Selection in 3D games and simulations is a well-studied problem. Many techniques have been created to address many of the typical scenarios a user could experience. For any single scenario with consistent conditions, there is likely a technique which is well suited. If there isn't, then there is an opportunity for one to be created to best suit the expected conditions of that new scenario. It is critical that the user be given an appropriate technique to interact with their environment. Without it, the entire experience is at risk of becoming burdensome and not enjoyable. With all of the different possible scenarios, it can become problematic when two or more are part of the same program. If they are put closely together, or even intertwined, then the developer is often forced to pick a single technique that works so-so in both, but is likely not optimal for either, or maybe optimal in just one of them. In this case, the user is left to perform selections with a technique that is lacking in one way or another, which can increase errors and frustration. In our research, we have outlined different selection scenarios, all of which were classified by their level of object density (number of objects in scene) and object velocity. We then performed an initial study on how it impacts performance of various selection techniques, including a new selection technique that we developed just for this test, called Expand. Our results showed, among other things, that a standard Raycast technique works well in slow moving and sparse environments, while revealing that our new Expand technique works well in denser environments. With the results from our first study, we sought to develop something that would bridge the gap in performance between those selection techniques tested. Our idea was a framework that could harvest several different selection techniques and determine which was the most optimal at any time. Each selection technique would report how effective it was, given the provided scenario conditions. The framework was responsible for activating the appropriate selection technique when the user made a selection attempt. With this framework in hand, we performed two additional user studies to determine how effective it could be in actual use, and to identify its strengths and weaknesses. Each study compared several selection techniques individually against the framework which utilized them collectively, picking the most suitable. Again, the same scenarios from our first study were reused. From these studies, we gained a deeper understanding of the many challenges associated with automatic selection technique determination. The results from these two studies showed that transitioning between techniques was potentially viable, but rife with design challenges that made its optimization quite difficult. In an effort to sidestep some of the issues surrounding the switching of discrete techniques, we sought to attack the problem from the other direction, and make a single technique act similarly to two techniques, adjusting dynamically to conditions. We performed a user study to analyze the performance of such a technique, with promising results. While the qualitative differences were small, the user feedback did indicate that users preferred this technique over the others, which were static in nature. Finally, we sought to gain a deeper understanding of existing selection techniques that were dynamic in nature, and study how they were designed, and how they could be improved. We scrutinized the attributes of each technique that were already being adjusted dynamically or that could be adjusted and innovated new ways in which the technique could be improved upon. Within this analysis, we also gave thought to how each technique could be best integrated into the Auto-Select framework we proposed earlier. This overall analysis of the latest selection techniques left us with an array of new variants that warrant being created and tested against their existing versions. Our overall research goal was to perform an analysis of selection techniques that intelligently adapt to their environment. We believe that we achieved this by performing several iterative development cycles, including user studies and ultimately leading to innovation in the field of selection. We conclude our research with yet more questions left to be answered. We intend to pursue further research regarding some of these questions, as time permits.

The Quality of Service (QoS) routing approach is gaining an increasing interest in the Internet community due to the new emerging Internet applications such as real-time multimedia applications. These applications require better levels of quality of services than those supported by best effort networks. Therefore providing such services is crucial to many real time and multimedia applications which have strict quality of service requirements regarding bandwidth and timeliness of delivery. QoS routing is a major component in any QoS architecture and thus has been studied extensively in the literature. Scalability is considered one of the major issues in designing efficient QoS routing algorithms due to the high cost of QoS routing both in terms of computational effort and communication overhead. Localized quality of service routing is a promising approach to overcome the scalability problem of the conventional quality of service routing approach. The localized quality of service approach eliminates the communication overhead because it does not need the global network state information. The main aim of this thesis is to contribute towards the localised routing area by proposing and developing some new models and algorithms. Toward this goal we make the following major contributions. First, a scalable and efficient QoS routing algorithm based on a localised approach to QoS routing has been developed and evaluated. Second, we have developed a path selection technique that can be used with existing localized QoS routing algorithms to enhance their scalability and performance. Third, a scalable and efficient hierarchical QoS routing algorithm based on a localised approach to QoS routing has been developed and evaluated.

