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IEEE Open Journal of Antennas and Propagation 2021年 2卷 676-678页

作者： Feng, Naixing Zhang, Yuxian Zeng, Qingsheng Tong, Mei Song Joines, William T. Wang, Guo Ping Institute of Microscale Optoelectronics Shenzhen University Shenzhen China College of Astronautics Nanjing University of Aeronautics and Astronautics Nanjing China Key Laboratory of Embedded System and Service Computing of the Ministry of Education College of Electronics and Information Engineering Tongji University Shanghai China Department of Electrical and Computer Engineering Duke University DurhamNC United States

In this response letter, we focus on addressing Yu Hang Yang's comments so that the correctness and validity of the proposed auxiliary-differential-equation-based (ADE) Crank-Nicolson finite-difference time-domain method with direct-splitting technique (CNDS-FDTD) are demonstrated. Moreover, related applications in modeling 2D material nanostructure problems are shown. © 2020 IEEE.

关键词： Finite difference time domain method

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An Automatic Tooth Position and Dental Disease Detection Algorithm Based on YOLOv8

An Automatic Tooth Position and Dental Disease Detection Alg...

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International Conference on computer and Communications (ICCC)

作者： Hongliang Wang Ke Diao Lin Wu Xuerao Li Xin Zhou Xiaozhou Liu Shenyang Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Liaoning Province Human-Computer Interaction System Engineering Research Center Based on Digital Twin Shenyang China Shenyang Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Shenyang China School and Hospital of Stomatology China Medical University Liaoning Provincial Key Laboratory of Oral Diseases Shenyang China Shenyang Institute of Computing Technology Chinese Academy of Sciences Liaoning Province Human-Computer Interaction System Engineering Research Center Based on Digital Twin Shenyang China

ISBN: (数字)9798331507077

ISBN: (纸本)9798331507084

With the development of digital dental imaging technology, panoramic tomographic images have become an effective tool for diagnosing dental diseases. However, due to the diversity and complexity of dental lesions, accurately identifying tooth positions and pathological conditions from these images remains challenging. To address the issue of low target detection accuracy caused by the high similarity and complex textures of teeth, this paper introduces YOLOv8-Teeth, an enhanced teeth detection algorithm designed for accurate identification of teeth positions and dental diseases from panoramic radiographs. Key innovations include integrating the C2f_ConvNeXtv2 module for improved feature extraction, employing the ASPP module for optimized multi-scale feature capture, and utilizing Focal-EloU Loss for refined bounding box regression. Experimental results on a custom dataset of 476 panoramic radiographs demonstrate significant performance gains, achieving a mean average precision (mAP) of 91.3%, precision of 93.6%, and recall of 88.7%. These improvements offer promising potential for early diagnosis and treatment planning in dental care.

关键词： Technological innovation Accuracy Computational modeling Teeth Tomography Feature extraction Real-time systems Dentistry Detection algorithms Diseases

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A Modular Framework for Task-Agnostic, Energy Shaping Control of Lower-Limb Exoskeletons

TechRxiv

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TechRxiv 2024年

作者： Lin, Jianping Thomas, Gray Cortright Divekar, Nikhil V. Peddinti, Vamsi Gregg, Robert D. State Key Laboratory of Mechanical System and Vibration School of Mechanical Engineering Shanghai Jiao Tong University Shanghai200240 China Department of Mechanical Engineering Texas A&M University College StationTX77843 United States Department of Robotics University of Michigan Ann ArborMI48109 United States Department of Electrical and Computer Engineering University of Michigan Ann ArborMI48109 United States

Various backdrivable lower-limb exoskeletons have demonstrated the electromechanical capability to assist volitional motions of able-bodied users and people with mild to moderate gait disorders, but there does not exist a control framework that can be deployed on any joint(s) to assist any activity of daily life in a provably stable manner. This paper presents the modular, multi-task optimal energy shaping (M-TOES) framework, which uses a convex, data-driven optimization to train an analytical control model to instantaneously estimate assistive joint torques across activities for any lower-limb exoskeleton joint configuration. The presented modular energy basis is sufficiently descriptive to fit normative human joint torques (given normative feedback from signals available to a given joint configuration) across sit-stand transitions, stair ascent/descent, ramp ascent/descent, and level walking at different speeds. We evaluated controllers for four joint configurations (unilateral/bilateral, hip/knee) of the modular M-BLUE exoskeleton on eight able-bodied users navigating a multi-activity course comprising the primary activities of daily life. The two unilateral conditions significantly lowered overall muscle activation across all tasks and subjects (p © 2024, CC BY-NC-SA.

