The fight-or-flight response (FFR) is an instantaneous organismic response driven by emotions to external stimuli. However, most current intelligent systems requiring real-time environmental response overlooked this fundamental mechanism. Meanwhile, endowing systems with pre-response adaptive decision-making ability based on environment complexity (EC) considerations also necessitates in-depth research. This work proposes a decision-making model with a bionic memristive circuit comprising n FFR circuits and one environment enrichment (EE) module. The FFR circuit simulates FFR and implements long-term emotion memory (LTEM), memory forgetting, emotion generalization and fast emotion arousal. The EE module allows the circuit to dynamically adjust response targets in multi-stimulus scenarios by considering EC. Consequently, the circuit achieves adaptive and minimal delay responses to changeable stimuli via pre-response decision-making, validated by PSPICE simulations. The use of memristors facilitates online in-situ operating and in-memory computing, which makes the circuit expected to be deployed on multi-nozzle fire-fighting robots, enabling them to adaptively prioritize target processing in real time based on the urgency of various targets in multi-fire points scenarios.

Medical institutions store a vast amount of patient information, and various types of medical data face severe security challenges in cloud storage environments. This paper proposes an efficient multi-type medical multimedia data encryption scheme based on the memristive Hopfield neural network (MHNN). First, a class of MHNNs is constructed, which exhibits isomorphic extreme multistability under different initial conditions. The systems are capable of generating large-scale coexisting chaotic attractors, whose spatial positions, vortex numbers, and amplitudes can be independently regulated, thereby significantly expanding the generation capacity and diversity of chaotic sequences. Based on this, a multi-type data encryption algorithm is designed, which uniformly encodes and integrates different types of medical data into a secure transmission structure. Furthermore, by exploiting multistable characteristics of the MHNN, a two-layer key system composed of a master key and a selection key is established, enabling a secure medical data scheme that supports multi-party collaborative annotation. Experimental results demonstrate that the proposed encryption scheme achieves excellent performance in both security and efficiency, providing a novel and effective solution for secure storage and collaborative processing of multi-type medical multimedia data.

The existing group formation-containment (GFC) studies for multi-agent systems (MASs) depend on system model information and generally neglect input saturation constraint, thereby limiting their applicability to MASs with unknown system model and input saturation. This paper investigates the data-driven GFC control problem of nonlinear MASs with asymmetric input saturation. First, a novel communication topology selection algorithm with relaxed topology conditions is proposed. Then, to tackle the challenge posed by asymmetric input saturation, a novel nonquadratic performance index function with a simplified formulation is designed and the corresponding Hamilton-Jacobi-Bellman equation is derived. On this basis, an effective value iteration algorithm is proposed to determine the optimal GFC control policy, accompanied by the rigorous mathematical analysis. By establishing the critic-actor framework based on generalized fuzzy hyperbolic model, a novel data-driven algorithm is proposed to achieve GFC under asymmetric input saturation, which overcomes the dependence on system model. Finally, some simulation results are provided to verify the effectiveness and superiority of the proposed data-driven GFC algorithm.

Medical image translation plays a crucial role in assisting clinical diagnosis by enabling cross-modal synthesis (e.g., Computed Tomography to Magnetic Resonance Imaging) and super-resolution, effectively addressing clinical challenges such as radiation exposure, prolonged scan times, and allergic reactions to contrast agents. However, existing approaches primarily focus on developing specialized models for specific tasks, limiting their adaptability across different applications. Developing a general model capable of handling arbitrary medical image translation tasks not only enhances cross-domain generalization but also aligns with the broader trend of artificial intelligence evolving from specialized to general-purpose solutions. Achieving such one-for-all model, however, presents three key challenges: (1) varying task complexity, (2) modality discrepancies, and (3) structural variations across anatomical regions within the same modality. To tackle the first challenge, we utilize an advanced diffusion-based training paradigm to endow the denoising model with extensive pattern coverage capabilities, thereby handling tasks of varying difficulty levels. Subsequently, a General Diffusion Transformer incorporating a Fuzzy Mixture-of-Experts (FMoE) module and an Entropy-guided Attention Soft Prompt (EASP) module is proposed. The FMoE module, equipped with nonlinear modeling capabilities, is designed to address modality discrepancies, while the EASP module is employed to enhance the model's perception of structural variations in images. Extensive qualitative and quantitative experiments demonstrate the effectiveness of the proposed model in the arbitrary medical image translation task.

