Made available in DSpace on 2016-06-02T20:06:12Z (GMT). No. of bitstreams: 1 DissDAZ.pdf: 2962567 bytes, checksum: 5c6271a67fae12d6b0160ac8ed9351a2 (MD5) Previous issue date: 2006-02-20 / Universidade Federal de Minas Gerais / (See full text for download) / A crescente necessidade do desenvolvimento de eficientes técnicas computacionais e estatísticas para analisar a profusão de dados biológicos transformaram o modelo Markoviano oculto (HMM), caso particular das redes bayesianas ou probabilísticas, em uma alternativa interessante para analisar sequências de DNA. Uma razão do interesse no HMM é a sua flexibilidade em descrever segmentos heterogêneos da sequência através de uma mesma estrutura de dependência entre as variáveis, supostamente conhecida. No entanto, na maioria dos problemas práticos, a estrutura de dependência não é conhecida e precisa ser também estimada. A maneira mais comum para estimação de estrutra de um HMM é o uso de métodos de seleção de modelos. Outra solução é a utilização de metodologias para estimação da estrutura de uma rede probabilística. Neste trabalho, propomos o HMM de segunda ordem e seus estimadores bayesianos, definimos o fator de Bayes e o DIC para seleção do HMM mais adequado a uma sequência específica, verificamos seus desempenhos e a performance da metodologia proposta por Friedman e Koller (2003) em conjunto de dados simulados e aplicamos estas metodologias em duas sequências de DNA: o intron 7 do gene a - fetoprotein dos cimpanzés e o genoma do parasita Bacteriophage lambda, para o qual o modelo de segunda ordem é mais adequado.

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES / This work is a study of the relationship between the intrinsic dimension of feature vectors applied to the classification of video signals in order to perform lip reading. In pattern recognition tasks, the extraction of relevant features is crucial for a good performance of the classifiers. The starting point of this work was the reproduction of the work of J.R. Movellan [1], which classifies lips gestures with HMM using only the video signal from the Tulips1 database. The database consists of videos of volunteers’ mouths while they utter the first 4 numerals in English. The original work uses feature vectors of high dimensionality in relation to the size of the database. Consequently, the adjustment of HMM classifiers has become problematic and the maximum accuracy was only 66.67%. Alternative strategies for feature extraction and classification schemes were proposed in order to analyze the influence of the intrinsic dimension in the performance of classifiers. The best solution, in terms of results, achieved an accuracy of approximately 83%. / Este trabalho é um estudo da relação entre a dimensão intrínseca de vetores de características aplicados à classificação de sinais de vídeo no intuito de realizar-se a leitura labial. Nas tarefas de reconhecimento de padrões, a extração de características relevantes é crucial para um bom desempenho dos classificadores. O ponto de partida deste trabalho foi a reprodução do trabalho de J.R. Movellan [1], que realiza a classificação de gestos labiais com HMM na base de dados Tulips1, utilizando somente o sinal de vídeo. A base é composta por vídeos das bocas de voluntários enquanto esses pronunciam os primeiros 4 numerais em inglês. O trabalho original utiliza vetores de características de dimensão muito alta em relação ao tamanho da base. Consequentemente, o ajuste de classificadores HMM se tornou problemático e só se alcançou 66,67% de acurácia. Estratégias de extração de características e esquemas de classificação alternativos foram propostos, a fim de analisar a influência da dimensão intrínseca no desempenho de classificadores. A melhor solução, em termos de resultados, obteve uma acurácia de aproximadamente 83%. / São Cristóvão, SE

Current Ambient Intelligence and Intelligent Environment research focuses on the interpretation of a subject’s behaviour at the activity level by logging the Activity of Daily Living (ADL) such as eating, cooking, etc. In general, the sensors employed (e.g. PIR sensors, contact sensors) provide low resolution information. Meanwhile, the expansion of ubiquitous computing allows researchers to gather additional information from different types of sensor which is possible to improve activity analysis. Based on the previous research about sitting posture detection, this research attempts to further analyses human sitting activity. The aim of this research is to use non-intrusive low cost pressure sensor embedded chair system to recognize a subject’s activity by using their detected postures. There are three steps for this research, the first step is to find a hardware solution for low cost sitting posture detection, second step is to find a suitable strategy of sitting posture detection and the last step is to correlate the time-ordered sitting posture sequences with sitting activity. The author initiated a