关键词： Exoskeleton (Robotics)

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Efficient Module-Lattice-based Certificateless Online/Offline Signcryption Scheme for Internet of Medical Things

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IEEE Internet of Things Journal 2025年

作者： Bai, Ye He, Debiao Yang, Zhichao Luo, Min Peng, Cong Wuhan University Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan430072 China Wuhan University School of Cyber Science and Engineering Wuhan430072 China Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan250014 China Naval University of Engineering Department of Information Security Wuhan430000 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Industrial Network and Information System Security Jinan250014 China

The Internet of Medical Things (IoMT) has achieved remote diagnosis and real-time health monitoring through intelligent sensor devices and Internet of Things (IoT) technology, providing great convenience for analyzing medical conditions between doctors and patients. However, sensitive information such as patient medical data may face security challenges such as data leakage and abuse during transmission in the IoMT. To ensure the confidentiality and unforgeability of medical data transmission, scholars have proposed many cryptographic schemes. With the development of quantum computers, schemes based on traditional cryptographic primitives have become insecure. Existing cryptographic schemes for IoMT cannot simultaneously meet the security requirements of high communication performance, low computational overhead, and resistance to quantum attacks. Therefore, we propose an efficient module-lattice-based certificateless online/offline signcryption (MLCLOOSC) scheme resistant to quantum attacks while meeting the confidentiality and unforgeability requirements under Type I and Type II attacks. Compared with five recent CLOOSC schemes, theoretical analysis, and experimental test results show that the proposed scheme outperforms the other five schemes regarding computational and communication costs and security. Therefore, our scheme is more suitable for application in IoMT scenarios. © 2014 IEEE.

关键词： Quantum computers

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digital Over-the-Air Federated Learning in Multi-Antenna systems

arXiv

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arXiv 2023年

作者： Wang, Sihua Chen, Mingzhe Shen, Cong Yin, Changchuan Brinton, Christopher G. The Beijing Laboratory of Advanced Information Network The Beijing Key Laboratory of Network System Architecture and Convergence Beijing University of Posts and Telecommunications Beijing100876 China The Department of Electrical and Computer Engineering Institute for Data Science and Computing University of Miami Coral GablesFL33146 United States The Charles L. Brown Department of Electrical and Computer Engineering University of Virginia CharlottesvilleVA United States The School of Electrical and Computer Engineering Purdue University West LafayetteIN United States

In this paper, the performance optimization of federated learning (FL), when deployed over a realistic wireless multiple-input multiple-output (MIMO) communication system with digital modulation and over-the-air computation (AirComp) is studied. In particular, a MIMO system is considered in which edge devices transmit their local FL models (trained using their locally collected data) to a parameter server (PS) using beamforming to maximize the number of devices scheduled for transmission. The PS, acting as a central controller, generates a global FL model using the received local FL models and broadcasts it back to all devices. Due to the limited bandwidth in a wireless network, AirComp is adopted to enable efficient wireless data aggregation. However, fading of wireless channels can produce aggregate distortions in an AirComp-based FL scheme. To tackle this challenge, we propose a modified federated averaging (FedAvg) algorithm that combines digital modulation with AirComp to mitigate wireless fading while ensuring the communication efficiency. This is achieved by a joint transmit and receive beamforming design, which is formulated as an optimization problem to dynamically adjust the beamforming matrices based on current FL model parameters so as to minimize the transmitting error and ensure the FL performance. To achieve this goal, we first analytically characterize how the beamforming matrices affect the performance of the FedAvg in different iterations. Based on this relationship, an artificial neural network (ANN) is used to estimate the local FL models of all devices and adjust the beamforming matrices at the PS for future model transmission. The algorithmic advantages and improved performance of the proposed methodologies are demonstrated through extensive numerical experiments. Copyright © 2023, The Authors. All rights reserved.

