Vlsi And Parallel Computing For Pattern Recognition And Artificial Intelligence
Vlsi And Parallel Computing For Pattern Recognition And Artificial Intelligence

Author: N Ranganathan

Publisher: World Scientific

Published: 1995-06-30

Total Pages: 298

ISBN-13: 9814500232

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This book covers parallel algorithms and architectures and VLSI chips for a range of problems in image processing, computer vision, pattern recognition and artificial intelligence. The specific problems addressed include vision and image processing tasks, Fast Fourier Transforms, Hough Transforms, Discrete Cosine Transforms, image compression, polygon matching, template matching, pattern matching, fuzzy expert systems and image rotation. The collection of papers gives the reader a good introduction to the state-of-the-art, while for an expert this serves as a good reference and a source of some new contributions in this field.

VLSI & Parallel Computing for Pattern Recognition & Artificial Intelligence
VLSI & Parallel Computing for Pattern Recognition & Artificial Intelligence

Author: N. Ranganathan

Publisher: World Scientific

Published: 1995

Total Pages: 304

ISBN-13: 9789810223120

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This book covers parallel algorithms and architectures and VLSI chips for a range of problems in image processing, computer vision, pattern recognition and artificial intelligence. The specific problems addressed include vision and image processing tasks, Fast Fourier Transforms, Hough Transforms, Discrete Cosine Transforms, image compression, polygon matching, template matching, pattern matching, fuzzy expert systems and image rotation. The collection of papers gives the reader a good introduction to the state-of-the-art, while for an expert this serves as a good reference and a source of some new contributions in this field.

Pattern Recognition
Pattern Recognition

Author: R‚jean Plamondon

Publisher: World Scientific

Published: 1991

Total Pages: 404

ISBN-13: 9789810206048

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This book contains 15 reviewed papers selected from among those presented at the 4th Vision Interface Conference in Halifax, Canada 14 - 18 May 1990. The papers are grouped into three sections which deal with parallel architectures and neural networks, algorithms for analysis and processing, and systems and applications.

Parallel VLSI Neural System Design
Parallel VLSI Neural System Design

Author: David Zhang

Publisher: Springer

Published: 1999

Total Pages: 284

ISBN-13:

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Aimed at researchers and graduate engineers working in the area of VLSI circuit and system design, as well as being a reference for senior undergraduate level courses on parallel neural computing and VLSI system applications, Parallel VLSI Neural System Design will prove useful in contributing to the understanding of this new and exciting discipline of ANNs System Engineering.

Hardware Annealing in Analog VLSI Neurocomputing
Hardware Annealing in Analog VLSI Neurocomputing

Author: Bank W. Lee

Publisher: Springer Science & Business Media

Published: 2012-12-06

Total Pages: 251

ISBN-13: 1461539846

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Rapid advances in neural sciences and VLSI design technologies have provided an excellent means to boost the computational capability and efficiency of data and signal processing tasks by several orders of magnitude. With massively parallel processing capabilities, artificial neural networks can be used to solve many engineering and scientific problems. Due to the optimized data communication structure for artificial intelligence applications, a neurocomputer is considered as the most promising sixth-generation computing machine. Typical applica tions of artificial neural networks include associative memory, pattern classification, early vision processing, speech recognition, image data compression, and intelligent robot control. VLSI neural circuits play an important role in exploring and exploiting the rich properties of artificial neural networks by using pro grammable synapses and gain-adjustable neurons. Basic building blocks of the analog VLSI neural networks consist of operational amplifiers as electronic neurons and synthesized resistors as electronic synapses. The synapse weight information can be stored in the dynamically refreshed capacitors for medium-term storage or in the floating-gate of an EEPROM cell for long-term storage. The feedback path in the amplifier can continuously change the output neuron operation from the unity-gain configuration to a high-gain configuration. The adjustability of the vol tage gain in the output neurons allows the implementation of hardware annealing in analog VLSI neural chips to find optimal solutions very efficiently. Both supervised learning and unsupervised learning can be implemented by using the programmable neural chips.

