In this thesis, we also investigated various conversion techniques concerning how to improve the performance of multiplierless fast 1-D DCT, and row column 2-D DCT fast algorithms. We have explored a number of choices of conversion techniques having an impact on the performance of multiplierless fast DCT algorithms. Based on our analytical analysis, and experiment results, we have the following findings: (1) a transform based on a reversible inverse generally performs better than a version based on a traditional inverse; (2) a transform with a delayed uniform normalization step can achieve a much better performance; (3) a lifting structure transform can usually achieve better performance than its non-lifting structure version; (4) using an optimized configuration of non-zero digits to approximate the coefficients can help to achieve a much better performance than using a non-optimized configuration. / This thesis proposes effective methods to convert fast DCT algorithms, including 1-D DCT, row column 2-D DCT, and direct 2-D DCT, into their multiplierless versions. The basic conversion techniques used include: (1) to convert any butterfly structures in a DCT algorithm into lifting steps; (2) to use an optimized configuration of non-zero digits to approximate the coefficients so that multiplications can be converted into shift and add operations. We devised an effective algorithm based on the remainder theorem for finding an MSD representation, with minimum wordlength, of any float constant. As the approximation errors of different coefficients often affect the MSE of an approximated fast DCT algorithm differently, we developed an efficient search algorithm for finding an optimized configuration of non-zero digits for approximating each of the coefficients with an appropriate number of non-zero signed digits so that the approximated algorithm could achieve a minimum MSE. / When compared to those multiplierless fast 1-D DCT algorithms developed by others, the multiplierless 1-D DCT fast algorithms developed via our proposed conversion method can achieve similar or better performance in terms of MSE and PSNR. While the published methods were use to approximate only the kernels of the 1-D DCT fast algorithms with butterfly structures, our proposed methods can approximate both the kernels and the normalization steps of any 1-D DCT, row column 2-D DCT, and direct 2-D DCT fast algorithms. / Chan, Kwong Wing Raymond. / "February 2007." / Adviser: Lee Moon Chuen. / Source: Dissertation Abstracts International, Volume: 68-09, Section: B, page: 6172. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2007. / Includes bibliographical references (p. 110-117). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Electronic reproduction. [Ann Arbor, MI] : ProQuest Information and Learning, [200-] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstract in English and Chinese. / School code: 1307.

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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.

"Dissertation submitted, September 2003." / Includes bibliographical references (leaves 159-172) / xvi, 172 leaves : ill. ; 30 cm. / Title page, contents and abstract only. The complete thesis in print form is available from the University Library. / Thesis (Ph.D.)--University of Adelaide, School of Electrical and Electronic Engineering, 2004

Thesis (Ph. D.)--University of Texas at Austin, 2006. / Vita. Includes bibliographical references.

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

<p>The demand for efficient communication and data storage is continuously increasing and <i>signal representation</i> and <i>compression</i> are important factors in digital communication and storage systems.</p><p>This work deals with <i>Frame based</i> signal representation and compression. The emphasis is on the design of frames suited for efficient representation, or for low bit rate compression, of classes of signals.</p><p>Traditional signal decompositions such as transforms, wavelets, and filter banks, generate expansions using an analysis-synthesis setting. In this thesis we concentrate on the synthesis or <i>reconstruction</i> part of the signal expansion, having a system with no explicit analysis stage. We want to investigate the use of an <i>overcomplete</i> set of vectors, a frame or an overcomplete dictionary, for signal representations and allow sparse representations. Effective signal representations are desirable in many applications, where signal compression is one example. Others can be signal analysis for different purposes, reconstruction of signals from a limited observation set, feature extraction in pattern recognition and so forth.</p><p>The lack of an explicit analysis stage originates some questions on finding the optimal representation. Finding an optimal sparse representation from an overcomplete set of vectors is NP-complete, and suboptimal vector selection methods are more practical. We have used some existing methods like different variations of the Matching Pursuit (MP) [52] algorithm, and we developed a robust regularized FOCUSS to be able to use FOCUSS (FOCal Underdetermined System Solver [29]) under lossy conditions.</p><p>In this work we develop techniques for frame design, the Method of Optimal Directions (MOD), and propose methods by which such frames can successfully be used in frame based signal representation and in compression schemes. A Multi Frame Compression (MFC) scheme is presented and experiments with several signal classes show that the MFC scheme works well at low bit rates using MOD designed frames. Reconstruction experiments provides complimentary evidence of the good properties of the MOD algorithm.</p>

