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A Novel Memristive Multiscroll Multistable Neural Network With Application to Secure Medical image Communication

IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2025年第2期35卷 1774-1786页

作者： Zhang, Sen Peng, Xuenan Wang, Xiaoping Chen, Chengjie Zeng, Zhigang Nanjing Univ Informat Sci & Technol Sch Artificial Intelligence Nanjing 210044 Peoples R China Xiangtan Univ Sch Math & Comp Sci Xiangtan 411100 Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Educ Minist China Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China Jiangsu Univ Technol Sch Elect & Informat Engn Changzhou 213001 Peoples R China

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.

关键词： Memristors Electromagnetic radiation Neurons Mathematical models Biological neural networks Magnetic hysteresis Brain modeling Circuits and systems Chaotic communication Solid modeling Memristor multiscroll attractor Hopfield neural network initial offset boosting secure communication

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A symmetric image encryption scheme based on hybrid analog-digital chaotic system and parameter selection mechanism

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MULTIMEDIA TOOLS AND APPLICATIONS 2021年第14期80卷 20883-20905页

作者： Zheng, Jun Hu, Hanping Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Minist Educ Key Lab Image Informat Proc & Intelligent Control Wuhan 430074 Peoples R China

In recent years, various chaos-based image encryption algorithms have been proposed to meet the growing demand for real-time secure image transmission. However, chaotic system that is the core component of chaos-based cryptosystem usually degrades under finite computing precision, causing many security issues. In this paper, a novel cryptosystem with analog-digital hybrid chaotic model is proposed. Firstly, the analog Chen chaotic system and the digital Logistic map are adopted to depict the capability of the hybrid model, in which analog system is used to perturb digital system. Dynamic analyses demonstrate that the hybrid method has better complexity, larger chaotic parameter range and good ability to counteract dynamical degradation. The chaos-based key streams generated by the perturbed Logistic map are more suitable for image encryption. Secondly, a parameter selection mechanism is introduced to increase security. The state variables of Chen chaotic system and cipher image are involved in parameter selection process to dynamically change the parameter of the perturbed Logistic map. The involvement of cipher image makes the key streams relevant to plain image and can resist known/chosen-plaintext attacks. Performance, security and comparison analyses indicate that this cryptosystem has high security, low time complexity, and ability to resist common attacks.

关键词： Chaotic cryptography Dynamical degradation Hybrid chaotic system Parameter selection mechanism image encryption

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Synchronization of memristive neural networks with unknown parameters via event-triggered adaptive control

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NEURAL NETWORKS 2021年 139卷 255-264页

作者： Zhou, Yufeng Zhang, Hao Zeng, Zhigang Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Educ Minist China Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China

This paper considers the drive-response synchronization of memristive neural networks (MNNs) with unknown parameters, where the unbounded discrete and bounded distributed time-varying delays are involved. Aiming at the unknown parameters of MNNs, the updating law of weight in response system and the gain of adaptive controller are proposed to realize the synchronization of delayed MNNs. In view of the limited communication and bandwidth, the event-triggered mechanism is introduced to adaptive control, which not only decreases the times of controller update and the amount of data sending out but also enables synchronization when parameters of MNNs are unknown. In addition, a relative threshold strategy, which is relative to fixed threshold strategy, is proposed to increase the inter-execution intervals and to improve the control effect. When the parameters of MNNs are known, the algebraic criteria of synchronization are established via event-triggered state feedback control by exploiting inequality techniques and calculus theorems. Finally, one simulation is presented to validate the effectiveness of the proposed results. (C) 2021 Elsevier Ltd. All rights reserved.

关键词： Event-triggered adaptive control Synchronization Memristive neural networks Distributed delays Unbounded discrete delays

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Comprehensive analysis of immune-related lncRNAs and their clinical relevance in gastric adenocarcinoma

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FUNCTIONAL & INTEGRATIVE GENOMICS 2023年第1期23卷 28-28页

作者： Jin, Yan Ma, Jianfei Wang, Lidong Zhengzhou Univ State Key Lab Esophageal Canc Prevent & Treatment Zhengzhou 450052 Henan Peoples R China Zhengzhou Univ Affiliated Hosp 1 Henan Key Lab Esophageal Canc Res Zhengzhou 450052 Henan Peoples R China Xinxiang Med Univ Sch Basic Med Sci Dept Human Anat & Histoembryol Xinxiang 453003 Henan Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Informat Proc & Intelligent Control Luoyu Rd 1037 Wuhan 430074 Peoples R China

