L’objectif de cette thèse consiste en l’élaboration de deux méthodologies visant à révéler des effets jusqu’alors masqués en modélisation décisionnelle. Dans la première partie, nous cherchons à mettre en œuvre une méthode d’analyse locale des critères de choix dans un contexte de choix binaires. Dans une seconde partie, nous mettons en avant les effets de génération dans l’étude des comportements de choix. Dans les deux parties, notre démarche de recherche combine de nouveaux outils d’analyse prédictive (Support Vector Machines, FANOVA, PLS) aux outils traditionnels de statistique inférentielle, afin d’enrichir les résultats habituels par des informations complémentaires sur les effets masqués que constituent les effets locaux dans les fonctions de choix binaires, et les effets de génération dans l’analyse temporelle des comportement de choix. Les méthodologies proposées, respectivement nommées AEL et APC-PLS, sont appliquées sur des cas réels, afin d’en illustrer le fonctionnement et la pertinence. / The objective of this thesis is the development of two methodologies to reveal previously hidden effects in decision modeling. In the first part, we try to implement a method of local analysis in order to select criteria in the context of binary choices. In a second part, we highlight the effects of generations in the study of consumer behavior. In both parts, our research approach combines new predictive analytical tools (such as Support Vector Machines, FANOVA, PLS) to traditional tools of inferential statistics, to enrich the usual results by additional on the masked effects, which are the local effects in the binary choice functions, and the effects of generation in temporal choice behavior analysis.The proposed methodologies, respectively named AEL and APC- PLS are both applied to real cases in order to illustrate their operation and relevance.

As técnicas computacionais de aprendizagem de máquina para classificação ou categorização de dados estão sendo cada vez mais utilizadas no contexto de extração de informações ou padrões em bases de dados volumosas em variadas áreas de aplicação. Em paralelo, a aplicação destes métodos computacionais para identificação de padrões, bem como a classificação de dados relacionados ao consumo dos bens de informação é considerada uma tarefa complexa, visto que tais padrões de decisão do consumo estão relacionados com as preferências dos indivíduos e dependem de uma composição de características individuais, variáveis culturais, econômicas e sociais segregadas e agrupadas, além de ser um tópico pouco explorado no mercado brasileiro. Neste contexto, este trabalho realizou o estudo experimental a partir da aplicação do processo de Descoberta do conhecimento (KDD), o que inclui as etapas de seleção e Mineração de Dados, para um problema de classificação binária, indivíduos brasileiros que consomem e não consomem um bem de informação, filmes em salas de cinema, a partir dos dados obtidos na Pesquisa de Orçamento Familiar (POF) 2008-2009, pelo Instituto Brasileiro de Geografia e Estatística (IBGE). O estudo experimental resultou em uma análise comparativa da aplicação de duas técnicas de aprendizagem de máquina para classificação de dados, baseadas em aprendizado supervisionado, sendo estas Naïve Bayes (NB) e Support Vector Machine (SVM). Inicialmente, a revisão sistemática realizada com o objetivo de identificar estudos relacionados a aplicação de técnicas computacionais de aprendizado de máquina para classificação e identificação de padrões de consumo indica que a utilização destas técnicas neste contexto não é um tópico de pesquisa maduro e desenvolvido, visto que não foi abordado em nenhum dos trabalhos estudados. Os resultados obtidos a partir da análise comparativa realizada entre os algoritmos sugerem que a escolha dos algoritmos de aprendizagem de máquina para Classificação de Dados está diretamente relacionada a fatores como: (i) importância das classes para o problema a ser estudado; (ii) balanceamento entre as classes; (iii) universo de atributos a serem considerados em relação a quantidade e grau de importância destes para o classificador. Adicionalmente, os atributos selecionados pelo algoritmo de seleção de variáveis Information Gain sugerem que a decisão de consumo de cultura, mais especificamente do bem de informação, filmes em cinema, está fortemente relacionada a aspectos dos indivíduos relacionados a renda, nível de educação, bem como suas preferências por bens culturais / Machine learning techniques for data classification or categorization are increasingly being used for extracting information or patterns from volumous databases in various application