In dieser Arbeit werden planare plasmonische Schichtsysteme unter Verwendung der transparenten leitfähigen Oxide (TCOs) Zinkgalliumoxid (GZO) und Indiumzinnoxid (ITO) untersucht, die mittels Molekularstrahlepitaxie realisiert werden. Es wird gezeigt, dass solche hochdotierten Schichten aus GZO und ITO sich wie ein Drude-Metall mit einer einstellbaren Plasmafrequenz verhalten und Oberflächenplasmon-Polaritonen (SPPs) aufweisen, die über einen breiten NIR-Spektralbereich abstimmbar sind. Die TCOs können in mehreren Schichten mit unabhängig voneinander einstellbaren Dicken und Dotierungen der einzelnen Schichten gezüchtet werden. Diese abstimmbaren Mehrschichtstrukturen ermöglichen die Realisierung plasmonischer Konfigurationen, die für eine Vielzahl komplexer hybridisierter SPP-Zustände maßgeschneidert sind. Unter anderem wird unter Ausnutzung der Photon-Plasmon-Hybridisierung ein Stopped-Light-Resonator auf Basis von ITO demonstriert. Das Mehrschichtenregime kann zu einem Übergitter aus periodisch abwechselnd dotierten und undotierten TCO-Schichten erweitert werden, das ein hyperbolisches Metamaterial (HMM) darstellt. Die Parameter dieses HMM können nach Bedarf eingestellt werden, was HMMs mit einer maßgeschneiderten Zusammensetzung ihrer einzigartigen spektralen Permittivitätsintervalle ermöglicht. Mithilfe von GZO wird ein HMM in eine planare optische Mikrokavität monolitisch eingebettet. Dieser neuartige NIR-Resonator weist eine anomale Modendispersion auf, einschließlich einem Kontinuum von Moden hoher Ordnung und einer von der Resonatorlänge unabhängigen Mode nullter Ordnung, welche Subwellenlängen-Resonanzen ermöglichen können. Es wird gezeigt, dass die Mode nullter Ordnung bei einer Kavitätslänge deutlich unterhalb ihrer Wellenlänge fortbesteht und ihre Dispersion durch den Füllfaktor des HMM steuerbar ist. Die Ergebnisse stellen somit ein neues allgemeines Konzept für die Realisierung eines Subwellenlängenresonators auf der Basis eines abstimmbaren HMM dar. / In this work, planar, layered plasmonic systems utilizing the transparent conducting oxides (TCOs) zinc gallium oxide (GZO) and indium tin oxide (ITO) facilitated by molecular beam epitaxy are investigated. It is shown that such highly doped layers of GZO and ITO prepared with behave as a Drude metal with a tunable plasma frequency and feature surface plasmon polaritons (SPPs) that are tunable over a broad NIR spectral range. TCOs can be grown in the multilayer regime with independently adjusted thicknesses and doping levels of the individual layers. These tunable multilayer structures allow for the realization of plasmonic configurations tailored to support a variety of intricate hybridized SPP states. Particularly, exploiting photon-plasmon hybridization, a stopped-light cavity is demonstrated using highly doped ITO. The multilayer regime can be extended into a superlattice of periodically alternating doped and undoped TCO layers that constitutes a hyperbolic metamaterial (HMM). The parameters of such an HMM can be set on-demand, thus allowing HMMs with a tailored composition of its unique spectral permittivity intervals. Utilizing GZO, an HMM is embedded in a planar optical microcavity monolithically. This novel type of a NIR optical resonator exhibits an anomalous resonant mode dispersion, including features like a high-order mode continuum and a cavity size independent zeroth-order mode, which can enable subwavelength resonances. It is demonstrated that the zeroth-order mode persists at cavity sizes significantly below its wavelength and its dispersion can be controlled by the fill factor of the HMM. Thus, the results propose a novel general concept for the realization of a subwavelength resonator on the basis of a tunable HMM.

Le LPC est un complément à la lecture labiale qui facilite la communication des malentendants. Sur le principe, il s'agit d'effectuer des gestes avec une main placée à côté du visage pour désambigüiser le mouvement des lèvres, qui pris isolément est insuffisant à la compréhension parfaite du message. Le projet RNTS TELMA a pour objectif de mettre en place un terminal téléphonique permettant la communication des malentendants en s'appuyant sur le LPC. Parmi les nombreuses fonctionnalités que cela implique, il est nécessaire de pouvoir reconnaître le geste manuel du LPC et de lui associer un sens. L'objet de ce travail est la segmentation vidéo, l'analyse et la reconnaissance des gestes de codeur LPC en situation de communication. Cela fait appel à des techniques de segmentation d'images, de classification, d'interprétation de geste, et de fusion de données. Afin de résoudre ce problème de reconnaissance de gestes, nous avons proposé plusieurs algorithmes originaux, parmi lesquels (1) un algorithme basé sur la persistance rétinienne permettant la catégorisation des images de geste cible et des images de geste de transition, (2) une amélioration des méthodes de multi-classification par SVM ou par classifieurs unaires via la théorie de l'évidence, assortie d'une méthode de conversion des probabilités subjectives en fonction de croyance, et (3) une méthode de décision partielle basée sur la généralisation de la Transformée Pignistique, afin d'autoriser les incertitudes dans l'interprétation de gestes ambigus.

