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Enhancing Multi-Label ECG Classification via Task-Guided Lead Correlations in Internet of Medical Things

IEEE Internet of Things Journal 2025年第12期12卷 20544-20555页

作者： Yuan, Xiaoyan Wang, Wei Chen, Junxin Fang, Kai Bashir, Ali Kashif Mondal, Tapas Hu, Xiping Deen, M. Jamal Beijing Institute of Technology School of Medical Technology Beijing100081 China Shenzhen MSU-BIT University Guangdong-Hong Kong-Macao Joint Laboratory for Emotion Intelligence and Pervasive Computing Artificial Intelligence Research Institute Guangdong Shenzhen518172 China Dalian University of Technology School of Software Dalian116621 China Zhejiang A and F University College of Mathematics and Computer Science Hangzhou311300 China Manchester Metropolitan University Department of Computing and Mathematics United Kingdom Chitkara University Institute of Engineering and Technology Centre for Research Impact and Outcome Chitkara University Punjab Rajpura140401 India McMaster University Faculty of Health Science and the Department of Pediatrics Hamilton Canada McMaster University McMaster School of Biomedical Engineering Hamilton Canada McMaster University Electrical and Computer Engineering Department Hamilton Canada

With the rise of the Internet of Things (IoT), wearable devices have enabled real-time health monitoring, particularly through physiological signals like electrocardiograms (ECG). The standard 12-lead ECG records the electrical activity of the heart from multiple perspectives, providing valuable insights into cardiac health. However, existing 12-lead ECG analysis methods often treat leads as channel-level arrangements or rely on spatial adjacency to predefine lead connections, limiting their ability to capture the complex spatial and functional relationships between leads fully. To address this limitation, we propose TGLLNet, a task-driven model that automatically learns inter-lead relationships to improve multi-label ECG classification. TGLLNet adaptively learns lead connectivity patterns and relational strengths, enhancing ECG representation and improving model generalizability across tasks. Specifically, TGLLNet employs a Temporal Graph Construction (ETGC) module to convert ECG signals into temporal graphs and uses a Residual Pyramid Graph Convolution (RPG) module for multi-level graph embeddings, utilizing a Graph Convolutional Network (GCN) with independently learnable adjacency matrices. Combined with a Temporal Context Convolution (TCC) module, TGLLNet captures spatio-temporal dependencies, significantly improving ECG representation. Experimental results on 7 tasks from PTB-XL and CPSC2018 datasets demonstrate that TGLLNet outperforms existing methods, showing superior generalizability across different tasks. Our code is available at https://***/rosemary333/TGLLnet. © 2014 IEEE.

关键词： Graph embeddings

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Towards an ultrafast 3D imaging scanning LiDAR system:a review

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Photonics Research 2024年第8期12卷 1709-1729页

作者： ZHI LI YAQI HAN LICAN WU ZIHAN ZANG MAOLIN DAI SZE YUN SET SHINJI YAMASHITA QIAN LI H.Y.FU Tsinghua Shenzhen International Graduate School Tsinghua UniversityShenzhen 518055China Department of Electrical Engineering and Information Systems The University of TokyoTokyo 113-8656Japan Research Center for Advanced Science and Technology The University of TokyoTokyo 153-8904Japan School of Electronic and Computer Engineering Peking UniversityShenzhen 518055China

Light detection and ranging(LiDAR),as a hot imaging technology in both industry and academia,has undergone rapid innovation and *** current mainstream direction is towards system miniaturization and *** are many metrics that can be used to evaluate the performance of a LiDAR system,such as lateral resolution,ranging accuracy,stability,size,and *** recently,with the continuous enrichment of LiDAR application scenarios,the pursuit of imaging speed has attracted tremendous research ***,for autonomous vehicles running on motorways or industrial automation applications,the imaging speed of LiDAR systems is a critical *** this review,we will focus on discussing the upper speed limit of the LiDAR *** on the working mechanism,the limitation of optical parts on the maximum imaging speed is *** beam scanner has the greatest impact on imaging *** provide the working principle of current popular beam scanners used in LiDAR systems and summarize the main constraints on the scanning ***,we highlight the spectral scanning LiDAR as a new paradigm of ultrafast ***,to further improve the imaging speed,we then review the parallel detection methods,which include multiple-detector schemes and multiplexing ***,we summarize the LiDAR systems with the fastest point acquisition rate reported *** the outlook,we address the current technical challenges for ultrafast LiDAR systems from different aspects and give a brief analysis of the feasibility of different approaches.

