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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 by its cell dynamics (e.g., state equations) and synaptic law, which specifies each cell's interaction with its neighbors. We focus in this paper on reaction4iffusion CNNs having a linear synaptic law that approximates a spatial Laplacian operator. Such a synaptic law can be realized by one or more layers of linear resistor couplings. An autonomous CNN made of third-order universal cells and coupled to each other by only one layer of linear resistors provides a unified active medium for generating trigger (autowave) waves, target (concentric) waves, spiral waves, and scroll waves. When a second layer of linear resistors is added to couple a second capacitor voltage in each cell to its neighboring cells, the resulting CNN can be used to generate various turingpatterns. Although the equations describing these autonomous CNNs represent an excellent approximation to the nonlinear partial differential equations describing reaction-diffusion systems if the number of cells is sufficiently large, they can exhibit new phenomena (e.g., propagation failure) that can not be obtained from their limiting partial differential equations. This demonstrates that the autonomous CNN is in some sense more general than its associated nonlinear partial differential equations. To demonstrate how an autonomous CNN can serve as a unifying paradigm for pattern formation and active wave propagation, several well-known examples chosen from different disciplines are mapped into a generic reaction-diffusion CNN made of thirdorder cells. Finally, several examples that can not be modeled by reaction-diffusion equations are mapped into other classes of autonomous CNNs in order to illustrate the universality of the CNN paradigm. 'We use the term cell to mean an artificial neuron in this paper.

A procedure for the design of allpole filters with low sensitivity to component tolerance is presented. The filters are based on resistance-capacitance (RC) ladder structures combined with single operational amplifiers. It is shown that by the use of impedance tapering, in which L-sections of the RC ladder are successively impedance-scaled upwards, from the driving source to the amplifier input, the sensitivity of the filter characteristics to component tolerances can be significantly decreased. Impedance tapering is achieved by the appropriate choice of component values. The design procedure, therefore, adds nothing to the cost of conventional circuits; component count and topology remain unchanged, whereas the component values selected for impedance tapering account for the considerable decrease in componenttolerance sensitivity.

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 designed for multichannel long-term recordings of signals of slight muscle movements. A wavelet-based, hierarchical cluster analysis algorithm estimates the number of classes [motor units (MUs)], distinguishes single MUAPs from superpositions, and sets up the shape of the template for each class. Using three channels and a weighted averaging method to track action potential (AP) shape changes improve the analysis. In the last step, nonclassified segments, i.e., segments containing superimposed APs, are decomposed into their units using class-mean signals. Based on experiments on simulated and long-term recorded EMG signals, our software is capable of providing reliable decompositions with satisfying accuracy. EMG-LODEC is suitable for the study of MU discharge patterns and recruitment order in healthy subjects and patients during long-term measurements.

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