Navegação autônoma é um tópico abrangente cuja atenção por parte da comunidade de robôs móveis vemaumentando ao longo dos anos. O problema consiste em guiar um robô de forma inteligente por um determinado percurso sem ajuda humana. Esta dissertação apresenta um sistema de navegação para ambientes abertos baseado em visão estéreo. Uma câmera estéreo é utilizada na captação de imagens do ambiente e, utilizando o mapa de disparidades gerado por um método estéreo semi-global, dois métodos de detecção de obstáculos são utilizando para segmentar as imagens em regiões navegáveis e não navegáveis. Posteriormente esta classificação é utilizada em conjunto com um método de desvio de obstáculos, resultando em um sistema completo de navegação autônoma. Os resultados obtidos por está dissertação incluem a avaliação de dois métodos estéreo, esta sendo favorável ao método estéreo empregado (semi-global). Foram feitos testes visando avaliar a qualidade e custo computacional de dois métodos para detecção de obstáculos, um baseado em plano e outro baseado em cone. Tais testes deixaram claras as limitações de ambos os métodos e levaram a uma implementação paralela do método baseado em cone. Utilizando uma unidade de processamento gráfico, a versão paralelizada do método baseado em cone atingiu um ganho no tempo computacional de aproximadamente dez vezes. Por fim, os resultados demonstrarão o sistema completo em funcionamento, onde a plataforma robótica utilizada, um veículo elétrico, foi capaz de desviar de pessoas e cones alcançando seu objetivo seguramente / Autonomous navigation is a broad topic that has received increasing attention from the community of mobile robots over the years. The problem is to guide a robot in a smart way for a certain route without human help. This dissertation presents a navigation system for open environments based on stereo vision. A stereo camera is used to capture images of the environment and based on the disparity map generated by a semi-global stereo method, two obstacle detection methods are used to segment the images into navigable and non-navigable regions. Subsequently, this classification is employed in conjunction with a obstacle avoidance method, resulting in a complete autonomous navigation system. The results include an evaluation two stereo methods, this being favorable to the employed stereo method (semi-global). Tests were performed to evaluate the quality and computational cost of two methods for obstacle detection, a plane based one and a cone based. Such tests have left clear the limitations of both methods and led to a parallel implementation of the cone based method. Using a graphics processing unit, a parallel version of the cone based method reached a gain in computational time of approximately ten times. Finally, the results demonstrate the complete system in operation, where the robotic platform used, an electric vehicle, was able to dodge people and cones reaching its goal safely

Proton computed tomography (pCT) is an image modality that will improve treatment planning for patients receiving proton radiation therapy compared with the current techniques, which are based on X-ray CT. Images are reconstructed in pCT by solving a large and sparse system of linear equations. The size of the system necessitates matrix-partitioning and parallel reconstruction algorithms to be implemented across some sort of cluster computing architecture. The prototypical algorithm to solve the pCT system is the algebraic reconstruction technique (ART) that has been modified into parallel versions called block-iterative-projection (BIP) methods and string-averaging-projection (SAP) methods. General purpose graphics processing units (GPGPUs) have hundreds of stream processors for massively parallel calculations. A GPGPU cluster is a set of nodes, with each node containing a set of GPGPUs. This thesis describes a proton simulator that was developed to generate realistic pCT data sets. Simulated data sets were used to compare the performance of a BIP implementation against a SAP implementation on a single GPGPU with the data stored in a sparse matrix structure called the compressed sparse row (CSR) format. Both BIP and SAP algorithms allow for parallel computation by creating row partitions of the pCT linear system. The difference between these two general classes of algorithms is that BIP permits parallel computations within the row partitions yet sequential computations between the row partitions, whereas SAP permits parallel computations between the row partitions yet sequential computations within the row partitions. This thesis also introduces a general partitioning scheme to be applied to a GPGPU cluster to achieve a pure parallel ART algorithm while providing a framework for column partitioning to the pCT system, as well as show sparse visualization patterns that can be found via specified ordering of the equations within the matrix.

