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arXiv 2024年

作者： Chen, Jian Huang, Rixing Zhao, Xueqian Fan, Qingxi Chang, Kan Zhang, Zhenrong Li, Guangyuan Guangxi Key Laboratory of Multimedia Communications and Network Technology School of Computer Electronics and Information Guangxi University Nanning530004 China School of Electrical Engineering Guangxi University Nanning530004 China CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Shenzhen College of Advanced Technology University of Chinese Academy of Sciences Shenzhen518055 China

The coupling between dual-band or multi-band quasi-bound states in the continuum (q-BICs) is of great interest for their rich physics and promising applications. Here, we report tunable collective electromagnetic induced transparency-like (EIT-like) phenomenon due to coupling between dual-band collective electric dipolar and magnetic quadrupolar q-BICs, which are supported by an all-dielectric metasurface composed of periodic tilted silicon quadrumers. We show that this collective EIT-like phenomenon with strong slow light effect can be realized by varying the nanodisk diameter or the tilt angle, and that the transparency window wavelength, the quality factor, and the group index can all be tuned by changing the nanodisk size. We further find that as the nanodisk size decreases, the slow light effect becomes stronger, and higher sensitivity can be obtained for the refractive index sensing. Interestingly, the sensitivity first increases exponentially and then reaches a plateau as the nanodisk size decreases, or equivalently as the group index increases. We therefore expect this work will advance the understanding of the collective EIT-like effect due to coupling between q-BICs, and the findings will have potential applications in slow-light enhanced biochemical sensing. © 2024, CC0.

关键词： Refractive index

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A differential public PUF design for lightweight authentication 16

A differential public PUF design for lightweight authenticat...

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2021 International Conference on Synthesis, Modeling, Analysis and Simulation Methods and Applications to Circuit Design, SMACD 2021 and 16th Conference on PhD Research in Microelectronics and Electronics, PRIME 2021

作者： Duan, Shengyu Sai, Gaole School of Computer Engineering and Science Shanghai University Shanghai200444 China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences China Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology China

ISBN: (纸本)9783800755899

Physical Unclonable Functions (PUFs) have emerged as a promising primitive to provide a hardware keyless security mechanism for integrated circuit applications. Public PUFs (PPUFs) address the crucial PUF vulnerability of requiring databases to store the secrete reference information. This paper investigates one of the PPUF families, differential PPUF (dPPUF), which exploits complexly interacted logic to create time gap between actual execution and simulation, and to prevent simulation attacks. Conventional dPPUFs have high costs. We observe the repressers have large impact on simulation time, but take a great proportion in current dPPUF structure. We thereby present a represser network with specially designed interconnection and shared logic, to maintain a high circuit complexity and to reduce the area cost. We show the proposed dPPUF has similar resilience over simulation attacks as the conventional design. Area cost can be reduced by up to 44%, and thus the proposed design can be used for lightweight authentication. © VDE VERLAG GMBH.

关键词： Authentication

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A population randomization-based multi-objective genetic algorithm for gesture adaptation in human-robot interaction

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Science China(Information Sciences) 2021年第1期64卷 188-200页

作者： Luefeng CHEN Wanjuan SU Min LI Min WU Witold PEDRYCZ Kaoru HIROTA School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Department of Electrical and Computer Engineering University of Alberta Tokyo Institute of Technology

In recent years, vision-based gesture adaptation has attracted great attention from many experts in the field of human-robot interaction, and many methods have been proposed and successfully applied, such as particle swarm optimization and genetic algorithm. However, the reduction of the error and energy consumption of a robot while paying attention to more subtle attitude changes is very important and *** view of these problems, we propose a population randomization-based multi-objective genetic *** gesture signal is processed with a slight change by imitating the biological evolution mechanisms. In the proposed algorithm, a random out-of-order matrix is added in the process of population evolution synthesis to prevent the premature grouping convergence of the new population. The weights of the objective function and the elite retention strategy are adopted, and the most adaptable individuals in each generation are inherited directly in the next generation without any recombination or mutation. To verify the effectiveness of the algorithm, preliminary application experiments are performed on the gesture adaptation of a robotic arm. The results are compared with the original signal, and the comparison shows that by using the proposed method, the energy consumption is reduced, and the end error is decreased to less than 3 mm while ensuring the tracking effect of the robotic arm. These obtained results meet the communication requirements for human-robot interactions such as handshakes. Moreover, the proposed method has better performance, uses less energy, and has a smaller tracking error than the particle swarm optimization, the single-objective genetic algorithm, and the traditional multi-objective genetic algorithm. A preliminary application experiment indicates that the robotic arm can adapt to human gestures in real time.

