La plupart des réseaux WSN utilisent une bande passante industrielle, scientifique et médicale (ISM) sans licence, qui crée un phénomène probable d'interférence sur un canal donné. Le débit du système est influencé par les interférences car il peut être encombré, causant des pertes de paquets, des retransmissions, une instabilité de liaison et un comportement de protocole incohérent. Dans la recherche sur les réseaux de capteurs sans fil, la simulation est l’une des approches essentielles pour évaluer un protocole de système ou de performance. La précision des résultats estimés dépend des paramètres de simulation sélectionnés. Dans les analyses existantes sur WSN, des modèles d'interférence simples sont utilisés dans les simulations. Cependant, ces modèles d'interférence ne sont pas assez précis pour l'analyse pratique d'applications de réseau de capteurs sans fil. De plus, la croissance rapide dans le domaine des réseaux WSN implique la nécessité de créer de nouveaux simulateurs dotés de capacités plus spécifiques pour lutter contre les effets de propagation par brouillage et par trajets multiples. La recherche a pour objectif principal de rechercher un environnement de simulation approprié permettant aux chercheurs de vérifier de nouvelles idées et de comparer les solutions futures. / Most of WSNs use unlicensed Industrial, Scientific and Medical (ISM) frequency band that makes interference probable phenomenon on a given channel.System throughput gets influenced by the interference as it can congest wireless medium, cause packet drops, re-transmissions, link instability, and inconsistent protocol behavior. In wireless sensor network research, simulation is one of the essential approaches to asses and evaluate system or protocol performance. The accuracy of estimated results depends on selected simulation parameters. In existing analysis on WSN, simple interference models are used in simulations. However, these interference models are not accurate enough for practical wireless sensor network applications analysis.Moreover, the rapid growth in the field of WSNs entails the need of creating new simulators that have more specific capabilities to tackle interference and multipath propagation effects.Finding a suitable simulation environment that allows researchers to verify new ideas and compare proposed future solutions is main task of this research.

Submitted by Divisão de Documentação/BC Biblioteca Central (ddbc@ufam.edu.br) on 2018-05-22T13:53:20Z No. of bitstreams: 2 license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) versaofinal.pdf: 8756608 bytes, checksum: e4b5f21e17776e8f9af04b6752317a59 (MD5) / Approved for entry into archive by Divisão de Documentação/BC Biblioteca Central (ddbc@ufam.edu.br) on 2018-05-22T14:16:29Z (GMT) No. of bitstreams: 2 license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) versaofinal.pdf: 8756608 bytes, checksum: e4b5f21e17776e8f9af04b6752317a59 (MD5) / Made available in DSpace on 2018-05-22T14:16:29Z (GMT). No. of bitstreams: 2 license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) versaofinal.pdf: 8756608 bytes, checksum: e4b5f21e17776e8f9af04b6752317a59 (MD5) Previous issue date: 2018-03-09 / CAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / Relative humidity interferes in many aspects in the life of the human being, and due to the many consequences that a low or a high percentage can entail, the control of its level is of paramount importance. Thus, the modeling of extreme situations of this variable can aid in the planning of human activities that are susceptible to their harmful effects, such as public health. The main interest is to predict, based on probability density functions applied to observed data, the values that may occur in a certain locality. The Generalized Distribution of Extreme Values has been widely used for this purpose and research using Time Series analysis of meteorological and climatic data. In this work, a statistical model is proposed for prediction of rates and temporal proportions and/or spatially dependents. The model was constructed by marginalizing the Kumaraswamy G-exponentialised distribution conditioned to a random field with positive alpha-stable distribution. Some properties of this model were presented, procedures for estimation and inference were discussed and an MCEM algorithm was developed to estimate the parameters. As a particular case, the model was used for spatial prediction of relative humidity in weather stations at Amazonas state, Brazil. / A umidade relativa interfere em vários aspectos na vida do ser humano, e devido as muitas consequências que um baixo ou um alto percentual podem acarretar, o controle de seu nível é de suma importância. Dessa forma, a modelagem de situações extremas dessa variável pode auxiliar no planejamento de atividades humanas que sejam suscetíveis aos seus efeitos danosos, como a saúde pública. O principal interesse é prever com base em funções densidade de probabilidade aplicadas aos dados observados, os valores que possam ocorrer em uma certa localidade. A distribuição Generalizada de Valores Extremos tem sido amplamente utilizada com essa finalidade e pesquisas utilizando análise de Séries Temporais de dados meteorológicos e climáticos. Neste trabalho, é proposto um modelo estatístico para predição de taxas e proporções temporais e/ou espacialmente dependentes. O modelo foi construído através da marginalização da distribuição Kumaraswamy G-exponencializada condicionada a um campo aleatório com distribuição alfaestável positivo. Algumas propriedades desse modelo foram apresentadas, procedimentos para estimação e inferência foram discutidos e um algoritmo MCEM foi desenvolvido parar estimar os parâmetros. Como um caso particular, o modelo foi utilizado para predição espacial da umidade relativa do ar observada nas estações meteorológicas do Estado do Amazonas.

