Synchronization of nonlinear systems is a crucial problem in many applications, including system identification, data forecasting, compressive sensing, coupled oscillator topologies, and neuromorphic systems. Despite many efficient synchronization techniques being developed, there are some unresolved issues such as fast and reliable synchronization using short or noisy fragments of available data. In this paper, we use time-reversible integration to obtain a synchronization technique as a generalization of the well-known Pecora-Carroll method. The proposed time-symmetric synchronization technique employs the time reversibility of a discrete system obtained by the symmetric integration method. This approach allows the complete synchronization of two chaotic systems using minimal, sparse, or noisy sync data from one state variable without any controller. An example of rapid unidirectional time-symmetric synchronization of several test chaotic systems is shown to verify the performance of the proposed technique. We show that the time-reversible approach works for both conservative and dissipative systems, but highly depends on initial conditions. To increase the overall performance of the time-symmetric synchronization scheme, we suggest using a computationally simple and easy-to-implement time-reversible semi-implicit numerical integration method. Several possible applications include chaos-based communications, chaotic signal filtering, and systems based on coupled oscillators.
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http://dx.doi.org/10.1103/PhysRevE.111.014213 | DOI Listing |
Anal Chim Acta
May 2025
Key Laboratory of Smart Agriculture System Integration, Ministry of Education, China Agricultural University, Beijing, 100083, China. Electronic address:
Background: Immunomagnetic separation is essential for screening pathogenic bacteria to prevent food poisoning. However, free immunomagnetic nanobeads (IMNBs) coexist with IMNB-bacteria conjugates (IBCs) after traditional immunomagnetic separation resulting in the infeasibility for IMNBs on IBCs to further act as signal label in bacterial detection. Although we have demonstrated that magnetophoretic separation at a high flowrate could separate IBCs from IMNBs, partial IMNBs were still found with IBCs due to chaotic flows and resulted in inevitable interferences.
View Article and Find Full Text PDFPeerJ Comput Sci
February 2025
Institute Science of Mathematics, Universiti Malaya, Petaling Jaya, Kuala Lumpur, Malaysia.
The transmission of healthcare data plays a vital role in cities worldwide, facilitating access to patient's health information across healthcare systems and contributing to the enhancement of care services. Ensuring secure healthcare transmission requires that the transmitted data be reliable. However, verifying this reliability can potentially compromise patient privacy.
View Article and Find Full Text PDFThe permanent magnet synchronous generator (PMSG) system becomes unstable when unpredicted chaos appears, and current approaches do not take how to lessen this chaos phenomenon into account. Motivated by the ability of projective synchronization (PS) to adjust the chaotic system trajectory, this research aims to use PS to reduce the chaos in PMSG system. For better control in the time estimation of PS and the robustness of systems, an adaptive predefined-time robust zeroing neural dynamic controller (APTRZNDC) for the PS between PMSG systems is proposed.
View Article and Find Full Text PDFChaos
March 2025
Neuroscience Institute and Department of Mathematics and Statistics, Georgia State University, 100 Piedmont Ave., Atlanta, Georgia 30303, USA.
This paper investigates the origin and onset of chaos in a mathematical model of an individual neuron, arising from the intricate interaction between 3D fast and 2D slow dynamics governing its intrinsic currents. Central to the chaotic dynamics are multiple homoclinic connections and bifurcations of saddle equilibria and periodic orbits. This neural model reveals a rich array of codimension-2 bifurcations, including Shilnikov-Hopf, Belyakov, Bautin, and Bogdanov-Takens points, which play a pivotal role in organizing the complex bifurcation structure of the parameter space.
View Article and Find Full Text PDFACS Appl Mater Interfaces
March 2025
University of Sheffield, Sheffield, S13JD, United Kingdom.
Physical dynamic reservoirs are well-suited for edge systems, as they can efficiently process temporal input at a low training cost by utilizing the short-term memory of the device for in-memory computation. However, the short-term memory of two-terminal memristor-based reservoirs limits the duration of the temporal inputs, resulting in more reservoir outputs per sample for classification. Additionally, forecasting requires multiple devices (20-25) for the prediction of a single time step, and long-term forecasting requires the reintroduction of forecasted data as new input, increasing system complexity and costs.
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