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arXiv 2023年

作者： Li, Minghui Wang, Xianlong Yu, Zhifei Hu, Shengshan Zhou, Ziqi Zhang, Longling Zhang, Leo Yu School of Software Engineering Huazhong University of Science and Technology China School of Cyber Science and Engineering Huazhong University of Science and Technology China Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security China School of Computer Science and Technology Huazhong University of Science and Technology China School of Information and Communication Technology Griffith University Australia

Convolution-based unlearnable examples (UEs) employ class-wise multiplicative convolutional noise to training samples, severely compromising model performance. This fire-new type of UEs have successfully countered all defense mechanisms against UEs. The failure of such defenses can be attributed to the absence of norm constraints on convolutional noise, leading to severe blurring of image features. To address this, we first design an Edge Pixel-based Detector (EPD) to identify convolution-based UEs. Upon detection of them, we propose the first defense scheme against convolution-based UEs, COrrupting these samples via random matrix multiplication by employing bilinear INterpolation (COIN) such that disrupting the distribution of class-wise multiplicative noise. To evaluate the generalization of our proposed COIN, we newly design two convolution-based UEs called VUDA and HUDA to expand the scope of convolution-based UEs. Extensive experiments demonstrate the effectiveness of detection scheme EPD and that our defense COIN outperforms 11 state-of-the-art (SOTA) defenses, achieving a significant improvement on the CIFAR and ImageNet datasets. Copyright © 2023, The Authors. All rights reserved.

关键词： Convolution

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Research on Higher-Order Operator Transformation Algorithms for Syntax Transformation-Based Model Checking

Research on Higher-Order Operator Transformation Algorithms ...

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IEEE International Conference on Automation, Electronics and Electrical engineering (AUTEEE)

作者： Ji Liu Jie Liu Lingyun Luo Fei Yang Mingxing Liu Yu Tu School of Computer Science University of South China Hengyang Hunan China Hunan Engineering Research Center of Software Evaluation and Testing for Intellectual Equipment School of Computer Science University of South China Hengyang Hunan China China Nuclear Power Institute Research and Design Institute Chengdu Sichuan China

ISBN: (数字)9798350377033

ISBN: (纸本)9798350377040

Model checking technology addresses the limitations of traditional testing methods, which cannot cover all possible system states. Syntax transformation-based model checking methods convert source programs into equivalent forms and leverage existing model checking tools for verification, providing adaptability to various syntactic structures. The L2C language, widely used in safety-critical domains, lacks effective model checking support for its advanced features, particularly higher-order operators. To address this issue, this paper proposes a syntax transformation-based higher-order operator model checking method that converts L2C programs into equivalent Lustre programs. The method employs the ANTLR tool for lexical analysis, syntax analysis, abstract syntax tree generation, and simplification, enabling effective model checking of higher-order operators in the L2C language.

关键词： Computational modeling Semantics Symbols Syntactics Model checking Stability analysis Iterative algorithms Thermal stability Tree traversal Testing

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Video Stabilization via Prediction with Time-Series Network and Image Inpainting with Pyramid Fusion

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Chinese Journal of Electronics 2021年第6期30卷 1103-1110页

作者： CHENG Keyang LI Shichao RONG Lan WANG Wenshan SHI Wenxi ZHAN Yongzhao School of Computer Science and Communication Engineering Jiangsu University Jiangsu Province Big Data Ubiquitous Perception and Intelligent Agricultural Application Engineering Research Center Cyber Space Security Academy of Jiangsu University National Engineering Laboratory for Public Safety Risk Perception and Control by Big Data Xinjiang Lianhaichuangzhi Information Technology Co. Ltd.