In robotics and autonomous driving, accurate depth estimation is vital yet challenging under dynamic scenes and extreme lighting. Conventional frame-based cameras offer rich context but suffer from motion blur and limited dynamic range, while event cameras provide high temporal resolution and dynamic range but lack global scene structure. Therefore, recent studies explore frame-event fusion depth estimation methods to leverage these two complementary modalities to achieve robust performance. However, due to the mismatch in temporal and spatial resolution, there is an inherent contradiction between high spatial resolution frames captured at sparse temporal intervals and event streams characterized by spatial sparsity but high temporal resolution, rendering cross-modal feature fusion ineffective. Moreover, the limited availability of frame-event depth datasets further undermines the model's generalization capability across different scenes. To address the above challenges, we propose HAFUNet, a Hierarchical Attention Fusion Network for depth estimation via frame-event fusion. Our method contains: (1) a pre-trained Dual-Stream Encoder (DSEer) to extract complementary features from frame and event inputs; (2) a Cross-modal Feature Interaction Module (CFIM) that aligns and fuses spatial-channel features across modalities; and (3) a Hierarchical Attention Decoder (HADer) that progressively refines depth predictions via attention-guided convolution. Experiments on synthetic and real-world datasets show that HAFUNet surpasses existing methods in depth accuracy and robustness. These results demonstrate the strength of our fusion strategy in diverse environments. Code is available at https://github.com/SiYZhangwh/HAFUNet.

Multimodal emotion recognition in conversation (MERC) has garnered substantial research attention recently. Existing MERC methods face several challenges: (1) they fail to fully harness direct inter-modal cues, possibly leading to less-than-thorough cross-modal modeling; (2) they concurrently extract information from the same and different modalities at each network layer, potentially triggering conflicts from the fusion of multi-source data; (3) they lack the agility required to detect dynamic sentimental changes, perhaps resulting in inaccurate classification of utterances with abrupt sentiment shifts. To address these issues, a novel approach named GraphSmile is proposed for tracking intricate emotional cues in multimodal dialogues. GraphSmile comprises two key components, i.e., GSF and SDP modules. GSF ingeniously leverages graph structures to alternately assimilate inter-modal and intra-modal emotional dependencies layer by layer, adequately capturing cross-modal cues while effectively circumventing fusion conflicts. SDP is an auxiliary task to explicitly delineate the sentiment dynamics between utterances, promoting the model's ability to distinguish sentimental discrepancies. GraphSmile is effortlessly applied to multimodal sentiment analysis in conversation (MSAC), thus enabling simultaneous execution of MERC and MSAC tasks. Empirical results on multiple benchmarks demonstrate that GraphSmile can handle complex emotional and sentimental patterns, significantly outperforming baseline models.

This work proposes a bio-inspired decision-making memristive circuit drawing on Hull’s secondary learning system. This circuit can not only mimic the decision-making initiated by secondary drive stimuli and shaped and guided by secondary reinforcers via integrating classical conditioning (CC) and operant conditioning (OC), but also consider the factors that influence decision-making, such as demand states, incentive motivation, and habit strength. These bionic functions have not yet been implemented by existing memristive circuits. Our circuit primarily includes CC module, drive regulation module, habit memory module, incentive generation module, and winner-takes-all module, which is designed through a modular hierarchical circuit design method. Memristors play a core role in our circuit and enable the circuit to perform brain-like online learning in an in-memory computing way, which has power and area advantages. The PSPICE-based simulations in various scenarios show that our circuit has a strong adaptive decision-making ability since more bionic features are considered. The proposed circuit can be applied to a bionic intelligent robot, enabling the robot capable of autonomous associative learning abilities to perform complex tasks such as detection and rescue.Note to Practitioners—This work is motivated by the problem of neuromorphic circuit design for bio-inspired learning and decision-making. To realize brain-like online in-situ learning in an in-memory computing way and enhance the adaptability of the circuit in dynamic environment, a memristive circuit integrating CC and OC is proposed. Referring to Hull’s secondary learning system, the proposed circuit takes into account factors that affect decision-making, such as demand states, incentive motivation, and habit strength, as well as the fact that decision-making processes can be evoked by secondary drive stimuli and shaped by secondary reinforcers, which is unaddressed by existing memristor-based works. The bionic foraging simulations in various scenarios show that our circuit can make favorable adaptive decisions based on various cues categorized by associative memories when engaging with their environment. Such a bio-inspired memristive circuit system can be applied to bionic robots or rescue detection robots through large-scale integration to achieve adaptive learning and decision-making of complex tasks with low power consumption.

This paper focuses on addressing the practical finite-time synchronization (PFTS) problem of heterogeneous fractional-order complex dynamical networks (FCDNs) through event-triggered feedback control (ETFC). Firstly, a novel practical finite-time stability lemma is proposed based on the fractional-order differential inequality$_{t_{0}}^{C}D_{t}^{\alpha } V\left ({{ t }}\right) \le - {p_{1}}V\left ({{ t }}\right) - {p_{2}}{V^{\beta } }\left ({{ t }}\right) + q$, which plays a crucial role in analyzing PFTS. Secondly, a novel ETFC protocol is designed where the information transmission of the controller occurs at a sequence of state-dependent instants. Thirdly, using the aforementioned lemma and fractional Lyapunov theory, synchronization criteria for heterogeneous FCDNs can be derived, and Zeno behavior is excluded. Finally, the numerical example involving the PFTS of a fractional-order Lorenz’s circuit is provided to demonstrate the effectiveness of the proposed theoretical results.

