1-bit analog-to-digital conversion is very attractive for low-complexity communications receivers. A major drawback is, however, the small spectral efficiency when sampling at symbol rate. This can be improved through oversampling by exploiting the signal distortion caused by the transmission channel. This paper analyzes the achievable data rate of band-limited communications channels that are subject to additive noise and inter-symbol-interference with 1-bit quantization and oversampling at the receiver. It is shown that not only the channel noise but also the inter-symbol-interference can be exploited to benefit from oversampling.

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L’objectif de cette thèse est de proposer des solutions et améliorations de performance à certains problèmes de sélection des paramètres acoustiques pertinents dans le cadre de la reconnaissance de la parole. Ainsi, notre première contribution consiste à proposer une nouvelle méthode de sélection de paramètres pertinents fondée sur un développement exact de la redondance entre une caractéristique et les caractéristiques précédemment sélectionnées par un algorithme de recherche séquentielle ascendante. Le problème de l’estimation des densités de probabilités d’ordre supérieur est résolu par la troncature du développement théorique de cette redondance à des ordres acceptables. En outre, nous avons proposé un critère d’arrêt qui permet de fixer le nombre de caractéristiques sélectionnées en fonction de l’information mutuelle approximée à l’itération j de l’algorithme de recherche. Cependant l’estimation de l’information mutuelle est difficile puisque sa définition dépend des densités de probabilités des variables (paramètres) dans lesquelles le type de ces distributions est inconnu et leurs estimations sont effectuées sur un ensemble d’échantillons finis. Une approche pour l’estimation de ces distributions est basée sur la méthode de l’histogramme. Cette méthode exige un bon choix du nombre de bins (cellules de l’histogramme). Ainsi, on a proposé également une nouvelle formule de calcul du nombre de bins permettant de minimiser le biais de l’estimateur de l’entropie et de l’information mutuelle. Ce nouvel estimateur a été validé sur des données simulées et des données de parole. Plus particulièrement cet estimateur a été appliqué dans la sélection des paramètres MFCC statiques et dynamiques les plus pertinents pour une tâche de reconnaissance des mots connectés de la base Aurora2. / The objective of this thesis is to propose solutions and performance improvements to certain problems of relevant acoustic features selection in the framework of the speech recognition. Thus, our first contribution consists in proposing a new method of relevant feature selection based on an exact development of the redundancy between a feature and the feature previously selected using Forward search algorithm. The estimation problem of the higher order probability densities is solved by the truncation of the theoretical development of this redundancy up to acceptable orders. Moreover, we proposed a stopping criterion which allows fixing the number of features selected according to the mutual information approximated at the iteration J of the search algorithm. However, the mutual information estimation is difficult since its definition depends on the probability densities of the variables (features) in which the type of these distributions is unknown and their estimates are carried out on a finite sample set. An approach for the estimate of these distributions is based on the histogram method. This method requires a good choice of the bin number (cells of the histogram). Thus, we also proposed a new formula of computation of bin number that allows minimizing the estimator bias of the entropy and mutual information. This new estimator was validated on simulated data and speech data. More particularly, this estimator was applied in the selection of the static and dynamic MFCC parameters that were the most relevant for a recognition task of the connected words of the Aurora2 base.

The aim of the present study has been to investigate how titles of social rank used in Tudor and Stuart England are modified by attributive adjectives in pre-adjacent position and the implications that become possible to observe. Using the Corpus of Early English Correspondence Sampler (CEECS) the present work set out to examine adjectival modification, gender perspectives and MI (Mutual Information) scores in order to gain a deeper understanding of how and why titles were modified in certain ways. The titles under scrutiny are Lord, Lady, Sir, Dame, Madam, Master and Mistress and these have been analysed following theories and frameworks pertaining to the scientific discipline of sociohistorical linguistics.    The findings of the present study suggest that male titles were modified more frequently than, and differently from, female titles. The adjectives used as pre-modifiers, in turn, stem from different semantic domains which reveals differences in attitudes from the language producers towards the referents and in what traits are described regarding the holders of the titles. Additionally, a type/token ratio investigation reveals that the language producers were keener on using a more varied vocabulary when modifying female titles and less so when modifying male titles. The male terms proved to be used more formulaically than the female terms, as well. Lastly, an analysis of MI scores concludes that the most frequent collocations are not necessarily the most relevant ones.    A discussion regarding similarities and differences to other studies is carried out, as well, which, further, is accompanied by suggestions for future research.

