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A significant increase in the information processing abilities of CNN's demands powerful information processing at the cell level. In this paper, the defining formula, the main properties, and several applications of a novel coupling cell are presented. Since it is able to implement any Boolean function, its functionality expands on those of digital RAM's by adding new capabilities such as learning and interpolation. While it is able to embed all previously accumulated knowledge regarding useful binary information processing tasks performed by standard CNN's, the pyramidal universal cell provides a broader context for defining other useful processing tasks, including extended gray scale or color image processing as well. Examples of applications in image processing are provided in this paper. Implementation issues are also considered. Assuming some compromise between area and speed, a VLSI implementation of CNN's based on pyramidal cells offers a speedup of up to one million times when compared to corresponding software implementations.

A novel image scanning and transmission system is proposed, where the traditional raster scan is replaced with a new one, called a chaotic scan. The result is a low complexity image transmission system with encryption and spread spectrum capabilities well suited for compressed sensing applications. Due to the uncorrelated nature of the consecutively scanned pixels, it allows a form of progressive compression and fast discovery of the relevant image features using only a small fraction of the transmitted pixels. The key ingredient of the proposed system is a chaotic counter addressing the sensor array, based on a cellular automaton exhibiting a pseudo-random chaotic behavior and binary synchronization property. [ABSTRACT FROM AUTHOR]