A universal variable extension method for designing multi-scroll/wing chaotic systems

© 2023 IEEE. This is the accepted manuscript version of an article which has been published in final form at https://doi.org/10.1109/TIE.2023.3299020Developing a universal design method to construct different multiscroll/wing chaotic systems (MS/WCSs) has been challenging. This article proposes a general design method for MS $/$ WCSs called the universal variable extension method (UVEM). It is a simple but effective approach that generates one-direction (1-D) and 2-D multiscroll/wing chaotic attractors. Using any double-scroll/wing chaotic system as the basic system, the UVEM is able to construct different MS/WCSs. Employing Chua's chaotic system and Lorenz chaotic system as two examples, we construct two MSCSs (including 1-D and 2-D) and two MWCSs (including 1-D and 2-D), respectively. Theoretical analysis and numerical simulation show that the constructed MS/WCSs not only can generate 1-D and 2-D multiscroll/wing chaotic attractors but also have 1-D and 2-D initial boosting behaviors. This means that the MS/WCSs designed by the UVEM are very sensitive to their initial states, and have better unpredictability and more complex chaotic behaviors. To show the simplicity of UVEM in hardware implementation, we develop a field-programmable gate array-based digital hardware platform to implement the designed MS $/$ WCSs. Finally, a new pseudorandom number generator is proposed to investigate the application of the MS/WCSs. All P-values obtained by the NIST SP800-22 test are larger than 0.01, which indicates that the MS/WCSs designed by UVEM have high randomness.Peer reviewe

Generating n-Scroll Chaotic Attractors From A Memristor-based Magnetized Hopfield Neural Network

© 2023 IEEE. This is the accepted manuscript version of an article which has been published in final form at https://doi.org/10.1109/TCSII.2022.3212394This brief presents a novel method to generate n-scroll chaotic attractors. First, a magnetized Hopfield neural network (HNN) with three neurons is modeled by introducing an improved multi-piecewise memristor to describe the effect of electromagnetic induction. Theoretical analysis and numerical simulation show that the memristor-based magnetized HNN can generate multi-scroll chaotic attractors with arbitrary number of scrolls. The number of scrolls can be easily changed by adjusting the memristor control parameters. Besides, complex initial offset boosting behavior is revealed from the magnetized HNN. Finally, a magnetized HNN circuit is designed and various typical attractors are verified.Peer reviewe

A multi-stable memristor and its application in a neural network

© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Nowadays, there is a lot of study on memristorbased systems with multistability. However, there is no study on memristor with multistability. This brief constructs a mathematical memristor model with multistability. The origin of the multi-stable dynamics is revealed using standard nonlinear theory as well as circuit and system theory. Moreover, the multi-stable memristor is applied to simulate a synaptic connection in a Hopfield neural network. The memristive neural network successfully generates infinitely many coexisting chaotic attractors unobserved in previous Hopfield-type neural networks. The results are also confirmed in analog circuits based on commercially available electronic elements.Peer reviewe

Diversified Butterfly Attractors of Memristive HNN With Two Memristive Systems and Application in IoMT for Privacy Protection

© 2024, IEEE. This is an open access accepted manuscript distributed under the terms of the Creative Commons Attribution License (CC BY), https://creativecommons.org/licenses/by/4.0/Memristors are often used to emulate neural synapses or to describe electromagnetic induction effects in neural networks. However, when these two things occur in one neuron concurrently, what dynamical behaviors could be generated in the neural network? Up to now, it has not been comprehensively studied in the literature. To this end, this paper constructs a new memristive Hopfield neural network (HNN) by simultaneously introducing two memristors into one Hopfield-type neuron, in which one memristor is employed to mimic an autapse of the neuron and the other memristor is utilized to describe the electromagnetic induction effect. Dynamical behaviors related to the two memristive systems are investigated. Research results show that the constructed memristive HNN can generate Lorenz-like double-wing and four-wing butterfly attractors by changing the parameters of the first memristive system. Under the simultaneous influence of the two memristive systems, the memristive HNN can generate complex multi-butterfly chaotic attractors including multi-double-wing-butterfly attractors and multi-four-wing-butterfly attractors, and the number of butterflies contained in an attractor can be freely controlled by adjusting the control parameter of the second memristive system. Moreover, by switching the initial state of the second memristive system, the multi-butterfly memristive HNN exhibits initial-boosted coexisting double-wing and four-wing butterfly attractors. Undoubtedly, such diversified butterfly attractors make the proposed memristive HNN more suitable for chaos-based engineering applications. Finally, based on the multi-butterfly memristive HNN, a novel privacy protection scheme in the IoMT is designed. Its effectiveness is demonstrated through encryption tests and hardware experiments.Peer reviewe

Grid Multi-Butterfly Memristive Neural Network With Three Memristive Systems: Modeling, Dynamic Analysis, and Application in Police IoT

© 2024, IEEE. This is an open access accepted manuscript distributed under the terms of the Creative Commons Attribution License (CC BY), https://creativecommons.org/licenses/by/4.0/Nowadays, the Internet of Things (IoT) technology has been widely applied in the police security system. However, with more and more image data that concerns crime scenes being transmitted through the police IoT, there are some new security and privacy issues. Therefore, how to design a safe and efficient secret image sharing solution suitable for police IoT has become a very urgent task. In this work, a grid multi-butterfly memristive Hopfield neural network (HNN) with three memristive systems is constructed and its complex dynamics are deeply analyzed. Among them, the first memristive system is modeled by emulating a self connection synapse, the second memristive system is modeled by coupling two neurons, and the third memristive system is modeled by describing external electromagnetic radiation. Dynamic analyses show that the proposed memristive HNN can not only generate two kinds of 1-directional (1D) multi-butterfly chaotic attractors but also produce complex grid (2D) multi-butterfly chaotic attractors. More importantly, by switching the initial states of the second and third memristive systems, the grid multi-butterfly memristive HNN exhibits initial-boosted plane coexisting multi-butterfly attractors. Moreover, the number of butterflies contained in a multi-butterfly attractor and coexisting attractors can be easily adjusted by changing memristive parameters. Based on these complex dynamics, an image security solution is designed to show the application of the newly constructed grid multi-butterfly memristive HNN to police IoT security. Security performances indicate the designed scheme can resist various attacks and has high robustness. Finally, the test results are further demonstrated through RPI-based hardware experimentsPeer reviewe