关键词： LiDAR scanner system

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Machine Learning Stroke Prediction in Smart Healthcare:Integrating Fuzzy K-Nearest Neighbor and Artificial Neural Networks with Feature Selection Techniques

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computers, Materials & Continua 2025年第3期82卷 5115-5134页

作者： Abdul Ahad Ira Puspitasari Jiangbin Zheng Shamsher Ullah Farhan Ullah Sheikh Tahir Bakhsh Ivan Miguel Pires Department Information System Study Program Faculty of Science and TechnologyUniversitas AirlanggaSurabaya60286Indonesia School of Software Northwestern Polytechnical UniversityXi’an710072China Research Center for Quantum Engineering Design Faculty of Science and TechnologyUniversitas AirlanggaSurabaya60286Indonesia School of Computer Science and Software Engineering Shenzhen UniversityShenzhen518061China Cybersecurity Center Prince Mohammad Bin Fahd University617Al JawharahKhobarDhahran34754Saudi Arabia Cardiff School of Technologies Cardiff Metropolitan UniversityWestern AvenueCardiffCF52YBUK Instituto de Telecomunicacoes Escola Superior de Tecnologia e Gestao de AguedaUniversidade de Aveiroágueda3750-127Portugal

This research explores the use of Fuzzy K-Nearest Neighbor(F-KNN)and Artificial Neural Networks(ANN)for predicting heart stroke incidents,focusing on the impact of feature selection methods,specifically Chi-Square and Best First Search(BFS).The study demonstrates that BFS significantly enhances the performance of both *** BFS preprocessing,the ANN model achieved an impressive accuracy of 97.5%,precision and recall of 97.5%,and an Receiver Operating Characteristics(ROC)area of 97.9%,outperforming the Chi-Square-based ANN,which recorded an accuracy of 91.4%.Similarly,the F-KNN model with BFS achieved an accuracy of 96.3%,precision and recall of 96.3%,and a Receiver Operating Characteristics(ROC)area of 96.2%,surpassing the performance of the Chi-Square F-KNN model,which showed an accuracy of 95%.These results highlight that BFS improves the ability to select the most relevant features,contributing to more reliable and accurate stroke *** findings underscore the importance of using advanced feature selection methods like BFS to enhance the performance of machine learning models in healthcare applications,leading to better stroke risk management and improved patient outcomes.

关键词： Fuzzy K-nearest neighbor artificial neural network accuracy precision recall F-measure Chi-Square best search first heart stroke

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Physics-based fuid simulation in computer graphics: Survey, research trends, and challenges

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Computational Visual Media 2024年第5期10卷 803-858页

作者： Xiaokun Wang Yanrui Xu Sinuo Liu Bo Ren Jiri Kosinka Alexandru C.Telea Jiamin Wang Chongming Song Jian Chang Chenfeng Li Jian Jun Zhang Xiaojuan Ban Beijing Advanced Innovation Center for Materials Genome Engineering and School of Intelligence Science and Technologyand Shunde Innovation School University of Science and Technology BeijingBeijing 100083China National Center for Computer Animation Faculty of Media and CommunicationBournemouth UniversityPoole BH125BBUK. Bernoulli Institute University of GroningenGroningen 9700 AKthe Netherlands School of Computer Science Peking UniversityBeijing 100871China. TMCC College of Computer ScienceNankai UniversityTianjin 300350China Department of Information and Computing Sciences Utrecht UniversityUtrecht 3584 CCthe Netherlands. Zienkiewicz Institute for Modelling Data and AISwansea UniversitySwansea SA18ENUK. Institute of Materials Intelligent Technology Liaoning Academy of MaterialsShenyang 110004China.

