Yes / Microstrip patch antennas with harmonic suppression are designed and optimised, using a genetic algorithm and applying a novel adaptive meshing program to generate a wire-grid simulation. A coaxially-fed air-dielectric patch antenna design with a folded patch was investigated. It was confirmed that antennas with excellent performances could be designed by this method. / MSCRC

A number of methods for adaptation of existing all-hexahedral grids by localized refinement have been developed; however, none ideally fit all refinement needs. This thesis presents the structure to a method of two-refinement developed for conformal, structured, all-hexahedral grids that offers flexibility beyond what has been offered to date. The method is fundamentally based on pillowing pairs of sheets of hexes. This thesis also suggests an implementation of the method, shows the results of examples refined using it and compares these results to results from implementing three-refinement on the same examples.

The work presented in this PhD studies and proposes cellular computation parallel models able to address different types of NP-hard optimization problems defined in the Euclidean space, and their implementation on the Graphics Processing Unit (GPU) platform. The goal is to allow both dealing with large size problems and provide substantial acceleration factors by massive parallelism. The field of applications concerns vehicle embedded systems for stereovision as well as transportation problems in the plane, as vehicle routing problems. The main characteristic of the cellular model is that it decomposes the plane into an appropriate number of cellular units, each responsible of a constant part of the input data, and such that each cell corresponds to a single processing unit. Hence, the number of processing units and required memory are with linear increasing relationship to the optimization problem size, which makes the model able to deal with very large size problems.The effectiveness of the proposed cellular models has been tested on the GPU parallel platform on four applications. The first application is a stereo-matching problem. It concerns color stereovision. The problem input is a stereo image pair, and the output a disparity map that represents depths in the 3D scene. The goal is to implement and compare GPU/CPU winner-takes-all local dense stereo-matching methods dealing with CFA (color filter array) image pairs. The second application focuses on the possible GPU improvements able to reach near real-time stereo-matching computation. The third and fourth applications deal with a cellular GPU implementation of the self-organizing map neural network in the plane. The third application concerns structured mesh generation according to the disparity map to allow 3D surface compressed representation. Then, the fourth application is to address large size Euclidean traveling salesman problems (TSP) with up to 33708 cities.In all applications, GPU implementations allow substantial acceleration factors over CPU versions, as the problem size increases and for similar or higher quality results. The GPU speedup factor over CPU was of 20 times faster for the CFA image pairs, but GPU computation time is about 0.2s for a small image pair from Middlebury database. The near real-time stereovision algorithm takes about 0.017s for a small image pair, which is one of the fastest records in the Middlebury benchmark with moderate quality. The structured mesh generation is evaluated on Middlebury data set to gauge the GPU acceleration factor and quality obtained. The acceleration factor for the GPU parallel self-organizing map over the CPU version, on the largest TSP problem with 33708 cities, is of 30 times faster.

