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Accountable Distributed Access Control With Privacy Preservation for Blockchain-Enabled Internet of Things Systems: A Zero-Trust Security Scheme

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IEEE Internet of Things Journal 2025年第11期12卷 17936-17947页

作者： Fang, He Xu, Li Nan, Guoshun Zheng, Danyang Zhao, Haitao Wang, Xianbin Fujian Normal University College of Computer and Cyber Security Fuzhou350007 China Beijing University of Posts and Telecommunications National Engineering Research Center for Mobile Network Technologies Beijing100876 China Southwest Jiaotong University School of Computing and Artificial Intelligence Chengdu610031 China Nanjing University of Posts and Telecommunications Department of Wireless Communication Key Lab of Jiangsu Province Nanjing210003 China Western University Department of Electrical and Computer Engineering LondonONN6A 5B9 Canada

While being able to avoid single point failures, emerging decentralized security techniques are facing new challenges of reliability, robustness, and privacy preservation in blockchain-enabled Internet-of-Things (IoT) systems. To circumvent these issues, a zero-trust security scheme is proposed through distributed access control, enhanced authentication, dynamic authorization, and privacy preservation enabled by the consortium blockchain. The proposed scheme integrates three key components, i.e., a distributed recommendation mechanism, where multiple authorized nodes are utilized as referrers to efficiently confer their trust on a new public entity for enhanced authentication;an anonymous credential generation strategy, which is developed for the new entity to further protect its privacy from linking attacks;and an adaptive reputation update strategy, which is proposed for evaluating the nodes' behaviors in the system for accountability and dynamic multiple-level authorization. The proposed scheme is implemented in a Hyperledge Fabric and the results show that it significantly enhances security and protects private information. © 2014 IEEE.

关键词： Differential privacy

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Seamless Distributed Power Management of DC Microgrid under Cyber Attacks with Bidirectional Power Flow

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IEEE Journal of Emerging and Selected Topics in Power Electronics 2025年

作者： Tran, Dat Thanh Kim, Kyeong-Hwa Lai, Jih-Sheng Seoul National University of Science and Technology Research Center for Electrical and Information Technology Department of Electrical and Information Engineering Seoul01811 Korea Republic of Department of Electrical and Computer Engineering BlacksburgVA24061 United States

A seamless distributed power management for the dc microgrid (DCMG) with bidirectional power flow is presented in this paper to achieve the overall system stabilization even under severe cyber attacks. First, a distributed secondary control (DSC) based on the V*-P droop curves is presented to ensure the power and voltage regulation for the DCMG system consisting of the electric vehicle (EV), wind turbine, battery, load, and utility grid agents under uncertain conditions. The proposed scheme automatically adjusts the utility grid droop curve to minimize electricity expenditure under electricity price change. To eliminate the negative effect of severe cyber attacks such as false data injection (FDI) and denial-of-service (DoS) in the distributed DCMG system, a resilient DSC based on the compensation term is utilized in the proposed scheme. In addition, by modifying the DSC structure of each power agent, the proposed scheme reduces the overshoot of the dc bus voltage even in the presence of the electricity price change, agent power variation, sudden utility grid disconnection, or critical state-of-charge (SOC) levels. Furthermore, the proposed distributed DCMG system utilizes only the unidirectional digital communication links (DCLs) to cut down the system cost and communication burden, which greatly simplifies communication structure. The efficiency and feasibility of the proposed distributed power management are validated by simulation and experimental results under various conditions. © 2025 IEEE.