Firing multistability in a locally active memristive neuron model

Funding Information: This work is supported by The Major Research Project of the National Natural Science Foundation of China (91964108), The National Natural Science Foundation of China (61971185), The Open Fund Project of Key Laboratory in Hunan Universities (18K010). Publisher Copyright: © 2020, Springer Nature B.V. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.The theoretical, numerical and experimental demonstrations of firing dynamics in isolated neuron are of great significance for the understanding of neural function in human brain. In this paper, a new type of locally active and non-volatile memristor with three stable pinched hysteresis loops is presented. Then, a novel locally active memristive neuron model is established by using the locally active memristor as a connecting autapse, and both firing patterns and multistability in this neuronal system are investigated. We have confirmed that, on the one hand, the constructed neuron can generate multiple firing patterns like periodic bursting, periodic spiking, chaotic bursting, chaotic spiking, stochastic bursting, transient chaotic bursting and transient stochastic bursting. On the other hand, the phenomenon of firing multistability with coexisting four kinds of firing patterns can be observed via changing its initial states. It is worth noting that the proposed neuron exhibits such firing multistability previously unobserved in single neuron model. Finally, an electric neuron is designed and implemented, which is extremely useful for the practical scientific and engineering applications. The results captured from neuron hardware experiments match well with the theoretical and numerical simulation results.Peer reviewedFinal Accepted Versio

Synchronization of inertial memristive neural networks with time-varying delays via static or dynamic event-triggered control

Funding Information: This work was supported in part by the National Natural Science Foundation of China under Grant 61971185, the Major Research Project of the National Natural Science Foundation of China under Grant 91964108 and the Open Fund Project of Key Laboratory in Hunan Universities under Grant 18K010. Publisher Copyright: © 2020 Elsevier B.V.This paper investigates the synchronization problem of inertial memristive neural networks (IMNNs) with time-varying delays via event-triggered control (ETC) scheme and state feedback controller for the first time. First, two types of state feedback controllers are designed; the first type of controller is added to the transformational first-order system, and the second type of controller is added to the original second-order system. Next, based on each feedback controller, static event-triggered control (SETC) condition and dynamic event-triggered control (DETC) condition are presented to significantly reduce the update times of controller and decrease the computing cost. Then, some sufficient conditions are given such that synchronization of IMNNs with time-varying delays can be achieved under ETC schemes. Finally, a numerical simulation and some data analyses are given to verify the validity of the proposed results.Peer reviewe

Memristor-Based Attention Network for Online Real-Time Object Tracking

© 2024 IEEE. This is the accepted manuscript version of an article which has been published in final form at https://doi.org/10.1109/TCAD.2024.3437345Most existing visual object tracking approaches are implemented based on von Neumann computation systems, which inevitably have the problems of high latency. Additionally, remote server processing of video resources requires a large amount of data transmission over the Internet, which limits real-time tracking performance. The integration of visual object tracking technology into electronic devices has become a new trend. However, current visual object tracking approaches have high algorithm complexity, making it difficult to design circuits to implement the corresponding functions. In this paper, a memristor-based attention network and its corresponding algorithm are proposed to achieve online real-time tracking under parallel computing. Memristors are used to construct attention encoding circuits to record changes of the target in historical frames, and adjust attention signals to the target online and in real-time during the tracking process, avoiding the latency problem of the von Neumann architecture. Inspired by the working process of γ-GABAergic interneuron and tripartite synapse, we propose an attention allocation module to selectively allocate attention values. Combining the Winner-Take-All principle, we design a target localization circuit and an optimal attention zone selection circuit for parallel computation to track the location of the target. Finally, experiments and analyses on OTB-100, NFS, and VOT-RTb2022 benchmark datasets verify that the proposed memristor-based attention network has promising tracking performance and achieves a tracking speed of 1000 FPS, demonstrating superior real-time performance.Peer reviewe

Nonvolatile CMOS memristor, reconfigurable array and its application in power load forecasting

© 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. This is the accepted manuscript version of a conference paper which has been published in final form at https://doi.org/10.1109/TII.2023.3341256The high cost, low yield, and low stability of nano-materials significantly hinder the application and development of memristors. To promote the application of memristors, researchers proposed a variety of memristor emulators to simulate memristor functions and apply them in various fields. However these emulators lack nonvolatile characteristics, limiting their scope of application. This paper proposes an innovative nonvolatile memristor circuit based on complementary metal-oxide-semiconductor (CMOS) technology, expanding the horizons of memristor emulators. The proposed memristor is fabricated in a reconfigurable array architecture using the standard CMOS process, allowing the connection between memristors to be altered by configuring the on-off state of switches. Compared to nano-material memristors, the CMOS nonvolatile memristor circuit proposed in this paper offers advantages of low manufacturing cost and easy mass production, which can promote the application of memristors. The application of the reconfigurable array is further studied by constructing an Echo State Network (ESN) for short-term load forecasting in the power system.Peer reviewe