Emotional support conversation (ESC) aims to alleviate the negative emotions of help-seekers by providing psychological assistance. Existing approaches typically overlook the abundant annotations contained in the ESC dataset, such as the situation descriptions and feedback scores of seekers, which limits their performance. In an effort to utilize the annotation information to enhance the emotional support ability of the backbone, we propose a three-stage training method called BlenderBot-ThTra for ESC systems. The proposed BlenderBot-ThTra involves the following three training processes: fine-tuning with supplemental feedback utterance, fine-tuning with auxiliary situation restoration, and calibration with the helpfulness estimation. The first stage aims to intensify the backbone's perception of conversational context, the second stage propels the backbone into excavating the causes of the emotional distress faced by the seeker. In the third stage, we leverage a Bayesian method based on the seeker's feedback scores to train a helpfulness evaluation model, then exploit a contrastive learning method to calibrate the ESC backbone. We conduct experiments on the standard multiturn ESC dataset, and the results demonstrate that BlenderBot-ThTra has a significant advantage in generating more supportive and adaptive responses.

Owing to their ability to effectively characterize the memory effect of magnetic flux, specifically in relation to the effect of external electromagnetic radiation, memristors have elicited widespread interest in the construction of neural networks with complex dynamics. This work proposes a novel memristive multiscroll multistable neural network (MMSMSNN), wherein multistable threshold memristors are used to describe external electromagnetic radiation effects. Numerical simulations show that the MMSMSNN can yield any number of cubic lattice multiscroll attractors by adjusting the internal parameters of memristors. Another highlight is that it can also be able to yield abundant initial offset boosting behaviors, i.e., different kinds of infinitely many homogeneous coexisting attractors, including linearly arranged homogeneous coexisting attractors, planar lattice-distributed homogeneous coexisting attractors, and cubic lattice-distributed homogeneous coexisting attractors. In addition, hardware experiments based on the CH32V307 microcontroller are carried out to demonstrate the numerical findings. Finally, a new secure medical image communication scheme is designed to investigate the MMSMSNN in practical applications, and performance analyses reveal its superiority and high security.

This study investigates the semi-global fixed-time stability (SGFTS) and global fixed-time stability (GFTS) of nonlinear impulsive systems (NISs). A key challenge in analyzing the SGFTS of such systems lies in the evolving integration methods caused by the impulses. To address this, we dynamically partition the semi-global attraction set (SGAS) and solve the corresponding differential equations within each subset. Additionally, by constructing the transition dynamics of impulse points and iteratively computing these points, we establish the conditions for SGFTS under both stabilizing and destabilizing impulses. For GFTS, the primary difficulty arises from the distinct trajectories and dynamics of points located inside and outside the SGAS. To overcome this, we introduce the concept of the maximum-minimum impulse interval and derive a sufficient condition that ensures the system can enter the SGAS from a distance under a finite number of impulses. Furthermore, we develop a criterion for GFTS under varying impulse degrees and provide convergence time estimation based on the research on SGFTS of NIS. Finally, numerical examples are presented to validate the theoretical results. Notably, in Example 3, a fixed-time impulse controller is designed based on the proposed theoretical framework to achieve global stabilization of complex systems. This example highlights the potential applications of this work in the field of control.

This article focuses on achieving the finite-time synchronization (FTS) for fractional complex dynamical networks (FCDNs) using hybrid impulsive control. Initially, a novel framework for local FTS is developed, building upon the relaxed inequality${}_{{t_{k}}}^{C}D_{t}^{{\alpha}}V( t ) \le \chi V( t ) - \eta$. To expand the attraction domain within the local FTS framework, a piecewise fractional-order differential inequality based on impulsive control systems is proposed. Subsequently, a new hybrid control strategy is designed by integrating a simple feedback controller with an impulsive controller involving a finite number of impulses, which can be accurately calculated using the proposed impulsive degree. Additionally, a set of local/global FTS criteria is formulated, and the settling time can be explicitly estimated. Lastly, an illustrative example is presented to demonstrate the effectiveness of the derived results.

Thanks to the biomimetic properties of synaptic plasticity, memristors are often utilized to mimic biological neuronal synapses. This article presents a new memristor synapse coupling (MSC) approach for producing multidirectional multidouble-scroll attractors. Through adopting flux-controlled hyperbolic memristor synapses to couple a Hopfield neural network, a novel multidirectional multidouble-scroll Hopfield neural network (MDMDSHNN) is constructed. Theoretical results and numerical calculations indicate that MDMDSHNN is capable of producing any desired amount of multidirectional multidouble-scroll attractors, including unidirectional (1-D), bidirectional (2-D), and three-directional (3-D) multidouble-scroll attractors. Furthermore, an infinite amount of initial offset-boosted coexisting multidouble-scroll chaotic attractors possessing identical shapes but different positions, i.e., homogeneous extreme multistability are also found via switching the memristor initial values. Furthermore, to validate the physical implementability and practicality of MDMDSHNN, the digital hardware platform is performed. Finally, to investigate MDMDSHNN in practical application, an image encryption scheme with superior security performance is given by employing the homogeneous multidouble-scroll chaotic sequences, further illustrating good superiority and effectiveness of the present MSC method.

