This study employs Bayesian Hidden Markov Models as method to explore vehicle traffic flow conditions in an urban area in Stockholm, based on sensor data from separate road positions. Inter-arrival times are used as the observed sequences. These sequences of inter-arrival times are assumed to be generated from the distributions of four different (and hidden) traffic flow states; nightly free flow, free flow, mixture and congestion. The filtered and smoothed probability distributions of the hidden states and the most probable state sequences are obtained by using the forward, forward-backward and Viterbi algorithms. The No-U-Turn sampler is used to sample from the posterior distributions of all unknown parameters. The obtained results show in a satisfactory way that the Hidden Markov Models can detect different traffic flow conditions. Some of the models have problems with divergence, but the obtained results from those models still show satisfactory results. In fact, two of the models that converged seemed to overestimate the presence of congested traffic and all the models that not converged seem to do adequate estimations of the probability of being in a congested state. Since the interest of this study lies in estimating the current traffic flow condition, and not in doing parameter inference, the model choice of Bayesian Hidden Markov Models is satisfactory. Due to the unsupervised nature of the problematization of this study, it is difficult to evaluate the accuracy of the results. However, a model with simulated data and known states was also implemented, which resulted in a high classification accuracy. This indicates that the choice of Hidden Markov Models is a good model choice for estimating traffic flow conditions.

La reconnaissance d’activités humaines est un sujet de recherche d’actualité comme en témoignent les nombreux travaux de recherche sur le sujet. Dans ce cadre, la reconnaissance des activités physiques humaines est un domaine émergent avec de nombreuses retombées attendues dans la gestion de l’état de santé des personnes et de certaines maladies, les systèmes de rééducation, etc.Cette thèse vise la proposition d’une approche pour la reconnaissance automatique et non-intrusive d’activités physiques quotidiennes, à travers des capteurs inertiels de type accéléromètres, placés au niveau de certains points clés du corps humain. Les approches de reconnaissance d’activités physiques étudiées dans cette thèse, sont catégorisées en deux parties : la première traite des approches supervisées et la seconde étudie les approches non-supervisées. L’accent est mis plus particulièrement sur les approches non-supervisées ne nécessitant aucune labellisation des données. Ainsi, nous proposons une approche probabiliste pour la modélisation des séries temporelles associées aux données accélérométriques, basée sur un modèle de régression dynamique régi par une chaine de Markov cachée. En considérant les séquences d’accélérations issues de plusieurs capteurs comme des séries temporelles multidimensionnelles, la reconnaissance d’activités humaines se ramène à un problème de segmentation jointe de séries temporelles multidimensionnelles où chaque segment est associé à une activité. L’approche proposée prend en compte l’aspect séquentiel et l’évolution temporelle des données. Les résultats obtenus montrent clairement la supériorité de l’approche proposée par rapport aux autres approches en termes de précision de classification aussi bien des activités statiques et dynamiques, que des transitions entre activités. / Human activity recognition is currently a challengeable research topic as it can be witnessed by the extensive research works that has been conducted recently on this subject. In this context, recognition of physical human activities is an emerging domain with expected impacts in the monitoring of some pathologies and people health status, rehabilitation procedures, etc. In this thesis, we propose a new approach for the automatic recognition of human activity from raw acceleration data measured using inertial wearable sensors placed at key points of the human body. Approaches studied in this thesis are categorized into two parts : the first one deals with supervised-based approaches while the second one treats the unsupervised-based ones. The proposed unsupervised approach is based upon joint segmentation of multidimensional time series using a Hidden Markov Model (HMM) in a multiple regression context where each segment is associated with an activity. The model is learned in an unsupervised framework where no activity labels are needed. The proposed approach takes into account the sequential appearance and temporal evolution of data. The results clearly show the satisfactory results of the proposed approach with respect to other approaches in terms of classification accuracy for static, dynamic and transitional human activities

