Radio frequency (RF) stealth anti-sorting technology can improve the battlefield survival rate of radar and is one of the research hotspots in the radar field. In this study, the signal design principle of anti-sequential difference histogram (SDIF) sorting was explored for the main sorting algorithm of the SDIF. Furthermore, we designed a piecewise linear chaotic system with interval number parameterization based on random disturbance and proposed a method to modulate the repetition period of widely spaced signal pulses using a chaotic system. Then, considering the difficulty of the traditional signal processing method to measure the velocity of the highly random anti-sorting signals designed in this paper, we used compressed sensing (CS) technology to process the echoes of the signals to solve the velocity and distance of the detection targets. Finally, simulation verification was performed from the correctness of the signal design principle, the performance of the chaotic system, the anti-sorting performance of the designed signals and the recovery and reconstruction performance of the signals by CS. The results show that: (a) the signal design principle presented in this paper can guide the signal design correctly; (b) the performance of the piecewise linear chaotic system with interval number parameterization is better than that of the classical one-dimensional chaotic system; (c) the anti-sorting signal modulated by the chaotic system can achieve anti-SDIF sorting, and the anti-sorting signals designed in this paper can be processed to obtain the velocity and distance of the targets.
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http://dx.doi.org/10.3390/e24111559 | DOI Listing |
This paper proposes a covert chaotic encryption (CCE) scheme based on compressive sensing (CS). The chaotic sequences used are generated by a six-dimensional hyper-chaotic D-system, where the sequence is utilized for a chaotic index sparse block (CISB), the sequence is used for generating the CS measurement matrix effectively, the , , and sequences are employed for variable-parameter iterative Arnold transformations, and the sequence is used for dual-random least significant bit (LSB) scrambling and embedding. The combination of these technologies enabled the scheme to achieve multi-domain, multi-dimensional, ultra-high-security encryption for multimedia image data.
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View Article and Find Full Text PDFInt J Drug Policy
January 2025
MGH Institute for Technology Assessment, Harvard Medical School, Boston, MA, 02144, USA. Electronic address:
The overdose epidemic in the United States is evolving, with a rise in stimulant (cocaine and/or methamphetamine)-only and opioid and stimulant-involved overdose deaths for reasons that remain unclear. We conducted interviews and group model building workshops in Massachusetts and South Dakota. Building on these data and extant research, we identified six dynamic hypotheses, explaining changes in stimulant-involved overdose trends, visualized using causal loop diagrams.
View Article and Find Full Text PDFChaos
January 2025
Physics Institute, University of São Paulo, 05508-090 São Paulo, SP, Brazil.
In this work, we investigate the dynamics of a discrete-time prey-predator model considering a prey reproductive response as a function of the predation risk, with the prey population growth factor governed by two parameters. The system can evolve toward scenarios of mutual or only of predators extinction, or species coexistence. We analytically show all different types of equilibrium points depending on the ranges of growth parameters.
View Article and Find Full Text PDFChaos
January 2025
School of Mathematics and Statistics, University College Dublin, Dublin 4 D04 V1W8, Ireland.
Synaptic plasticity plays a fundamental role in neuronal dynamics, governing how connections between neurons evolve in response to experience. In this study, we extend a network model of θ-neuron oscillators to include a realistic form of adaptive plasticity. In place of the less tractable spike-timing-dependent plasticity, we employ recently validated phase-difference-dependent plasticity rules, which adjust coupling strengths based on the relative phases of θ-neuron oscillators.
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