Automatic Generation of Cellular Neural Networks for Distributed Sensor Data Processing

In this paper, the main interest is the fusion and the control of data that is obtained from a set of sensors. This task requires the use of a both effective and versatile computational model. The chosen architecture is the already known for its suitability cellular neural network (CNN). This specific model, adopts some significant features, such as: continuous-time dynamics, local interconnection, reliability, simple implementation, low power consumption and as far as its behavior is concerned, great flexibility. Furthermore, it is taken into consideration, that depending on the application, the corresponding network dimension may vary. In order to confront this problem, a methodology is proposed for the automatic generation of CNNs of variable dimensions. The above task is achieved by developing an algorithm, which enables the combination of the basic CNN circuit counterparts, so as to produce the desired network dimensions.