There has been an increase in demand for efficient wireless systems. Smart antennas using position location are one possible way to improve the capacity of cellular systems. In order to deploy such systems successfully, the wireless network must properly exploit the processing of spatial information (ie. The uplink angle of arrival) through wireless channel models. Geometric modelling is a technique to model the wireless environment. When compared to other methods such as ray tracing simulations, geometric models allow one to classify a wide varity of environments within a single model. Secondly, there have been much research in the past to obtain empirical measurements in many different environment settings. These measurements have been recorded, however, there has been no research undertaken to systematically compare and validate the empirical findings with current geometric models. The goal of this research is to compare and constrast geometric models with empirical data in order to show which models are best suited for specific wireless environments. The uplink angle of arrival (AOA) probability distribution is the fading metric that will be used to compare and contrast these models.

There has been an increase in demand for efficient wireless systems. Smart antennas using position location are one possible way to improve the capacity of cellular systems. In order to deploy such systems successfully, the wireless network must properly exploit the processing of spatial information (ie. The uplink angle of arrival) through wireless channel models. Geometric modelling is a technique to model the wireless environment. When compared to other methods such as ray tracing simulations, geometric models allow one to classify a wide varity of environments within a single model. Secondly, there have been much research in the past to obtain empirical measurements in many different environment settings. These measurements have been recorded, however, there has been no research undertaken to systematically compare and validate the empirical findings with current geometric models. The goal of this research is to compare and constrast geometric models with empirical data in order to show which models are best suited for specific wireless environments. The uplink angle of arrival (AOA) probability distribution is the fading metric that will be used to compare and contrast these models.

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No. of bitstreams: 1 DissCHVP.pdf: 4899707 bytes, checksum: e7de60b5431e48ddbc2b9016dae268c7 (MD5) Previous issue date: 2016-03-10 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) / Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) / The magnetic resonance (MR) imaging has become an indispensable tool for the diagnosis and study of various diseases and syndromes of the central nervous system, such as Alzheimer’s disease (AD). In order to perform the precise diagnosis of a disease, as well as the evolutionary monitoring of a certain treatment, the neuroradiologist doctor often needs to measure and assess volume and shape changes in certain brain structures along a series of MR images. For that, the previous delineation of the structures of interest is necessary. In general, such task is manually done, with limited help from a computer, and therefore it has several problems. For this reason, many researchers have turned their efforts towards the development of automatic techniques for segmentation of brain structures in MR images. Among the various approaches proposed in the literature, techniques based on deformable models and anatomical atlases are among those which present the best results. However, one of the main difficulties in applying geometric deformable models is the initial positioning of the model. Thus, this research aimed to develop an atlas of 3D salient points (automatically detected from a set of MR images) and to investigate the applicability of such atlas in guiding the initial positioning of geometric deformable models representing brain structures, with the purpose of helping the automatic segmentation of such structures in MR images. The processing pipeline included the use of a 3D salient point detector based on the phase congruency measure, an adaptation of the shape contexts technique to create point descriptors and the estimation of a B-spline transform to map pairs of matching points. The results, evaluated using the Jaccard and Dice metrics before and after the model initializations, showed a significant gain in the tests involving synthetically deformed images of normal patients, but for images of clinical patients with AD the gain was marginal and can still be improved in future researches. Some ways to do such improvements are discussed in this work. / O imageamento por ressonância magnética (RM) tornou-se uma ferramenta indispensável no diagnóstico e estudo de diversas doenças e síndromes do sistema nervoso central, tais como a doença de Alzheimer (DA). Para que se possa realizar o diagnóstico preciso de uma doença, bem como o acompanhamento evolutivo de um determinado tratamento, o médico neurorradiologista frequentemente precisa medir e avaliar alterações de volume e forma em determinadas estruturas do cérebro ao longo de uma série de imagens de RM. Para isso, a delineação prévia das estruturas de interesse nas imagens é necessária. Em geral, essa tarefa é realizada manualmente, com ajuda limitada de um computador, e portanto possui diversos problemas. Por esse motivo, vários pesquisadores têm voltado seus esforços para o desenvolvimento de técnicas automáticas de segmentação de estruturas cerebrais em imagens de RM. Dentre as várias abordagens propostas na literatura, técnicas baseadas em modelos deformáveis e atlas anatômicos estão entre as que apresentam os melhores resultados. No entanto, uma das principais dificuldades na aplicação de modelos geométricos deformáveis é o posicionamento inicial do modelo. Assim, esta pesquisa teve por objetivo desenvolver um atlas de pontos salientes 3D (automaticamente detectados em um conjunto de imagens de RM) e investigar a aplicabilidade de tal atlas em guiar o posicionamento inicial de modelos geométricos deformáveis representando estruturas cerebrais, com o propósito de auxiliar a segmentação automática de tais estruturas em imagens de RM. O arcabouço de processamento incluiu o uso de um detector de pontos salientes 3D baseado na medida de congruência de fase, uma adaptação da técnica shape contexts para a criação de descritores de pontos e a estimação de uma transformação B-spline para mapear pares de pontos correspondentes. Os resultados, avaliados com as métricas Jaccard e Dice antes e após a inicialização dos modelos, mostraram um ganho significativo em testes envolvendo imagens sinteticamente deformadas de pacientes normais, mas em imagens de pacientes clínicos com DA o ganho foi marginal e ainda pode ser melhorado em pesquisas futuras. Algumas maneiras de se realizar tais melhorias são discutidas neste trabalho. / FAPESP: 2015/02232-1 / CAPES: 2014/11988-0