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Combining intrinsic and sliding-induced polarizations for multistates in two-dimensional ferroelectrics

Physical Review B 2025年第8期111卷 L081407-L081407页

作者： Chuhan Tang Zhiqiang Tian Tao Ouyang Anlian Pan Mingxing Chen Key Laboratory for Matter Microstructure and Function of Hunan Province Key Laboratory of Low-Dimensional Quantum Structures and Quantum Control of Ministry of Education School of Physics and Electronics Hunan Normal University Changsha 410081 China Hunan Key Laboratory for Micro-Nano Energy Materials and Device and School of Physics and Optoelectronics Xiangtan University Hunan Xiangtan 411105 China Key Laboratory for Micro-Nano Physics and Technology of Hunan Province College of Materials Science and Engineering Hunan University Changsha 410082 China State Key Laboratory of Powder Metallurgy Central South University Changsha 410083 China

Going beyond the bistability paradigm of the charge polarization in ferroelectrics is highly desired for ferroelectric (FE) memory devices toward ultrahigh-density information storage. Here, we propose to build multistates by combining the intrinsic and sliding-induced polarizations. The physics is that there is at least one order of magnitude difference in the energy barriers between these two types of polarizations, which leads to a significant difference in the electric fields for reversing the polarization. This difference, along with the symmetry breaking, allows for a unique flipping mechanism involving layer-by-layer sliding followed by layer-by-layer flipping during the transformation of the multistates. As a result, six and ten switchable states can be achieved for the 1T″ bilayers and trilayers, respectively. We further illustrate the concept in H-stacking bilayers and trilayers of 1T″ transition-metal dichalcogenides by first-principles calculations. Our study provides a route to design polarization multistates for developing next-generation memory devices.

关键词： Ferroelectricity

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TransRain: A Hybrid Deep Learning and Image Registration Approach for Accurate Satellite Precipitation Estimation with Ground-Based Validation 4

TransRain: A Hybrid Deep Learning and Image Registration App...

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4th International Computational Imaging Conference, CITA 2024

作者： Guo, Yanan Cao, Xiaoqun Zhou, Mengge College of Computer Science National University of Defense Technology Hunan Changsha China College of Meteorology and Oceanography National University of Defense Technology Hunan Changsha China

ISBN: (纸本)9781510688834

To make full use of multi-source precipitation observation information and to solve the current problem of low accuracy of precipitation estimation using satellite images, a new satellite precipitation estimation method is proposed. In this study, we construct a new image registration algorithm based on Transformer and convolutional neural network to fuse rain gauge measurements with satellite image data to reduce errors in satellite data. The spatial correspondence between moving and fixed images are accurately mined by Transformer to enhance feature extraction of precipitation areas and effectively compensate for the lack of trace precipitation information. The convolution layer is also used to effectively capture local information, which results in a high-resolution feature map and enables the model to obtain richer features, thus improving the estimation of quantitative precipitation. Experimental results confirm the effectiveness of the method in satellite precipitation image registration. The quantitative analysis shows that the algorithm effectively improves the precipitation estimation accuracy and can achieve efficient and accurate precipitation estimation, which is of great value to the research of extreme precipitation estimation. © 2025 SPIE.

关键词： Satellite imagery

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Understanding Deep Learning RF Wireless Side-Channel Attacks: Perspectives on Noisy Scenarios 25

Understanding Deep Learning RF Wireless Side-Channel Attacks...

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Proceedings of the 2025 4th International Conference on Cryptography, Network Security and Communication technology

作者： Bijia Cao Yuanyuan Chen Xiaoxia Wang Qixiang Ouyang Huanyu Wang Junnian Wang School of Physics and Electronics Hunan University of Science and Technology Xiangtan Hunan China School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan Hunan China

ISBN: (纸本)9798400712623

Recently developed Radio Frequency Wireless Side-Channel Attacks (RF-WSCAs) have become a significant threat to the widespread deployment of IoT edge devices as they eliminate the need for physical proximity to the target. By analyzing side-channel emissions from mixed-signal chips (unintentionally coupled with radio transmission signals), attackers can compromise cryptographic implementations over long distances. Existing work demonstrates the feasibility of extracting AES keys from Bluetooth devices at a distance of 15-metres using CNN models trained on traces. However, they may overestimate the threat of RF WSCAs by ignoring the effects caused by ambient noise and interference. In this study, we conduct a detailed examination of the extent to which environmental noise and interference can affect the attack efficiency of RF-WSCAs. We first proposed a 20 input CNN architecture that achieved similar attack results to the SOTA model. Then, we conducted a comprehensive analysis of RF-WSCAs from the perspective of noisy scenarios.