关键词： Cyber attacks

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Optical polarized orthogonal matrix

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Photonics research 2025年第2期13卷 373-381页

作者： SHUJUN ZHENG JIAREN TAN XIANMIAO XU HONGJIE LIU YI YANG XIAO LIN XIAODI TAN Information Photonics Research Center College of Photonic and Electronic EngineeringFujian Normal University Department of Electrical and Computer Engineering Duke University College of Photonic and Electronic Engineering Key Laboratory of Opto-Electronic Science and for Medicine of Ministry of EducationFujian Provincial Key Laboratory of Photonics TechnologyFujian Provincial Engineering Technology Research Center of Photoelectric Sensing ApplicationFujian Normal University

Multiplexing technology serves as an effective approach to increase both information storage and transmission capability. However, when exploring multiplexing methods across various dimensions, the polarization dimension encounters limitations stemming from the finite orthogonal combinations. Given that only two mutually orthogonal polarizations are identifiable on the basic Poincaré sphere, this poses a hindrance to polarization modulation. To overcome this challenge, we propose a construction method for the optical polarized orthogonal matrix(OPOM), which is not constrained by the number of orthogonal combinations. Furthermore, we experimentally validate its application in high-dimensional multiplexing of polarization holography. We explore polarization holography technology, capable of recording amplitude, phase, and polarization, for the purpose of recording and selective reconstruction of polarization multi-channels. Our research reveals that, despite identical polarization states, multiple images can be independently manipulated within distinct polarization channels through orthogonal polarization combinations, owing to the orthogonal selectivity among information.

关键词： Multiplex holography Optical data storage Optical encryption Orbital angular momentum multiplexing Signal transmission Three dimensional displays

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Exploiting the Potential of Self-Supervised Monocular Depth Estimation via Patch-Based Self-Distillation

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IEEE Internet of Things Journal 2025年第11期12卷 18042-18055页

作者： Yu, Ting Pan, Shan Chen, Wei Tian, Zijian Wang, Zehua Yu, F. Richard Leung, Victor C. M. Postdoctoral Research Station China RongTong Academy of Sciences Group Corporation Limited Beijing100022 China China RongTong Artificial Intelligence Research Center Beijing100022 China China University of Mining and Technology School of Artificial Intelligence Beijing100083 China China University of Mining and Technology School of Computer Science and Technology Xuzhou221116 China School of Artificial Intelligence Beijing100083 China The University of British Columbia Department of Electrical and Computer Engineering VancouverBCV6T 1Z4 Canada Carleton University Department of Systems and Computer Engineering OttawaONK1S 5B6 Canada Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China

Perceiving scene depth and 3D structure is one of the key tasks for Internet of Video Things (IoVT) devices to understand and interact with the environment. Self-supervised monocular depth estimation has demonstrated significant potential in leveraging large-scale unlabeled datasets to achieve competitive performance, thereby playing an increasingly important role in depth estimation. Despite recent methods providing additional supervisory signals through self-distillation strategies to improve depth estimation, an effective method for generating pseudo-depth labels suitable for addressing occlusion issues among elements far from the camera remains unexplored. To address this limitation, we propose a Patch-based Self-Distillation Learning Framework to exploit the potential of self-supervised monocular depth estimation in recovering fine-grained scene depth. In the proposed framework, elements far from the camera within the input image are enlarged by enlarging and cropping operations in the patch-based self-distillation branch. Guided by photometric consistency, the model learns the detailed occlusion relationships among elements from the enlarged patches, producing patch depth maps with fine structures. In the main branch, which takes full-scale images as input, patch depth maps serve as pseudo-depth labels through self-distillation loss to provide additional supervisory signals for regions where photometric consistency fails to offer effective supervision. This forces the depth estimation network to recover fine structures of elements far from the camera in full-scale input images. Regarding the architecture of the depth estimation network, we introduce a bin-center prediction. In this prediction, a global aggregator based on self-attention provides additional scene structure queries for adaptive scene depth discretization. Finally, to encourage the model to explore more general cues for depth inference beyond road plane cues, we propose a PatchMix data augmentati

关键词： Image enhancement

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MSEMG: Surface Electromyography Denoising with a Mamba-based Efficient Network