关键词： Beamforming

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A Medium-Voltage Multilevel Hybrid Converter Using 3.3kV Silicon Carbide MOSFETs and Silicon IGBT Modules

A Medium-Voltage Multilevel Hybrid Converter Using 3.3kV Sil...

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Annual IEEE Conference on Applied Power Electronics Conference and Exposition (APEC)

作者： Fei Diao Pengyu Lai Feng Guo Xinyuan Du Peng Sun Yue Zhao Zhong Chen Yufei Li Department of Electrical Engineering University of Arkansas Fayetteville AR USA State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources North China Electric Power University Beijing China Department of Electrical and Computer Engineering Princeton University Princeton NJ USA

In this work, a medium voltage (MV) five-level (5-L) hybrid active neutral-point-clamped (ANPC) converter with the inner-interleaved configuration is proposed featuring improved output quality and ease of filtering design. The proposed hybrid ANPC converter consists of the silicon (Si) IGBT based low switching frequency cells and the silicon carbide (SiC) MOSFETs based high switching frequency cells to achieve low harmonics at premium efficiency and reasonable cost. By adopting the inner-interleaved configuration within SiC high switching frequency cell, the proposed hybrid ANPC has increased voltage levels and much higher equivalent switching frequency compared to the conventional ANPC. To validate the effectiveness of the proposed design, an MV converter is prototyped in this work using 3.3 kV Si IGBT half-bridge modules and custom designed compact 3.3 kV SiC H-bridge modules. The design process of the low inductance SiC H-bridge module is also presented. Finally, both simulation and experimental results are presented to validate the performance of the proposed five-level hybrid ANPC converter.

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OneForecast: A Universal Framework for Global and Regional Weather Forecasting

arXiv

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arXiv 2025年

作者： Gao, Yuan Wu, Hao Shu, Ruiqi Dong, Huanshuo Xu, Fan Chen, Rui Yan, Yibo Wen, Qingsong Hu, Xuming Wang, Kun Wu, Jiahao Li, Qing Xiong, Hui Huang, Xiaomeng Department of Earth System Science Ministry of Education Key Laboratory for Earth System Modeling Institute for Global Change Studies Tsinghua University China TEG Tencent China Department and Computer and Science University of Science and Technology of China China Institute for Interdisciplinary Information Sciences Tsinghua University China Department of Computer Science and Engineering The HongKong University of Science and Technology Hong Kong AI Thrust The Hong Kong University of Science and Technology Guangzhou China Squirrel Ai Learning China School of Computer Science and Engineering Nanyang Technological University Singapore Department of Computing The Hong Kong Polytechnic University Hong Kong

Accurate weather forecasts are important for disaster prevention, agricultural planning, and water resource management. Traditional numerical weather prediction (NWP) methods offer physically interpretable high-accuracy predictions but are computationally expensive and fail to fully leverage rapidly growing historical data. In recent years, deep learning methods have made significant progress in weather forecasting, but challenges remain, such as balancing global and regional high-resolution forecasts, excessive smoothing in extreme event predictions, and insufficient dynamic system modeling. To address these issues, this paper proposes a global-regional nested weather forecasting framework based on graph neural networks (GNNs). By combining a dynamic system perspective with multi-grid theory, we construct a multi-scale graph structure and densify the target region to capture local high-frequency features. We introduce an adaptive information propagation mechanism, using dynamic gating units to deeply integrate node and edge features for more accurate extreme event forecasting. For high-resolution regional forecasts, we propose a neural nested grid method to mitigate boundary information loss. Experimental results show that the proposed method performs excellently across global to regional scales and short-term to long-term forecasts, especially in extreme event predictions (e.g., typhoons), significantly improving forecast accuracy. Our codes are available at https://***/YuanGao-YG/OneForecast. Copyright © 2025, The Authors. All rights reserved.

