Due to the increasing competition from developing countries, companies are struggling to reduce their manufacturing costs. In the field of tool manufacturing, manufacturers are under pressure to produce new products as quickly as possible at minimum cost with high accuracy. Laminated tooling, where parts are manufactured layer by layer, is a promising technology to reduce production costs. Laminated tooling is based on taking sheets of metal and stacking them to produce the final product after cutting each layer profile using laser cutting or other techniques. It is also a powerful tool to make complex tools with conformal cooling channels. In conventional injection moulds and casting dies the cooling channels are drilled in straight paths whereas the cavity has a complex profile. In these cases the cooling system may not be sufficiently effective resulting in a longer cooling time and loss of productivity. Furthermore, conventional cooling channels are limited to circular cross sections, while conformal cooling channels could follow any curved path with variable and non circular cross sections. One of the issues in laminated tooling is the surface jaggedness. The surface jaggedness depends on the layers' thicknesses and surface geometry. If the sheets are thin, the surface quality is improved, but the cost of layer profile cutting is increased. On the other hand, increasing the layers' thicknesses reduces the lamination process cost, but it increases the post processing cost. One solution is having variable thicknesses for the layers and optimally finding the set of layer thicknesses to achieve the minimum surface jaggedness and the number of layers at the same time. In practice, the choice of layers thicknesses depends on the availability of commercial sheet metals. One solution to reduce the number of layers without compromising the surface jaggedness is to use a non-uniform lamination technique in which the layers' thicknesses are changed according to the surface geometry. Another factor in the final surface quality is the lamination direction which can be used to reduce the number of laminations. Optimization by considering lamination direction can be done assuming one or multiple directions. In this thesis, an optimization method to minimize the surface jaggedness and the number of layers in laminated tooling is presented. In this optimization, the layers' thicknesses are selected from a set of available sheet metals. Also, the lamination direction as one of the optimization parameters is studied. A modified version of genetic algorithm is created for the optimization purpose in this research. The proposed method is presented as an optimization package which is applicable to any injection mould, hydroforming or sheet metal forming tool to create an optimized laminated prototype based on the actual model.

This thesis deals with questions relating to the maximum density of rank-n matroids in a minor-closed class. Consider a minor-closed class M of matroids that does not contain a given rank-2 uniform matroid. The growth rate function is defined by h_M(n) = max(|N| : N ∈ M simple, r(N) ≤ n). The Growth Rate Theorem, due to Geelen, Kabell, Kung, and Whittle, shows that the growth rate function is either linear, quadratic, or exponential in n. In the case of exponentially dense classes, we conjecture that, for sufficiently large n, h_M(n) = (q^(n+k) − 1)/(q-1) − c, where q is a prime power, and k and c are non-negative integers depending only on M. We show that this holds for several interesting classes, including the class of all matroids with no U_{2,t}-minor. We also consider more general minor-closed classes that exclude an arbitrary uniform matroid. Here the growth rate, as defined above, can be infinite. We define a more suitable notion of density, and prove a growth rate theorem for this more general notion, dividing minor-closed classes into those that are at most polynomially dense, and those that are exponentially dense.

The classical mean-variance model, proposed by Harry Markowitz in 1952, has been one of the most powerful tools in the field of portfolio optimization. In this model, parameters are estimated by their sample counterparts. However, this leads to estimation risk, which the model completely ignores. In addition, the mean-variance model fails to incorporate behavioral aspects of investment decisions. To remedy the problem, the notion of ambiguity aversion has been addressed by several papers where investors acknowledge uncertainty in the estimation of mean returns. We extend the idea to the variances and correlation coefficient of the portfolio, and study their impact. The performance of the portfolio is measured in terms of its Sharpe ratio. We consider different cases where one parameter is assumed to be perfectly estimated by the sample counterpart whereas the other parameters introduce ambiguity, and vice versa, and investigate which parameter has what impact on the performance of the portfolio.

Side channel attacks, where an attacker learns some physical information about the state of a device, are one of the ways in which cryptographic schemes are broken in practice. "Provably secure" schemes are subject to these attacks since the traditional models of security do not account for them. The theoretical community has recently proposed leakage resilient cryptography in an effort to account for side channel attacks in the security model. This thesis provides an in-depth look into what security guarantees public key leakage resilient schemes provide in practice.

In this thesis, the basic theory is convex optimization theory[1]. And we study the problem about how to transfer to convex optimization problem from the filter design problem. So that we can guarantee the solution is the globally optimized solution. As we get the filter coefficients, we quantize them, then to reduce the quantization bits of the filter coefficients by using the algorithm[2]. At last, we try to change the sequence of quantization, and compared the result with the result of the method[2].

A widespread use of robotic technology in civilian and military applications has generated a need for advanced motion planning algorithms that are real-time implementable. These algorithms are required to navigate autonomous vehicles through obstacle-rich environments. This research has led to the development of the multilayer trajectory generation approach. It is built on the principle of separation of concerns, which partitions a given problem into multiple independent layers, and addresses complexity that is inherent at each level. We partition the motion planning algorithm into a roadmap layer and an optimal control layer. At the roadmap layer, elements of computational geometry are used to process the obstacle rich environment and generate feasible sets. These are used by the optimal control layer to generate trajectories while satisfying dynamics of the vehicle. The roadmap layer ignores the dynamics of the system, and the optimal control layer ignores the complexity of the environment, thus achieving a separation of concern. This decomposition enables computationally tractable methods to be developed for addressing motion planning in complex environments. The approach is applied in known and unknown environments. The methodology developed in this thesis has been successfully applied to a 6 DOF planar robotic testbed. Simulation results suggest that the planner can generate trajectories that navigate through obstacles while satisfying dynamical constraints.