areas. Simultaneously, the application of these computational methods to identify patterns, as well as data classification related to the consumption of information goods is considered a complex task, since such decision consumption paterns are related to the preferences of individuals and depend on a composition of individual characteristics, cultural, economic and social variables segregated and grouped, as well as being not a topic explored in the Brazilian market. In this context, this study performed an experimental study of application of the Knowledge Discovery (KDD) process, which includes data selection and data mining steps, for a binary classification problem, Brazilian individuals who consume and do not consume a information good, film at theaters in Brazil, from the microdata obtained from the Brazilian Household Budget Survey (POF), 2008-2009, performed by the Brazilian Institute of Geography and Statistics (IBGE). The experimental study resulted in a comparative analysis of the application of two machine-learning techniques for data classification, based on supervised learning, such as Naïve Bayes (NB) and Support Vector Machine (SVM). Initially, a systematic review with the objective of identifying studies related to the application of computational techniques of machine learning to classification and identification of consumption patterns indicates that the use of these techniques in this context is not a mature and developed research topic, since was not studied in any of the papers analyzed. The results obtained from the comparative analysis performed between the algorithms suggest that the choice of the machine learning algorithms for data classification is directly related to factors such as: (i) importance of the classes for the problem to be studied; (ii) balancing between classes; (iii) universe of attributes to be considered in relation to the quantity and degree of importance of these to the classifiers. In addition, the attributes selected by the Information Gain variable selection algorithm suggest that the decision to consume culture, more specifically information good, film at theaters, is directly related to aspects of individuals regarding income, educational level, as well as preferences for cultural goods

Neste trabalho, foram utilizadas as vantagens da ferramenta matemática de análise temporal e espectral, a transformada wavelet discreta (DWT), além dos coeficientes de predição linear (LPC) e do algoritmo de inteligência artificial, Least Squares Support Vector Machines (LS-SVM), para aplicações em análise de sinais de voz e classificação de vozes patológicas. Inúmeros trabalhos na literatura têm demonstrado o grande interesse existente por ferramentas auxiliares ao diagnóstico de patologias da laringe. Os componentes da DWT forneceram parâmetros de medida para a análise e classificação das vozes patológicas, principalmente aquelas provenientes de pacientes com edema de Reinke e nódulo nas pregas vocais. O banco de dados com as vozes patológicas foi obtido do Departamento de Otorrinolaringologia e Cirurgia de Cabeça e Pescoço do Hospital das Clínicas da Faculdade de Medicina de Ribeirão Preto (FMRP-USP). Utilizando-se o algoritmo de reconhecimento de padrões, LS-SVM, mostrou-se que a combinação dos componentes da DWT de Daubechies com o filtro LP inverso levou a um classificador de bom desempenho alcançando mais de 90% de acerto na classificação das vozes patológicas. / The main objective of this work was to use the advantages of the time-frequency analysis mathematical tool, discrete wavelet transform (DWT), besides the linear prediction coefficients (LPC) and the artificial intelligence algorithm, Least Squares Support Vector Machines (LS-SVM), for applications in voice signal analysis and classification of pathological voices. A large number of works in the literature has been shown that there is a great interest for auxiliary tools to the diagnosis of laryngeal pathologies. DWT components gave measure parameters for the analysis and classification of pathological voices, mainly that ones from patients with Reinke\'s edema and nodule in the vocal folds. It was used a data bank with pathological voices from the Otolaryngology and the Head and Neck Surgery sector of the Clinical Hospital of the Faculty of Medicine at Ribeirão Preto, University of Sao Paulo (FMRP-USP), Brazil. Using the automatic learning algorithm applied in pattern recognition problems, LS-SVM, results have showed that the combination of Daubechies\' DWT components and inverse LP filter leads to a classifier with good performance reaching more than 90% of accuracy in the classification of the pathological voices.