Dans cette thèse nous intéressons à la conception d'un robot mobile capable d’analyser le comportement et le mouvement d’une personne en environnement intérieur et encombré, par exemple le domicile d’une personne âgée. Plus précisément, notre objectif est de doter le robot des capacités de perception visuelle de la posture humaine de façon à mieux maîtriser certaines situations qui nécessitent de comprendre l’intention des personnes avec lesquelles le robot interagit, ou encore de détecter des situations à risques comme les chutes ou encore d’analyser les capacités motrices des personnes dont il a la garde. Le suivi de la posture dans un environnement dynamique et encombré relève plusieurs défis notamment l'apprentissage en continue du fond de la scène et l'extraction la silhouette qui peut être partiellement observable lorsque la personne est dans des endroits occultés. Ces difficultés rendent le suivi de la posture une tâche difficile. La majorité des méthodes existantes, supposent que la scène est statique et la personne est toujours visible en entier. Ces approches ne sont pas adaptées pour fonctionner dans des conditions réelles. Nous proposons, dans cette thèse, un nouveau système de suivi capable de suivre la posture de la personne dans ces conditions réelles. Notre approche utilise une grille d'occupation avec un modèle de Markov caché pour apprendre en continu l'évolution de la scène et d'extraire la silhouette, ensuite un algorithme de filtrage particulaire hiérarchique est utilisé pour reconstruire la posture. Nous proposons aussi un nouvel algorithme de gestion d'occlusion capable d'identifier et d'exclure les parties du corps cachées du processus de l'estimation de la pose. Finalement, nous avons proposé une base de données contenant des images RGB-D avec la vérité-terrain dans le but d'établir une nouvelle référence pour l'évaluation des systèmes de capture de mouvement dans un environnement réel avec occlusions. La vérité-terrain est obtenue à partir d'un système de capture de mouvement à base de marqueur de haute précision avec huit caméras infrarouges. L'ensemble des données est disponible en ligne. La deuxième contribution de cette thèse, est le développement d'une méthode de localisation visuelle à partir d'une caméra du type RGB-D montée sur un robot qui se déplace dans un environnement dynamique. En effet, le système de capture de mouvement que nous avons développé doit équiper un robot se déplaçant dans une scène. Ainsi, l'estimation de mouvement du robot est importante pour garantir une extraction de silhouette correcte pour le suivi. La difficulté majeure de la localisation d'une caméra dans un environnement dynamique, est que les objets mobiles de la scène induisent un mouvement supplémentaire qui génère des pixels aberrants. Ces pixels doivent être exclus du processus de l'estimation du mouvement de la caméra. Nous proposons ainsi une extension de la méthode de localisation dense basée sur le flux optique pour isoler les pixels aberrants en utilisant l'algorithme de RANSAC. / In this thesis we are interested in designing a mobile robot able to analyze the behavior and movement of a a person in indoor and cluttered environment. Our goal is to equip the robot by visual perception capabilities of the human posture to better analyze situations that require understanding of person with which the robot interacts, or detect risk situations such as falls or analyze motor skills of the person. Motion capture in a dynamic and crowded environment raises multiple challenges such as learning the background of the environment and extracting the silhouette that can be partially observable when the person is in hidden places. These difficulties make motion capture difficult. Most of existing methods assume that the scene is static and the person is always fully visible by the camera. These approaches are not able to work in such realistic conditions. In this thesis, We propose a new motion capture system capable of tracking a person in realistic world conditions. Our approach uses a 3D occupancy grid with a hidden Markov model to continuously learn the changing background of the scene and to extract silhouette of the person, then a hierarchical particle filtering algorithm is used to reconstruct the posture. We propose a novel occlusion management algorithm able to identify and discards hidden body parts of the person from process of the pose estimation. We also proposed a new database containing RGBD images with ground truth data in order to establish a new benchmark for the assessment of motion capture systems in a real environment with occlusions. The ground truth is obtained from a motion capture system based on high-precision marker with eight infrared cameras. All data is available online. The second contribution of this thesis is the development of a new visual odometry method to localize an RGB-D camera mounted on a robot moving in a dynamic environment. The major difficulty of the localization in a dynamic environment, is that mobile objects in the scene induce additional movement that generates outliers pixels. These pixels should be excluded from the camera motion estimation process in order to produce accurate and precise localization. We thus propose an extension of the dense localization method based on the optical flow method to remove outliers pixels using the RANSAC algorithm.

