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 article 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 the 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 multibutterfly chaotic attractors, including multidouble-wing-butterfly attractors and multifour-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 multibutterfly 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 the chaos-based engineering applications. Finally, based on the multibutterfly memristive HNN, a novel privacy protection scheme in the Internet of Medical Things is designed. Its effectiveness is demonstrated through the encryption tests and hardware experiments.

By integrating memristors into a Hopfield neural network (HNN), a diverse range of dynamical behavior can be generated, which has significant implications for modeling and biomimetic applications of artificial neurons. However, research on the firing dynamics of HNNs remains relatively limited. In response, a memristive tri-neurons Hopfield neural network (MTN-HNN) was constructed, with the synapse of the second neuron replaced by the proposed memristor. A theoretical and experimental investigation of the dynamics of this neural network was conducted using general analytical tools, such as phase diagrams, Lyapunov exponents, bifurcation diagrams, and others. Experimental results indicate that the dynamics of the MTN-HNN is influenced by the internal parameters of the memristor, enabling the network to extend attractors in up to two directions and thereby form grid multi-scrolls. Notably, the MTN-HNN exhibits various firing modes, including periodic and chaotic bursting. Finally, an encryption scheme was proposed to demonstrate the potential of the MTN-HNN, and both the custom digital circuits and the encryption scheme were successfully implemented on a Field-Programmable Gate Array (FPGA).

Transistor sizing and spacing are constantly decreasing due to the continuous advancement of CMOS technology. The charge of the sensitive nodes in the static random access memory (SRAM) cell gradually decreases, making the SRAM cell more and more sensitive to soft errors, such as single-node upsets (SNUs) and double-node upsets (DNUs). Therefore, two types of radiation-hardened SRAM cells are proposed in this article. First, a low-power DNU self-recovery S6P8N cell is proposed. This cell can realize SNU self-recovery from all sensitive nodes as well as realize partial DNUs self-recovery and has low-power consumption overhead. Second, we propose a high-speed DNU self-recovery S8P6N cell, which has a soft-error tolerance level similar to the S6P8N. Furthermore, it reduces the read access time (RAT) and write access time (WAT). Simulation results show that the proposed cells are self-recovery for all SNUs and most of DNUs. Compared with RHD12, QCCM12T, QUCCE12T, RHMD10T, SEA14T, RHM-12T, S4P8N, S8P4N, RH-14T, HRLP16T, CC18T, and RHM, the average power consumption of S6P8N is reduced by 48.78%, and the average WAT is reduced by 6.62%. While the average power consumption of S8P6N is reduced by 23.64%, and the average WAT and RAT by 9.07% and 36.84%, respectively.

As CMOS process sizes continue to shrink, static randomized memory (SRAM) cells have become very sensitive to the charge generated by high-energy radiation particles, making them more susceptible to Single Event Upsets(SEUs). In this paper, a highly reliable and low-power radiation hardened 20T SRAM (RH-20T) cell is proposed. RH-20T is able to recover from single-node upsets (SNU) at all sensitive nodes, and also the structure has ten node pairs for self-recovery from double-node upsets(DNU). Simulation results demonstrate its advantages in terms of reliability and power consumption over typical existing radiation-hardened SRAM cell designs, such as ZHANG14T, QUCCE12T, RHMD10T, SEA14T, SCCS, S4P8N, S8P4N, DNUSRM, SESRS and CC18T, the RH-20T achieves an average power consumption reduction of 44.61%, and an average write access time (WAT) reduction of 19.78%.

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 multibutterfly 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 (1-D) multibutterfly chaotic attractors but also produce complex grid (2-D) multibutterfly chaotic attractors. More importantly, by switching the initial states of the second and third memristive systems, the grid multibutterfly memristive HNN exhibits initial-boosted plane coexisting multibutterfly attractors. Moreover, the number of butterflies contained in a multibutterfly 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 multibutterfly 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 Raspberry Pi-based hardware experiments.

With the continuous shrinking of the size of the semiconductor process, the multi-node upset (MNU) brought about by the charge-sharing effect in the nano-integrated circuit has a huge impact on the reliability of the chip. In this paper, a low-delay quadruple-node-upset self-recoverable (LDQNUSR) latch is proposed, which employs seven identical multi-level soft-error interception modules (SIM), each of which is composed of six two-input C-element (CEs) and an inverter. Due to the error interception characteristics of each SIM and the mutual feedback mechanism, this latch has complete quadruple-node-upset (QNU) self-recovery capabilities. Simulation results show that the proposed latch can tolerate all QNUs and can self-recover from any QNUs. In addition, latch overhead can be reduced due to the use of high-speed transmission gates and clock gating techniques. The proposed latch has lower delay compared to the latest LDAVPM latch.