MSEMG: Surface Electromyography Denoising with a Mamba-based...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Liu, Yu-Tung Wang, Kuan-Chen Chao, Rong Siniscalchi, Sabato Marco Yeh, Ping-Cheng Tsao, Yu Department of Electronics and Electrical Engineering National Yang Ming Chiao Tung University Taiwan Graduate Institute of Communication Engineering National Taiwan University Taiwan Department of Computer Science and Information Engineering National Taiwan University Taiwan University of Palermo Italy Research Center for Information Technology Innovation Academic Sinica Taiwan

ISBN: (纸本)9798350368741

Surface electromyography (sEMG) recordings can be contaminated by electrocardiogram (ECG) signals when the monitored muscle is closed to the heart. Traditional signal processing-based approaches, such as high-pass filtering and template subtraction, have been used to remove ECG interference but are often limited in their effectiveness. Recently, neural network-based methods have shown greater promise for sEMG denoising, but they still struggle to balance both efficiency and effectiveness. In this study, we introduce MSEMG, a novel system that integrates the Mamba state space model with a convolutional neural network to serve as a lightweight sEMG denoising model. We evaluated MSEMG using sEMG data from the Non-Invasive Adaptive Prosthetics database and ECG signals from the MIT-BIH Normal Sinus Rhythm Database. The results show that MSEMG outperforms existing methods, generating higher-quality sEMG signals using fewer parameters. © 2025 IEEE.

关键词： Deep neural network ECG interference removal State space model Surface electromyography

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Dynamic Interaction Analysis of Coupled Axial-Torsional-Lateral Mechanical Vibrations in Rotary Drilling Systems

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Structural Durability & Health Monitoring 2025年第1期19卷 77-103页

作者： Sabrina Meddah Sid Ahmed Tadjer Abdelhakim Idir Kong Fah Tee Mohamed Zinelabidine Doghmane Madjid Kidouche Applied Automation Laboratory Faculty of Hydrocarbons and ChemistryUniversity of BoumerdesBoumerdes35000Algeria Institute of Electrical and Electronic Engineering(IGEE) University of BoumerdesBoumerdes35000Algeria Laboratory of the Electrification of Industrial Enterprises University of BoumerdesBoumerdes35000Algeria Electrical Engineering Department Mohamed Boudiaf University of M’silaM’sila28000Algeria Department of Civil and Environmental Engineering King Fahd University of Petroleum&MineralsDhahran31261Saudi Arabia Interdisciplinary Research Center for Construction and Building Materials King Fahd University of Petroleum&Minerals(KFUPM)Dhahran31261Saudi Arabia

Maintaining the integrity and longevity of structures is essential in many industries,such as aerospace,nuclear,and *** achieve the cost-effectiveness of large-scale systems in petroleum drilling,a strong emphasis on structural durability and monitoring is *** study focuses on the mechanical vibrations that occur in rotary drilling systems,which have a substantial impact on the structural integrity of drilling *** study speciﬁcally investigates axial,torsional,and lateral vibrations,which might lead to negative consequences such as bit-bounce,chaotic whirling,and high-frequency *** events not only hinder the efﬁciency of drilling but also lead to exhaustion and harm to the system’s components since they are difﬁcult to be detected and controlled in real *** study investigates the dynamic interactions of these vibrations,speciﬁcally in their high-frequency modes,usingﬁeld data obtained from measurement while ***ﬁndings have demonstrated the effect of strong coupling between the high-frequency modes of these vibrations on drilling sys-tem *** obtained results highlight the importance of considering the interconnected impacts of these vibrations when designing and implementing robust control ***,integrating these compo-nents can increase the durability of drill bits and drill strings,as well as improve the ability to monitor and detect ***,by exploiting theseﬁndings,the assessment of structural resilience in rotary drilling systems can be ***,the study demonstrates the capacity of structural health monitoring to improve the quality,dependability,and efﬁciency of rotary drilling systems in the petroleum industry.

