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arXiv 2024年

作者： Guo, Weiyu Sun, Ying Xu, Yijie Qiao, Ziyue Yang, Yongkui Xiong, Hui Artificial Intelligence Thrust HKUST Guangzhou China School of Computing and Information Technology Great Bay University China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences China Department of Computer Science and Engineering HKUST Hong Kong Guangzhou HKUST Fok Ying Tung Research Institute China

Surface electromyography (sEMG) based gesture recognition offers a natural and intuitive interaction modality for wearable devices. Despite significant advancements in sEMG-based gesture recognition models, existing methods often suffer from high computational latency and increased energy consumption. Additionally, the inherent instability of sEMG signals, combined with their sensitivity to distribution shifts in real-world settings, compromises model robustness. To tackle these challenges, we propose a novel SpGesture framework based on Spiking Neural Networks, which possesses several unique merits compared with existing methods: (1) Robustness: By utilizing membrane potential as a memory list, we pioneer the introduction of Source-Free Domain Adaptation into SNN for the first time. This enables SpGesture to mitigate the accuracy degradation caused by distribution shifts. (2) High Accuracy: With a novel Spiking Jaccard Attention, SpGesture enhances the SNNs’ ability to represent sEMG features, leading to a notable rise in system accuracy. To validate SpGesture’s performance, we collected a new sEMG gesture dataset which has different forearm postures, where SpGesture achieved the highest accuracy among the baselines (89.26%). Moreover, the actual deployment on the CPU demonstrated a latency below 100ms, well within real-time requirements. This impressive performance showcases SpGesture’s potential to enhance the applicability of sEMG in real-world scenarios. The code is available at https://***/guoweiyu/SpGesture/. © 2024, CC BY-NC-ND.

关键词： Gesture recognition

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Electrical contact with dielectric breakdown of interfacial gap

arXiv

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arXiv 2024年

作者： Xu, Yang Wu, Yue Jackson, Robert L. School of Mechanical Engineering Hefei University of Technology Hefei230009 China Anhui Province Key Laboratory of Digital Design and Manufacturing Hefei230009 China Shanghai Advanced Research Institute Chinese Academy of Sciences Shanghai201210 China Department of Mechanical Engineering Auburn University AL36849 United States

Electrical contact is fundamental to almost every aspect of modern industry, including the fast-growing electric vehicle industry. In metallic contacts in atmospheric conditions, most of the electrical current passes via the micro-junctions formed between two electrodes. The classic electrical contact theory predicts an infinite current density at the circular contact periphery. In the present work, we explore the influence of the dielectric breakdown of air outside the contact area on the electrical contact interface. Incorporating the discharging boundary condition governed by the modified Paschen law, we develop the numerical model as well as two sets of closed-form solutions for low applied voltage cases where two electrodes are in solid-solid contact and complete separation, respectively. For Hertzian contact, the present work theoretically proves that the ignorance of discharge can lead to a singular current density at the contact periphery and an overestimation of the electrical contact resistance. The current density monotonically increases along the radial direction to a finite value at the contact area periphery, followed by a monotonic drop within the discharge zone. The present study serves as a foundation for the modeling of discharging rough surface electrical contact and sheds light on the machine element surface damages caused by the electrical discharge machining. © 2024, CC BY.

关键词： Electric discharge machining

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Meta-hallucinator: Towards few-shot cross-modality cardiac image segmentation

arXiv

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arXiv 2023年

作者： Zhao, Ziyuan Zhou, Fangcheng Zeng, Zeng Guan, Cuntai Kevin Zhou, S. Nanyang Technological University Singapore A*STAR Singapore A*STAR Singapore National University of Singapore Singapore School of Biomedical Engineering Suzhou Institute for Advanced Research University of Science and Technology of China Suzhou China Institute of Computing Technology CAS Beijing China

Domain shift and label scarcity heavily limit deep learning applications to various medical image analysis tasks. Unsupervised domain adaptation (UDA) techniques have recently achieved promising cross-modality medical image segmentation by transferring knowledge from a label-rich source domain to an unlabeled target domain. However, it is also difficult to collect annotations from the source domain in many clinical applications, rendering most prior works suboptimal with the label-scarce source domain, particularly for few-shot scenarios, where only a few source labels are accessible. To achieve efficient few-shot cross-modality segmentation, we propose a novel transformation-consistent meta-hallucination framework, meta-hallucinator, with the goal of learning to diversify data distributions and generate useful examples for enhancing cross-modality performance. In our framework, hallucination and segmentation models are jointly trained with the gradient-based meta-learning strategy to synthesize examples that lead to good segmentation performance on the target domain. To further facilitate data hallucination and cross-domain knowledge transfer, we develop a self-ensembling model with a hallucination-consistent property. Our meta-hallucinator can seamlessly collaborate with the meta-segmenter for learning to hallucinate with mutual benefits from a combined view of meta-learning and self-ensembling learning. Extensive studies on MM-WHS 2017 dataset for cross-modality cardiac segmentation demonstrate that our method performs favorably against various approaches by a lot in the few-shot UDA scenario. Copyright © 2023, The Authors. All rights reserved.

