Attempt to distinguish electric signals of a dichotomous nature

Abstract

Three types of electric signals were analyzed: Ion current fluctuations in membrane channels (ICFMC), Seismic electric signals activities (SES), and "artificial" noises (AN). The wavelet transform, when applied to the conventional time domain, does not allow a classification of these signals, but does so in the "natural" time domain. A classification also becomes possible, if we study <chi(q)>-<chi>(q) versus q, where chi stands for the "natural" time. For q values approximately between 1 and 2 the signals are classified and ICFMC lies between the other two types. For q=1, the "entropy" S identical with <chilnchi>-<chi>ln<chi> of ICFMC almost equals that of a "uniform" distribution, while the AN and SES have larger and smaller S values, respectively. The recent [P. Varotsos, N. Sarlis, and E. Skordas, Phys. Rev. E 67, 021109 (2003)] finding that, in short time scales, both SES and AN (which are shown to be non-Markovian) result in comparable detrended fluctuation analysis exponents alpha in (1.0,1.5) is revisited. Even a Markovian dichotomous time series, in short time scales, leads to similar alpha exponents.

Entropy of seismic electric signals: Analysis in natural time under time reversal

“…The results are shown in Fig.3, in which we plot the S and S − values versus α DF A . Since the analysis of the SES activities in natural time result [7,8] in DFA exponents α DF A around unity, hereafter we are solely focused in Fig.3 in the range 0.8 < α DF A < 1.…”

“…It is a dynamic entropy depending on the sequential order of pulses [5,6] and exhibits [9] concavity, positivity and Lesche [12,13] stability. When the system enters the critical stage (infinitely ranged long range temporal correlations [3,4]), the S-value is smaller [8,10] than the value S u (= 1/2 ln 2 − 1/4 ≈ 0.0966) of a "uniform" distribution (defined in Refs. [3,4,6,8], e.g.…”

“…When the system enters the critical stage (infinitely ranged long range temporal correlations [3,4]), the S-value is smaller [8,10] than the value S u (= 1/2 ln 2 − 1/4 ≈ 0.0966) of a "uniform" distribution (defined in Refs. [3,4,6,8], e.g. when when all p k are equal), i.e.,…”

Quantifying signals with power-law correlations: A comparative study of detrended fluctuation analysis and detrended moving average techniques

“…There is growing evidence that output signals of many physical [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15], biological [16,17,18,19], physiological [20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35] and economic systems [36,37,38,39,40,41,42,43], where multiple component feedback interactions play a central role, exhibit complex self-similar fluctuations over a broad range of space and/or time scales. These fluctuating signals can be characterized by long-range power-law correlations.…”

M W9 Tohoku earthquake in 2011 in Japan: precursors uncovered by natural time analysis

“…The variance of v weighted for p k , designated by j 1 , is given by (Varotsos et al 2001b(Varotsos et al , 2003a(Varotsos et al , b, 2005(Varotsos et al , 2011a …”