Submitted by ANA PAULA ALVES GUIMARÃES null (annapaulasun@gmail.com) on 2017-02-04T20:28:04Z No. of bitstreams: 1 dissertação-final.pdf: 4630588 bytes, checksum: 8c2806b890a1b7889d8d26b4a11e97bf (MD5) / Approved for entry into archive by LUIZA DE MENEZES ROMANETTO (luizamenezes@reitoria.unesp.br) on 2017-02-07T13:18:18Z (GMT) No. of bitstreams: 1 guimaraes_apa_me_ilha.pdf: 4630588 bytes, checksum: 8c2806b890a1b7889d8d26b4a11e97bf (MD5) / Made available in DSpace on 2017-02-07T13:18:18Z (GMT). No. of bitstreams: 1 guimaraes_apa_me_ilha.pdf: 4630588 bytes, checksum: 8c2806b890a1b7889d8d26b4a11e97bf (MD5) Previous issue date: 2016-12-20 / O monitoramento da condição estrutural é uma área que vem sendo bastante estudada por permitir a construção de sistemas que possuem a capacidade de identificar um determinado dano em seu estágio inicial, podendo assim evitar sérios prejuízos futuros. O ideal seria que estes sistemas tivessem o mínimo de interferência humana. Sistemas que abordam o conceito de aprendizagem têm a capacidade de serem autômatos. Acredita-se que por possuírem estas propriedades, os algoritmos de aprendizagem de máquina sejam uma excelente opção para realizar as etapas de identificação, localização e avaliação de um dano, com capacidade de obter resultados extremamente precisos e com taxas mínimas de erros. Este trabalho tem como foco principal utilizar o algoritmo support vector machine no auxílio do monitoramento da condição de estruturas e, com isto, obter melhor exatidão na identificação da presença ou ausência do dano, diminuindo as taxas de erros através das abordagens da aprendizagem de máquina, possibilitando, assim, um monitoramento inteligente e eficiente. Foi utilizada a biblioteca LibSVM para análise e validação da proposta. Desta forma, foi possível realizar o treinamento e classificação dos dados promovendo a identificação dos danos e posteriormente, empregando as predições efetuadas pelo algoritmo, foi possível determinar a localização dos danos na estrutura. Os resultados de identificação e localização dos danos foram bastante satisfatórios. / Structural health monitoring (SHM) is an area that has been extensively studied for allowing the construction of systems that have the ability to identify damages at an early stage, thus being able to avoid serious future losses. Ideally, these systems have the minimum of human interference. Systems that address the concept of learning have the ability to be autonomous. It is believed that by having these properties, the machine learning algorithms are an excellent choice to perform the steps of identifying, locating and assessing damage with ability to obtain highly accurate results with minimum error rates. This work is mainly focused on using support vector machine algorithm for monitoring structural condition and, thus, get better accuracy in identifying the presence or absence of damage, reducing error rates through the approaches of machine learning. It allows an intelligent and efficient monitoring system. LIBSVM library was used for analysing and validation of the proposed approach. Thus, it was feasible to conduct training and classification of data promoting the identification of damages. It was also possible to locate the damages in the structure. The results of identification and location of the damage was quite satisfactory.

Submitted by Otávio Henrique Perez null (otavio_perez@hotmail.com) on 2017-03-03T20:13:38Z No. of bitstreams: 1 DissertacaoOtavioHenriquePerez.pdf: 2570606 bytes, checksum: dd0f73a1627a83d453f101ef3a973d23 (MD5) / Approved for entry into archive by LUIZA DE MENEZES ROMANETTO (luizamenezes@reitoria.unesp.br) on 2017-03-09T17:45:41Z (GMT) No. of bitstreams: 1 perez_oh_me_sjrp.pdf: 2570606 bytes, checksum: dd0f73a1627a83d453f101ef3a973d23 (MD5) / Made available in DSpace on 2017-03-09T17:45:41Z (GMT). No. of bitstreams: 1 perez_oh_me_sjrp.pdf: 2570606 bytes, checksum: dd0f73a1627a83d453f101ef3a973d23 (MD5) Previous issue date: 2017-02-23 / Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) / Neste trabalho iremos abordar aspectos qualitativos e geométricos a respeito de campos de vetores suaves por partes. Nosso foco será estudar bifurcações locais e globais de codimensão um e dois e também algumas relações de equivalência para campos vetoriais suaves por partes definidos no plano. Classificaremos e caracterizaremos bifurcações genéricas por meio do retrato de fase e do diagrama de bifurcação dos campos envolvidos. Também faremos uma breve introdução sobre Sistemas Slow-Fast. / In this work we study qualitative and geometric aspects of piecewise smooth vector fields. Our focus is to study local and global bifurcations of codimension one and two and some equivalence relations for piecewise smooth vector fields defined on the plane. We will classify and characterize generic bifurcations using the phase portrait and the bifurcation diagram of the vector fields involved. We also incorporate a brief introduction about Slow-Fast Systems. / FAPESP: 2014/18707-6

