In summary, the viewpoint of random graphs indeed provides us an opportunity of improving some existing machine learning algorithms. / In this thesis, we establish three machine learning models on random graphs: Heat Diffusion Models on Random Graphs, Predictive Random Graph Ranking, and Random Graph Dependency. The heat diffusion models on random graphs lead to Graph-based Heat Diffusion Classifiers (G-HDC) and a novel ranking algorithm on Web pages called DiffusionRank. For G-HDC, a random graph is constructed on data points. The generated random graph can be considered as the representation of the underlying geometry, and the heat diffusion model on them can be considered as the approximation to the way that heat flows on a geometric structure. Experiments show that G-HDC can achieve better performance in accuracy in some benchmark datasets. For DiffusionRank, theoretically we show that it is a generalization of PageRank when the heat diffusion coefficient tends to infinity, and empirically we show that it achieves the ability of anti-manipulation. / Predictive Random Graph Ranking (PRGR) incorporates DiffusionRank. PRGR aims to solve the problem that the incomplete information about the Web structure causes inaccurate results of various ranking algorithms. The Web structure is predicted as a random graph, on which ranking algorithms are expected to be improved in accuracy. Experimental results show that the PRGR framework can improve the accuracy of the ranking algorithms such as PageRank and Common Neighbor. / Three special forms of the novel Random Graph Dependency measure on two random graphs are investigated. The first special form can improve the speed of the C4.5 algorithm, and can achieve better results on attribute selection than gamma used in Rough Set Theory. The second special form of the general random graph dependency measure generalizes the conditional entropy because it becomes equivalent to the conditional entropy when the random graphs take their special form-equivalence relations. Experiments demonstrates that the second form is an informative measure, showing its success in decision trees on small sample size problems. The third special form can help to search two parameters in G-HDC faster than the cross-validation method. / Yang, haixuan. / "August 2007." / Advisers: Irwin King; Michael R. Lyu. / Source: Dissertation Abstracts International, Volume: 69-02, Section: B, page: 1125. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2007. / Includes bibliographical references (p. 184-197). / 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.

by Man-lai Tang. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1991. / Includes bibliographical references. / Chapter PART I ： --- ANALYSIS OF DATA WITH POLYTOMOUS VARIABLES --- p.1 / Chapter Chapter 1 --- Introduction --- p.1 / Chapter Chapter 2 --- Estimation of the Model with Incomplete Data --- p.5 / Chapter §2.1 --- The Model --- p.5 / Chapter §2.2 --- Two-stage Estimation Method --- p.7 / Chapter Chapter 3 --- Generalization to Several Populations --- p.16 / Chapter §3.1 --- The Model --- p.16 / Chapter §3.2 --- Two-stage