Simulation of Time-Multiplexing Cellular Neural Networks with Numerical Integration Algorithms

Abstract

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.

An Efficient Numerical Integration Algorithm for Cellular Neural Network Based Hole-Filler Template Design

“…where we use the minimum value of x i j (t) in equation (12). Since the cell is inside the holes, its initial output of non-diagonal black pixels remain unchanged.…”

“…Three of the most widely used Numerical Integration Algorithms are used in Raster CNN Simulation described here. They are the Euler's Algorithm, RK-Gill Algorithm discussed by Oliveria [8] and the RK-Butcher Algorithm discussed by Badder [5,6] and Murugesh and Murugesan [10,11,12].…”

“…The RK-Butcher algorithm discussed by Badder [5,6] and Murugesh and Murugesan [10,11,12], is an explicit method. It starts with a simple Euler step.…”

An Efficient Face Detection and Recognition System Using RVJA and SCNN

“…In numerous felds like the monitoring system of the bank self-service cash machine, the new face-brushing technology of Alipay, the verifcation of identity by each application face scanning, along with the face unlocking of the mobile phone, FDR is applied with the continuous enhancement of science and technology [10]. Te FDR methodologies, which have been well-studied in the computer vision domain, have been amalgamated with these systems in an attempt to handle certain external issues like computational cost, face capture angle, facial expression, the existence of hair, along with facial alteration relying on the luminosity, time, usage of accessories or ornaments, classifer performance, ethnic variations amongst others, and longer distances as of the camera [11,12].…”