In this thesis, a maximum likelihood receiver to obtain the target range profile that uses the clutter prediction derived from the target-free previous observations is suggested as a fast time processor for pulse compression radar systems. The maximum likelihood receiver is proposed to overcome the range sidelobe problem, which is introduced by the pulse compression method. Conventional methods, such as the matched filter receiver, as fast time processor result in the targets with high radar cross sec- tion masking the low radar cross section targets at the neighboring range cells / since sidelobes of the matched filter is determined by the autocorrelation of the spreading code and linearly proportional to target signal power. An unbiased estimator, like the maximum likelihood receiver in this thesis work does not su&crarr / er from such issues. In addition to that, to suppress the signal dependent interference, namely the clutter, at the output of fast time processor / the previous target-free observations are col- lected and utilized to predict the clutter signal for next time instant. This prediction is used in the maximum likelihood receiver. The clutter prediction is done for the stationary case and the internal clutter motion case, and their SINR performances with the maximum likelihood receiver are evaluated. In conclusion, such an approach managed to have an unbiased estimation of target range profile and the clutter suppression advantage in the fast time.

In todays society, a growing number of users are demanding more sophisticated services from wireless communication devices. In order to meet these rising demands, it has been proposed to increase the capacity of the wireless channel by using more than one antenna at the transmitter and receiver, thereby creating multiple-input multiple-output (MIMO) channels. Using MIMO communication techniques is a promising way to improve wireless communication technology because in a rich-scattering environment the capacity increases linearly with the number of antennas. However, increasing the number of transmit antennas also increases the complexity of detection at an exponential rate. So while MIMO channels have the potential to greatly increase the capacity of wireless communication systems, they also force a greater computational burden on the receiver. Even suboptimal MIMO detectors that have relatively low complexity, have been shown to achieve unprecedented high spectral efficiency. However, their performance is far inferior to the optimal MIMO detector, meaning they require more transmit power. The fact that the optimal MIMO detector is an impractical solution due to its prohibitive complexity, leaves a performance gap between detectors that require reasonable complexity and the optimal detector. The objective of this research is to bridge this gap and provide new solutions for managing the inherent performance-complexity trade-off in MIMO detection. The optimally-ordered decision-feedback (BODF) detector is a standard low-complexity detector. The contributions of this thesis can be regarded as ways to either improve its performance or reduce its complexity - or both. We propose a novel algorithm to implement the BODF detector based on noise-prediction. This algorithm is more computationally efficient than previously reported implementations of the BODF detector. Another benefit of this algorithm is that it can be used to easily upgrade an existing linear detector into a BODF detector. We propose the partial decision-feedback detector as a strategy to achieve nearly the same performance as the BODF detector, while requiring nearly the same complexity as the linear detector. We propose the family of Chase detectors that allow the receiver to trade performance for reduced complexity. By adapting some simple parameters, a Chase detector may achieve near-ML performance or have near-minimal complexity. We also propose two new detection strategies that belong to the family of Chase detectors called the B-Chase and S-Chase detectors. Both of these detectors can achieve near-optimal performance with less complexity than existing detectors. Finally, we propose the double-sorted lattice-reduction algorithm that achieves near-optimal performance with near-BODF complexity when combined with the decision-feedback detector.

Timing of ordering of inventory items is of very great importance in Materials Requirement Planning. Uncertainties in timing can have an adverse effect on the system performance. Most often the lead time variation contribute to timing uncertainties; and their effects are reflected in added costs. Lead time error effects are investigated in this thesis. The study attempts to estimate the effects through some relevant costs, and their variations across the lot sizing rules. The hypotheses for this study are 1) Between any two lot sizing rules, there will be a significant difference in error coats due to combined effect of purchased lead time error and manufacturing lead time errors; 2) Relative cost performance of lot sizing rules in MRP is influenced by the lead time errors; 3) There will be a difference in error cost between lot for l o t rule and least total cost rule even with single source of lead time variation. To carry out the study a MRP programme was developed, in FORTRAN 77 with provisions to include the lot sizing rules while exploding the structure. The lot sizing rules used in the study are Lot for Lot, Silver and Meal heuristics, Wagner-Whitin algorithm, Least total cost, Least unit cost and Part Period balancing. A simulation model is developed using GPSS/PC, to test the hypotheses. An hypothetical production situation with three end items, each with a different product structure is designed. In addition, a master production schedule and a job shop are also structured. Appropriate distributions are assumed for both manufacturing lead times and purchase lead times. These provide the stochastic variables in the simulation experiments. A series of experiments were carried out with the model to investigate into the variations of costs amongst lot sizing rules. Results of the simulation experiments prove that there are costs associated with lead time errors in MRP. These error costs vary significantly with different lot sizing rules. It is also found that the resultant error costs vary significantly even with a single source of lead time variation. Least unit cost rule gives the beat performance having least error costs. Lot for Lot rule has shown the worst performance amongst the lot sizing rules considered. Other interesting results have emerged out of the study.

