Vlsi Parallel Computing For Pattern Recognition Artificial Intelligence
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Vlsi Parallel Computing For Pattern Recognition Artificial Intelligence
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Author : N. Ranganathan
language : en
Publisher: World Scientific
Release Date : 1995
Vlsi Parallel Computing For Pattern Recognition Artificial Intelligence written by N. Ranganathan and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 1995 with Technology & Engineering categories.
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
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Author : N. Ranganathan
language : en
Publisher:
Release Date : 1995
Vlsi Parallel Computing For Pattern Recognition Artificial Intelligence written by N. Ranganathan and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1995 with categories.
Parallel Vlsi Neural System Design
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Author : David Zhang
language : en
Publisher: Springer
Release Date : 1999
Parallel Vlsi Neural System Design written by David Zhang and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 1999 with Computers categories.
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.
Pattern Recognition Architectures Algorithms And Applications
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Author : Rejean Plamondon
language : en
Publisher: World Scientific
Release Date : 1991-08-12
Pattern Recognition Architectures Algorithms And Applications written by Rejean Plamondon and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 1991-08-12 with Computers categories.
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 Processing For Artificial Intelligence
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Author : Laveen N. Kanal
language : en
Publisher:
Release Date : 1994
Parallel Processing For Artificial Intelligence written by Laveen N. Kanal and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1994 with categories.
Hardware Annealing In Analog Vlsi Neurocomputing
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Author : Bank W. Lee
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06
Hardware Annealing In Analog Vlsi Neurocomputing written by Bank W. Lee and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-06 with Technology & Engineering categories.
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
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Author : Bing J. Sheu
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06
Neural Information Processing And Vlsi written by Bing J. Sheu and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-06 with Technology & Engineering categories.
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.
Advances In Pattern Recognition And Artificial Intelligence
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Author : Marleah Blom
language : en
Publisher: World Scientific
Release Date : 2021-11-16
Advances In Pattern Recognition And Artificial Intelligence written by Marleah Blom and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-11-16 with Computers categories.
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.
Hardware Annealing In Analog Vlsi Neurocomputing
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Author : Bank W. Lee
language : en
Publisher: Springer
Release Date : 2012-12-06
Hardware Annealing In Analog Vlsi Neurocomputing written by Bank W. Lee and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-06 with Technology & Engineering categories.
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.
Internet Scale Pattern Recognition
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Author : Anang Hudaya Muhamad Amin
language : en
Publisher: CRC Press
Release Date : 2012-11-20
Internet Scale Pattern Recognition written by Anang Hudaya Muhamad Amin and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-11-20 with Computers categories.
For machine intelligence applications to work successfully, machines must perform reliably under variations of data and must be able to keep up with data streams. Internet-Scale Pattern Recognition: New Techniques for Voluminous Data Sets and Data Clouds unveils computational models that address performance and scalability to achieve higher levels of reliability. It explores different ways of implementing pattern recognition using machine intelligence. Based on the authors’ research from the past 10 years, the text draws on concepts from pattern recognition, parallel processing, distributed systems, and data networks. It describes fundamental research on the scalability and performance of pattern recognition, addressing issues with existing pattern recognition schemes for Internet-scale data deployment. The authors review numerous approaches and introduce possible solutions to the scalability problem. By presenting the concise body of knowledge required for reliable and scalable pattern recognition, this book shortens the learning curve and gives you valuable insight to make further innovations. It offers an extendable template for Internet-scale pattern recognition applications as well as guidance on the programming of large networks of devices.