Submitted by Filipe dos Santos (fsantos@pucsp.br) on 2018-04-11T11:51:01Z No. of bitstreams: 1 José Pedro Pacheco do Amaral.pdf: 926727 bytes, checksum: c26daf41edd0fb00e4d2136efbe543fe (MD5) / Made available in DSpace on 2018-04-11T11:51:01Z (GMT). No. of bitstreams: 1 José Pedro Pacheco do Amaral.pdf: 926727 bytes, checksum: c26daf41edd0fb00e4d2136efbe543fe (MD5) Previous issue date: 2018-02-28 / Fundação São Paulo - FUNDASP / Pontifícia Universidade Católica de São Paulo - PUCSP / This study aims at addressing the liability of the assignor of shares in a limited company regarding the hidden liabilities verified in the limited company after the acquisition of shares by the assignee. To this end, based on a broad conceptual framework of the proposed theme, our study was divided into three parts. Firstly, we present the core elements for the subsequent confrontation of the proposed problematic: the concepts of limited company and of social capital; the characteristics of the shares, a will that will be transferred from the assignor to the assignee; the legal transaction of the assignment of shares; the purchase price of shares; and the concept of hidden liabilities. Secondly, we approach the institutes provided by law that may possibly support the liability of the assignor’s liability by the hidden liabilities. In this perspective, we address the interpretation of Article 1,003, sole paragraph of the Brazilian Civil Code: the institutes of error, fraud and injury; the redhibitory defects; the possibility of annulment of the legal transaction; and the analogical application of Article 1,146 of the Brazilian Civil Code to the assignment of shares. Finally, in the last part, we address the contractual practices applied to characterize or even to prevent the assignor’s liability by the hidden liabilities. We approach the due diligence of the representations and warranties clauses included in the share assignment contract, as well as of the clauses included in the share assignment contract that deal expressly with the transferor's liability by the hidden liabilities / A presente dissertação de mestrado versa sobre a responsabilidade do cedente de quotas de sociedade limitada em função de passivos não contabilizados (ocultos) verificados na sociedade limitada após a aquisição de quotas pelo cessionário. Para esse fim, apoiados em ampla bibliografia sobre o tema proposto, o estudo foi dividido em três partes. Na primeira, apresentamos os elementos fundamentais para o posterior enfrentamento da problemática proposta: os conceitos de sociedade limitada e de capital social; as características das quotas, bem que será transferido do cedente ao cessionário; o negócio jurídico de transferência das quotas; o preço de aquisição das quotas; e o conceito de passivo oculto. Na segunda parte, expomos os institutos previstos em lei que eventualmente possam embasar a responsabilidade do cedente pelo passivo oculto. Nesse sentido, abordamos a interpretação do parágrafo único do art. 1.003 do Código Civil: os institutos do erro, dolo e lesão; os vícios redibitórios; a possibilidade de anulação do negócio jurídico; e a aplicação analógica do art. 1.146 do Código Civil à cessão de quotas. Finalmente, na última parte, tratamos de práticas contratuais utilizadas para caracterizar ou mesmo prevenir a responsabilidade do cedente pelo passivo oculto. No caso, abordamos a due diligence das cláusulas de representations and warranties inseridas em contratos de cessão de quotas, assim como das próprias cláusulas inseridas em contratos de cessão de quotas que tratam, expressamente, da responsabilidade do cedente pelo passivo oculto

Made available in DSpace on 2015-03-04T18:50:59Z (GMT). No. of bitstreams: 1 Thesis_Tharso_Dominisini_Fernandes_2008.pdf: 433414 bytes, checksum: 974d6b0bd3b829341f4f36f9c8d29a72 (MD5) Previous issue date: 2008-03-13 / Fundação Carlos Chagas Filho de Amparo a Pesquisa do Estado do Rio de Janeiro / Quantum computers may solve certain problems asymptotically faster than the classical computers. Quantum algorithms, such as Shor s algorithm, may be considered as a particular case of the Hidden Subgroup Problem (HSP). The HSP consists in finding a subgroup H of a group G by evaluating a function f, which is constant in cosets of H and distinct for each coset. The HSP for Abelian groups is efficiently solved in a quantum computer, but