As técnicas computacionais de aprendizagem de máquina para classificação ou categorização de dados estão sendo cada vez mais utilizadas no contexto de extração de informações ou padrões em bases de dados volumosas em variadas áreas de aplicação. Em paralelo, a aplicação destes métodos computacionais para identificação de padrões, bem como a classificação de dados relacionados ao consumo dos bens de informação é considerada uma tarefa complexa, visto que tais padrões de decisão do consumo estão relacionados com as preferências dos indivíduos e dependem de uma composição de características individuais, variáveis culturais, econômicas e sociais segregadas e agrupadas, além de ser um tópico pouco explorado no mercado brasileiro. Neste contexto, este trabalho realizou o estudo experimental a partir da aplicação do processo de Descoberta do conhecimento (KDD), o que inclui as etapas de seleção e Mineração de Dados, para um problema de classificação binária, indivíduos brasileiros que consomem e não consomem um bem de informação, filmes em salas de cinema, a partir dos dados obtidos na Pesquisa de Orçamento Familiar (POF) 2008-2009, pelo Instituto Brasileiro de Geografia e Estatística (IBGE). O estudo experimental resultou em uma análise comparativa da aplicação de duas técnicas de aprendizagem de máquina para classificação de dados, baseadas em aprendizado supervisionado, sendo estas Naïve Bayes (NB) e Support Vector Machine (SVM). Inicialmente, a revisão sistemática realizada com o objetivo de identificar estudos relacionados a aplicação de técnicas computacionais de aprendizado de máquina para classificação e identificação de padrões de consumo indica que a utilização destas técnicas neste contexto não é um tópico de pesquisa maduro e desenvolvido, visto que não foi abordado em nenhum dos trabalhos estudados. Os resultados obtidos a partir da análise comparativa realizada entre os algoritmos sugerem que a escolha dos algoritmos de aprendizagem de máquina para Classificação de Dados está diretamente relacionada a fatores como: (i) importância das classes para o problema a ser estudado; (ii) balanceamento entre as classes; (iii) universo de atributos a serem considerados em relação a quantidade e grau de importância destes para o classificador. Adicionalmente, os atributos selecionados pelo algoritmo de seleção de variáveis Information Gain sugerem que a decisão de consumo de cultura, mais especificamente do bem de informação, filmes em cinema, está fortemente relacionada a aspectos dos indivíduos relacionados a renda, nível de educação, bem como suas preferências por bens culturais / Machine learning techniques for data classification or categorization are increasingly being used for extracting information or patterns from volumous databases in various application areas. Simultaneously, the application of these computational methods to identify patterns, as well as data classification related to the consumption of information goods is considered a complex task, since such decision consumption paterns are related to the preferences of individuals and depend on a composition of individual characteristics, cultural, economic and social variables segregated and grouped, as well as being not a topic explored in the Brazilian market. In this context, this study performed an experimental study of application of the Knowledge Discovery (KDD) process, which includes data selection and data mining steps, for a binary classification problem, Brazilian individuals who consume and do not consume a information good, film at theaters in Brazil, from the microdata obtained from the Brazilian Household Budget Survey (POF), 2008-2009, performed by the Brazilian Institute of Geography and Statistics (IBGE). The experimental study resulted in a comparative analysis of the application of two machine-learning techniques for data classification, based on supervised learning, such as Naïve Bayes (NB) and