De nombreux problèmes d'optimisation propres à différents secteurs industriels et académiques (énergie, chimie, transport, etc.) nécessitent de concevoir des méthodes de plus en plus efficaces pour les résoudre. Afin de répondre à ces besoins, l'objectif de cette thèse est de développer une bibliothèque composée de plusieurs métaheuristiques hybrides distribuées et massivement parallèles. Dans un premier temps, nous avons étudié le problème du voyageur de commerce et sa résolution par la méthode colonie de fourmis afin de mettre en place les techniques d'hybridation et de parallélisation. Ensuite, deux autres problèmes d'optimisation ont été traités, à savoir, le problème d'affectation quadratique (QAP) et le problème de la résolution structurale des zéolithes (ZSP). Pour le QAP, plusieurs variantes basées sur une recherche taboue itérative avec des diversifications adaptatives ont été proposées. Le but de ces propositions est d'étudier l'impact de : l'échange des données, des stratégies de diversification et des méthodes de coopération. Notre meilleure variante est comparée à six des meilleurs travaux de la littérature. En ce qui concerne le ZSP, deux nouvelles formulations de la fonction objective sont proposées pour évaluer le potentiel des structures zéolitiques trouvées. Ces formulations sont basées sur le principe de pénalisation et de récompense. Deux algorithmes génétiques hybrides et parallèles sont proposés pour générer des structures zéolitiques stables. Nos algorithmes ont généré actuellement six topologies stables, parmi lesquelles trois ne sont pas répertoriées sur le site Web du SC-IZA ou dans l'Atlas of Prospective Zeolite Structures. / Many optimization problems specific to different industrial and academic sectors (energy, chemicals, transportation, etc.) require the development of more effective methods in resolving. To meet these needs, the aim of this thesis is to develop a library of several hybrid metaheuristics distributed and massively parallel. First, we studied the traveling salesman problem and its resolution by the ant colony method to establish hybridization and parallelization techniques. Two other optimization problems have been dealt, which are, the quadratic assignment problem (QAP) and the zeolite structure problem (ZSP). For the QAP, several variants based on an iterative tabu search with adaptive diversification have been proposed. The aim of these proposals is to study the impact of: the data exchange, the diversification strategies and the methods of cooperation. Our best variant is compared with six from the leading works of the literature. For the ZSP two new formulations of the objective function are proposed to evaluate the potential of the zeolites structures founded. These formulations are based on reward and penalty evaluation. Two hybrid and parallel genetic algorithms are proposed to generate stable zeolites structures. Our algorithms have now generated six stable topologies, three of them are not listed in the SC-JZA website or in the Atlas of Prospective Zeolite Structures.

Les neurosciences computationnelles ont permis de développer des outils mathématiques et informatiques permettant la création, puis la simulation de modèles représentant le comportement de certaines composantes de notre cerveau à l’échelle cellulaire. Ces derniers sont utiles dans la compréhension des interactions physiques et biochimiques entre les différents neurones, au lieu d’une reproduction fidèle des différentes fonctions cognitives comme dans les travaux sur l’intelligence artificielle. La construction de modèles décrivant le cerveau dans sa globalité, en utilisant une homogénéisation des données microscopiques est plus récent, car il faut prendre en compte la complexité géométrique des différentes structures constituant le cerveau. Il y a donc un long travail de reconstitution à effectuer pour parvenir à des simulations. D’un point de vue mathématique, les différents modèles sont décrits à l’aide de systèmes d’équations différentielles ordinaires, et d’équations aux dérivées partielles. Le problème majeur de ces simulations vient du fait que le temps de résolution peut devenir très important, lorsque des précisions importantes sur les solutions sont requises sur les échelles temporelles mais également spatiales. L’objet de cette étude est d’étudier les différents modèles décrivant l’activité électrique du cerveau, en utilisant des techniques innovantes de parallélisation des calculs, permettant ainsi de gagner du temps, tout en obtenant des résultats très précis. Quatre axes majeurs permettront de répondre à cette problématique : description des modèles, explication des outils de parallélisation, applications sur deux modèles macroscopiques. / Computational Neuroscience helped develop mathematical and computational tools for the creation, then simulation models representing the behavior of certain components of our brain at the cellular level. These are helpful in understanding the physical and biochemical interactions between different neurons, instead of a faithful reproduction of various cognitive functions such as in the work on artificial intelligence. The construction of models describing the brain as a whole, using a homogenization microscopic data is newer, because it is necessary to take into account the geometric complexity of the various structures comprising the brain. There is therefore a long process of rebuilding to be done to achieve the simulations. From a mathematical point of view, the various models are described using ordinary differential equations, and partial differential equations. The major problem of these simulations is that the resolution time can become very important when important details on the solutions are required on time scales but also spatial. The purpose of this study is to investigate the various models describing the electrical activity of the brain, using innovative techniques of parallelization of computations, thereby saving time while obtaining highly accurate results. Four major themes will address this issue: description of the models, explaining parallelization tools, applications on both macroscopic models.