Physics-based fluid simulation has played an increasingly important role in the computer graphics *** methods in this area have greatly improved the generation of complex visual effects and its computational *** techniques have emerged to deal with complex boundaries,multiphase fluids,gas-liquid interfaces,and fine *** parallel use of machine learning,image processing,and fluid control technologies has brought many interesting and novel research *** this survey,we provide an introduction to theoretical concepts underpinning physics-based fuid simulation and their practical implementation,with the aim for it to serve as a guide for both newcomers and seasoned researchers to explore the field of physics-based fuid simulation,with a focus on developments in the last *** by the distribution of recent publications in the field,we structure our survey to cover physical background;discretization approaches;computational methods that address scalability;fuid interactions with other materials and interfaces;and methods for expressive aspects of surface detail and *** a practical perspective,we give an overview of existing implementations available for the above methods.

关键词： computer graphics physical simulation fluid simulation fluid coupling

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Transformer-based Text Classification on Unified Bangla Multi-class Emotion Corpus 25

Transformer-based Text Classification on Unified Bangla Mult...

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25th International Arab Conference on Information technology, ACIT 2024

作者： Sourav, Md Sakib Ullah Mahmud, Mohammad Sultan Zheng, Hua Aljaidi, Mohammad Talukder, Md. Simul Hasan Bin Sulaiman, Rejwan Nur, Abdullah Hafez Al-Qerem, Ahmad Shandong University of Finance and Economics School of Management Science and Engineering Jinan China Shenzhen University College of Computer Science and Software Engineering Senzhen China Software Engineering Institute of Guangzhou Department of Software Engineering Guangzhou510990 China Zarqa University Department of Computer Science Zarqa Jordan University of Business and Technology College of Engineering Jeddah21448 Saudi Arabia Dhaka University of Engineering and Technology Department of Electrical and Electronic Engineering Dhaka Bangladesh Northumbria University Newcastle Tayne School of Computer Science & Technology Northumbria United Kingdom International Islamic University Chittagong Department of Electronic and Telecommunication Engineering Chittagong Bangladesh Zarqa University Faculty of Information Technology Department of Computer Science Zarqa Jordan

ISBN: (纸本)9798331540012

Due to its importance in studying people's thoughts on various Web 2.0 services, emotion classification is a critical undertaking. Most existing research is focused on the English language, with little work on low-resource languages. Though sentiment analysis, particularly emotion classification in English, has received increasing attention in recent years, little study has been done in the context of Bangla, one of the world's most widely spoken languages. In this research, we propose a complete set of approaches for identifying and extracting emotions from Bangla texts. We provide a Bangla emotion classifier for six classes, i.e., anger, disgust, fear, joy, sadness, and surprise, from Bangla words using transformer-based models, which exhibit phenomenal results in recent days, especially for high-resource languages. The Unified Bangla Multi-class Emotion Corpus (UBMEC) is used to assess the performance of our models. UBMEC is created by combining two previously released manually labelled datasets of Bangla comments on six emotion classes with fresh manually labelled Bangla comments created by us. The corpus dataset and code we used in this work are publicly available. © 2024 IEEE.

关键词： Classification (of information)

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Nonlinear Deep Subspace Network for Micro-expression Recognition 3

Nonlinear Deep Subspace Network for Micro-expression Recogni...

