WorldCat Identities

Deville, Yannick 1964-

Overview
Works: 44 works in 75 publications in 2 languages and 524 library holdings
Genres: Conference papers and proceedings 
Roles: Editor, Author, Thesis advisor, htt, Other, Opponent
Publication Timeline
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Most widely held works by Yannick Deville
Latent variable analysis and signal separation : 14th International Conference, LVA/ICA 2018, Guildford, UK, July 2-5, 2018, proceedings by LVA/ICA( )

12 editions published in 2018 in English and held by 244 WorldCat member libraries worldwide

This book constitutes the proceedings of the 14th International Conference on Latent Variable Analysis and Signal Separation, LVA/ICA 2018, held in Guildford, UK, in July 2018. The 52 full papers were carefully reviewed and selected from 62 initial submissions. As research topics the papers encompass a wide range of general mixtures of latent variables models but also theories and tools drawn from a great variety of disciplines such as structured tensor decompositions and applications; matrix and tensor factorizations; ICA methods; nonlinear mixtures; audio data and methods; signal separation evaluation campaign; deep learning and data-driven methods; advances in phase retrieval and applications; sparsity-related methods; and biomedical data and methods
NONLINEAR BLIND SOURCE SEPARATION AND BLIND MIXTURE IDENTIFICATION by Yannick Deville( )

4 editions published in 2021 in English and held by 107 WorldCat member libraries worldwide

This book provides a detailed survey of the methods that were recently developed to handle advanced versions of the blind source separation problem, which involve several types of nonlinear mixtures. Another attractive feature of the book is that it is based on a coherent framework. More precisely, the authors first present a general procedure for developing blind source separation methods. Then, all reported methods are defined with respect to this procedure. This allows the reader not only to more easily follow the description of each method but also to see how these methods relate to one another. The coherence of this book also results from the fact that the same notations are used throughout the chapters for the quantities (source signals and so on) that are used in various methods. Finally, among the quite varied types of processing methods that are presented in this book, a significant part of this description is dedicated to methods based on artificial neural networks, especially recurrent ones, which are currently of high interest to the data analysis and machine learning community in general, beyond the more specific signal processing and blind source separation communities. Presents advanced configurations of the blind source separation problem, involving bilinear, linear-quadratic and polynomial mixing models; Provides a detailed and coherent description of the methods reported in the literature for handling these types of mixing phenomena; Focuses on complex configurations involving nonlinear mixing transforms
Signaux temporels et spatiotemporels : analyse des signaux, théorie de l'information, traitement d'antenne, séparation aveugle de sources by Yannick Deville( Book )

1 edition published in 2011 in French and held by 83 WorldCat member libraries worldwide

Nonlinear Blind Source Separation and Blind Mixture Identification : Methods for Bilinear, Linear-quadratic and Polynomial Mixtures by Yannick Deville( )

1 edition published in 2021 in English and held by 17 WorldCat member libraries worldwide

Algorithmes temporels rapides à point fixe pour la séparation aveugle de mélanges convolutifs et/ou sous-déterminés by Johan Thomas( Book )

2 editions published in 2007 in French and held by 3 WorldCat member libraries worldwide

La première partie de cette thèse est consacrée aux mélanges convolutifs (sur-)déterminés. Nous cherchons à étendre l'algorithme FastICA aux mélanges convolutifs en proposant un algorithme à point fixe opérant dans le domaine temporel. Nous introduisons un processus de blanchiment spatio-temporel non-causal, qui, en initialisant les paramètres d'extraction d'une façon particulière, permet d'utiliser des itérations d'optimisation de type point fixe. L'estimation des contributions dans les observations est réalisée grâce à un critère quadratique optimisé par un filtre de Wiener non-causal. Dans la deuxième partie, consacrée aux mélanges sous-déterminés instantanés et convolutifs, nous cherchons aussi à étendre l'algorithme FastICA en nous basant sur le concept de séparation différentielle. Nous proposons des procédures de blanchiment différentiel des observations qui permettent l'emploi d'itérations à point fixe pour séparer entre elles des sources dites utiles. En convolutif, les contributions sont estimées au moyen d'un filtre de Wiener différentiel non-causal. La troisième partie, consacrée aux mélanges instantanés de sources contenant un grand nombre d'échantillons, met d'abord en évidence une limitation de l'algorithme FastICA, qui, à chaque itération d'optimisation, calcule des statistiques sur l'ensemble des échantillons disponibles. Le critère du kurtosis étant polynômial relativement aux coefficients d'extraction, nous proposons une procédure d'identification de ce polynôme qui permet d'effectuer l'optimisation dans un espace de calcul moins gourmand. Ce principe est ensuite appliqué aux mélanges instantanés sous-déterminés
Méthodes de séparation aveugle de sources fondées sur des transformées temps-fréquence : application à des signaux de parole by Matthieu Puigt( Book )

