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International Telemetering Conference Proceedings / October 17-20, 1994 / Town & Country Hotel and Conference Center, San Diego, California / A significant problem in the processing chain of a low resolution quantization system is the Analog to Digital converter quantization error. The classical model of quantization treats the error generated as a random additive process that is independent of the input and uniformly distributed. This model is valid for complex or random input signals that are large relative to a least significant bit. But the model fails catastrophically for small, simple signals applied to high resolution quantization systems, and in addition, the model fails for simple signals applied to low resolution quantization systems, i.e. one to 6 bits resolution. This paper will discuss a means of correcting this problem by the application of dither. Two methods of dither will be discussed as well as a real-life implementation of the techniques.

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

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

published_or_final_version / abstract / toc / Computer Science and Information Systems / Master / Master of Philosophy

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No description available.

Several pseudorandom number generators are described and compared on the basis of cost of generation and length of period of the sequences that are produced. The major statistical tests, which are used to obtain a measure of randomness for the different generators are discussed and compared. Four pseudorandom number generators are programmed in GPSS and are used to generate interarrival and service times for an M/M/1 queuing system. The results of each of the trials are compared to the theoretical results which can be obtained from queuing theory.

Random numbers are a basic part in a Simulation Model, and they are also used in random sampling. These techniques are employed by quality engineers in the successful execution of their jobs. The every-day use of random numbers, however, often leads to a sense of complacency in he mind of engineers toward the exacting requirements that should be satisfied by the random number process to generate a genuine random number. Microcomputers have become a common and powerful tool that helps managers and engineers in their simulation experiments by providing sequences of random numbers. This research presents a sequence of eight tests to test the Radio Shack microcomputer system random number generator for uniformity and randomness; then, this Radio Shack random number generator is used to generate uniform and non-uniform deviates and a non-parametric test is performed to test these deviates for randomness. Two computer programs written in the BASIC language are used to test for randomness. The first one to test the Radio Shack random number generator and the second one to test the uniform and non-uniform deviates.