Estimation Method --- p.18 / Chapter Chapter 4 --- Computation of the Estimates --- p.23 / Chapter §4.1 --- Maximum Likelihood Estimates in Stage I --- p.23 / Chapter §4.2 --- Generalized Least Squares Estimates in Stage II --- p.27 / Chapter §4.3 --- Approximation for the weight matrix W --- p.28 / Chapter Chapter 5 --- Some Illustrative Examples --- p.31 / Chapter §5.1 --- Single Population --- p.31 / Chapter §5.2 --- Multisample --- p.37 / Chapter PART II ： --- ANALYSIS OF CONTINUOUS AND POLYTOMOUS VARIABLES --- p.42 / Chapter Chapter 6 --- Introduction --- p.42 / Chapter Chapter 7 --- Several Populations Structural Equation Models with Continuous and Polytomous Variables --- p.44 / Chapter §7.1 --- The Model --- p.44 / Chapter §7.2 --- Analysis of the Model --- p.45 / Chapter Chapter 8 --- Analysis of Structural Equation Models of Polytomous and Continuous Variables with Incomplete Data by Multisample Technique --- p.54 / Chapter §8.1 --- Motivation --- p.54 / Chapter §8.2 --- The Model --- p.55 / Chapter §8.3 --- The Method --- p.56 / Chapter Chapter 9 --- Computation of the Estimates --- p.60 / Chapter §9.1 --- Optimization Procedure --- p.60 / Chapter §9.2 --- Derivatives --- p.61 / Chapter Chapter 10 --- Some Illustrative Examples --- p.65 / Chapter §10.1 --- Multisample Example --- p.65 / Chapter §10.2 --- Incomplete Data Example --- p.67 / Chapter §10.3 --- The LISREL Program --- p.69 / Chapter Chapter 11 --- Conclusion --- p.71 / Tables --- p.73 / Appendix --- p.85 / References --- p.89

by Terry Shing-fong Lew. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1994. / Includes bibliographical references (leaves 40-42). / Chapter Chapter 1 --- Introduction --- p.Page1 / Chapter Chapter 2 --- Measures of Association for Polytomous Variables --- p.Page5 / Chapter §2.1 --- "Notations," --- p.5 / Chapter §2.2 --- "Pearson Product-moment Correlation Coefficient," --- p.6 / Chapter §2.3 --- "Spearman Rank Correlation Coefficient," --- p.7 / Chapter §2.4 --- "Kendall's Tau-b," --- p.9 / Chapter §2.5 --- "Polychoric Correlation Coefficient," --- p.9 / Chapter Chapter 3 --- Monte Carlo Study of Measures of Association for Polytomous Variables with Multivariate Normal Distribution --- p.Page 13 / Chapter §3.1 --- "Design," --- p.13 / Chapter §3.2 --- "Results and Findings," --- p.18 / Chapter §3.3 --- "Discussion," --- p.23 / Chapter §3.4 --- "Implications," --- p.26 / Chapter Chapter 4 --- Monte Carlo Studies for Polytomous Variables with Non-normal Distribution --- p.Page 27 / Chapter §4.1 --- "Elliptica1-t Distribution," --- p.27 / Chapter §4.2 --- "Design," --- p.28 / Chapter §4.3 --- "Results and Findings," --- p.30 / Chapter §4.4 --- "Discussion," --- p.33 / Chapter §4.5 --- "Implications," --- p.34 / Chapter Chapter 5 --- Conclusion --- p.Page36 / References --- p.Page40 / Figures --- p.Page43 / Tables --- p.Page51