Two computationally fast fluid mechanical models for vertical axis turbines are the streamtube and the vortex model. The streamtube model is the fastest, allowing three-dimensional modeling of the turbine, but lacks a proper time-dependent description of the flow through the turbine. The vortex model used is two-dimensional, but gives a more complete time-dependent description of the flow. Effects of a velocity profile and the inclusion of struts have been investigated with the streamtube model. Simulations with an inhomogeneous velocity profile predict that the power coefficient of a vertical axis turbine is relatively insensitive to the velocity profile. For the struts, structural mechanic loads have been computed and the calculations show that if turbines are designed for high flow velocities, additional struts are required, reducing the efficiency for lower flow velocities.Turbines in channels and turbine arrays have been studied with the vortex model. The channel study shows that smaller channels give higher power coefficients and convergence is obtained in fewer time steps. Simulations on a turbine array were performed on five turbines in a row and in a zigzag configuration, where better performance is predicted for the row configuration. The row configuration was extended to ten turbines and it has been shown that the turbine spacing needs to be increased if the misalignment in flow direction is large.A control system for the turbine with only the rotational velocity as input has been studied using the vortex model coupled with an electrical model. According to simulations, this system can obtain power coefficients close to the theoretical peak values. This control system study has been extended to a turbine farm. Individual control of each turbine has been compared to a less costly control system where all turbines are connected to a mutual DC bus through passive rectifiers. The individual control performs best for aerodynamically independent turbines, but for aerodynamically coupled turbines, the results show that a mutual DC bus can be a viable option.Finally, an implementation of the fast multipole method has been made on a graphics processing unit (GPU) and the performance gain from this platform is demonstrated.

Synthetic Vision Systems (SVS) provide an aircraft pilot with a virtual 3-D image of surrounding terrain which is generated from a digital elevation model stored in an onboard database. SVS improves the pilot's situational awareness at night and in inclement weather, thus reducing the chance of accidents such as controlled flight into terrain. A terrain database integrity monitor is needed to verify the accuracy of the displayed image due to potential database and navigational system errors. Previous research has used existing aircraft sensors to compare the real terrain position with the predicted position. We propose an improvement to one of these models by leveraging the stream computing capabilities of commercial graphics hardware. "Brook for GPUs," a system for implementing stream computing applications on programmable graphics processors, is used to execute a streaming ray-casting algorithm that correctly simulates the beam characteristics of a radar altimeter during all phases of flight.

