Cette thèse est le fruit de l’interaction de deux disciplines qui sont la détection de changements dans des images multitemporelles et le raisonnement évidentiel à l’aide de la théorie de Dempster-Shafer (DST). Aborder le problème de détection et d’analyse de changements par la DST nécessite la détermination d’un cadre de discernement exhaustif et exclusif. Ce problème s’avère complexe en l’absence des informations a priori sur les images. Nous proposons dans ce travail de recherche un nouvel algorithme de clustering basé sur l’algorithme Fuzzy-C-Means (FCM) afin de définir les classes sémantiques existantes. L’idée de cet algorithme est la représentation de chaque classe par un nombre varié de centroïdes afin de garantir une meilleure caractérisation de classes. Afin d’assurer l’exhaustivité du cadre de discernement, un nouvel indice de validité de clustering permettant de déterminer le nombre optimal de classes sémantiques est proposé. La troisième contribution consiste à exploiter la position du pixel par rapport aux centroïdes des classes et les degrés d’appartenance afin de définir la distribution de masse qui représente les informations. La particularité de la distribution proposée est la génération d’un nombre réduit des éléments focaux et le respect des axiomes mathématiques en effectuant la transformation flou-masse. Nous avons souligné la capacité du conflit évidentiel à indiquer les transformations multi-temporelles. Nous avons porté notre raisonnement sur la décomposition du conflit global et l’estimation des conflits partiels entre les couples des éléments focaux pour mesurer le conflit causé par le changement. Cette stratégie permet d’identifier le couple de classes qui participent dans le changement. Pour quantifier ce conflit, nous avons proposé une nouvelle mesure de changement notée CM. Finalement, nous avons proposé un algorithme permettant de déduire la carte binaire de changements à partir de la carte de conflits partiels. / This thesis is the interaction result of two disciplines that are the change detection in multitemporal images and the evidential reasoning using the Dempster-Shafer theory (DST). Addressing the problem of change detection and analyzing by the DST, requires the determination of an exhaustive and exclusive frame of discernment. This issue is complex when images lake prior information. In this research work, we propose a new clustering algorithm based on the Fuzzy-C-Means (FCM) algorithm in order to define existing semantic classes. The idea of this algorithm is the representation of each class by a varied number of centroids in order to guarantee a better characterization of classes. To ensure the frame of discernment exhaustiveness, we proposed a new cluster validity index able to identify the optimal number of semantic classes. The third contribution is to exploit the position of the pixel in relation to class centroids and its membership distribution in order to define the mass distribution that represents information. The particularity of the proposed distribution, is the generation of a reduced set of focal elements and the respect of mathematical axioms when performing the fuzzy-mass transformation. We have emphasized the capacity of evidential conflict to indicate multi-temporal transformations. We reasoned on the decomposition of the global conflict and the estimation of the partial conflicts between the couples of focal elements to measure the conflict caused by the change. This strategy allows to identify the couple of classes that participate in the change. To quantify this conflict, we proposed a new measure of change noted CM. Finally, we proposed an algorithm to deduce the binary map of changes from the partial conflicts map.