Les travaux présentés dans ce mémoire s’inscrivent dans le cadre duprojet PANsafer (Vers un Passage A Niveau plus sûr), lauréat de l’appel ANR-VTT2008. Ce projet est labellisé par les deux pôles de compétitivité i-Trans et Véhiculedu Futur. Le travail de la thèse est mené conjointement par le laboratoire IRTESSETde l’UTBM et le laboratoire LEOST de l’IFSTTAR.L’objectif de cette thèse est de développer un système de perception permettantl’interprétation de scénarios dans l’environnement d’un passage à niveau. Il s’agitd’évaluer des situations potentiellement dangereuses par l’analyse spatio-temporelledes objets présents autour du passage à niveau.Pour atteindre cet objectif, le travail est décomposé en trois étapes principales. Lapremière étape est consacrée à la mise en place d’une architecture spatiale des capteursvidéo permettant de couvrir de manière optimale l’environnement du passageà niveau. Cette étape est mise en oeuvre dans le cadre du développement d’unsimulateur d’aide à la sécurité aux passages à niveau en utilisant un système deperception multi-vues. Dans ce cadre, nous avons proposé une méthode d’optimisationpermettant de déterminer automatiquement la position et l’orientation descaméras par rapport à l’environnement à percevoir.La deuxième étape consisteà développer une méthode robuste de suivi d’objets enmouvement à partir d’une séquence d’images. Dans un premier temps, nous avonsproposé une technique permettant la détection et la séparation des objets. Le processusde suivi est ensuite mis en oeuvre par le calcul et la rectification du flotoptique grâce respectivement à un modèle gaussien et un modèle de filtre de Kalman.La dernière étape est destinée à l’analyse des trajectoires 2D reconstruites parl’étape précédente pour l’interprétation de scénarios. Cette analyse commence parune modélisation markovienne des trajectoires 2D. Un système de décision à basede théorie de l’évidence est ensuite proposé pour l’évaluation de scénarios, aprèsavoir modélisé les sources de danger.L’approche proposée a été testée et évaluée avec des données issues de campagnesexpérimentales effectuées sur site réel d’un passage à niveau mis à disposition parRFF. / The main objective of this thesis is to develop a system for monitoringthe close environment of a level crossing. It aims to develop a perception systemallowing the detection and the evaluation of dangerous situations around a levelcrossing.To achieve this goal, the overall problem of this work has been broken down intothree main stages. In the first stage, we propose a method for optimizing automaticallythe location of video sensors in order to cover optimally a level crossingenvironment. This stage addresses the problem of cameras positioning and orientationin order to view optimally monitored scenes.The second stage aims to implement a method for objects tracking within a surveillancezone. It consists first on developing robust algorithms for detecting and separatingmoving objects around level crossing. The second part of this stage consistsin performing object tracking using a Gaussian propagation optical flow based modeland Kalman filtering.On the basis of the previous steps, the last stage is concerned to present a newmodel to evaluate and recognize potential dangerous situations in a level crossingenvironment. This danger evaluation method is built using Hidden Markov Modeland credibility model.Finally, synthetics and real data are used to test the effectiveness and the robustnessof the proposed algorithms and the whole approach by considering various scenarioswithin several situations.This work is developed within the framework of PANsafer project (Towards a saferlevel crossing), supported by the ANR-VTT program (2008) of the French NationalAgency of Research. This project is also labelled by Pôles de compétitivité "i-Trans"and "Véhicule du Futur". All the work, presented in this thesis, has been conductedjointly within IRTES-SET laboratory from UTBM and LEOST laboratory fromIFSTTAR.

Le décalage de phase de lecture en -1 est un mécanisme de traduction non conventionnel des ARNm en protéines. Il contrôle la production de deux peptides différents à partir d'un messager unique. Bien que les exemples actuels de décalage de phase de lecture en -1 soient en grande partie limités aux génomes viraux et aux transposons, quelques événements bactériens et eucaryotes sont également documentés. Mon travail de thèse a eu pour objet la recherche de gènes contrôlés par décalage de phase de lecture chez la levure Saccharomyces cerevisiæ, par des approches de biologie expérimentale et de bioinformatique. Une partie de cette étude a été réalisée en collaboration. Au cours de ces travaux, l'étude du mécanisme du décalage de phase de lecture eucaryote a aussi été abordée. Mes résultats ont permis de mettre en évidence l'effet de la modification des ARNt présents au site E du ribosome au moment du décalage sur l'efficacité de changement de cadre de lecture en -1.

The growth of multimedia content in recent years motivated the research on audio classification and content retrieval area. In this thesis, a general environmental audio classification and retrieval approach is proposed in which higher level semantic classes (outdoor, nature, meeting and violence) are obtained from lower level acoustic classes (emergency alarm, car horn, gun-shot, explosion, automobile, motorcycle, helicopter, wind, water, rain, applause, crowd and laughter). In order to classify an audio sample into acoustic classes, MPEG-7 audio features, Mel Frequency Cepstral Coefficients (MFCC) feature and Zero Crossing Rate (ZCR) feature are used with Hidden Markov Model (HMM) and Support Vector Machine (SVM) classifiers. Additionally, a new classification method is proposed using Genetic Algorithm (GA) for classification of semantic classes. Query by Example (QBE) and keyword-based query capabilities are implemented for content retrieval.