Support Vector Machine (SVM). Initially, a systematic review with the objective of identifying studies related to the application of computational techniques of machine learning to classification and identification of consumption patterns indicates that the use of these techniques in this context is not a mature and developed research topic, since was not studied in any of the papers analyzed. The results obtained from the comparative analysis performed between the algorithms suggest that the choice of the machine learning algorithms for data classification is directly related to factors such as: (i) importance of the classes for the problem to be studied; (ii) balancing between classes; (iii) universe of attributes to be considered in relation to the quantity and degree of importance of these to the classifiers. In addition, the attributes selected by the Information Gain variable selection algorithm suggest that the decision to consume culture, more specifically information good, film at theaters, is directly related to aspects of individuals regarding income, educational level, as well as preferences for cultural goods

Este trabalho descreve os métodos e os resultados encontrados a partir da implementação de métodos estáticos para análise da estabilidade de tensão em sistemas elétricos de potência. A determinação da margem de estabilidade de tensão foi feita através do cálculo do menor valor singular da matriz jacobiana associada às equações de fluxo de carga, comumente utilizado como índice estático de colapso de tensão. As não linearidades e descontinuidades relatadas nas referências estudadas e encontradas nos testes realizados, levaram-nos a propor o uso da razão entre o menor e o maior valores singulares da mesma matriz jacobiana, na expectativa de que este índice tivesse um comportamento menos instável do que o menor valor singular, o que não foi confirmado nos testes realizados. Identifica-se também as regiões do sistema elétrico mais afetadas pela instabilidade, o que é feito através da determinação da barra crítica do sistema e da classificação das barras de carga em ilhas de controle de tensão. A barra crítica é identificada através do cálculo do vetor tangente do sistema, conforme proposto nas referências citadas no trabalho. Como alternativa ao vetor tangente para a identificação da barra crítica, propôs-se usar o vetor singular à direita associado ao menor valor singular da matriz jacobiana. A comparação da capacidade de identificação da barra crítica por esses dois vetores mostrou uma clara vantagem do uso do vetor tangente. A rotina para identificação das ilhas de controle de tensão foi adaptada a partir de um método desenvolvido para a análise de coerência em barras de carga, e os resultados encontrados foram bastante satisfatórios. Os métodos implementados foram testados em diversas situações, com o objetivo de se analisar os efeitos dos modelos de carga ZIP com elevadas parcelas de impedância constante, dos limitadores de potência reativa dos geradores e da repartição do incremento da carga de potência ativa entre os geradores. / This work describes the methods and results got from the implementation of static methods for power systems voltage stability analisys. The power system voltage stability margin was predicted by the smallest load flow jacobian\'s singular value, commonly used as a prediction index to voltage stability. lt is investigated the use of ratio of the smallest single value by the biggest one as voltage colapse index, assuming that it\'s less unstable than the singular value itself, specialy near the collapse point. The results presented shown a clear advantage of using the smallest singular value instead of this singular value rate. The identification of the system\'s regions affected by the voltage drop is made by the tangent vector and by the voltage island identification method proposed on this work. Is compared the ability to identify system\'s critical bus by the tangent vector and right singular vetor of the smallest jacobian\'s singular value. In this case, tests results show the superiority of tangent vector. All the simulations presented are compared to allow the analysis of the voltage dependents load models (with high percentual of constant impedances), reactive limiters and generators load sharing efects over the smallest singular value, the rate of the smallest single value by the biggest one, voltage island classification and the critical bus identification.

