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fluctuation-functions
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Levels of complexity in turbulent time series for weakly and high Reynolds number
, Article Physica A: Statistical Mechanics and its Applications ; Volume 391, Issue 11 , 2012 , Pages 3151-3158 ; 03784371 (ISSN) ; Sharif University of Technology
2012
Abstract
We use the detrended fluctuation analysis (DFA), the detrended cross correlation analysis (DCCA) and the magnitude and sign decomposition analysis to study the fluctuations in the turbulent time series and to probe long-term nonlinear levels of complexity in weakly and high turbulent flow. The DFA analysis indicate that there is a time scaling region in the fluctuation function, segregating regimes with different scaling exponents. We discuss that this time scaling region is related to inertial range in turbulent flows. The DCCA exponent implies the presence of power-law cross correlations. In addition, we conclude its multifractality for high Reynold's number in inertial range. Further, we...
Deep sparse graph functional connectivity analysis in AD patients using fMRI data
, Article Computer Methods and Programs in Biomedicine ; Volume 201 , 2021 ; 01692607 (ISSN) ; Fatemizadeh, E ; Motie Nasrabadi, A ; Sharif University of Technology
Elsevier Ireland Ltd
2021
Abstract
Functional magnetic resonance imaging (fMRI) is a non-invasive method that helps to analyze brain function based on BOLD signal fluctuations. Functional Connectivity (FC) catches the transient relationship between various brain regions usually measured by correlation analysis. The elements of the correlation matrix are between -1 to 1. Some of them are very small values usually related to weak and spurious correlations due to noises and artifacts. They can not be concluded as real strong correlations between brain regions and their existence could make a misconception and leads to fake results. It is crucial to make a conclusion based on reliable and informative correlations. In order to...
Deep sparse graph functional connectivity analysis in AD patients using fMRI data
, Article Computer Methods and Programs in Biomedicine ; Volume 201 , 2021 ; 01692607 (ISSN) ; Fatemizadeh, E ; Motie Nasrabadi, A ; Sharif University of Technology
Elsevier Ireland Ltd
2021
Abstract
Functional magnetic resonance imaging (fMRI) is a non-invasive method that helps to analyze brain function based on BOLD signal fluctuations. Functional Connectivity (FC) catches the transient relationship between various brain regions usually measured by correlation analysis. The elements of the correlation matrix are between -1 to 1. Some of them are very small values usually related to weak and spurious correlations due to noises and artifacts. They can not be concluded as real strong correlations between brain regions and their existence could make a misconception and leads to fake results. It is crucial to make a conclusion based on reliable and informative correlations. In order to...