Bon nombre de problèmes d’ingénierie requièrent l’estimation de l’état de systèmes dynamiques. La modélisation de l’espace des états du système est faite à travers un vecteur d’état qui contient toutes informations utiles pour la description du système. Les problèmes d’estimation d’état sont aussi connus comme problèmes inverses non stationnaires. Ils sont d'un grand intérêt dans de nombreuses applications pratiques, afin de produire une estimation séquentielle des variables souhaitées, à partir de modèles stochastiques et de mesures expérimentales. Ceci dans le but d’optimiser statistiquement l’erreur. Ce travail a pour objectif d’appliquer des méthodes de Filtres à Particules à des thermiques et de combustion. Ces algorithmes sont appliqués successivement à un problème de conduction de chaleur, à un problème de solidification et finalement à un problème de propagation d’incendies. / Many areas of engineering require state estimation of dynamic systems. State relevant information to describe the desired system. The state estimation problems are also known as transient inverse problems. They are of great interest in many practical applications, in order to produce sequential estimates of the desired variables through stochastic models and experimental measurements, in such a way that the error is statistically minimized. In this work we solve state estimation problems with the Bayesian class of particle filters, in heat transfer and combustion. These algorithms havebeen applied to problems of one-dimensional transient heat conduction, solidification and fire propagation.

Computer networks security is a topic which has been extensively researched. This research is fully justified when one notices the dimensions of the problem faced. One can easily identify different kinds of networks, a large quantity of network protocols, and an overwhelming amount of user applications that make extensive use of networks for the purposes those applications were built. This conforms a vast research field, where it is possible for a researcher to set his or her interests over a set of threats, vulnerabilities, or types of attacks, and devise a mechanism to prevent the attack, mitigate its effects or repair the final damages, based upon the specific characteristics of the scenario. Our research group on Computer Networks has been researching on certain kinds of computer networks security risks, specially those affecting wireless networks. In previous doctoral works [13], detection and exclusion methods for dealing with malicious nodes in mobile ad hoc networks (MANETs) had been proposed, from the point of view of every individual network node, using a technique called Intrusion Detection Systems (IDS) based on Watchdog methods. In this scope, we pretend to optimize network throughput removing misbehaved nodes from the network communication processes, a task performed specifically by the Watchdog systems. When isolated security techniques obtain good results on dealing with one type of attacks, a way to improve the whole network performance could be establishing mechanisms for cooperatively sharing information between well-behaved nodes to speed up misbehaved node detection and increase accuracy. Obviously, these mechanisms will have a cost in terms of network transmission overhead and also a small computing time overhead needed to analize the received data and to obtain an opinion about a suspect node. The key issue here it to adequately balance the costs and the benefits related to these cooperation techniques to ensure that the overall network performance is increased if compared with a non-collaborative one. / Serrat Olmos, MD. (2013). Cooperation techniques to improve peer-to-peer wireless networks security [Tesis doctoral]. Editorial Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/32831 / Alfresco