Dans beaucoup de pays du monde, on observe une importante augmentation du nombre de personnes âgées vivant seules. Depuis quelques années, un nombre significatif de projets de recherche sur l’assistance aux personnes âgées ont vu le jour. La plupart de ces projets utilisent plusieurs modalités (vidéo, son, détection de chute, etc.) pour surveiller l'activité de la personne et lui permettre de communiquer naturellement avec sa maison "intelligente", et, en cas de danger, lui venir en aide au plus vite. Ce travail a été réalisé dans le cadre du projet ANR VERSO de recherche industrielle, Sweet-Home. Les objectifs du projet sont de proposer un système domotique permettant une interaction naturelle (par commande vocale et tactile) avec la maison, et procurant plus de sécurité à l'habitant par la détection des situations de détresse. Dans ce cadre, l'objectif de ce travail est de proposer des solutions pour la reconnaissance des sons de la vie courante dans un contexte réaliste. La reconnaissance du son fonctionnera en amont d'un système de Reconnaissance Automatique de la Parole. Les performances de celui-ci dépendent donc de la fiabilité de la séparation entre la parole et les autres sons. Par ailleurs, une bonne reconnaissance de certains sons, complétée par d'autres sources informations (détection de présence, détection de chute, etc.) permettrait de bien suivre les activités de la personne et de détecter ainsi les situations de danger. Dans un premier temps, nous nous sommes intéressés aux méthodes en provenance de la Reconnaissance et Vérification du Locuteur. Dans cet esprit, nous avons testé des méthodes basées sur GMM et SVM. Nous avons, en particulier, testé le noyau SVM-GSL (SVM GMM Supervector Linear Kernel) utilisé pour la classification de séquences. SVM-GSL est une combinaison de SVM et GMM et consiste à transformer une séquence de vecteurs de longueur arbitraire en un seul vecteur de très grande taille, appelé Super Vecteur, et utilisé en entrée d'un SVM. Les expérimentations ont été menées en utilisant une base de données créée localement (18 classes de sons, plus de 1000 enregistrements), puis le corpus du projet Sweet-Home, en intégrant notre système dans un système plus complet incluant la détection multi-canaux du son et la reconnaissance de la parole. Ces premières expérimentations ont toutes été réalisées en utilisant un seul type de coefficients acoustiques, les MFCC. Par la suite, nous nous sommes penchés sur l'étude d'autres familles de coefficients en vue d'en évaluer l'utilisabilité en reconnaissance des sons de l'environnement. Notre motivation fut de trouver des représentations plus simples et/ou plus efficaces que les MFCC. En utilisant 15 familles différentes de coefficients, nous avons également expérimenté deux approches pour transformer une séquence de vecteurs en un seul vecteur, à utiliser avec un SVM linéaire. Dans le première approche, on calcule un nombre fixe de coefficients statistiques qui remplaceront toute la séquence de vecteurs. La seconde approche (une des contributions de ce travail) utilise une méthode de discrétisation pour trouver, pour chaque caractéristique d'un vecteur acoustique, les meilleurs points de découpage permettant d'associer une classe donnée à un ou plusieurs intervalles de valeurs. La probabilité de la séquence est estimée par rapport à chaque intervalle. Les probabilités obtenues ainsi sont utilisées pour construire un seul vecteur qui remplacera la séquence de vecteurs acoustiques. Les résultats obtenus montrent que certaines familles de coefficients sont effectivement plus adaptées pour reconnaître certaines classes de sons. En effet, pour la plupart des classes, les meilleurs taux de reconnaissance ont été observés avec une ou plusieurs familles de coefficients différentes des MFCC. Certaines familles sont, de surcroît, moins complexes et comptent une seule caractéristique par fenêtre d'analyse contre 16 caractéristiques pour les MFCC / In many countries around the world, the number of elderly people living alone has been increasing. In the last few years, a significant number of research projects on elderly people monitoring have been launched. Most of them make use of several modalities such as video streams, sound, fall detection and so on, in order to monitor the activities of an elderly person, to supply them with a natural way to communicate with their “smart-home”, and to render assistance in case of an emergency. This work is part of the Industrial Research ANR VERSO project, Sweet-Home. The goals of the project are to propose a domotic system that enables a natural interaction (using touch and voice command) between an elderly person and their house and to provide them a higher safety level through the detection of distress situations. Thus, the goal of this work is to come up with solutions for sound recognition of daily life in a realistic context. Sound recognition will run prior to an Automatic Speech Recognition system. Therefore, the speech recognition’s performances rely on the reliability of the speech/non-speech separation. Furthermore, a good recognition of a few kinds of sounds, complemented by other sources of information (presence detection, fall detection, etc.) could allow for a better