L’hétérogénéité des environnements ainsi que la diversité des profils et des besoins des patients représentent des contraintes majeures qui remettent en question l’utilisation à grande échelle des systèmes d’assistance à l’autonomie à domicile (AAL). En effet, afin de répondre à l’évolution de l’état des patients et de leurs besoins humains, les environnements AAL sont en évolution continue par l’introduction ou la disparition de capteurs, de dispositifs d’interaction et de services d’assistance. Par conséquent, une plateforme générique et dynamique capable de s’adapter à différents environnements et d’intégrer de nouveaux capteurs, dispositifs d’interaction et services d’assistance est requise. La mise en œuvre d’un tel aspect dynamique peut produire une situation d’incertitude dérivée des problèmes techniques liés à la fiabilité des capteurs ou à des problèmes de réseau. Par conséquent, la notion d’incertitude doit être introduite dans la représentation de contexte et la prise de décision afin de faire face à ce problème. Au cours de cette thèse, j’ai développé une plateforme dynamique et extensible capable de s’adapter à différents environnements et aux besoins des patients. Ceci a été réalisé sur la base de l’approche Plug&Play sémantique que j’ai proposé. Afin de traiter le problème d’incertitude de l’information lié à des problèmes techniques, j’ai proposé une approche de mesure d’incertitude en utilisant les caractéristiques intrinsèques des capteurs et leurs comportements fonctionnels. J’ai aussi fourni un modèle de représentation sémantique et de raisonnement avec incertitude associé avec la théorie de Dempster-Shafer (DST) pour la prise de décision / The heterogeneity of the environments as well as the diversity of patients’ needs and profiles are major constraints that challenge the spread of ambient assistive living (AAL) systems. AAL environments are usually evolving by the introduction or the disappearance of sensors, devices and assistive services to respond to the evolution of patients’ conditions and human needs. Therefore, a generic framework that is able to adapt to such dynamic environments and to integrate new sensors, devices and assistive services at runtime is required. Implementing such a dynamic aspect may produce an uncertainty derived from technical problems related to sensors reliability or network problems. Therefore, a notion of uncertain should be introduced in context representation and decision making in order to deal with this problem. During this thesis, I have developed a dynamic and extendible framework able to adapt to different environments and patients’ needs. This was achieved based on my proposed approach of semantic Plug&Play mechanism. In order to handle the problem of uncertain information related to technical problems, I have proposed an approach for uncertainty measurement based on intrinsic characteristics of the sensors and their functional behaviors, then I have provided a model of semantic representation and reasoning under uncertainty coupled with the Dempster-Shafer Theory of evidence (DST) for decision making

Les travaux présentés dans ce mémoire s’inscrivent dans le cadre duprojet PANsafer (Vers un Passage A Niveau plus sûr), lauréat de l’appel ANR-VTT2008. Ce projet est labellisé par les deux pôles de compétitivité i-Trans et Véhiculedu Futur. Le travail de la thèse est mené conjointement par le laboratoire IRTESSETde l’UTBM et le laboratoire LEOST de l’IFSTTAR.L’objectif de cette thèse est de développer un système de perception permettantl’interprétation de scénarios dans l’environnement d’un passage à niveau. Il s’agitd’évaluer des situations potentiellement dangereuses par l’analyse spatio-temporelledes objets présents autour du passage à niveau.Pour atteindre cet objectif, le travail est décomposé en trois étapes principales. Lapremière étape est consacrée à la mise en place d’une architecture spatiale des capteursvidéo permettant de couvrir de manière optimale l’environnement du passageà niveau. Cette étape est mise en oeuvre dans le cadre du développement d’unsimulateur d’aide à la sécurité aux passages à niveau en utilisant un système deperception multi-vues. Dans ce cadre, nous avons proposé une méthode d’optimisationpermettant de déterminer automatiquement la position et l’orientation descaméras par rapport à l’environnement à percevoir.La deuxième étape consisteà développer une méthode robuste de suivi d’objets enmouvement à partir d’une séquence d’images. Dans un premier temps, nous avonsproposé une technique permettant la détection et la séparation des objets. Le processusde suivi est ensuite mis en oeuvre par le calcul et la rectification du flotoptique grâce respectivement à un modèle gaussien et un modèle de filtre de Kalman.La dernière étape est destinée à l’analyse des trajectoires 2D reconstruites parl’étape précédente pour l’interprétation de scénarios. Cette analyse commence parune modélisation markovienne des trajectoires 2D. Un système de décision à basede théorie de l’évidence est ensuite proposé pour l’évaluation de scénarios, aprèsavoir modélisé les sources de danger.L’approche proposée a été testée et évaluée avec des données issues de campagnesexpérimentales effectuées sur site réel d’un passage à niveau mis à disposition parRFF. / The main objective of this thesis is to develop a system for monitoringthe close environment of a level crossing. It aims to develop a perception systemallowing the detection and the evaluation of dangerous situations around a levelcrossing.To achieve this goal, the overall problem of this work has been broken down intothree main stages. In the first stage, we propose a method for optimizing automaticallythe location of video sensors in order to cover optimally a level crossingenvironment. This stage addresses the problem of cameras positioning and orientationin order to view optimally monitored scenes.The second stage aims to implement a method for objects tracking within a surveillancezone. It consists first on developing robust algorithms for detecting and separatingmoving objects around level crossing. The second part of this stage consistsin performing object tracking using a Gaussian propagation optical flow based modeland Kalman filtering.On the basis of the previous steps, the last stage is concerned to present a newmodel to evaluate and recognize potential dangerous situations in a level crossingenvironment. This danger evaluation method is built using Hidden Markov Modeland credibility model.Finally, synthetics and real data are used to test the effectiveness and the robustnessof the proposed algorithms and the whole approach by considering various scenarioswithin several situations.This work is developed within the framework of PANsafer project (Towards a saferlevel crossing), supported by the ANR-VTT program (2008) of the French NationalAgency of Research. This project is also labelled by Pôles de compétitivité "i-Trans"and "Véhicule du Futur". All the work, presented in this thesis, has been conductedjointly within IRTES-SET laboratory from UTBM and LEOST laboratory fromIFSTTAR.