prototype type of sensing system called IntelliChair for sitting posture detection. Two experiments are proceeded in order to determine the hardware architecture of IntelliChair system. The prototype looks at the sensor selection and integration of various sensor and indicates the best for a low cost, non-intrusive system. Subsequently, this research implements signal process theory to explore the frequency feature of sitting posture, for the purpose of determining a suitable sampling rate for IntelliChair system. For second and third step, ten subjects are recruited for the sitting posture data and sitting activity data collection. The former dataset is collected byasking subjects to perform certain pre-defined sitting postures on IntelliChair and it is used for posture recognition experiment. The latter dataset is collected by asking the subjects to perform their normal sitting activity routine on IntelliChair for four hours, and the dataset is used for activity modelling and recognition experiment. For the posture recognition experiment, two Support Vector Machine (SVM) based classifiers are trained (one for spine postures and the other one for leg postures), and their performance evaluated. Hidden Markov Model is utilized for sitting activity modelling and recognition in order to establish the selected sitting activities from sitting posture sequences.2. After experimenting with possible sensors, Force Sensing Resistor (FSR) is selected as the pressure sensing unit for IntelliChair. Eight FSRs are mounted on the seat and back of a chair to gather haptic (i.e., touch-based) posture information. Furthermore, the research explores the possibility of using alternative non-intrusive sensing technology (i.e. vision based Kinect Sensor from Microsoft) and find out the Kinect sensor is not reliable for sitting posture detection due to the joint drifting problem. A suitable sampling rate for IntelliChair is determined according to the experiment result which is 6 Hz. The posture classification performance shows that the SVM based classifier is robust to “familiar” subject data (accuracy is 99.8% with spine postures and 99.9% with leg postures). When dealing with “unfamiliar” subject data, the accuracy is 80.7% for spine posture classification and 42.3% for leg posture classification. The result of activity recognition achieves 41.27% accuracy among four selected activities (i.e. relax, play game, working with PC and watching video). The result of this thesis shows that different individual body characteristics and sitting habits influence both sitting posture and sitting activity recognition. In this case, it suggests that IntelliChair is suitable for individual usage but a training stage is required.

The vital signs monitoring data of an infant receiving intensive care are a rich source of information about its health condition. One major concern about the state of health of such patients is the onset of neonatal sepsis, a life-threatening bloodstream infection. As early signs are subtle and current diagnosis procedures involve slow laboratory testing, sepsis detection based on the monitored physiological dynamics is a clinically significant task. This challenging problem can be thoroughly modelled as real-time inference within a machine learning framework. In this thesis, we develop probabilistic dynamical models centred around the goal of providing useful predictions about the onset of neonatal sepsis. This research is characterised by the careful incorporation of domain knowledge for the purpose of extracting the infant’s true physiology from the monitoring data. We make two main contributions. The first one is the formulation of sepsis detection as learning and inference in an Auto-Regressive Hidden Markov Model (AR-HMM). The model investigates the extent to which physiological events observed in the patient’s monitoring traces could be used for the early detection of neonatal sepsis. In addition, the proposed approach involves exact marginalisation over missing data at inference time. When applying the ARHMM on a real-world dataset, we found that it can produce effective predictions about the onset of sepsis. Second, both sepsis and clinical event detection are formulated as learning and inference in a Hierarchical Switching Linear Dynamical System (HSLDS). The HSLDS models dynamical systems where complex interactions between modes of operation can be represented as a twolevel hidden discrete hierarchical structure. For neonatal condition monitoring, the lower layer models clinical events and is controlled by upper layer variables with semantics sepsis/nonsepsis. The model parameterisation and estimation procedures are adapted to the specifics of physiological monitoring data. We demonstrate that the performance of the HSLDS for the detection of sepsis is not statistically different from the AR-HMM, despite the fact that the latter model is given “ground truth” annotations of the patient’s physiology.