by King-hong Leung. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1994. / Includes bibliographical references (leaves 110-111). / Chapter Chapter 1 --- Introduction --- p.1 / Chapter Chapter 2 --- Prediction Problem of Factor Scores --- p.5 / Chapter 2.1 --- The Basic Model --- p.5 / Chapter 2.2 --- Regression Formula in Predicting Factor Scores --- p.7 / Chapter 2.3 --- The Model with Polytomous Variables --- p.9 / Chapter Chapter 3 --- Prediction Methods of Factor Scores --- p.11 / Chapter 3.1 --- Model with Continuous and Polytomous Variables --- p.11 / Chapter 3.2 --- Model with Polytomous Variables --- p.16 / Chapter Chapter 4 --- Monte-Carlo Study --- p.20 / Chapter 4.1 --- Model with Continuous and Polytomous Variables --- p.20 / Chapter 4.1.1 --- Design of the Monte-Carlo Study --- p.20 / Chapter 4.1.2 --- Results of the Monte-Carlo Study --- p.24 / Chapter 4.2 --- Model with Polytomous Variables --- p.30 / Chapter 4.2.1 --- Design of the Monte-Carlo Study --- p.30 / Chapter 4.2.2 --- Results of the Monte-Carlo Study --- p.33 / Chapter Chapter 5 --- Summary and Conclusion --- p.38 / Tables --- p.41 / Figures --- p.56 / References --- p.110

by Siu Chi Sang Obadiah. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1999. / Includes bibliographical references (leaves 66-68). / Abstracts in English and Chinese. / Chapter 1 --- Pseudorandom Number Generator --- p.5 / Chapter 1.1 --- Introduction --- p.5 / Chapter 1.2 --- Statistical Indistingushible and Entropy --- p.7 / Chapter 1.3 --- Example of PNG --- p.9 / Chapter 2 --- Basic Knowledge of Cellular Automata --- p.12 / Chapter 2.1 --- Introduction --- p.12 / Chapter 2.2 --- Elementary and Totalistic Cellular Automata --- p.14 / Chapter 2.3 --- Four classes of Cellular Automata --- p.17 / Chapter 2.4 --- Entropy --- p.20 / Chapter 3 --- Theoretical analysis of the CA PNG --- p.26 / Chapter 3.1 --- The Generator --- p.26 / Chapter 3.2 --- Global Properties --- p.27 / Chapter 3.3 --- Stability Properties --- p.31 / Chapter 3.4 --- Particular Initial States --- p.33 / Chapter 3.5 --- Functional Properties --- p.38 / Chapter 3.6 --- Computational Theoretical Properties --- p.42 / Chapter 3.7 --- Finite Size Behaviour --- p.44 / Chapter 3.8 --- Statistical Properties --- p.51 / Chapter 3.8.1 --- statistical test used --- p.54 / Chapter 4 --- Practical Implementation of the CA PNG --- p.56 / Chapter 4.1 --- The implementation of the CA PNG --- p.56 / Chapter 4.2 --- Applied to the set of integers --- p.58 / Chapter 5 --- Application to Cryptography --- p.61 / Chapter 5.1 --- Stream Cipher --- p.61 / Chapter 5.2 --- One Time Pad --- p.62 / Chapter 5.3 --- Probabilistic Encryption --- p.63 / Chapter 5.4 --- Probabilistic Encryption with RSA --- p.64 / Chapter 5.5 --- Prove yourself --- p.65 / Bibliography

Tang Fung Chu. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1999. / Includes bibliographical references (leaves 79-81). / Abstracts in English and Chinese. / Chapter Chapter 1. --- Introduction --- p.1 / Chapter Chapter 2. --- The Multivariate Model and Bayesian Analysis of Stochastic Prior Information --- p.4 / Chapter 2.1 --- The Model --- p.4 / Chapter 2.2 --- Identification of the Model --- p.5 / Chapter 2.3 --- Bayesian Analysis of Stochastic Prior Information --- p.8 / Chapter 2.4 --- Computational Procedure --- p.10 / Chapter 2.4.1 --- Optimization Procedures --- p.11 / Chapter 2.4.2 --- Analytical Expressions --- p.12 / Chapter Chapter 3. --- Example and Simulation Study --- p.18 / Chapter 3.1 --- Example --- p.18 / Chapter 3.2 --- Simulation Study --- p.19 / Chapter 3.2.1 --- Designs --- p.20 / Chapter 3.2.2 --- Results --- p.23 / Chapter Chapter 4. --- Conclusion --- p.26 / Tables --- p.29 / References --- p.79