Multimodal Emotion Recognition in Conversation (ERC) plays an influential role in the field of human-computer interaction and conversational robotics since it can motivate machines to provide empathetic services. Multimodal data modeling is an up-and-coming research area in recent years, which is inspired by human capability to integrate multiple senses. Several graph-based approaches claim to capture interactive information between modalities, but the heterogeneity of multimodal data makes these methods prohibit optimal solutions. In this work, we introduce a multimodal fusion approach named Graph and Attention based Two-stage Multi-source Information Fusion (GA2MIF) for emotion detection in conversation. Our proposed method circumvents the problem of taking heterogeneous graph as input to the model while eliminating complex redundant connections in the construction of graph. GA2MIF focuses on contextual modeling and cross-modal modeling through leveraging Multi-head Directed Graph ATtention networks (MDGATs) and Multi-head Pairwise Cross-modal ATtention networks (MPCATs), respectively. Extensive experiments on two public datasets (i.e., IEMOCAP and MELD) demonstrate that the proposed GA2MIF has the capacity to validly capture intra-modal long-range contextual information and inter-modal complementary information, as well as outperforms the prevalent State-Of-The-Art (SOTA) models by a remarkable margin.

Multimodal emotion recognition in conversation (ERC) has garnered growing attention from research communities in various fields. In this paper, we propose a Crossmodal Fusion Network with Emotion-Shift Awareness (CFNESA) for ERC. Extant approaches employ each modality equally without distinguishing the amount of emotional information in these modalities, rendering it hard to adequately extract complementary information from multimodal data. To cope with this problem, in CFN-ESA, we treat textual modality as the primary source of emotional information, while visual and acoustic modalities are taken as the secondary sources. Besides, most multimodal ERC models ignore emotion-shift information and overfocus on contextual information, leading to the failure of emotion recognition under emotion-shift scenario. We elaborate an emotion-shift module to address this challenge. CFNESA mainly consists of unimodal encoder (RUME), cross-modal encoder (ACME), and emotion-shift module (LESM). RUME is applied to extract conversation-level contextual emotional cues while pulling together data distributions between modalities; ACME is utilized to perform multimodal interaction centered on textual modality; LESM is used to model emotion shift and capture emotion-shift information, thereby guiding the learning of the main task. Experimental results demonstrate that CFN-ESA can effectively promote performance for ERC and remarkably outperform state-of-the-art models.

Reservoir computing (RC) contains two significant variants: wide RC and deep RC. The hybrid wide-deep architecture absorbs their strengths with a powerful parallel processing capability while enhancing the memory capacity of reservoirs. However, the fully analog RC circuit combining the two structures has yet to be proposed, mainly due to unmanageable hierarchical signal processing. Here we report a full-analog memristive RC circuit with a hybrid wide-deep architecture comprising an input module, mask module, reservoir module, and readout module. The input module can generate continuous voltages with temporal sequences. The mask module provides parallel mask processes, laying the foundation for implementing a wide RC structure. The reservoir module includes dynamic memristors and postprocessing circuits. Dynamic memristors can produce high-dimensional reservoir states, and postprocessing circuits allow memristive reservoir circuits to be cascaded to achieve a deep RC structure. The readout module mainly consists of a nonvolatile memristor crossbar and an analog integrator, enabling an efficient multiplication-and-accumulation operation. The simulation results in LTspice illustrate that the memory capacity of the proposed circuit is 91.6% higher than that of wide RC. Moreover, it can efficiently perform temporal tasks, obtaining a high accuracy of 98.99% in arrhythmia detection.

In this contribution, the problem of fixed-time stabilization in multi-weighted complex networks via the novel adaptive pinning nonchattering control based on the linear matrix inequality (LMI) method, as well as its application to image protection is addressed. Different from the traditional methods, a novel fixed-time stable form is proposed and the convergence time is estimated based on beta function. Next, utilizing the designed continuous adaptive control strategy, a sufficient LMI condition is presented to ensure the fixed-time stabilization of multi-weighted complex networks. Furthermore, the novel nonchattering adaptive pinning control protocol is given to guarantee the fixed-time stabilization of the system only by controlling a small number of nodes. Note that a scheme of how to select the number of control nodes is put forward accordingly. Finally, the effectiveness of the proposed method is verified by the actual financial model. Meanwhile, a number of encryption experiments are carried out based on three networks, and the mean and variance of the encryption performance are calculated to show the stability and robustness of image encryption different from the existing research works.

