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Updated: Jul 6, 2026

Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons
Published on: June 9, 2023
Time shifts and correlations in synchronized chaos
Jonathan N Blakely1, Matthew W Pruitt, Ned J Corron
1U. S. Army Research, Engineering, and Development Command, AMSRD-AMR-WS-ST, Redstone Arsenal, Alabama 35898, USA.
We developed a new transfer function method to predict synchronization characteristics in coupled systems. This approach accurately estimates waveform correlation and time shifts, enabling improved coupling designs.
Area of Science:
- Complex Systems
- Nonlinear Dynamics
- Network Science
Background:
- Coupled dynamical systems often exhibit synchronized behavior.
- Unidirectional coupling schemes are prevalent in various scientific and engineering fields.
- Predicting and controlling synchronization properties remains a key challenge.
Purpose of the Study:
- To introduce a novel transfer function-based method for predicting synchronization characteristics.
- To quantify the correlation and time shift in unidirectional coupling schemes.
- To enable the design of improved coupling strategies for enhanced synchronization.
Main Methods:
- Derivation of a transfer function from the coupling model.
- Application of the transfer function to simulated coupled Rossler oscillators.
- Validation using an experimental system of coupled chaotic electronic circuits.
Main Results:
- The transfer function accurately estimates correlation between drive and response waveforms.
- The method provides reliable predictions of the time shift (lag or anticipation) in synchronized states.
- Demonstrated significant improvements in correlation and maximum achievable time shift using designed coupling schemes.
Conclusions:
- The developed transfer function offers a powerful tool for analyzing and predicting synchronization in unidirectional coupled systems.
- This method facilitates the design of novel coupling schemes with enhanced performance.
- The findings have implications for controlling and optimizing synchronized behavior in complex networks.
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