Avui en dia, la Interacció Home Màquina (IHM) és una de les disciplines més estudiades amb l'objectiu de millorar les interaccions humanes amb sistemes reals actuals i futurs. Cada vegada més gent utilitza més dispositius electrònics a la vida quotidiana Aquesta incursió electrònica es deu principalment a dues raons. D'una banda, la facilitat d'accés a aquesta tecnologia però d'altra banda, unes interfícies més amigables que permeten un ús més fàcil i intuitiu. Simplement fa falta observar els ordinadors personals d'avui en dia, les computadores de butxaca i inclús els telèfons mòbils. Tots aquests nous dispositius permeten que usuaris poc experimentats puguin fer ús de les tecnologies més punteres. D'altra banda, la inclusió de les tecnologies de la parla estan arribant a ser més comunes gràcies a què els sistemes de reconeixement i de síntesi de veu han millorat considerablement el seu funcionament i fiabilitat.L'objectiu final de les tecnologies de la parla és crear sistemes tan naturals com els éssers humans per tal de fer que el seu ús es pugui extendre a qualsevol racó de la vida quotidiana Els conversors de Text-a-Parla (o sintetitzadors) són un dels mòduls que més esforç investigador han rebut amb l'objectiu de millorar la seva naturalitat i expressivitat. L'ús de sintetitzadors s'ha ampliat durant els últims temps degut a l'alta qualitat aconseguida en aplicacions de domini restringit i el bon comportament en aplicacions de propòsit general. De totes formes, encara queda un llarg camí per recòrrer pel que respecta a la qualitat en aplicacions de domini obert. A més a més, algunes de les tendències dels sistemes sintetitzadors comporten reduir el tamany de les bases de dades, sistemes flexibles per adaptar locutors i estils de locució i sistemes entrenables.Aquesta tesi doctoral presentarà un sintetizador de veu basat en l'entorn probabilístic dels Models Ocults de Makov (MOM) que tractarà amb els principals temes estudiats a l'actualitat, tal com l'adaptació de l'estil del locutor, sistemes conversors de veu entrenables i bases de dades de tamany reduit. Es descriurà el funcionament convencional dels algoritmes i es propondran millores en diferents àmbits com per exemple l'expressivitat. A la vegada, es presenta un sistema híbrid punter que combina models estadístics i de concatenació de veu. Els resultats obtinguts mostren com les propostes d'aquest treball donen un pas endavant en l'àmbit de la creació de veu sintètica utilitzant models estadístics. / Hoy en día, la Interacción Hombre-Máquina (IHM) es una de las disciplinas más estudiadas con el objetivo de mejorar las interacciones humanas con sistemas reales para el presente y para el futuro venidero. Más y más dispositivos electrónicos son usados por más gente en la vida diaria. Esta incursión electrónica se debe principalmente a dos razones. Por un lado, el indudable aumento en la accesibilidad económica a esta tecnología pero por otra parte, unos interfaces más amigables que permiten un uso más fácil e intuitivo. Simplemente hace falta observar hoy en día los ordenadores personales, las computadoras de bolsillo e incluso los teléfonos móviles. Todos estos nuevos dispositivos admiten que usuarios poco experimentados puedan hacer uso de las tecnologías más punteras. Por otra parte, la inclusión de las tecnologías del habla está llegando a ser más común gracias a que los sistemas de reconocimiento y de síntesis de voz han estado mejorando su funcionamiento y fiabilidad.El objetivo final de las tecnologías del habla es crear sistemas tan naturales como los seres humanos para que su uso se pueda extender a cualquier rincón de la vida diaria. Los conversores de Texto-a-Voz (o sintetizadores) son de los módulos que más esfuerzo investigador han recibido con el objetivo de mejorar su naturalidad y la expresividad. El uso de los sintetizadores se ha ampliado durante los últimos tiempos debido a la alta calidad alcanzada en usos de dominio restringido y el buen comportamiento en aplicaciones de propósito general. De todas formas, todavía queda un largo camino por recorrer por lo que respecta a la calidad en aplicaciones de dominio abierto. Además, algunas de las tendencias de los sistemas sintetizadores conllevan reducir el tamaño de las bases de datos, sistemas flexibles para adaptar locutores y estilos de locución y sistemas entrenables.Esta tesis doctoral presentará un sintetizador de voz basado en el entorno probabilístico de los Modelos Ocultos de Markov (MOM) que lidiará con los principales temas estudiados en la actualidad tales como adaptación del estilo de locutor, sistema conversores de voz entrenables y bases de datos de tamaño reducido. Se describirá el funcionamiento convencional de los algoritmos y se propondrán mejoras en varios ámbitos tales como la expresividad. A la vez se presenta un sistema híbrido puntero que combina modelos estadísticos y de concatenación de voz. Los resultados obtenidos muestran como las propuestas de este trabajo dan un paso adelante en el ámbito de la creación de voz sintética usando modelos estadísticos. / Nowadays, Human Computer Interaction (HCI) is one of the most studied disciplines in order to improve real human interactions with machines on the present time and for the incoming future. More and more electronic devices of the daily life are used by more people. This electronic incursion is mainly due to two reasons. On the one hand, the undoubted increasing of the economical accessibility to this technology but on the other hand, the more friendly interfaces allow an easier and more intuitive use. As a matter of fact, nowadays it is only necessary to observe the personal computer interfaces, pocket size computers and even mobile telephones. All these new interfaces let little experienced users make use of cutting edge technologies. Moreover, the inclusion of speech technologies in these systems is becoming more usual since speech recognition and synthesis systems have improved their performance and reliability.The purpose of speech technology is to provide systems with a natural human interface so the use can be extended to daily life. Text-to-Speech (TTS) systems are one of the main modules under intense research activity in order to improve their naturalness and expressiveness. The use of synthesizers has been extended during the last times due to the high-quality reached in real limited domain applications and the good performance in generic purposes applications. However, there is still a long way to go with respect to quality and open domain systems.This work will present a TTS system based on a statistical framework using Hidden Markov Models (HMMs) that will deal with the main topics under study in recent years such as voice style adaptation, trainable TTS systems and low print databases. Moreover, a cutting edge hybrid approach combining concatenative and statistical synthesis will also be presented. Ideas and results in this work show a step forward in the HMM-based TTS system field