<p>The main theme of this dissertation is statistical estimation and information theory. There are three related topics including "distributed estimation", "an information geometric approach to ML estimation with incomplete data" and "joint identification and estimation in non-linear state space using Bayesian filters". The expectationmaximization (EM) algorithm, as an iterative estimation technique for dealing with incomplete data is the common bond that binds these three topics together.</p> <p>1. <em>Distributed estimation</em></p> <p>Distributed estimation involves the study of estimation theory in an information theoretic framework. This field concerns the following question: "What if the purpose of communications in a distributed environment is parameter estimation rather than source reconstruction?" The first part of this thesis is dedicated to designing low-complexity iterative algorithms for distributed estimation. The algorithm design, in this case, involves transmission of statistics via communication systems. Therefore, the first question raised is "whether the code rates in distributed estimation are different from those in conventional communications?" Surprisingly, under certain conditions, the answer is found to be negative. It is shown that for fixed parameters, the achievable rates coincide with rates in conventional distributed coding of correlated sources (i.e. Slepian-Wolf region). In order to prove the main theorem, we also devise a novel distributed binning scheme and a new theorem in Large deviation theory that are used for proving our distributed coding theorem. The proof of the converse is implemented by a generalized <em>Fano's inequality</em> for distributed estimation.</p> <p>Determination of the region of achievable rates for efficient estimation of a general source is an extremely difficult problem. This fact is the motivation for proving a theorem that provides a method for determining the region of achievable rates for a large class of sources with a convex mutual information with respect to the unknown parameters.</p> <p>With a given set of rates, an efficient implementation of universal coding schemes for distributed estimation based on the expectation maximization (EM) technique is presented. Since the correlation channel between the sources is assumed to be unknown at the joint decoder, previously proposed distributed coding schemes are not useful for this purpose. Therefore, LDPC-based coset-coding schemes are extended to the case where the correlation channel is unknown at the decoder. The basic idea is to implement a low-complexity version of the EM algorithm on a factor~graph that includes an LDPC decoding mechanism.</p> <p>2. <em>Information geometric approach to ML estimation with incomplete data</em></p> <p>The stochastic maximum likelihood estimation of parameters with incomplete data is cast in an information geometric framework. In this vein we develop the information geometric identification (IGID) algorithm, that provides an alternative iterative solution to the incomplete-data estimation problem. The algorithm consists of iterative alternating projections on two sets of probability distributions (PD); i.e., likelihood PD's and data empirical distributions. A Gaussian assumption on the source distribution permits a closed form lowcomplexity solution for these projections. The method is applicable to a wide range of problems; however the emphasis is on semi-blind identification of unknown parameters in a multi-input multi-output (MIMO) communications system.</p> <p>3. <em>Joint identification and estimation in non-linear state space using Bayesian filters</em></p> <p>There are situations in estimation where nonlinear state-space models where the model parameters or the model structure itself are not known a priori or are known only partially. In these scenarios, standard estimation algorithms like the extended Kalman Filter (EKF), which assume perfect knowledge of the model parameters, are not accurate. The nonlinear state estimation problem with possibly non-Gaussian noise in the presence of measurement model uncertainty is modeled as a special case of maximum likelihood estimation with incomplete data. The EM algorithm is used to solve the problem. The expectation (E) step is implemented by a particle filter that is initialized by a Monte-Carlo Markov chain algorithm. In the maximization (M) step, a nonlinear regression method, here using a mixture of Gaussians (MoG), is used to approximate (identify) the uncertain model equations. The proposed procedure is used to solve a highly nonlinear bearing-only tracking problem, as well as the sensor registration problem in a multi-sensor fusion scenario.</p> / Doctor of Philosophy (PhD)

The Internet has been plagued with endless spam for over 15 years. However, in the last five years spam has morphed from an annoying advertising tool to a social engineering attack vector. Much of today's unwanted email tries to deceive users into replying with passwords, bank account information, or to visit malicious sites which steal login credentials and spread malware. These email-based attacks are known as phishing attacks. Much has been published about these attacks which try to appear real not only to users and subsequently, spam filters. Several sources indicate traditional content filters have a hard time detecting phishing attacks because the emails lack the traditional features and characteristics of spam messages. This thesis tests the hypothesis that by separating the messages into three categories (ham, spam and phish) content filters will yield better filtering performance. Even though experimentation showed three-way classification did not improve performance, several additional premises were tested, including the validity of the claim that phishing emails are too much like legitimate emails and the ability of Naive Bayes classifiers to properly classify emails.

Ce travail a pour objectif de développer des techniques théoriques et expérimentales pour la détermination des propriétés thermophysiques et terme source. Deux formes de comportement temporel pour le terme source ont été étudiées : un constant et un qui varie dans le temps. La variation dans le temps a été considérée comme une pulse carrée ou une variation sinusoïdale. Deux formes d’échauffement ont été utilisées : une résistance électrique et un laser diode. Pour l’acquisition des données une caméra de thermographie par infrarouge a été utilisée. La stratégie nodale a été utilisée pour contourner le problème des grosses quantités de données générées par la caméra. Le problème direct a été résolu par différences finies, et deux approches pour la solution du problème inverse ont été utilisées, en fonction du comportement temporel du terme source. Les deux approches sont basées sur des méthodes d’inférence statistiques dans une approche Bayésienne, avec la méthode de Monte Carlo via les Chaînes de Markov pour le terme source constant, et le filtre de Kalman pour le problème dont le terme source varie dans le temps. Des manipulations contrôlées ont été faites dans un échantillon avec des propriétés thermophysiques déterminées par des méthodes classiques dans la littérature. / This work deals with the development of new theoretical and experimental techniques for the efficient estimation of thermophysical properties and source-term in micro and macro-scale. Two kinds of source term were studied: a constant and a time varying source term. The time wise variation of the source term had a sinusoidal and a pulse form. Two devices were used for the sample heating: An electrical resistance and a laser diode. For the data acquisition, an infrared camera was used, providing a full cartography of properties of the medium and also non-contact temperature measurements. The direct problem was solved by the finite differences method, and two approaches were used for the solution of the inverse problem, depending on the time varying behavior of the source term. Both approaches deal with the parameters estimation within the Bayesian framework, using the Markov Chain Monte Carlo (MCMC) method via the Metropolis Hastings (MH) algorithm for the constant source term, and the Kalman filter for the time-varying source term. The nodal strategy is presented as a method to deal with the large number of experimental data problems. Experiments were carried out in a sample with well-known thermophysical properties, determined by classical methods.