关键词： multi-objective genetic algorithm gesture adaptation human-robot interaction

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Hierarchy-Aware and Channel-Adaptive Semantic Communication for Bandwidth-Limited Data Fusion

arXiv

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arXiv 2025年

作者： Guo, Lei Chen, Wei Sun, Yuxuan Ai, Bo Pappas, Nikolaos Quek, Tony State Key Laboratory of Advanced Rail Autonomous Operation Beijing Jiaotong University China School of Electronic and Information Engineering Beijing Jiaotong University Beijing China Department of Computer and Information Science Linköping University Linköping Sweden Information Systems Technology and Design Singapore University of Technology and Design Singapore

Obtaining high-resolution hyperspectral images (HR-HSI) is costly and data-intensive, making it necessary to fuse low-resolution hyperspectral images (LR-HSI) with high-resolution RGB images (HR-RGB) for practical applications. However, traditional fusion techniques, which integrate detailed information into the reconstruction, significantly increase bandwidth consumption compared to directly transmitting raw data. To overcome these challenges, we propose a hierarchy-aware and channel-adaptive semantic communication approach for bandwidth-limited data fusion. A hierarchical correlation module is proposed to preserve both the overall structural information and the details of the image required for super-resolution. This module efficiently combines deep semantic and shallow features from LR-HSI and HR-RGB. To further reduce bandwidth usage while preserving reconstruction quality, a channel-adaptive attention mechanism based on Transformer is proposed to dynamically integrate and transmit the deep and shallow features, enabling efficient data transmission and high-quality HR-HSI reconstruction. Experimental results on the CAVE and Washington DC Mall datasets demonstrate that our method outperforms single-source transmission, achieving up to a 2 dB improvement in peak signal-to-noise ratio (PSNR). Additionally, it reduces bandwidth consumption by two-thirds, confirming its effectiveness in bandwidth-constrained environments for HR-HSI reconstruction tasks. Copyright © 2025, The Authors. All rights reserved.

关键词： Bandwidth

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HandDGCL: Two-hand 3D reconstruction based disturbing graph contrastive learning

HandDGCL: Two-hand 3D reconstruction based disturbing graph ...

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作者： Han, Bing Yao, Chao Wang, Xiaokun Chang, Jian Ban, Xiaojuan Beijing Advanced Innovation Center for Materials Genome Engineering Institute of Artificial Intelligence University of Science and Technology Beijing Beijing China Shunde Innovation School University of Science and Technology Beijing Foshan China School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China School of Intelligence Science and Technology University of Science and Technology Beijing Beijing China Bournemouth University Fern Barrow Dorset Poole United Kingdom Key Laboratory of Intelligent Bionic Unmanned Systems Ministry of Education University of Science and Technology Beijing Beijing China

Virtual reality (VR) and augmented reality (AR) applications are becoming increasingly prevalent. However, constructing realistic 3D hands, especially when two hands are interacting, from a single RGB image remains a major challenge due to severe mutual occlusion and the enormous diversity of hand poses. In this article, we propose a disturbing graph contrastive learning strategy for two-hand 3D reconstruction. This involves a graph disturbance network designed to generate graph feature pairs to enhance the consistency of the two-hand pose features. A contrastive learning module leverages high-quality generative features for a strong feature expression. We further propose a similarity distinguish method to divide positive and negative features for accelerating the model convergence. Additionally, a multi-term loss is designed to balance the relation among the hand pose, the visual scale and the viewpoint position. Our model has achieved state-of-the-art results in the InterHand2.6M benchmark. Ablation studies show the model's great ability to correct unreasonable hand movements. In subjective assessments, our graph disturbance learning method significantly improves the construction of realistic 3D hands, especially when two hands are interacting. © 2023 John Wiley & Sons, Ltd.

关键词： Image reconstruction

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CEBRA Method: Decoding Brain Activity for advanced Brain-computer Interface Technology 46

CEBRA Method: Decoding Brain Activity for Advanced Brain-Com...