关键词： Weather forecasting

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PrintListener: Uncovering the Vulnerability of Fingerprint Authentication via the Finger Friction Sound

arXiv

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arXiv 2024年

作者： Zhou, Man Su, Shuao Wang, Qian Li, Qi Zhou, Yuting Ma, Xiaojing Li, Zhengxiong School of Cyber Science and Engineering Huazhong University of Science and Technology China School of Cyber Science and Engineering Wuhan University China Institute for Network Sciences and Cyberspace Tsinghua University China Department of Computer Science and Engineering University of Colorado Denver United States Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology China

Fingerprint authentication has been extensively employed in contemporary identity verification systems owing to its rapidity and cost-effectiveness. Due to its widespread use, fingerprint leakage may cause sensitive information theft, enormous economic and personnel losses, and even a potential compromise of national security. As a fingerprint that can coincidentally match a specific proportion of the overall fingerprint population, MasterPrint rings the alarm bells for the security of fingerprint authentication. In this paper, we propose a new side-channel attack on the minutiae-based Automatic Fingerprint Identification system (AFIS), called PrintListener, which leverages users’ fingertip swiping actions on the screen to extract fingerprint pattern features (the first-level features) and synthesizes a stronger targeted PatternMasterPrint with potential second-level features. The attack scenario of PrintListener is extensive and covert. It only needs to record users’ fingertip friction sound and can be launched by leveraging a large number of social media platforms. Extensive experimental results in real-world scenarios show that Printlistener can significantly improve the attack potency of MasterPrint. © 2024, CC0.

关键词： Friction

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Adaptive Estimation for Environmental Monitoring Using an Autonomous Underwater Vehicle

Adaptive Estimation for Environmental Monitoring Using an Au...

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IEEE Conference on Decision and Control

作者： Ziwen Yang Joana Fonseca Shanying Zhu Cailian Chen Xinping Guan Karl H. Johansson Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai China Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Digital Futures

This paper considers the problem of monitoring and adaptively estimating an environmental field, such as temperature or salinity, using an autonomous underwater vehicle (AUV). The AUV moves in the field and persistently measures environmental scalars and its position in its local coordinate frame. The environmental scalars are approximately linearly distributed over the region of interest, and an adaptive estimator is designed to estimate the gradient. By orthogonal decomposition of the velocity of the AUV, a linear time-varying system is equivalently constructed, and the sufficient conditions on the motion of the AUV are established, under which the global exponential stability of the estimation error system is rigorously proved. Furthermore, an estimate of the exponential convergence rate is given, and a reference trajectory that maximizes the estimate of the convergence rate is obtained for the AUV to track. Numerical examples verify the stability and efficiency of the system.

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Fast target-aware learning for few-shot video object segmentation

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Science China(Information Sciences) 2022年第8期65卷 71-86页

作者： Yadang CHEN Chuanyan HAO Zhi-Xin YANG Enhua WU Engineering Research Center of Digital Forensics Ministry of EducationSchool of Computer and SoftwareNanjing University of Information Science and Technology School of Education Science and Technology Nanjing University of Posts and Telecommunications State Key Laboratory of Internet of Things for Smart City Department of Electromechanical EngineeringUniversity of Macau State Key Laboratory of Computer Science Institute of SoftwareUniversity of Chinese Academy of Sciences Faculty of Science and Technology University of Macau

Few-shot video object segmentation(FSVOS) aims to segment a specific object throughout a video sequence when only the first-frame annotation is given. In this study, we develop a fast target-aware learning approach for FSVOS, where the proposed approach adapts to new video sequences from its firstframe annotation through a lightweight procedure. The proposed network comprises two models. First, the meta knowledge model learns the general semantic features for the input video image and up-samples the coarse predicted mask to the original image size. Second, the target model adapts quickly from the limited support set. Concretely, during the online inference for testing the video, we first employ fast optimization techniques to train a powerful target model by minimizing the segmentation error in the first frame and then use it to predict the subsequent frames. During the offline training, we use a bilevel-optimization strategy to mimic the full testing procedure to train the meta knowledge model across multiple video *** proposed method is trained only on an individual public video object segmentation(VOS) benchmark without additional training sets and compared favorably with state-of-the-art methods on DAVIS-2017, with a J &F overall score of 71.6%, and on YouT ubeVOS-2018, with a J &F overall score of 75.4%. Meanwhile,a high inference speed of approximately 0.13 s per frame is maintained.

关键词： video object segmentation few-shot target-aware meta knowledge bilevel-optimization

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