An algorithm for displaying gray level images using a small number of fixed quantization levels is proposed. The algorithm, called multilevel halftoning, is based on the Cellular Neural Networks (CNN) paradigm. It tracks the CNN transient outputs and selects the image which is subjectively perceived to be the best when reduced to the allowed number of gray levels. The selection criterion is based on the "visually compensated" mean square error that takes into account the specifics of the human visual system. The results of the proposed algorithm were validated in subjective quality experiments with human subjects.

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In the most general case the finding of the shear stress distribution on the cross section of prismatic bar subjected to torsion is a specific problem that can be solved in two steps, The first of them consists in finding the so-called stress function, and the second one in finding the shear stresses on the basis of the formerly found stress function. The stress function is the solution of Poisson's partial differential equation for given conditions of unambiguity that in the elasticity theory describes the torsion of prismatic bars in terms of stresses. Modeling by means of electrical networks is one of a few possible ways to find the stress function. This papcr describes how Chua and Yang's cellular neural networks can be used as an analogous model to find the stress function of a twisted prismatic bar, which serves to calculate the shear stress distribution. Effectiveness of the presented method is illustrated by the solutions of two problems. The method can be applied in mechanical and civil engineering.