Neural Information Processing and VLSI
Neural Information Processing and VLSI

Author: Bing J. Sheu

Publisher: Springer Science & Business Media

Published: 2012-12-06

Total Pages: 569

ISBN-13: 1461522471

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Neural Information Processing and VLSI provides a unified treatment of this important subject for use in classrooms, industry, and research laboratories, in order to develop advanced artificial and biologically-inspired neural networks using compact analog and digital VLSI parallel processing techniques. Neural Information Processing and VLSI systematically presents various neural network paradigms, computing architectures, and the associated electronic/optical implementations using efficient VLSI design methodologies. Conventional digital machines cannot perform computationally-intensive tasks with satisfactory performance in such areas as intelligent perception, including visual and auditory signal processing, recognition, understanding, and logical reasoning (where the human being and even a small living animal can do a superb job). Recent research advances in artificial and biological neural networks have established an important foundation for high-performance information processing with more efficient use of computing resources. The secret lies in the design optimization at various levels of computing and communication of intelligent machines. Each neural network system consists of massively paralleled and distributed signal processors with every processor performing very simple operations, thus consuming little power. Large computational capabilities of these systems in the range of some hundred giga to several tera operations per second are derived from collectively parallel processing and efficient data routing, through well-structured interconnection networks. Deep-submicron very large-scale integration (VLSI) technologies can integrate tens of millions of transistors in a single silicon chip for complex signal processing and information manipulation. The book is suitable for those interested in efficient neurocomputing as well as those curious about neural network system applications. It has been especially prepared for use as a text for advanced undergraduate and first year graduate students, and is an excellent reference book for researchers and scientists working in the fields covered.

Massively Parallel, Optical, and Neural Computing in the United States
Massively Parallel, Optical, and Neural Computing in the United States

Author: Gilbert Kalb

Publisher: IOS Press

Published: 1992

Total Pages: 220

ISBN-13: 9789051990973

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A survey of products and research projects in the field of highly parallel, optical and neural computers in the USA. It covers operating systems, language projects and market analysis, as well as optical computing devices and optical connections of electronic parts.

Parallel Processing on VLSI Arrays
Parallel Processing on VLSI Arrays

Author: Josef A. Nossek

Publisher: Springer Science & Business Media

Published: 2012-12-06

Total Pages: 136

ISBN-13: 1461540364

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Guest Editor: JOSEF A. NOSSEK This is a special issue of the Journal of VLSI Signal Processing comprising eight contributions invited for publica tion on the basis of novel work presented in a special session on "Parallel Processing on VLSI Arrays" at the International Symposium on Circuits and Systems (ISCAS) held in New Orleans in May 1990. Massive parallelism to cope with high-speed requirements stemming from real-time applications and the restrictions in architectural and circuit design, such as regularity and local connectedness, brought about by the VLSI technology are the key questions addressed in these eight papers. They can be grouped into three subsections elaborating on: • Simulation of continuous physical systems, i. e. , numerically solving partial differential equations. • Neural architectures for image processing and pattern recognition. • Systolic architectures for implementing regular and irregular algorithms in VLSI technology. The paper by A. Fettweis and O. Nitsche advocates a signal processing approach for the numerical integration of partial differential equations (PD Es). It is based on the principles of multidimensional wave digital filters (MDWDFs) thereby preserving the passivity of energy dissipating physical systems. It is particularly suited for systems ofPDEs involving time and finite propagation speed. The basic ideas are explained using Maxwell's equa tions as a vehicle for the derivation of a multidimensional equivalent circuit representing the spatially infinitely extended arrangement with only very few circuit elements.

Advances In Pattern Recognition And Artificial Intelligence
Advances In Pattern Recognition And Artificial Intelligence

Author: Marleah Blom

Publisher: World Scientific

Published: 2021-11-16

Total Pages: 277

ISBN-13: 9811239029

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This book includes reviewed papers by international scholars from the 2020 International Conference on Pattern Recognition and Artificial Intelligence (held online). The papers have been expanded to provide more details specifically for the book. It is geared to promote ongoing interest and understanding about pattern recognition and artificial intelligence. Like the previous book in the series, this book covers a range of topics and illustrates potential areas where pattern recognition and artificial intelligence can be applied. It highlights, for example, how pattern recognition and artificial intelligence can be used to classify, predict, detect and help promote further discoveries related to credit scores, criminal news, national elections, license plates, gender, personality characteristics, health, and more.Chapters include works centred on medical and financial applications as well as topics related to handwriting analysis and text processing, internet security, image analysis, database creation, neural networks and deep learning. While the book is geared to promote interest from the general public, it may also be of interest to graduate students and researchers in the field.