L'apnée est une complication fréquente chez les nouveaux-nés prématurés. L'un des problèmes les plus fréquents est l'épisode d'apnée bradycardie dont la répétition influence de manière négative le développement de l'enfant. C'est pourquoi les enfants prématurés sont surveillés en continu par un système de monitoring. Depuis la mise en place de ce système, l'espérance de vie et le pronostic de vie des prématurés ont été considérablement améliorés et ainsi la mortalité réduite. En effet, les avancées technologiques en électronique, informatique et télécommunications ont conduit à l'élaboration de systèmes multivoies de monitoring néonatal de plus en plus performants. L'un des principaux signaux exploités dans ces systèmes est l'électrocardiogramme (ECG). Toutefois, même si l'analyse de l'ECG a évolué au fil des années, l'ensemble des informations qu'il fournit n'est pas encore totalement exploité dans les processus de décision, notamment en monitoring en Unité de Soins Intensifs en Néonatalogie (USIN). L'objectif principal de cette thèse est d'améliorer la prise en compte des dynamiques multi-dimensionnelles en proposant de nouvelles approches basées sur un formalisme bayésien, pour la détection précoce des apnées bradycardies chez le nouveau-né prématuré. Aussi, dans cette thèse, nous proposons deux approches bayésiennes, basées sur les caractéristiques de signaux biologiques en vue de la détection précoce de l'apnée bradycardie des nouveaux-nés prématurés. Tout d'abord avec l'approche de Markov caché, nous proposons deux extensions du Modèle de Markov Caché (MMC) classique. La première, qui s'appelle Modèle de Markov Caché Couplé (MMCC), créé une chaîne de Markov à chaque dimension de l'observation et établit un couplage entre les chaînes. La seconde, qui s'appelle Modèle Semi-Markov Caché Couplé (MSMCC), combine les caractéristiques du modèle de MSMC avec le mécanisme de couplage entre canaux. Pour les deux nouveaux modèles (MMCC et MSMCC), les algorithmes récursifs basées sur la version classique de Forward-Backward sont introduits pour résoudre les problèmes d'apprentissage et d'inférence dans le cas couplé. En plus des modèles de Markov, nous proposons deux approches passées sur les filtres de Kalman pour la détection d'apnée. La première utilise les modifications de la morphologie du complexe QRS et est inspirée du modèle générateur de McSharry, déjà utilisé en couplant avec un filtre de Kalman étendu dans le but de détecter des changements subtils de l'ECG, échantillon par échantillon. La deuxième utilise deux modèles AR (l'un pour le processus normal et l'autre pour le processus de bradycardie). Les modèles AR sont appliqués sur la série RR, alors que le filtre de Kalman suit l'évolution des paramètres du modèle AR et fournit une mesure de probabilité des deux processus concurrents. / Apnea-bradycardia episodes (breathing pauses associated with a significant fall in heart rate) are the most common disease in preterm infants. Consequences associated with apnea-bradycardia episodes involve a compromise in oxygenation and tissue perfusion, a poor neuromotor prognosis at childhood and a predisposing factor to sudden-death syndrome in preterm newborns. It is therefore important that these episodes are recognized (early detected or predicted if possible), to start an appropriate treatment and to prevent the associated risks. In this thesis, we propose two Bayesian Network (BN) approaches (Markovian and Switching Kalman Filter) for the early detection of apnea bradycardia events on preterm infants, using different features extracted from electrocardiographic (ECG) recordings. Concerning the Markovian approach, we propose new frameworks for two generalizations of the classical Hidden Markov Model (HMM). The first framework, Coupled Hidden Markov Model (CHMM), is accomplished by assigning a Markov chain (channel) to each dimension of observation and establishing a coupling among channels. The second framework, Coupled Hidden semi Markov Model (CHMM), combines the characteristics of Hidden semi Markov Model (HSMM) with the above-mentioned coupling concept. For each framework, we present appropriate recursions in order to use modified Forward-Backward (FB) algorithms to solve the learning and inference problems. The proposed learning algorithm is based on Maximum Likelihood (ML) criteria. Moreover, we propose two new switching Kalman Filter (SKF) based algorithms, called wave-based and R-based, to present an index for bradycardia detection from ECG. The wave-based algorithm is established based on McSarry's dynamical model for ECG beat generation which is used in an Extended Kalman filter algorithm in order to detect subtle changes in ECG sample by sample. We also propose a new SKF algorithm to model normal beats and those with bradycardia by two different AR processes.