关键词： AES

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Privacy-Preserving Publishing Mechanism for Real-Time Vehicle Trajectory in Vehicle-to-Grid Networks

Privacy-Preserving Publishing Mechanism for Real-Time Vehicl...

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Power Electronics, computer Applications (ICPECA), IEEE International Conference on

作者： Yulong Wang Jianbo Xu Bo Gong School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan China

ISBN: (数字)9798331533694

ISBN: (纸本)9798331533700

The Vehicle-to-Grid (V2G) network is a smart grid technology generated under the background of the rapid development of new energy technology, which allows mobile energy storage vehicles (MESVs) to realize bidirectional energy exchange with the grid system. As distributed energy storage units within the V2G network, MESVs help meet the grid's load-balancing demands. To accurately schedule grid loads, V2G networks rely on MESVs' real-time trajectory data. After collecting this data, the central authority (CA) publishes it to the grid enterprise (GM) for load-balancing decisions. However, before publication, the CA must process the data to protect the location privacy of MESVs and prevent GM from extracting sensitive information. To address this privacy protection challenge during data publication, we propose a differential privacy-based mechanism. Our mechanism uses the unscented Kalman filter to predict and correct the published data at the sampling point and intelligently adjusts the sampling interval. Additionally, we introduce a privacy budget allocation method based on regional access frequency and travel time, enhancing privacy protection of trajectory in high-access areas and peak hours. Through security analysis and experimental validation, our mechanism effectively preserves the privacy of real-time vehicle trajectory data while ensuring data validity.

关键词： Vehicle-to-grid Data privacy Privacy Publishing Real-time systems Trajectory Kalman filters Security Protection Radio spectrum management

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RDP: Ranked Differential Privacy for Facial Feature Protection in Multi-Scale Sparsified Subspaces

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IEEE Internet of Things Journal 2025年第11期12卷 17333-17348页

作者： Ou, Lu Liao, Shaolin Gao, Shihui Huang, Guandong Qin, Zheng Hunan University School of Journalism and Communication Hunan Changsha China Sun Yat-sen University Guangdong Guangzhou China Illinois Institute of Technology ChicagoIL United States Purdue University West LafayetteIN United States Hunan University College of Computer Science and Electronic Engineering Hunan Changsha China State Grid Hunan Materials Company Changsha China

With the widespread sharing of personal face images on the Internet, systems based on face recognition encounter the real threat of being breached by potential adversaries who are able to access individuals' face images and use them to intrude the systems. In this paper, we propose a novel privacy protection method in the sparsified multi-scale feature subspaces to protect sensitive facial features, taking care of the influence or weight-ranked subspace coefficients on the privacy budget, named "Ranked Differential Privacy (RDP)". After the multi-scale subspaces' decomposition, the lightweight Laplacian noise is added to the dimension-reduced sparsified subspaces' coefficients according to the geometric superposition method. Then, we rigorously prove that the RDP satisfies ϵ0-Differential Privacy. After that, the non-linear Lagrange multiplier (LM) method is formulated for the constraint optimization problem of maximizing the utility of protected face images of high visualization quality with sanitizing noise, under a given privacy budget ϵ0. Then, two methods are proposed to solve the nonlinear LM problem and obtain the optimal noise scale parameters: 1) the analytical Normalization Approximation (NA) method with identical average noise scale parameter for real-time online applications;and 2) the LM optimization method via Gradient Descent (LMGD) to obtain the nonlinear solution through iterative updating for more accurate offline applications. Experimental results on two real-world datasets show that our proposed RDP outperforms other state-of-the-art methods: at a privacy budget of ϵ0=0.2, the peak signal-to-noise ratio (PSNR) of the optimized RDP is about ~10 dB higher than (10 times as high as) the highest PSNR of all state-of-the-art methods compared. © 2014 IEEE.