Chung Chi Keung. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1999. / Includes bibliographical references (leaves 80-84). / Abstracts in English and Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 2 --- Partition Maximum Likelihood Estimation of the General Model --- p.5 / Chapter 2.1 --- Introduction --- p.5 / Chapter 2.2 --- Model --- p.5 / Chapter 2.3 --- The Partition Maximum Likelihood Procedure --- p.8 / Chapter 2.3.1 --- PML estimation of pa --- p.9 / Chapter 2.3.2 --- PML estimation of pab --- p.13 / Chapter 2.3.3 --- Asymptotic properties of the first-stage PML estimates --- p.15 / Chapter 3 --- Bayesian Analysis of Stochastic Prior Information --- p.19 / Chapter 3.1 --- Introduction --- p.19 / Chapter 3.2 --- Bayesian analysis of the Model --- p.20 / Chapter 3.2.1 --- "Case 1, Γ = σ2I" --- p.21 / Chapter 3.2.2 --- Case 2，Г as diagonal matrix with different diagonal el- ements --- p.24 / Chapter 3.2.3 --- "Case 3, Г as a general positive definite matrix" --- p.26 / Chapter 4 --- Simulation Design and Numerical Example --- p.29 / Chapter 4.1 --- Simulation Design --- p.29 / Chapter 4.1.1 --- Model --- p.29 / Chapter 4.1.2 --- Methods of evaluation --- p.32 / Chapter 4.1.3 --- Data analysis --- p.33 / Chapter 4.2 --- Numerical Example --- p.34 / Chapter 4.2.1 --- Model --- p.35 / Chapter 5 --- Conclusion and Discussion --- p.42 / APPENDIX I to V --- p.44-50 / TABLES 1 to 10 --- p.51-77 / FIGURES 1 to 3 --- p.78-79 / REFERENCE --- p.80-84

by Lai-seung Chan. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1998. / Includes bibliographical references (leaves 58-59). / Abstract also in Chinese. / Chapter Chapter 1. --- Introduction --- p.1 / Chapter Chapter 2. --- The Bivariate Model and Maximum Likelihood Estimation --- p.5 / Chapter 2.1 --- The Model --- p.5 / Chapter 2.2 --- Likelihood function of the model --- p.7 / Chapter 2.3 --- Derivatives of likelihood equations --- p.8 / Chapter 2.4 --- Asymptotic properties --- p.11 / Chapter 2.5 --- Optimization procedures --- p.12 / Chapter Chapter 3. --- Generalization to Multivariate Model --- p.14 / Chapter 3.1 --- The Model --- p.14 / Chapter 3.2 --- The Partition Maximum Likelihood (PML) Estimation --- p.15 / Chapter 3.3 --- Asymptotic properties of the PML estimates --- p.19 / Chapter 3.4 --- Optimization procedures --- p.21 / Chapter Chapter 4. --- Simulation Study --- p.22 / Chapter 4.1 --- Designs --- p.22 / Chapter 4.2 --- Results --- p.26 / Chapter Chapter 5. --- Conclusion --- p.30 / Tables --- p.32 / References --- p.58

