V. Cheung

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This thesis proposes a method of classifying stochastic, non-stationary, self-similar signals which originate from non-linear systems and may be comprised of multiple signals, using a multifractal analysis and neural networks. The first stage of the signal classification process entails the extraction of the most important features of the signal. To perform(More)
This paper describes a system capable of classifying stochastic, self-affine, nonstationary signals produced by nonlinear systems. The classification and analysis of these signals is important because they are generated by many real-world processes. The first stage of the signal classification process entails the transformation of the signal into the(More)
Graphical Epitome Processing 2013 This thesis introduces principled, broadly applicable, and efficient patch-based models for data processing applications. Recently, " epitomes " were introduced as patch-based probability models that are learned by compiling together a large number of examples of patches from input images. This thesis describes how epitomes(More)
—This paper describes a system capable of classifying stochas-tic self-affine nonstationary signals produced by nonlinear systems. The classification and the analysis of these signals are important because these are generated by many real-world processes. The first stage of the signal classification process entails the transformation of the signal into the(More)
The Chinese University of Hong Kong holds the copyright of this thesis. Any person(s) intending to use a part or whole of the materials in the thesis in a proposed publication must seek copyright release from the Dean of the Graduate School. Abstract Nowadays, distributed systems are becoming more and more popular in the provision of enriched information to(More)
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