In Natural Language Processing (NLP), speech and text are parsed and generated with language models and parser models, and translated with translation models. Each model contains a set of numerical parameters which are found by applying a suitable training algorithm to a set of training data. Many such training algorithms are instances of the Expectation-Maximization (EM) algorithm. In [BSV15], a generic EM algorithm for NLP is described. This work presents a particular speech model, the Hidden Markov model, and its standard training algorithm, the Baum-Welch algorithm. It is then shown that the Baum-Welch algorithm is an instance of the generic EM algorithm introduced by [BSV15], from which follows that all statements about the generic EM algorithm also apply to the Baum-Welch algorithm, especially its correctness and convergence properties.

Les modèles latents sont très utilisés en pratique, comme en génomique, économétrie, reconnaissance de parole... Comme la modélisation paramétrique des densités d’émission, c’est-à-dire les lois d’une observation sachant l’état latent, peut conduire à de mauvais résultats en pratique, un récent intérêt pour les modèles latents non paramétriques est apparu dans les applications. Or ces modèles ont peu été étudiés en théorie. Dans cette thèse je me suis intéressée aux propriétés asymptotiques des estimateurs (dans le cas fréquentiste) et de la loi a posteriori (dans le cadre Bayésien) dans deux modèles latents particuliers : les modèles de Markov caché et les modèles de mélange. J’ai tout d’abord étudié la concentration de la loi a posteriori dans les modèles non paramétriques de Markov caché. Plus précisément, j’ai étudié la consistance puis la vitesse de concentration de la loi a posteriori. Enfin je me suis intéressée à l’estimation efficace du paramètre de mélange dans les modèles semi paramétriques de mélange. / Latent models have been widely used in diverse fields such as speech recognition, genomics, econometrics. Because parametric modeling of emission distributions, that is the distributions of an observation given the latent state, may lead to poor results in practice, in particular for clustering purposes, recent interest in using non parametric latent models appeared in applications. Yet little thoughts have been given to theory in this framework. During my PhD I have been interested in the asymptotic behaviour of estimators (in the frequentist case) and the posterior distribution (in the Bayesian case) in two particuliar non parametric latent models: hidden Markov models and mixture models. I have first studied the concentration of the posterior distribution in non parametric hidden Markov models. More precisely, I have considered posterior consistency and posterior concentration rates. Finally, I have been interested in efficient estimation of the mixture parameter in semi parametric mixture models.

