Tong, Fung Ling. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2007. / Includes bibliographical references (leaves 94-96). / Abstracts in English and Chinese. / Abstract --- p.i / 摘要 --- p.iii / Acknowledgement --- p.iii / Table of Contents --- p.iv / List of Figures --- p.vii / List of Tables --- p.x / Chapter Chapter 1 - --- Introduction --- p.1 / Chapter Chapter 2 - --- State of the art in Vision-based Localization --- p.6 / Chapter 2.1 --- Extended Kalman Filter-based Localization --- p.6 / Chapter 2.1.1 --- Overview of the EKF algorithm --- p.6 / Chapter 2.1.2 --- Process of the EKF-based localization algorithm --- p.8 / Chapter 2.1.3 --- Recent EKF-based vision-based localization algorithms --- p.10 / Chapter 2.1.4 --- Advantages of the EKF-based localization algorithms --- p.11 / Chapter 2.1.5 --- Disadvantages of the EKF-based localization algorithm --- p.11 / Chapter 2.2 --- Monte Carlo Localization --- p.12 / Chapter 2.2.1 --- Overview of MCL --- p.12 / Chapter 2.2.2 --- Recent MCL-based localization algorithms --- p.14 / Chapter 2.2.3 --- Advantages of the MCL-based algorithm --- p.15 / Chapter 2.2.4 --- Disadvantages of the MCL-based algorithm --- p.16 / Chapter 2.3 --- Summary --- p.16 / Chapter Chapter 3 - --- Vision-based Localization as an Optimization Problem --- p.18 / Chapter 3.1 --- "Relationship between the World, Camera and Robot Body Coordinate System" --- p.18 / Chapter 3.2 --- Formulation of the Vision-based Localization as an Optimization Problem --- p.21 / Chapter 3.3 --- Summary --- p.26 / Chapter Chapter 4 - --- Existing Search Algorithms --- p.27 / Chapter 4.1 --- Overview of the Existing Search Algorithms --- p.27 / Chapter 4.2 --- Search Algorithm for the Proposed Objective Function --- p.28 / Chapter 4.3 --- Summary --- p.30 / Chapter Chapter 5 - --- Proposed Vision-based Localization using Genetic Algorithm --- p.32 / Chapter 5.1 --- Mechanism of Genetic Algorithm --- p.32 / Chapter 5.2 --- Formation of Chromosome --- p.35 / Chapter 5.3 --- Fitness Function --- p.39 / Chapter 5.4 --- Mutation and Crossover --- p.40 / Chapter 5.5 --- Selection and Stopping Criteria --- p.42 / Chapter 5.6 --- Adaptive Search Space --- p.44 / Chapter 5.7 --- Overall Flow of the Proposed Algorithm --- p.46 / Chapter 5.8 --- Summary --- p.47 / Chapter Chapter 6 - --- Experimental Results --- p.48 / Chapter 6.1 --- Test Robot --- p.48 / Chapter 6.2 --- Simulator --- p.49 / Chapter 6.2.1 --- Camera states simulation --- p.49 / Chapter 6.2.2 --- Oscillated walking motion simulation --- p.50 / Chapter 6.2.3 --- Input images simulation --- p.50 / Chapter 6.3 --- Computer for simulations --- p.51 / Chapter 6.4 --- Position and Orientation errors --- p.51 / Chapter 6.5 --- Experiment 1 一 Feature points with quantized noise --- p.53 / Chapter 6.5.1 --- Setup --- p.53 / Chapter 6.5.2 --- Results --- p.56 / Chapter 6.6 --- Experiment 2 一 Feature points added with Gaussian noise --- p.62 / Chapter 6.6.1 --- Setup --- p.62 / Chapter 6.6.2 --- Results --- p.62 / Chapter 6.7 --- Experiment 3 一 Noise reduction performance of the adaptive search space strategy --- p.77 / Chapter 6.7.1 --- Setup --- p.77 / Chapter 6.7.2 --- Results --- p.79 / Chapter 6.8 --- Experiment 4 一 Comparison with benchmark algorithms --- p.83 / Chapter 6.8.1 --- Setup --- p.83 / Chapter 6.8.2 --- Results --- p.85 / Chapter 6.9 --- Discussions --- p.88 / Chapter 6.10 --- Summary --- p.90 / Chapter Chapter 7- --- Conclusion --- p.91 / References --- p.94

Um dos problemas fundamentais em finanças é a escolha de ativos para investimento. O primeiro método para solucionar este problema foi desenvolvido por Markowitz em 1952 com a análise de como a variância dos retornos de um ativo impacta no risco do portifólio no qual o mesmo está inserido. Apesar da importância de sua contribuição, o método desenvolvido para a otimização de carteiras não leva em consideração características como a existência de lotes de compra para os ativos e a existência de custos de transação. Este trabalho apresenta uma abordagem alternativa para o problema de otimização de carteiras utilizando algoritmos genéticos. Para tanto são utilizados três algoritmos, o algoritmo genético simples, o algoritmo genético multiobjetivo (Multi Objective Genetic Algorithm - MOGA) e o algoritmo genético de ordenação não dominante (Non Dominated Sorting Genetic Algorithm - NSGA II). O desempenho apresentado pelos algoritmos genéticos neste trabalho mostram a perspectiva para a solução desse problema tão importante e complexo, obtendo-se soluções de alta qualidade e com menor esforço computacional. / One of the basic problems in finance is the choice of assets for investment. The first method to solve this problem was developed by Markowitz in 1952 with the analysis of how the variance of the returns of an asset impacts in the portfolio risk in which the same is inserted. Despite the importance of its contribution, the method developed for the portfolio optimization does not consider characteristics as the existence of round lots and transaction costs. This work presents an alternative approach for the portfolio optimization problem using genetic algorithms. For that three algorithms are used, the simple genetic algorithm, the multi objective genetic algorithm (MOGA) and the non dominated sorting genetic algorithm (NSGA II). The performance presented for the genetic algorithms in this work shows the perspective for the solution of this so important and complex problem, getting solutions of high quality and with lesser computational effort.

