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All-optical networks with sparse wavelength conversion /Subramaniam, Suresh, January 1997 (has links)
Thesis (Ph. D.)--University of Washington, 1997. / Vita. Includes bibliographical references (leaves [119]-125).
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Evaluation of ANSI compression in a bulk data file transfer system /Chaulklin, Douglas Gary. January 1991 (has links)
Project report (M. Eng.)--Virginia Polytechnic Institute and State University, 1991. / Abstract. Includes bibliographical references (leaves 63-64). Also available via the Internet.
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Low bit rate compression of Marine imagery using fast ECVQ /Johnson, Mary Holland. January 1999 (has links)
Thesis (Ph. D.)--University of Washington, 1999. / Vita. Includes bibliographical references (leaves 97-102).
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A reference guide to JPEG compression /Goodenow, Daniel P. January 1993 (has links)
Thesis (M.S.)--Rochester Institute of Technology, 1993. / Typescript. Includes bibliographical references.
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Efficient software implementation of the JBIG compression standard /Smith, Craig M. January 1993 (has links)
Thesis (M.S.)--Rochester Institute of Technology, 1993. / Typescript. Includes bibliographical references (leaves 72-74).
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Evaluation of ANSI compression in a bulk data file transfer systemChaulklin, Douglas Gary 20 January 2010 (has links)
This report evaluates the use of a newly proposed
American National Standard Institute (ANSI) standard for data
compression in a bulk data transmission system. An overview
of the transmission system, the current compression method,
and the ANSI algorithm are presented. A dynamic systems model
is used to analyze the benefits and impacts of various
alternatives to addressing the needs of the system. A
decision model is built to summarize the alternatives based on
the perceived problem contexts. / Master of Engineering
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Associative neural networks: properties, learning, and applications.January 1994 (has links)
by Chi-sing Leung. / Thesis (Ph.D.)--Chinese University of Hong Kong, 1994. / Includes bibliographical references (leaves 236-244). / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Background of Associative Neural Networks --- p.1 / Chapter 1.2 --- A Distributed Encoding Model: Bidirectional Associative Memory --- p.3 / Chapter 1.3 --- A Direct Encoding Model: Kohonen Map --- p.6 / Chapter 1.4 --- Scope and Organization --- p.9 / Chapter 1.5 --- Summary of Publications --- p.13 / Chapter I --- Bidirectional Associative Memory: Statistical Proper- ties and Learning --- p.17 / Chapter 2 --- Introduction to Bidirectional Associative Memory --- p.18 / Chapter 2.1 --- Bidirectional Associative Memory and its Encoding Method --- p.18 / Chapter 2.2 --- Recall Process of BAM --- p.20 / Chapter 2.3 --- Stability of BAM --- p.22 / Chapter 2.4 --- Memory Capacity of BAM --- p.24 / Chapter 2.5 --- Error Correction Capability of BAM --- p.28 / Chapter 2.6 --- Chapter Summary --- p.29 / Chapter 3 --- Memory Capacity and Statistical Dynamics of First Order BAM --- p.31 / Chapter 3.1 --- Introduction --- p.31 / Chapter 3.2 --- Existence of Energy Barrier --- p.34 / Chapter 3.3 --- Memory Capacity from Energy Barrier --- p.44 / Chapter 3.4 --- Confidence Dynamics --- p.49 / Chapter 3.5 --- Numerical Results from the Dynamics --- p.63 / Chapter 3.6 --- Chapter Summary --- p.68 / Chapter 4 --- Stability and Statistical Dynamics of Second order BAM --- p.70 / Chapter 4.1 --- Introduction --- p.70 / Chapter 4.2 --- Second order BAM and its Stability --- p.71 / Chapter 4.3 --- Confidence Dynamics of Second Order BAM --- p.75 / Chapter 4.4 --- Numerical Results --- p.82 / Chapter 4.5 --- Extension to higher order BAM --- p.90 / Chapter 4.6 --- Verification