In this thesis we develop several kinds of latent variable models in order to address three types of bio-statistical problem. The three problems are the treatment effect of carcinogens on tumour development, spatial interactions between plant species and motor unit number estimation (MUNE). The three types of data looked at are: highly heterogeneous longitudinal count data, quadrat counts of species on a rectangular lattice and lastly, electrophysiological data consisting of measurements of compound muscle action potential (CMAP) area and amplitude. Chapter 1 sets out the structure and the development of ideas presented in this thesis from the point of view of: model structure, model selection, and efficiency of estimation. Chapter 2 is an introduction to the relevant literature that has in influenced the development of this thesis. In Chapter 3 we use the EM algorithm for an application of an autoregressive hidden Markov model to describe longitudinal counts. The data is collected from experiments to test the effect of carcinogens on tumour growth in mice. Here we develop forward and backward recursions for calculating the likelihood and for estimation. Chapter 4 is the analysis of a similar kind of data using a more sophisticated model, incorporating random effects, but estimation this time is conducted from the Bayesian perspective. Bayesian model selection is also explored. In Chapter 5 we move to the two dimensional lattice and construct a model for describing the spatial interaction of tree types. We also compare the merits of directed and undirected graphical models for describing the hidden lattice. Chapter 6 is the application of a Bayesian hierarchical model (MUNE), where the latent variable this time is multivariate Gaussian and dependent on a covariate, the stimulus. Model selection is carried out using the Bayes Information Criterion (BIC). In Chapter 7 we approach the same problem by using the reversible jump methodology (Green, 1995) where this time we use a dual Gaussian-Binary representation of the latent data. We conclude in Chapter 8 with suggestions for the direction of new work. In this thesis, all of the estimation carried out on real data has only been performed once we have been satisfied that estimation is able to retrieve the parameters from simulated data. Keywords: Amyotrophic lateral sclerosis (ALS), carcinogens, hidden Markov models (HMM), latent variable models, longitudinal data analysis, motor unit disease (MND), partially ordered Markov models (POMMs), the pseudo auto- logistic model, reversible jump, spatial interactions.

La maintenance prédictive joue un rôle important dans le maintien des systèmes de production continue car elle peut aider à réduire les interventions inutiles ainsi qu'à éviter des pannes imprévues. En effet, par rapport à la maintenance conditionnelle, la maintenance prédictive met en œuvre une étape supplémentaire, appelée le pronostic. Les opérations de maintenance sont planifiées sur la base de la prédiction des états de détérioration futurs et sur l'estimation de la vie résiduelle du système. Dans le cadre du projet européen FP7 SUPREME (Sustainable PREdictive Maintenance for manufacturing Equipment en Anglais), cette thèse se concentre sur le développement des modèles de détérioration stochastiques et sur des méthodes d'estimation de la vie résiduelle (Remaining Useful Life – RUL en anglais) associées pour les adapter aux cas d'application du projet. Plus précisément, les travaux présentés dans ce manuscrit sont divisés en deux parties principales. La première donne une étude détaillée des modèles de détérioration et des méthodes d'estimation de la RUL existant dans la littérature. En analysant leurs avantages et leurs inconvénients, une adaptation d’une approche de l'état de l'art est mise en œuvre sur des cas d'études issus du projet SUPREME et avec les données acquises à partir d’un banc d'essai développé pour le projet. Certains aspects pratiques de l’implémentation, à savoir la question de l'échange d'informations entre les partenaires du projet, sont également détaillées dans cette première partie. La deuxième partie est consacrée au développement de nouveaux modèles de détérioration et les méthodes d'estimation de la RUL qui permettent d'apporter des éléments de solutions aux problèmes de modélisation de détérioration et de prédiction de RUL soulevés dans le projet SUPREME. Plus précisément, pour surmonter le problème de la coexistence de plusieurs modes de détérioration, le concept des modèles « multi-branche » est proposé. Dans le cadre de cette thèse, deux catégories des modèles de type multi-branche sont présentées correspondant aux deux grands types de modélisation de l'état de santé des système, discret ou continu. Dans le cas discret, en se basant sur des modèles markoviens, deux modèles nommés Mb-HMM and Mb-HsMM (Multi-branch Hidden (semi-)Markov Model en anglais) sont présentés. Alors que dans le cas des états continus, les systèmes linéaires à sauts markoviens (JMLS) sont mis en œuvre. Pour chaque modèle, un cadre à deux phases est implémenté pour accomplir à la fois les tâches de diagnostic et de pronostic. A travers des simulations numériques, nous montrons que les modèles de type multi-branche peuvent donner des meilleures performances pour l'estimation de la RUL par rapport à celles obtenues par des modèles standards mais « mono-branche ». / Predictive maintenance plays a crucial role in maintaining continuous production systems since it can help to reduce unnecessary intervention actions and avoid unplanned breakdowns. Indeed, compared to the widely used condition-based maintenance (CBM), the predictive maintenance implements an additional prognostics stage. The maintenance actions are then planned based on the prediction of future deterioration states and residual life of the system. In the framework of the European FP7 project SUPREME (Sustainable PREdictive Maintenance for manufacturing Equipment), this thesis concentrates on the development of stochastic deterioration models and the associated remaining useful life (RUL) estimation methods in order to be adapted in the project application cases. Specifically, the thesis research work is divided in two main parts. The first one gives a comprehensive review of the deterioration models and RUL estimation methods existing in the literature. By analyzing their advantages and disadvantages, an adaption of the state of the art approaches is then implemented for the problem considered in the SUPREME project and for the data acquired from a project's test bench. Some practical implementation aspects, such as the issue of delivering the proper RUL information to the maintenance decision module are also detailed in this part. The second part is dedicated to the development of innovative contributions beyond the state-of-the-are in order to develop enhanced deterioration models and RUL estimation methods to solve original prognostics issues raised in the SUPREME project. Specifically, to overcome the co-existence problem of several deterioration modes, the concept of the "multi-branch" models is introduced. It refers to the deterioration models consisting of different branches in which each one represent a deterioration mode. In the framework of this thesis, two multi-branch model types are presented corresponding to the discrete and continuous cases of the systems' health state. In the discrete case, the so-called Multi-branch Hidden Markov Model (Mb-HMM) and the Multi-branch Hidden semi-Markov model (Mb-HsMM) are constructed based on the Markov and semi-Markov models. Concerning the continuous health state case, the Jump Markov Linear System (JMLS) is implemented. For each model, a two-phase framework is carried out for both the diagnostics and prognostics purposes. Through numerical simulations and a case study, we show that the multi-branch models can help to take into account the co-existence problem of multiple deterioration modes, and hence give better performances in RUL estimation compared to the ones obtained by standard "single branch" models.