This PhD thesis aims to study financial processes which have semi-heavy-tailed marginal distributions and may exhibit memory. The traditional Black-Scholes model is expanded to incorporate memory via an integral operator, resulting in a class of market models which still preserve the completeness and arbitragefree conditions needed for replication of contingent claims. This approach is used to estimate the implied volatility of the resulting model. The first part of the thesis investigates the semi-heavy-tailed behaviour of financial processes. We treat these processes as continuous-time random walks characterised by a transition probability density governed by a fractional Riesz- Bessel equation. This equation extends the Feller fractional heat equation which generates a-stable processes. These latter processes have heavy tails, while those processes generated by the fractional Riesz-Bessel equation have semi-heavy tails, which are more suitable to model financial data. We propose a quasi-likelihood method to estimate the parameters of the fractional Riesz- Bessel equation based on the empirical characteristic function. The second part considers a dynamic model of complete financial markets in which the prices of European calls and puts are given by the Black-Scholes formula. The model has memory and can distinguish between historical volatility and implied volatility. A new method is then provided to estimate the implied volatility from the model. The third part of the thesis considers the problem of classification of financial markets using high-frequency data. The classification is based on the measure representation of high-frequency data, which is then modelled as a recurrent iterated function system. The new methodology developed is applied to some stock prices, stock indices, foreign exchange rates and other financial time series of some major markets. In particular, the models and techniques are used to analyse the SET index, the SET50 index and the MAI index of the Stock Exchange of Thailand.

Le travail réalisé dans le cadre de cette thèse a pour but de caractériser des signaux aléatoires, rencontrés dans le domaine aérien et sous-marin, en s’appuyant sur une approche statistique. En traitement du signal, l'analyse statistique a longtemps été fondée sous l'hypothèse de Gaussianité des données. Cependant, ce modèle n'est plus valide dès lors que la densité de probabilité des données se caractérise par des phénomènes de queues lourdes et d'asymétrie. Une famille de lois est particulièrement adaptée pour représenter de tels phénomènes : les distributions α-stables. Dans un premier temps, les distributions α-stables ont été présentées et utilisées pour estimer des données synthétiques et réelles, issues d'un sondeur monofaisceau, dans une stratégie de classification de fonds marins. La classification est réalisée à partir de la théorie des fonctions de croyance, permettant ainsi de prendre en compte l'imprécision et l'incertitude liées aux données et à l'estimation de celles-ci. Les résultats obtenus ont été comparés à un classifieur Bayésien. Dans un second temps, dans le contexte de la surveillance maritime, une approche statistique à partir des distributions α-stables a été réalisée afin de caractériser les échos indésirables réfléchis par la surface maritime, appelés aussi fouillis de mer, où la surface est observée en configuration bistatique. La surface maritime a d'abord été générée à partir du spectre d'Elfouhaily puis la Surface Équivalente Radar (SER) de celle-ci a été déterminée à partir de l'Optique Physique (OP). Les distributions de Weibull et ont été utilisées et comparées au modèle α-stable. La validité de chaque modèle a été étudiée à partir d'un test de Kolmogorov-Smirnov. / The purpose of this thesis is to characterize random signals, observed in air and underwater context, by using a statistical approach. In signal processing, the hypothesis of Gaussian model is often used for a statistical study. However, the Gaussian model is not valid when the probability density function of data are characterized by heavy-tailed and skewness phenomena. A family of laws can fit these phenomena: the α-stable distributions. Firstly, the α-stable distribution have been used to estimate generated and real data, extracted from a mono-beam echo-sounder, for seabed sediments classification. The classification is made by using the theory of belief functions, which can take into account the imprecision and uncertainty of data and theirs estimations. The results have been compared to a Bayesian approach. Secondly, in a context a marine surveillance, a statistical study from the α-stable distribution has been made to characterize undesirable echo reflected by a sea surface, called sea clutter, where the sea surface is considered in a bistatic configuration. The sea surface has been firstly generated by the Elfouhaily sea spectrum and the Radar Cross Section (RCS) of the sea surface has been computed by the Physical Optics (PO). The Weibull and distributions have been used and the results compared to the α-stable model. The validity of each model has been evaluated by a Kolmogorov-Smirnov test.