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3rd Workshop on Facial Micro-Expression: advanced Techniques for Multi-Modal Facial Expression Analysis, FME 2023

作者： Feng, Weijia Xu, Manlu Chen, Yuanxu Wang, Xiaofeng Guo, Jia Dai, Lei Wang, Nan Zuo, Xinyu Li, Xiaoxue College of Computer and Information Engineering Tianjin Normal University Tianjin China Ping An Technology Shenzhen China Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control Tianjin University of Technology Tianjin China

ISBN: (纸本)9798400702853

Deep learning (DL) models have been widely studied in the field of micro-expression recognition (MER). However, micro-expressions (MEs) suffer from small number of samples and difficulty in extracting subtle and transient features, resulting in limited improvement in the recognition performance. In addition, DL models are prone to overfitting problems and are difficult to extract discriminative features of facial actions from ME images or sequences. To address these issues, we propose a MER method that combines a nonlinear deep subspace network and optical flow features. Firstly, facial motion features are captured using optical flow computation, and then the optical flow features are input into a kernel principal component analysis network (KPCANet) to further learn deeper spatio-temporal features. Finally, a linear support vector machine (SVM) is used for ME classification. Experiments conducted on four public spontaneous ME datasets including SMIC, CASME, CASME II and SAMM validate the effectiveness of the proposed method. The experimental results demonstrate that the proposed method achieves even better recognition performance compared to existing state-of-the-art MER methods. © 2023 ACM.

关键词： Principal component analysis

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Semisupervised Breast MRI Density Segmentation Integrating Fine and Rough Annotations

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第3期6卷 690-699页

作者： Xie, Tianyu Sun, Yue Yang, Hongxu Li, Shuo Song, Jinhong Yang, Qimin Chen, Hao Wu, Mingxiang Tan, Tao Macao Polytechnic University Faculty of Applied Sciences 999078 China Eindhoven University of Technology EindhovenAZ 5612 Netherlands Case Western Reserve University Department of Computer and Data Science Department of Biomedical Engineering ClevelandOH44106 United States Jiangsu JITRI Sioux Technologies Company Ltd. Suzhou215000 China University of Twente Department of Biomechanical Engineering EnschedeNB 7522 Netherlands Shenzhen People's Hospital Department of Radiology Shenzhen518020 China

This article introduces an enhanced teacher-student model featuring a novel Vnet architecture that integrates high-pass and low-pass filters to improve the segmentation of breast magnetic resonance imaging (MRI) images. The model effectively utilizes finely annotated, roughly annotated, and unannotated data to achieve precise breast tissue density segmentation. The teacher-student framework incorporates three specialized Vnet networks, each tailored to different types of annotations. By integrating cosine contrast loss functions between finely and roughly annotated models, and innovatively applying high-pass and low-pass filters within the Vnet architecture, the segmentation performance is significantly enhanced. This hybrid filtering approach enables the model to capture both fine-grained and coarse structural details, leading to more accurate segmentation across various MRI image datasets. Experimental results demonstrate the superiority of the proposed method, achieving Dice values of 0.833 on the finely annotated shenzhen dataset and 0.780 on the Duke dataset, using 15 finely annotated, 15 roughly annotated, and 58 unlabeled samples provided by shenzhen People's Hospital. These findings underscore its potential clinical application in breast density assessment. © 2024 IEEE.

关键词： Students

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Exploring the Use of Particle and Kalman Filters for Obstacle Detection in Mobile Robots

Periodica polytechnica Electrical engineering and computer s...

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Periodica polytechnica Electrical engineering and computer science 2023年第4期67卷 384-393页

作者： Gyenes, Zoltán Bölöni, Ladislau Szádeczky-Kardoss, Emese Gincsainé Department of Control Engineering and Information Technology Faculty of Electrical Engineering and Informatics Budapest University of Technology and Economics Muegyetem rkp. 3. BudapestH-1111 Hungary Department of Computer Science University of Central Florida 4328 Scorpius St OrlandoFL32816-2362 United States

The present study aims to explore the adaptation of estimation methodologies, specifically Particle filters and Kalman filters, for the purpose of determining the position and velocity vector of obstacles within the operational workspace of mobile robots. These algorithms are commonly employed in the motion planning tasks of mobile robots for the estimation of their own position. The proposed methodology utilizes LiDAR sensor data to estimate the position vectors and calculate the velocity vectors of obstacles. Additionally, an uncertainty parameter can be determined using the introduced perception method. The performance of the newly adapted algorithms is evaluated through comparison of the absolute error in position and velocity vector estimations. © 2023 Budapest university of technology and Economics. All rights reserved.

