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Neural Network Parallel Computing
Neural Network Parallel Computing is the first book available to the professional market on neural network computing for optimization problems. This introductory book is not only for the novice reader, but for experts in a variety of areas including parallel computing, neural network computing, computer science, communications, graph theory, computer aided design for VLSI circuits, molecular biology, management science, and operations research. The goal of the book is to facilitate an understanding as to the uses of neural network models in real-world applications. Neural Network Parallel Computing presents a major breakthrough in science and a variety of engineering fields. The computational power of neural network computing is demonstrated by solving numerous problems such as N-queen, crossbar switch scheduling, four-coloring and k-colorability, graph planarization and channel routing, RNA secondary structure prediction, knight's tour, spare allocation, sorting and searching, and tiling. Neural Network Parallel Computing is an excellent reference for researchers in all areas covered by the book. Furthermore, the text may be used in a senior or graduate level course on the topic.
Analog VLSI Neural Networks
This book brings together in one place important contributions and state-of-the-art research in the rapidly advancing area of analog VLSI neural networks. The book serves as an excellent reference, providing insights into some of the most important issues in analog VLSI neural networks research efforts.
Cryptology and Network Security
This book constitutes the refereed proceedings of the 4th International Conference on Cryptology and Network Security, CANS 2005, held in Xiamen, China in December 2005. The 28 revised full papers presented together with 2 invited papers were carefully reviewed and selected from 118 submissions. The papers are organized in topical sections on cryptanalysis, intrusion detection and viruses, authentication and signature, signcryption, e-mail security, cryptosystems, privacy and tracing, information hiding, firewalls, denial of service and DNS security, and trust management.
VLSI — Compatible Implementations for Artificial Neural Networks
This book introduces several state-of-the-art VLSI implementations of artificial neural networks (ANNs). It reviews various hardware approaches to ANN implementations: analog, digital and pulse-coded. The analog approach is emphasized as the main one taken in the later chapters of the book. The area of VLSI implementation of ANNs has been progressing for the last 15 years, but not at the fast pace originally predicted. Several reasons have contributed to the slow progress, with the main one being that VLSI implementation of ANNs is an interdisciplinaly area where only a few researchers, academics and graduate students are willing to venture. The work of Professors Fakhraie and Smith, present...
High-Performance CMOS Continuous-Time Filters
High-Performance CMOS Continuous-Time Filters is devoted to the design of CMOS continuous-time filters. CMOS is employed because the most complex integrated circuits have been realized with this technology for two decades. The most important advantages and drawbacks of continuous-time filters are clearly shown. The transfer function is one of the most important filter parameters but several others (like intermodulation distortion, power-supply rejection ratio, noise level and dynamic range) are fundamental in the design of high-performance systems. Special attention is paid to the practical aspects of the design, which shows the difference between an academic design and an industrial design....
Frequency Compensation Techniques for Low-Power Operational Amplifiers
Frequency Compensation Techniques for Low-Power Operational Amplifiers is intended for professional designers of integrated amplifiers, emphasizing low-voltage and low-power solutions. The book bridges the gap between the professional designer's needs and available techniques for frequency compensation. It does so by explaining existing techniques and introducing several new techniques including Hybrid Nested Miller compensation, Multipath Miller Zero cancellation and Multipath Conditionally Stable compensation. All compensation techniques are treated in a stage-number-based order, progressing from a single transistor to circuits with six stages and more. Apart from discussing the mathematic...
Feed-Forward Neural Networks
Feed-Forward Neural Networks: Vector Decomposition Analysis, Modelling and Analog Implementation presents a novel method for the mathematical analysis of neural networks that learn according to the back-propagation algorithm. The book also discusses some other recent alternative algorithms for hardware implemented perception-like neural networks. The method permits a simple analysis of the learning behaviour of neural networks, allowing specifications for their building blocks to be readily obtained. Starting with the derivation of a specification and ending with its hardware implementation, analog hard-wired, feed-forward neural networks with on-chip back-propagation learning are designed i...
Graph Algorithms and Applications 2
This book contains Volumes 4 and 5 of the Journal of Graph Algorithms and Applications (JGAA). The first book of this series, Graph Algorithms and Applications I, published in March 2002, contains Volumes 1-3 of JGAA. JGAA is a peer-reviewed scientific journal devoted to the publication of high-quality research papers on the analysis, design, implementation, and applications of graph algorithms. Areas of interest include computational biology, computational geometry, computer graphics, computer-aided design, computer and interconnection networks, constraint systems, databases, graph drawing, graph embedding and layout, knowledge representation, multimedia, software engineering, telecommunica...
Digital Rights Management: Concepts, Methodologies, Tools, and Applications
"This reference is a comprehensive collection of recent case studies, theories, research on digital rights management, and its place in the world today"--
Neuromorphic Systems Engineering
Neuromorphic Systems Engineering: Neural Networks in Silicon emphasizes three important aspects of this exciting new research field. The term neuromorphic expresses relations to computational models found in biological neural systems, which are used as inspiration for building large electronic systems in silicon. By adequate engineering, these silicon systems are made useful to mankind. Neuromorphic Systems Engineering: Neural Networks in Silicon provides the reader with a snapshot of neuromorphic engineering today. It is organized into five parts viewing state-of-the-art developments within neuromorphic engineering from different perspectives. Neuromorphic Systems Engineering: Neural Networ...
