Search results for: Yannick Deville

Items from 1 to 20 out of 83 results

article

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

Alain Deville, Yannick Deville

Quantum Information Processing > 2019 > 18 > 10 > 1-16

If a pure state of a qubit pair is developed over the four basis states, an equality between the four coefficients of that development, verified if and only if that state is unentangled, is already known. This paper considers an arbitrary pure state of an N-qubit system, developed over the $$2^{N}$$ 2N basis states. It is shown that the state is unentangled if and only if a well-chosen collection...

article

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

Sylvain Jay, Mireille Guillaume, Audrey Minghelli, Yannick Deville, more

Remote Sensing of Environment > 2017 > 200 > C > 352-367

Hyperspectral remote sensing is now an established tool to determine shallow water properties over large areas, usually by inverting a semi-analytical model of water reflectance. However, various sources of error may make the observed subsurface remote-sensing reflectance deviate from the model, resulting in an increased retrieval error when inverting the model based on classical least-squares fitting...

article

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

Yannick Deville, Alain Deville

Digital Signal Processing > 2017 > 67 > C > 30-51

Blind Quantum Source Separation (BQSS) deals with multi-qubit states, called “mixed states”, obtained by applying an unknown “mixing function” (which typically corresponds to undesired coupling, e.g. between qubits implemented as close electron spins 1/2) to unknown multi-qubit “source states”, which are product states (and pure in the simplest case, considered in this paper). Some other properties...

article

A second-order blind source separation method for bilinear mixtures

Lina Jarboui, Yannick Deville, Shahram Hosseini, Rima Guidara, more

Multidimensional Systems and Signal Processing > 2018 > 29 > 3 > 1153-1172

In this paper, we are interested in the problem of Blind Source Separation using a Second-order Statistics (SOS) method in order to separate autocorrelated and mutually independent sources mixed according to a bilinear (BL) model. In this context, we propose a new approach called Bilinear Second-order Blind Source Separation, which is an extension of linear SOS methods, devoted to separate sources...

chapter

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

Yannick Deville, Alain Deville

2017 IEEE International Workshop of Electronics, Control, Measurement, Signals and their Application to Mechatronics (ECMSM) > 1 - 6

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

Classical, i.e. non-quantum, blind source separation (BSS) methods estimate unknown source signals by using only mixed signals obtained by transferring these source signals through a mixing transform, which typically has unknown parameter values. We developed quantum versions of BSS (called BQSS) and we extended them to the blind estimation of the unknown mixing/coupling parameters, thus achieving...

chapter

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

Fatima Zohra Benhalouche, Moussa Sofiane Karoui, Yannick Deville, Issam Boukerch, more

2017 IEEE International Workshop of Electronics, Control, Measurement, Signals and their Application to Mechatronics (ECMSM) > 1 - 6

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

Multi-sharpening consists in fusing a multispectral image with a hyperspectral one, to produce an unobservable image with the high spatial resolution of the former and the high spectral resolution of the latter. In this paper, a new fusion method, based on the spectral unmixing concept, is proposed. The proposed method, related to linear-quadratic spectral unmixing techniques, and based on linear-quadratic...

article

Hypersharpening by Joint-Criterion Nonnegative Matrix Factorization

Moussa Sofiane Karoui, Yannick Deville, Fatima Zohra Benhalouche, Issam Boukerch

IEEE Transactions on Geoscience and Remote Sensing > 2017 > 55 > 3 > 1660 - 1670

Hypersharpening aims at combining an observable low-spatial resolution hyperspectral image with a high-spatial resolution remote sensing image, in particular a multispectral one, to generate an unobservable image with the high spectral resolution of the former and the high spatial resolution of the latter. In this paper, two such new fusion methods are proposed. These methods, related to linear spectral...