2 editions published in 2007 in French and held by 3 WorldCat member libraries worldwide

Several time-frequency (TF) blind source separation (BSS) methods have been proposed in this thesis. In the systems output that have been used, a contribution of each source is estimated, using only mixed signals. All the methods proposed in this manuscript find tiny TF zones where only one source is active and estimate the mixing parameters in these zones. These approaches are particularly well suited for non-stationary sources (speech, music). We first studied and improved linear instantaneous methods based on variance or correlation criteria, that have been previously proposed by our team. They yield excellent performance for signals of speech and can also separate spectra from astrophysical data. However, the nature of the mixtures that they can process limits their application fields. We have extended these approaches to more realistic mixtures. The first extensions consider attenuated and delayed mixtures of sources, which corresponds to mixtures in anechoic chamber. They require less restrictive sparsity assumptions than some approaches previously proposed in the literature, while addressing the same type of mixtures. We have studied the contribution of clustering techniques to our approaches and have achieved good performance for mixtures of speech signals. Lastly, a theoretical extension of these methods to general convolutive mixtures is described. It needs strong sparsity hypotheses and we have to solve classical indeterminacies of frequency-domain BSS methods
Evolution des très petites particules de poussière dans le cycle cosmique de la matière : méthodes de séparation aveugle de sources et spectro-imagerie avec le télescope spatial Spitzer by Olivier Berné( Book )

2 editions published in 2008 in English and held by 3 WorldCat member libraries worldwide

Les très petites particules de poussière carbonée dominent l'émission dans l'infrarouge (IR) moyen d'un grand nombre d'objets astrophysiques de notre galaxie, et celui des galaxies extérieures. Il est admis que les bandes observées dans ces spectres sont dues à l'émission d'hydrocarbures polycycliques aromatiques (PAH) ou de populations proches chimiquement des PAH. Cependant l'origine des variations de la forme de ces spectres suivant l'environnement considéré reste inexpliquée. Afin de progresser sur cette question, nous avons appliqué des algorithmes de séparation aveugle de sources aux données de spectro-imagerie de nébuleuses par réflexion obtenues avec le télescope spatial Spitzer de la NASA. Cette analyse nous a permis de mettre en évidence l'évolution physico-chimique des très petites particules de poussière : les macromolécules de type PAH sont produites par évaporation de très petits grains sous l'effet du champ ultraviolet et ensuites ionisées. Cette évolution, intimement liée aux conditions physiques du milieu, est à l'origine des variations spectrales observées dans ces nébuleuses. Ces résultats nous ont permis d'obtenir une base de spectres pour l'analyse de l'émission des très petites particules de poussière dans le cycle cosmique de la matière. Nous avons montré qu'il est possible d'expliquer la forme du spectre infrarouge moyen observé dans les nébuleuses planétaires et les disques protoplanétaires en la reliant aux conditions physiques qui règnent dans ces objets. L'étude de ces environnements circumstellaires nous a également permis de mettre en évidence de nouvelles populations de très petites particules
Contrôlabilité des alliages inter-métalliques Titane-Aluminium by Arnaud Talon( Book )

2 editions published in 2005 in French and held by 3 WorldCat member libraries worldwide

Le travail présenté concerne la contrôlabilité des aluminures de titane TiAl élaborés par frittage. Cette étude porte sur la recherche de moyens de contrôle non destructifs (ultrasons, radiographie et tomographie X, ressuage) adaptés ou offrant de bonnes perspectives d'amélioration par des méthodes de traitement du signal. Des échantillons ont été réalisés avec des manques de matière mais aussi avec des inclusions réelles de différentes natures et formes. On a pu ainsi évaluer les avantages et inconvénients des méthodes proposées en fonction des différents défauts. Enfin une étude de l'influence des inclusions sur les propriétés mécaniques a été réalisée. Des méthodes de traitement du signal ont été appliquées pour améliorer la détectabilité des défauts par contrôle ultrasonore. Nous avons mis en œuvre des méthodes telles que l'analyse cepstrale, la transformation de Hilbert, des méthodes à haute résolution ainsi que des techniques de débruitage par la transformée en ondelettes
Endmember Variability in hyperspectral image unmixing by Lucas Drumetz( )

