Due to the recent interest in broadband antennas a microstrip patch antenna was developed to meet the need for a cheap, low profile, broadband antenna. This antenna could be used in a wide range of applications such as in the communications industry for cell phones or satellite communication. Particle Swarm Optimization was used to design the dual-linear polarization broadband microstrip antenna and impedance matching network. This optimization method greatly reduced the time needed to find viable antenna parameters. A dual input patch antenna with over 30% bandwidth in the X-band was simulated using Ansoft's High Frequency Structural Simulator (HFSS) in conjunction with Particle Swarm Optimization. A single input and a dual input antenna was then fabricated. The fabricated antennas were composed of stacked microstrip patches over a set of bowtie apertures in the ground plane that were perpendicular to one another. A dual offset microstrip feedline was used to feed the aperture. Two different layers were used for the microstrip feedline of each polarization. The resulting measured impedance bandwidth was even wider than predicted. The antenna pattern was measured at several frequencies over the antenna bandwidth and was found to have good gain, consistent antenna patterns and low cross polarization.

The fundamental character of excitation system is to provide the direct-current power for field windings for synchronous generators. Excitation control system controls the generator output voltage and reactive power by varying the field winding¡¦s currents. Therefore, it can improve the transient stability of power system. The thesis proposed a process for modeling and simulation on a brushless coal-fired unit, since the 40 years-old magnetic amplifier (Type WMA MAG-A-STAT) Automatic Voltage Regulator (AVR) was replaced by a Programmable Logical Controller based digital redundancy system, for the purpose to verify the excitation system model and dynamic response gains in the future power system study. To establish the generator excitation system and simulations on a popular software program MATLAB/SIMULINK, we wish to manipulate the effective and precise simulation test on a personal-computer and apply Particle Swarm Optimization (PSO) to find the global optimal solution for AVR controller settings. This thesis contributes in building a reliable excitation system model with dynamic response figures for power system network planning and dispatch.

In this dissertation, particle swarm optimization (PSO) is utilized for fractal image compression (FIC) and active contour model (ACM). The dissertation is divided into two parts. The first part is concerned with the FIC and the second part with ACM. FIC is promising both theoretically and practically for image compression. However, since the encoding speed of the traditional full search method is very time-consuming, FIC with full search is unsuitable for real-time applications. In this dissertation, several novel PSO-based approaches incorporating the edge property of the image blocks are proposed to speedup the encoder and preserve the image quality. Instead of the full search, a direction map is built according to the edge type of the image blocks, which directs the particles in the swarm to regions consisting of candidates of higher similarity. Therefore, the searching space is reduced and the speedup can be achieved. Also, since the strategy is performed according to the edge property, better visual effect can be preserved. Experimental results show that the visual-based particle swarm optimization speeds up the encoder 125 times faster with only 0.89 dB decay of image quality in comparison to the full search method. The second part of the dissertation is concerned with the active contour model for automatic object boundary identification. In the traditional methods for ACM, each control point searches its new position in a small nearby window. Consequently, the boundary concavities cannot be searched accurately. Some improvements have been made in the past to enlarge the searching space, yet they are still time-consuming. To overcome these drawbacks, a novel multi-population PSO technique is adopted in this dissertation to enhance the concavity searching capability and reduce the search time but in a larger searching window. In the proposed scheme, to each control point in the contour there is a corresponding swarm of particles with the best swarm particle as the new control point. The proposed optimizer not only inherits the spirit of the original PSO in each swarm but also shares information of the surrounding swarms. Experimental results demonstrate that the proposed method can improve the search of object concavities without extra computation time.

The gradual changes in the world development have brought energy issues back into high profile. An ongoing challenge for countries around the world is to balance the development gains against its effects on the environment. The energy management is the key factor of any sustainable development program. All the aspects of development in agriculture, power generation, social welfare and industry in Iran are crucially related to the energy and its revenue. Forecasting end-use natural gas consumption is an important Factor for efficient system operation and a basis for planning decisions. In this thesis, particle swarm optimization (PSO) used to forecast long run natural gas consumption in Iran. Gas consumption data in Iran for the previous 34 years is used to predict the consumption for the coming years. Four linear and nonlinear models proposed and six factors such as Gross Domestic Product (GDP), Population, National Income (NI), Temperature, Consumer Price Index (CPI) and yearly Natural Gas (NG) demand investigated.