关键词： Mobile robots

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QoE-oriented Soft Caching with Content Recommendation for Edge Computing Networks

QoE-oriented Soft Caching with Content Recommendation for Ed...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Wang, Chenyang Chen, Yan Sun, Chuan Jia, Bosen Wang, Xiaofei Taleb, Tarik Leung, Victor C. M. Shenzhen University College of Computer Science & Software Engineering Shenzhen China Shenzhen518132 China Ruhr University Bochum Faculty of Electrical Engineering and Information Technology Bochum Germany Nanyang Technological University College of Computing and Data Science Singapore China Unicom Digital Technology Co. Ltd Beijing China Tianjin University College of Intelligence and Computing Tianjin China University of British Columbia Vancouver Canada

ISBN: (纸本)9798350351255

Mobile Edge Caching (MEC) can potentially alleviate Internet transmission congestion by delivering content at the network edge. However, current MEC solutions suffer from low resource utilization efficiency and often fail to meet user Quality of Experience (QoE), primarily due to dynamic user requests and obsessive pursuit of direct caching hits. Given the prevalence of recommendation systems, users often lack precise requests when using recommendation-based applications like TikTok and Taobao, insted passively enjoying recommended content. In this paper, we introduce a recommendation-enabled MEC architecture to enhance resource utilization and QoE. We develop a recommendation-enabled soft caching model and formulate the optimization problem as maximizing joint system revenue. To address this, we propose an attention-assisted federated learning deep Q-network algorithm. We conduct the simulations by using the real-world MIND dataset. The results demonstrate that our proposed algorithm outperforms existing baselines, demonstrating its effectiveness in improving resource utilization and QoE. © 2024 IEEE.

关键词： Mobile edge computing

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A CH9 Low-Profile Circularly-Polarized Antenna for Indoor Positioning 7

A CH9 Low-Profile Circularly-Polarized Antenna for Indoor Po...

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7th World Conference on Computing and Communication Technologies, WCCCT 2024

作者： Zhang, Zhenfeng Feng, Botao Gu, Wenzhe Ding, Xiao Sun, Sanshan Deng, Li Shenzhen University College of Electronics and Information Engineering Shenzhen518060 China Huizhou University School of Computer Science and Engineering Huizhou516007 China Macau University of Science and Technology Faculty of Humanities and Arts 999078 China Sichuan Normal University College of Physics and Electronic Engineering Sichuan China Beijing University of Posts and Telecommunications College of Information and Communication Engineering Beijing China

ISBN: (纸本)9798350372328

This paper presents a low-profile CH9 circularly polarized 3-element multiple input multiple output (MIMO) antenna for precise positioning applications. The feeding structure of the antenna employs a conventional wilkinson power divider to achieve circular polarization, where the feeding structure and the radiating patch are located on the same layer of the dielectric substrate with a grounded backside. The -10-dB bandwidth of each antenna element ranges from 7.04 to 8.55 GHz, with a gain larger than 2.21 dBi within the desired bandwidth. In order to enhance localization accuracy and minimize the impact of spatial correlation, the distance interval between antenna elements is designed to be 12.5 mm. Experimental results show that when ports 1, 2 and 3 are excited with equal amplitude phase differences of 0 °, 90 ° and -90 °, respectively, the frequency bandwidth of the MIMO antenna is ranging from 7.61 to 8.48 GHz, with corresponding gain fluctuating from 5.39 to 8.07 dBi. In addition, the circularly polarized bandwidth is from 7 to 8.89 GHz, which fully covers the CH9 frequency band. © 2024 IEEE.

关键词： Bandwidth

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