In this thesis work, minimization of platform-based coupling between the antennas of two VHF radios on an aircraft platform and two HF radios on a ship platform is aimed. For this purpose / an optimal antenna placement, which yields minimum average coupling between the antennas over the whole frequency band of operation is determined for each platform. Two important global optimization techniques, namely Genetic Algorithm Optimization and Particle Swarm Optimization, are used in determination of these optimal antenna placements. Aircraft &amp / ship platforms and antennas placed on them are modeled based on their real electrical and physical properties in CST &ndash / MWS (Microwave Studio) simulation tool. For each platform, antenna placements and coupling results determined by two different optimization techniques and performances of these optimization techniques are compared with each other. At the end of this thesis work / for each platform, far-field radiation pattern performances of the antennas at their optimal places are analyzed in terms of directivity and coverage.

One of the major problems encountered in wireless location is the effect caused by non-line of sight (NLOS) propagation. When the direct path from the mobile station (MS) to base stations (BSs) is blocked by obstacles or buildings, the signal arrival times will delay. That will make the signal measurements include an error due to the excess path propagation. If we use the NLOS signal measurements for localization, that will make the system localization performance reduce greatly. In the thesis, a time-of-arrival (TOA) based location system with NLOS mitigation algorithm is proposed. The proposed method uses least squares-support vector machine (LS-SVM) with optimal parameters selection by particle swarm optimization (PSO) for establishing regression model, which is used in the estimation of propagation distances and reduction of the NLOS propagation errors. By using a weighted objective function, the estimation results of the distances are combined with suitable weight factors, which are derived from the differences between the estimated measurements and the measured measurements. By applying the optimality of the weighted objection function, the method is capable of mitigating the NLOS effects and reducing the propagation range errors. Computer simulation results in ultra-wideband (UWB) environments show that the proposed NLOS mitigation algorithm can reduce the mean and variance of the NLOS measurements efficiently. The proposed method outperforms other methods in improving localization accuracy under different NLOS conditions.

In recent years, the awareness of environmental protection has made the power dispatch model no longer purely economical-oriented. This thesis proposed the application of particle swarm optimization (PSO) algorithm and interactive compromise programming method to solve the 24-hour two-objective power dispatch problem. Considering simultaneously the lowest generating cost and the lowest pollution emission, the two mutually-conflicting objectives will choose a compromised dispatch model. This thesis joined the mixed-integer programming problem of optimal power flow (MIOPF) with the dynamic economic dispatch (DED), making this dispatch solution more realistic without electrical violations; The MIOPF considers both continuous and discrete types of variables. The continuous variables are the generating unit real power output and the generator-bus voltage magnitudes; the discrete variables are the shunt capacitor banks and transformer tap setting. Simulation were run on the standard IEEE 30 Bus system. In order to avoid the PSO local optimality problem, this thesis proposed the utilization of the PSO algorithm with time-varying acceleration coefficients (PSO_TVAC) plus the local random search method (LRS), so it can quickly and effectively reach the optimal solution, without advantages of performance and accuracy of PSO. This thesis also proposed the consideration of the available transfer capability (ATC) on transmission lines of the existing dispatch model. Applying sensitivity factors to calculate each generator¡¦s available transfer capability that can be offered in the analyzed time interval, enables the creation of a new constraint. Joined with the dynamic economic dispatch problem, it will make possible that a load client wishes to raise its demand. Simultaneously taking care of the minimum cost and the limits of system security, better dispatch results could be expected.

Stability of the power quality is one of the objectives that power companies always try to assure. With energy shortage and the increases of fuel cost over years, reduction of expenses in all areas is another effort of the power company. Dealing with the above problems, Taiwan Power Company sets up a standard voltage for secondary side of each primary substation. Standard voltage is a commitment of expected 69kV primary substation bus voltage. A proper setting of the standard voltage can reduce voltage variation, in the secondary substation, and reduce the operation frequencies of the on load tap changer. Besides, it can prolong the service life and the maintenance cycle, and it can also reduce maintenance cost of each main transformer. This study proposes a method to calculate the standard voltage to improve the shortcomings that the voltage used to be set up with experience rule. The load and voltage data were used to build a neural network model. Improved particle swarm optimizer was used to find the parameters of the radial basis function neural network in order to build an efficient network. This network uses improved particle swarm optimizer again to the standard voltage. The proposed approach has been verified by the comparison of winter and summer standard voltages on the Tainan primary substation of taipower with accurate results.