Increasing evidence has demonstrated that lncRNA plays a significant role in the immunity regulation of gastric adenocarcinoma. However, the immune-related lncRNAs and the prognostic value in immunotherapies remain largely unexplored. We collected immune-related lncRNA and the associated pathways of gastric cancer from the ImmLnc database. The cox regression model is used to analyze the prognostic value of these lncRNAs. Gastric cancer is further divided into different subtypes based on these lncRNAs. Tumor microenvironment analysis, functional enrichment analysis, and genomic alteration analysis are performed for different subtypes. Furthermore, chemotherapeutic and immunotherapeutic sensitivity are also analyzed among different subtypes. Nine lncRNAs are identified as significant regulators of the immune pathway of gastric cancer. Gastric cancer can be classified into 5 subtypes based on these lncRNAs. Tumor microenvironment analysis shows that cluster C3 has the highest immune score and C5 has the lowest score. Functional analysis shows that these subtypes are enriched with distinct biological processes. Genomic analysis shows that LAMA2 mutation is a protective factor in C3 but a risk factor in C5. Furthermore, these subtypes are found to respond distinctly to the same chemotherapeutic and immunotherapeutic drugs. In this study, we analyzed the immune-related lncRNA and identified the crucial role in the regulation of immune properties, biological processes, and immunotherapeutic sensitivity. These findings can improve our understanding of the epigenetic immunoregulation of lncRNA and advance the research of immunotherapy.

关键词： Gastric cancer lncRNA Immune Subtype Immunotherapy

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Design of In-Situ Learning Bidirectional Associative Memory Neural Network Circuit With Memristor Synapse

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IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE 2021年第5期5卷 743-754页

作者： Shi, Jichen Zeng, Zhigang Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Educ Minist China Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China

Memristor is considered as a promising synaptic device for neural networks because of its tunable and non-volatile resistance states, which is similar to the biological synapses. In this article, a novel network circuit based on memristor synapses is proposed for bidirectional associative memory with in-situ learning method. An analog neuron circuit is designed to emulate the cubic activation function of neural networks. A memristive synapse circuit is constructed to map both positive and negative weights on a single memristor. Moreover, an in-situ learning circuit fitting memristor's nonlinear characteristic is proposed. Feedback control strategy is incorporated in this learning circuit to adjust the resistance of the memristor and avoid the encoding error of the memristor's write voltage. The performance of the proposed network circuit is verified by the training and recalling simulations. The comparison between the proposed approach and related works is analyzed to demonstrate the advantage of the proposed circuit design.

关键词： Memristors Neurons Synapses Training Computer architecture Biological neural networks Integrated circuit modeling Artificial neural network bidirectional associative memory memristor synaptic weight circuit design

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Multidirectional Multidouble-Scroll Hopfield Neural Network With Application to image Encryption

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2025年第1期55卷 735-746页

作者： Zhang, Sen Chen, Chengjie Zhang, Yunzhen Cai, Jianming Wang, Xiaoping Zeng, Zhigang Nanjing Univ Informat Sci & Technol Sch Artificial Intelligence Nanjing 210044 Peoples R China Jiangsu Univ Technol Sch Elect & Informat Engn Changzhou 213001 Peoples R China Xuchang Univ Sch Informat Engn Xuchang 461000 Peoples R China Changzhou Univ Sch Microelect & Control Engn Changzhou 213000 Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Key Lab Image Proc & Intelligent Control Educ Minist China Wuhan 430074 Peoples R China

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.

关键词： Hopfield neural network image encryption initial offset boosting memristive synapse multidouble-scroll attractor

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High-level LoRA and hierarchical fusion for enhanced micro-expression recognition

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VISUAL COMPUTER 2025年第7期41卷 4533-4546页

作者： Shao, Zhiwen Cheng, Yifan Zhou, Yong Xiang, Xiang Li, Jian Liu, Bing Yeung, Dit-Yan China Univ Min & Technol Sch Comp Sci & Technol Xuzhou 221116 Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat MoE Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China Hong Kong Univ Sci & Technol Dept Comp Sci & Engn Kowloon Clear Water Bay Hong Kong 999077 Peoples R China Minist Educ Mine Digitizat Engn Res Ctr Xuzhou 221116 Peoples R China Tencent Inc Youtu Lab Shanghai 200233 Peoples R China