A presente dissertação examina um método de determinação da conectividade cerebral cujo uso vem se tornando popular nos últimos anos, o partial direct coherence (PDC), que se destaca dentre outros métodos por possibilitar a verificação das relações imediatas de sinais multivariados. Este método representa a conectividade cerebral no domínio da frequência e tem íntima relação com a noção de \"causalidade\" de Granger (GRANGER, 1969), que possibilita quantificar a influência mútua entre séries temporais observadas. De um ponto de vista computacional, o referido método faz uso de modelos de séries temporais que hoje têm implementação bastante eficiente em termos de algoritmos off-line, mas cujo sucesso depende da presunção de estacionariedade dos dados, fato que é somente verdadeiro em trechos relativamente curtos de sinais de origem cerebral, como no caso do EEG (Eletroencefalograma). O objetivo deste trabalho é criar um sistema que calcule o PDC, continuamente, em tempo real e que possua a mesma precisão do método off-line, além de ser uma plataforma de estudos para implementações e testes de métodos de determinação da conectividade neural em tempo real. A plataforma desenvolvida é modular, incentivando futuros trabalhos na mesma, e mostrouse eficaz quanto a precisão numérica dos resultados do cálculo do PDC. As características de tempo real foram atingidas com algumas restrições, que dependem da configuração do usuário e do número de canais que um sinal possui. / This thesis examines a method of determination of brain connectivity whose use becomes popular in recent years, the partial direct coherence (PDC) that stands out in comparison with other methods for making possible the verification of immediate relations of multivariate signal. This method represents the brain connectivity in the frequency domain and has a close relationship with the notion of Granger causality (GRANGER, 1969) that makes it possible to quantify the mutual influence between observed time series. From a computational perspective, the above method makes use of time series models, which today has very efficient implementation in terms of off-line algorithm, but whose success depends on presume that the data is stationary, a fact that is only true in relatively short stretches of cerebral signals, especially in the case of EEG. The objective of this thesis is to create a system that calculates the PDC continuously and in real time maintaining the same precision of the off-line method. Furthermore being a research platform for implementations and tests of new methods for determining neural connectivity in real time. The developed platform is modular encouraging future work on it, and was effective in the numerical accuracy of the PDC calculation results. The real time characteristics were achieved with some restrictions that depend of the user configuration and the number of channels that the signal has.

La quête de performance a été une constante à travers l’histoire des systèmes informatiques. Il y a plus d’une décennie maintenant, le modèle de traitement séquentiel montrait ses premiers signes d’épuisement pour satisfaire les exigences de performance. Les barrières du calcul séquentiel ont poussé à un changement de paradigme et ont établi le traitement parallèle comme standard dans les systèmes informatiques modernes. Avec l’adoption généralisée d’ordinateurs parallèles, de nombreux algorithmes et applications ont été développés pour s’adapter à ces nouvelles architectures. Cependant, dans des applications non conventionnelles, avec des exigences d’interactivité et de temps réel, la parallélisation efficace est encore un défi majeur. L’exigence de performance en temps réel apparaît, par exemple, dans les simulations interactives où le système doit prendre en compte l’entrée de l’utilisateur dans une itération de calcul de la boucle de simulation. Le même type de contrainte apparaît dans les applications d’analyse de données en continu. Par exemple, lorsque des donnes issues de capteurs de trafic ou de messages de réseaux sociaux sont produites en flux continu, le système d’analyse doit être capable de traiter ces données à la volée rapidement sur ce flux tout en conservant un budget de mémoire contrôlé La caractéristique dynamique des données soulève plusieurs problèmes de performance tel que la décomposition du problème pour le traitement en parallèle et la maintenance de la localité mémoire pour une utilisation efficace du cache. Les optimisations classiques qui reposent sur des modèles pré-calculés ou sur l’indexation statique des données ne conduisent pas aux performances souhaitées. Dans cette thèse, nous abordons les problèmes dépendants de données sur deux applications différentes : la première dans le domaine de la simulation physique interactive et la seconde sur l’analyse des données en continu. Pour