[EN] Handwritten Text Segmentation (HTS) is a task within the Document Layout Analysis field that aims to detect and extract the different page regions of interest found in handwritten documents. HTS remains an active topic, that has gained importance with the years, due to the increasing demand to provide textual access to the myriads of handwritten document collections held by archives and libraries. This thesis considers HTS as a task that must be tackled in two specialized phases: detection and extraction. We see the detection phase fundamentally as a recognition problem that yields the vertical positions of each region of interest as a by-product. The extraction phase consists in calculating the best contour coordinates of the region using the position information provided by the detection phase. Our proposed detection approach allows us to attack both higher level regions: paragraphs, diagrams, etc., and lower level regions like text lines. In the case of text line detection we model the problem to ensure that the system's yielded vertical position approximates the fictitious line that connects the lower part of the grapheme bodies in a text line, commonly known as the baseline. One of the main contributions of this thesis, is that the proposed modelling approach allows us to include prior information regarding the layout of the documents being processed. This is performed via a Vertical Layout Model (VLM). We develop a Hidden Markov Model (HMM) based framework to tackle both region detection and classification as an integrated task and study the performance and ease of use of the proposed approach in many corpora. We review the modelling simplicity of our approach to process regions at different levels of information: text lines, paragraphs, titles, etc. We study the impact of adding deterministic and/or probabilistic prior information and restrictions via the VLM that our approach provides. Having a separate phase that accurately yields the detection position (base- lines in the case of text lines) of each region greatly simplifies the problem that must be tackled during the extraction phase. In this thesis we propose to use a distance map that takes into consideration the grey-scale information in the image. This allows us to yield extraction frontiers which are equidistant to the adjacent text regions. We study how our approach escalates its accuracy proportionally to the quality of the provided detection vertical position. Our extraction approach gives near perfect results when human reviewed baselines are provided. / [ES] La Segmentación de Texto Manuscrito (STM) es una tarea dentro del campo de investigación de Análisis de Estructura de Documentos (AED) que tiene como objetivo detectar y extraer las diferentes regiones de interés de las páginas que se encuentran en documentos manuscritos. La STM es un tema de investigación activo que ha ganado importancia con los años debido a la creciente demanda de proporcionar acceso textual a las miles de colecciones de documentos manuscritos que se conservan en archivos y bibliotecas. Esta tesis entiende la STM como una tarea que debe ser abordada en dos fases especializadas: detección y extracción. Consideramos que la fase de detección es, fundamentalmente, un problema de clasificación cuyo subproducto son las posiciones verticales de cada región de interés. Por su parte, la fase de extracción consiste en calcular las mejores coordenadas de contorno de la región utilizando la información de posición proporcionada por la fase de detección. Nuestro enfoque de detección nos permite atacar tanto regiones de alto nivel (párrafos, diagramas¿) como regiones de nivel bajo (líneas de texto principalmente). En el caso de la detección de líneas de texto, modelamos el problema para asegurar que la posición vertical estimada por el sistema se aproxime a la línea ficticia que conecta la parte inferior de los cuerpos de los grafemas en una línea de texto, comúnmente conocida como línea base. Una de las principales aportaciones de esta tesis es que el enfoque de modelización propuesto nos permite incluir información conocida a priori sobre la disposición de los documentos que se están procesando. Esto se realiza mediante un Modelo de Estructura Vertical (MEV). Desarrollamos un marco de trabajo basado en los Modelos Ocultos de Markov (MOM) para abordar tanto la detección de regiones como su clasificación de forma integrada, así como para estudiar el rendimiento y la facilidad de uso del enfoque propuesto en numerosos corpus. Así mismo, revisamos la simplicidad del modelado de nuestro enfoque para procesar regiones en diferentes niveles de información: líneas de texto, párrafos, títulos, etc. Finalmente, estudiamos el impacto de añadir información y restricciones previas deterministas o probabilistas a través de el MEV propuesto que nuestro enfoque proporciona. Disponer de un método independiente que obtiene con precisión la posición de cada región detectada (líneas base en el caso de las líneas de texto) simplifica enormemente el problema que debe abordarse durante la fase de extracción. En esta tesis proponemos utilizar un mapa de