关键词： Differential privacy

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Towards intelligent and trustworthy task assignments for 5G-enabled industrial communication systems

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Digital Communications and Networks 2025年第1期11卷 246-255页

作者： Mingfeng Huang Anfeng Liu Neal N.Xiong Athanasios V.Vasilakos School of Computer and Communication Engineering Changsha University of Science&TechnologyChangsha410114China School of Electronic Information Central South UniversityChangsha410083China Department of Computer Science and Mathematics Sul Ross State UniversityAlpineTX 79830USA College of Mathematics and Computer Science Fuzhou UniversityFuzhou350116China Center for AI Research University of AgderGrimstad 999026Norway

With the unprecedented prevalence of Industrial Internet of Things(IIoT)and 5G technology,various applications supported by industrial communication systems have generated exponentially increased processing tasks,which makes task assignment inefficient due to insufficient *** this paper,an Intelligent and Trustworthy task assignment method based on Trust and Social relations(ITTS)is proposed for scenarios with many tasks and few ***,ITTS first makes initial assignments based on trust and social influences,thereby transforming the complex large-scale industrial task assignment of the platform into the small-scale task assignment for each ***,an intelligent Q-decision mechanism based on workers'social relation is proposed,which adopts the first-exploration-then-utilization principle to allocate *** when a worker cannot cope with the assigned tasks,it initiates dynamic worker recruitment,thus effectively solving the worker shortage problem as well as the cold start *** importantly,we consider trust and security issues,and evaluate the trust and social circles of workers by accumulating task feedback,to provide the platform a reference for worker recruitment,thereby creating a high-quality worker ***,extensive simulations demonstrate ITTS outperforms two benchmark methods by increasing task completion rates by 56.49%-61.53%and profit by 42.34%-47.19%.

关键词： Industrial Internet of Things Insufficient workers Trust evaluation Social relation Task assignment

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ADA-FInfer: Inferring Face Representations From Adaptive Select Frames for High-Visual-Quality Deepfake Detection

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IEEE Transactions on Dependable and Secure Computing 2025年第3期22卷 3011-3027页

作者： Hu, Juan Liang, Jinwen Qin, Zheng Liao, Xin Zhou, Wenbo Lin, Xiaodong Hunan University College of Computer Science and Electronic Engineering Changsha410012 China The Hong Kong Polytechnic University Department of Computing Hong Kong University of Science and Technology of China CAS Key Laboratory of Electromagnetic Space Information Anhui 230026 China University of Guelph School of Computer Science GuelphONN1G 2W1 Canada

Interpretable deepfake detection is gaining attentionfor providing explainable, trustworthy results, avoiding the limitations of ‘black-box’ models. Current interpretable methods focus on visible artifacts in low-visual-quality deepfakes, but theseartifacts become less apparent in high-visual-quality deepfakesgenerated by advanced models. With advancements in deep generative models, producing high-visual-quality deepfakes has become a strategy to evade detection. To address this, we proposeADA − FInfer, an adaptive frame selection and interpretable facerepresentation inference method for detecting high-visual-qualitydeepfakes. ADA − FInfer adaptively selects frames by analyzingoptical flow to reveal manipulations. We also introduce an adaptiveattack method that manipulates specific frames, and our adaptiveselection strategy shows resistance to such attacks. ADA − FInferuses an encoder to learn face representations from source and targetfaces, applying a representation-prediction loss to maximize the distinction between real and fake videos. To provide further insights,we employ the joint entropy, mutual information, and conditionalentropy analyses to explain the method’s effectiveness. Extensiveexperiments and ablation studies demonstrate that ADA − FInferachieves promising performance in detecting high-visual-qualitydeepfakes. © 2004-2012 IEEE.