le problème de simulation, nous présentons un algorithme GPU parallèle pour calculer les multiples plus courts chemins et des diagrammes de Voronoi sur un graphe en forme de grille. Pour le problème d’analyse de données en continu, nous présentons une structure de données parallélisable, basée sur des Packed Memory Arrays, pour indexer des données dynamiques géo-référencées tout en conservant une bonne localité de mémoire. / A busca por desempenho tem sido uma constante na história dos sistemas computacionais. Ha mais de uma década, o modelo de processamento sequencial já mostrava seus primeiro sinais de exaustão pare suprir a crescente exigência por performance. Houveram "barreiras"para a computação sequencial que levaram a uma mudança de paradigma e estabeleceram o processamento paralelo como padrão nos sistemas computacionais modernos. Com a adoção generalizada de computadores paralelos, novos algoritmos foram desenvolvidos e aplicações reprojetadas para se adequar às características dessas novas arquiteturas. No entanto, em aplicações menos convencionais, com características de interatividade e tempo real, alcançar paralelizações eficientes ainda representa um grande desafio. O requisito por desempenho de tempo real apresenta-se, por exemplo, em simulações interativas onde o sistema deve ser capaz de reagir às entradas do usuário dentro do tempo de uma iteração da simulação. O mesmo tipo de exigência aparece em aplicações de monitoramento de fluxos contínuos de dados (streams). Por exemplo, quando dados provenientes de sensores de tráfego ou postagens em redes sociais são produzidos em fluxo contínuo, o sistema de análise on-line deve ser capaz de processar essas informações em tempo real e ao mesmo tempo manter um consumo de memória controlada A natureza dinâmica desses dados traz diversos problemas de performance, tais como a decomposição do problema para processamento em paralelo e a manutenção da localidade de dados para uma utilização eficiente da memória cache. As estratégias de otimização tradicionais, que dependem de modelos pré-computados ou de índices estáticos sobre os dados, não atendem às exigências de performance necessárias nesses cenários. Nesta tese, abordamos os problemas dependentes de dados em dois contextos diferentes: um na área de simulações baseada em física e outro em análise de dados em fluxo contínuo. Para o problema de simulação, apresentamos um algoritmo paralelo, em GPU, para computar múltiplos caminhos mínimos e diagramas de Voronoi em um grafo com topologia de grade. Para o problema de análise de fluxos de dados, apresentamos uma estrutura de dados paralelizável, baseada em Packed Memory Arrays, para indexar dados dinâmicos geo-localizados ao passo que mantém uma boa localidade de memória. / The quest for performance has been a constant through the history of computing systems. It has been more than a decade now since the sequential processing model had shown its first signs of exhaustion to keep performance improvements. Walls to the sequential computation pushed a paradigm shift and established the parallel processing as the standard in modern computing systems. With the widespread adoption of parallel computers, many algorithms and applications have been ported to fit these new architectures. However, in unconventional applications, with interactivity and real-time requirements, achieving efficient parallelizations is still a major challenge. Real-time performance requirement shows up, for instance, in user-interactive simulations where the system must be able to react to the user’s input within a computation time-step of the simulation loop. The same kind of constraint appears in streaming data monitoring applications. For instance, when an external source of data, such as traffic sensors or social media posts, provides a continuous flow of information to be consumed by an online analysis system. The consumer system has to keep a controlled memory budget and deliver a fast processed information about the stream Common optimizations relying on pre-computed models or static index of data are not possible in these highly dynamic scenarios. The dynamic nature of the data brings up several performance issues originated from the problem decomposition for parallel processing and from the data locality maintenance for efficient cache utilization. In this thesis we address data-dependent problems on two different applications: one on physically based simulations and another on streaming data analysis. To deal with the simulation problem, we present a parallel GPU algorithm for computing multiple shortest paths and Voronoi diagrams on a grid-like graph. Our contribution to the streaming data analysis problem is a parallelizable data structure, based on packed memory arrays, for indexing dynamic geo-located data while keeping good memory locality.

