An Extremely Simple Multiwing Chaotic System: Dynamics Analysis, Encryption Application, and Hardware Implementation

Lin, Hairong; Wang, Chunhua; Yu, Fei; Xu, Cong; Hong, Qinghui; Yao, Wei; Sun, Yichuang

doi:10.1109/tie.2020.3047012

Cited by 91 publications

(28 citation statements)

References 40 publications

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“…For the chaotic bursting, except for the period-m bursting, there exist other kinds of bursting with different numbers or amplitudes of spikes. Additionally, from the point of view of chaos [48][49], both stochastic bursting and chaotic bursting are also chaotic behaviors, but they are different from chaos firing that is a completely chaotic oscillation [50].…”

Section: Networkmentioning

confidence: 99%

Neural Bursting and Synchronization Emulated by Neural Networks and Circuits

Lin

Wang

Chen

et al. 2021

IEEE Trans. Circuits Syst. I

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Nowadays, research, modeling, simulation and realization of brain-like systems to reproduce brain behaviors have become urgent requirements. In this paper, neural bursting and synchronization are imitated by modeling two neural network models based on the Hopfield neural network (HNN). The first neural network model consists of four neurons, which correspond to realizing neural bursting firings. Theoretical analysis and numerical simulation show that the simple neural network can generate abundant bursting dynamics including multiple periodic bursting firings with different spikes per burst, multiple coexisting bursting firings, as well as multiple chaotic bursting firings with different amplitudes. The second neural network model simulates neural synchronization using a coupling neural network composed of two above small neural networks. The synchronization dynamics of the coupling neural network is theoretically proved based on the Lyapunov stability theory. Extensive simulation results show that the coupling neural network can produce different types of synchronous behaviors dependent on synaptic coupling strength, such as anti-phase bursting synchronization, anti-phase spiking synchronization, and complete bursting synchronization. Finally, two neural network circuits are designed and implemented to show the effectiveness and potential of the constructed neural networks.

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Section: Networkmentioning

confidence: 99%

Neural Bursting and Synchronization Emulated by Neural Networks and Circuits

Lin

Wang

Chen

et al. 2021

IEEE Trans. Circuits Syst. I

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“…As the main application of chaotic system, PRNG has been paid more and more attention in the field of image encryption [57][58][59]. At the same time, FPGA also provides strong support for the engineering application of chaotic system [60]. Therefore, as the basis of different engineering applications based on PRNG, FPGA has been paid more and more attention.…”

Section: Design and Implementation Of Image Encryption And Decryption System Based On Prngmentioning

confidence: 99%

Design and FPGA Implementation of a Pseudo-random Number Generator Based on a Hopfield Neural Network Under Electromagnetic Radiation

Zhang

Shen

et al. 2021

Front. Phys.

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When implementing a pseudo-random number generator (PRNG) for neural network chaos-based systems on FPGAs, chaotic degradation caused by numerical accuracy constraints can have a dramatic impact on the performance of the PRNG. To suppress this degradation, a PRNG with a feedback controller based on a Hopfield neural network chaotic oscillator is proposed, in which a neuron is exposed to electromagnetic radiation. We choose the magnetic flux across the cell membrane of the neuron as a feedback condition of the feedback controller to disturb other neurons, thus avoiding periodicity. The proposed PRNG is modeled and simulated on Vivado 2018.3 software and implemented and synthesized by the FPGA device ZYNQ-XC7Z020 on Xilinx using Verilog HDL code. As the basic entropy source, the Hopfield neural network with one neuron exposed to electromagnetic radiation has been implemented on the FPGA using the high precision 32-bit Runge Kutta fourth-order method (RK4) algorithm from the IEEE 754-1985 floating point standard. The post-processing module consists of 32 registers and 15 XOR comparators. The binary data generated by the scheme was tested and analyzed using the NIST 800.22 statistical test suite. The results show that it has high security and randomness. Finally, an image encryption and decryption system based on PRNG is designed and implemented on FPGA. The feasibility of the system is proved by simulation and security analysis.

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“…Yu et al [22][23][24]showed that electromagnetic radiation can not only change the number of equilibrium points in neural network, but also affect the dynamic behavior of neural network. Lin et al [25][26][27] discussed in detail the latest progress of neuron and neural network models on neurodynamics, which is helpful to apply dynamics to the study of brain function.Xie et al [28]proposed a fractional order multistable multi stable local active memristor is proposed.The experimental results show that, compared with the integer memristors, the fractional memristors have stronger local activity and greater nonvolatile storage characteristics.…”

Section: Introductionmentioning

confidence: 99%

A Study of a Fractional-order Model of Coronavius Disease 2019(COVID-19) after Mass Caccination

Wang

2022

Preprint

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New coronavirus (COVID-19) broke out at the end of 2019. Today, the epidemic has spread all over the world. The number of confirmed cases is 250 million and the number of deaths has reached about 5000000, which undoubtedly caused serious public health disasters all over the world. In the face of the problem that the virus continues to mutate and the virus spreads faster and survives for a longer time after mutation, as well as the situation of asymptomatic infected persons, the study of the transmission law and epidemic trend of COVID-19 provides theoretical support for how to effectively prevent epidemics, which is of great research value. Most of the previous studies on COVID-19 adopt SIR model and variants of SIR model, and there are few theoretical studies on the impact of asymptomatic infection on the transmission, prevention and control of infectious diseases.Because the time of large-scale vaccination of COVID-19 vaccine in China is about the middle of 2021, so far, no scholars have studied the transmission law and prevention and control of COVID-19 virus after large-scale vaccination. In addition,most researchers use the integer-order models.Generally speaking, integer-order may not capture satisfactory model attributes. Based on this, this paper proposes a SEQIR model with quarantine item, sets up seven fractional order equations, studies and compares the epidemic prevention measures taken by the two places in the face of the re outbreak of the epidemic before mass vaccination (Beijing) and after vaccination (Hunan), calculates the size of the basic regeneration number $R_{0}$ in the two places and reports according to the real data of the two places, different numerical simulations are carried out.Studies have shown that mass vaccination and nucleic acid detection can effectively reduce the transmission of the virus. In addition, in the area where the epidemic situation is concentrated, a large number of nucleic acid tests in time can enable asymptomatic infected persons to be detected in time. After isolating asymptomatic infected people, asymptomatic infected people will infect fewer susceptible people.

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An Extremely Simple Multiwing Chaotic System: Dynamics Analysis, Encryption Application, and Hardware Implementation

Cited by 91 publications

References 40 publications

Neural Bursting and Synchronization Emulated by Neural Networks and Circuits

Neural Bursting and Synchronization Emulated by Neural Networks and Circuits

Design and FPGA Implementation of a Pseudo-random Number Generator Based on a Hopfield Neural Network Under Electromagnetic Radiation

A Study of a Fractional-order Model of Coronavius Disease 2019(COVID-19) after Mass Caccination

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