关键词： Face recognition

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MCFusion: Frequency Domain Characteristics Enhancement and Feature Compensation Fusion Network for RGB-T Object Detection

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IEEE Sensors Journal 2025年第11期25卷 20880-20893页

作者： Gao, Yinbo Liao, Zhuhua Liu, Yizhi Yi, Aiping Zhang, Guoqiang Hunan University of Science and Technology China Hunan University of Science and Technology Department of Computer Science and Engineering Xiangtan China Hainan Normal University Department of Information Science and Technology Haikou China

The multi-modal object detection technology based on visible-thermal vision sensors has drawn significant attention as it is capable of achieving reliable object detection in complex scenes with challenging lighting conditions such as low light or backlight. However, there has been a lack of focus on the frequency domain feature information of the visible and thermal modalities themselves, as well as the complementarity of cross-modal features. Furthermore, current visible and thermal feature fusion methods only utilize feature information from the current layer, neglecting context information. Therefore, this paper proposes a novel network framework for enhancing frequency domain Characteristics and fusing cross-layer and cross-modal features to compensate for these limitations. This framework introduces two key modules: the Frequency Domain Characteristics Enhancement (FCE) module and the Cross-Layer and Cross-Modal Feature Compensation Fusion (CCF) module. The FCE module consists of two sub-modules. Reduce high-frequency information loss(FCE-RHFL) module operates on the visible modality to reduce high-frequency information loss, utilizing methods such as high-frequency masking and frequency recombination. Meanwhile, Enhance high-frequency information representation(FCE-EHFR) module enhances high-frequency information representation for the thermal modality through convolutions with different kernels and frequency domain enhancement techniques. In the CCF module, cross-modal and cross-layer feature compensation methods are employed to compensate for differences in modalities, followed by capture complementary information across modalities using a query-guided cross-attention mechanism. Finally, we conduct experimental comparisons on the KAIST and FLIR datasets, and the experimental results demonstrate that our method has excellent performance and robust detection results. © 2001-2012 IEEE.

关键词： Frequency domain analysis

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The health prediction of assembly robot based on feature fusion and weighted mahalanobis distance

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Reliability engineering and System Safety 2025年 262卷

作者： Guibing, G.A.O. Cao, Mingyu Jun, W.A.N.G. College of mechanical and Electrical Engineering Hunan University of Science and Technology Xiangtan411201 China

Predicting the health status of assembly robots serves as an effective means to enhance the reliability of manufacturing systems. To precisely predict the health status of robots and thereby improve the safety and reliability of manufacturing systems, a health status prediction method for assembly robots based on feature fusion and weighted Mahalanobis distance(WMD) is *** the issues of slow processing speed and suboptimal feature fusion in robot high-dimensional monitoring data processing with SAE, a method of optimizing the hyperparameters and selecting the appropriate loss function of SAE is employed to achieve the deep fusion of features of robot monitoring data, and the manifold learning method is incorporated to select the health status-sensitive *** address the limitations of existing robot health state prediction models, particularly the risk of false "healthy" predictions caused by the complexity of numerous monitored parameters, the Cox-Box transformation is applied to the WMD of sensitive features. A robot health state monitoring model is then designed based on the transformed WMD, enabling more accurate and reliable monitoring of robot health status. An assembly robot on a production line is used as an example to demonstrate the effectiveness of the proposed method in monitoring robot health status. © 2025 Elsevier Ltd

关键词： Robotic assembly

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On the Zero Coprime Equivalence of Multivariate Polynomial Matrices

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Wuhan university Journal of Natural sciences 2025年第1期30卷 32-42页

作者： CHEN Zuo LI Dongmei GUO Xu School of Computer Science and Engineering Hunan University of Science and TechnologyXiangtan 411201HunanChina School of Mathematics and Statistics Hunan University of Science and TechnologyXiangtan 411201HunanChina

The zero coprime system equivalence is one of important research in the theory of multidimensional system equivalence,and is closely related to zero coprime equivalence of multivariate polynomial *** first discuss the relation between zero coprime equivalence and unimodular equivalence for polynomial ***,we investigate the zero coprime equivalence problem for several classes of polynomial matrices,some novel findings and criteria on reducing these matrices to their Smith normal forms are ***,an example is provided to illustrate the main results.

关键词： multidimensional system multivariate polynomial matrix zero coprime equivalence unimodular equivalence Smith normal form

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