by Vincent Wing-Yun Sit. / Thesis submitted in: June 1997. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1998. / Includes bibliographical references (leaves 1-4 (3rd gp.)). / Abstract also in Chinese. / TABLE OF CONTENTS / LIST OF FIGURES / LIST OF TABLES / ACKNOWLEDGEMENTS / ABSTRACT / Chapter 1. --- INTRODUCTION --- p.1 / Chapter 1.1 --- ASYNCHRONOUS DESIGN --- p.2 / Chapter 1.1.1 --- POTENTIAL ADVANTAGES --- p.2 / Chapter 1.1.2 --- DESIGN METHODOLOGIES --- p.2 / Chapter 1.1.3 --- SYSTEM CHARACTERISTICS --- p.3 / Chapter 1.2 --- ASYNCHRONOUS MEMORY --- p.5 / Chapter 1.2.1 --- MOTIVATION --- p.5 / Chapter 1.2.2 --- DEFINITION --- p.9 / Chapter 1.3 --- PROPOSED MEMORY DESIGN --- p.10 / Chapter 1.3.1 --- CONTROL INTERFACE --- p.10 / Chapter 1.3.2 --- OVERVIEW --- p.11 / Chapter 1.3.3 --- HANDSHAKE CONTROL PROTOCOL --- p.13 / Chapter 2. --- THEORY --- p.16 / Chapter 2.1 --- VARIABLE BIT LINE LOAD --- p.17 / Chapter 2.1.1 --- DEFINITION --- p.17 / Chapter 2.1.2 --- ADVANTAGE --- p.17 / Chapter 2.2 --- CURRENT SENSING COMPLETION DETECTION --- p.18 / Chapter 2.2.1 --- BLOCK DIAGRAM --- p.19 / Chapter 2.2.2 --- GENERAL LSD CURRENT SENSOR --- p.21 / Chapter 2.2.3 --- CMOS LSD CURRENT SENSOR --- p.23 / Chapter 2.3 --- VOLTAGE SENSING COMPLETION DETECTION --- p.28 / Chapter 2.3.1 --- DATA READING IN MEMORY CIRCUIT --- p.29 / Chapter 2.3.2 --- BLOCK DIAGRAM --- p.30 / Chapter 2.4 --- MULTIPLE DELAYS COMPLETION GENERATION --- p.32 / Chapter 2.4.1 --- ADVANTAGE --- p.32 / Chapter 2.4.2 --- BLOCK DIAGRAM --- p.33 / Chapter 3. --- IMPLEMENTATION --- p.35 / Chapter 3.1 --- 1M-BIT SRAM FRAMEWORK --- p.36 / Chapter 3.1.1 --- INTRODUCTION --- p.36 / Chapter 3.1.2 --- FRAMEWORK --- p.36 / Chapter 3.2 --- CONTROL CIRCUIT --- p.40 / Chapter 3.2.1 --- CONTROL SIGNALS --- p.40 / Chapter 3.2.1.1 --- EXTERNAL CONTROL SIGNALS --- p.40 / Chapter 3.2.1.2 --- INTERNAL CONTROL SIGNALS --- p.41 / Chapter 3.2.2 --- READ / WRITE STATE TRANSITION GRAPHS --- p.42 / Chapter 3.2.3 --- IMPLEMENTATION --- p.43 / Chapter 3.3 --- BIT LINE SEGMENTATION --- p.45 / Chapter 3.3.1 --- FOUR REGIONS SEGMENTATION --- p.46 / Chapter 3.3.2 --- OPERATION --- p.50 / Chapter 3.3.3 --- MEMORY CELL --- p.51 / Chapter 3.4 --- CURRENT SENSING COMPLETION DETECTION --- p.52 / Chapter 3.4.1 --- ONE BIT DATA BUS --- p.53 / Chapter 3.4.2 --- EIGHT BITS DATA BUS --- p.55 / Chapter 3.5 --- VOLTAGE SENSING COMPLETION DETECTION --- p.57 / Chapter 3.5.1 --- ONE BIT DATA BUS --- p.57 / Chapter 3.5.2 --- EIGHT BITS DATA BUS --- p.59 / Chapter 3.6 --- MULTIPLE DELAYS COMPLETION GENERATION --- p.60 / Chapter 4. --- SIMULATION --- p.63 / Chapter 4.1 --- SIMULATION ENVIRONMENT --- p.64 / Chapter 4.1.1 --- SIMULATION PARAMETERS --- p.64 / Chapter 4.1.2 --- MEMORY TIMING SPECIFICATIONS --- p.64 / Chapter 4.1.3 --- BIT LINE LOAD DETERMINATION --- p.67 / Chapter 4.2 --- BENCHMARK SIMULATION --- p.69 / Chapter 4.2.1 --- CIRCUIT SCHEMATIC --- p.69 / Chapter 4.2.2 --- RESULTS --- p.71 / Chapter 4.3 --- CURRENT SENSING COMPLETION DETECTION --- p.73 / Chapter 4.3.1 --- CIRCUIT SCHEMATIC --- p.73 / Chapter 4.3.2 --- SENSE AMPLIFIER CURRENT CHARACTERISTICS --- p.75 / Chapter 4.3.3 --- RESULTS --- p.76 / Chapter 4.3.4 --- OBSERVATIONS --- p.80 / Chapter 4.4 --- VOLTAGE SENSING COMPLETION DETECTION --- p.82 / Chapter 4.4.1 --- CIRCUIT SCHEMATIC --- p.82 / Chapter 4.4.2 --- RESULTS --- p.83 / Chapter 4.5 --- MULTIPLE DELAYS COMPLETION GENERATION --- p.89 / Chapter 