W.K. Lee. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1980. / Bibliography: leaves 107-111.

by Yeung Wai Leung. / Thesis (Ph.D.)--Chinese University of Hong Kong, 1997. / Includes bibliographical references (p. 248-[259]). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Mode of access: World Wide Web.

With the increased demand for complex digital signal processing systems, real-time signal processing requires higher throughput systems. In the past, the throughput has been increased by increasing the clock rates, but synchronization can become increasingly more difficult. Recently there has been renewed interest in designing asynchronous digital systems. In an asynchronous system, there is no global clock, and all modules communicate through handshaking. In this thesis we demonstrate an implementation of an FIR filter using asynchronous digital circuit techniques. These asynchronous design techniques are used to test whether a practical signal processing filter can be implemented with asynchronous logic. A third-order four-bit filter is developed and simulated with SPICE, comparing favorably with other available technologies in speed and power consumption. Although in practice 8-16 bits are needed, this work is sufficient to demonstrate the feasibility of asynchronous circuits for filtering applications. A chip is laid out in 2 micron CMOS, and testing shows that it has a speed-power product comparable with asynchronous designs fabricated by others. / Graduation date: 1994

Thesis (M. Phil.)--University of Hong Kong, 2001. / Includes bibliographical references (leaves 80-81).

Thesis (M.S.)--West Virginia University, 2004. / Title from document title page. Document formatted into pages; contains xiii, 148 p. : ill. (some col.). Vita. Includes abstract. Includes bibliographical references (p. 97-98).

published_or_final_version / Mathematics / Master / Master of Philosophy

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In this dissertation we develop four new methods for image restoration. The common feature of all these methods is that the object estimates have a nonlinear dependence on the image data and that iterative methods of solution are needed. The restoration algorithms have been compared with some previously developed methods by means of computer simulations. The problem of restoring noisy images where the spread function is known is treated in two ways. First, this restoration problem is regarded as a constrained least squares optimization problem. Different methods of enforcing smoothness on the restoration are considered. It is shown that the use of an arc length penalty function permits better restoration of edges than can be obtained by pure quadratic penalty functions. We also treat some methods for enforcing upper and lower bounds on the restoration. The second approach taken on the known spread function restoration problem is statistical. Here we consider the image forming system as a communication channel in which the unknown object to be estimated is one member from a random ensemble. We propose a new approach to restoration based on maximum entropy methods. This new approach allows one to easily synthesize estimators to comply with various prior constraints the image restorer wishes to impose. We show how this new maximum entropy synthesis procedure relates to previous uses of maximum entropy principles for the restoration problem. The problem of restoring atmospherically degraded images is treated in Chapter 4. Here, in addition to random noise in the image, we are faced with a randomly changing spread function. We formulated two algorithms for restoration that have better noise immunity than any previously proposed methods. Both proposed methods are based on processing a series of short exposure speckle images. The first method is an ad hoc successive least squares estimation procedure which uses the second order moments of the image and the spread function discrete Fourier transforms (DFT). The second method, which performs even better than the first, is a maximum likelihood estimation algorithm to find the object's DFT. The maximum likelihood algorithm uses both the first and second moments of the transfer function and the image's DFT.

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