Conselho Nacional de Pesquisa e Desenvolvimento Científico e Tecnológico - CNPq / This work presents a study of the spectral dynamics characteristics of audio signals. More specifically, we aim at detecting regularities that can be modeled in typical domestic sounds, in order to classify them. Our starting point is the work of Sehili et al. [2], in which a household sounds classification system based on GMM is proposed. The Sehili system is reproduced in this work as a baseline system. Following the same protocol of experiments, a 73 % recognition rate is achieved. Afterwards, three sets of experiments are performed, arranged so that each new approach incorporates a new technique to highlight a different aspect of the spectral dynamics. The first technique is the insertion of the discrete gradient information of feature vectors, a strategy aimed at a local spectral dynamic analysis, and resultes in a perceptible increase in recognition rate. The next experiment is conducted with a HMM based classifier, in which the spectral dynamic should be encoded in state transition probability matrices. The tests with the HMM do not result in improved recognition rates. The last experiment is based on a features extraction method, proposed by the author, called Patterns of Energy Envelope per Band (PEEB). The PEEB is an extractor that highlight the signal spectral dynamics inside narrow bands. In domestic sounds recognition tests, the classification system based on a combination of PEEB, MFCC and GMM strategies resulted in a significant improvement over all other systems tested. We conclude, based on our results, that the spectral dynamics of the studied dataset plays an important role in the classification task. However, the approaches for spectral dynamic information extraction, studied in this work, are not definitive, for it is clear that they can be further developed. For example, in the case of PEEB, the recognition rate is strongly dependent on the sound class, suggesting more elaborate forms of fusion of PEEB and MFCC features for each class. / Este trabalho é um estudo da característica da dinâmica espectral em sinais sonoros, com vistas a encontrar as regularidades que podem ser modeladas em sons tipicamente domésticos, com o objetivo de classificá-los. O ponto de partida é o trabalho de Sehili et al. [1], no qual é proposto um sistema de classificação de sons domésticos baseado em GMM. O sistema de Sehili é reproduzido neste trabalho como marco zero na análise da dinâmica espectral, seguindo o mesmo roteiro dos experimentos. A partir daí, três conjuntos de experimentos são realizados, organizados de forma que, a cada novo experimento, uma técnica – que destaca um aspecto diferente da dinâmica espectral – seja incorporada. A primeira técnica analisada é a inserção da informação de gradiente discreto dos vetores de características, estratégia que representa uma análise de dinâmica espectral local e que resulta num aumento perceptível na taxa de classificação. O próximo experimento é realizado com um classificador baseado em HMM, no qual a informação de dinâmica espectral deve ser codificada na matriz de probabilidades de transição de estados do modelo. Os testes com o HMM não resultam em melhora na taxa de reconhecimento das classes de sons. O último experimento é baseado num extrator de características proposto pelo autor, chamado de Padrões de Envelopes de Energia por Banda (PEEB). O PEEB é um extrator que destaca os padrões de evolução espectro-temporais do sinais. Nos testes de reconhecimento de sons domésticos, o sistema de classificação baseado numa combinação das estratégias PEEB, MFCC e GMM resultam numa melhora significativa em relação a todos os outros sistemas testados. Conclui-se, com base nos resultados, que a dinâmica espectral dos sinais da base estudada é relevante à tarefa de classificação. No entanto, as maneiras de extração da informação de dinâmica espectral estudadas neste trabalho não são definitivas, pois ainda há muito espaço para desenvolvê-las. Por exemplo, no caso do PEEB, nota-se que a taxa de classificação fortemente é dependente da classe sonora, sugerindo formas mais elaboradas de fusão das características PEEB e MFCC para cada classe.