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46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Yang, Jingcheng Kulwa, Frank Liu, Xuanwei Lu, Yiqing Fu, Yufa Li, Guanglin Huai, Yaping Zhang, Xin Li, Yongcheng Shenzhen Dapeng New District Nanao People's Hospital Guangdong Shenzhen518000 China Shenzhen Longhua District Central Hospital Department of Rehabilitation Medicine Guangdong Shenzhen518000 China Kunming University of Science and Technology Faculty of Information Engineering and Automation Kunming650500 China Kunming University of Science and Technology Brain Cognition and Brain-Computer Intelligence Integration Group Kunming650500 China Cas Key Laboratory of Human-Machine Intelligence Synergy Systems Shenzhen518055 China University of Chinese Academy of Sciences Shenzhen College of Advanced Technology Shenzhen518055 China Shandong Zhongke Advanced Technology Co. Ltd. Jinan250014 China

ISBN: (纸本)9798350371499

The emerging neurorehabilitation technology, Brain-computer Interface (BCI), provides a novel prospect for stroke recovery. However, decoding brain activity during the movement present substantial challenges, and feature extraction is crutial to build a better decoder. In this study the CEBRA method were employed to extract features first based on electroencephalogram(EEG) data during Motor Execution(ME) and Motor lmagery (Ml) tasks for 20 participants (including 10 stroke patients). The results revealed that, in MI tasks, CEBRA-RF (Random Forests) achieved an average classification accuracy of 91.33%, with an average F1-score of 91.19%, and CEBRA-SVM (Support Vector Machine) achieved an average classification accuracy of 91.32%, with an average F1-score of 90.83%. Compared to other conventional feature extraction methods, CEBRA shows significantly higher accuracy (t-test, p © 2024 IEEE.

关键词： Patient rehabilitation

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IRS-Enhanced Anti-Jamming Precoding Against DISCO Physical Layer Jamming Attacks

IRS-Enhanced Anti-Jamming Precoding Against DISCO Physical L...

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IEEE International Conference on Communications (ICC)

作者： Huan Huang Hongliang Zhang Yi Cai Yunjing Zhang A. Lee Swindlehurst Zhu Han Electronic Information School Soochow University Suzhou China State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Peking University Beijing China Department of Electrical and Computer Engineering University of Houston Houston USA Center for Pervasive Communications and Computing University of California Irvine USA

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

Illegitimate intelligent reflective surfaces (IRSs) can pose significant physical layer security risks on multi-user multiple-input single-output (MU-MISO) systems. Recently, a DISCO approach has been proposed an illegitimate IRS with random and time-varying reflection coefficients, referred to as a “disco” IRS (DIRS). Such DIRS can attack MU-MISO systems without relying on either jamming power or channel state information (CSI), and classical anti-jamming techniques are in-effective for the DIRS-based fully-passive jammers (DIRS-based FPJs). In this paper, we propose an IRS-enhanced anti-jamming precoder against DIRS-based FPJs that requires only statistical rather than instantaneous CSI of the DIRS-jammed channels. Specifically, a legitimate IRS is introduced to reduce the strength of the DIRS-based jamming relative to the transmit signals at a legitimate user (LU). In addition, the active beamforming at the legitimate access point (AP) is designed to maximize the signal-to-jamming-plus-noise ratios (SJNRs). Numerical results are presented to evaluate the effectiveness of the proposed IRS-enhanced anti-jamming precoder against DIRS-based FPJs.

关键词： Array signal processing Precoding Computational modeling Interference MISO communication Physical layer security Reflection coefficient

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On Improving Boundary Quality of Instance Segmentation in Cluttered and Chaotic Scenarios

On Improving Boundary Quality of Instance Segmentation in Cl...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Biqi Yang Xiaojie Gao Xianzhi Li Yun-Hui Liu Chi-Wing Fu Pheng-Ann Heng Department of Computer Science and Engineering The Chinese University of Hong Kong Department of Mechanical and Automation Engineering The Chinese University of Hong Kong School of Computer Science and Technology Huazhong University of Science and Technology Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Chinese Academy of Sciences Shenzhen Institutes of Advanced Technology

Instance segmentation is a long-standing task for supporting robotic bin picking. However, objects of diverse classes can be closely packed with occlusions in cluttered and chaotic scenes, hence, even recent methods could have difficulty in locating clear and precise boundaries to distinguish nearby objects. In this work, we aim to improve the boundary quality of the instance masks for robust and precise instance segmentation in these challenging scenarios. Technical-wise, we first formulate an IoU-based Boundary-aware Mask head (IBM head) for predicting the instance-level mask, boundary, and their corresponding IoU scores. With this core module, we then follow the coarse-to-fine strategy and design our pipeline with two stages: an 1IoUNet to learn localization-based objectness cue and a hierarchical mask refiner to produce sharper and cleaner boundaries. We deploy the IBM head throughout the framework. Extensive experimental results on three grasping benchmarks manifest that our method attains the best instance segmentation performance, compared with the state-of-the-art approaches. Practically, we conduct real-world picking tests to show that with the objectness and boundary IoU scores as guidance, we are able to filter invalid (occluded) instances and select high-fidelity (exposed) instances for grasping.

