Time multiplexed color image processing based on a CNN with cell-state outputs
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
An area efficient implementation of a cellular neural network
ANNES '95 Proceedings of the 2nd New Zealand Two-Stream International Conference on Artificial Neural Networks and Expert Systems
Implementation of Time-Multiplexed CNN Building Block Cell
MICRONEURO '96 Proceedings of the 5th International Conference on Microelectronics for Neural Networks and Fuzzy Systems
Digital Image Processing Using MATLAB
Digital Image Processing Using MATLAB
Simulation of time-multiplexing cellular neural networks with numerical integration algorithms
ICCS'06 Proceedings of the 6th international conference on Computational Science - Volume Part I
Hole-Filler Cellular Neural Network Simulation by RKGHM(5,5)
Journal of Mathematical Imaging and Vision
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In practical sense owing to hardware limitations, it is not possible to have a one-one mapping between the CNN hardware processors and all the pixels of the image. The time-multiplexing approach plays a pivotal role in the area of simulating hardware models and testing hardware implementations of cellular non-linear networks (CNNs). In this framework, time-multiplexing scheme is used to process large images using small CNN arrays. Using a novel integration algorithm by formulating an embedded technique involving RK technique based on arithmetic mean (AM) and Heronian mean (HeM) with error control for general CNNs is presented. Simulation results and comparison have also been made to show the efficiency of the numerical integration algorithms. The analytic expression for local truncation error (LTE) has been derived. It is found that the RK-embedded HeM gives promising results in comparison with the Harmonic mean. A more quantitative analysis has been carried out to clearly visualise the goodness and robustness of the proposed algorithm.