English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

This article deals with the estimation of fractal dimension ofspatio-temporal patterns that are generated by numerically solving the SwiftHohenberg (SH) equation. The patterns were converted into a spatial series(analogous to time series) which were shown to be chaotic by evaluating thelargest Lyapunov exponent. We have applied several nonlinear time-seriesanalysis techniques like Detrended fluctuation and Rescaled range on thesespatial data to obtain Hurst exponent values that reveal spatial series data tobe long range correlated. We have estimated fractal dimension from the Hurstand power law exponent and found the value lying between 1 and 2. The noveltyof our approach lies in estimating fractal dimension using image to dataconversion and spatial series analysis techniques, crucial for experimentallyobtained images.

Fractal dimension analysis of spatio-temporal patterns using image processing and nonlinear time-series analysis