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Super-resolution of Fisheye Rectified Image based on Deep Multi-Path Cascaded Network

Super-resolution of Fisheye Rectified Image based on Deep Mu...

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International Conference on Big Data and Information Analytics (BigDIA)

作者： Weili Li Zhe Li Haoming Wang Xiaoqing Yin YiQin Ma Science and Technology on Information Systems Engineering Laboratory National University of Defense Technology Changsha China College of Computer Science National University of Defense Technology Changsha China College of Advanced Interdisciplinary Studies National University of Defense Technology Changsha China

Fisheye imaging technique plays an important role in various areas. However, fisheye lenses can cause severe image distortion, which requires distortion rectification for these images. However, after distortion rectification, the images will suffer from the problem of resolution reduction, and the degree of resolution reduction varies in different regions. This problem will directly affect the subsequent process of image content analysis. Therefore, it is needed to adopt a super-resolution reconstruction algorithm to enhance the resolution of the fisheye rectified image. This paper designs a multi-path cascaded deep network which performs super-resolution reconstruction of fisheye rectified images. Through the multi-path deep network structure, the features of the fisheye rectified image are learned from multiple aspects at the same time, and the residual learning approach is introduced to further diminish the difficulty of network training and improve the super-resolution results. The mean square error loss is used to supervise the training process of the proposed deep work. The super-resolution reconstruction model is obtained which takes the input low-resolution fisheye rectified image as input and generate the super-resolution result. The results of super-resolution reconstruction experiments demonstrate that the proposed multi-path cascaded deep network can effectively enhance the resolution of fisheye rectified images, and can provide higher quality images for fisheye image analysis.

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Vulnerability Assessment and Optimization of Urban Rail systems Against Extreme Perturbations: A Case Study on the Zhengzhou Metro Flooding

SSRN

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SSRN 2024年

作者： Liu, Zehai Yin, Jiateng Liu, Ronghui D’Ariano, Andrea State Key Laboratory of Advanced Rail Autonomous Operation Beijing Jiaotong University China School of Systems Science Beijing Jiaotong University 100044 China Institute for Transport Studies University of Leeds United Kingdom Department of Civil Computer Science and Aeronautical Technologies Engineering Rome Tre University Italy

With the rapid development of urban rail networks in recent years, these complex transportation systems are facing significant challenges in both preparing for and recovering from the growing impact of extreme perturbations caused by various factors such as extreme weather, equipment faults, and security threats. Assessing and improving the vulnerability of urban rail systems to perturbations is crucial for ensuring efficient and safe mobility services for passengers. While most existing studies have focused on the vulnerability assessment of physical transportation networks from the perspective of infrastructures, our study aims to investigate the vulnerability of urban rail systems to extreme perturbations from the perspective of rail managers. Specifically, for the assessment of system vulnerability, we first construct a graph model with the definition of nodes, edges, and edge weights using a hybrid DEMATEL-ISM-ANP method to capture the causal relationships among different factors leading to perturbations, such as technical system factors, organizational factors, and human factors. Then, for the optimization of system vulnerability, we develop a mixed integer programming (MIP) formulation based on the constructed graph model to identify the most vulnerable factors against perturbations. We implement our approach in a case study of the Zhengzhou metro flooding disaster, which occurred on Zhengzhou metro line 5 on July 20, 2021, resulting in the death of 14 passengers. The results indicate that our approach can correctly identify the factors that significantly influence system vulnerability in the Zhengzhou metro flooding. Our findings support that the Zhengzhou metro flooding disaster was primarily caused by human factors, such as the failure of emergency commands from dispatchers. We also conduct experiments to draw valuable insights for early warnings of urban rail systems against extreme perturbations. © 2024, The Authors. All rights reserved.

关键词： Disasters

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