2 editions published in 2016 in English and held by 3 WorldCat member libraries worldwide

The fine spectral resolution of hyperspectral remote sensing images allows an accurate analysis of the imaged scene, but due to their limited spatial resolution, a pixel acquired by the sensor is often a mixture of the contributions of several materials. Spectral unmixing aims at estimating the spectra of the pure materials (called endmembers) in the scene, and their abundances in each pixel. The endmembers are usually assumed to be perfectly represented by a single spectrum, which is wrong in practice since each material exhibits a significant intra-class variability. This thesis aims at designing unmixing algorithms to better handle this phenomenon. First, we perform the unmixing locally in well chosen regions of the image where variability effects are less important, and automatically discard wrongly estimated local endmembers using collaborative sparsity. In another approach, we refine the abundance estimation of the materials by taking into account the group structure of an image-derived endmember dictionary. Second, we introduce an extended linear mixing model, based on physical considerations, modeling spectral variability in the form of scaling factors, and develop optimization algorithms to estimate its parameters. This model provides easily interpretable results and outperforms other state-of-the-art approaches. We finally investigate two applications of this model to confirm its relevance
Méthodes de séparation aveugle de sources non linéaires, étude du modèle quadratique 2X2 by Chahinez Chaouchi( Book )

2 editions published in 2011 in French and held by 3 WorldCat member libraries worldwide

This thesis presents blind source separation (BSS) methods for a particular model of mixture, the quadratic one. The first part presents the separating structure (basic and extended versions).The equilibrium points of the structure and their local stability are then studied. We propose two methods of BSS. The first method uses the cumulants and the second is based on a maximum likelihood approach. We validate our results by numerical tests
Méthodes de séparation aveugle de sources pour l'imagerie hyperspectrale : application à la télédétection urbaine et à l'astrophysique by Inès Meganem( Book )

2 editions published in 2012 in French and held by 3 WorldCat member libraries worldwide

In this work, we developed Blind Source Separation methods (BSS) for hyperspectral images, concerning two applications : urban remote sensing and astrophysics. The first part of this work concerned spectral unmixing for urban images, with the aim of finding, by an unsupervised method, the materials present in the scene, by extracting their spectra and their proportions. Most existing methods rely on a linear model, which is not valid in urban environments because of 3D structures. Therefore, the first step was to derive a mixing model adapted to urban environments, starting from physical equations based on radiative transfer theory. The derived linear-quadratic model, and possible hypotheses on the mixing coefficients, are justified by results obtained with simulated realistic images. We then proposed, for the unmixing, BSS methods based on NMF (Non-negative Matrix Factorization). These methods are based on gradient computation taking into account the quadratic terms.The first method uses a gradient descent algorithm with a constant step, from which we then derived a Newton version. The last proposed method is a multiplicative NMF algorithm. These methods give better performance than a linear method from the literature. Concerning astrophysics, we developed BSS methods for dense field images of the MUSE instrument. Due to the PSF (Point Spread Function) effect, information contained in the pixels can result from contributions of many stars. Hence, there is a need for BSS, to extract from these signals that are mixtures, the star spectra which are our "sources". The mixing model is linear but spectrally non-invariant. We proposed a BSS method based on positivity. This approach uses the parametric model of MUSE FSF (Field Spread Function). The implemented method is iterative and alternates spectra estimation using least squares (with positivity constraint) and FSF parameter estimation by a projected gradient descent algorithm. The proposed method yields good performance with simulated MUSE images
Méthodes markoviennes pour la séparation aveugle de signaux et images by Rima Guidara( Book )

2 editions published in 2009 in French and held by 3 WorldCat member libraries worldwide

This thesis presents new Markovian methods for blind separation of instantaneous linear mixtures of one-dimensional signals and images. In the first part, we propose several improvements to an existent method for separating temporal signals. The new method exploits simultaneously non-Gaussianity, autocorrelation and non-stationarity of the sources. Excellent performance is obtained for the separation of artificial mixtures of speech signals, and we succeed to separate real mixtures of astrophysical spectra. An extension to image separation is then proposed. The dependence within the image pixels is modelled by non-symetrical half-plane Markov random fields. Very good performance is obtained for the separation of artificial mixtures of natural images and noiseless observations of the Planck satellite. The results obtained with a low level noise are acceptable
Méthodes de séparation aveugle de sources pour le démélange d'images de télédétection by Djaouad Benachir( Book )