is quantum computers can solve the HSP in non-Abelian groups efficiently? This question has been regularly discussed by the scientific community due to the importance of some applications, such as the graph isomorphism problem and the short vector in a lattice. In this dissertation we review the Ivanyos et al. (2007a) that address HSP in nilpotent groups of class 2. We make a brief review on Quantum Computing; we address some characteristics of nilpotent groups and solvable groups, with special attention to nilpotent groups of class 2; we discuss the standard method of solution of the HSP in Abelian groups; we present the main characteristics of the polycyclic sequences and important reductions of the HSP in classes of nilpotent groups using the properties of polycyclic sequences. Finally, we present an efficient algorithm to solve the HSP in nilpotent groups of class 2. / Computadores quânticos prometem resolver certos problemas assintoticamente mais rápido do que os computadores clássicos. Algoritmos quânticos, como o algoritmo de Shor, podem ser considerados casos particulares do chamado Problema do Subgrupo Oculto(PSO). O PSO consiste em encontrar um subgrupo H de um grupo G por meio de avaliações de uma função f que é constante em classes laterais de H e distinta em classes laterais diferentes. O PSO em grupos abelianos é resolvido eficientemente em um computador quântico, mas será que os computadores quânticos podem resolver o PSO em grupos não abelianos? Esta questão tem sido discutida regularmente pela comunidade científica devido a importantes aplicações, como é o caso do problema de isomorfismo de grafos e do problema do menor vetor em um reticulado. Nesta dissertação é feita uma revisão do trabalho de Ivanyos et al. (2007a), o qual apresenta uma solução para o PSO em grupos nilpotentes de classe 2. Com esta finalidade, é elaborada uma breve revisão sobre a Computação Quântica; são mostradas algumas características dos grupos nilpotentes e dos grupos solúveis, dando uma atenção especial aos grupos nilpotentes de classe 2; é exposto o método padrão de solução do PSO em grupos abelianos; também são exibidas as principais características de sequencias policıclicas e reduções¸de grupos nilpotentes usando as propriedades de sequencias policıclicas

In this paper we study parameter estimation via the Expectation Maximization (EM) algorithm for a continuous-time hidden Markov model with diffusion and point process observation. Inference problems of this type arise for instance in credit risk modelling. A key step in the application of the EM algorithm is the derivation of finite-dimensional filters for the quantities that are needed in the E-Step of the algorithm. In this context we obtain exact, unnormalized and robust filters, and we discuss their numerical implementation. Moreover, we propose several goodness-of-fit tests for hidden Markov models with Gaussian noise and point process observation. We run an extensive simulation study to test speed and accuracy of our methodology. The paper closes with an application to credit risk: we estimate the parameters of a hidden Markov model for credit quality where the observations consist of rating transitions and credit spreads for US corporations.

Feature extraction is the main driving force behind the advancement of the image processing techniques infields suchas image quality assessment, objectdetection, and object recognition. In this work, we perform a comprehensive and in-depth study on feature extraction for the following applications: image macro-uniformity assessment, 2.5D printing quality assessment, streak defect detection, and pedestrian detection. Firstly, a set of multi-scale wavelet-based features is proposed, and a quality predictor is trained to predict the perceived macro-uniformity. Secondly, the 2.5D printing quality is characterized by a set of merits that focus on the surface structure.Thirdly, a set of features is proposed to describe the streaks, based on which two detectors are developed: the first one uses Support Vector Machine (SVM) to train a binary classifier to detect the streak; the second one adopts Hidden Markov Model (HMM) to incorporates the row dependency information within a single streak. Finally, a novel set of pixel-difference features is proposed to develop a computationally efficient feature extraction method for pedestrian detection.