distancias que tiene en cuenta la información de escala de grises de la imagen. Esto nos permite obtener fronteras de extracción que son equidistantes a las regiones de texto adyacentes. Estudiamos como nuestro enfoque aumenta su precisión de manera proporcional a la calidad de la detección y descubrimos que da resultados casi perfectos cuando se le proporcionan líneas de base revisadas por humanos. / [CAT] La Segmentació de Text Manuscrit (STM) és una tasca dins del camp d'investigació d'Anàlisi d'Estructura de Documents (AED) que té com a objectiu detectar I extraure les diferents regions d'interès de les pàgines que es troben en documents manuscrits. La STM és un tema d'investigació actiu que ha guanyat importància amb els anys a causa de la creixent demanda per proporcionar accés textual als milers de col·leccions de documents manuscrits que es conserven en arxius i biblioteques. Aquesta tesi entén la STM com una tasca que ha de ser abordada en dues fases especialitzades: detecció i extracció. Considerem que la fase de detecció és, fonamentalment, un problema de classificació el subproducte de la qual són les posicions verticals de cada regió d'interès. Per la seva part, la fase d'extracció consisteix a calcular les millors coordenades de contorn de la regió utilitzant la informació de posició proporcionada per la fase de detecció. El nostre enfocament de detecció ens permet atacar tant regions d'alt nivell (paràgrafs, diagrames ...) com regions de nivell baix (línies de text principalment). En el cas de la detecció de línies de text, modelem el problema per a assegurar que la posició vertical estimada pel sistema s'aproximi a la línia fictícia que connecta la part inferior dels cossos dels grafemes en una línia de text, comunament coneguda com a línia base. Una de les principals aportacions d'aquesta tesi és que l'enfocament de modelització proposat ens permet incloure informació coneguda a priori sobre la disposició dels documents que s'estan processant. Això es realitza mitjançant un Model d'Estructura Vertical (MEV). Desenvolupem un marc de treball basat en els Models Ocults de Markov (MOM) per a abordar tant la detecció de regions com la seva classificació de forma integrada, així com per a estudiar el rendiment i la facilitat d'ús de l'enfocament proposat en nombrosos corpus. Així mateix, revisem la simplicitat del modelatge del nostre enfocament per a processar regions en diferents nivells d'informació: línies de text, paràgrafs, títols, etc. Finalment, estudiem l'impacte d'afegir informació i restriccions prèvies deterministes o probabilistes a través del MEV que el nostre mètode proporciona. Disposar d'un mètode independent que obté amb precisió la posició de cada regió detectada (línies base en el cas de les línies de text) simplifica enormement el problema que ha d'abordar-se durant la fase d'extracció. En aquesta tesi proposem utilitzar un mapa de distàncies que té en compte la informació d'escala de grisos de la imatge. Això ens permet obtenir fronteres d'extracció que són equidistants de les regions de text adjacents. Estudiem com el nostre enfocament augmenta la seva precisió de manera proporcional a la qualitat de la detecció i descobrim que dona resultats quasi perfectes quan se li proporcionen línies de base revisades per humans. / Bosch Campos, V. (2020). Advances in Document Layout Analysis [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/138397 / TESIS

In this thesis, methods of implementing an online gesture recognition system for the novel Stylaero Board device are investigated. Two methods are evaluated - one based on LSTMs and one based on HMMs - on three kinds of gestures: Tap, circle, and flick motions. A method’s performance was measured in its accuracy in determining both whether any of the above listed gestures were performed and, if so, which gesture, in an online single-pass scenario. Insight was acquired regarding the technical challenges and possible solutions to the online aspect of the problem. Poor performance was, however, observed in both methods, with a likely culprit identified as low quality of training data, due to an arduous and complex gesture performance capturing process. Further research improving on the process of gathering data is suggested.

Financial data is often said to follow different market regimes. These regimes, which not possible to observe directly, are assumed to influence the observable returns. In this thesis such regimes are modeled using hidden Markov models. We will investigate whether the five different currency pairs EUR/NOK, USD/NOK, EUR/USD, EUR/SEK, and USD/SEK exhibit market regimes that can be described using hidden Markov modeling. We will find the most optimal number of states and study the mean, variance, and correlations in each market regime. / Finansiella data sägs ofta följa olika marknadsregimer. Dessa marknadsregimer kan inte observeras direkt men antas ha inflytande på de observerade avkastningarna. I denna uppsats undersöks om de fem valutaparen EUR/NOK, USD/NOK, EUR/USD, EUR/SEK och USD/SEK tycks följa separata marknadsregimer som kan detekteras med hjälp av en dold Markovkedja.