This thesis presents an efficient self-consistent Hybrid Spectral Ray Tracing (HSRT) technique for analysis and design of multi-scale sub-millimeter wave problems, where sub-wavelength features are modeled using rigorous methods, and complex structures with dimensions in the order of tens or even hundreds of wavelengths are modeled by asymptotic methods. Quasi-optical devices are used in imaging arrays for sub-millimeter and terahertz applications, THz time-domain spectroscopy (THz-TDS), high-speed wireless communications, and space applications to couple terahertz radiation from space to a hot electron bolometer. These devices and structures, as physically small they have become, are very large in terms of the wavelength of the driving quasi-optical sources and may have dimension in the tens or even hundreds of wavelengths. Simulation and design optimization of these devices and structures is an extremely challenging electromagnetic problem. The analysis of complex electrically large unbounded wave structures using rigorous methods such as method of moments (MoM), finite element method (FEM), and finite difference time domain (FDTD) method can become almost impossible due to the need for large computational resources. Asymptotic high-frequency techniques are used for analysis of electrically large quasi-optical systems and hybrid methods for solving multi-scale problems. Spectral Ray Tracing (SRT) has a number of unique advantages as a candidate for hybridization. The SRT method has the advantages of Spectral Theory of Diffraction (STD). STD can model reflection, refraction and diffraction of an arbitrary wave incident on the complex structure, which is not the case for diffraction theories such as Geometrical Theory of Diffraction (GTD), Uniform theory of Diffraction (UTD) and Uniform Asymptotic Theory (UAT). By including complex rays, SRT can effectively analyze both near-fields and far-fields accurately with minimal approximations. In this thesis, a novel matrix representation of SRT is presented that uses only one spectral integration per observation point and applied to modeling a hemispherical and hyper-hemispherical lens. The hybridization of SRT with commercially available FEM and MoM software is proposed in this work to solve the complexity of multi-scale analysis. This yields a computationally efficient self-consistent HSRT algorithm. Various arrangements of the Hybrid SRT method such as FEM-SRT, and MoM-SRT, are investigated and validated through comparison of radiation patterns with Ansoft HFSS for the FEM method, FEKO for MoM, Multi-level Fast Multipole Method (MLFMM) and physical optics. For that a bow-tie terahertz antenna backed by hyper-hemispherical silicon lens, an on-chip planar dipole fabricated in SiGe:C BiCMOS technology and attached to a hyper-hemispherical silicon lens and a double-slot antenna backed by silica lens will be used as sample structures to be analyzed using the HSRT. Computational performance (memory requirement, CPU/GPU time) of developed algorithm is compared to other methods in commercially available software. It is shown that the MoM-SRT, in its present implementation, is more accurate than MoM-PO but comparable in speed. However, as shown in this thesis, MoM-SRT can take advantage of parallel processing and GPU. The HSRT algorithm is applied to simulation of on-chip dipole antenna backed by Silicon lens and integrated with a 180-GHz VCO and radiation pattern compared with measurements. The radiation pattern is measured in a quasi-optical configuration using a power detector. In addition, it is shown that the matrix formulation of SRT and HSRT are promising approaches for solving complex electrically large problems with high accuracy. This thesis also expounds on new measurement setup specifically developed for measuring integrated antennas, radiation pattern and gain of the embedded on-chip antenna in the mmW/ terahertz range. In this method, the radiation pattern is first measured in a quasi-optical configuration using a power detector. Subsequently, the radiated power is estimated form the integration over the radiation pattern. Finally, the antenna gain is obtained from the measurement of a two-antenna system.

Monte Carlo (MC) simulations are widely used in the field of medical biophysics, particularly for modelling light propagation in biological tissue. The iterative nature of MC simulations and their high computation time currently limit their use to solving the forward solution for a given set of source characteristics and tissue optical properties. However, applications such as photodynamic therapy treatment planning or image reconstruction in diffuse optical tomography require solving the inverse problem given a desired light dose distribution or absorber distribution, respectively. A faster means for performing MC simulations would enable the use of MC-based models for such tasks. In this thesis, a gold standard MC code called MCML was accelerated using two distinct hardware-based approaches, namely designing custom hardware on field-programmable gate arrays (FPGAs) and programming commodity graphics processing units (GPUs). Currently, the GPU-based approach is promising, offering approximately 1000-fold speedup with 4 GPUs compared to an Intel Xeon CPU.

