by Wan, Kwok Fai. / Thesis (Ph.D.)--Chinese University of Hong Kong, 1995. / Includes bibliographical references (leaves 138-148). / Chapter Chapter 1. --- Adaptive signal processing and its applications --- p.1 / Chapter 1.1. --- Introduction --- p.1 / Chapter 1.2. --- Applications of adaptive system --- p.3 / Chapter 1.2.1. --- Adaptive noise cancellation --- p.3 / Chapter 1.2.2. --- Adaptive echo cancellation --- p.5 / Chapter 1.2.3. --- Adaptive line enhancement --- p.5 / Chapter 1.2.4. --- Adaptive linear prediction --- p.7 / Chapter 1.2.5. --- Adaptive system identification --- p.8 / Chapter 1.3. --- Algorithms for adaptive systems --- p.10 / Chapter 1.4. --- Transform domain adaptive filtering --- p.12 / Chapter 1.5 --- The motivation and organization of the thesis --- p.13 / Chapter Chapter 2. --- Time domain split-path adaptive filter --- p.16 / Chapter 2.1. --- Adaptive transversal filter and the LMS algorithm --- p.17 / Chapter 2.1.1. --- Wiener-Hopf solution --- p.17 / Chapter 2.1.2. --- The LMS adaptive algorithm --- p.20 / Chapter 2.2. --- Split structure adaptive filtering --- p.23 / Chapter 2.2.1. --- Split structure of an adaptive filter --- p.24 / Chapter 2.2.2. --- Split-path structure for a non-symmetric adaptive filter --- p.25 / Chapter 2.3. --- Split-path adaptive median filtering --- p.29 / Chapter 2.3.1. --- Median filtering and median LMS algorithm --- p.29 / Chapter 2.3.2. --- The split-path median LMS (SPMLMS) algorithm --- p.32 / Chapter 2.3.3. --- Convergence analysis of SPMLMS --- p.36 / Chapter 2.4. --- Computer simulation examples --- p.41 / Chapter 2.5. --- Summary --- p.45 / Chapter Chapter 3. --- Multi-stage split structure adaptive filtering --- p.46 / Chapter 3.1. --- Introduction --- p.46 / Chapter 3.2. --- Split structure for a symmetric or an anti-symmetric adaptive filter --- p.48 / Chapter 3.3. --- Multi-stage split structure for an FIR adaptive filter --- p.56 / Chapter 3.4. --- Properties of the split structure LMS algorithm --- p.59 / Chapter 3.5. --- Full split-path adaptive algorithm for system identification --- p.66 / Chapter 3.6. --- Summary --- p.71 / Chapter Chapter 4. --- Transform domain split-path adaptive algorithms --- p.72 / Chapter 4.1. --- Introduction --- p.73 / Chapter 4.2. --- general description of transforms --- p.74 / Chapter 4.2.1. --- Fast Karhunen-Loeve transform --- p.75 / Chapter 4.2.2. --- Symmetric cosine transform --- p.77 / Chapter 4.2.3. --- Discrete sine transform --- p.77 / Chapter 4.2.4. --- Discrete cosine transform --- p.78 / Chapter 4.2.5. --- Discrete Hartley transform --- p.78 / Chapter 4.2.6. --- Discrete Walsh transform --- p.79 / Chapter 4.3. --- Transform domain adaptive filters --- p.80 / Chapter 4.3.1. --- Structure of transform domain adaptive filters --- p.80 / Chapter 4.3.2. --- Properties of transform domain adaptive filters --- p.83 / Chapter 4.4. --- Transform domain split-path LMS adaptive predictor --- p.84 / Chapter 4.5. --- Performance analysis of the TRSPAF --- p.93 / Chapter 4.5.1. --- Optimum Wiener solution --- p.93 / Chapter 4.5.2. --- Steady state MSE and convergence speed --- p.94 / Chapter 4.6. --- Computer simulation examples --- p.96 / Chapter 4.7. --- Summary --- p.100 / Chapter Chapter 5. --- Tracking optimal convergence factor for transform domain split-path adaptive algorithm --- p.101 / Chapter 5.1. --- Introduction --- p.102 / Chapter 5.2. --- The optimal convergence factors of TRSPAF --- p.104 / Chapter 5.3. --- Tracking optimal convergence factors for TRSPAF --- p.110 / Chapter 5.3.1. --- Tracking optimal convergence factor for gradient-based algorithms --- p.111 / Chapter 5.3.2. --- Tracking optimal convergence factors for LMS algorithm --- p.112 / Chapter 5.4. --- Comparison of optimal convergence factor tracking method with self-orthogonalizing method --- p.114 / Chapter 5.5. --- Computer simulation results --- p.116 / Chapter 5.6. --- Summary --- p.121 / Chapter Chapter 6. --- A unification between split-path adaptive filtering and discrete Walsh transform adaptation --- p.122 / Chapter 6.1. --- Introduction --- p.122 / Chapter 6.2. --- A new ordering of the Walsh functions --- p.124 / Chapter 6.3. --- Relationship between SM-ordered Walsh function and other Walsh functions --- p.126 / Chapter 6.4. --- Computer simulation results --- p.132 / Chapter 6.5. --- Summary --- p.134 / Chapter Chapter 7. --- Conclusion --- p.135 / References --- p.138

