Autowaves for image processing on a two-dimensional CNN array of excitable nonlinear circuits: flat and wrinkled labyrinths

A two-dimensional (2-D) cellular neural network (CNN) array of resistively coupled Chua circuits which can be designed to implement some elementary aspects of spatial recognition, namely, distinguishing open curves from closed ones and locating the shortest path between two locations, is described. In the latter, two situations are analyzed: flat and wrinkled surfaces. The 2-D CNN array of Chua circuits is shown to be capable of finding the shortest path between two points on a wrinkled labyrinth. The performance of this parallel processing approach was examined using computer simulations, although this method can be implemented in real time via VLSI technology