In this thesis we provide a model for a bufferless optical burst switching (OBS) and an optical packet switching (OPS) network. The thesis is divided in three parts. <p><p>In the first part we introduce the basic functionality and structure of OBS and OPS networks. We identify the blocking probability as the main performance parameter of interest. <p><p>In the second part we study the statistical properties of the traffic that will likely run through these networks. We use for this purpose a set of traffic traces obtained from the Universidad Politécnica de Catalunya. Our conclusion is that traffic entering the optical domain in future OBS/OPS networks will be long-range dependent (LRD). <p><p>In the third part we present the model for bufferless OBS/OPS networks. This model takes into account the results from the second part of the thesis concerning the LRD nature of traffic. It also takes into account specific issues concerning the functionality of a typical bufferless packet-switching network. The resulting model presents scalability problems, so we propose an approximative method to compute the blocking probability from it. We empirically evaluate the accuracy of this method, as well as its scalability. / Doctorat en Sciences de l'ingénieur / info:eu-repo/semantics/nonPublished

Les filtres non-linéaires de rang sont souvent utilisés dans le but de rehausser la qualité d'une image numérique. Leur application permet de faciliter l'interprétation visuelle et la compréhension du contenu des images que ce soit pour un opérateur humain ou pour un traitement automatique ultérieur. Dans le pipeline d'une chaîne habituelle de traitement des images, ces filtres sont appliqués généralement dans la phase de pré-traitement, juste après l'acquisition et avant le traitement et l'analyse d'image proprement dit.<p>Les filtres de rang sont considérés comme un important goulot d'étranglement dans la chaîne de traitement, à cause du tri des pixels dans chaque voisinage, à effectuer pour tout pixel de l'image. Les temps de calcul augmentent de façon significative avec la taille de l'image à traiter, la taille du voisinage considéré et lorsque le rang approche la médiane.<p>Cette thèse propose deux solutions à l'accélération du temps de traitement des filtres de rang.<p>La première solution vise l'exploitation des différents niveaux de parallélisme des ordinateurs personnels d'aujourd'hui, notamment le parallélisme de données et le parallélisme inter-processeurs. Une telle approche présente un facteur d'accélération de l'ordre de 10 par rapport à une approche classique qui fait abstraction du matériel grâce aux compilateurs des langages évolués. Si le débit résultant des pixels traités, de l'ordre d'une dizaine de millions de pixels par seconde, permet de travailler en temps réel avec des applications vidéo, peu de temps reste pour d'autres traitements dans la chaîne.<p>La deuxième solution proposée est basée sur le concept de calcul reconfigurable et réalisée à l'aide des circuits FPGA (Field Programmable Gate Array). Le système décrit combine les algorithmes de type bit-série et la haute densité des circuits FPGA actuels. Il en résulte un système de traitement hautement parallèle, impliquant des centaines d'unités de traitement par circuit FPGA et permet d'arriver à un facteur d'accélération supplémentaire de l'ordre de 10 par rapport à la première solution présentée. Un tel système, inséré entre une source d'image numérique et un système hôte, effectue le calcul des filtres de rang avec un débit de l'ordre de centaine de millions de pixels par seconde. / Doctorat en sciences appliquées / info:eu-repo/semantics/nonPublished

Indiana University-Purdue University Indianapolis (IUPUI) / Object segmentation, quantification, and shape modeling are important areas inmedical image processing. By combining these techniques, researchers can find valuableways to extract and represent details on user-desired structures, which can functionas the base for subsequent analyses such as feature classification, regression, and prediction. This thesis presents a new framework for building a three-dimensional (3D) hippocampal atlas model with subfield information mapped onto its surface, with which hippocampal surface registration can be done, and the comparison and analysis can be facilitated and easily visualized. This framework combines three powerful tools for automatic subcortical segmentation and 3D surface modeling. Freesurfer and Functional magnetic resonance imaging of the brain's Integrated Registration and Segmentation Tool (FIRST) are employed for hippocampal segmentation and quantification, while SPherical HARMonics (SPHARM) is employed for parametric surface modeling. This pipeline is shown to be effective in creating a hippocampal surface atlas using the Alzheimer's Disease Neuroimaging Initiative Grand Opportunity and phase 2 (ADNI GO/2) dataset. Intra-class Correlation Coefficients (ICCs) are calculated for evaluating the reliability of the extracted hippocampal subfields. The complex folding anatomy of the hippocampus offers many analytical challenges, especially when informative hippocampal subfields are usually ignored in detailed morphometric studies. Thus, current research results are inadequate to accurately characterize hippocampal morphometry and effectively identify hippocampal structural changes related to different conditions. To address this challenge, one contribution of this study is to model the hippocampal surface using a parametric spherical harmonic model, which is a Fourier descriptor for general a 3D surface. The second contribution of this study is to extend hippocampal studies by incorporating valuable hippocampal subfield information. Based on the subfield distributions, a surface atlas is created for both left and right hippocampi. The third contribution is achieved by calculating Fourier coefficients in the parametric space. Based on the coefficient values and user-desired degrees, a pair of averaged hippocampal surface atlas models can be reconstructed. These contributions lay a solid foundation to facilitate a more accurate, subfield-guided morphometric analysis of the hippocampus and have the potential to reveal subtle hippocampal structural damage associated.

Indiana University-Purdue University Indianapolis (IUPUI) / In general, profiling sensors are low-cost crude imagers that typically utilize a sparse detector array, whereas traditional cameras employ a dense focal-plane array. Profiling sensors are of particular interest in applications that require classification of a sensed object into broad categories, such as human, animal, or vehicle. However, profiling sensors have many other applications in which reliable classification of a crude silhouette or profile produced by the sensor is of value. The notion of a profiling sensor was first realized by a Near-Infrared (N-IR), retro-reflective prototype consisting of a vertical column of sparse detectors. Alternative arrangements of detectors have been implemented in which a subset of the detectors have been offset from the vertical column and placed at arbitrary locations along the anticipated path of the objects of interest. All prior work with the N-IR, retro-reflective profiling sensors has consisted of wired detectors. This thesis surveys prior work and advances this work with a wireless profiling sensor prototype in which each detector is a wireless sensor node and the aggregation of these nodes comprises a profiling sensor’s field of view. In this novel approach, a base station pre-processes the data collected from the sensor nodes, including data realignment, prior to its classification through a back-propagation neural network. Such a wireless detector configuration advances deployment options for N-IR, retro-reflective profiling sensors.