关键词： Rotary drilling systems mechanical vibrations structural durability dynamic interaction analysis ﬁeld data analysis

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Skull Fracture Visualization Using Disk Harmonic Maps

Skull Fracture Visualization Using Disk Harmonic Maps

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IEEE International Symposium on Biomedical Imaging

作者： Nicolas Hadjittoouli Costas Pitris Department of Electrical and Computer Engineering KIOS Research and Innovation Center of Excellence University of Cyprus Nicosia Cyprus

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

Skull fractures, especially those of the base and facial regions, are challenging to identify due to the intricate anatomy and their subtle morphological characteristics. They necessitate meticulous attention and allocation of sufficient time to ensure accurate diagnosis and optimal patient care. This paper introduces a novel approach that converts a head CT to a mesh representation, in order to utilize disk harmonic mapping and eventually create a flatten map of the skull for improved visualization. In such a view, fractures appear continuous with enhanced contrast. The effectiveness of this technique was evaluated using the CQ500 dataset. This alternative visualization of a flattened skull can provide radiologists and emergency department personnel a valuable addition to conventional radiological views, increasing the effectiveness and speed of skull fracture detection.

关键词： Visualization Accuracy Head Computed tomography Image processing Harmonic analysis computer aided diagnosis Resource management Personnel Biomedical imaging

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Domain Aware Multi-task Pretraining of 3D Swin Transformer for T1-Weighted Brain MRI 17th

Domain Aware Multi-task Pretraining of 3D Swin Transformer ...

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17th Asian Conference on computer Vision, ACCV 2024

作者： Kim, Jonghun Kim, Mansu Park, Hyunjin Department of Electrical and Computer Engineering Sungkyunkwan University Suwon Korea Republic of Center for Neuroscience Imaging Research Institute for Basic Science Suwon Korea Republic of AI Graduate School Gwangju Institute of Science and Technology Gwangju Korea Republic of

ISBN: (纸本)9789819609000

The scarcity of annotated medical images is a major bottleneck in developing learning models for medical image analysis. Hence, recent studies have focused on pretrained models with fewer annotation requirements that can be fine-tuned for various downstream tasks. However, existing approaches are mainly 3D adaptions of 2D approaches ill-suited for 3D medical imaging data. Motivated by this gap, we propose novel domain-aware multi-task learning tasks to pretrain a 3D Swin Transformer for brain magnetic resonance imaging (MRI). Our method considers the domain knowledge in brain MRI by incorporating brain anatomy and morphology as well as standard pretext tasks adapted for 3D imaging in a contrastive learning setting. We pretrain our model using large-scale brain MRI data of 13,687 samples spanning several large-scale databases. Our method outperforms existing supervised and self-supervised methods in three downstream tasks of Alzheimer’s disease classification, Parkinson’s disease classification, and age prediction tasks. The ablation study of the proposed pretext tasks shows the effectiveness of our pretext tasks. Our code is available at ***/jongdory/DAMT. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Neurodegenerative diseases

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Cutting Skill Assessment by Motion Analysis Using Deep Learning and Spatial Marker Tracking

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IEEE Transactions on Biomedical engineering 2025年第6期72卷 1963-1972页

作者： Su, Bai-Quan Ma, Xu-Dong Li, Weihan Kuang, Zi-Ao Gong, Yi Wang, Gang Zhang, Qingqian Liu, Wenyong Li, Changsheng Gao, Li Wang, Junchen Beijing University of Posts and Telecommunications Medical Robotics Laboratory School of Automation Beijing100876 China University of Tokyo School of Mechanical Engineering Tokyo113-0033 Japan Beijing University of Posts and Telecommunications School of Computer Science Beijing100876 China Peking University General Surgery Department Beijing100191 China Beihang University Key Laboratory for Biomechanics and Mechanobiology of Chinese Education Ministry Beijing Advanced Innovation Centre for Biomedical Engineering School of Biological Science and Medical Engineering Beijing100083 China Beijing Institute of Technology School of Mechatronical Engineering Beijing100081 China Beijing Institute of Technology Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing100081 China Peking University Department of Periodontology National Stomatological Center School and Hospital of Stomatology National Clinical Research Center for Oral Diseases National Engineering Research Center of Oral Biomaterials and Digital Medical Devices Beijing100081 China Beihang University School of Mechanical Engineering and Automation Beijing100083 China

