop (SSP).

Identification des spectres infrarouges des populations de poussiere carbonees interstellaires par separation aveugle de sources

Y. Deville, C. Joblin, Olivier Bern, 2007 .

Single-preparation unsupervised quantum machine learning: concepts and applications

Y. Deville, A. Deville, 2021, 2101.01442.

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

Y. Deville, N. Charkani, 1997, ESANN.

An NMF-based method for jointly handling mixture nonlinearity and intraclass variability in hyperspectral blind source separation

Y. Deville, X. Briottet, G. Faury, 2022, Digit. Signal Process..

Two Unitary Quantum Process Tomography Algorithms Robust to Systematic Errors

Y. Deville, François Verdeil, 2022, MaxEnt 2022.

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

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

Pure-state tomography with parallel unentangled measurements

Y. Deville, Franccois Verdeil, 2022, Physical Review A.

Hyperspectral Imagery for Environmental Urban Planning

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

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..

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

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

Blind source separation methods based on output nonlinear correlation for bilinear mixtures of an arbitrary number of possibly correlated signals

Y. Deville, S. Hosseini, 2020, 2020 IEEE 11th Sensor Array and Multichannel Signal Processing Workshop (SAM).

Independent Component Analysis and Bayesian Separation Methods

Y. Deville, S. Hosseini, Leonardo Tomazeli Duarte, 2020 .

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

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

Separability/identifiability properties of low-rank matrix factorization methods for bilinear mixtures of source signals

Y. Deville, 2019 .

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

Yannick Deville, Shahram Hosseini, Inès Meganem, 2016 .

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 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 .

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

Yannick Deville, Y. Deville, 2014 .

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

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

Separation of Sparse Signals in Overdetermined Linear-Quadratic Mixtures

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

Exact and Approximate Quantum Independent Component Analysis for Qubit Uncoupling

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

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

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

A blind quantum source separation method based on mutual information for retrieving two qubits with cylindrical-symmetry heisenberg coupling

Y. Deville, A. Deville, 2011, 2011 7th International Symposium on Image and Signal Processing and Analysis (ISPA).

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..

Overview of source separation applications

Christian Jutten, Ricardo Vigário, Yannick Deville, 2010 .

Blind separation of piecewise stationary non-Gaussian sources

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

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.

Fast Blind Separation of Long Mixture Recordings Using Multivariate Polynomial Identification

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

Contribution of non-negative matrix factorization to the classification of remote sensing images

Moussa Sofiane Karoui, Y. Deville, D. Ducrot, 2008, Remote Sensing.

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.

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).

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).

Spatially-Weighted Factor Analysis for Extraction of Source-Oriented Mineralization Feature in 3D Coordinates of Surface Geochemical Signal

S. H. Tabatabaei, Y. Deville, S. Hosseini, 2021, Natural Resources Research.

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).

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

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

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

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

Blind Maximum Likelihood Separation of a Linear-Quadratic Mixture

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

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

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

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

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

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

Yannick Deville, Nabil Charkani, Ovidiu Albu, 2002 .

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).

Analysis of convolutive source separation methods based on self-normalized weight updating terms

Y. Deville, N. Charkani, Y. Deville, 1999, Proceedings of the IEEE Signal Processing Workshop on Higher-Order Statistics. SPW-HOS '99.

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..

New Single-Preparation Methods for Unsupervised Quantum Machine Learning Problems

Y. Deville, A. Deville, 2021, IEEE Transactions on Quantum Engineering.

Statistical intrusion detection and eavesdropping in quantum channels with coupling: multiple-preparation and single-preparation methods

Y. Deville, A. Mansour, C. Ioana, 2021, Quantum Information Processing.

Local hyperspectral data multisharpening based on linear/linear-quadratic nonnegative matrix factorization by integrating lidar data

Moussa Sofiane Karoui, Abdelaziz Ouamri, Y. Deville, 2015, SPIE Remote Sensing.

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).

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

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

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

Y. Deville, A. Deville, 2011, Quantum Information Processing.

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

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

Random-coefficient pure states versus statistical mixtures

Y. Deville, A. Deville, 2022, 2201.03248.

Hyperspectral and Multispectral Image Fusion with Automated Extraction of Image-Based Endmember Bundles and Sparsity-Based Unmixing to Deal with Spectral Variability

Salah Eddine Brezini, Y. Deville, S. Brezini, 2023, Sensors.

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..

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

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

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..

Using a Panchromatic Image to Improve Hyperspectral Unmixing

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

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

Jocelyn Chanussot, Yannick Deville, Xavier Briottet, 2020 .

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

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

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..

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

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

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.

Time-domain fast fixed-point algorithms for convolutive ICA

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

Differential Fast Fixed-Point BSS for Underdetermined Linear Instantaneous Mixtures

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

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

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

Exploiting the higher-order statistics of random-coefficient pure states for quantum information processing

Y. Deville, A. Deville, 2023, Quantum Information Processing.

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.