Les maladies neurodégénératives, telles que la maladie d'Alzheimer (AD) et Charcot Marie Tooth (CMT), sont des maladies complexes. Leurs mécanismes pathologiques ne sont toujours pas bien compris et les progrès dans la recherche et le développement de nouvelles thérapies potentielles modifiant la maladie sont lents. Les données catégorielles, comme les échelles de notation et les données sur les études d'association génomique (GWAS), sont largement utilisées dans les maladies neurodégénératives dans le diagnostic, la prédiction et le suivi de la progression. Il est important de comprendre et d'interpréter ces données correctement si nous voulons améliorer la recherche sur les maladies neurodégénératives. Le but de cette thèse est d'utiliser la théorie psychométrique moderne: théorie de la réponse d’item pour analyser ces données catégoriques afin de mieux comprendre les maladies neurodégénératives et de faciliter la recherche de médicaments correspondante. Tout d'abord, nous avons appliqué l'analyse de Rasch afin d'évaluer la validité du score de neuropathie Charcot-Marie-Tooth (CMTNS), un critère important d'évaluation principal pour les essais cliniques de la maladie de CMT. Nous avons ensuite adapté le modèle Rasch à l'analyse des associations génétiques pour identifier les gènes associés à la maladie d'Alzheimer. Cette méthode résume les génotypes catégoriques de plusieurs marqueurs génétiques tels que les polymorphisme nucléotidique (SNPs) en un seul score génétique. Enfin, nous avons calculé l'information mutuelle basée sur la théorie de réponse d’item pour sélectionner les items sensibles dans ADAS-cog, une mesure de fonctionnement cognitif la plus utilisées dans les études de la maladie d'Alzheimer, afin de mieux évaluer le progrès de la maladie. / Neurodegenerative diseases, such as Alzheimer’s disease (AD) and Charcot Marie Tooth (CMT), are complex diseases. Their pathological mechanisms are still not well understood, and the progress in the research and development of new potential disease-modifying therapies is slow. Categorical data like rating scales and Genome-Wide Association Studies (GWAS) data are widely utilized in the neurodegenerative diseases in the diagnosis, prediction and progression monitor. It is important to understand and interpret these data correctly if we want to improve the disease research. The purpose of this thesis is to use the modern psychometric Item Response Theory to analyze these categorical data for better understanding the neurodegenerative diseases and facilitating the corresponding drug research. First, we applied the Rasch analysis in order to assess the validity of the Charcot-Marie-Tooth Neuropathy Score (CMTNS), a main endpoint for the CMT disease clinical trials. We then adapted the Rasch model to the analysis of genetic associations and used to identify genes associated with Alzheimer’s disease by summarizing the categorical genotypes of several genetic markers such as Single Nucleotide Polymorphisms (SNPs) into one genetic score. Finally, to select sensitive items in the most used psychometrical tests for Alzheimer’s disease, we calculated the mutual information based on the item response model to evaluate the sensitivity of each item on the ADAS-cog scale.