Le travail présenté dans ce mémoire étudie et propose des modèles de calcul parallèles de type cellulaire pour traiter différents problèmes d’optimisation NP-durs définis dans l’espace euclidien, et leur implantation sur des processeurs graphiques multi-fonction (Graphics Processing Unit; GPU). Le but est de pouvoir traiter des problèmes de grande taille tout en permettant des facteurs d’accélération substantiels à l’aide du parallélisme massif. Les champs d’application visés concernent les systèmes embarqués pour la stéréovision de même que les problèmes de transports définis dans le plan, tels que les problèmes de tournées de véhicules. La principale caractéristique du modèle cellulaire est qu’il est fondé sur une décomposition du plan en un nombre approprié de cellules, chacune comportant une part constante de la donnée, et chacune correspondant à une unité de calcul (processus). Ainsi, le nombre de processus parallèles et la taille mémoire nécessaire sont en relation linéaire avec la taille du problème d’optimisation, ce qui permet de traiter des instances de très grandes tailles.L’efficacité des modèles cellulaires proposés a été testée sur plateforme parallèle GPU sur quatre applications. La première application est un problème d’appariement d’images stéréo. Elle concerne la stéréovision couleur. L’entrée du problème est une paire d’images stéréo, et la sortie une carte de disparités représentant les profondeurs dans la scène 3D. Le but est de comparer des méthodes d’appariement local selon l’approche winner-takes-all et appliquées à des paires d’images CFA (color filter array). La deuxième application concerne la recherche d’améliorations de l’implantation GPU permettant de réaliser un calcul quasi temps-réel de l’appariement. Les troisième et quatrième applications ont trait à l’implantation cellulaire GPU des réseaux neuronaux de type carte auto-organisatrice dans le plan. La troisième application concerne la génération de maillages structurés appliquée aux cartes de disparité afin de produire des représentations compressées des surfaces 3D. Enfin, la quatrième application concerne le traitement d’instances de grandes tailles du problème du voyageur de commerce euclidien comportant jusqu’à 33708 villes.Pour chacune des applications, les implantations GPU permettent une accélération substantielle du calcul par rapport aux versions CPU, pour des tailles croissantes des problèmes et pour une qualité de résultat obtenue similaire ou supérieure. Le facteur d’accélération GPU par rapport à la version CPU est d’environ 20 fois plus vite pour la version GPU sur le traitement des images CFA, cependant que le temps de traitement GPU est d’environ de 0,2s pour une paire d’images de petites tailles de la base Middlebury. L’algorithme amélioré quasi temps-réel nécessite environ 0,017s pour traiter une paire d’images de petites tailles, ce qui correspond aux temps d’exécution parmi les plus rapides de la base Middlebury pour une qualité de résultat modérée. La génération de maillages structurés est évaluée sur la base Middlebury afin de déterminer les facteurs d’accélération et qualité de résultats obtenus. Le facteur d’accélération obtenu pour l’implantation parallèle des cartes auto-organisatrices appliquée au problème du voyageur de commerce et pour l’instance avec 33708 villes est de 30 pour la version parallèle. / The work presented in this PhD studies and proposes cellular computation parallel models able to address different types of NP-hard optimization problems defined in the Euclidean space, and their implementation on the Graphics Processing Unit (GPU) platform. The goal is to allow both dealing with large size problems and provide substantial acceleration factors by massive parallelism. The field of applications concerns vehicle embedded systems for stereovision as well as transportation problems in the plane, as vehicle routing problems. The main characteristic of the cellular model is that it decomposes the plane into an appropriate number of cellular units, each responsible of a constant part of the input data, and such that each cell corresponds to a single processing unit. Hence, the number of processing units and required memory are with linear increasing relationship to the optimization problem size, which makes the model able to deal with very large size problems.The effectiveness of the proposed cellular models has been tested on the GPU parallel platform on four applications. The first application is a stereo-matching problem. It concerns color stereovision. The problem input is a stereo image pair, and the output a disparity map that represents depths in the 3D scene. The goal is to implement and compare GPU/CPU winner-takes-all local dense stereo-matching methods dealing with CFA (color filter array) image pairs. The second application focuses on the possible GPU improvements able to reach near real-time stereo-matching computation. The third and fourth applications deal with a cellular GPU implementation of the self-organizing map neural network in the plane. The third application concerns structured mesh generation according to the disparity map to allow 3D surface compressed representation. Then, the fourth application is to address large size Euclidean traveling salesman problems (TSP) with up to 33708 cities.In all applications, GPU implementations allow substantial acceleration factors over CPU versions, as the problem size increases and for similar or higher quality results. The GPU speedup factor over CPU was of 20 times faster for the CFA image pairs, but GPU computation time is about 0.2s for a small image pair from Middlebury database. The near real-time stereovision algorithm takes about 0.017s for a small image pair, which is one of the fastest records in the Middlebury benchmark with moderate quality. The structured mesh generation is evaluated on Middlebury data set to gauge the GPU acceleration factor and quality obtained. The acceleration factor for the GPU parallel self-organizing map over the CPU version, on the largest TSP problem with 33708 cities, is of 30 times faster.