Abstract Data transmission between smart meters and data center is facing network security threats in advanced metering infrastructure of smart grid. The traditional solution is to move the data to the data center to build a centralized attack detection model, or divide the collected data into several independent and identically distributed datasets to build a distributed attack detection model. However, the long‐distance transmission and the centralized storage of data not only increase the communication overhead and time overhead, but also increase the risk of being attacked, causing privacy disclosure during the process of building the model. In this paper, we propose an efficient intrusion detection framework Fed_ADBN based on federated attention deep belief network and client selection. Clients cooperate with the data center to jointly build a horizontal federated learning framework. Under the premise of protecting data security by keeping data on the clients, we design a client selection algorithm based on client computing power, communication quality and security risks, which can improve the operating efficiency of federated learning. We also deploy a deep belief neural network with attention mechanism in each client to accurately detect possible network attacks in AMI network in real time. Experimental results show that compared with state‐of‐the‐art methods, the proposed framework can not only maintain good detection accuracy but also protect privacy.

Memristors have recently demonstrated great promise in constructing memristive neural networks with complex dynamics. This article proposes a memristive Hopfield neural network with three memristive coupling synaptic weights. The complex dynamical behaviors of the triple-memristor Hopfield neural network (TM-HNN), which have never been observed in previous Hopfield-type neural networks, include space multistructure chaotic attractors and space initial-offset coexisting behaviors. Bifurcation diagrams, Lyapunov exponents, phase portraits, Poincaré maps, and basins of attraction are used to reveal and examine the specific dynamics. Theoretical analysis and numerical simulation show that the number of space multistructure attractors can be adjusted by changing the control parameters of the memristors, and the position of space coexisting attractors can be changed by switching the initial states of the memristors. Extreme multistability emerges as a result of the TM-HNN’s unique dynamical behaviors, making it more suitable for applications based on chaos. Moreover, a digital hardware platform is developed and the space multistructure attractors as well as the space coexisting attractors are experimentally demonstrated. Finally, we design a pseudorandom number generator to explore the potential application of the proposed TM-HNN.

Neural networks have been widely and deeply studied in the field of computational neurodynamics. However, coupled neural networks and their brain-like chaotic dynamics have not been noticed yet. In this article, we focus on the coupled neural network-based brain-like initial boosting coexisting hyperchaos and its application in biomedical image encryption. We first construct a memristive-coupled neural network (MCNN) model based on two subneural networks and one multistable memristor synapse. Then we investigate its coupling strength-related dynamical behaviors, initial states-related dynamical behaviors, and initial-boosted coexisting hyperchaos using bifurcation diagrams, phase portraits, Lyapunov exponents, and attraction basins. The numerical results demonstrate that the proposed MCNN not only can generate hyperchaotic attractors with high complexity but also can boost the attractor positions by switching their initial states. This makes the MCNN more suitable for many chaos-based engineering applications. Moreover, we design a biomedical image encryption scheme to explore the application of the MCNN. Performance evaluations show that the designed cryptosystem has several advantages in the keyspace, information entropy, and key sensitivity. Finally, we develop a field-programmable gate array test platform to verify the practicability of the presented MCNN and the designed medical image cryptosystem.

In this paper, we consider a smart power model, where some subscribers share several energy providers and there are some malicious users in this power grid. The energy providers are managed by a power market scheduling center (PMSC), which broadcasts electricity price to subscribers and energy providers. The energy providers and subscribers update their capacities and energy consumption requirements, respectively, according to the electricity prices received. In order to identify the malicious users and the unstable energy providers, the mechanism of identification and processing (MIP) for the malicious users and unstable energy providers is proposed. By integrating the MIP, we proposed a heuristic algorithm called the dynamic pricing algorithm with malicious users and unstable energy providers (DPAMU) to get the optimal electricity price as well as the optimal power requirement and the load capacity. Finally, the simulation results show that the proposed DPAMU has good convergence performance and can shave and clip the peak load effectively.