2 editions published between 2014 and 2015 in French and held by 2 WorldCat member libraries worldwide

Within this thesis, we propose new blind source separation (BSS) methods intended for instantaneous linear mixtures, aimed at remote sensing applications. The first contribution is based on the combination of two broad classes of BSS methods : Independent Component Analysis (ICA), and Non-negative Matrix Factorization (NMF). We show how the physical constraints of our problem can be used to eliminate some of the indeterminacies related to ICA and provide a first approximation of endmembers spectra and associated sources. These approximations are then used to initialize an NMF algorithm with the goal of improving them. The results we reached are satisfactory as compared with the classical methods used in our undertaken tests. The second proposed method is based on sparsity as well as on geometrical properties. We begin by highlighting some properties facilitating the presentation of the hypotheses considered 153 in the method. We then provide the broad lines of this approach which is based on the determination of the two-source zones that are contained in a remote sensing image, with the help of a correlation criterion. From the intersections of the lines generated by these two-source zones, we detail how to obtain the columns of the mixing matrix and the sought sources. The obtained results are quite attractive as compared with those reached by several methods from literature
Méthodes de séparation aveugle de sources applicables à des signaux de parole by Benoît Albouy( Book )

2 editions published in 2004 in French and held by 2 WorldCat member libraries worldwide

Différentes méthodes de séparation aveugle de sources (SAS) ont été proposées au cours de cette thèse, afin d'extraire en sortie des structures utilisées, une contribution de chacune des sources à l'aide uniquement des mélanges reçus. Nous avons tout d'abord développé deux approches de SAS à segmentation temporelle, avec des structures de séparation symétrique et asymétrique, afin de résoudre le problème de deux mélanges convolutifs de deux sources présentant des silences. Ces méthodes nécessitent une identification de filtres de séparation, réalisée à l'aide de rapports de densité spectrale de puissance, et une détection de zones temporelles mono-sources. Ces approches fournissent alors de bonnes performances lors de tests de cocktail party, de rehaussement de la parole et de reconnaissance automatique de la parole. Afin de réduire l'hypothèse précédente de parcimonie des sources, des approches à segmentation temps-fréquence (TF) ont ensuite été proposées pour N mélanges linéaires instantanés puis pour N mélanges à atténuations et retards. La détection de quelques zones TF mono-sources permet alors d'obtenir des performances très intéressantes pour des signaux non-stationnaires comme ceux de parole
N-qubit system in a pure state: a necessary and sufficient condition for unentanglement by Alain Deville( )

1 edition published in 2019 in English and held by 2 WorldCat member libraries worldwide

Méthodes de démélange et de fusion des images multispectrales et hyperspectrales de télédétection spatiale by Fatima Zohra Benhalouche( Book )

2 editions published in 2018 in French and held by 2 WorldCat member libraries worldwide

In this thesis, we focused on two main problems of the spatial remote sensing of urban environments which are: "spectral unmixing" and "fusion". In the first part of the thesis, we are interested in the spectral unmixing of hyperspectral images of urban scenes. The developed methods are designed to unsupervisely extract the spectra of materials contained in an imaged scene. Most often, spectral unmixing methods (methods known as blind source separation) are based on the linear mixing model. However, when facing non-flat landscape, as in the case of urban areas, the linear mixing model is not valid any more, and must be replaced by a nonlinear mixing model. This nonlinear model can be reduced to a linear-quadratic/bilinear mixing model. The proposed spectral unmixing methods are based on matrix factorization with non-negativity constraint, and are designed for urban scenes. The proposed methods generally give better performance than the tested literature methods. The second part of this thesis is devoted to the implementation of methods that allow the fusion of multispectral and hyperspectral images, in order to improve the spatial resolution of the hyperspectral image. This fusion consists in combining the high spatial resolution of multispectral images and high spectral resolution of hyperspectral images. The implemented methods are designed for urban remote sensing data. These methods are based on linear-quadratic spectral unmixing techniques and use the non-negative matrix factorization. The obtained results show that the developed methods give good performance for hyperspectral and multispectral data fusion. They also show that these methods significantly outperform the tested literature approaches
Méthodes de traitement du signal pour l'analyse quantitative de gaz respiratoires à partir d'un unique capteur MOX by Stéphanie Madrolle( )

1 edition published in 2018 in French and held by 2 WorldCat member libraries worldwide