Legionella pneumophila, the causative agent of Legionnaires' disease, is a water born pathogenic bacteria commonly found in natural and manmade water systems such as rivers, lakes, wet soil, hot and cold water storage systems (being able to survive at temperatures between 6-63 °C, and proliferating between 20-45 °C), showerheads, cooling towers and spa pools. The main pathway of exposure to Legionella is by inhaling the aerosols containing the microorganism. Legionnaires' disease can be fatal if not diagnosed and treated at the right time. Practical Legionella control starts with a risk assessment of the water system and followed by the regular monitoring and water sampling. UK Health and Safety Executive (HSE) have implemented strict legislations to protect the public from Legionnaires' disease. This research highlights and addresses three major data gaps identified in Legionella control and management strategy employed in the UK and worldwide; namely, (i) the underestimation of microbiological threat in current cold water storage sampling strategy, (ii) the inability of current qPCR diagnostic methods to detect live Legionella in water samples, and (iii) the lack of predictive 'risk management system' for Legionella control in domestic water systems. During my PhD, 15 relevant cold water storage tanks (selected from more than 6000 tanks surveyed at different sites located in different London Boroughs) were used to investigate the risk factors that contribute towards Legionella proliferation, and revealed serious shortcomings in the appropriateness of the water sample taken for regulatory testing. Secondly, molecular biology research was carried out to develop an accurate, reliable and rapid testing method for the detection and quantification of live Legionella using qPCR techniques. This was successfully achieved by extracting RNA from a Legionella lenticule, converting the RNA into cDNA and amplifying the cDNA using qPCR techniques. Finally, regular monitoring data from 120 London buildings (60 known to be Legionella positive and 60 known to be Legionella negative) was used to identify the possible risk factors contributing towards Legionella outbreaks. Data for these factors was then used to develop a predictive risk model for Legionella contamination using Principal Component Analysis (PCA). The model was validated with 66 new London buildings and 9 out of London buildings. The model showed 100% accuracy in predicting the risk of Legionella by distinguishing infected and non-infected sites in London as well as for the sites in out of London.

O desafio de encontrar corretamente genes eucarioticos codificadores de proteinas nas sequencias genomicas e um problema em aberto. Neste trabalho, implementamos uma plata- forma, com o objetivo de melhorar a forma com que preditores de genes sao implementados e avaliados. Tres novas ferramentas foram implementadas: ToPS (Toolkit of Probabilistic Models of Sequences) foi o primeiro arcabouco orientado a objetos que fornece ferramentas para implementacao, manipulacao, e combinacao de modelos probabilisticos para representar sequencias de simbolos; MYOP (Make Your Own Predictor) e um sistema que tem como objetivo facilitar a construcao de preditores de genes; e SGEval utiliza grafos de splicing para comparar diferente anotacoes com eventos de splicing alternativos. Utilizamos nossas ferramentas para o desenvolvimentos de preditores de genes em onze genomas distintos: A. thaliana, C. elegans, Z. mays, P. falciparum, D. melanogaster, D. rerio, M. musculus, R. norvegicus, O. sativa, G. max e H. sapiens. Com esse desenvolvimento, estabelecemos um protocolo para implementacao de novos preditores. Alem disso, utilizando a nossa plata- forma, desenvolvemos um fluxo de trabalho para predicao de genes no projeto do genoma da cana de acucar, que ja foi utilizado em 109 sequencias de BAC geradas pelo BIOEN (FAPESP Bioenergy Program). / The challenge of correctly identify eukaryotic protein-coding genes in the genomic se- quences is an open problem. In this work, we implemented a plataform with the aim of improving the way that gene predictors are implemented and evaluated. ToPS (Toolkit of Probabilistic Models of Sequence) was the first object-oriented framework that provides tools for implementation, manipulation, and combination of probabilistic models that represent sequences of symbols. MYOP (Make Your Own Predictor) facilitates the construction of gene predictors. SGEval (Splicing Graph Evaluation) uses splicing graphs to compare dif- ferent annotations with alternative splicing events. We used our plataform to develop gene finders in eleven distinct genomes: A. thaliana, C. elegans, Z. mays, P. falciparum, D. me- lanogaster, D. rerio, M. musculus, R. norvegicus, O. sativa, G. max e H. sapiens. With this development, we established a protocol for implementing new gene predictors. In addi- tion, using our platform, we developed a pipeline to find genes in the 109 sugarcane BAC sequences produced by BIOEN (FAPESP Bioenergy Program).