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Morphing an aircraft wingtip can provide substantial performance improvement. Most civil transport aircraft are optimized for range but for other flight conditions such as take-off and climb they are used as constraints. These constraints could potentially reduce the performance of an aircraft at cruise. By altering the shape of the wingtip, we can force the load distribution to adapt to the required flight condition to improve performance. Using a Variable Geometry Truss Mechanism (VGTM) concept to morph the wingtip of an aircraft with a Multidisciplinary Design Optimization (MDO) framework, the current work will attempt to find an optimal wing and wingtip shape to minimize fuel consumption for multiple morphing stages during cruise. This optimization routine was conducted with a Particle Swarm Optimization (PSO) algorithm using different fidelity tools to analyze the aerodynamic and structural disciplines.

Morphing an aircraft wingtip can provide substantial performance improvement. Most civil transport aircraft are optimized for range but for other flight conditions such as take-off and climb they are used as constraints. These constraints could potentially reduce the performance of an aircraft at cruise. By altering the shape of the wingtip, we can force the load distribution to adapt to the required flight condition to improve performance. Using a Variable Geometry Truss Mechanism (VGTM) concept to morph the wingtip of an aircraft with a Multidisciplinary Design Optimization (MDO) framework, the current work will attempt to find an optimal wing and wingtip shape to minimize fuel consumption for multiple morphing stages during cruise. This optimization routine was conducted with a Particle Swarm Optimization (PSO) algorithm using different fidelity tools to analyze the aerodynamic and structural disciplines.

This thesis describes the development and experimental results of a system to explore cloning of facial expressions between dissimilar face models, so new faces can be animated using the animations from existing faces. The system described in this thesis uses fuzzy membership functions and subtractive clustering to represent faces and expressions in an intermediate space. This intermediate space allows expressions for face models with different resolutions to be compared. The algorithm is trained for each pair of faces using particle swarm optimization, which selects appropriate weights and radii to construct the intermediate space. These techniques allow the described system to be more flexible than previous systems, since it does not require prior knowledge of the underlying implementation of the face models to function.

This thesis is concerned with the development of a novel discrete particle swarm optimization (PSO) technique and its application to the discrete optimization of digital filter frequency response characteristics on the one hand, and the high-level synthesis of bit-parallel digital filter data-paths on the other. Two different techniques are presented for the optimization of sharp-transition band frequency response masking (FRM) digital filters, one of which is based on the conventional finite impulse-response (FIR) digital subfilters, and a new hardware-efficient approach which is based on utilizing infinite impulse-response (IIR) digital subfilters. It is shown that further hardware efficiency can be achieved by realizing the IIR interpolation subfilters by using the bilinear-LDI approach. The corresponding discrete PSO is carried out over the canonical signed digit (CSD) multiplier coefficient space for direct mapping to digital hardware considering simultaneous magnitude and group-delay frequency response characteristics. A powerful encoding scheme is developed for the high-level synthesis of digital filters based on discrete PSO, which preserves the data dependency relationships in the digital filter data-paths. In addition, a constrained discrete PSO is developed to overcome the limitations which would manifest themselves if the conventional PSO were to be used. Several examples are presented to demonstrate the application of discrete PSO to the design, high-level synthesis and optimization of digital filters. / Communications

In this thesis, a Particle Swarm Optimization (PSO) method combined with a ray propagation method is presented as a means to optimally locate multiple antennas in an indoor environment. This novel approach uses Particle Swarm Optimisation combined with geometric partitioning. The PSO algorithm uses swarm intelligence to determine the optimal transmitter location within the building layout. It uses the Keenan-Motley indoor propagation model to determine the fitness of a location. If a transmitter placed at that optimum location, transmitting a maximum power is not enough to meet the coverage requirements of the entire indoor space, then the space is geometrically partitioned and the PSO initiated again independently in each partition. The method outputs the number of antennas, their effective isotropic radiated power (EIRP) and physical location required to meet the coverage requirements. An example scenario is presented for a real building at Loughborough University and is compared against a conventional planning technique used widely in practice.