by Leung Pak Ming. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1980. / Bibliography: leaves 199-206.

Kwong Lap-ming. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2001. / Includes bibliographical references (leaves 101-[104]). / Abstracts in English and Chinese. / Chapter 1 --- Introduction to Image Communication --- p.1 / Chapter 1.1 --- Existing Approach improving Image Communication --- p.8 / Chapter 1.1.1 --- Data Compression --- p.8 / Chapter 1.1.2 --- Progressive Image Transmission --- p.10 / Chapter 2 --- Review of Progressive Image Transmission Methods --- p.13 / Chapter 2.1 --- Pyramidal Image Coding --- p.14 / Chapter 2.1.1 --- Expansive Image Pyramid --- p.15 / Chapter 2.1.2 --- Non-Expansive Image Pyramid --- p.18 / Chapter 2.1.3 --- Pros and Cons of Pyramidal Data Structure --- p.19 / Chapter 2.2 --- Hierarchical Data Structure with Bit Plane Transmission --- p.20 / Chapter 2.2.1 --- Bitwise Condensed Quadtree method (BCQ)[6] --- p.21 / Chapter 2.2.2 --- Progressive Transmission of Full-Search VQ[7] --- p.25 / Chapter 2.2.3 --- Pros and Cons of Hierarchical Data Structure with Bit Plane Transmission --- p.26 / Chapter 2.3 --- Embedded Transform Coding --- p.27 / Chapter 2.3.1 --- SPIHT method [16] --- p.27 / Chapter 2.3.2 --- Embedded DCT Method [13] --- p.30 / Chapter 2.3.3 --- Pros and Cons of Embedded Transform Coding --- p.31 / Chapter 2.4 --- Summary --- p.32 / Chapter 3 --- Progressive Refinement of Colormapped Image --- p.34 / Chapter 3.1 --- Colormapped Image --- p.36 / Chapter 3.1.1 --- Pros and Cons of the Usage of Colormapped Image --- p.37 / Chapter 3.2 --- Progressive Refinement in both Spatial and Contrast Resolutions --- p.38 / Chapter 4 --- The Design of Progressive Refinement of Colormapped Image --- p.42 / Chapter 4.1 --- The Scalar Quantization in the YCrCb color space --- p.44 / Chapter 4.1.1 --- The Color Space for Color Quantization --- p.44 / Chapter 4.1.2 --- Color Quantization --- p.44 / Chapter 4.1.3 --- The Order of Quantization --- p.47 / Chapter 4.1.4 --- Pixel Mapping --- p.55 / Chapter 4.2 --- Reordering Pixels --- p.58 / Chapter 4.3 --- Transmission Sequence --- p.61 / Chapter 4.4 --- Changing the progression rate of the spatial and the contrast resolution --- p.62 / Chapter 4.5 --- Data Transmission --- p.69 / Chapter 4.6 --- Displaying the image --- p.70 / Chapter 5 --- Results Analysis & Performance Evaluation --- p.72 / Chapter 5.1 --- Traffic overhead --- p.72 / Chapter 5.2 --- Performance Evaluation --- p.73 / Chapter 5.2.1 --- Experiment --- p.73 / Chapter 5.2.2 --- Comparison with other Methods --- p.81 / Chapter 5.2.3 --- Image quality variation --- p.93 / Chapter 6 --- Discussion and Conclusion --- p.97 / Chapter 6.1 --- Discussion --- p.97 / Chapter 6.2 --- Conclusion --- p.99 / Bibliography --- p.101

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Blind Source Separation is a modern signal processing technique which recovers both the unknown sources and unknown mixing systems from only measured mixtures of signals. It has application in diverse fields such as communication, image processing, geological exploration and biomedical signal processing etc. This project studies the BSS problem, develop separation methods and reveal the potential for mechanical engineering applications. There are two models for blind source separation corresponding to the two ways that the sources are mixed, the instantaneous mixing model and the convolved mixing model. The author carried out a theoretical study of the first model by proposing an idea called Redundant Data Elimination which leads to geometric interpretation of the model, explains that circular distribution property is the reason why Gaussian signal mixtures can not be separated, and showed that this idea can improve separation accuracy for unsymmetrically distributed sources. This new idea enabled evaluation and comparison of two well-known algorithms and proposal of a simplified algorithm based on Joint Approximate Diagonalization of fourth order cumulant matrices, which is further developed by determining an optimized parameter value for separation convergence. Also based on the understanding from the RDE, an outlier spherical projection method is proposed to improve separation accuracy against outlier errors. Mechanical vibration or acoustic problems belong to the second model. After some theoretical study of the problem and the model, a novel application of the Blind Least Mean Square algorithm using Gray's variable norm as cost function is applied to engine vibration data to separate piston slap, fuel injection noise and cylinder pressure effects. Further, the algorithm is combined with a deflation algorithm for successive subtraction of recovered source responses from the measured mixture to enable the recovery of more sources. The algorithms are verified to be successful by simulation, and the separated engine sources are proved reasonable by analysing the engine operation and physical properties of the sources. The author also studied the relationship between these two models, the problems of different approaches for solving the model such as the frequency domain approach and the Bussgang approach, and sets out future research interests.

With the development and production of integrated circuits at the 22nm node, optical lithography faces increasing challenges to keep up with the specifications on its performance along various metrics, such as pattern fidelity and process window. The past few years saw the emergence of source mask optimization (SMO) as an important technique in computational lithography, which allows lithographers to rise to the challenges by exploiting a larger design space on both mask and illumination configuration, and integrates with methods such as inverse imaging. Yet, many methods that are used to tackle SMO problem arising in the inverse imaging involve heavy computation and slow convergence, making the technique unappealing for full-chip simulations or large circuits. Therefore, the purpose of this research is to take advantage of computational imaging technologies to solve source and mask design problems, extending the lifetime of optical lithography. The computational burden results in part from identical optimization over the whole mask pattern, consequently, we propose a weighted SMO scheme which applies different degrees of correction in the corresponding regions so that the optimal solutions are reached with fewer iterations. Additionally, undesirably long time is also attributed to the algorithm adopted to solve SMO problem. A fast algorithm based on augmented Lagrangian methods is therefore developed, which use the quasi-Newton method to accelerate convergence, thereby shortening the overall execution time. However, as semiconductor lithography is pushed to even smaller dimensions, mask topography effects have to be taken into account for a more accurate solution of SMO. At this stage, intensive computation is spent mainly in rigorous 3D mask modeling and simulations. To address this issue, we devise an optimization framework incorporating pupil aberrations into SMO procedure, which is performed based on the thin mask model so as to ensure a faster speed. We apply the above approaches to various mask geometries with different critical dimensions. Compared to conventional SMO, simulation results show that the proposed methods lead to better pattern fidelity and larger process window, especially in rigorous calculations. This demonstrates that the source and mask design generated through our algorithms are more practical. More importantly, the improved performance is not at the cost of speed. Instead, our methods take the least time to achieve it. This allows the advantages of computational imaging technologies to be worth exploring for further applications in optical lithography. / published_or_final_version / Electrical and Electronic Engineering / Doctoral / Doctor of Philosophy

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published_or_final_version / Computer Science / Doctoral / Doctor of Philosophy

published_or_final_version / Mechanical Engineering / Doctoral / Doctor of Philosophy

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No description available.