Micro-expression recognition (MER) remains challenging due to its subtle and fleeting nature. Existing methods often suffer from insufficient training data or rely on handcrafted features. Inspired by recent advancements in large language model fine-tuning and visual foundation models (VFMs), we propose HLoRA-MER, a novel framework that combines high-level low-rank adaptation (HLoRA) and a hierarchical fusion module (HFM). HLoRA fine-tunes the high-level layers of a VFM to capture facial muscle movement information, while HFM aggregates inter-frame and spatio-temporal features. Experiments on benchmark datasets demonstrate that HLoRA-MER outperforms state-of-the-art methods, achieving an F1-score of 84.24%\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$84.24\%$$\end{document} and 83.07%\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$83.07\%$$\end{document} on CASME II and SAMM, respectively, with only 197k trainable parameters. Our approach offers a promising solution for MER in both constrained and unconstrained scenarios. The code is available at https://***/CYF-cuber/HLoRA_MER_dinov2.

关键词： Micro-expression recognition High-level low-rank adaptation Hierarchical fusion

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Analysis of a new coupled hyperchaotic model and its topological types

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NONLINEAR DYNAMICS 2021年第2期105卷 1937-1952页

作者： Ming, Hao Hu, Hanping Zheng, Jun Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Minist Educ Key Lab Image Informat Proc & Intelligent Control Wuhan 430074 Peoples R China

In order to construct the high-dimensional discrete hyperchaotic systems systemically, this paper proposes a new coupled chaotic model. It has a wide chaotic parameter range and can relax the election of the coupling coefficient. Sufficient conditions are derived to prove the existence of Li-Yorke chaos in the proposed model. Meanwhile, the existence of hyperchaos is also demonstrated. To discern the effects of different coupling types on the chaotic dynamics more comprehensively, we further explore the dynamical behaviors with various coupling structures by using Lyapunov spectrum, bifurcation analysis, and phase portraits. We investigate the interaction relationship between coupled units and give suggestions for selecting coupling types. The results indicate that the coupled model has more complex and more stable chaotic performance when there exists a loop in its topological structure. Further, synchronization is also discussed in this work;analysis results illustrate that the proposed model cannot be suppressed to periodic points at sufficiently high coupling strengths. This paper suggests an effective method that may contribute to studying hyperchaos design and coupled chaotic systems.

关键词： Coupled chaos Hyperchaos Topological structure Chaos synchronization

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Synchronization of recurrent neural networks with unbounded delays and time-varying coefficients via generalized differential inequalities

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NEURAL NETWORKS 2021年 143卷 161-170页

作者： Zhang, Hao Zeng, Zhigang Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Educ Minist China Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China

In this paper, we revisit the drive-response synchronization of a class of recurrent neural networks with unbounded delays and time-varying coefficients, contrary to usual in the literature about time-varying neural networks, the signs of self-feedback coefficients are permitted to be indefinite or the time varying coefficients can be unbounded. A generalized scalar delay differential inequality considering indefinite self-feedback coefficient and unbounded delay simultaneously is established, which covers the existing result with bounded delay, the applicabilities of the sufficient conditions are discussed. Some novel criteria for network synchronization are then derived by constructing different candidate functions. These results have been improved in some aspects compared with the existing ones. Differential inequality in vector form is also derived to obtain a more refined synchronization criterion which removes some strong assumptions. Three examples are presented to verify the effectiveness and show the superiorities of our theoretical results. (C) 2021 Elsevier Ltd. All rights reserved.

关键词： Asymptotic synchronization Unbounded delay Time-varying neural networks Generalized delay differential inequality

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Gated PE-NL-MA: A multi -modal attention based network for video understanding

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NEUROCOMPUTING 2021年 423卷 148-157页

作者： Xie, Chengyang Wang, Xiaoping Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Minist China Key Lab Image Proc & Intelligent Control Educ Wuhan 430074 Peoples R China

In multi-modal learning tasks such as video understanding, the most important operations are feature extraction, feature enhancement for single modality and feature aggregation between modalities. In this paper, we present two attention based algorithms, the Position-embedding Non-local (PE-NL) Network and the Multi-modal Attention (MA) feature aggregation method. Inspired by Non-local Neural Networks and Transformers, our PE-NL is a self-attention liked feature enhancement operation and it can capture long-range dependencies and model relative positions. The MA aggregation method merges visual and audio modals while reduces feature dimension and the number of parameters without losing too much accuracy. Both of PE-NL and MA blocks can be plugged into many multi-modal learning architectures. Our Gated PE-NL-MA network achieves competitive results on Youtube-8M dataset. (c) 2020 Elsevier B.V. All rights reserved.

关键词： Neural network Multi-modal Attention mechanism Non-local Video understanding

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