monitoring of the person's activities that leads to a better detection of dangerous situations. We first had been interested in methods from the Speaker Recognition and Verification field. As part of this, we have experimented methods based on GMM and SVM. We had particularly tested a Sequence Discriminant SVM kernel called SVM-GSL (SVM GMM Super Vector Linear Kernel). SVM-GSL is a combination of GMM and SVM whose basic idea is to map a sequence of vectors of an arbitrary length into one high dimensional vector called a Super Vector and used as an input of an SVM. Experiments had been carried out using a locally created sound database (containing 18 sound classes for over 1000 records), then using the Sweet-Home project's corpus. Our daily sounds recognition system was integrated into a more complete system that also performs a multi-channel sound detection and speech recognition. These first experiments had all been performed using one kind of acoustical coefficients, MFCC coefficients. Thereafter, we focused on the study of other families of acoustical coefficients. The aim of this study was to assess the usability of other acoustical coefficients for environmental sounds recognition. Our motivation was to find a few representations that are simpler and/or more effective than the MFCC coefficients. Using 15 different acoustical coefficients families, we have also experimented two approaches to map a sequence of vectors into one vector, usable with a linear SVM. The first approach consists of computing a set of a fixed number of statistical coefficients and use them instead of the whole sequence. The second one, which is one of the novel contributions of this work, makes use of a discretization method to find, for each feature within an acoustical vector, the best cut points that associates a given class with one or many intervals of values. The likelihood of the sequence is estimated for each interval. The obtained likelihood values are used to build one single vector that replaces the sequence of acoustical vectors. The obtained results show that a few families of coefficients are actually more appropriate to the recognition of some sound classes. For most sound classes, we noticed that the best recognition performances were obtained with one or many families other than MFCC. Moreover, a number of these families are less complex than MFCC. They are actually a one-feature per frame acoustical families, whereas MFCC coefficients contain 16 features per frame

Spectral clustering is often used to partition a data set into a specified number of clusters. Both the unweighted and the vertex-weighted approaches use eigenvectors of the Laplacian matrix of a graph. Our focus is on using vertex-weighted methods to refine clustering of observations. An eigenvector corresponding with the second smallest eigenvalue of the Laplacian matrix of a graph is called a Fiedler vector. Coefficients of a Fiedler vector are used to partition vertices of a given graph into two clusters. A vertex of a graph is classified as unassociated if the Fiedler coefficient of the vertex is close to zero compared to the largest Fiedler coefficient of the graph. We propose a vertex-weighted spectral clustering algorithm which incorporates a vector of weights for each vertex of a given graph to form a vertex-weighted graph. The proposed algorithm predicts association of equidistant or nearly equidistant data points from both clusters while the unweighted clustering does not provide association. Finally, we implemented both the unweighted and the vertex-weighted spectral clustering algorithms on several data sets to show that the proposed algorithm works in general.

The role of scalar mesons in radiative vector meson decays is investigated. The effects of scalar-isoscalar f_{0}(980) and scalar-isovector a_{0}(980) mesons are studied in the mechanism of the radiative Phi-&gt / pi{+}pi{-}gamma and phi-&gt / pi{0}eta gamma decays, respectively. A phenomenological approach is used to study the radiative phi-&gt / pi{+}p{-}gamma decay by considering the contributions of sigma-meson, rho-meson and f_{0}-meson. The interference effects between different contributions are analyzed and the branching ratio for this decay is calculated. The radiative phi-&gt / pi{0}eta gamma decay is studied within the framework of a phenomenological approach in which the contributions of rho-meson, chiral loop and a_{0}-meson are considered. The interference effects between different contributions are examined and the coupling constants g_{phi a_{0} gamma} and g_{a_{0}K{+}K{-}} are estimated using the experimental branching ratio for the phi-&gt / pi{0}eta gamma decay. Furthermore, the radiative rho{0}pi{+}pi{-}gamma$ and rho{0}-&gt / pi{0}pi{0}gamma decays are studied to investigate the role of scalar-isoscalar sigma-meson. The branching ratios of the rho{0}-&gt / pi{+}pi{-}gamma and rho{0}-&gt / pi{0}pi{0}gamma decays are calculated using a phenomenological approach by adding to the amplitude calculated within the framework of chiral perturbation theory and vector meson dominance the amplitude of sigma-meson intermediate state. In all the decays studied the scalar meson intermediate states make important contributions to the overall amplitude.