Discrete memristors have been employed in discrete maps for the purpose of chaos generation and regulation. In this paper, a novel universal model for discrete memristors is proposed to generate multi-attractors. The classical Hénon map and Rulkov neuron are chosen as two examples to verify the effectiveness of the proposed memristor. Coexisting homogeneous attractors are identified in the phase space by memristor-induced offset boosting. An arbitrarily desired number of coexisting attractors is extracted by the appropriate feedback strength of the memristor. What adds further interest to this case is that the amplitude is rescaled by a memristor-related parameter that works well over an infinite range. Number-related parameters are extracted to rescale the oscillation range of the chaotic signals. Moreover, CH32-based circuit implementation is built, which aligns with numerical simulation results. Finally, coexisting homogeneous chaotic signals are tested to explore their robust performance in the application of pseudo-random number generator.

This article investigates the quasi-synchronization for fractional multiweighted coupled neural networks (FMCNNs) with discontinuous activation functions and mismatched parameters. First, under the generalized Caputo fractional-order derivative operator, a novel piecewise fractional differential inequality is established to study the convergence of fractional systems, which significantly extends some related published results. Subsequently, by exploiting the new inequality and Lyapunov stability theory, some sufficient quasi-synchronization conditions of FMCNNs are presented by aperiodic intermittent control. Meanwhile, the exponential convergence rate and synchronization error's bound are given explicitly. Finally, the validity of theoretical analysis is confirmed by numerical examples and simulations.

This work explores the fixed-time stabilization of fuzzy uncertain systems based on conditional integral sliding mode control. The difficulties in solving such problems include the integral windup problem and the severe chattering problem when the system converges. Moreover, the design of the traditional fixed-time controller algorithm in the integral sliding mode control becomes complicated. In order to overcome these difficulties: 1) a piecewise function which can bring distant states closer to the sliding surface is considered in the integral function to prevent the integral windup problem, 2) a class of state variable controllers with odd power is designed to solve the chattering problem due to there is no sign function, 3) a fixed-time control algorithm is introduced into the integral function and the controller to guarantee that both the reachability and sliding motion phases are fixed-time stable. Furthermore, based on 1) and 3), an appropriate fixed-time conditional integral sliding mode controller is constructed to ensure the fixed-time reachability of the designed sliding mode surface. Besides, combining 1), 2) and 3), a class of chattering-free fixed-time integral sliding-mode controllers is established to obtain the fixed-time reachability of the sliding mode surface and the fixed-time stability of sliding mode dynamics. This method dramatically reduces the chattering problem and can adjust integral windup. Finally, two numerical simulations are presented to verify the theoretical results, showing the robustness of the designed overshoot factor and the chattering-free scheme for sliding mode control.

This article mainly explores the fixed-time control problem of fuzzy semi-Markov systems with uncertainties and unknown transition rates. Firstly, the T-S fuzzy semi-Markov system is established by using the membership relation of fuzzy logic and Markov probability property. Then, unlike the existing fixed-time control strategies, this work uses a hyperbolic sine function to replace the traditional multiple powers fixed-time control method and construct a novel fixed-time adaptive integral sliding mode control strategy, which reduces the complexity of the controller and adaptive law while optimizing the sliding mode surface and improving the fixed-time convergence performance of the system. Moreover, compared with current methods that require the assumption that the unknown function satisfies the Lipschitz condition or is bounded, the fully connected neural network is introduced to approximate the unknown nonlinear function in the system, improving the intelligence and practicality of the controller. Finally, the theoretical results are verified through numerical simulation, showing the superior performance of achieving fixed-time stability through the proposed control scheme, the gap in the study of fixed-time control using hyperbolic sine functions and fully connected neural networks is filled.

Emotion Recognition in Conversation (ERC) plays a significant part in Human-Computer Interaction (HCI) systems since it can provide empathetic services. Multimodal ERC can mitigate the drawbacks of uni-modal approaches. Recently, Graph Neural Networks (GNNs) have been widely used in a variety of fields due to their superior performance in relation modeling. In multimodal ERC, GNNs are capable of extracting both long-distance contextual information and inter-modal interactive information. Unfortunately, since existing methods such as MMGCN directly fuse multiple modalities, redundant information may be generated and diverse information may be lost. In this work, we present a directed Graph based Cross-modal Feature Complementation (GraphCFC) module that can efficiently model contextual and interactive information. GraphCFC alleviates the problem of heterogeneity gap in multimodal fusion by utilizing multiple subspace extractors and Pair-wise Cross-modal Complementary (PairCC) strategy. We extract various types of edges from the constructed graph for encoding, thus enabling GNNs to extract crucial contextual and interactive information more accurately when performing message passing. Furthermore, we design a GNN structure called GAT-MLP, which can provide a new unified network framework for multimodal learning. The experimental results on two benchmark datasets show that our GraphCFC outperforms the state-of-the-art (SOTA) approaches.