Due to the poor filling effect of the video image defect commonly used in the video stabilization field, the video is seemed still unstable after the image stabilization process, which seriously affects the visual effect. To solve this problem, we improve a video stabilization method based on time-series network prediction and pyramid fusion restoration is proposed to optimize the visual effect after image *** flow of the proposed method is as follows: First,it is adaptive to determine whether the defect of the corresponding frame at the current time needs padding inpainting. Then, for the frame that needs to be inpainting,the frames generated before the current moment are sent to the model combining the convolutional neural networks and the gate recurrent unit to predict the part to be filled. Next the current defect image and the complete image to be filled are brought into the Laplacian pyramid reconstruction, and the improved weighted optimal suture is introduced for splicing during the fusion. Finally, the video frame is cut after reconstruction. The method is tested on a data set composed of videos commonly used in the field of video stabilization. The experimental results show that the average peak signal to noise ratio of the method is 2 to 5 d B higher than that of the comparison algorithm, and the average structural similarity index is improved by about 2% to 7% compared with the contrast algorithm.

关键词： image restoration image sequences video frame cutting improved weighted optimal suture gate recurrent unit video signal processing video stabilization method image fusion Laplacian pyramid reconstruction padding inpainting image resolution neural nets convolutional neural networks video image defect pyramid fusion restoration image inpainting visual effect optimization image reconstruction time-series network prediction image stabilization process

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Threshold voltage modulation in monolayer MoS_(2) field-effect transistors via selective gallium ion beam irradiation

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science China Materials 2022年第3期65卷 741-747页

作者： Baoshan Tang Yunshan Zhao Changjie Zhou Mingkun Zhang Huili Zhu Yida Li Jin Feng Leong Hao Shuai Hao Gong Weifeng Yang Department of Microelectronics and Integrated Circuit School of Electronic Science and EngineeringXiamen UniversityXiamen 361005China Department of Electrical and Computer Engineering National University of SingaporeSingapore 117576Singapore NNU-SULI Thermal Energy Research Center(NSTER)&Center for Quantum Transport and Thermal Energy Science(CQTES) School of Physics and TechnologyNanjing Normal UniversityNanjing 210023China Department of Physics School of ScienceJimei UniversityXiamen 361021China Engineering Research Center of Integrated Circuits for Next-Generation Communications Ministry of EducationSouthern University of Science and TechnologyShenzhen 518055China Department of Materials Science and Engineering National University of SingaporeSingapore 117575Singapore

Electronic regulation of two-dimensional(2 D)transition metal dichalcogenides(TMDCs)is a crucial step towards next-generation optoelectronics and ***,we demonstrate controllable and selective-area defect engineering in 2D molybdenum disulfide(MoS_(2))using a focused ion beam with a low-energy gallium ion(Ga^(+))*** find that the surface defects of MoS_(2)can be tuned by the precise control of ion energy and ***,the fieldeffect transistors based on the monolayer MoS_(2)show a significant threshold voltage modulation over 70 V after Ga+***-principles calculations reveal that the Ga impurities in the monolayer MoS_(2)introduce a defect state near the Fermi level,leading to a shallow acceptor level of 0.25 eV above the valence band *** defect engineering strategy enables direct writing of complex pattern at the atomic length scale in a controlled and facile manner,tailoring the electronic properties of 2D TMDCs for novel devices.

关键词： two-dimensional transition metal dichalcogenides field-effect transistors defect engineering Ga ion irradiation

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AI-Native Network Digital Twin for Intelligent Network Management in 6G

arXiv

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arXiv 2024年

作者： Wu, Wen Huang, Xinyu Luan, Tom H. The Frontier Research Center Peng Cheng Laboratory Guangdong Shenzhen China The Department of Electrical and Computer Engineering University of Waterloo WaterlooON Canada The School of Cyber Science and Engineering Xi'an Jiaotong University Xi’an China

As a pivotal virtualization technology, network digital twin is expected to accurately reflect real-time status and abstract features in the on-going sixth generation (6G) networks. In this article, we propose an artificial intelligence (AI)-native network digital twin framework for 6G networks to enable the synergy of AI and network digital twin, thereby facilitating intelligent network management. In the proposed framework, AI models are utilized to establish network digital twin models to facilitate network status prediction, network pattern abstraction, and network management decision-making. Furthermore, potential solutions are proposed for enhance the performance of network digital twin. Finally, a case study is presented, followed by a discussion of open research issues that are essential for AI-native network digital twin in 6G networks. Copyright © 2024, The Authors. All rights reserved.