by Ho King Choi. / Thesis (Ph.D.)--Chinese University of Hong Kong, 1991. / Includes bibliographical references. / Chapter 1. --- Introduction --- p.1 / Chapter 1.1 --- Adaptive Filter and Adaptive System --- p.1 / Chapter 1.2 --- Applications of Adaptive Filter --- p.4 / Chapter 1.2.1 --- System Identification --- p.4 / Chapter 1.2.2 --- Noise Cancellation --- p.6 / Chapter 1.2.3 --- Echo Cancellation --- p.8 / Chapter 1.2.4 --- Speech Processing --- p.10 / Chapter 1.3 --- Chapter Summary --- p.14 / References --- p.15 / Chapter 2. --- Adaptive Filter Structures and Algorithms --- p.17 / Chapter 2.1 --- Filter Structures for Adaptive Filtering --- p.17 / Chapter 2.2 --- Adaptation Algorithms --- p.22 / Chapter 2.2.1 --- The LMS Adaptation Algorithm --- p.24 / Chapter 2.2.1.1 --- Convergence Analysis --- p.28 / Chapter 2.2.1.2 --- Steady State Performance --- p.33 / Chapter 2.2.2 --- The RLS Adaptation Algorithm --- p.35 / Chapter 2.3 --- Chapter Summary --- p.39 / References --- p.41 / Chapter 3. --- Parallel Split Adaptive System --- p.45 / Chapter 3.1 --- Parallel Form Adaptive Filter --- p.45 / Chapter 3.2 --- Joint Process Estimation with a Split-Path Adaptive Filter --- p.49 / Chapter 3.2.1 --- The New Adaptive System Identification Configuration --- p.53 / Chapter 3.2.2 --- Analysis of the Split-Path System Modeling Structure --- p.57 / Chapter 3.2.3 --- Comparison with the Non-Split Configuration --- p.63 / Chapter 3.2.4 --- Some Notes on Even Filter Order Case --- p.67 / Chapter 3.2.5 --- Simulation Results --- p.70 / Chapter 3.3 --- Autoregressive Modeling with a Split-Path Adaptive Filter --- p.75 / Chapter 3.3.1 --- The Split-Path Adaptive Filter for AR Modeling --- p.79 / Chapter 3.3.2 --- Analysis of the Split-Path AR Modeling Structure --- p.84 / Chapter 3.3.3 --- Comparison with Traditional AR Modeling System --- p.89 / Chapter 3.3.4 --- Selection of Step Sizes --- p.90 / Chapter 3.3.5 --- Some Notes on Odd Filter Order Case --- p.94 / Chapter 3.3.6 --- Simulation Results --- p.94 / Chapter 3.3.7 --- Application to Noise Cancellation --- p.99 / Chapter 3.4 --- Chapter Summary --- p.107 / References --- p.109 / Chapter 4. --- Serial Split Adaptive System --- p.112 / Chapter 4.1 --- Serial Form Adaptive Filter --- p.112 / Chapter 4.2 --- Time Delay Estimation with a Serial Split Adaptive Filter --- p.125 / Chapter 4.2.1 --- Adaptive TDE --- p.125 / Chapter 4.2.2 --- Split Filter Approach to Adaptive TDE --- p.132 / Chapter 4.2.3 --- Analysis of the New TDE System --- p.136 / Chapter 4.2.3.1 --- Least-mean-square Solution --- p.138 / Chapter 4.2.3.2 --- Adaptation Algorithm and Performance Evaluation --- p.142 / Chapter 4.2.4 --- Comparison with Traditional Adaptive TDE Method --- p.147 / Chapter 4.2.5 --- System Implementation --- p.148 / Chapter 4.2.6 --- Simulation Results --- p.148 / Chapter 4.2.7 --- Constrained Adaptation for the New TDE System --- p.156 / Chapter 4.3 --- Chapter Summary --- p.163 / References --- p.165 / Chapter 5. --- Extension of the Split Adaptive Systems --- p.167 / Chapter 5.1 --- The Generalized Parallel Split System --- p.167 / Chapter 5.2 --- The Generalized Serial Split System --- p.170 / Chapter 5.3 --- Comparison between the Parallel and the Serial Split Adaptive System --- p.172 / Chapter 5.4 --- Integration of the Two Forms of Split Predictors --- p.177 / Chapter 5.5 --- Application of the Integrated Split Model to Speech Encoding --- p.179 / Chapter 5.6 --- Chapter Summary --- p.188 / References --- p.139 / Chapter 6. --- Conclusions --- p.191 / References --- p.197

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