Image processing applications via time-multiplexing cellular neural network simulator with numerical integration algorithms

A novel approach to simulate cellular neural networks (CNN) is presented in this paper. The approach, time-multiplexing simulation, is prompted by the need to simulate hardware models and test hardware implementations of CNN. For practical applications, due to hardware limitations, it is impossible...

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Bibliographic Details
Published in:International journal of computer mathematics Vol. 87; no. 4; pp. 840 - 848
Main Author: Murugesh, V.
Format: Journal Article
Language:English
Published: Abingdon Taylor & Francis 01.03.2010
Taylor & Francis Ltd
Subjects:
Algorithms
Blocking
cellular neural networks
Computer simulation
Differential equations
Dynamical systems
Euler algorithm
G.1.7
Hardware
Image processing systems
Mathematical models
Neural networks
Optimization algorithms
ordinary differential equations
Processors
RK-Butcher algorithm
RK-Gill algorithm
Simulation
ISSN:0020-7160, 1029-0265
Online Access:Get full text
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Summary:A novel approach to simulate cellular neural networks (CNN) is presented in this paper. The approach, time-multiplexing simulation, is prompted by the need to simulate hardware models and test hardware implementations of CNN. For practical applications, due to hardware limitations, it is impossible to have a one-to-one mapping between the CNN hardware processors and all the pixels of the image. This simulator provides a solution by processing the input image block by block, with the number of pixels in a block being the same as the number of CNN processors in the hardware. The algorithm for implementing this simulator is presented along with popular numerical integration algorithms. Some simulation results and comparisons are also presented.
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ISSN:0020-7160
1029-0265
DOI:10.1080/00207160802217219