The Quality of Service (QoS) routing approach is gaining an increasing interest in the Internet community due to the new emerging Internet applications such as real-time multimedia applications. These applications require better levels of quality of services than those supported by best effort networks. Therefore providing such services is crucial to many real time and multimedia applications which have strict quality of service requirements regarding bandwidth and timeliness of delivery. QoS routing is a major component in any QoS architecture and thus has been studied extensively in the literature. Scalability is considered one of the major issues in designing efficient QoS routing algorithms due to the high cost of QoS routing both in terms of computational effort and communication overhead. Localized quality of service routing is a promising approach to overcome the scalability problem of the conventional quality of service routing approach. The localized quality of service approach eliminates the communication overhead because it does not need the global network state information. The main aim of this thesis is to contribute towards the localised routing area by proposing and developing some new models and algorithms. Toward this goal we make the following major contributions. First, a scalable and efficient QoS routing algorithm based on a localised approach to QoS routing has been developed and evaluated. Second, we have developed a path selection technique that can be used with existing localized QoS routing algorithms to enhance their scalability and performance. Third, a scalable and efficient hierarchical QoS routing algorithm based on a localised approach to QoS routing has been developed and evaluated.

Dans ce travail de thèse, on s'intéresse é l'emploi de techniques de traitement de signal de systèmes de communication MIMO (Multiple Input Multiple Output) pour des applications aux réseaux de capteurs sans fil. Les contraintes énergétiques de cette classe de réseau font appel à des topologies particulières et le réseau peut être perçu comme étant un ensemble de grappes de nœuds capteurs. Ceci ouvre la porte à des techniques avancées de communication de type MIMO. Dans un premier temps, les différents aspects caractérisant les réseaux de capteurs sans fil sont introduits. Puis, les efforts engagés pour optimiser la conservation de l'énergie dans ces réseaux sont résumés. Les concepts de base de systèmes MIMOs sont abordés dans le deuxième chapitre et l'exploration par voie numérique de différentes pistes de la technologie MIMO sont exposées. Nous nous intéressons à des techniques de diversité de polarisation dans le cadre de milieux de communication riches en diffuseurs. Par la suite, des méthodes de type beamforming sont proposées pour la localisation dans les réseaux de capteurs sans fil. Le nouvel algorithme de localisation est présenté et les performances sont évaluées. Nous identifions la configuration pour la communication inter-grappes qui permet pour les meilleurs compromis entre énergie et efficacité spectrale dans les réseaux de capteurs sans fil. Finalement, nous envisageons la technique de sélection de nœuds capteurs afin de réduire la consommation de l'énergie dans le réseau de capteur sans fil. / The aim of this work is to study from a signal processing point of view the use of MIMO (Multiple Input Multiple Output) communication systems for algorithms dedicated to wireless sensor networks. We investigate energy-constrained wireless sensor networks and we focus on cluster topology of the network. This topology permits for the use of MIMO communication system model. First, we review different aspects that characterize the wireless sensor network. Then, we introduce the existing strategies for energy conservation in the network. The basic concepts of MIMO systems are presented in the second chapter and numerical results are provided for evaluating the performances of MIMO techniques. Of particular interest, polarization diversity over rich scattering environment is studied. Thereafter, beamforming approach is proposed for the development of an original localization algorithm in wireless sensor network. The novel algorithm is described and performances are evaluated by simulation. We determine the optimal system configuration between a pair of clusters that permits for the highest capacity to energy ratio in the fourth chapter. The final chapter is devoted to sensor nodes selection in wireless sensor network. The aim of using such technique is to make energy conservation in the network.