Monte Carlo (MC) simulations are widely used in the field of medical biophysics, particularly for modelling light propagation in biological tissue. The iterative nature of MC simulations and their high computation time currently limit their use to solving the forward solution for a given set of source characteristics and tissue optical properties. However, applications such as photodynamic therapy treatment planning or image reconstruction in diffuse optical tomography require solving the inverse problem given a desired light dose distribution or absorber distribution, respectively. A faster means for performing MC simulations would enable the use of MC-based models for such tasks. In this thesis, a gold standard MC code called MCML was accelerated using two distinct hardware-based approaches, namely designing custom hardware on field-programmable gate arrays (FPGAs) and programming commodity graphics processing units (GPUs). Currently, the GPU-based approach is promising, offering approximately 1000-fold speedup with 4 GPUs compared to an Intel Xeon CPU.

This thesis presents an efficient self-consistent Hybrid Spectral Ray Tracing (HSRT) technique for analysis and design of multi-scale sub-millimeter wave problems, where sub-wavelength features are modeled using rigorous methods, and complex structures with dimensions in the order of tens or even hundreds of wavelengths are modeled by asymptotic methods. Quasi-optical devices are used in imaging arrays for sub-millimeter and terahertz applications, THz time-domain spectroscopy (THz-TDS), high-speed wireless communications, and space applications to couple terahertz radiation from space to a hot electron bolometer. These devices and structures, as physically small they have become, are very large in terms of the wavelength of the driving quasi-optical sources and may have dimension in the tens or even hundreds of wavelengths. Simulation and design optimization of these devices and structures is an extremely challenging electromagnetic problem. The analysis of complex electrically large unbounded wave structures using rigorous methods such as method of moments (MoM), finite element method (FEM), and finite difference time domain (FDTD) method can become almost impossible due to the need for large computational resources. Asymptotic high-frequency techniques are used for analysis of electrically large quasi-optical systems and hybrid methods for solving multi-scale problems. Spectral Ray Tracing (SRT) has a number of unique advantages as a candidate for hybridization. The SRT method has the advantages of Spectral Theory of Diffraction (STD). STD can model reflection, refraction and diffraction of an arbitrary wave incident on the complex structure, which is not the case for diffraction theories such as Geometrical Theory of Diffraction (GTD), Uniform theory of Diffraction (UTD) and Uniform Asymptotic Theory (UAT). By including complex rays, SRT can effectively analyze both near-fields and far-fields accurately with minimal approximations. In this thesis, a novel matrix representation of SRT is presented that uses only one spectral integration per observation point and applied to modeling a hemispherical and hyper-hemispherical lens. The hybridization of SRT with commercially available FEM and MoM software is proposed in this work to solve the complexity of multi-scale analysis. This yields a computationally efficient self-consistent HSRT algorithm. Various arrangements of the Hybrid SRT method such as FEM-SRT, and MoM-SRT, are investigated and validated through comparison of radiation patterns with Ansoft HFSS for the FEM method, FEKO for MoM, Multi-level Fast Multipole Method (MLFMM) and physical optics. For that a bow-tie terahertz antenna backed by hyper-hemispherical silicon lens, an on-chip planar dipole fabricated in SiGe:C BiCMOS technology and attached to a hyper-hemispherical silicon lens and a double-slot antenna backed by silica lens will be used as sample structures to be analyzed using the HSRT. Computational performance (memory requirement, CPU/GPU time) of developed algorithm is compared to other methods in commercially available software. It is shown that the MoM-SRT, in its present implementation, is more accurate than MoM-PO but comparable in speed. However, as shown in this thesis, MoM-SRT can take advantage of parallel processing and GPU. The HSRT algorithm is applied to simulation of on-chip dipole antenna backed by Silicon lens and integrated with a 180-GHz VCO and radiation pattern compared with measurements. The radiation pattern is measured in a quasi-optical configuration using a power detector. In addition, it is shown that the matrix formulation of SRT and HSRT are promising approaches for solving complex electrically large problems with high accuracy. This thesis also expounds on new measurement setup specifically developed for measuring integrated antennas, radiation pattern and gain of the embedded on-chip antenna in the mmW/ terahertz range. In this method, the radiation pattern is first measured in a quasi-optical configuration using a power detector. Subsequently, the radiated power is estimated form the integration over the radiation pattern. Finally, the antenna gain is obtained from the measurement of a two-antenna system.