关键词： Domain Knowledge

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Pre-Equalization Aided Grant-Free Massive Access in Massive MIMO System

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IEEE Transactions on Vehicular Technology 2025年

作者： Wang, Yueqing Mei, Yikun Gao, Zhen Wan, Ziwei Ning, Boyu Mi, De Muhaidat, Sami School of Information and Electronics Beijing Institute of Technology Beijing100081 China Beijing Institute of Technology MIIT Key Laboratory of Complex-Field Intelligent Sensing Beijing100081 China Yangtze Delta Region Academy Jiaxing314019 China Beijing Institute of Technology Advanced Technology Research Institute Jinan250307 China University of Electronic Science and Technology of China National Key Laboratory of Wireless Communications Chengdu611731 China Birmingham City University School of Computing and Digital Technology BirminghamB55JU United Kingdom Khalifa University KU 6 G Research Center Department of Computer and Information Engineering Abu Dhabi United Arab Emirates

The spatial diversity and multiplexing advantages of massive multi-input-multi-output (mMIMO) can significantly improve the capacity of massive non-orthogonal multiple access (NOMA) in machine type communications. However, state-of-the-art grant-free massive NOMA schemes for mMIMO systems require accurate estimation of random access channels to perform activity detection and the following coherent data demodulation, which suffers from excessive pilot overhead and access latency. To address this, we propose a pre-equalization aided grant-free massive access scheme for mMIMO systems, where an iterative detection scheme is conceived. Specifically, the base station (BS) firstly activates one of its antennas (i.e., beacon antenna) to broadcast a beacon signal, which facilitates the user equipment (UEs) to perform downlink channel estimation and pre-equalize the uplink random access signal with respect to the channels associated with the beacon antenna. During the uplink transmission stage, the BS detects UEs' activity and data by using the proposed iterative detection algorithm, which consists of three modules: coarse data detection (DD), data-aided channel estimation (CE), and fine DD. In the proposed algorithm, the joint activity and DD is firstly performed based on the signals received by the beacon antenna. Subsequently, the DD is further refined by iteratively performing data-aided CE module and fine DD module using signals received by all BS antennas. Our simulation results demonstrate that the proposed scheme outperforms state-of-the-art mMIMO-based grant-free massive NOMA schemes with the same access latency. © 1967-2012 IEEE.

关键词： Channel estimation

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Advances and Open Challenges in Federated Foundation Models

引用

IEEE Communications Surveys and Tutorials 2025年

作者： Ren, Chao Yu, Han Peng, Hongyi Tang, Xiaoli Zhao, Bo Yi, Liping Tan, Alysa Ziying Gao, Yulan Li, Anran Li, Xiaoxiao Li, Zengxiang Yang, Qiang School of Electrical Engineering and Computer Science Kth Royal Institute of Technology Sweden College of Computing and Data Science Nanyang Technological University Singapore College of Computer Nankai University China School of Medicine Yale University United States Digital Research Institute of Enn Group Langfang China Department of Electrical and Computer Engineering The University of British Columbia VancouverBC Canada Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong WeBank Shenzhen China

The integration of Foundation Models (FMs) with Federated Learning (FL) presents a transformative paradigm in Artificial Intelligence (AI). This integration offers enhanced capabilities, while addressing concerns of privacy, data decentralization and computational efficiency. This paper provides a comprehensive survey of the emerging field of Federated Foundation Models (FedFM), elucidating their synergistic relationship and exploring novel methodologies, challenges, and future directions that the FL research field needs to focus on in order to thrive in the age of FMs. A systematic multi-tiered taxonomy is proposed, categorizing existing FedFM approaches for model training, aggregation, trustworthiness, and incentivization. Key challenges, including how to enable FL to deal with high complexity of computational demands, privacy considerations, contribution evaluation, and communication efficiency, are thoroughly discussed. Moreover, this paper explores the intricate challenges of communication, scalability and security inherent in training/fine-tuning FMs via FL. It highlights the potential of quantum computing to revolutionize the processes of training, inference, optimization and security. This survey also introduces the implementation requirement of FedFM and some practical FedFM applications. It highlights lessons learned with a clear understanding of our findings for FedFM. Finally, this survey not only provides insights into the current state and challenges of FedFM, but also offers a blueprint for future research directions, emphasizing the need for developing trustworthy solutions. It serves as a foundational guide for researchers and practitioners interested in contributing to this interdisciplinary and rapidly advancing field. © 2025 IEEE.