La quête de performance a été une constante à travers l'histoire des systèmes informatiques.Il y a plus d'une décennie maintenant, le modèle de traitement séquentiel montrait ses premiers signes d'épuisement pour satisfaire les exigences de performance.Les barrières du calcul séquentiel ont poussé à un changement de paradigme et ont établi le traitement parallèle comme standard dans les systèmes informatiques modernes.Avec l'adoption généralisée d'ordinateurs parallèles, de nombreux algorithmes et applications ont été développés pour s'adapter à ces nouvelles architectures.Cependant, dans des applications non conventionnelles, avec des exigences d'interactivité et de temps réel, la parallélisation efficace est encore un défi majeur.L'exigence de performance en temps réel apparaît, par exemple, dans les simulations interactives où le système doit prendre en compte l'entrée de l'utilisateur dans une itération de calcul de la boucle de simulation.Le même type de contrainte apparaît dans les applications d'analyse de données en continu.Par exemple, lorsque des donnes issues de capteurs de trafic ou de messages de réseaux sociaux sont produites en flux continu, le système d'analyse doit être capable de traiter ces données à la volée rapidement sur ce flux tout en conservant un budget de mémoire contrôlé.La caractéristique dynamique des données soulève plusieurs problèmes de performance tel que la décomposition du problème pour le traitement en parallèle et la maintenance de la localité mémoire pour une utilisation efficace du cache.Les optimisations classiques qui reposent sur des modèles pré-calculés ou sur l'indexation statique des données ne conduisent pas aux performances souhaitées.Dans cette thèse, nous abordons les problèmes dépendants de données sur deux applications différentes: la première dans le domaine de la simulation physique interactive et la seconde sur l'analyse des données en continu.Pour le problème de simulation, nous présentons un algorithme GPU parallèle pour calculer les multiples plus courts chemins et des diagrammes de Voronoi sur un graphe en forme de grille.Pour le problème d'analyse de données en continu, nous présentons une structure de données parallélisable, basée sur des Packed Memory Arrays, pour indexer des données dynamiques géo-référencées tout en conservant une bonne localité de mémoire. / The quest for performance has been a constant through the history of computing systems. It has been more than a decade now since the sequential processing model had shown its first signs of exhaustion to keep performance improvements.Walls to the sequential computation pushed a paradigm shift and established the parallel processing as the standard in modern computing systems. With the widespread adoption of parallel computers, many algorithms and applications have been ported to fit these new architectures. However, in unconventional applications, with interactivity and real-time requirements, achieving efficient parallelizations is still a major challenge.Real-time performance requirement shows-up, for instance, in user-interactive simulations where the system must be able to react to the user's input within a computation time-step of the simulation loop. The same kind of constraint appears in streaming data monitoring applications. For instance, when an external source of data, such as traffic sensors or social media posts, provides a continuous flow of information to be consumed by an on-line analysis system. The consumer system has to keep a controlled memory budget and delivery fast processed information about the stream.Common optimizations relying on pre-computed models or static index of data are not possible in these highly dynamic scenarios. The dynamic nature of the data brings up several performance issues originated from the problem decomposition for parallel processing and from the data locality maintenance for efficient cache utilization.In this thesis we address data-dependent problems on two different application: one in physics-based simulation and other on streaming data analysis. To the simulation problem, we present a parallel GPU algorithm for computing multiple shortest paths and Voronoi diagrams on a grid-like graph. To the streaming data analysis problem we present a parallelizable data structure, based on packed memory arrays, for indexing dynamic geo-located data while keeping good memory locality.