chapter

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

Moussa Sofiane Karoui, Shahram Hosseini, Yannick Deville, Abdelaziz Ouamri, more

2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) > 1572 - 1576

2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

In this paper, a new approach is proposed for linear endmember spectra extraction from a highly mixed hyperspectral image combined with high spatial resolution multispectral data containing pure pixels. This new approach, which is applied to unmix the considered hyperspectral image, is based on a modified version of nonnegative matrix factorization (NMF) coupled with nonnegative least squares (NLS)...

chapter

Blind Separation of Nonstationary Sources by Spectral Decorrelation

Shahram Hosseini, Yannick Deville

Lecture Notes in Computer Science > Independent Component Analysis and Blind Signal Separation > Linear Mixture Models > 279-286

This paper demonstrates and exploits some interesting frequency-domain properties of nonstationary signals. Considering these properties, two new methods for blind separation of linear instantaneous mixtures of mutually uncorrelated, nonstationary sources are proposed. These methods are based on spectral decorrelation of the sources. The second method is particularly important because it allows the...

chapter

Blind Separation of Non-stationary Images Using Markov Models

Rima Guidara, Shahram Hosseini, Yannick Deville

Lecture Notes in Computer Science > Independent Component Analysis and Signal Separation > Miscellaneous > 722-729

In recent works, we presented a blind image separation method based on a maximum likelihood approach, where we supposed the sources to be stationary, spatially autocorrelated and following Markov models. To make this method more adapted to real-world images, we here propose to extend it to non-stationary image separation. Two approaches, respectively based on blocking and kernel smoothing, are then...

chapter

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

Fatima Zohra Benhalouche, Yannick Deville, Moussa Sofiane Karoui, Abdelaziz Ouamri

2016 IEEE 26th International Workshop on Machine Learning for Signal Processing (MLSP) > 1 - 6

2016 IEEE 26th International Workshop on Machine Learning for Signal Processing (MLSP)

In this paper, a new projected-gradient method for linear-quadratic matrix factorization is proposed for extracting hyperspectral endmember spectra. The proposed method is designed for a linear-quadratic mixing model involved in urban hyperspectral remote sensing images. The reduction of the number of considered variables, when optimizing the used cost function, constitutes the main originality of...

chapter

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

Ahmed Selloum, Shahram Hosseini, Yannick Deville, Thierry Contini

2016 IEEE 26th International Workshop on Machine Learning for Signal Processing (MLSP) > 1 - 6

2016 IEEE 26th International Workshop on Machine Learning for Signal Processing (MLSP)

We first derive a mathematical model describing data generated by the spectrograph of the future Euclid space mission. Our model takes into account the special mixing effect resulting from slitless spectroscopy. We then propose two new blind source separation methods, exploiting the non-negativity of data involved in these mixtures and adapted to this special model, to separate the spectra from the...

chapter

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

Shahram Hosseini, Yannick Deville, Leonardo T. Duarte, Ahmed Selloum

2016 IEEE 26th International Workshop on Machine Learning for Signal Processing (MLSP) > 1 - 6

2016 IEEE 26th International Workshop on Machine Learning for Signal Processing (MLSP)

This paper proposes a new constrained method, based on nonnegative matrix factorization, for blindly separating linear-quadratic (LQ) mixtures of mutually uncorrelated source signals when the sources and mixing parameters are all nonnegative. The uncorrelatedness of the sources is used as a regularization term in the cost function. The main advantage of exploiting uncorrelatedness in this manner is...

chapter

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

Yves-Marie Batany, Daniela Donno, Leonardo Tomazeli Duarte, Herve Chauris, more

2016 24th European Signal Processing Conference (EUSIPCO) > 1628 - 1632

2016 24th European Signal Processing Conference (EUSIPCO)

This paper addresses sparse component analysis, a powerful framework for blind source separation and extraction that is built upon the assumption that the sources of interest are sparse in a known domain. We propose and discuss a necessary and sufficient condition under which the ℓ₀ pseudo-norm can be used as a contrast function in the blind source extraction problem in both instantaneous and convolutive...

chapter

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

Yannick Deville, Alain Deville

2016 24th European Signal Processing Conference (EUSIPCO) > 503 - 507

2016 24th European Signal Processing Conference (EUSIPCO)