Submitted by Joana Azevedo (joanad@id.uff.br) on 2017-08-15T18:24:14Z No. of bitstreams: 1 Dissert Wilson Vanderlei Costa Sousa.pdf: 2498072 bytes, checksum: 2cbeac670037f8e2920f8b2932194e62 (MD5) / Approved for entry into archive by Biblioteca da Escola de Engenharia (bee@ndc.uff.br) on 2017-09-04T16:45:28Z (GMT) No. of bitstreams: 1 Dissert Wilson Vanderlei Costa Sousa.pdf: 2498072 bytes, checksum: 2cbeac670037f8e2920f8b2932194e62 (MD5) / Made available in DSpace on 2017-09-04T16:45:28Z (GMT). No. of bitstreams: 1 Dissert Wilson Vanderlei Costa Sousa.pdf: 2498072 bytes, checksum: 2cbeac670037f8e2920f8b2932194e62 (MD5) Previous issue date: 2015-03-26 / Partindo do princípio de que os custos ocultos podem estar presentes em todas as entidades, inclusive nas instituições públicas brasileiras – onde as disfunções resultantes da interação de estruturas atrofiadas com comportamentos atrofiados fazem com que estes custos se perpetuem – esta pesquisa busca responder, dentro do contexto da administração pública, como a Gestão do Capital Intelectual pode contribuir para melhor administrar os custos ocultos. Neste sentido, deve-se levar em consideração que as técnicas contabilísticas não podem reconhecer esse tipo de custo e que a Nova Gestão Pública tem buscado técnicas de gerenciamento aplicáveis ao setor público que resultem no aumento da eficiência, transparência e qualidade dos serviços prestados ao cidadão. Para tanto, primeiramente buscou-se na literatura os principais conceitos, origens, natureza e classificação dos custos ocultos e do Capital Intelectual, bem como os modelos e técnicas para sua identificação e mensuração. A partir daí, realizou-se um estudo de caso no Departamento de Contabilidade e Finanças da Universidade Federal Fluminense e áreas correlacionadas. Foi aplicado um questionário baseado em método de reconhecimento e mensuração do Capital Intelectual do setor público, o qual pode apresentar resultados que direcionam para insuficiência, estagnação ou excelência, sendo os dois primeiros grandes indicadores da ocorrência de custos ocultos. Foi possível verificar que a Gestão do Capital Intelectual no Serviço Público pode ser aplicada de forma a contribuir na regulação dos comportamentos e estruturas atrofiadas, objetivando com isso diminuir as disfunções que geram custos ocultos e produzem efeitos destrutivos para a performance eficiente do serviço público. / Assuming that hidden costs may be present in all entities, including Brazilian public institutions – where dysfunctions resulted from the interaction between both atrophied structures and atrophied behaviors make these costs to perpetuate themselves – this research aims to answer, within public administration context, how intellectual capital management can help to improve hidden costs management as well. In this sense, it must be taken that accounting techniques cannot recognize this kind of cost and the New Public Management has searched for management techniques applicable to public sector that result in increasing efficiency, transparency and quality of services provided to citizens. To do so, first one has sought in literature about the main concepts, origins, nature and classification of hidden costs and Intellectual Capital, as well as models and techniques for its identification and measurement. Thereafter, one has performed a case study in the Accounting and Finances Department of Federal Fluminense University and its related areas.It has been applied a survey based on a method for recognition and measurement of intellectual capital in public sector, which can produce results that lead to failure, stagnation or excellence, the first two ones are considered main indicators of the occurrence of hidden costs. Was possible to verify that the Intellectual Capital Management in Public Service can be applied in order to contribute for the regulation of behavior and atrophied structures, aiming this way to reduce dysfunctions that generate hidden costs and produce destructive effects for the efficient performance of the public service.