关键词： Federated learning

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Multi-site Organ Segmentation with Federated Partial Supervision and Site Adaptation

arXiv

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arXiv 2023年

作者： Liu, Pengbo Sun, Mengke Zhou, S. Kevin School of Biomedical Engineering Suzhou Institute for Advanced Research University of Science and Technology of China Suzhou215123 China Beijing UCAS Space Technology Co. Ltd. Beijing China Institute of Computing Technology CAS Beijing100190 China

Objective and Impact Statement: Accurate organ segmentation is critical for many clinical applications at different clinical sites, which may have their specific application requirements that concern different organs. Introduction: However, learning high-quality, site-specific organ segmentation models is challenging as it often needs on-site curation of a large number of annotated images. Security concerns further complicate the matter. Methods: The paper aims to tackle these challenges via a two-phase aggregation-then-adaptation approach. The first phase of federated aggregation learns a single multi-organ segmentation model by leveraging the strength of ‘bigger data’, which are formed by (i) aggregating together datasets from multiple sites that with different organ labels to provide partial supervision, and (ii) conducting partially supervised learning without data breach. The second phase of site adaptation is to transfer the federated multi-organ segmentation model to site-specific organ segmentation models, one model per site, in order to further improve the performance of each site’s organ segmentation task. Furthermore, improved marginal loss and exclusion loss functions are used to avoid ‘knowledge conflict’ problem in a partially supervision mechanism. Results and Conclusion: Extensive experiments on five organ segmentation datasets demonstrate the effectiveness of our multi-site approach, significantly outperforming the site-per-se learned models and achieving the performance comparable to the centrally learned models. © 2023, CC BY.

关键词： Image segmentation

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From Food Waste to High-Capacity Hard Carbon for Rechargeable Sodium-Ion Batteries

SSRN

引用

SSRN 2023年

作者： Kalibek, Madina Rakhymbay, Lunara Zhakiyeva, Zhanar Bakenov, Zhumabay Myung, Seung-Taek Konarov, Aishuak Department of Chemical and Materials Engineering School of Engineering and Digital Sciences Nazarbayev University 53 Kabanbay Batyr Ave. Astana Kazakhstan Hybrid Materials Research Center Department of Nanotechnology and Advanced Materials Engineering Sejong Battery Institute Sejong University Seoul05006 Korea Republic of

In this study, we introduce a straightforward and effective approach to produce P-doped hard carbon using coffee grounds as the precursor, with H3PO4 serving as the doping agent. By varying the concentrations of H3PO4 (1M, 2M, and 3M), we aimed to determine the optimal doping level for maximizing the incorporation of phosphorus ions into the carbon framework. Our investigation revealed that using 2M of H3PO4 as the dopant material for hard carbon led to promising electrochemical performance when employed as an anode material for sodium-ion batteries. The P-doped hard carbon, carbonized at 1300 °C, exhibited an impressive reversible capacity of 341 mAh g-1 at a current density of 20 mA g-1, with an initial Coulombic efficiency (ICE) of 83%. This outstanding electrochemical performance of P-doped hard carbon can be attributed to its unique properties, including a porous agglomerated structure, a significant interlayer spacing, and the formation of C-P bonds. © 2023, The Authors. All rights reserved.

关键词： Sodium-ion batteries

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Evidence-Aware Multi-Modal Data Fusion and its Application to Total Knee Replacement Prediction

Evidence-Aware Multi-Modal Data Fusion and its Application t...

引用

Proceedings of the digital Image computing: Technqiues and Applications (DICTA)

作者： Xinwen Liu Jing Wang S. Kevin Zhou Craig Engstrom Shekhar S. Chandra School of Electrical Engineering and Computer Science The University of Queensland Brisbane Australia The Commonwealth Scientific and Industrial Research Organisation Canberra Australia Center for Medical Imaging Robotics Analytic Computing & Learning (MIRACLE) School of Biomedical Engineering & Suzhou Institute for Advanced Research University of Science and Technology of China Suzhou China Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS Beijing China School of Human Movement and Nutrition Sciences The University of Queensland Brisbane Australia

ISBN: (数字)9798350379037

ISBN: (纸本)9798350379044

Deep neural networks have been widely studied to predict a medical condition, such as total knee replacement (TKR). It has shown that data of different modalities, such as imaging data, clinical variables, and demographic information, provide complementary information and thus can improve the prediction accuracy together. However, the data sources of various modalities may not always be of high quality, and each modality may have only partial information of medical condition. Thus, predictions from different modalities can be in conflict, and the final prediction may fail in the presence of such a conflict. Therefore, it is important to account for the reliability of each source data and the prediction output when making a final decision. In this paper, we propose an evidence-aware multimodal data fusion framework based on the Dempster-Shafer theory (DST). The backbone models contain an image branch, a non-image branch and a fusion branch. For each branch, there is an evidence network that takes the extracted features as input and outputs an evidence score, which is designed to represent the reliability of the output from the current branch. The output probabilities along with the evidence scores from multiple branches are combined with the Dempster's combination rule to make a final prediction. Experimental results on the public OA initiative (OAI) dataset for the TKR prediction task show that the proposed method has better performance by accounting for conflicts from various modalities.