Our recent investigations of blind quantum source separation and process tomography methods for Heisenberg-coupled quantum bits (qubits) were focused on introducing a new separation principle, based on output disentanglement. We here extend them by proposing a more advanced implementation of their cost function and optimization algorithm. This leads us to move from a feedback to a feedforward adapting...

chapter

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

Charlotte Revel, Yannick Deville, Veronique Achard, Xavier Briottet

2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS) > 1 - 5

2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS)

In hyperspectral imagery, unmixing methods are often used to analyse the composition of the pixels. Such methods usually suppose that a unique spectral signature, called an endmember, can be associated with each pure material present in the scene. This assumption is no more valid for materials that exhibit spectral variability due to illumination conditions, weathering, slight variations of the composition,...

chapter

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

Fatima Zohra Benhalouche, Yannick Deville, Moussa Sofiane Karoui, Abdelaziz Ouamri

2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) > 6565 - 6568

IGARSS 2016 - 2016 IEEE International Geoscience and Remote Sensing Symposium

In this paper, a new projected-gradient method for bilinear matrix factorization with nonnegativity constraints is proposed for extracting hyperspectral endmember spectra. The proposed method is designed for a bilinear mixing model faced in urban hyperspectral remote sensing images. Experiments based on realistic synthetic data, generated according to the considered bilinear mixing model, are conducted...

chapter

A time-frequency blind source separation method based on segmented coherence function

Benoit Albouy, Yannick Deville

Lecture Notes in Computer Science > Artificial Neural Nets Problem Solving Methods > Sources Separation > 289-296

In this paper, we introduce a new blind source separation (BSS) method for linear instantaneous mixtures, which only assumes the sources to be uncorrelated. It is based on the time-segmented frequency- dependent real coherence function of the observed signals. This parameter makes it possible to detect time-frequency zones where only one source is active. Such zones are then used to identify the required...

chapter

Blind separation of linear-quadratic mixtures of real sources using a recurrent structure

Shahram Hosseini, Yannick Deville

Lecture Notes in Computer Science > Artificial Neural Nets Problem Solving Methods > Sources Separation > 241-248

In this paper, we propose an approach for separating linearquadratic mixtures of independent real sources. The method is based on parametric identification of a recurrent separating structure by means of an adaptive algorithm which uses the higher-order statistics of the outputs of this structure. We study the local stability of the recurrent structure and show experimentally that when it is stable...

chapter

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

Yannick Deville, Shahram Hosseini

Lecture Notes in Computer Science > Latent Variable Analysis and Signal Separation > Theory > 237-244

Retrieving unknown sources from nonlinear mixtures of them requires one to define a separating structure, before proceeding to methods for estimating mixing or separating parameters in blind configurations. Recurrent neural networks are attractive separating structures for a wide range of nonlinear mixing models. In a previous paper, we proposed such a network for the non-blind version of linear-quadratic...

