Information measures through velocity time series in a seepage affected alluvial sinuous channel

2020 ◽  
Vol 34 (11) ◽  
pp. 1925-1938
Author(s):  
Jyotismita Taye ◽  
Abhijit D. Lade ◽  
Anja Mihailović ◽  
Dragutin T. Mihailović ◽  
Bimlesh Kumar
2020 ◽  
Author(s):  
Luca Faes ◽  
Riccardo Pernice ◽  
Gorana Mijatovic ◽  
Yuri Antonacci ◽  
Jana Cernanova Krohova ◽  
...  

SummaryWhile cross-spectral and information-theoretic approaches are widely used for the multivariate analysis of physiological time series, their combined utilization is far less developed in the literature. This study introduces a framework for the spectral decomposition of multivariate information measures, which provides frequency-specific quantifications of the information shared between a target and two source time series and of its expansion into amounts related to how the sources contribute to the target dynamics with unique, redundant and synergistic information. The framework is illustrated in simulations of linearly interacting stochastic processes, showing how it allows to retrieve amounts of information shared by the processes within specific frequency bands which are otherwise not detectable by time-domain information measures, as well as coupling features which are not detectable by spectral measures. Then, it is applied to the time series of heart period, systolic and diastolic arterial pressure and respiration variability measured in healthy subjects monitored in the resting supine position and during head-up tilt. We show that the spectral measures of unique, redundant and synergistic information shared by these variability series, integrated within specific frequency bands of physiological interest, reflect the mechanisms of short term regulation of cardiovascular and cardiorespiratory oscillations and their alterations induced by the postural stress.


Entropy ◽  
2021 ◽  
Vol 23 (3) ◽  
pp. 337
Author(s):  
Hong-Jia Chen ◽  
Luciano Telesca ◽  
Michele Lovallo ◽  
Chien-Chih Chen

The seismo-electrical coupling is critical to understand the mechanism of geoelectrical precursors to earthquakes. A novel seismo-electrical model, called Chen–Ouillon–Sornette (COS) model, has been developed by combining the Burridge–Knopoff spring-block system with the mechanisms of stress-activated charge carriers (i.e., electrons and holes) and pressure-stimulated currents. Such a model, thus, can simulate fracture-induced electrical signals at a laboratory scale or earthquake-related geoelectrical signals at a geological scale. In this study, by using information measures of time series analysis, we attempt to understand the influence of diverse electrical conditions on the characteristics of the simulated electrical signals with the COS model. We employ the Fisher–Shannon method to investigate the temporal dynamics of the COS model. The result showed that the electrical parameters of the COS model, particularly for the capacitance and inductance, affect the levels of the order/disorder in the electrical time series. Compared to the field observations, we infer that the underground electrical condition has become larger capacitance or smaller inductance in seismogenic processes. Accordingly, this study may provide a better understanding of the mechanical–electrical coupling of the earth’s crust.


2018 ◽  
Vol 212 ◽  
pp. 01033
Author(s):  
Elena Chernetsova ◽  
Anatoly Shishkin

A method for calculating loads combination on a building is considered using information measures of the connectivity of signals received from sensors of various physical nature, united in a wireless monitoring network. The method includes the definition of the most powerful information measure on the ensemble of process realizations with known a priori load data by the criterion of connectedness of time series. Then, based on the selected information measure, the connectivity of the signals for the ensemble of realizations of the random process of loads to the building from the network formed by the wireless monitoring data bank of time series is calculated. The volume of the data bank sufficient to make the correct decision about the combination of loads on the building with a predetermined error probability is calculated on the basis of a consistent criterion for the ratio of Wald probabilities. This method is easily algorithmized and can be used to develop an automated decision support system.


2009 ◽  
Vol 79 (4) ◽  
Author(s):  
Roberto Monetti ◽  
Wolfram Bunk ◽  
Thomas Aschenbrenner ◽  
Ferdinand Jamitzky

Entropy ◽  
2019 ◽  
Vol 21 (11) ◽  
pp. 1080 ◽  
Author(s):  
Elsa Siggiridou ◽  
Christos Koutlis ◽  
Alkiviadis Tsimpiris ◽  
Dimitris Kugiumtzis

Granger causality and variants of this concept allow the study of complex dynamical systems as networks constructed from multivariate time series. In this work, a large number of Granger causality measures used to form causality networks from multivariate time series are assessed. These measures are in the time domain, such as model-based and information measures, the frequency domain, and the phase domain. The study aims also to compare bivariate and multivariate measures, linear and nonlinear measures, as well as the use of dimension reduction in linear model-based measures and information measures. The latter is particular relevant in the study of high-dimensional time series. For the performance of the multivariate causality measures, low and high dimensional coupled dynamical systems are considered in discrete and continuous time, as well as deterministic and stochastic. The measures are evaluated and ranked according to their ability to provide causality networks that match the original coupling structure. The simulation study concludes that the Granger causality measures using dimension reduction are superior and should be preferred particularly in studies involving many observed variables, such as multi-channel electroencephalograms and financial markets.


2016 ◽  
Vol 39 (1) ◽  
pp. 81-95
Author(s):  
Behzad Mansouri ◽  
Rahim Chinipa

<p>This article is concerned with the problem of discrimination between two classes of locally stationary time series based on minimum discrimination information. We view the observed signals as realizations of Gaussian locally stationary wavelet (LSW) processes. The asymptotic Kullback - Leibler discrimination information and Chernoff discrimination information are developed as discriminant criteria for LSW processes. The simulation study showed that our procedure performs as well as other procedures and in some cases better than some other classification methods. Applications to classifying real data show the usefulness of our discriminant criteria.</p>


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