Emotion Recognition in Conversation (ERC) has attracted widespread attention in the natural language processing field due to its enormous potential for practical applications. Existing ERC methods face challenges in achieving generalization to diverse scenarios due to insufficient modeling of context, ambiguous capture of dialogue relationships and overfitting in speaker modeling. In this work, we present a Hybrid Continuous Attributive Network (HCAN) to address these issues in the perspective of emotional continuation and emotional attribution. Specifically, HCAN adopts a hybrid recurrent and attention-based module to model global emotion continuity. Then a novel Emotional Attribution Encoding (EAE) is proposed to model intra- and inter-emotional attribution for each utterance. Moreover, aiming to enhance the robustness of the model in speaker modeling and improve its performance in different scenarios, A comprehensive loss function emotional cognitive loss $\mathcal{L}_{EC}$ is proposed to alleviate emotional drift and overcome the overfitting of the model to speaker modeling. Our model achieves state-of-the-art performance on three datasets, demonstrating the superiority of our work. Another extensive comparative experiments and ablation studies on three benchmarks are conducted to provided evidence to support the efficacy of each module. Further exploration of generalization ability experiments shows the plug-and-play nature of the EAE module in our method.

In this work, a bionic memristive circuit with the functions of emotional learning and generation is proposed, which can perform brain-like emotional learning and generation based on various types of input information. The proposed circuit is designed based on the brain emotional learning theory in the limbic system, which mainly includes three layers of design: 1) the bottom layer is the design of the basic unit modules, such as neuron and synapse; 2) the middle layer is the design of the functional modules related to emotional learning in the limbic system, such as the amygdala, thalamus, and so on; and 3) the top layer is the design of the overall circuit, which is used to realize the function of the emotional generation. A 2-D emotional space composed of valence and arousal signals is adopted. According to the above bottom-up circuit design method, the valence and arousal signals can be generated, respectively, by designing corresponding emotional learning circuits, so as to form continuous emotions. The volatile and nonvolatile memristors are mainly used to mimic the functions of the neuron and synapse at the bottom layer of the circuit to achieve the core emotional learning function of the middle layer, thereby constructing a brain-like information processing architecture to realize the function of the emotional generation in the top layer. The simulation results in PSPICE show that the proposed circuit can learn and generate emotions like humans. If the proposed circuit is applied to a humanoid robot platform through further research, the robot may have the ability of personalized emotional interaction with humans, so that it can be effectively used in emotional companionship and other aspects.

This article investigates fixed-time stabilization of fuzzy neural networks with distributed delay by designing an adaptive sliding mode controller. First, according to stability theory and related inequalities, a new fixed-time stability theorem is put forward, and the settling time is given. In order to stabilize the system, a new integral sliding mode surface is designed, and the corresponding sliding mode control strategy and adaptive sliding mode control strategy are established. Some criteria that can be obtained, and it is shown that the neuronal states of neural networks will arrive at the sliding surface in a fixed time, and then approach zero along the sliding surface. Compared with existing sliding mode control techniques, this work extends the previous related results by choosing different parameters of the controller and the sliding mode manifold to gain various protocols. Finally, two examples are provided to verify the validity of the theorems in this work.

Multimodal Emotion Recognition is challenging because of the heterogeneity gap among different modalities. Due to the powerful ability of feature abstraction, Deep Neural Networks (DNNs) have exhibited significant success in bridging the heterogeneity gap in cross-modal retrieval and generation tasks. In this work, a DNNs-based Multi-channel Weight-sharing Autoencoder with Cascade Multi-head Attention (MCWSA-CMHA) is proposed to generically address the affective heterogeneity gap in MER. Specifically, multimodal heterogeneity features are extracted by multiple independent encoders, and then a scalable heterogeneous feature fusion module (CMHA) is realized by connecting multiple multi-head attention modules in series. The core of the proposed algorithm is to reduce the heterogeneity between the output features of different encoders through the unsupervised training of MCWSA, and then to model the affective interactions between different modal features through the supervised training of CMHA. Experimental results demonstrate that the proposed MCWSA-CMHA achieves outperformance on two publicly available datasets compared with the state-of-the-art techniques. In addition, visualization experiments and approximation experiments are used to verify the effectiveness of each module in the proposed algorithm, and the experimental results show that the proposed MCWSA-CMHA can mine more emotion-related information among multimodal features compared with other fusion methods.

As an emerging in-memory element, memristor has been widely used in various neural network circuits to represent the weights and accelerate the calculation. However, the Transformer Network (TN), one of the most important models for machine vision and natural language processing in recent years, has not yet been full-circuit implemented using memristors due to the complex calculation process and data storage. In order to carry out the computation of the TN more efficiently, this work proposes a memristor-based full-circuit implementation of the TN capable of: 1) a memristor crossbar module to preserve the weights of the TN and perform the vector-matrix multiplications; 2) an analog signal memory module to store the analog signal directly in near-memory mode; 3) function circuit modules to achieve five transformations, namely Softmax, Layer Normalization, ReLU, Multiply-add and Residual; 4) a timing signal generation module to schedule operations of the circuit. The proposed TN circuit can complete all calculations directly based on the analog signal without using any analog-digital converter (ADC), digital-analog converter (DAC) and digital memory. In addition, character image recognition experiments are carried out in PSPICE to verify the functional correctness of the designed circuit. The corresponding signal retention rates of the analog memory, the performance of the whole circuit, and the non-idealities of the memristors are also analyzed. The results indicate that the circuit has advantages in terms of area overhead, energy efficiency and anti-noise.

