On linear filtering capabilities of 1-D CNNs with minimum-size templates
Radu P. Matei, Liviu Goraş · 2003
In this paper we investigate the linear filtering capabilities of the standard cellular neural network in the general case of non-symmetric templates. We refer to 1D Cellular Neural Networks (CNN's) with templates of minimum size (1/spl times/3). A detailed analysis of the spatial transfer function is made, emphasizing the useful filtering functions that can be obtained. We present an approach from a designer's point of view, establishing a set of relations to be satisfied by the template parameters, in order to obtain the desired filtering function with specified characteristics - central frequency, bandwidth, selectivity. Symmetric templates are treated as a particular case. For each type of filtering the characteristics are shown and simulation results are presented as well.