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Keywords

BLIND SOURCE SEPARATION (26)
HYPERSPECTRAL IMAGING (16)
INDEPENDENT COMPONENT ANALYSIS (10)
MATHEMATICAL MODEL (9)
CORRELATION (8)
SOURCE SEPARATION (8)
INDEXES (7)
COST FUNCTION (6)
DATA MODELS (6)
SPARSE COMPONENT ANALYSIS (6)
ADAPTATION MODELS (5)
MATERIALS (5)
SPATIAL RESOLUTION (5)
DATA FUSION (4)
ENDMEMBER SPECTRA EXTRACTION (4)
ESTIMATION (4)
EUROPE (4)
HYPERSPECTRAL UNMIXING (4)
MATRIX DECOMPOSITION (4)
NMF (4)
NONLINEAR MIXTURE (4)
QUANTUM BIT (QUBIT) (4)
SENSORS (4)
SIGNAL PROCESSING ALGORITHMS (4)
SPECTRAL UNMIXING (4)
VECTORS (4)
COUPLINGS (3)
ENTANGLEMENT (3)
HYPERSPECTRAL IMAGES (3)
IMAGE RECONSTRUCTION (3)
LINEAR-QUADRATIC MIXING MODEL (3)
LINEAR-QUADRATIC MIXTURE (3)
MAXIMUM LIKELIHOOD (3)
MULTI-SHARPENING (3)
NOISE (3)
NON-NEGATIVE MATRIX FACTORIZATION (NMF) (3)
OPTIMIZATION (3)
SEMI-BLIND SOURCE SEPARATION (3)
SILICON (3)
SPARSE MATRICES (3)
SPARSITY (3)
STANDARDS (3)
BILINEAR MATRIX FACTORIZATION (2)
BLIND SOURCE SEPARATION (BSS) (2)
CLUSTERING (2)
COMPUTATIONAL EFFICIENCY (2)
CONVERGENCE (2)
CONVOLUTIVE MIXTURE (2)
COVARIANCE MATRICES (2)
CYLINDRICAL-SYMMETRY HEISENBERG COUPLING (2)
DATA MINING (2)
EIGENVALUES AND EIGENFUNCTIONS (2)
ELECTRONIC MAIL (2)
EQUATIONS (2)
ESTIMATION ERROR (2)
EUCLID MISSION (2)
HYPER/MULTISPECTRAL IMAGERY (2)
HYPER/MULTISPECTRAL IMAGING (2)
HYPERSHARPENING (2)
HYPERSPECTRAL IMAGE (2)
ICA (2)
IEEE XPLORE (2)
INSTRUMENTS (2)
INTRA-CLASS VARIABILITY (2)
LINEAR-QUADRATIC NONNEGATIVE MATRIX FACTORIZATION (2)
LINEAR-QUADRATIC SPECTRAL UNMIXING (2)
MULTISPECTRAL SPATIAL UNMIXING (2)
NOISE MEASUREMENT (2)
NON-NEGATIVE MATRIX FACTORIZATION (2)
NON-STATIONARITY (2)
NON-STATIONARY SIGNALS (2)
NONNEGATIVE MATRIX FACTORIZATION (2)
PARTIAL SOURCE SEPARATION (2)
PIXEL (2)
PIXEL-BY-PIXEL NONNEGATIVE MATRIX FACTORISATION (NMF) (2)
POLYNOMIALS (2)
PORTABLE DOCUMENT FORMAT (2)
PROJECTED GRADIENT (2)
REAL DATA SET (2)
SHAPE (2)
SHORT-TIME FOURIER TRANSFORM (2)
SLITLESS SPECTROSCOPY (2)
SPECTRUM DECONTAMINATION (2)
SPEECH (2)
STABILITY ANALYSIS (2)
TIME-DOMAIN ANALYSIS (2)
TIME-FREQUENCY ANALYSIS (2)
TIME–FREQUENCY ANALYSIS (2)
TOMOGRAPHY (2)
UNDERDETERMINED MIXTURES (2)
UNDERDETERMINED OR NOISY MIXTURE (2)
URBAN AREAS (2)
URBAN IMAGES (2)
ℓ<INF>0</INF> PSEUDO-NORM (1)
ABSTRACTS (1)
ADAPTIVE QUANTUM INFORMATION PROCESSING (1)
ALGORITHM DESIGN AND ANALYSIS (1)
ANALYTICAL MODELS (1)
ARTIFICIAL NEURAL NETWORKS (1)
ASTRONOMY (1)
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Springer (22)
Elsevier (16)

INFONA - science communication portal

Search results for: Yannick Deville

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

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

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

A second-order blind source separation method for bilinear mixtures

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

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

Hypersharpening by Joint-Criterion Nonnegative Matrix Factorization

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

Blind Separation of Nonstationary Sources by Spectral Decorrelation

Blind Separation of Non-stationary Images Using Markov Models

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

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

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

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

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

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

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

A time-frequency blind source separation method based on segmented coherence function

Blind separation of linear-quadratic mixtures of real sources using a recurrent structure

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

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