The application of Moore's Law would not be feasible by using the computing systems fabrication principles that are prevalent today. Fundamental changes in the field of computing are needed to keep Moore's Law operational. Different quantum technologies are available to take the advancement of computing into the future. Logic in quantum technology uses gates that are very different from those used in contemporary technology. Limiting itself to reversible operations, this thesis presents different methods to realize these logic gates. Two methods using Generalized Ternary Gates and Muthukrishnan Stroud Gates are presented for synthesis of ternary logic gates. Realizations of well-known quantum gates like the Feynman gate, Toffoli Gate, 2-qudit and 3-qudit SW AP gates are shown. In addition a new gate, the Inverse SW AP gate, is proposed and its realization is also presented.

In this thesis we present a new model for identifying dependencies within a gene regulatory cycle. The model incorporates both probabilistic and temporal aspects, but is kept deliberately simple to make it amenable for learning from the gene expression data of microarray experiments. A key simplifying feature in our model is the use of a compression function for collapsing multiple causes of gene expression into a single cause. This allows us to introduce a learning algorithm which avoids the over-fitting tendencies of models with many parameters. We have validated the learning algorithm on simulated data, and carried out experiments on real microarray data. In doing so, we have discovered novel, yet plausible, biological relationships.

Many of the colour images captured by different types of digital camera do not provide quality colour image according to human visual perception. In this study we explore technique for colour correction of the colour images from the Waikato Image Ranger. Colour images were captured using three different illuminants with the Waikato Image Ranger. The colour image formed from the ranger data do not have good quality because illuminants used do not match usual RGB standard illuminants. The spectral power distribution values of the illuminants were measured using spectroradiometer. To calculate tristimulus values the reflectance function of the scene is required. A mechanism of calculating the reflectance functions from the ranger data using genetic algorithm was explained. The reflectance functions are approximated using variable Gaussian basis functions, and fit to the ranger colour triplets by genetic algorithms. From the estimated reflectance functions standard CRT RGB values were calculated. It was found that the genetic algorithm approach was for too slow for practical purposes and produced images with far too much colour variation.

[Truncated abstract] Genetic algorithms (GAs) have been extensively used and studied in computer science, yet there is no generally accepted methodology for exploring which parameters significantly affect performance, whether there is any interaction between parameters and how performance varies with respect to changes in parameters. This thesis presents a rigorous yet practical statistical methodology for the exploratory study of GAs. This methodology addresses the issues of experimental design, blocking, power and response curve analysis. It details how statistical analysis may assist the investigator along the exploratory pathway. The statistical methodology is demonstrated in this thesis using a number of case studies with a classical genetic algorithm with one-point crossover and bit-replacement mutation. In doing so we answer a number of questions about the relationship between the performance of the GA and the operators and encoding used. The methodology is suitable, however, to be applied to other adaptive optimization algorithms not treated in this thesis. In the first instance, as an initial demonstration of our methodology, we describe case studies using four standard test functions. It is found that the effect upon performance of crossover is predominantly linear while the effect of mutation is predominantly quadratic. Higher order effects are noted but contribute less to overall behaviour. In the case of crossover both positive and negative gradients are found which suggests using rates as high as possible for some problems while possibly excluding it for others. .... This is illustrated by showing how the use of Gray codes impedes the performance on a lower modality test function compared with a higher modality test function. Computer animation is then used to illustrate the actual mechanism by which this occurs. Fourthly, the traditional concept of a GA is that of selection, crossover and mutation. However, a limited amount of data from the literature has suggested that the niche for the beneficial effect of crossover upon GA performance may be smaller than has traditionally been held. Based upon previous results on not-linear-separable problems an exploration is made by comparing two test problem suites, one comprising non-rotated functions and the other comprising the same functions rotated by 45 degrees in the solution space rendering them not-linear-separable. It is shown that for the difficult rotated functions the crossover operator is detrimental to the performance of the GA. It is conjectured that what makes a problem difficult for the GA is complex and involves factors such as the degree of optimization at local minima due to crossover, the bias associated with the mutation operator and the Hamming Distances present in the individual problems due to the encoding. Furthermore, the GA was tested on a real world landscape minimization problem to see if the results obtained would match those from the difficult rotated functions. It is demonstrated that they match and that the features which make certain of the test functions difficult are also present in the real world problem. Overall, the proposed methodology is found to be an effective tool for revealing relationships between a randomized optimization algorithm and its encoding and parameters that are difficult to establish from more ad-hoc experimental studies alone.

Thesis (M. Phil.)--University of Hong Kong, 2007. / Also available online.

Thesis (M.S.)--University of Nevada, Reno, 2006. / "May, 2006." Includes bibliographical references (leaves 78-82). Online version available on the World Wide Web.

Thesis (M. Phil.)--University of Hong Kong, 2007. / Title proper from title frame. Also available in printed format.

Thesis (M. Phil.)--University of Hong Kong, 2007. / Title proper from title frame. Also available in printed format.