WAVELET MULTIFRACTAL DETRENDED FLUCTUATION ANALYSIS OF ENCRYPTION AND DECRYPTION MATRICES

2013 ◽  
Vol 24 (09) ◽  
pp. 1350069 ◽  
Author(s):  
J. S. MURGUÍA ◽  
M. MEJÍA CARLOS ◽  
C. VARGAS-OLMOS ◽  
M. T. RAMÍREZ-TORRES ◽  
H. C. ROSU
Keyword(s):  
Hurst Exponent ◽  
Detrended Fluctuation Analysis ◽  
Fluctuation Analysis ◽  
Scaling Method ◽  
Multifractal Detrended Fluctuation Analysis ◽  
Encryption And Decryption ◽  
Singularity Exponent ◽  
Generalized Hurst Exponent ◽  
Different Dimensions ◽  
Detrended Fluctuation

In this paper, we study in detail the multifractal features of the main matrices of an encryption system based on a rule-90 cellular automaton. For this purpose, we consider the scaling method known as the wavelet transform multifractal detrended fluctuation analysis (WT-MFDFA). In addition, we analyze the multifractal structure of the matrices of different dimensions, and find that there are minimal differences in all the examined multifractal quantities such as the multifractal support, the most frequent singularity exponent, and the generalized Hurst exponent.

Fish Sound Characterization Using Multifractal Detrended Fluctuation Analysis

2020 ◽  
Vol 19 (01) ◽  
pp. 2050009 ◽  
Author(s):  
Kranthikumar Chanda ◽  
Shubham Shet ◽  
Bishwajit Chakraborty ◽  
Arvind K. Saran ◽  
William Fernandes ◽  
...  
Keyword(s):  
Hurst Exponent ◽  
Detrended Fluctuation Analysis ◽  
Linear Method ◽  
Multifractal Spectrum ◽  
Fluctuation Analysis ◽  
Multifractal Detrended Fluctuation Analysis ◽  
Generalized Hurst Exponent ◽  
Estuarine Systems ◽  
Detrended Fluctuation ◽  
Sound Data

This work involves the application of a non-linear method, multifractal detrended fluctuation analysis (MFDFA), to describe fish sound data recorded from the open waters of two major estuarine systems. Applying MFDFA, the second-order Hurst exponent [Formula: see text] values are found to be [Formula: see text] and [Formula: see text] for the fish families Batrachoididae (common name: Toadfish) and Sciaenidae (common name: Croakers, drums), respectively. The generalized Hurst exponent [Formula: see text]-related width parameters [Formula: see text] are found to be [Formula: see text] and [Formula: see text], respectively, for toadfish and Sciaenidae vocalizations, implying greater heterogeneity and multifractal characteristics. The results suggest that the Sciaenidae fish calls are smoother in comparison with Batrachoididae. Clustering of multifractal spectrum-related parameters with respect to toadfish and Sciaenidae vocalization characteristics is observed in this analyses.

scholarly journals A probe into the Multifractal Behaviour of Total Ozone Time Series through Detrended Fluctuation Analysis

2021 ◽  
Author(s):  
Sombit Chakraborty ◽  
Surajit Chattopadhyay
Keyword(s):  
Time Series ◽  
Hurst Exponent ◽  
Detrended Fluctuation Analysis ◽  
Total Ozone ◽  
Fluctuation Analysis ◽  
Multifractal Detrended Fluctuation Analysis ◽  
Order Polynomial ◽  
Local Trend ◽  
Detrended Fluctuation ◽  
Daily Total

Abstract The present study reports a multifractal detrended fluctuation analysis of total ozone time series. Considering daily total ozone concentration (TOC) data ranging from 2015 to 2019, we have created a new profile by subtracting the trend. Subsequently we have divided the profile \({X}_{i}\) into non intersecting segments of equal time scale varying from 25 to 30. Fitting a second order polynomial, we have eliminated the local trend from each segment and thereafter we have computed the detrended variance. Finally the multifractal behaviour has been identified and the singularity spectra has helped us in obtaining the generalised Hurst exponent which in this case has come out to be greater than 0.5.

Multifractal detrended fluctuation analysis of temperature in Spain (1960–2019)

2021 ◽  
pp. 126118
Author(s):  
Javier Gómez-Gómez ◽  
Rafael Carmona-Cabezas ◽  
Ana B. Ariza-Villaverde ◽  
Eduardo Gutiérrez de Ravé ◽  
Francisco José Jiménez-Hornero
Keyword(s):  
Detrended Fluctuation Analysis ◽  
Fluctuation Analysis ◽  
Multifractal Detrended Fluctuation Analysis ◽  
Detrended Fluctuation

Multifractal characterization of mechanical vibration signals through improved empirical mode decomposition-based detrended fluctuation analysis

2016 ◽  
Vol 231 (22) ◽  
pp. 4139-4149 ◽  
Author(s):  
Du Wenliao ◽  
Guo Zhiqiang ◽  
Gong Xiaoyun ◽  
Xie Guizhong ◽  
Wang Liangwen ◽  
...  
Keyword(s):  
Empirical Mode Decomposition ◽  
Detrended Fluctuation Analysis ◽  
Fluctuation Analysis ◽  
Intrinsic Mode Functions ◽  
Vibration Signals ◽  
Multifractal Detrended Fluctuation Analysis ◽  
Mode Decomposition ◽  
Kolmogorov Smirnov ◽  
Detrended Fluctuation ◽  
Smirnov Test

A novel multifractal detrended fluctuation analysis based on improved empirical mode decomposition for the non-linear and non-stationary vibration signal of machinery is proposed. As the intrinsic mode functions selection and Kolmogorov–Smirnov test are utilized in the detrending procedure, the present approach is quite available for contaminated data sets. The intrinsic mode functions selection is employed to deal with the undesired intrinsic mode functions named pseudocomponents, and the two-sample Kolmogorov–Smirnov test works on each intrinsic mode function and Gaussian noise to detect the noise-like intrinsic mode functions. The proposed method is adaptive to the signal and weakens the effect of noise, which makes this approach work well for vibration signals collected from poor working conditions. We assess the performance of the proposed procedure through the classic multiplicative cascading process. For the pure simulation signal, our results agree with the theoretical results, and for the contaminated time series, the proposed method outperforms the traditional multifractal detrended fluctuation analysis methods. In addition, we analyze the vibration signals of rolling bearing with different fault types, and the presence of multifractality is confirmed.

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