Proteins are essential for the growth, survival and maintenance of the cell. Understanding the functional roles of proteins helps to decipher the working of macromolecular assemblies and cellular machinery of living organisms. A thorough investigation of the link between sequence, structure and function of proteins, helps in building a comprehensive understanding of the complex biological systems. Proteins have been observed to be composed of single and multiple domains. Analysis of proteins encoded in diverse genomes shows the ubiquitous nature of multi-domain proteins. Though the majority of eukaryotic proteins are multi-domain in nature, 3-D structures of only a small proportion of multi-domain proteins are known due to difficulties in crystallizing such proteins. While functions of individual domains are generally extensively studied, the complex interplay of functions of domains is not well understood for most multi-domain proteins. Paucity of structural and functional data, affects our understanding of the evolution of structure and function of multi-domain proteins. The broad objective of this thesis is to achieve an enhanced understanding of structure and function of protein domains by computational analysis of sequence and structural data. Special attention is paid in the first few chapters of this thesis on the multi-domain proteins. Classification of multi-domain proteins by implementation of an alignment-free sequence comparison method has been achieved in Chapters 2 and 3. Studies on organization, interactions and interdependence of domain-domain interactions in multi-domain proteins with respect to sequential separation between domains and N to C-terminal domain order have been described in Chapters 4 and 5. The functional and structural repertoire of organisms can be comprehensively studied and compared using functional and structural domain annotations. Chapter 6, 7 and 8 represent the proteome-wide structure and function comparisons of various pathogenic and non-pathogenic microorganisms. These comparisons help in identifying proteins implicated in virulence of the pathogen and thus predict putative targets for disease treatment and prevention. Chapter 1 forms an introduction to the main subject area of this thesis. Starting with describing protein structure and function, details of the four levels of hierarchical organization of protein structure have been provided, along with the databases that document protein sequences and structures. Classification of protein domains considered as the realm of function, structure and evolution has been described. The usefulness of classification of proteins at the domain level has been highlighted in terms of providing an enhanced understanding of protein structure and function and also their evolutionary relatedness. The details of structure, function and evolution of multi-domain proteins have also been outlined in chapter 1. ! Chapter 2 aims to achieve a biologically meaningful classification scheme for multi-domain protein sequences. The overall function of a multi-domain protein is determined by the functional and structural interplay of its constituent domains. Traditional sequence-based methods utilize only the domain-level information to classify proteins. This does not take into account the contributions of accessory domains and linker regions towards the overall function of a multi-domain protein. An alignment-free protein sequence comparison tool, CLAP (CLAssification of Proteins) previously developed in this laboratory, was assessed and improved when the author joined the group. CLAP was developed especially to handle multi-domain protein sequences without a requirement of defining domain boundaries and sequential order of domains (domain architecture). ! The working principle of CLAP involves comparison of all against all windows of 5-residue sequence patterns between two protein sequences. The sequences compared could be full-length comprising of all the domains in the two proteins. This compilation of comparison is represented as the Local Matching Scores (LMS) between protein sequences (nslab.iisc.ernet.in/clap/). It has been previously shown that the execution time of CLAP is ~7 times faster than other protein sequence comparison methods that employ alignment of sequences. In Chapter 2, CLAP-based classification has been carried out on two test datasets of proteins containing (i) Tyrosine phosphatase domain family and (ii) SH3-domain family. The former dataset comprises both single and multi-domain proteins that sometimes consist of domain repeats of the tyrosine phosphatase domain. The latter dataset consists only of multi-domain proteins with one copy of the SH3-domain. At the domain-level CLAP-based classification scheme resulted in a clustering similar to that obtained from an alignment-based method, ClustalW. CLAP-based clusters obtained for full-length datasets were shown to comprise of proteins with similar functions and domain architectures. Hence, a protein classification scheme is shown to work efficiently that is independent of domain definitions and requires only the full-length amino acid sequences as input.! Chapter 3 explores the limitations of CLAP in large-scale protein sequence comparisons. The potential advantages of full-length protein sequence classification, combined with the availability of the alignment-free sequence comparison tool, CLAP, motivated the conceptualization of full-length sequence classification of the entire protein repertoire. Before undertaking this mammoth task, working of CLAP was tested for a large dataset of 239,461 protein sequences. Chapter 3 discusses the technical details of computation, storage and retrieval of CLAP scores for a large dataset in a feasible timeframe. CLAP scores were examined for protein pairs of same domain architecture and ~22% of these showed 0 CLAP similarity scores. This led to investigation of the sensitivity of CLAP with respect to sequence divergence. Several test datasets of proteins belonging to the same SCOP fold were constructed and CLAP-based classification of these proteins was examined at inter and intra-SCOP family level. CLAP was successful in efficiently clustering evolutionary related proteins (defined as proteins within the same SCOP superfamily) if their sequence identity >35%. At lower sequence identities, CLAP fails to recognize any evolutionary relatedness. Another test dataset consisting of two-domain proteins with domain order swapped was constructed. Domain order swap refers to domain architectures of type AB and BA, consisting of domains A and B. A condition that the sequence identities of homologous domains were greater than 35% was imposed. CLAP could effectively cluster together proteins of the same domain architectures in this case. Thus, the sequence identity threshold of 35% at the domain-level improves the accuracy of CLAP. The analysis also showed that for highly divergent sequences, the expectation of 5-residue pattern match was likely a stringent criterion. Thus, a modification in the 5-residue identical pattern match criterion, by considering even similar residue and gaps within matched patterns may be required to effectuate CLAP-based clustering of remotely related protein sequences. Thus, this study highlights the limitations of CLAP with respect to large-scale analysis and its sensitivity to sequence divergence. ! Chapters 4 and 5 discuss the computational analysis of inter-domain interactions with respect to sequential distance and domain order. Knowledge of domain composition and 3-D structures of individual domains in a multi-domain protein may not be sufficient to predict the tertiary structure of the multi-domain protein. Substantial information about the nature of domain-domain interfaces helps in prediction of the tertiary as well as the quaternary structure of a protein. Therefore, chapter 4 explores the possible