L'imagerie par résonance magnétique (IRM) est, aujourd'hui, un outil puissant permettant l'observation in vivo de l'anatomie cérébrale. Utilisée en routine clinique, la multiplicité des pondérations d'acquisition permet aux médecins d'accéder à une information riche, abondante, et donc particulièrement adaptée au diagnostic de tumeurs cérébrales.<br /> <br />Cette thèse porte sur la problématique de segmentation des images IRM cérébrales pour l'aide au diagnostic des tumeurs cérébrales. Il s'agit donc de développer des méthodes de segmentation précises et fiables permettant la localisation des tumeurs cérébrales, en particulier infiltrantes dont les frontières ne sont pas nettes.<br />L'approche de segmentation adoptée est une approche multi-échos - donc multi-sources - fondée sur la théorie de l'évidence (ou théorie de Dempster-Shafer) apte à gérer l'incertitude des données à traiter et l'aspect multi-sources des informations manipulées. Dans un premier temps, nous nous attachons à montrer l'aptitude de la théorie de l'évidence à traiter les informations imprécises et incertaines que sont les images IRM au travers d'une démarche de type reconnaissance des formes crédibiliste. Dans un second temps, nous proposons une méthode d'intégration d'informations contextuelles fondée sur une combinaison pondérée de fonctions de croyance. La méthode de segmentation ainsi définie est appliquée à différents volumes cérébraux permettant une détection des zones tumorales. Des comparaisons avec des segmentations menées par des experts cliniciens et des méthodes de la littérature montrent l'intérêt des outils méthodologiques proposés à définir les volumes tumoraux recherchés. Enfin, nous nous sommes intéressées au conflit généré par le processus d'intégration des informations contextuelles. Nous montrons que le conflit est une information à part entière, représentative de la position des frontières entre les différentes structures anatomiques de la scène observée (le cerveau). Cette information frontière peut être utilisée en coopération avec la segmentation région initialement obtenue permettant ainsi d'obtenir un processus de segmentation complet reposant sur une approche de type "régions" et une approche de type "contours"

The technique of sensor fusion addresses the issues relating to the optimality of decision-making in the multiple-sensor framework. The advances in sensor fusion enable to perform intrusion detection for both rare and new attacks. This thesis discusses this assertion in detail, and describes the theoretical and experimental work done to show its validity. The attack-detector relationship is initially modeled and validated to understand the detection scenario. The different metrics available for the evaluation of intrusion detection systems are also introduced. The usefulness of the data set used for experimental evaluation has been demonstrated. The issues connected with intrusion detection systems are analyzed and the need for incorporating multiple detectors and their fusion is established in this work. Sensor fusion provides advantages with respect to reliability and completeness, in addition to intuitive and meaningful results. The goal for this work is to investigate how to combine data from diverse intrusion detection systems in order to improve the detection rate and reduce the false-alarm rate. The primary objective of the proposed thesis work is to develop a theoretical and practical basis for enhancing the performance of intrusion detection systems using advances in sensor fusion with easily available intrusion detection systems. This thesis introduces the mathematical basis for sensor fusion in order to provide enough support for the acceptability of sensor fusion in performance enhancement of intrusion detection systems. The thesis also shows the practical feasibility of performance enhancement using advances in sensor fusion and discusses various sensor fusion algorithms, its characteristics and related design and implementation is-sues. We show that it is possible to build performance enhancement to intrusion detection systems by setting proper threshold bounds and also by rule-based fusion. We introduce an architecture called the data-dependent decision fusion as a framework for building intrusion detection systems using sensor fusion based on data-dependency. Furthermore, we provide information about the types of data, the data skewness problems and the most effective algorithm in detecting different types of attacks. This thesis also proposes and incorporates a modified evidence theory for the fusion unit, which performs very well for the intrusion detection application. The future improvements in individual IDSs can also be easily incorporated in this technique in order to obtain better detection capabilities. Experimental evaluation shows that the proposed methods have the capability of detecting a significant percentage of rare and new attacks. The improved performance of the IDS using the algorithms that has been developed in this thesis, if deployed fully would contribute to an enormous reduction of the successful attacks over a period of time. This has been demonstrated in the thesis and is a right step towards making the cyber space safer.