4.5.1 --- CIRCUIT SCHEMATIC --- p.89 / Chapter 4.5.2 --- RESULTS --- p.90 / Chapter 5. --- TESTING --- p.97 / Chapter 5.1 --- TEST CHIP DESIGN --- p.98 / Chapter 5.1.1 --- BLOCK DIAGRAM --- p.98 / Chapter 5.1.2 --- SCHEMATIC --- p.100 / Chapter 5.1.3 --- LAYOUT --- p.102 / Chapter 5.2 --- HSPICE POST-LAYOUT SIMULATION RESULTS --- p.104 / Chapter 5.2.1 --- GRAPHICAL RESULTS --- p.105 / Chapter 5.2.2 --- VOLTAGE SENSING COMPLETION DETECTION --- p.108 / Chapter 5.2.3 --- MULTIPLE DELAYS COMPLETION GENERATION --- p.114 / Chapter 5.3 --- MEASUREMENTS --- p.117 / Chapter 5.3.1 --- LOGIC RESULTS --- p.118 / Chapter 5.3.1.1 --- METHOD --- p.118 / Chapter 5.3.1.2 --- RESULTS --- p.118 / Chapter 5.3.2 --- TIMING RESULTS --- p.119 / Chapter 5.3.2.1 --- METHOD --- p.119 / Chapter 5.3.2.2 --- GRAPHICAL RESULTS --- p.121 / Chapter 5.3.2.3 --- VOLTAGE SENSING COMPLETION DETECTION --- p.123 / Chapter 5.3.2.4 --- MULTIPLE DELAYS COMPLETION GENERATION --- p.125 / Chapter 6. --- DISCUSSION --- p.127 / Chapter 6.1 --- CURRENT SENSING COMPLETION DETECTION --- p.128 / Chapter 6.1.1 --- COMMENTS AND CONCLUSION --- p.128 / Chapter 6.1.2 --- SUGGESTION --- p.128 / Chapter 6.2 --- VOLTAGE SENSING COMPLETION DETECTION --- p.129 / Chapter 6.2.1 --- RESULTS COMPARISON --- p.129 / Chapter 6.2.1.1 --- GENERAL --- p.129 / Chapter 6.2.1.2 --- BIT LINE LOAD --- p.132 / Chapter 6.2.1.3 --- BIT LINE SEGMENTATION --- p.133 / Chapter 6.2.2 --- RESOURCE CONSUMPTION --- p.133 / Chapter 6.2.2.1 --- AREA --- p.133 / Chapter 6.2.2.2 --- POWER --- p.134 / Chapter 6.2.3 --- COMMENTS AND CONCLUSION --- p.134 / Chapter 6.3 --- MULTIPLE DELAY COMPLETION GENERATION --- p.135 / Chapter 6.3.1 --- RESULTS COMPARISON --- p.135 / Chapter 6.3.1.1 --- GENERAL --- p.135 / Chapter 6.3.1.2 --- BIT LINE LOAD --- p.136 / Chapter 6.3.1.3 --- BIT LINE SEGMENTATION --- p.137 / Chapter 6.3.2 --- RESOURCE CONSUMPTION --- p.138 / Chapter 6.3.2.1 --- AREA --- p.138 / Chapter 6.3.2.2 --- POWER --- p.138 / Chapter 6.3.3 --- COMMENTS AND CONCLUSION --- p.138 / Chapter 6.4 --- GENERAL COMMENTS --- p.139 / Chapter 6.4.1 --- COMPARISON OF THE THREE TECHNIQUES --- p.139 / Chapter 6.4.2 --- BIT LINE SEGMENTATION --- p.141 / Chapter 6.5 --- APPLICATION --- p.142 / Chapter 6.6 --- FURTHER DEVELOPMENTS --- p.144 / Chapter 6.6.1 --- INTERACE WITH TWO-PHASE HCP --- p.144 / Chapter 6.6.2 --- DATA BUS EXPANSION --- p.146 / Chapter 6.6.3 --- SPEED OPTIMIZATION --- p.147 / Chapter 6.6.4 --- MODIFIED WRITE COMPLETION METHOD --- p.150 / Chapter 7. --- CONCLUSION --- p.152 / Chapter 7.1 --- PROBLEM DEFINITION --- p.152 / Chapter 7.2 --- IMPLEMENTATION --- p.152 / Chapter 7.3 --- EVALUATION --- p.153 / Chapter 7.4 --- COMMENTS AND SUGGESTIONS --- p.155 / Chapter 8. --- REFERENCES --- p.R-l / Chapter 9. --- APPENDIX --- p.A-l / Chapter 9.1 --- HSPICE SIMULATION PARAMETERS --- p.A-l / Chapter 9.1.1 --- TYPICAL SIMULATION CONDITION --- p.A-l / Chapter 9.1.2 --- FAST SIMULATION CONDITION --- p.A-3 / Chapter 9.1.3 --- SLOW SIMULATION CONDITION --- p.A-4 / Chapter 9.2 --- SRAM CELL LAYOUT AND NETLIST --- p.A-5 / Chapter 9.3 --- TEST CHIP SPECIFICATIONS --- p.A-8 / Chapter 9.3.1 --- GENERAL SPECIFICATIONS --- p.A-8 / Chapter 9.3.2 --- PIN ASSIGNMENT --- p.A-9 / Chapter 9.3.3 --- TIMING DIAGRAMS AND SPECIFICATIONS --- p.A-10 / Chapter 9.3.4 --- SCHEMATICS AND LAYOUTS --- p.A-11 / Chapter 9.3.4.1 --- STANDARD MEMORY COMPONENTS --- p.A-12 / Chapter 9.3.4.2 --- DVSCD AND MDCG COMPONENTS --- p.A-20 / Chapter 9.3.5 --- MICROPHOTOGRAPH --- p.A-25