In this article, we propose a novel chaotic neuron circuit with memristive neural synapses, construct an architecture of memristive chaotic neural network (MCNN) and implement associative memory application of bipolar images. The proposed neuron circuit mainly consists of synapse module and neuron module with chaotic dynamics characteristics. The synapse module is composed of memristors which represent synaptic weights. The neuron module employs voltage feedback operational amplifiers to accomplish integral operation and output function. MCNN utilizes a memristor crossbar array to perform matrix operations and can process the information in parallel. In addition, the proposed circuit of MCNN can accomplish continuous recursive operations and meet different applications due to the programmability of the memristor. The ex-situ method is utilized to train the memristor crossbar array. Furthermore, the associative memory applications of bipolar images are carried out based on the constructed circuits of MCNN with three and nine neurons. The simulation results in PSPICE software testify the functions of the MCNN circuit.

Memristors are widely used to construct multi-scroll/wing chaotic systems with complex dynamics. However, the generation of a multi-scroll/wing attractor is typically not induced by the memristor but depends on other nonlinear functions in the system, which does not take advantage of the unique features of the memristor for chaos-based applications. To address this issue, the present paper introduces a memristor coupling (MC) method to construct a novel memristive Sprott A system (MSAS) through coupling a flux-controlled memristor with multi-piecewise linear memductance into the chaotic Sprott A system. From theoretical analysis and numerical simulations, the MSAS is shown to be able to generate any number of multi-type hidden attractors, including multi-one-scroll, multi-double-scroll and multi-double-wing hidden attractors. In addition, it has two kinds of multistabilities, that is, heterogeneous multistability and homogeneous multistability. Based on these unique properties, different numbers of coexisting heterogeneous hidden attractors and coexisting homogeneous hidden attractors are derived respectively by switching the memristor initial states. These interesting dynamical properties are comprehensively investigated using nonlinear analysis tools. Furthermore, hardware experiments are implemented to demonstrate the feasibility of the MSAS and the effectiveness of the MC method. Finally, a new pseudo-random number generator (PRNG) is proposed to explore the practical applications of the MSAS. Performance evaluation results verify the high-quality randomness of the designed PRNG.

Biological long short-term memory (B-LSTM) can effectively help human process all kinds of received information. In this work, a memristive B-LSTM circuit which mimics a conversion from short-term memory to long-term memory is proposed. That is, the stronger the signal, the more profound the memory and the higher the output. On this basis, an image binarization circuit using adaptive row threshold algorithm is proposed. It can make the image remain a deep impression on the strong pixel information and effectively filter the relatively weak pixel information. In combination with the function of image binarization, a memristive circuit for eyes state detection is proposed by adding corresponding horizontal projection calculation, subtraction calculation and judgement open or closed eyes modules. The proposed circuit can detect whether there is a blink between two adjacent facial images, which uses the characteristics of memristor to detect the difference of horizontal projection between two images. Due to the use of memristor, the proposed circuit can realize in-memory computing, which fundamentally avoids the problem of storage wall and shorten the execution time. Finally, an expectation application in fatigue driving based on the proposed method is demonstrated, which indicates the practicability of the circuit design in this work.

In order to overcome the essential difficulties in conventional nonlinear control with iteratively adjusting multiple parameters, a novel method for designing multidirection multibutterfly chaotic attractors (MDMBCAs) without reconstructing nonlinear functions is proposed. By using a unified pulse control in a modified Lorenz system, a family of complete multibutterfly attractors can be produced, including 1-D, 2-D, and 3-D multibutterfly attractors. Theoretical analysis and numerical simulations show that arbitrary MDMBCA all can be generated by conducting the pulse-control in corresponding state variable direction (1-D), plane (2-D), or space (3-D). Meanwhile, the number of butterfly attractors can be controlled with the number of pulsed excitation. Furthermore, we design a module-based unified realization circuit and arbitrary MDMBCA can be obtained by selecting corresponding pulsed-excitation. Our theoretical analysis, MATLAB simulations and circuit experiments together show the effectiveness and universality of the proposed methodology. It should be especially pointed out that the proposed method is a universal scheme and can be applied in the arbitrary double-wing chaotic system.