A distributed adaptive block matching algorithm: DIS-ABMA

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

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

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

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

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

Stability analysis and optimization of time-domain convolutive source separation algorithms

N. Charkani, Y. Deville, J. Herault, 1997, First IEEE Signal Processing Workshop on Signal Processing Advances in Wireless Communications.

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

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

2022 Beyond the density operator and T r ( ρ ˆ A ): Exploiting the higher-order statistics of random-coeﬃcient pure states for quantum information processing

Y. Deville, 2022 .

Quantum process tomography with unknown single-preparation input states: Concepts and application to the qubit pair with internal exchange coupling

Y. Deville, A. Deville, 2019, Physical Review A.

The Blind Version of Quantum Process Tomography: Operating with Unknown Input Values

Y. Deville, A. Deville, 2017 .

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

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

From Blind Quantum Source Separation to Blind Quantum Process Tomography

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

Nonlinear Blind Source Separation and Blind Mixture Identification

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

Minerals Detection and Mapping in the Southwestern Algeria Gara-Djebilet Region with a Multistage Informed NMF-Based Unmixing Approach Using Prisma Remote Sensing Hyperspectral Data

Fatima Zohra Benhalouche, M. S. Karoui, Lahsen Wahib Kebir, 2022, IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium.

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.

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

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

Detecting And Mapping Kaolinite In The Algerian Central Hoggar With A Partial Linear Nmf-Based Unmixing Method

Fatima Zohra Benhalouche, M. S. Karoui, Lahsen Wahib Kebir, 2020, 2020 Mediterranean and Middle-East Geoscience and Remote Sensing Symposium (M2GARSS).

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

Y. Deville, O. Bern'e, G. Faury, 2020, Astronomy & Astrophysics.

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

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

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).

Blind quantum source separation and process tomography systems for time-varying coupling

Y. Deville, A. Deville, 2017, 2017 IEEE International Workshop of Electronics, Control, Measurement, Signals and their Application to Mechatronics (ECMSM).

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).

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).

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

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

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.

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.

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

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

Use and misuse of variances for quantum systems in pure or mixed states

Y. Deville, A. Deville, 2023, 2303.05827.

Linear–Quadratic Mixing Model for Reflectances in Urban Environments

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

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).

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.

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..

Analysis of the emission of very small dust particles from Spitzer spectro-imagery data using blind signal separation methods

Y. Deville, A. Abergel, O. Berné, 2007, astro-ph/0703072.

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).

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

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

Multi-sharpening hyperspectral remote sensing data by Multiplicative Joint-Criterion Linear-Quadratic Nonnegative Matrix Factorization

Fatima Zohra Benhalouche, M. S. Karoui, Y. Deville, 2017, 2017 IEEE International Workshop of Electronics, Control, Measurement, Signals and their Application to Mechatronics (ECMSM).

Hypersharpening by Joint-Criterion Nonnegative Matrix Factorization

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

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).

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

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

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).

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.

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 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).

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).

Spatial sparsity-based blind source separation method including non-negative matrix factorization for multispectral image unmixing

M. S. Karoui, Y. Deville, S. Hosseini, 2011, 2011 10th International Workshop on Electronics, Control, Measurement and Signals.

Separation of galaxy spectra measured with slitless spectroscopy

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

Clarifying the link between von Neumann and thermodynamic entropies

Y. Deville, A. Deville, 2012, The European Physical Journal H.

Modeling and Unsupervised Unmixing Based on Spectral Variability for Hyperspectral Oceanic Remote Sensing Data with Adjacency Effects

Fatima Zohra Benhalouche, M. S. Karoui, Y. Deville, 2023, Remote Sensing.

Blind Source Separation of Overdetermined Linear-Quadratic Mixtures

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

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..

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).

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.

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).

Blind Source Separation of Spectrally Filtered Geochemical Signals to Recognize Multi-depth Ore-Related Enrichment Patterns

S. H. Tabatabaei, Y. Deville, S. Hosseini, 2023, Mathematical Geosciences.

Blind Processing Methods for Quantum Channels: Identification, Equalization and Source Separation

Y. Deville, A. Deville, 2023, International Workshop on Machine Learning for Signal Processing.

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

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

Chapter 19 – Audio applications

Y. Deville, E. Vincent, E. Vincent, 2010 .

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 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).

Hyperspectral and multispectral data fusion based on linear-quadratic nonnegative matrix factorization

Moussa Sofiane Karoui, Abdelaziz Ouamri, Y. Deville, 2017 .

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

M. S. Karoui, Y. Deville, S. Hosseini, 2012, 2012 IEEE International Geoscience and Remote Sensing Symposium.

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).

Markovian blind separation of non-stationary temporally correlated sources

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

Predicting minimum uncertainties in the inversion of ocean color geophysical parameters based on Cramer-Rao bounds.

Y. Deville, B. Lafrance, M. Guillaume, 2018, Optics express.