Prélevés de manière non invasive, les gaz respiratoires sont constitués de nombreux composés organiques volatils (VOCs) dont la quantité dépend de l'état de santé du sujet. L'analyse quantitative de l'air expiré présente alors un fort intérêt médical, que ce soit pour le diagnostic ou le suivi de traitement. Dans le cadre de ma thèse, nous proposons d'étudier un dispositif d'analyse des gaz respiratoires, et notamment de ces VOCs. Cette thèse multidisciplinaire aborde différents aspects, tels que le choix des capteurs, du matériel et des modes d'acquisition, l'acquisition des données à l'aide d'un banc gaz, et ensuite le traitement des signaux obtenus de manière à quantifier un mélange de gaz. Nous étudions la réponse d'un capteur à oxyde métallique (MOX) à des mélanges de deux gaz (acétone et éthanol) dilués dans de l'air synthétique (oxygène et azote). Ensuite, nous utilisons des méthodes de séparation de sources de manière à distinguer les deux gaz, et déterminer leur concentration. Pour donner des résultats satisfaisants, ces méthodes nécessitent d'utiliser plusieurs capteurs dont on connait la forme mathématique du modèle décrivant l'interaction du mélange avec le capteur, et qui présentent une diversité suffisante dans les mesures d'étalonnage pour estimer les coefficients de ce modèle. Dans cette thèse, nous montrons que les capteurs MOX peuvent être décrits par un modèle de mélange linéaire quadratique, et qu'un mode d'acquisition fonctionnant en double température permet de générer deux capteurs virtuels à partir d'un unique capteur physique. Pour quantifier précisément les composants du mélange à partir des mesures sur ces capteurs (virtuels), nous avons conçu des méthodes de séparation de sources, supervisées et non supervisées appliquées à ce modèle non-linéaire : l'analyse en composantes indépendantes, des méthodes de moindres carrés (algorithme de Levenberg-Marquardt), et une méthode bayésienne ont été étudiées. Les résultats expérimentaux montrent que ces méthodes permettent d'estimer les concentrations de VOCs contenus dans un mélange de gaz, de façon précise, en ne nécessitant que très peu de points de calibration
Séparation de sources en ligne dans des environnements réverbérants en exploitant la localisation des sources by Jack Harris( )

1 edition published in 2015 in French and held by 2 WorldCat member libraries worldwide

Methods for improving the real-time performance and speed of various source enhancement and separation are considered. Two themes of research are considered so far: a method which relies only on second order statistics to enhance a target source exploiting video cues. Secondly, a higher-order statistics method, independent vector analysis is implemented in real-time on a digital signal processor, where an alternative source prior has been used performance is shown to have improved
Méthodes de séparation aveugle de sources et applications : des statistiques d'ordre supérieur à l'analyse temps-fréquence by Frédéric Abrard( Book )

2 editions published in 2003 in French and held by 2 WorldCat member libraries worldwide

Apport de la prise en compte de la variabilité intra-classe dans les méthodes de démélange hyperspectral pour l'imagerie urbaine by Charlotte Revel( Book )

2 editions published in 2016 in French and held by 2 WorldCat member libraries worldwide

This work is devoted to unmixing for urban areas. We particularly focused on the impact of intra-class variability on unmixing. We first described the results of a study highlighting intra-class variability assessed in real images. It appeared that this phenomenon was significant and had to be included in the mixing models. Based on the state of the art we developed 2 new mixing models dealing with intra-class variability. The first one is a linear one. The second one is a linear-quadratic one which allows to consider multiple scattering effects on buildings. First only the linear mixing model was considered. Currently it does not exist any unmixing method able to deal with this new model. So two methods were developed, UP-NMF and IP-NMF. UP-NMF is a new unmixing method based on an extension of the standard NMF. To overcome UP-NMF limitations an extended method is proposed, IP-NMF, which limit the spreading of each class by adding an inertia constraint in the cost function. These methods were firstly tested on a semi-synthetic data set. These tests allowed us to study the impact of the initialisation on our methods performance and also to fix the inertia parameter. We also compared the results of UP-NMF and IP-NMF to the results obtained with standard methods. The second tests were performed on an image taken above Toulouse. It appeared that IP-NMF is less sensitive to an error in the estimation of classes number than standard methods. Finally we developed a linear-quadratic method, LQIP-NMF, dealing with the non-linear mixing model previously described. In cases of high intra-class variability, the quadratic terms are drowned in the large variability of materials. So it seems that it is not relevant to taking into account these non-linearities
 
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French (25)

English (22)