关键词： Medical conditions Soft sensors digital images Data integration Imaging Artificial neural networks Reliability theory Feature extraction Reliability engineering Knee replacement

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CCAT-NET: A NOVEL TRANSFORMER BASED SEMI-SUPERVISED FRAMEWORK FOR COVID-19 LUNG LESION SEGMENTATION

arXiv

引用

arXiv 2022年

作者： Liu, Mingyang Xiao, Li Jiang, Huiqin He, Qing Henan Institutes of Advanced Technology Research Center of Digital Medical Image Technique Zhengzhou University Zhengzhou450052 China School of Information Engineering Research Center of Digital Medical Image Technique Zhengzhou University China Institute of Computing Technology CAS Beijing100190 China Ningbo Huamei Hospital University of the Chinese Academy of Sciences Ningbo China

The spread of the novel coronavirus disease 2019 (COVID-19) has claimed millions of lives. Automatic segmentation of lesions from CT images can assist doctors with screening, treatment, and monitoring. However, accurate segmentation of lesions from CT images can be very challenging due to data and model limitations. Recently, Transformer-based networks have attracted a lot of attention in the area of computer vision, as Transformer outperforms CNN at a bunch of tasks. In this work, we propose a novel network structure that combines CNN and Transformer for the segmentation of COVID-19 lesions. We further propose an efficient semi-supervised learning framework to address the shortage of labeled data. Extensive experiments showed that our proposed network outperforms most existing networks and the semi-supervised learning framework can outperform the base network by 3.0% and 8.2% in terms of Dice coefficient and sensitivity. Copyright © 2022, The Authors. All rights reserved.

关键词： Coronavirus

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A review on current research status of the surface modification of Zn-based biodegradable metals

引用

Bioactive Materials 2022年第1期7卷 192-216页

作者： Wei Yuan Dandan Xia Shuilin Wu Yufeng Zheng Zhenpeng Guan Julietta V.Rau School of Materials Science and Engineering Peking UniversityBeijing100871China National Engineering Laboratory for Digital and Material Technology of Stomatology National Clinical Research Center for Oral DiseasesBeijing Key Laboratory of Digital StomatologyNational Medical Products Administration Key Laboratory for Dental MaterialsResearch Center of Engineering and Technology for Digital DentistryMinistry of HealthBeijing100081China School of Materials Science&Engineering The Key Laboratory of Advanced Ceramics and Machining Technology by the Ministry of Education of ChinaTianjin UniversityTianjin300072China Orthopedics Department Peking University Shougang HospitalNo.9 Jinyuanzhuang RdShijingshan DistrictBeijing100144China Istituto di Struttura della Materia Consiglio Nazionale delle Ricerche(ISM-CNR)Via del Fosso del Cavaliere100-00133RomeItaly Sechenov First Moscow State Medical University Institute of PharmacyDepartment of AnalyticalPhysical and Colloid ChemistryTrubetskaya 8build.2119991MoscowRussia

Recently,zinc and its alloys have been proposed as promising candidates for biodegradable metals(BMs),owning to their preferable corrosion behavior and acceptable biocompatibility in cardiovascular,bone and gastrointestinal environments,together with Mg-based and Fe-based ***,there is the desire for surface treatment for Zn-based BMs to better control their biodegradation ***,the implantation of some Zn-based BMs in cardiovascular environment exhibited intimal activation with mild ***,for orthopedic applications,the biodegradation rates of Zn-based BMs are relatively slow,resulting in a long-term retention after fulfilling their ***,excessive Zn2+release during degradation will cause in vitro cytotoxicity and in vivo delayed *** this review,we firstly summarized the current surface modification methods of Zn-based alloys for the industrial *** we comprehensively summarized the recent progress of biomedical bulk Zn-based BMs as well as the corresponding surface modification *** but not least,the future perspectives towards the design of surface bio-functionalized coatings on Zn-based BMs for orthopedic and cardiovascular applications were also briefly proposed.

关键词： Zn-based biodegradable metals Surface modification Corrosion behavior Biocompatibility Osseointegration

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