Depuis quelques années les détecteurs à pixels hybrides ont ouvert la voie au développement de la tomographie à rayon X spectrale ou tomodensitométrie (TDM) spectrale. La TDM spectrale permet d’extraire plus d’information concernant la structure interne de l’objet par rapport à la TDM d’absorption classique. Un de ses objectifs dans l’imagerie médicale est d’identifier et quantifier des composants d’intérêt dans un objet, tels que des marqueurs biologique appelés agents de contraste (iode, baryum, etc.). La majeure partie de l’état de l’art procède en deux étapes : - la "pré-reconstruction" qui consiste à séparer les composants dans l’espace des projections puis reconstruire, - la "post-reconstruction", qui reconstruit l’objet puis sépare les composants.On s’intéresse dans ce travail de thèse à une approche qui consiste à séparer et reconstruire simultanément les composants de l’objet. L’état de l’art des méthodes de reconstruction et séparation simultanées de données de TDM spectrale reste à ce jour peu fourni et les approches de reconstruction existantes sont limitées dans leurs performances et ne tiennent souvent pas compte de la complexité du modèle d’acquisition.L’objectif principal de ce travail de thèse est de proposer des approches de reconstruction et séparation tenant compte de la complexité du modèle afin d’améliorer la qualité des images reconstruites. Le problème à résoudre est un problème inverse, mal-posé, non-convexe et de très grande dimension. Pour le résoudre, nous proposons un algorithme proximal à métrique variable. Des résultats prometteurs sont obtenus sur des données réelles et montrent des avantages en terme de qualité de reconstruction. / In recent years, hybrid pixel detectors have paved the way for the development of spectral X ray tomography or spectral tomography (CT). Spectral CT provides more information about the internal structure of the object compared to conventional absorption CT. One of its objectives in medical imaging is to obtain images of components of interest in an object, such as biological markers called contrast agents (iodine, barium, etc.).The state of the art of simultaneous reconstruction and separation of spectral CT data methods remains to this day limited. Existing reconstruction approaches are limited in their performance and often do not take into account the complexity of the acquisition model.The main objective of this thesis work is to propose better quality reconstruction approaches that take into account the complexity of the model in order to improve the quality of the reconstructed images. Our contribution considers the non-linear polychromatic model of the X-ray beam and combines it with an earlier model on the components of the object to be reconstructed. The problem thus obtained is an inverse, non-convex and misplaced problem of very large dimensions.To solve it, we propose a proximal algorithmwith variable metrics. Promising results are shown on real data. They show that the proposed approach allows good separation and reconstruction despite the presence of noise (Gaussian or Poisson). Compared to existing approaches, the proposed approach has advantages over the speed of convergence.

The introduction of autonomous vehicles in traffic is driven by expected gains in multiple areas, such as improvement of health and safety, better resource utilization, pollution reduction and greater convenience. The development of more competent algorithms will determine the rate and level of success for the ambitions around autonomous vehicles. In this thesis work an intersection management system for a mix of autonomous and manually driven vehicles is created. The purpose is to investigate the strategy to combine turn intention prediction for manually driven vehicles with scheduling of autonomous vehicle. The prediction method used is support vector machine (SVM) and scheduling of vehicles have been made by dividing the intersection into an occupancy grid and apply different safety levels. Real-life data comprising recordings of large volumes of traffic through an intersection has been combined with simulated vehicles to assess the relevance of the new algorithms. Measurements of collision rate and traffic flow showed that the algorithms behaved as expected. A miniature vehicle based on a prototype for an autonomous RC-car has been designed with the purpose of testing of the algorithms in a laboratory setting. / Införandet av autonoma fordon i trafiken drivs av förväntade vinster i flera områden, såsom förbättring av hälsa och säkerhet, bättre resursutnyttjande, minskning av föroreningar och ökad bekvämlighet. Utvecklingen av mer kompetenta algoritmer kommer att bestämma hastigheten och nivån på framgång för ambitionerna kring autonoma fordon. I detta examensarbete skapas ett korsningshanteringssystem för en blandning av autonoma och självkörande bilar. Syftet är att undersöka strategin att kombinera prediktion av hur manuellt styrda bilar kommer att svänga med att schemalägga autonoma bilar utifrån detta. Prediktionsmetoden som använts är support vector machine (SVM) och schemaläggning av bilar har gjorts genom att dela upp korsningen i ett occupancy grid och tillämpa olika säkerhetsmarginaler. Verklig data från inspelningar av stora volymer trafik genom en korsning har kombinerats med simulerade fordon för att bedöma relevansen av de nya algoritmerna. Mätningar av kollisioner och trafikflöde visade att algoritmerna uppträdde som förväntat. Ett miniatyrfordon baserat på en prototyp av en självkörande radiostyrd bil har tagits fram i syfte att testa algoritmerna i laboratoriemiljö.