Tato práce zkoumá existující možnosti a metody detekce korelovaných mutací v proteinech. Práce začíná teoretickým úvodem do zkoumané problematiky. Využití informací o korelovaných mutacích je především při predikci terciální struktury proteinu či hledání oblastí s významnou funkcí. Dále následuje přehled v současnosti používaných metod detekce a jejich výhody a nevýhody. V této práci jsou zkoumány zejména metody založené na statistice (například Pearsonově korelačním koeficientu nebo Pearsonově chi^2 testu), informační teorii (Mutual information - MI) a pravděpodobnosti (ELSC nebo Spidermonkey). Dále jsou popsány nejdůležitější nástroje s informací o tom, které metody používají a jakým způsobem. Také je diskutována možnost návrhu optimálního algoritmu. Jako optimální z hlediska úspěšnosti detekce je doporučeno využít více zmíněných metod. Také je doporučeno při detekci využít fyzikálně-chemických vlastností aminokyselin. V praktické části byla vyvinuta metoda využívající fyzikálně-chemických vlastností aminokyselin a fylogenetických stromů. Výsledky detekce byly porovnány s nástroji CAPS, CRASP a CMAT.

This thesis deals with speech analysis of people who suffer from Parkinson’s disease. Purpose of this thesis is to obtain statistical sample of speech parameters which helps to determine if examined person is suffering from Parkinson’s disease. Statistical sample is based on hypokinetic dysarthria detection. For speech signal pre-processing DC-offset removal and pre-emphasis are used. The next step is to divide signal into frames. Phonation parameters, MFCC and PLP coefficients are used for characterization of framed speech signal. After parametrization the speech signal can be analyzed by statistical methods. For statistical analysis in this thesis Spearman’s and Pearson’s correlation coefficients, mutual information, Mann-Whitney U test and Student’s t-test are used. The thesis results are the groups of speech parameters for individual long czech vowels which are the best indicator of the difference between healthy person and patient suffering from Parkinson’s disease. These result can be helpful in medical diagnosis of a patient.

Les Systèmes de Contrôle Embarqués Distribués (SCED) utilisent les réseaux de communication dans les boucles de rétroaction. Étant donné que les systèmes SCED ont une puissance de batterie, une bande passante de communication et une puissance de calcul limitée, les débits des données ou des informations transmises sont bornées et ils peuvent affecter leur stabilité. Ceci nous amène à élargir le spectre de notre étude et y intégrer une étude sur la relation entre la théorie du contrôle d’un coté et celle de l’information de l’autre. La contrainte de débit de données induit la quantification des signaux tandis que les aspects de calcul temps réel et de communication induit des événements asynchrones qui ne sont plus réguliers ou périodiques. Ces deux phénomènes donnent au SCED une double nature, continue et discrète, et en font des cas d’étude spécifiques. Dans cette thèse, nous analysons la stabilité et la performance de SCED du point de vue de la théorie de l’information et du contrôle. Pour les systèmes linéaires, nous montrons l’importance du compromis entre la quantité d’information communiquée et les objectifs de contrôle, telles que la stabilité, la contrôlabilité/observabilité et les performances. Une approche de conception conjointe de contrôle et de communication (en termes de débit d’information au sens de Shannon) des SCED est étudiée. Les principaux résultats de ces travaux sont les suivants : nous avons prouvé que la réduction d’entropie (ce qui correspond à la réduction d’incertitude) dépend du Grammien de contrôlabilité. Cette réduction est également liée à l’information mutuelle de Shannon. Nous avons démontré que le Grammien de contrôlabilité constitue une métrique de l’entropie théorique de l’information en ce qui concerne les bruits induits par la quantification. La réduction de l’influence de ces bruits est équivalente à la réduction de la norme du Grammien de contrôlabilité. Nous avons établi une nouvelle relation entre la matrice d’information de Fisher (FIM) et le Grammien de Contrôlabilité (CG) basé sur la théorie de l’estimation et la théorie de l’information. Nous proposons un algorithme qui distribue de manière optimale les capacités de communication du réseau entre un nombre "n" d’actionneurs et/ou systèmes concurrents se basant sur la réduction de la norme du Grammien de Contrôlabilité / The Networked Embedded Control System (NECS) uses communication networks in the feedback loops. Since the embedded systems have the limited battery power along with limited bandwidth and computing power, the feedback data rates are limited. The rate of communications can drastically affect system stability. Hence, there is a strong need for understanding and merging the Control Theory with Communication or Information Theory. The data rate constraint introduces quantization into the feedback loop whereas the communication or computational model induces discrete events which are no more periodic. These two phenomena give the NECS a twofold nature : continuous and discrete, and render them specific. In this thesis we analyze the stability and performance of NECS from Informationtheoretic point of view. For linear systems, we show how fundamental are the tradeoffs between the communication-rate and control goals, such as stability, controllability / observability and performances. An integrated approach of control and communication (in terms of Shannon Information Rate) of NECS or distributed embedded control systems is studied. The main results are as follows : We showed that the entropy reduction which is same as uncertainty reduction is dependent on Controllability Gramian only. It is also related to Shannon Mutual-Information. We demonstrated that the gramian of controllability constitutes a metric of information theoretic entropy with respect to the noises induced by quantization. Reduction of these noises is equivalent to the design methods proposing a reduction of the controllability gramian norm. We established a new relation of Fisher Information Matrix (FIM) and Controllability Gramian (CG) based on estimation-theoretic and information-theoretic explanations. We propose an algorithm which optimally distributes the network capacity between a number "n" of competing actuators. The metric of this distribution is the Controllability Gramian

