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Unmanned Vehicles (UVs) have been significantly utilized in military and civil applications over the last decade. Path-planning of UVs plays an important role in effectively using the available resources such as the UVs and sensors as efficiently as possible. The main purpose of this thesis is to address two path planning problems involving a single UV. The two problems we consider are the quota problem and the budget problem. In the quota problem, the vehicle has to visit a sufficient number of targets to satisfy the quota requirement on the total prize collected in the tour. In the budget problem, the vehicle has to comply with a constraint of the distance traveled by the UV. We solve both these problems using a practical heuristic called the prize-multiplier approach. This approach first uses a primal-dual algorithm to first assign the targets to the UV. The Lin – Kernighan Heuristic (LKH) is then applied to generate a tour of the assigned targets for the UV. We tested this approach on two different vehicle models. One model is a simple vehicle which can move in any direction without a constraint on its turning radius. The other model is a Reeds-Shepp vehicle. We also modeled both problems in C++ using the multi-commodity flow formulations, and solved them to optimality by using the Concert Technology of CPLEX. We used the results generated by CPLEX to determine the quality of the solutions produced by the heuristics. By comparing the objective values of the obtained solutions and the running times of the heuristics and CPLEX, one can conclude that the proposed heuristics produce solutions with good quality to our problems within our desired time limits.

One of the main objectives in the Oil & Gas Industry is to constantly improve the reservoir management capabilities by using production optimization strategies that can positively impact the so-called net-present value (NPV) of a given project. In order to achieve this goal the industry is faced with the difficult task of maximizing hydrocarbon production and minimizing unwanted fluids, such as water, while sustaining or even enhancing the reservoir recovery factor by handling properly the fluids at surface facilities. A key element in this process is the understanding of the interactions between subsurface and subsurface dynamics in order to provide insightful production strategies which honor reservoir management surface facility constraints. The implementation of the ideal situation of fully coupling surface/subsurface has been hindered by the required computational efforts involved in the process. Consequently, various types of partially coupling that require less computational efforts are practically implemented. Due to importance of coupling surface and subsurface model on production optimization and taking the advantage of advancing computational performance, this research explores the concept of surface and subsurface model couplings and production optimization. The research aims at demonstrating the role of coupling of surface and subsurface model on production optimization under simple production constraint (i.e. production and injection pressure limit). The normal production prediction runs with various reservoir description (homogeneous-low permeability, homogeneous-high permeability, and heterogeneous permeability) and different fluid properties (dead-oil PVT and lived-oil PVT) were performed in order to understand the effect of coupling level, and coupling scheme with different reservoir descriptions and fluid properties on production and injection rate prediction. The result shows that for dead-oil PVT, the production rate from different coupling schemes in homogeneous and heterogeneous reservoir is less sensitive than lived-oil PVT cases. For lived-oil PVT, the production rate from different coupling schemes in homogeneous high permeability and heterogeneous permeability are more sensitive than homogeneous low permeability. The production optimization on water flooding under production and injection constraint cases is considered here also.

Multimedia services have become one of the major demands in wireless systems. As a result of growing demands for media services, traffic in wireless networks are increasing. Hence, optimization of multimedia delivery systems to efficiently consume the valuable transmission resources in wireless networks has gained a lot of interest. Raptor codes, with linear encoding and decoding time complexity are one branch of fountain codes (also known as rateless codes) which have found their ways in many recent communication standards as application layer forward error correcting (FEC) codes. Various attempts have been made in order to adapt these codes to wireless channels with their time varying nature. When multimedia delivery is targeted, some other issues such as delay should also be considered. Moreover, in multicast solutions, the system has to address demands of multiple clients. In this thesis, we investigate some optimization scenarios for wireless multimedia multicast systems wherein clients with heterogeneous channels and media quality demands subscribe to a video program. The video program is assumed as a multilayer source with possible spatial, temporal and fidelity layers. The point of optimization under various systems is to provide the clients of different quality constraints with their demanded services while imposing the minimum network/client cost (e.g. delay, power consumption, outage probability) or maximize the provided utility to the clients while considering their heterogeneous capabilities. To achieve these tasks, we study outage probability which serves as a measure to quantify the reliability of a service in a client's side. Packetized rateless multimedia multicast (PRMM) with few optimization criteria regarding the experienced delay in clients are studied and analytical solutions are obtained. A new optimization framework for rateless multimedia multicast is proposed in which, the provided utility to heterogeneous clients are maximized with respect to the clients channel and their quality demands. Application of this optimization in a rateless multimedia multicast system wherein the utility is defined based on perceptual quality experience of clients is also investigated. / Thesis (Master, Electrical & Computer Engineering) -- Queen's University, 2011-12-23 14:11:26.378

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Better knowledge in the areas of aerodynamics and optimization has allowed designers to develop efficient wingtip structures in recent years. However, the requirements faced by wingtip devices can be considerably different amongst an aircraft’s flight regimes. Traditional static wingtip devices are then a compromise between conflicting requirements, resulting in less than optimal performance within each regime. Alternatively, a morphing wingtip can reconfigure leading to improved performance over a range of dissimilar flight conditions. Developed within this thesis, is a modular morphing wingtip concept that centers on the use of variable geometry truss mechanisms to permit morphing. A conceptual design framework is established to aid in the development of the concept. The framework uses a metaheuristic optimization procedure to determine optimal continuous wingtip configurations. The configurations are then discretized for the modular concept. The functionality of the framework is demonstrated through a design study on a hypothetical wing/winglet within the thesis.

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