by Yuen Ching Wah. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1998. / Includes bibliographical references (leaves 61-[63]). / Abstract also in Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Why use cryptography? --- p.1 / Chapter 1.2 --- Why is authentication important ？ --- p.2 / Chapter 1.3 --- What is the relationship between authentication and digital sig- nature? --- p.3 / Chapter 1.4 --- Why is random number important? --- p.3 / Chapter 2 --- Background --- p.5 / Chapter 2.1 --- Cryptography --- p.5 / Chapter 2.1.1 --- Symmetric key cryptography --- p.5 / Chapter 2.1.2 --- Asymmetric key cryptography --- p.7 / Chapter 2.1.3 --- Authentication --- p.8 / Chapter 2.2 --- Elliptic curve cryptography --- p.9 / Chapter 2.2.1 --- Mathematical background for Elliptic curve cryptography --- p.10 / Chapter 2.3 --- Pseudorandom number generator --- p.12 / Chapter 2.3.1 --- Linear Congruential Generator --- p.13 / Chapter 2.3.2 --- Inversive Congruential Generator --- p.13 / Chapter 2.3.3 --- PN-sequence generator --- p.14 / Chapter 2.4 --- Digital Signature Scheme --- p.14 / Chapter 2.5 --- Babai's lattice vector algorithm --- p.16 / Chapter 2.5.1 --- First Algorithm: Rounding Off --- p.17 / Chapter 2.5.2 --- Second Algorithm: Nearest Plane --- p.17 / Chapter 3 --- Several Digital Signature Schemes --- p.18 / Chapter 3.1 --- DSA --- p.19 / Chapter 3.2 --- Nyberg-Rueppel Digital Signature --- p.21 / Chapter 3.3 --- EC.DSA --- p.23 / Chapter 3.4 --- EC-Nyberg-Rueppel Digital Signature Scheme --- p.26 / Chapter 4 --- Miscellaneous Digital Signature Schemes and their PRNG --- p.29 / Chapter 4.1 --- DSA with LCG --- p.30 / Chapter 4.2 --- DSA with PN-sequence --- p.33 / Chapter 4.2.1 --- Solution --- p.35 / Chapter 4.3 --- DSA with ICG --- p.39 / Chapter 4.3.1 --- Solution --- p.40 / Chapter 4.4 --- EC_DSA with PN-sequence --- p.43 / Chapter 4.4.1 --- Solution --- p.44 / Chapter 4.5 --- EC一DSA with LCG --- p.45 / Chapter 4.5.1 --- Solution --- p.46 / Chapter 4.6 --- EC-DSA with ICG --- p.46 / Chapter 4.6.1 --- Solution --- p.47 / Chapter 4.7 --- Nyberg-Rueppel Digital Signature with PN-sequence --- p.48 / Chapter 4.7.1 --- Solution --- p.49 / Chapter 4.8 --- Nyberg-Rueppel Digital Signature with LCG --- p.50 / Chapter 4.8.1 --- Solution --- p.50 / Chapter 4.9 --- Nyberg-Rueppel Digital Signature with ICG --- p.51 / Chapter 4.9.1 --- Solution --- p.52 / Chapter 4.10 --- EC- Nyberg-Rueppel Digital Signature with LCG --- p.53 / Chapter 4.10.1 --- Solution --- p.54 / Chapter 4.11 --- EC- Nyberg-Rueppel Digital Signature with PN-sequence --- p.55 / Chapter 4.11.1 --- Solution --- p.56 / Chapter 4.12 --- EC-Nyberg-Rueppel Digital Signature with ICG --- p.56 / Chapter 4.12.1 --- Solution --- p.57 / Chapter 5 --- Conclusion --- p.59 / Bibliography --- p.61