<p>In signal compression we distinguish between lossless and lossy compression. In lossless compression, the encoded signal is more bit efficient than the original signal and is exactly the same as the original one when decoded. In lossy compression, the encoded signal represents an approximation of the original signal, but it has less number of bits. In the latter situation, the major issue is to find the best possible rate-distortion (RD) tradeoff. The rate-distortion function (RDF) represents the theoretical lower bound of the distortion between the original and the reconstructed signal, subject to a given total bit rate for the compressed signal. This is with respect to any compression scheme. If the compression scheme is given, we can find its operational RDF (ORDF).</p><p>The main contribution of this dissertation is the presentation of a method that finds the operational rate-distortion optimal solution for an overcomplete signal decomposition. The idea of using overcomplete dictionaries, or frames, is to get a sparse representation of the signal. Traditionally, suboptimal algorithms, such as Matching Pursuit (MP), are used for this purpose. Given the frame and the Variable Length Codeword (VLC) table embedded in the entropy coder, the solution of the problem of establishing the best RD trade-off has a very high complexity. The proposed method reduces this complexity significantly by structuring the solution approach such that the dependent quantizer allocation problem reduces into an independent one. In addition, the use of a solution tree further reduces the complexity. It is important to note that this large reduction in complexity is achieved without sacrificing optimality. The optimal rate-distortion solution depends on the frame selection and the VLC table embedded in the entropy coder. Thus, frame design and VLC optimization is part of this work.</p><p>Extensive coding experiments are presented, where Gaussian AR(1) processes and various electrocardiogram (ECG) signals are used as input signals. The experiments demonstrate that the new approach outperforms Rate-Distortion Optimized (RDO) Matching Pursuit, previously proposed in [17], in the rate-distortion sense.</p>

The recent demand for wireless transceivers has created a flurry of research into nontraditional receiver architectures. The homodyne receiver, because of its high degree of integration, low complexity and low power consumption, has surfaced a desirable alternative to the well-known heterodyne receiver. However, distortions such as gain and phase imbalance severely degrade the performance of the homodyne receiver. These imbalances, which are caused by impairments of the employed analog devices, are intensified because quadrature demodulation is performed at very high frequencies with a weak input signal. Thus, there exists a great need for low complexity techniques to compensate for these imbalances. In this thesis, we present a new, simplified method for the estimation and the correction of the gain and phase imbalances in a homodyne receiver. The estimation process is based upon carrier re-injection during idle periods of the mobile unit and thus requires only few additional analog components. This approach will be shown to yield tight estimates of the gain and the phase error. Additionally, the correction is performed in the digital domain and thus can be implemented on a digital signal processor. The effectiveness of this method is demonstrated via simulations of an IS-54 transceiver. IS-54 is the North American TDMA standard for dual-mode cellular systems. / Graduation date: 1998

This thesis deals with modelling, analysis, and design of interactions between subsystems in chemical process plants and intracellular biochemical processes. In the first part, the focus is on the selection of decentralized feedback control structures for plants in the chemical process industry, with the aim of achieving a desired performance in the presence of interactions. The second part focuses on modelling and analysis of complex biochemical networks, with the aim of unravelling the impact of interactions between genes, proteins, and metabolites on cell functions. Decentralized control is almost the de-facto standard for control of large-scale systems, and in particular for systems in the process industry. An important task in the design of a decentralized control system is the selection of the control configuration, the so-called input-output pairing, which effectively decides the subsystems. Previous research addressing this problem has primarily focused on the effect of interactions on stability. In this thesis, the problem of selecting control configurations that can deliver a desired control performance is addressed. It is shown that existing measures of interactions, such as the relative gain array (RGA), are poor for selecting configurations for performance due to their inherent assumption of perfect control. Furthermore, several model based tools for the selection of control configurations based on performance considerations are proposed. Central functions in the cell are often linked to complex dynamic behaviors, such as sustained oscillations and multistability, in a biochemical reaction network. Determination of the specific interactions underlying such behaviors is important, for example, to determine sensitivity, robustness, and modelling requirements of given cell functions. A method for identifying the feedback connections and involved subsystems, within a biochemical network, that are the main sources of a complex dynamic behavior is proposed. The effectiveness of the method is illustrated on examples involving cell cycle control, circadian rhythms and glycolytic oscillations. Also, a method for identifying structured dynamic models of biochemical networks, based on experimental data, is proposed. The method is based on results from system identification theory, using time-series measurement data of expression profiles and concentrations of the involved biochemical components. Finally, in order to reduce the complexity of obtained network models, a method for decomposing large-scale networks into biologically meaningful subnetworks is proposed.