Hardware Implementation of a Desktop Supercomputer for High Performance Image Processing. Color Image Processing Using Cellular Neural Networks

Abstract

This report addresses the functional behavior of Cellular Neural Networks (CNN). The impact of variable convergence times on the proper operation of the network is discussed. A test method is presented to determine the functionality of the network. The function fault models assume that the cells are unable to switch between limiting states. The proposed method attains 100% stuck-at fault coverage without any extra hardware for its implementation. Moreover, the required number of test vectors is constant and independent of the array size which makes it suitable for practical implementations. The report discusses the new fault model, presents the algorithmic procedures and shows simulated testing results. Cellular neural Networks, Testing.

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Document Details

Document Type
Technical Report
Publication Date
Nov 01, 1994
Accession Number
ADA286455

Entities

People

  • Jose P. De Gyvez

Organizations

  • Texas Engineering Experiment Station

Tags

Communities of Interest

  • Advanced Electronics

DTIC Thesaurus Topics

  • Algorithms
  • Capacitors
  • Change Detection
  • Convergence
  • Detection
  • Differential Equations
  • Electrical Engineering
  • Engineering
  • Image Processing
  • Neural Networks
  • Nonlinear Differential Equations
  • Optical Scanning
  • Simulations
  • Simulators
  • Template Patterns
  • Test Methods
  • Transitions

Fields of Study

  • Computer science

Readers

  • Applied Combinatorial Optimization and Logic Circuit Design.
  • Neural Network Machine Learning.
  • Parallel and Distributed Computing.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Neural Networks