L'un des défis majeurs du monde de ces dernières décennies a été l'augmentation continue de la population des personnes âgées dans les pays développés. D’où la nécessité de fournir des soins de qualité à une population en croissance rapide, tout en réduisant les coûts des soins de santé. Dans ce contexte, de nombreux travaux de recherche portent sur l’utilisation des réseaux de capteurs sans fil dans les systèmes WBAN (Wireless Body Area Network), pour faciliter et améliorer la qualité du soin et de surveillance médicale à distance. Ces réseaux WBAN soulèvent de nouveaux défis technologiques en termes de sécurité et de protection contre les anomalies et les attaques. Le mode de communication sans fil utilisé entre ces capteurs et l’unité de traitement accentue ces vulnérabilités. En effet les vulnérabilités dans un système WBAN se décomposent en deux parties principales. La première partie se compose des attaques possibles sur le réseau des capteurs médicaux et sur le médium de communications sans fils entre ces capteurs et l’unité de traitement. La deuxième partie se compose des attaques possibles sur les communications à haut débit entre le système WBAN et le serveur médical. L’objectif de cette thèse est de répondre en partie aux problèmes de détection des attaques dans un système WBAN de surveillance médicale à distance. Pour atteindre cet objectif, nous avons proposé un algorithme pour détecter les attaques de brouillage radio (jamming attack) qui visent le médium de communications sans fils entre les capteurs et l’unité de traitement. Ainsi nous avons proposé une méthode de mesure de divergence pour détecter les attaques de type flooding qui visent les communications à haut débit entre le système WBAN et le serveur médical. / One of the major challenges of the world in recent decades is the continued increase in the elderly population in developed countries. Hence the need to provide quality care to a rapidly growing population while reducing the costs of health care is becoming a strategic challenge. In this context, many researches focus on the use of wireless sensor networks in WBAN (Wireless Body Area Network) systems to facilitate and improve the quality of medical care and remote monitoring. These WBAN systems pose new technological challenges in terms of security and protection against faults and attacks. The wireless communication mode used between the sensors and the collection node accentuates these vulnerabilities. Indeed vulnerabilities in a WBAN system are divided into two main parts. The first part consists of the possible attacks on the network of medical sensors and on the wireless communications medium between the sensors and the processing unit. The second part consists of possible attacks on high-speed communications between the WBAN system and the medical server. The objective of this thesis is to meet some of the problems of detecting attacks in a WBAN system for remote medical monitoring. To achieve this goal, we propose an algorithm to detect the jamming attacks targeting the wireless communications medium between the sensors and the processing unit. In addition we propose a method of measuring divergence to detect the flooding attacks targeting the high-speed communications between the WBAN system and the medical server.

This thesis deals with machine learning techniques for the extraction of structure and the analysis of the vertebrate olfactory pathway based on related methods. Some of its main contributions are summarized below. We have performed a systematic investigation for classification in biomedical images with the goal of recognizing a material in these images by its texture. This investigation included (i) different measures for evaluating the importance of image descriptors (features), (ii) methods to select a feature set based on these evaluations, and (iii) classification algorithms. Image features were evaluated according to their estimated relevance for the classification task and their redundancy with other features. For this purpose, we proposed a framework for relevance and redundancy measures and, within this framework, we proposed two new measures. These were the value difference metric and the fit criterion. Both measures performed well in comparison with other previously used ones for evaluating features. We also proposed a Hopfield network as a method for feature selection, which in experiments gave one of the best results relative to other previously used approaches. We proposed a genetic algorithm for clustering and tested it on several realworld datasets. This genetic algorithm was novel in several ways, including (i) the use of intra-cluster distance as additional optimization criterion, (ii) an annealing procedure, and (iii) adaptation of mutation rates. As opposed to many conventional clustering algorithms, our optimization framework allowed us to use different cluster validation measures including those which do not rely on cluster centroids. We demonstrated the use of the clustering algorithm experimentally with several cluster validity measures as optimization criteria. We compared the performance of our clustering algorithm to that of the often-used fuzzy c-means algorithm on several standard machine learning datasets from the University of California/Urvine (UCI) and obtained good results. The organization of representations in the brain has been observed at several stages of processing to spatially decompose input from the environment into features that are somehow relevant from a behavioral or perceptual standpoint. For the perception of smells, the analysis of such an organization, however, is not as straightforward because of the missing metric. Some studies report spatial clusters for several combinations of physico-chemical properties in the olfactory bulb at the level of the glomeruli. We performed a systematic study of representations based on a dataset of activity-related images comprising more than 350 odorants and covering the whole spatial array of the first synaptic level in the olfactory system. We found clustered representations for several physico-chemical properties. We compared the relevance of these properties to activations and estimated the size of the coding zones. The results confirmed and extended previous studies on olfactory coding for physico-chemical properties. Particularly of interest was the spatial progression by carbon chain that we found. We discussed our estimates of relevance and coding size in the context of processing strategies. We think that the results obtained in this study could guide the search into olfactory coding primitives and the understanding of the stimulus space. In a second study on representations in the olfactory bulb, we grouped odorants together by perceptual categories, such as floral and fruity. By the application of the same statistical methods as in the previous study, we found clustered zones for these categories. Furthermore, we found that distances between spatial representations were related to perceptual differences in humans as reported in the literature. This was possibly the first time that such an analysis had been done. Apart from pointing towards a spatial decomposition by perceptual dimensions, results indicate that distance relationships between representations could be perceptually meaningful. In a third study, we modeled axon convergence from olfactory receptor neurons to the olfactory bulb. Sensory neurons were stimulated by a set of biologically-relevant odors, which were described by a set of physico-chemical properties that covaried with the neural and glomerular population activity in the olfactory bulb. Convergence was mediated by the covariance between olfactory neurons. In our model, we could replicate the formation of glomeruli and concentration coding as reported in the literature, and further, we found that the spatial relationships between representational zones resulting from our model correlated with reported perceptual differences between odor categories. This shows that natural statistics, including similarity of physico-chemical structure of odorants, can give rise to an ordered arrangement of representations at the olfactory bulb level where the distances between representations are perceptually relevant. / <p>QC 20120224</p>