关键词： Intelligent networks

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Adaptive Extensive Cancellation Algorithm and Harmonic Enhanced Heart Rate Estimation based on MMWave Radar

arXiv

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arXiv 2025年

作者： Tang, Hui Yang, Zhan Rong, Yu Chai, Li Engineering Research Center of Metallurgical Automation and Measurement Technology Wuhan University of Science and Technology Wuhan430081 China The School of Electrical Computer and Energy Engineering Arizona State University TempeAZ85281 United States College of Control Science and Engineering Zhejiang University Hangzhou310027 China

Heart rate (HR) monitoring is crucial for assessing physical fitness, cardiovascular health, and stress management. Millimeter-wave radar offers a promising non-contact solution for long-term monitoring. However, accurate HR estimation remains challenging in low signal-to-noise ratio (SNR) conditions. To deal with both respiration harmonics and intermodulation interference, this paper proposes a cancellation-before-estimation strategy. Firstly, we present the adaptive extensive cancellation algorithm (ECA) to suppress respiratory and its low-order harmonics. Then, we propose an adaptive harmonic enhanced trace (AHET) method to avoid intermodulation interference by refining the HR search region. Various experimental results validate the effectiveness of the proposed methods, demonstrating improvements in accuracy, robustness, and computational efficiency compared to conventional approaches based on the FMCW (Frequency Modulated Continuous Wave) system. © 2025, CC BY-NC-ND.

关键词： Intermodulation

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Research on Remote Sensing Image Matching Based on Improved SuperPoint

Research on Remote Sensing Image Matching Based on Improved ...

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Artificial Intelligence Innovation (ICAII), International Conference on

作者： Zhuo Wang Hongli Li Yang Liu Kaimin Sun Xuhui Chen Yuntao Wu School of Computer Science Engineering Wuhan Institute of Technology Wuhan China School of Computer Science and Engineering Wuhan Institute of Technology Wuhan China ChangJiang Waterway Bureau Survey Center Wuhan China State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan University Wuhan China

Image matching is a fundamental technology for data processing and analysis in remote sensing robots. To address the limitations of deep learning models in image matching, particularly the poor matching performance and limited number of matching points in remote sensing images, this paper presents an enhanced SuperPoint model based on deep learning. It incorporates a sub-model called DC-SuperPoint for feature point extraction and description. Within the feature extraction network, the CBAM (Convolutional Block Attention Module) attention mechanism is introduced to enhance the salience of point features in the graph. Additionally, the network extraction branch for feature points and descriptors is replaced with deformable convolution DCNV2 (Deformable Convolutional Networks v2), which is specifically designed to learn different types of features in various scenarios. Comparative experiments are conducted on two different datasets to evaluate the algorithm. The experimental results demonstrate that the proposed improved algorithm achieves a matching accuracy of 49.2% for the images, surpassing the original algorithm by 30% in terms of the number of matching points. Moreover, it exhibits better adaptability to different orientations compared to the original model. The accuracy is significantly improved to a certain extent, and the overall performance surpasses that of the original algorithm. These findings indicate that the proposed method effectively enhances matching efficiency and accuracy in scenarios with varying levels of complexity.

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RPRB-Net: A Sign Language Recognition Based on Multi-dimension Attentions

RPRB-Net: A Sign Language Recognition Based on Multi-dimensi...

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Machine Vision, Image Processing and Imaging Technology (MVIPIT), International Conference on

作者： Tian Yang Cong Shen Xinyue Wang Xiaoyu Ma School of Computer Science and Engineering Tianjin University of Technology and Engineering Research Center of Learning-Based Intelligent System Ministry of Education Tianjin P.R.China