Throughout the last decade, vehicle localization has been attracting significant attention in a wide range of applications, including Navigation Systems, Road Tolling, Smart Parking, and Collision Avoidance. To deliver on their requirements, these applications need specific localization accuracy. However, current localization techniques lack the required accuracy, especially for mission critical applications. Although various approaches for improving localization accuracy have been reported in the literature, there is still a need for more efficient and more effective measures that can ascribe some level of accuracy to the localization process. These measures will enable localization systems to manage the localization process and resources so as to achieve the highest accuracy possible, and to mitigate the impact of inadequate accuracy on the target application. In this thesis, a framework for fusing different localization techniques is introduced in order to estimate the location of a vehicle along with location integrity assessment that captures the impact of the measurement conditions on the localization quality. Knowledge about estimate integrity allows the system to plan the use of its localization resources so as to match the target accuracy of the application. The framework introduced provides the tools that would allow for modeling the impact of the operation conditions on estimate accuracy and integrity, as such it enables more robust system performance in three steps. First, localization system parameters are utilized to contrive a feature space that constitutes probable accuracy classes. Due to the strong overlap among accuracy classes in the feature space, a hierarchical classification strategy is developed to address the class ambiguity problem via the class unfolding approach (HCCU). HCCU strategy is proven to be superior with respect to other hierarchical configuration. Furthermore, a Context Based Accuracy Classification (CBAC) algorithm is introduced to enhance the performance of the classification process. In this algorithm, knowledge about the surrounding environment is utilized to optimize classification performance as a function of the observation conditions. Second, a task-driven integrity (TDI) model is developed to enable the applications modules to be aware of the trust level of the localization output. Typically, this trust level functions in the measurement conditions; therefore, the TDI model monitors specific parameter(s) in the localization technique and, accordingly, infers the impact of the change in the environmental conditions on the quality of the localization process. A generalized TDI solution is also introduced to handle the cases where sufficient information about the sensing parameters is unavailable. Finally, the produce of the employed localization techniques (i.e., location estimates, accuracy, and integrity level assessment) needs to be fused. Nevertheless, these techniques are hybrid and their pieces of information are conflicting in many situations. Therefore, a novel evidence structure model called Spatial Evidence Structure Model (SESM) is developed and used in constructing a frame of discernment comprising discretized spatial data. SESM-based fusion paradigms are capable of performing a fusion process using the information provided by the techniques employed. Both the location estimate accuracy and aggregated integrity resultant from the fusion process demonstrate superiority over the employing localization techniques. Furthermore, a context aware task-driven resource allocation mechanism is developed to manage the fusion process. The main objective of this mechanism is to optimize the usage of system resources and achieve a task-driven performance. Extensive experimental work is conducted on real-life and simulated data to validate models developed in this thesis. It is evident from the experimental results that task-driven integrity assessment and control is applicable and effective on hybrid localization systems.