of the conditions of Newman's Lemma --- p.94 / Chapter 4.7 --- Chapter Summary --- p.95 / Chapter 5 --- Enhancement of BAM --- p.97 / Chapter 5.1 --- Background --- p.97 / Chapter 5.2 --- Review on Modifications of BAM --- p.101 / Chapter 5.2.1 --- Change of the encoding method --- p.101 / Chapter 5.2.2 --- Change of the topology --- p.105 / Chapter 5.3 --- Householder Encoding Algorithm --- p.107 / Chapter 5.3.1 --- Construction from Householder Transforms --- p.107 / Chapter 5.3.2 --- Construction from iterative method --- p.109 / Chapter 5.3.3 --- Remarks on HCA --- p.111 / Chapter 5.4 --- Enhanced Householder Encoding Algorithm --- p.112 / Chapter 5.4.1 --- Construction of EHCA --- p.112 / Chapter 5.4.2 --- Remarks on EHCA --- p.114 / Chapter 5.5 --- Bidirectional Learning --- p.115 / Chapter 5.5.1 --- Construction of BL --- p.115 / Chapter 5.5.2 --- The Convergence of BL and the memory capacity of BL --- p.116 / Chapter 5.5.3 --- Remarks on BL --- p.120 / Chapter 5.6 --- Adaptive Ho-Kashyap Bidirectional Learning --- p.121 / Chapter 5.6.1 --- Construction of AHKBL --- p.121 / Chapter 5.6.2 --- Convergent Conditions for AHKBL --- p.124 / Chapter 5.6.3 --- Remarks on AHKBL --- p.125 / Chapter 5.7 --- Computer Simulations --- p.126 / Chapter 5.7.1 --- Memory Capacity --- p.126 / Chapter 5.7.2 --- Error Correction Capability --- p.130 / Chapter 5.7.3 --- Learning Speed --- p.157 / Chapter 5.8 --- Chapter Summary --- p.158 / Chapter 6 --- BAM under Forgetting Learning --- p.160 / Chapter 6.1 --- Introduction --- p.160 / Chapter 6.2 --- Properties of Forgetting Learning --- p.162 / Chapter 6.3 --- Computer Simulations --- p.168 / Chapter 6.4 --- Chapter Summary --- p.168 / Chapter II --- Kohonen Map: Applications in Data compression and Communications --- p.170 / Chapter 7 --- Introduction to Vector Quantization and Kohonen Map --- p.171 / Chapter 7.1 --- Background on Vector quantization --- p.171 / Chapter 7.2 --- Introduction to LBG algorithm --- p.173 / Chapter 7.3 --- Introduction to Kohonen Map --- p.174 / Chapter 7.4 --- Chapter Summary --- p.179 / Chapter 8 --- Applications of Kohonen Map in Data Compression and Communi- cations --- p.181 / Chapter 8.1 --- Use Kohonen Map to design Trellis Coded Vector Quantizer --- p.182 / Chapter 8.1.1 --- Trellis Coded Vector Quantizer --- p.182 / Chapter 8.1.2 --- Trellis Coded Kohonen Map --- p.188 / Chapter 8.1.3 --- Computer Simulations --- p.191 / Chapter 8.2 --- Kohonen MapiCombined Vector Quantization and Modulation --- p.195 / Chapter 8.2.1 --- Impulsive Noise in the received data --- p.195 / Chapter 8.2.2 --- Combined Kohonen Map and Modulation --- p.198 / Chapter 8.2.3 --- Computer Simulations --- p.200 / Chapter 8.3 --- Error Control Scheme for the Transmission of Vector Quantized Data --- p.213 / Chapter 8.3.1 --- Motivation and Background --- p.214 / Chapter 8.3.2 --- Trellis Coded Modulation --- p.216 / Chapter 8.3.3 --- "Combined Vector Quantization, Error Control, and Modulation" --- p.220 / Chapter 8.3.4 --- Computer Simulations --- p.223 / Chapter 8.4 --- Chapter Summary --- p.226 / Chapter 9 --- Conclusion --- p.232 / Bibliography --- p.236
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Analog non-linear coding for improved performance in compressed sensingHu, Yichuan. January 2009 (has links)
Thesis (M.S.E.C.E.)--University of Delaware, 2009. / Principal faculty advisor: Javier Garcia-Frias, Dept. of Electrical & Computer Engineering. Includes bibliographical references.
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Ultrasonic non-destructive testing using digital pulse compression /Hui, Man-shan. January 1900 (has links)
Thesis (M. Phil.)--University of Hong Kong, 1981.
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Implementation of Low bit-rate image codec鄧世健, Tang, Sai-kin, Owen. January 1994 (has links)
published_or_final_version / Electrical and Electronic Engineering / Master / Master of Philosophy
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