relationship between the sequential distance separating two domains in a multi-domain protein and the extent of their interaction. With increasing sequential separation between any two domains, the extent of inter-domain interactions showed a gradual decrease. The trend was more apparent when sequential separation between domains is measured in terms of number of intervening domains. Irrespective of the linker length, extensive interactions were seen more often between contiguous domains than between non-contiguous domains. Contiguous domains show a broader interface area and lower proportion of non-interacting domains (interface area: 0 Å2 to - 4400 Å2, 2.3% non-interacting domains) than non-contiguous domains (interface area: 0 Å2 to - 2000 Å2, 34.7% non-interacting domains). Additionally, as inter-protein interactions are mediated through constituent domains, rules of protein-protein interactions were applied to domain-domain interactions. Tight binding between domains is denoted as putative permanent domain-domain interactions and domains that may dissociate and associate with relatively weak interactions to regulate functional activity are denoted as putative transient domain-domain interactions. An interface area threshold of 600 Å2 was utilized as a binary classifier to distinguish between putative permanent and putative transient domain-domain interactions. Therefore, the state of interaction of a domain pair is defined as either putative permanent or putative transient interaction. Contiguous domains showed a predominance of putative permanent nature of inter-domain interface, whereas non-contiguous domains showed a prevalence of putative transient interfaces. The state of interaction of various SCOP superfamily pairs was studied across different proteins in the dataset. SCOP superfamily pairs mostly showed a conserved state of interaction, i.e. either putative permanent or putative transient in all their occurrences across different proteins. Thus, it is noted that contiguous domains interact extensively more often than non-contiguous domains and specific superfamily pairs tend to interact in a conserved manner. In conclusion, a combination of interface area and other inter-domain properties along with experimental validation will help strengthen the binary classification scheme of putative permanent and transient domain-domain interactions.! Chapter 5 provides structural analysis of domain pairs occurring in different sequential domain orders in mutli-domain proteins. The function and regulation of a multi-domain protein is predominantly determined by the domain-domain interactions. These in turn are influenced by the sequential order of domains in a protein. With domains defined using evolutionary and structural relatedness (SCOP superfamily), their conservation of structure and function was studied across domain order reversal. A domain order reversal indicates different sequential orders of the concerned domains, which may be identified in proteins of same or different domain compositions. Domain order reversals of domains A and B can be indicated in protein pair consisting of the domain architectures xAxBx and xBxAx, where x indicates 0 or more domains. A total of 161 pairs of domain order reversals were identified in 77 pairs of PDB entries. For most of the comparisons between proteins with different domain composition and architecture, large differences in the relative spatial orientation of domains were observed. Although preservation of state of interaction was observed for ~75% of the comparisons, none of the inter-domain interfaces of domains in different order displayed high interface similarity. These domain order reversals in multi-domain proteins are contributed by a limited number of 15 SCOP superfamilies. Majority of the superfamilies undergoing order reversal either function as transporters or regulatory domains and very few are enzymes. A higher proportion of domain order reversals were observed in domains separated by 0 or 1 domains than those separated by more than 1 domain. A thorough analysis of various structural features of domains undergoing order reversal indicates that only one order of domains is strongly preferred over all possible orders. This may be due to either evolutionary selection of one of the orders and its conservation throughout generations, or the fact that domain order reversals rarely conserve the interface between the domains. Further studies (Chapters 6 to 8) utilize the available computational techniques for structural and functional annotation of proteins encoded in a few bacterial genomes. Based on these annotations, proteome-wide structure and function comparisons were performed between two sets of pathogenic and non-pathogenic bacteria. The first study compares the pathogenic Mycobacterium tuberculosis to the closely related organism Mycobacterium smegmatis which is non-pathogenic. The second study primarily identified biologically feasible host-pathogen interactions between the human host and the pathogen Leptospira interrogans and also compared leptospiral-host interactions of the pathogenic Leptospira interrogans and of the saprophytic Leptospira biflexa with the human host. Chapter 6 describes the function and structure annotation of proteins encoded in the genome of M. smegmatis MC2-155. M. smegmatis is a widely used model organism for understanding the pathophysiology of M. tuberculosis, the primary causative agent of tuberculosis in humans. M. smegmatis and M. tuberculosis species of the mycobacterial genus share several features like a similar cell-wall architecture, the ability to oxidise carbon monoxide aerobically and share a huge number of homologues. These features render M. smegmatis particularly useful in identifying critical cellular pathways of M. tuberculosis to inhibit its growth in the human host. In spite of the similarities between M. smegmatis and M. tuberculosis, there are stark differences between the two due to their diverse niche and lifestyle. While there are innumerable studies reporting the structure, function and interaction properties of M. tuberculosis proteins, there is a lack of high quality annotation of M. smegmatis proteins. This makes the understanding of the biology of M. smegmatis extremely important for investigating its competence as a good model organism for M. tuberculosis. With the implementation of available sequence and structural profile-based search procedures, functional and structural characterization could be achieved for ~92% of the M. smegmatis proteome. Structural and functional domain definitions were obtained for a total of 5695 of 6717 proteins in M. smegmatis. Residue coverage >70% was achieved for 4567 proteins, which constitute ~68% of the proteome. Domain unassigned regions more than 30 residues were assessed for their potential to be associated to a domain. For 1022 proteins with no recognizable domains, putative structural and functional information was inferred for 328 proteins by the use of distance relationship detection and fold recognition methods. Although 916 sequences of 1022 proteins with no recognizable domains were found to be specific to M. smegmatis species, 98 of these are specific to its MC2-155 strain. Of the 1828 M. smegmatis proteins classified as conserved hypothetical proteins, 1038 proteins were successfully characterized. A total of 33 Domains of Unknown Function (DUFs) occurring in M. smegmatis could be associated to structural domains. A high representation of the tetR and GntR family of transcription regulators was noted in the functional repertoire of M. smegmatis proteome. As M. smegmatis is a soil-dwelling bacterium, transcriptional regulators are crucial for helping it to adapt and survive the environmental stress. Similarly, the ABC transporter and MFS domain families are highly represented in the M. smegmatis proteome. These are important in enabling the