Recent advances in the miniaturization, functionality, and integration of integrated circuits and packages, such as the system-on-package (SOP) methodology, require increasing use of microvias that generates vertical signal paths in a high-density multilayer substrate. A scanning projection excimer laser system has been utilized to fabricate the microvias. In this thesis, a novel technique implementing statistical experimental design and neural networks (NNs) is used to characterize and model the excimer laser ablation process for microvia formation. Vias with diameters from 10 50 micrometer have been ablated in DuPont Kapton(r) E polyimide using an Anvik HexScan(tm) 2150 SXE pulsed excimer laser operating at 308 nm. Accurate NN models, developed from experimental data, are obtained for microvia responses, including ablated thickness, via diameter, wall angle, and resistance. Subsequent to modeling, NNs and genetic algorithms (GAs) are utilized to generate optimal process recipes for the laser tool. Such recipes can be used to produce desired microvia responses, including open vias, specific diameter, steep wall angle, and low resistance. With continuing advancement in the use of excimer laser systems in microsystems packaging has come an increasing need to offset capital equipment investment and lower equipment downtime. In this thesis, an automated in-line failure diagnosis system using NNs and Dempster-Shafer (D-S) theory is implemented. For the sake of comparison, an adaptive neuro-fuzzy approach is applied to achieve the same objective. Both the D-S theory and neuro-fuzzy logic are used to develop an automated inference system to specifically identify failures. Successful results in failure detection and diagnosis are obtained from the two approaches. The result of this investigation will benefit both engineering and management. Engineers will benefit from high yield, reliable production, and low equipment down-time. Business people, on the other hand, will benefit from cost-savings resulting from more production-worthy (i.e., lower maintenance) laser ablation equipment.

The heterogeneity of the environments as well as the diversity of patients' needs and profiles are major constraints that challenge the spread of ambient assistive living (AAL) systems. AAL environments are usually evolving by the introduction or the disappearance of sensors, devices and assistive services to respond to the evolution of patients' conditions and human needs. Therefore, a generic framework that is able to adapt to such dynamic environments and to integrate new sensors, devices and assistive services at runtime is required. Implementing such a dynamic aspect may produce an uncertainty derived from technical problems related to sensors reliability or network problems. Therefore, a notion of uncertain should be introduced in context representation and decision making in order to deal with this problem. During this thesis, I have developed a dynamic and extendible framework able to adapt to different environments and patients' needs. This was achieved based on my proposed approach of semantic Plug&Play mechanism. In order to handle the problem of uncertain information related to technical problems, I have proposed an approach for uncertainty measurement based on intrinsic characteristics of the sensors and their functional behaviors, then I have provided a model of semantic representation and reasoning under uncertainty coupled with the Dempster-Shafer Theory of evidence (DST) for decision making

Throughout the last decade, vehicle localization has been attracting significant attention in a wide range of applications, including Navigation Systems, Road Tolling, Smart Parking, and Collision Avoidance. To deliver on their requirements, these applications need specific localization accuracy. However, current localization techniques lack the required accuracy, especially for mission critical applications. Although various approaches for improving localization accuracy have been reported in the literature, there is still a need for more efficient and more effective measures that can ascribe some level of accuracy to the localization process. These measures will enable localization systems to manage the localization process and resources so as to achieve the highest accuracy possible, and to mitigate the impact of inadequate accuracy on the target application. In this thesis, a framework for fusing different localization techniques is introduced in order to estimate the location of a vehicle along with location integrity assessment that captures the impact of the measurement conditions on the localization quality. Knowledge about estimate integrity allows the system to plan the use of its localization resources so as to match the target accuracy of the application. The framework introduced provides the tools that would allow for modeling the impact of the operation conditions on estimate accuracy and integrity, as such it enables more robust system performance in three steps. First, localization system parameters are utilized to contrive a feature space that constitutes probable accuracy classes. Due to the strong overlap among accuracy classes in the feature space, a hierarchical classification strategy is developed to address the class ambiguity problem via the class unfolding approach (HCCU). HCCU strategy is proven to be superior with respect to other hierarchical configuration. Furthermore, a