1-bit analog-to-digital conversion is very attractive for low-complexity communications receivers. A major drawback is, however, the small spectral efficiency when sampling at symbol rate. This can be improved through oversampling by exploiting the signal distortion caused by the transmission channel. This paper analyzes the achievable data rate of band-limited communications channels that are subject to additive noise and inter-symbol-interference with 1-bit quantization and oversampling at the receiver. It is shown that not only the channel noise but also the inter-symbol-interference can be exploited to benefit from oversampling.

In this thesis, we propose many developments in the context of Structural Similarity. We address both node (local) similarity and graph (global) similarity. Concerning node similarity, we focus on improving the diffusive process leading to compute this similarity (e.g. Commute Times) by means of modifying or rewiring the structure of the graph (Graph Densification), although some advances in Laplacian-based ranking are also included in this document. Graph Densification is a particular case of what we call graph rewiring, i.e. a novel field (similar to image processing) where input graphs are rewired to be better conditioned for the subsequent pattern recognition tasks (e.g. clustering). In the thesis, we contribute with an scalable an effective method driven by Dirichlet processes. We propose both a completely unsupervised and a semi-supervised approach for Dirichlet densification. We also contribute with new random walkers (Return Random Walks) that are useful structural filters as well as asymmetry detectors in directed brain networks used to make early predictions of Alzheimer's disease (AD). Graph similarity is addressed by means of designing structural information channels as a means of measuring the Mutual Information between graphs. To this end, we first embed the graphs by means of Commute Times. Commute times embeddings have good properties for Delaunay triangulations (the typical representation for Graph Matching in computer vision). This means that these embeddings can act as encoders in the channel as well as decoders (since they are invertible). Consequently, structural noise can be modelled by the deformation introduced in one of the manifolds to fit the other one. This methodology leads to a very high discriminative similarity measure, since the Mutual Information is measured on the manifolds (vectorial domain) through copulas and bypass entropy estimators. This is consistent with the methodology of decoupling the measurement of graph similarity in two steps: a) linearizing the Quadratic Assignment Problem (QAP) by means of the embedding trick, and b) measuring similarity in vector spaces. The QAP problem is also investigated in this thesis. More precisely, we analyze the behaviour of $m$-best Graph Matching methods. These methods usually start by a couple of best solutions and then expand locally the search space by excluding previous clamped variables. The next variable to clamp is usually selected randomly, but we show that this reduces the performance when structural noise arises (outliers). Alternatively, we propose several heuristics for spanning the search space and evaluate all of them, showing that they are usually better than random selection. These heuristics are particularly interesting because they exploit the structure of the affinity matrix. Efficiency is improved as well. Concerning the application domains explored in this thesis we focus on object recognition (graph similarity), clustering (rewiring), compression/decompression of graphs (links with Extremal Graph Theory), 3D shape simplification (sparsification) and early prediction of AD. / Ministerio de Economía, Industria y Competitividad (Referencia TIN2012-32839 BES-2013-064482)