A performance model and a reliability-based optimization model for flexible pavements that accounts for the effects of rehabilitation actions are developed. The developed performance model can be effectively implemented in all the applications that require the reliability (performance) of pavements, before and after the rehabilitation actions. The response surface methodology in conjunction with Monte Carlo simulation is used to evaluate pavement fragilities. To provide more flexibility, the parametric regression model that expresses fragilities in terms of decision variables is developed. Developed fragilities are used as performance measures in a reliability-based optimization model. Three decision policies for rehabilitation actions are formulated and evaluated using a genetic algorithm. The multi-objective genetic algorithm is used for obtaining optimal trade-off between performance and cost. To illustrate the developed model, a numerical study is presented. The developed performance model describes well the behavior of flexible pavement before as well as after rehabilitation actions. The sensitivity measures suggest that the reliability of flexible pavements before and after rehabilitation actions can effectively be improved by providing an asphalt layer as thick as possible in the initial design and improving the subgrade stiffness. The importance measures suggest that the asphalt layer modulus at the time of rehabilitation actions represent the principal uncertainty for the performance after rehabilitation actions. Statistical validation of the developed response model shows that the response surface methodology can be efficiently used to describe pavement responses. The results for parametric regression model indicate that the developed regression models are able to express the fragilities in terms of decision variables. Numerical illustration for optimization shows that the cost minimization and reliability maximization formulations can be efficiently used in determining optimal rehabilitation policies. Pareto optimal solutions obtained from multi-objective genetic algorithm can be used to obtain trade-off between cost and performance and avoid possible conflict between two decision policies.

Horizontal well placement determination within a reservoir is a significant and difficult step in the reservoir development process. Determining the optimal well location is a complex problem involving many factors including geological considerations, reservoir and fluid properties, economic costs, lateral direction, and technical ability. The most thorough approach to this problem is that of an exhaustive search, in which a simulation is run for every conceivable well position in the reservoir. Although thorough and accurate, this approach is typically not used in real world applications due to the time constraints from the excessive number of simulations. This project suggests the use of a genetic algorithm applied to the horizontal well placement problem in a gas reservoir to reduce the required number of simulations. This research aims to first determine if well placement optimization is even necessary in a gas reservoir, and if so, to determine the benefit of optimization. Performance of the genetic algorithm was analyzed through five different case scenarios, one involving a vertical well and four involving horizontal wells. The genetic algorithm approach is used to evaluate the effect of well placement in heterogeneous and anisotropic reservoirs on reservoir recovery. The wells are constrained by surface gas rate and bottom-hole pressure for each case. This project's main new contribution is its application of using genetic algorithms to study the effect of well placement optimization in gas reservoirs. Two fundamental questions have been answered in this research. First, does well placement in a gas reservoir affect the reservoir performance? If so, what is an efficient method to find the optimal well location based on reservoir performance? The research provides evidence that well placement optimization is an important criterion during the reservoir development phase of a horizontal-well project in gas reservoirs, but it is less significant to vertical wells in a homogeneous reservoir. It is also shown that genetic algorithms are an extremely efficient and robust tool to find the optimal location.

In recent years, microstrip antennas are suitable for applications in wireless communication systems because they have the characteristics of compact size, light weight, low cost and easy to manufacture. So, they play an important role in the navigation equipment of the rocket, space shutter, personal communication, etc. However, in the design and synthesis of antennas, there are a large number of design variables that affect the antenna performance. In early stages, some researchers did not use any optimization tool in parameter tuning of antennas design. The one utilized most is the ¡§trial and error¡¨ method, which is very time-consuming in order to find a suitable solution to verify the possibilities of the antenna structure. Genetic algorithms have been shown to be effective in the design of broadband microstrip antenna. However, their effectiveness with various degrees depends on the skills of the different genetic algorithms. In this dissertation, we propose a Genetic Algorithm (GA)-based refined method to enhance the effectiveness and to solve the gap-coupled microstrip antenna design problem (largest impedance bandwidth). The refined method with optimization process improves the computing performance comparing with the conventional genetic algorithm. By the refined GA method, bandwidth can be widened up to 3.84 times that of a single excited patch. Furthermore, we present a new design for Ultra Wideband (UWB) antenna. In the new research topic, it is expected that the genetic algorithm can find out a range of feasible (range-based) solutions instead of a few of solutions. As a result, the manufacturing process will have more convenience and practicability. Finally, we propose a new method to overcome the problem of signal interference with the UWB system operations. A band notched characteristic is achieved for the antenna to restrain the interference bandwidth. The disclosed antenna and the circuitry for the antenna system are easily integrated. With the simple structure, the fabrication cost for the antenna is also reduced.

Evolutionary Structural Optimization (ESO) is a relatively new design tool used to improve and optimise the design of structures. In this method, a few elements of an initial design domain of finite elements are iteratively removed. Such a process is carried out repeatedly until an optimum design is achieved, or until a desired given area or volume is reached. In structural design, there is the demand for the development of design tools and methods that includes optimization. This need is the reason behind the development of methods like Evolutionary Structural Optimization (ESO). It is also this demand that this thesis seeks to satisfy. This thesis develops and examines the program named EVO, with the concept of structural optimization in the ESO process. Taking into account the stiffness and stress constraints, EVO allows a realistic and accurate approach to optimising a model in any given environment. Finally, in verifying the ESO algorithm&rsquo / s and EVO program&rsquo / s usefulness to the practical aspect of design, the work presented herein applies the ESO method to case studies. They concern the optimization of 2-D frames, and the optimization of 3-D spatial frames and beams with the prepared program EVO. Comparisons of these optimised models are then made to those that exist in literature.