ISBN: (数字)9798350306545

ISBN: (纸本)9798350306552

Sign Language Recognition (SLR) is the process of translating sign language into text or speech, which can also be viewed as a communication and expression tool between deaf individuals and hearing people. SLR using deep neural network has undergone significant development over the last decade. In this study, we design a feature-enhanced SLR technique based on serval spaces. To aid the network learning features from channel and spatial dimensions, we inject various attention mechanisms into the feature extractor backbone. As a result, we named our approach ResNet + Pyramid-Split-Attention + Recurrent-Criss-Cross-Attention + BiLSTM Network (RPRB-Net). Besides, we employ the BiLSTM architecture to encode high-level representations in temporal sequence. The network is ultimately trained using an end-to-end CTC alignment strategy. Extensive trials show that RPRB-Net achieves WERs of 3.5% and 23.6% on the USTC and RWTH-2014 datasets, respectively. Besides, it implies that RPRB-Net significantly surpasses the cutting-edge SLR approaches. Additionally, numerous ablation experiments have confirmed the efficiency of configuration.

关键词： Knowledge engineering Sign language Accuracy Image recognition Text recognition Machine vision Imaging

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A Few-Shot Network Flow Attack Classification via Graph Contrastive Learning

A Few-Shot Network Flow Attack Classification via Graph Cont...

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IEEE International Conference on Cyber Security and Cloud Computing (CSCloud)

作者： Binbin Gel Bo Li Xudong Mou Jun Zhao Xudong Liu School of Computer Science and Engineering Beihang University Beijing China Zhongguancun Laboratory Beijing China Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing Beijing China

ISBN: (数字)9798350376982

ISBN: (纸本)9798350376999

Accurately identifying network attacks is crucial for maintaining network security. However, these attacks are often hide within massive volumes of network traffic, posing significant challenges for traditional detection methods. Supervised learning approaches require substantial labeled data and struggle to adapt to unknown attack types, while unsupervised methods face difficulties in accurately pinpointing specific attack categories. To address these limitations, we propose a novel fewshot learning model for network flow attack classification based on graph contrastive learning. Our model leverages contrastive learning to enhance feature representation and generalization capabilities, enabling high-accuracy attack detection even with limited training data. Specifically, we first construct a multi- graph representation of network traffic and segment the data into snapshots. Then, we perform graph data augmentation within each snapshot to generate augmented sample pairs, which are used to pre-train the model via contrastive learning. Finally, we fine-tune the model parameters to achieve multi-class attack classification, leveraging the learned feature representations to identify various attack types, even those unseen during training. Experimental results demonstrate that our model exhibits excellent generalization ability and achieves high attack detection performance, even with limited training data.

关键词： Training Adaptation models Supervised learning Training data Contrastive learning Telecommunication traffic Data augmentation

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MSDAC: A multi-source domain adversarial framework for motion prediction in intracortical brain-computer interfaces

MSDAC: A multi-source domain adversarial framework for motio...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Haozhou Liu Banghua Yang Shouliang Guan Fenqi Rong Mengao Guo Ying Fang Bingyu Liu Yan Gao Yong Gu School of Mechatronic Engineering and Automation Research Center of Brain-Computer Engineering Shanghai University Shanghai China Lingang Laboratory Shanghai China National Center for Nanoscience and Technology Beijing China CAS Center for Excellence in Brain Science and Intelligence Technology Institute of Neurosciences Shanghai China

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

Intracortical brain-computer interfaces (iBCIs) restore motor function in patients with paralysis by converting neural activity into control signals for external devices. However, the frequent recalibration required by current decoding methods due to turnover and loss of recording neurons poses a challenge for achieving stable online decoding. To address these issues, we propose a multi-source domain adversarial classification (MSDAC) framework for cross-day decoding that utilizes an out-of-distribution (OOD) generalization approach. This framework divides the historical data into source domains by date and employs adversarial networks to minimize the distribution discrepancies among multiple source domains, thereby achieving robust domain-invariant characteristics and superior performance on unseen test data. The MSDAC framework was evaluated using five months of monkey center-out neural activity data and demonstrated exceptional performance. Without relying on test day data for model calibration or parameter updating, the MSDAC achieved an average decoding accuracy of 84.38% (day-5 to day-150, 27968 trials). These results underscore that the MSDAC-based decoding framework can be an ideal choice for establishing stable iBCI systems.

关键词： Accuracy Recurrent neural networks Neural activity Time series analysis Neurons Brain-computer interfaces Stability analysis Decoding Recording Paralysis

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