MODA: moving object detecting architecture

A type of cellular neural network (CNN) is described, which may be classified in the broader category of generalized cellular neural networks (GCNNs). Its novelty consists both in the task it performs and in its architecture and way of operation. The input to the network is a two-dimensional picture that is processed continuously in order to detect real time trajectories of moving objects in a noisy environment. MODA is designed by synthesis, so that it does not require learning, and it performs its task by implementing a nonlinear continuous functional in a vector space. The network, its architecture, its equations, and the method of design are described. In addition, the new network is compared with known paradigms of ANN and CNN. Results of simulations are also reported.< >