In this paper, a novel nonlinear function shift method for generating multiscroll attractors is proposed, and a memristor-based control circuit is used to realize the shift controller. Three types of shift modes, namely, horizontal shift, vertical shift, and combined shift, are added in a Jerk system. The dynamic behavior is analyzed through equilibria distribution, bifurcation diagram, Lyapunov exponent spectrum, and phase portraits. Research shows that various equilibria distributions and bifurcation phenomena can be obtained by adding different shifts, thereby producing diverse attractors including periodic orbits, single-scroll, double-scroll, and multiscroll attractors. Furthermore, symmetrical and asymmetrical attractors that are unusual dynamic behaviors can also be found. The circuit construction based on CMOS technology is given, and a memristor-based control circuit is designed to implement the proposed shift method. Different multiscroll attractors can be obtained by regulating the applied control signals instead of redesigning the nonlinear circuit, which simplifies the circuit design of multiscroll system. Our theoretical analysis, numerical simulations, and PSpice simulations together demonstrate the simpleness and effectiveness of the proposed methodology.

The multivalued memory achieved with memristors is a promising approach to enhance the memory density. Effective and compact methods of reading and writing for multivalued memories can significantly improve the performance of circuits. In this paper, we present a compact and efficient scheme of reading and writing for two memristors per transistor-based multivalued memory. With the VTEAM model of the memristor, the verification of feasibility of our reading operations and writing operations for multivalued memory is achieved through HSPICE simulation.

This paper proposes a novel memristor-based multiplexer implemented by using NOT-material implication. The proposed design can be extended to arbitrary N-bit inputs and the enable-port can be added to improve its structure. Furthermore, this structure can be applied in the cascade circuit and crossbar array. A novel peripheral read circuit is introduced to overcome the problem that it is difficult to read out the operation results stored in crossbar array with large scales. The feasibility and correctness of our design is verified by the HSPICE simulation results with voltage threshold adaptive memristor model.

Memristors have recently begun to be explored in logic operations. In this paper, a compact scheme using complementary resistive switching (CRS)-CMOS cells (CCCs) for logic operations and data storage is proposed. Several logic operations, including IMPLY, IMPLY-AND, AND, NAND, OR, and NOR, are realized with CCCs. Then the AND-OR logic and the OR-AND logic are presented to realize the programmable logic arrays built with CCCs, providing opportunities for memristor-CMOS integrated circuits.

A novel non-volatile 1-bit binary comparator based on pure memristors and an 8-bit comparator based on 1T1M (1 transistor per memristor) crossbar array are proposed in this paper. The proposed comparator is sneak-path free and has short delays. It can be extended to an arbitrary N-bit comparator with 7N memristors and 7N transistors. The worst case input-to-output delay is 4.8ns. A 64-bit comparator shows an maximum power dissipation of 9.38mW at 1.25GHz. Besides, the proposed comparator is nonvolatile and can combine computation with memory. The feasibility and correctness of our design is verified by HSPICE with VTEAM model.

Memristor-based material implication (M-IMP) logic is popular with logic operations, which provides a possibility that memory is operated directly. However, there is a small limitation that memristor is not able to reach the lowest resistance in M-IMP. In this brief, the M-IMP limitation and its influence are analyzed briefly. In addition, a circuit structure that performs a stateful logic operation on memristor memory based on a nanocrossbar is proposed, which can perfect the M-IMP limitation and eliminate the influence. Moreover, we simulate the proposed circuit design and the simulation results verify the correctness of the analysis.

Due to the conventional look-up-table (LUT) using the static random access memory (SRAM) cell, field programmable gate arrays (FPGAs) almost reach the limitation in term of the density, speed, and configuration overhead. This paper proposes an improved memristor-based LUT (MLUT) circuit which is compatible with the mainstream LUT circuit in FPGA. Any arbitrary combined logic functions can be implemented in the MLUT through specific configurations. Then the MLUT shows superior advantages over the conventional LUT such as smaller area overhead and fewer data transmission. As a case study, a one-bit full adder is simulated to verify that the design is of practice in PSPICE. Moreover, the adder can be cascaded into multibit full adder demonstrating competitiveness against the conventional configurable logic block in FPGA technology. MLUT can be a candidate to replace the conventional SRAM-based LUT and further improves the performance of FPGAs.

In this paper, we propose a novel Verilog-A based memristor model for effective simulation and application. Our proposed model captures desired nonlinear characteristics using voltage-based state control. This model is flexible and accurate, it can exhibit all the behaviors of HP memristive device and a general class memristive device resistive random access memory which is important in logic and memory design. Furthermore, we can antiserially connect two proposed models to capture the ideal I-V characteristics of complementary resistive switch (CRS). We demonstrate that our proposed CRS model-based crossbar arrays can significantly reduce sneak path currents with high noise margin compared to traditional memristor-based architectures.

Blood vessel segmentation is an important problem for quantitative structure analysis of retinal images, and many diseases are related to the structure changes. Manual segmentation is time consuming and computer aided segmentation is required to deal with large amount images. This paper presents a new supervised method for segmentation of blood vessels in retinal photographs. Multiple kernel learning (MKL) is introduced to deal with the problem, utilizing features from Hessian matrix based vesselness measure, response of multiscale Gabor filter, and multiple scale line strength features. The method is evaluated on the publicly available DRIVE and STARE databases. The performance of the MKL method is evaluated and experimental results show the high accuracy of the proposed method.