Context Based Accuracy Classification (CBAC) algorithm is introduced to enhance the performance of the classification process. In this algorithm, knowledge about the surrounding environment is utilized to optimize classification performance as a function of the observation conditions. Second, a task-driven integrity (TDI) model is developed to enable the applications modules to be aware of the trust level of the localization output. Typically, this trust level functions in the measurement conditions; therefore, the TDI model monitors specific parameter(s) in the localization technique and, accordingly, infers the impact of the change in the environmental conditions on the quality of the localization process. A generalized TDI solution is also introduced to handle the cases where sufficient information about the sensing parameters is unavailable. Finally, the produce of the employed localization techniques (i.e., location estimates, accuracy, and integrity level assessment) needs to be fused. Nevertheless, these techniques are hybrid and their pieces of information are conflicting in many situations. Therefore, a novel evidence structure model called Spatial Evidence Structure Model (SESM) is developed and used in constructing a frame of discernment comprising discretized spatial data. SESM-based fusion paradigms are capable of performing a fusion process using the information provided by the techniques employed. Both the location estimate accuracy and aggregated integrity resultant from the fusion process demonstrate superiority over the employing localization techniques. Furthermore, a context aware task-driven resource allocation mechanism is developed to manage the fusion process. The main objective of this mechanism is to optimize the usage of system resources and achieve a task-driven performance. Extensive experimental work is conducted on real-life and simulated data to validate models developed in this thesis. It is evident from the experimental results that task-driven integrity assessment and control is applicable and effective on hybrid localization systems.

Afin d'obtenir des simulations plus prédictives et plus précises du comportement mécanique des structures, des modèles matériau de plus en plus complexes ont été développés. Aujourd'hui, la caractérisation des propriétés des matériaux est donc un objectif prioritaire. Elle exige des méthodes et des tests d'identification dédiés dans des conditions les plus proches possible des cas de service. Cette thèse vise à développer une méthodologie d'identification efficace pour trouver les paramètres des propriétés matériau, en tenant compte de toutes les informations disponibles. L'information utilisée pour l'identification est à la fois théorique, expérimentale et empirique : l'information théorique est liée aux modèles mécaniques dont l'incertitude est épistémique; l'information expérimentale provient ici de la mesure de champs cinématiques obtenues pendant l'essai ct dont l'incertitude est aléatoire; l'information empirique est liée à l'information à priori associée à une incertitude épistémique ainsi. La difficulté principale est que l'information disponible n'est pas toujours fiable et que les incertitudes correspondantes sont hétérogènes. Cette difficulté est surmontée par l'utilisation de la théorie des fonctions de croyance. En offrant un cadre général pour représenter et quantifier les incertitudes hétérogènes, la performance de l'identification est améliorée. Une stratégie basée sur la théorie des fonctions de croyance est proposée pour identifier les propriétés élastiques macro et micro des matériaux multi-structures. Dans cette stratégie, les incertitudes liées aux modèles et aux mesures sont analysées et quantifiées. Cette stratégie est ensuite étendue pour prendre en compte l'information à priori et quantifier l'incertitude associée. / In order to obtain more predictive and accurate simulations of mechanical behaviour in the practical environment, more and more complex material models have been developed. Nowadays, the characterization of material properties remains a top-priority objective. It requires dedicated identification methods and tests in conditions as close as possible to the real ones. This thesis aims at developing an effective identification methodology to find the material property parameters, taking advantages of all available information. The information used for the identification is theoretical, experimental, and empirical: the theoretical information is linked to the mechanical models whose uncertainty is epistemic; the experimental information consists in the full-field measurement whose uncertainty is aleatory; the empirical information is related to the prior information with epistemic uncertainty as well. The main difficulty is that the available information is not always reliable and its corresponding uncertainty is heterogeneous. This difficulty is overcome by the introduction of the theory of belief functions. By offering a general framework to represent and quantify the heterogeneous uncertainties, the performance of the identification is improved. The strategy based on the belief function is proposed to identify macro and micro elastic properties of multi-structure materials. In this strategy, model and measurement uncertainties arc analysed and quantified. This strategy is subsequently developed to take prior information into consideration and quantify its corresponding uncertainty.