bacteria to uptake carbohydrate from diverse environmental sources. A lower number of virulent proteins were identified in M. smegmatis, which justifies its non-pathogenicity. Thus, a detailed functional and structural annotation of the M. smegmatis proteome was achieved in Chapter 6. Chapter 7 delineates the similarities and difference in the structure and function of proteins encoded in the genomes of the pathogenic M. tuberculosis and the non-pathogenic M. smegmatis. The protocol employed in Chapter 6 to achieve the proteome-wide structure and function annotation of M. smegmatis was also applied to M. tuberculosis proteome in Chapter 7. The number of proteins encoded by the genome of M. smegmatis strain MC2-155 (6717 proteins) is comparatively higher than that in M. tuberculosis strain H37Rv (4018 proteins). A total of 2720 high confidence orthologues sharing ≥30% sequence identity were identified in M. tuberculosis with respect to M. smegmatis. Based on the orthologue information, specific functional clusters, essential proteins, metabolic pathways, transporters and toxin-antitoxin systems of M. tuberculosis were inspected for conservation in M. smegmatis. Among the several categories analysed, 53 metabolic pathways, 44 membrane transporter proteins belonging to secondary transporters and ATP-dependent transporter classes, 73 toxin-antitoxin systems, 23 M. tuberculosis-specific targets, 10 broad-spectrum targets and 34 targets implicated in persistence of M. tuberculosis could not detect any orthologues in M. smegmatis. Several of the MFS superfamily transporters act as drug efflux pumps and are hence associated with drug resistance in M. tuberculosis. The relative abundances of MFS and ABC superfamily transporters are higher in M. smegmatis than in M. tuberculosis. As these transporters are involved in carbohydrate uptake, their higher representation in M. smegmatis than in M. tuberculosis highlights the lack of proficiency of M. tuberculosis to assimilate diverse carbon sources. In the case of porins, MspA-like and OmpA-like porins are selectively present in either M. smegmatis or M. tuberculosis. These differences help to elucidate protein clusters for which M. smegmatis may not be the best model organism to study M. tuberculosis proteins.! At the domain-level, ATP-binding domain of ABC transporters, tetracycline transcriptional regulator (tetR) domain family, major facilitator superfamily (MFS) domain family, AMP-binding domain family and enoyl-CoA hydrolase domain family are highly represented in both M. smegmatis and M. tuberculosis proteomes. These domains play an essential role in the carbohydrate uptake systems and drug-efflux pumps among other diverse functions in mycobacteria. There are several differentially represented domain families in M. tuberculosis and M. smegmatis. For example, the pentapeptide-repeat domain, PE, PPE and PIN domains although abundantly present in M. tuberculosis, are very rare in M. smegmatis. Therefore, such uniquely or differentially represented functional and structural domains in M. tuberculosis as compared to M. smegmatis may be linked to pathogenicity or adaptation of M. tuberculosis in the host. Hence, major differences between M. tuberculosis and M. smegmatis were identified, not only in terms of domain populations but also in terms of domain combinations. Thus, Chapter 7 highlights the similarities and differences between M. smegmatis and M. tuberculosis proteomes in terms of structure and function. These differences provide an understanding of selective utilization of M. smegmatis as a model organism to study M. tuberculosis. ! In Chapter 8, computational tools have been employed to predict biologically feasible host-pathogen interactions between the human host and the pathogenic, Leptospira interrogans. Sensitive profile-based search procedures were used to specifically identify practical drug targets in the genome of Leptospira interrogans, the causative agent of the globally widespread zoonotic disease, Leptospirosis. Traditionally, the genus Leptospira is classified into two species complex- the pathogenic L. interrogans and the non-pathogenic saprophyte L. biflexa. The pathogen gains entry into the human host through direct or indirect contact with fluids of infected animals. Several ambiguities exist in the understanding of L. interrogans pathogenesis. An integration of multiple computational approaches guided by experimentally derived protein-protein interactions, was utilized for recognition of host-pathogen protein-protein interactions. The initial step involved the identification of similarities of host and L. interrogans proteins with crystal structures of experimentally known transient protein-protein complexes. Further, conservation of interfacial nature was used to obtain high confidence predictions for putative host-pathogen protein-protein interactions. These predictions were subjected to further selection based on subcellular localization of proteins of the human host and L. interrogans, and tissue-specific expression profiles of the host proteins. A total of 49 protein-protein interactions mediated by 24 L. interrogans proteins and 17 host proteins were identified and these may be subjected to further experimental investigations to assess their in vivo relevance. The functional relevance of similarities and differences between the pathogenic and non-pathogenic leptospires in terms of interactions with the host has also been explored. For this, protein-protein interactions across human host and the non-pathogenic saprophyte L. biflexa were also predicted. Nearly 39 leptospiral-host interactions were recognized to be similar across both the pathogen and saprophyte in the context of processes that influence the host. The overlapping leptospiral-host interactions of L. interrogans and L. biflexa proteins with the human host proteins are primarily associated with establishment of its entry into the human host. These include adhesion of the leptospiral proteins to host cells, survival in host environment such as iron acquisition and binding to components of extracellular matrix and plasma. The disjoint sets of leptospiral-host interactions are species-specific interactions, more importantly indicative of the establishment of infection by L. interrogans in the human host and immune clearance of L. biflexa by the human host. With respect to L. interrogans, these specific interactions include interference with blood coagulation cascade and dissemination to target organs by means of disruption of cell junction assembly. On the other hand, species-specific interactions of L. biflexa proteins include those with components of host immune system. ! In spite of the limited availability of experimental evidence, these help in identifying functionally relevant interactions between host and pathogen by integrating multiple lines of evidence. Thus, inferences from computational prediction of host-pathogen interactions act as guidelines for experimental studies investigating the in vivo relevance of these predicted protein-protein interactions. This will further help in developing effective measures for treatment and disease prevention. In summary, Chapters 2 and 3 describe the implementation, advantages and limitations of the alignment-free full-length sequence comparison method, CLAP. Chapter 4 and 5 are dedicated to understand the domain-domain interactions in multi-domain protein sequences and structures. In Chapters 6, 7 and 8 the computational analyses of the mycobacterial species and leptospiral species helped in an enhanced understanding of the functional repertoire of these bacteria. These studies were undertaken by utilizing the biological sequence data available in public databases and implementation of powerful homology-detection techniques. The supplemental data associated with the chapters is provided in a compact disc attached with this thesis.!