Cellular Neural Networks and Visual Computing: Foundations and Applications (Google eBook)
Cellular Nonlinear/neural Network (CNN) technology is both a revolutionary concept and an experimentally proven new computing paradigm. Analogic cellular computers based on CNNs are set to change the way analog signals are processed and are paving the way to an analog computing industry. This unique undergraduate level textbook includes many examples and exercises, including CNN simulator and development software accessible via the Internet. It is an ideal introduction to CNNs and analogic cellular computing for students, researchers and engineers from a wide range of disciplines. Although its prime focus is on visual computing, the concepts and techniques described in the book will be of great interest to those working in other areas of research including modeling of biological, chemical and physical processes. Leon Chua, co-inventor of the CNN, and Tamás Roska are both highly respected pioneers in the field.
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3 Characteristics and analysis of simple CNN templates
4 Simulation of the CNN dynamics
5 Binary CNN characterization via Boolean functions
unified theoryand applications
7 Introduction to the CNN Universal Machine
Nonlinear dynamics and complete stability
12 Coupled CNN with linear synaptic weights
13 Uncoupled standard CNNs with nonlinear synaptic weights
14 Standard CNNs with delayed synaptic weights and motion analysis
15 Visual microprocessors analog and digital VLSI implementation of the CNN Universal Machine
16 CNN models in the visual pathwayand the Bionic Eye
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