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The time domain classes of signals include: static, quasistatic, periodic, repetitive, transient, random, and chaotic.
However, it can be applied only to data for which the majority of features do not vary systematically among the studied classes of signals.
Different kernels in Equation 1 define different distributions in the class, that are most specifically adapted to particular classes of signals [[6], pp. 71-76].
Since the above approaches define the similarity beforehand, they limit the applicability of the method to a certain application area or to certain classes of signals.
Level-crossing analog-to-digital converters (LC ADCs) have been considered in the literature and have been shown to efficiently sample certain classes of signals.
It is mechanically responsive and is capable of generating several classes of signals to regulate pulmonary function, ranging from calcium waves transmitted through gap junction channels to alterations in cell adhesion molecules to modulate interactions between leukocytes and ECs.
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The order, which takes effect March 1, 2014, creates two classes of signal boosters, for use by consumers and businesses, each with distinct requirements to minimize interference with wireless networks.
We find that these gain-related tradeoffs persist in mathematical models of biomolecular reaction mechanisms that are at the core of large classes of signaling systems.
Two classes of signal have been widely used to model stochastic fractal time series (Heneghan and McDarby, 2000): fractional Gaussian noise (fGn) and fractional Brownian motion (fBm).
Also essential for the differentiation of neural crest-derived cartilage are two classes of signaling molecules, FGFs[20] and endothelins[21].
In principle there were two classes of signaling systems described.
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