La quête de performance a été une constante à travers l’histoire des systèmes informatiques. Il y a plus d’une décennie maintenant, le modèle de traitement séquentiel montrait ses premiers signes d’épuisement pour satisfaire les exigences de performance. Les barrières du calcul séquentiel ont poussé à un changement de paradigme et ont établi le traitement parallèle comme standard dans les systèmes informatiques modernes. Avec l’adoption généralisée d’ordinateurs parallèles, de nombreux algorithmes et applications ont été développés pour s’adapter à ces nouvelles architectures. Cependant, dans des applications non conventionnelles, avec des exigences d’interactivité et de temps réel, la parallélisation efficace est encore un défi majeur. L’exigence de performance en temps réel apparaît, par exemple, dans les simulations interactives où le système doit prendre en compte l’entrée de l’utilisateur dans une itération de calcul de la boucle de simulation. Le même type de contrainte apparaît dans les applications d’analyse de données en continu. Par exemple, lorsque des donnes issues de capteurs de trafic ou de messages de réseaux sociaux sont produites en flux continu, le système d’analyse doit être capable de traiter ces données à la volée rapidement sur ce flux tout en conservant un budget de mémoire contrôlé La caractéristique dynamique des données soulève plusieurs problèmes de performance tel que la décomposition du problème pour le traitement en parallèle et la maintenance de la localité mémoire pour une utilisation efficace du cache. Les optimisations classiques qui reposent sur des modèles pré-calculés ou sur l’indexation statique des données ne conduisent pas aux performances souhaitées. Dans cette thèse, nous abordons les problèmes dépendants de données sur deux applications différentes : la première dans le domaine de la simulation physique interactive et la seconde sur l’analyse des données en continu. Pour le problème de simulation, nous présentons un algorithme GPU parallèle pour calculer les multiples plus courts chemins et des diagrammes de Voronoi sur un graphe en forme de grille. Pour le problème d’analyse de données en continu, nous présentons une structure de données parallélisable, basée sur des Packed Memory Arrays, pour indexer des données dynamiques géo-référencées tout en conservant une bonne localité de mémoire. / A busca por desempenho tem sido uma constante na história dos sistemas computacionais. Ha mais de uma década, o modelo de processamento sequencial já mostrava seus primeiro sinais de exaustão pare suprir a crescente exigência por performance. Houveram "barreiras"para a computação sequencial que levaram a uma mudança de paradigma e estabeleceram o processamento paralelo como padrão nos sistemas computacionais modernos. Com a adoção generalizada de computadores paralelos, novos algoritmos foram desenvolvidos e aplicações reprojetadas para se adequar às características dessas novas arquiteturas. No entanto, em aplicações menos convencionais, com características de interatividade e tempo real, alcançar paralelizações eficientes ainda representa um grande desafio. O requisito por desempenho de tempo real apresenta-se, por exemplo, em simulações interativas onde o sistema deve ser capaz de reagir às entradas do usuário dentro do tempo de uma iteração da simulação. O mesmo tipo de exigência aparece em aplicações de monitoramento de fluxos contínuos de dados (streams). Por exemplo, quando dados provenientes de sensores de tráfego ou postagens em redes sociais são produzidos em fluxo contínuo, o sistema de análise on-line deve ser capaz de processar essas informações em tempo real e ao mesmo tempo manter um consumo de memória controlada A natureza dinâmica desses dados traz diversos problemas de performance, tais como a decomposição do problema para processamento em paralelo e a manutenção da localidade de dados para uma utilização eficiente da memória cache. As estratégias de otimização tradicionais, que dependem de modelos pré-computados ou de índices estáticos sobre os dados, não atendem às exigências de performance necessárias nesses cenários. Nesta tese, abordamos os problemas dependentes de dados em dois contextos diferentes: um na área de simulações baseada em física e outro em análise de dados em fluxo contínuo. Para o problema de simulação, apresentamos um algoritmo paralelo, em GPU, para computar múltiplos caminhos mínimos e diagramas de Voronoi em um grafo com topologia de grade. Para o problema de análise de fluxos de dados, apresentamos uma estrutura de dados paralelizável, baseada em Packed Memory Arrays, para indexar dados dinâmicos geo-localizados ao passo que mantém uma boa localidade de memória. / The quest for performance has been a constant through the history of computing systems. It has been more than a decade now since the sequential processing model had shown its first signs of exhaustion to keep performance improvements. Walls to the sequential computation pushed a paradigm shift and established the parallel processing as the standard in modern computing systems. With the widespread adoption of parallel computers, many algorithms and applications have been ported to fit these new architectures. However, in unconventional applications, with interactivity and real-time requirements, achieving efficient parallelizations is still a major challenge. Real-time performance requirement shows up, for instance, in user-interactive simulations where the system must be able to react to the user’s input within a computation time-step of the simulation loop. The same kind of constraint appears in streaming data monitoring applications. For instance, when an external source of data, such as traffic sensors or social media posts, provides a continuous flow of information to be consumed by an online analysis system. The consumer system has to keep a controlled memory budget and deliver a fast processed information about the stream Common optimizations relying on pre-computed models or static index of data are not possible in these highly dynamic scenarios. The dynamic nature of the data brings up several performance issues originated from the problem decomposition for parallel processing and from the data locality maintenance for efficient cache utilization. In this thesis we address data-dependent problems on two different applications: one on physically based simulations and another on streaming data analysis. To deal with the simulation problem, we present a parallel GPU algorithm for computing multiple shortest paths and Voronoi diagrams on a grid-like graph. Our contribution to the streaming data analysis problem is a parallelizable data structure, based on packed memory arrays, for indexing dynamic geo-located data while keeping good memory locality.

