George S. Moschytz

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This tutorial paper proposes a subclass of cellular neural networks (CNN) having no inputs (i.e., autonomous) as a universal active substrate or medium for modeling and generating many pattern formation and nonlinear wave phenomena from numerous disciplines, including biology, chemistry, ecology, engineering, physics, etc. Each CNN is defined mathematically(More)
This paper presents a new approach to the adaptation of a wavelet filterbank based on perceptual and rate-distortion criteria. The system makes use of a wavelet-packet transform where each subband can have an individual time-segmentation. Boundary effects can be avoided by using overlapping blocks of samples and therefore switching bases is possible at(More)
An all-analog high-speed decoding technique is described which is suitable for magnetic recording (MR) and other computationally demanding applications. A decoder for a binary (18,9,5) tail-biting trellis code, which is much simpler than the codes used for MR, has been chosen to demonstrate this technique. It achieves a decoding rate of 100 Mbit/s at a(More)
Feature Extraction is an important step in fingerprint-based recognition systems. In this paper, a CNN Fingerprint Feature Extraction Algorithm is presented. It is applied to thinned fingerprints which have been previously obtained from real gray-scale, noisy fingerprints in the Image-Preprocessing stage, also by using CNNs. Examples are given to(More)
A methematical model of the single-fibre extra- and intracellular action potential is proposed whereby the transition from one to the other is given by an approximate analytical relationship. The model is given in both the time and the frequency domain, and has a simple mathematical form. Based on the new model an analytical description of the transfer(More)
This paper presents a method to decompose multichannel long-term intramuscular electromyogram (EMG) signals. In contrast to existing decomposition methods which only support short registration periods or single-channel recordings of signals of constant muscle effort, the decomposition software EMG-LODEC (ElectroMyoGram LOng-term DEComposition) is especially(More)
In this paper, we present an analytical design approach for the class of bipolar cellular neural networks (CNN’s) which yields optimally robust template parameters. We give a rigorous definition of absolute and relative robustness and show that all well-defined CNN tasks are characterized by a finite set of linear and homogeneous inequalities. This system(More)