Optimization is a mathematical technique that concerns the finding of maxima or minima of functions in some feasible region. There is no business or industry which is not involved in solving optimization problems. A variety of optimization techniques compete for the best solution. Particle Swarm Optimization (PSO) is a relatively new, modern, and powerful method of optimization that has been empirically shown to perform well on many of these optimization problems. It is widely used to find the global optimum solution in a complex search space. This thesis aims at providing a review and discussion of the most established results on PSO algorithm as well as exposing the most active research topics that can give initiative for future work and help the practitioner improve better result with little effort. This paper introduces a theoretical idea and detailed explanation of the PSO algorithm, the advantages and disadvantages, the effects and judicious selection of the various parameters. Moreover, this thesis discusses a study of boundary conditions with the invisible wall technique, controlling the convergence behaviors of PSO, discrete-valued problems, multi-objective PSO, and applications of PSO. Finally, this paper presents some kinds of improved versions as well as recent progress in the development of the PSO, and the future research issues are also given.

Scheduling of aperiodic and independent tasks in hard real-time symmetric multiprocessing systems is an NP-complete problem that is often solved using heuristics like particle swarm optimization (PSO). The performance of these class of heuristics, known as evolutionary algorithms, are often evaluated based on the number of iterations it takes to find a solution. Such metrics provide limited information on how the algorithm reaches a solution and how the process could be accelerated. This thesis presents a methodology to analyze the trajectory formed by candidate solutions in order to analyze them in both the time and frequency domains at a single scale. The analysis entails (i) the impact of different parameters for the PSO algorithm, and (ii) the evolutionary processes in the swarm. The work reveals that particles have a directed movement towards a solution during a transient phase, and then enter a steady state where they perform an unguided local search. The scheduling algorithm presented in this thesis uses a variation of the minimum total tardiness with cumulative penalties cost function, that can be extended to suit different system needs. The experimental results show that the scheduler is able to distribute tasks to meet the real-time deadlines over 1, 2, and 4 processors and up to 30 tasks with overall system loads of up to 50\% in fewer than 1,000 iterations. When scheduling greater loads, the scheduler reaches local solutions with 1 to 2 missed deadlines, while larger tasks sets take longer to converge. The trajectories of the particles during the scheduling algorithm are examined as a means to emphasize the impact of the behaviour on the application performance and give insight into ways to improve the algorithm for both space and terrestrial applications.

Recently, interest in the Graphics Processing Unit (GPU) for general purpose parallel applications development and research has grown. Much of the current research on the GPU focuses on the acceleration of regular problems, as irregular problems typically do not provide the same level of performance on the hardware. We explore the potential of the GPU by investigating four problems on the GPU with regular and/or irregular properties: lookback option pricing (regular), single-source shortest path (irregular), maximum flow (irregular), and the task matching problem using multi-swarm particle swarm optimization (regular with elements of irregularity). We investigate the design, implementation, optimization, and performance of these algorithms on the GPU, and compare the results. Our results show that the regular problem achieves greater performance and requires less development effort than the irregular problems. However, we find the GPU to still be capable of providing high levels of acceleration for irregular problems.

Filtering is a key step in digital image processing and analysis. It is mainly used for amplification or attenuation of some frequencies depending on the nature of the application. Filtering can either be performed in the spatial domain or in a transformed domain. The selection of the filtering method, filtering domain, and the filter parameters are often driven by the properties of the underlying image. This thesis presents three different kinds of biomedical image filtering applications, where the filter parameters are automatically determined from the underlying images. Filtering can be used for image enhancement. We present a robust image dependent filtering method for intensity inhomogeneity correction of biomedical images. In the presented filtering method, the filter parameters are automatically determined from the grey-weighted distance transform of the magnitude spectrum. An evaluation shows that the filter provides an accurate estimate of intensity inhomogeneity. Filtering can also be used for analysis. The thesis presents a filtering method for heart localization and robust signal detection from video recordings of rat embryos. It presents a strategy to decouple motion artifacts produced by the non-rigid embryonic boundary from the heart. The method also filters out noise and the trend term with the help of empirical mode decomposition. Again, all the filter parameters are determined automatically based on the underlying signal. Transforming the geometry of one image to fit that of another one, so called image registration, can be seen as a filtering operation of the image geometry. To assess the progression of eye disorder, registration between temporal images is often required to determine the movement and development of the blood vessels in the eye. We present a robust method for retinal image registration. The method is based on particle swarm optimization, where the swarm searches for optimal registration parameters based on the direction of its cognitive and social components. An evaluation of the proposed method shows that the method is less susceptible to becoming trapped in local minima than previous methods. With these thesis contributions, we have augmented the filter toolbox for image analysis with methods that adjust to the data at hand.