Enterprise Systems like customer-billing systems or financial transaction systems are required to process large volumes of data in a fixed period of time. Those systems are increasingly required to also provide near-time processing of data to support new service offerings. Common systems for data processing are either optimized for high maximum throughput or low latency. This thesis proposes the concept for an adaptive middleware, which is a new approach for designing systems for bulk data processing. The adaptive middleware is able to adapt its processing type fluently between batch processing and single-event processing. By using message aggregation, message routing and a closed feedback-loop to adjust the data granularity at runtime, the system is able to minimize the end-to-end latency for different load scenarios. The relationship of end-to-end latency and throughput of batch and message-based systems is formally analyzed and a performance evaluation of both processing types has been conducted. Additionally, the impact of message aggregation on throughput and latency is investigated. The proposed middleware concept has been implemented with a research prototype and has been evaluated. The results of the evaluation show that the concept is viable and is able to optimize the end-to-end latency of a system. The design, implementation and operation of an adaptive system for bulk data processing differs from common approaches to implement enterprise systems. A conceptual framework has been development to guide the development process of how to build an adaptive software for bulk data processing. It defines the needed roles and their skills, the necessary tasks and their relationship, artifacts that are created and required by different tasks, the tools that are needed to process the tasks and the processes, which describe the order of tasks.

Ces dernières années, les appareils photos/vidéos numériques grand public sont devenus omniprésents. On peut aujourd’hui trouver des systèmes de captures d’images dans toutes sortes d’appareils numériques comme les téléphones portables, les assistants personnels numériques etc. Malgré une augmentation croissante de la puissance et de la complexité de ces appareils, laqualité de la chaîne de capture d’image, composée du couple système optique/capteur est toujours contrainte à des limitations d’espace et de coût. Les défauts introduits sont nombreuxet dégradent considérablement la qualité des images produites : flou, déformations géométriques, artefacts de couleurs, effets de moire, bruits statiques et dynamiques, etc. Une idée intéressante est de corriger ces défauts de manière algorithmique en utilisant la puissance toujours croissante des architectures de traitements. Dans cette thèse nous nous intéressons particulièrement à deux problèmes issues de l’acquisition de l’image par le capteur : le dématriçage de la matrice de Bayer et la réduction du bruit. Dans la première partie, nous décrivons la structure générale de la chaîne de capture d’image dans les appareils photos/vidéos numériques. Nous présentons le rôle, le fonctionnement et les défauts introduits par chacun de ses éléments. Enfin, nous illustrons comment ces défauts peuvent être corriges par des traitements algorithmiques. Dans la deuxième partie, nous montrons comment l’information de couleur est introduite dans les capteurs numériques. Nous présentons ensuite un état de l’art des algorithmes de dématriçage. Un nouvel algorithme de reconstruction de la matrice de Bayer base sur le principe de l’interpolation directionnelle est propose. Il permet d’associer une qualité d’image produite sans artefacts avec une faible complexité de calculs. Pour mieux comprendre les comportements du bruit dans les capteurs numériques, nous énumérons ses différentes sources et leurs dépendances par rapport aux conditions de prises de vues. Apres avoir présenté l’état de l’art des méthodes de restauration des images bruitées, nous nous intéressons particulièrement aux algorithmes de débruitage à voisinage local et plus précisément au filtre bilatéral. Nous proposons un filtre bilatéral pour la mosaïque de Bayer, adaptatif en fonction de la puissance du bruit dans les images. Dans la troisième partie, nous présentons l’implémentation, l’optimisation et la simulation de l’exécution des algorithmes de dématriçage et de réduction du bruit proposes. La plateforme d’implémentation est le processeur TriMedia TM3270 de NXP semiconductors. Nous montrons que nous arrivons à traiter des images de taille 5 méga-pixels en moins de 0,5 secondes et des images de résolution VGA à une cadence supérieure à 25 images par seconde. Finalement, pour des raisons de standardisation, de rapidité d’exécution et de consommation d’énergie, nous avons conçu une architecture dédiée à l’algorithme de dématriçage propose. Cette architecture permet de multiplier par 10 la rapidité d’exécution obtenue sur le processeur TriMedia TM3270 / Digital cameras are now present