Made available in DSpace on 2016-06-02T19:06:09Z (GMT). No. of bitstreams: 1 5554.pdf: 2294386 bytes, checksum: ce6dc6cd7128337c0533ddd23c0bc601 (MD5) Previous issue date: 2013-06-07 / The mining of sequential patterns in data from environmental sensors is a challenging task: the data may show noise and may also contain sparse patterns that are difficult to detect. The knowledge extracted from environmental sensor data can be used to determine climate change, for example. However, there is a lack of methods that can handle this type of database. In order to reduce this gap, the algorithm Incremental Miner of Stretchy Time Sequences with Post-Processing (IncMSTS-PP) was proposed. The IncMSTS-PP applies incremental extraction of sequential patterns with post-processing based on ontology for the generalization of the patterns. The post-processing makes the patterns semantically richer. Generalized patterns synthesize the information and makes it easier to be interpreted. IncMSTS-PP implements the Stretchy Time Window (STW) that allows stretchy time patterns (patterns with temporal intervals) are mined from bases that have noises. In comparison with GSP algorithm, IncMSTS-PP can return 2.3 times more patterns and patterns with 5 times more itemsets. The post-processing module is responsible for the reduction in 22.47% of the number of patterns presented to the user, but the returned patterns are semantically richer. Thus, the IncMSTS-PP showed good performance and mined relevant patterns showing, that way, that IncMSTS-PP is effective, efficient and appropriate for domain of environmental sensor data. / A mineração de padrões sequenciais em dados de sensores ambientais é uma tarefa desafiadora: os dados podem apresentar ruídos e podem, também, conter padrões esparsos que são difíceis de serem detectados. O conhecimento extraído de dados de sensores ambientais pode ser usado para determinar mudanças climáticas, por exemplo. Entretanto, há uma lacuna de métodos que podem lidar com este tipo de banco de dados. Com o intuito de diminuir esta lacuna, o algoritmo Incremental Miner of Stretchy Time Sequences with Post- Processing (IncMSTS-PP) foi proposto. O IncMSTS-PP aplica a extração incremental de padrões sequencias com pós-processamento baseado em ontologia para a generalização dos padrões obtidos que acarreta o enriquecimento semântico desses padrões. Padrões generalizados sintetizam a informação e a torna mais fácil de ser interpretada. IncMSTS-PP implementa o método Stretchy Time Window (STW) que permite que padrões de tempo elástico (padrões com intervalos temporais) sejam extraídos em bases que apresentam ruídos. Em comparação com o algoritmo GSP, o IncMSTS-PP pode retornar 2,3 vezes mais sequencias e sequencias com 5 vezes mais itemsets. O módulo de pós-processamento é responsável pela redução em 22,47% do número de padrões apresentados ao usuário, porém os padrões retornados são semanticamente mais ricos, se comparados aos padrões não generalizados. Assim sendo, o IncMSTS-PP apresentou bons resultados de desempenho e minerou padrões relevantes mostrando, assim, que IncMSTS-PP é eficaz, eficiente e apropriado em domínio de dados de sensores ambientais.