Scheduling of aperiodic and independent tasks in hard real-time symmetric multiprocessing systems is an NP-complete problem that is often solved using heuristics like particle swarm optimization (PSO). The performance of these class of heuristics, known as evolutionary algorithms, are often evaluated based on the number of iterations it takes to find a solution. Such metrics provide limited information on how the algorithm reaches a solution and how the process could be accelerated. This thesis presents a methodology to analyze the trajectory formed by candidate solutions in order to analyze them in both the time and frequency domains at a single scale. The analysis entails (i) the impact of different parameters for the PSO algorithm, and (ii) the evolutionary processes in the swarm. The work reveals that particles have a directed movement towards a solution during a transient phase, and then enter a steady state where they perform an unguided local search. The scheduling algorithm presented in this thesis uses a variation of the minimum total tardiness with cumulative penalties cost function, that can be extended to suit different system needs. The experimental results show that the scheduler is able to distribute tasks to meet the real-time deadlines over 1, 2, and 4 processors and up to 30 tasks with overall system loads of up to 50\% in fewer than 1,000 iterations. When scheduling greater loads, the scheduler reaches local solutions with 1 to 2 missed deadlines, while larger tasks sets take longer to converge. The trajectories of the particles during the scheduling algorithm are examined as a means to emphasize the impact of the behaviour on the application performance and give insight into ways to improve the algorithm for both space and terrestrial applications.

Non-rigid shapes are generally known as objects whose three dimensional geometry may deform by internal and/or external forces. Deformable shapes are all around us, ranging from protein molecules, to natural objects such as the trees in the forest or the fruits in our gardens, and even human bodies. Two deformable shapes may be related by isometry, which means their intrinsic geometries are preserved, even though their extrinsic geometries are dissimilar. An important problem in the analysis of the deformable shapes is to identify the three-dimensional correspondence between two isometric shapes, given that the two shapes may be deviated from isometry by intrinsic distortions. A major challenge is that non-rigid shapes have large degrees of freedom on how to deform. Nevertheless, irrespective of how they are deformed, they may be aligned such that the geodesic distance between two arbitrary points on two shapes are nearly equal. Such alignment may be expressed by a permutation matrix (a matrix with binary entries) that corresponds to every paired geodesic distance in between the two shapes. The alignment involves searching the space over all possible mappings (that is all the permutations) to locate the one that minimizes the amount of deviation from isometry. A brute-force search to locate the correspondence is not computationally feasible. This thesis introduces a novel approach created to locate such correspondences, in spite of the large solution space that encompasses all possible mappings and the presence of intrinsic distortion. In order to find correspondences between two shapes, the first step is to create a suitable descriptor to accurately describe the deformable shapes. To this end, we developed deformation-invariant metric descriptors. A descriptor constitutes pair-wise geodesic distances among arbitrary number of discrete points that represent the topology of the non-rigid shape. Our descriptor provides isometric-invariant representation of the shape irrespective of its circumstantial deformation. Two isometric-invariant descriptors, representing two candidate deformable shapes, are the input parameters to our optimization algorithm. We then proceed to locate the permutation matrix that aligns the two descriptors, that minimizes the deviation from isometry. Once we have developed such a descriptor, we turn our attention to finding correspondences between non deformable shapes. In this study, we investigate the use of both classical and quantum particle swarm optimization (PSO) algorithms for this task. To explore the merits of variants of PSO, integer optimization involving test functions with large dimensions were performed, and the results and the analysis suggest that quantum PSO is more effective optimization method than its classical PSO counterpart. Further, a scheme is proposed to structure the solution space, composed of permutation matrices, in lexicographic ordering. The search in the solution space is accordingly simplified to integer optimization to find the integer rank of the targeted permutation matrix. Empirical results suggest that this scheme improves the scalability of quantum PSO across large solution spaces. Yet, quantum PSO's global search capability requires assistance in order to more effectively manoeuvre through the local extrema prevalent in the large solution spaces. A mutation based genetic algorithm (GA) is employed to augment the search diversity of quantum PSO when/if the swarm stagnates among the local extrema. The mutation based GA instantly disengages the optimization engine from the local extrema in order to reorient the optimization energy to the trajectories that steer to the global extrema, or the targeted permutation matrix. Our resultant optimization algorithm combines quantum Particle Swarm Optimization (PSO) and mutation based Genetic Algorithm (GA). Empirical results show that the optimization method presented is scalable and efficient on standard hardware across different solution space sizes. The performance of the optimization method, in simulations and on various near-isometric shapes, is discussed. In all cases investigated, the method could successfully identify the correspondence among the non-rigid deformable shapes that were related by isometry.