everywhere. They are commonly included in portable digital devices such as mobile phones and personal digital assistants. In spite of constant improvements in terms of computing power and complexity, the digital imaging chain quality, including sensor and lenses system, is still limited by space and cost constraints. An important number of degradations are introduced by this chain that significantly decrease overall image quality : including blurring effects, geometric distortions, color artefacts, moiré effects, static and dynamic noise. Correcting these defects in an algorithmic way, using the increasing power of embedded processing architecture present in mobile phones and PDAs may appear like an interesting solution. In this thesis we are especially interested in reducing two major defects of the sensor acquisition chain : Bayer matrix demosaicing artefacts and photon noise. In the first part, we describe the general imaging chain commonly used in digital cameras and video devices. We show the function, the inner working and the defects introduced by each of its elements. Finally we exhibit possible ways to correct these defects using algorithmic solutions. In the second part, we introduce the principle of Bayer demosaicing. We present the state of the art and we propose a new method based on a directed interpolation principle. Our method yields a good image quality while retaining a low computational complexity. We then enumerate several noise sources present in imaging digital sensors and their dependencies with imaging conditions. We are particularly interested in local algorithms and more specifically in the bilateral filter. After presenting the state of the art in denoising algorithm, we propose a new adaptive bilateral filter for sensor colour mosaic denoising. In the third part, we present the implementation, the optimization and the execution simulation of the proposed demosaicing and denoising algorithms. The implementation target is the TM3270 TriMedia processor from NXP Semiconductors. We show that it is possible to process 5 megapixels images in less than 0.5 seconds and more than 25 images per second at VGA resolution. Finally, for standardization, execution speed and power consumption reasons, we describe a dedicated architecture for our proposed demosaicing algorithm. This architecture improves the execution speed by a factor of 10 compared to the TriMedia TM3270 processor

This thesis aims to analyze how object detectors perform under winter weather conditions, specifically in areas with varying degrees of snow cover. The investigation will evaluate the effectiveness of commonly used object detection methods in identifying vehicles in snowy environments, including YOLO v8, Yolo v5, and Faster R-CNN. Additionally, the study explores the method of labeling vehicle objects within a set of image frames for the purpose of high-quality annotations in terms of correctness, details, and consistency. Training data is the cornerstone upon which the development of machine learning is built. Inaccurate or inconsistent annotations can mislead the model, causing it to learn incorrect patterns and features. Data augmentation techniques like rotation, scaling, or color alteration have been applied to enhance some robustness to recognize objects under different alterations. The study aims to contribute to the field of deep learning by providing valuable insights into the challenges of detecting vehicles in snowy conditions and offering suggestions for improving the accuracy and reliability of object detection systems. Furthermore, the investigation will examine edge devices' real-time tracking and detection capabilities when applied to aerial images under these weather conditions. What drives this research is the need to delve deeper into the research gap concerning vehicle detection using drones, especially in adverse weather conditions. It highlights the scarcity of substantial datasets before Mokayed et al. published the Nordic Vehicle Dataset. Using unmanned aerial vehicles(UAVs) or drones to capture real images in different settings and under various snow cover conditions in the Nordic region contributes to expanding the existing dataset, which has previously been restricted to non-snowy weather conditions. In recent years, the leverage of drones to capture real-time data to optimize intelligent transport systems has seen a surge. The potential of drones in providing an aerial perspective efficiently collecting data over large areas to precisely and timely monitor vehicular movement is an area that is imperative to address. To a greater extent, snowy weather conditions can create an environment of limited visibility, significantly complicating data interpretation and object detection. The emphasis is set on edge devices' real-time tracking and detection capabilities, which in this study introduces the integration of edge computing in drone technologies to explore the speed and efficiency of data processing in such systems.