The assessment of surgical skill is crucial for indicating a surgeon's proficiency. While motion analysis of surgical tools is widely used in endoscopic surgery, it is not commonly applied to open surgery. Instead, open surgery skill assessment relies on observing the trajectory of surgical tools on tissue. This observation-based method often lacks clear standards, leading to inaccurate assessments. This paper presents a method for evaluating cutting skill in open surgery through scalpel motion analysis. A 3D multiple-facet ArUco code cube is designed, and a dataset of tip coordinate system poses for various scalpels in the ArUco code coordinate system (ACS) is established using the pivot calibration method. The YOLOv8 model and an image dataset of different scalpels are used to identify the scalpel type and select its tip position. The tip position is then transformed from ACS to a binocular camera coordinate system (BCS), representing the incision curve made by the scalpel. Five assessment metrics are proposed to quantify the surgeon's cutting skill: average incision curvature deviation, incision length difference, incision endpoint deviation, average incision deviation, and average cutting jerk. Experiments involving twenty expert and novice surgeons performing four common incisions (straight line, polyline, semicircle, and cross line) demonstrate the metrics' effectiveness. The metrics provide a clear, objective display of individual cutting skills, and a combined ranking reveals comparative skill levels. This study offers a precise method for evaluating surgeons' cutting skills with a scalpel in open surgery. © 1964-2012 IEEE.

关键词： Surgery

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RGB-Based Category-Level Object Pose Estimation via Depth Recovery and Adaptive Refinement

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IEEE Robotics and automation Letters 2025年第6期10卷 5377-5384页

作者： Yang, Hui Sun, Wei Liu, Jian Zheng, Jin Zeng, Zhiwen Mian, Ajmal Hunan University National Engineering Research Center for Robot Visual Perception and Control Technology College of Electrical and Information Engineering Changsha410082 China Hunan University State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body Changsha410082 China Central South University School of Architecture and Art Changsha410082 China University of Western Australia Department of Computer Science and Software Engineering CrawleyWA6009 Australia

Category-level pose estimation methods have received widespread attention as they can be generalized to intra-class unseen objects. Although RGB-D-based category-level methods have made significant progress, reliance on depth image limits practical application. RGB-based methods offer a more practical and cost-effective solution. However, current RGB-based methods struggle with object geometry perception, leading to inaccurate pose estimation. We propose depth recovery and adaptive refinement for category-level object pose estimation from a single RGB image. We leverage DINOv2 to reconstruct the coarse scene-level depth from the input RGB image and propose an adaptive refinement network based on an encoder-decoder architecture to dynamically improve the predicted coarse depth and reduce its gap from the ground truth. We introduce a 2D–3D consistency loss to ensure correspondence between the point cloud obtained from depth projection and the objects in the 2D image. This consistency supervision enables the model to maintain alignment between the depth image and the point cloud. Finally, we extract features from the refined point cloud and feed them into two confidence-aware rotation regression branches and a translation and size prediction residual branch for end-to-end training. Decoupling the rotation matrix provides a more direct representation, which facilitates parameter optimization and gradient propagation. Extensive experiments on the REAL275 and CAMERA25 datasets demonstrate the superior performance of our method. Real-world estimation and robotic grasping experiments demonstrate our model robustness to occlusion, clutter environments, and low-textured objects. Our code and robotic grasping video are available at DA-Pose. © 2016 IEEE.

关键词： Depth perception

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