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Optical attenuation coefficient based optical coherence tomography angiography

Optics Communications 2025年 586卷

作者： Xiang, Ben Ding, Ning Jiang, Huiwen Liu, Jian Yu, Yao Luan, Jingmin Zhao, Yuqian Wang, Yi Yang, Yanqiu Wang, Fengwen Ma, Zhenhe School of Control Engineering Northeastern University at Qinhuangdao Qinhuangdao066004 China Hebei Key Laboratory of Micro-Nano Precision Optical Sensing and Measurement Technology Qinhuangdao066004 China College of Information Science and Engineering Northeastern University Shenyang110819 China School of Computer and Communication Engineering Northeastern University at Qinhuangdao Qinhuangdao066004 China

In this study, we reported an optical coherence tomography angiography (OCTA) method based on optical attenuation coefficients (OAC), called OAC-based decorrelation (OACD), for mapping high-quality microvascular images. OACD utilizes the OAC instead of amplitude or phase to calculate flow signals, so OACD can intrinsically suppress noise arising from phase errors and intensity variations in OCT signals caused by the blood flow in superficial vasculature. The performance of the newly proposed algorithm is tested against previously reported OCTA algorithms (i.e., split-spectrum amplitude and phase-gradient angiography (SSAPGA) and principal component analysis (PCA)). Compared to SSAGPA and PCA, OACD can provide enhanced motion contrast in phantoms and in vivo experiments, facilitating better differentiation between vessels and static tissue. Benefiting from these advantages, the vascular images processed by the OACD method exhibit increased vascular visibility and connectivity, enhancing overall image quality. Our method holds promise for disease diagnosis and monitoring. © 2025 Elsevier B.V.

关键词： Optical coherence tomography

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MUFFIN-HGCN: Multi-feature Fusion Hierarchical-GCN for 3D Object Detection

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IEEE Transactions on Vehicular Technology 2025年

作者： Liu, Chang Jiang, Aimin Zhou, Jia Li, Min Zhu, Yanping Kwan, Hon Keung Hohai University College of Information Science and Engineering Changzhou213200 China Changzhou University School of Microelectronics and Control Engineering Changzhou213164 China University of Windsor Department of Electrical and Computer Engineering WindsorONN9B 3P4 Canada

3D object detection play a pivotal role in various applications, such as autonomous driving and environmental perception. However, the challenging task of detecting targets (e.g., vehicles and pedestrians) in 3D point clouds obtained from LiDAR sensors is hindered by two primary factors. Firstly, point clouds are non-Euclidean data and lack rigid data properties. Secondly, these point clouds are sparse and deficient in semantic information. In this paper, we propose a novel deep learning framework for 3D object detection. The proposed approach involves two stages. Specifically, we first present an innovative fusion technique that combines images and point clouds using transformers. This technique enhances the integration of semantic and geometric features while accurately mapping points to pixels without relying on camera extrinsic parameters. Additionally, a multi-level graph convolutional network (GCN) architecture is further introduced for object detection. This architecture effectively handles the non-Euclidean characteristics of point clouds while achieving precise object identification within them. A comprehensive series of experiments on the KITTl and nuScenes datasets validate the effectiveness of the proposed framework. © 1967-2012 IEEE.

关键词： Object detection

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A Novel Thermal Analysis Method for Tubular PM Linear Motors Based on Transfer Learning

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IEEE Transactions on Transportation Electrification 2025年第3期11卷 7379-7388页

作者： Wu, Tao Dai, Peipei Xue, Gang Guo, Youguang Lei, Gang Zhu, Jianguo Wang, Yifei China University of Geosciences School of Automation Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China University of Technology Sydney School of Electrical and Data Engineering NSW2007 Australia University of Sydney School of Electrical and Computer Engineering SydneyNSW2006 Australia

Thermal modeling and analysis are critical for permanent magnet linear synchronous motors (PMLSMs), particularly in multi-physical analysis and motor design optimization. This paper proposes a new method for thermal steady state modeling and analysis based on transfer learning, which combines the thermal mechanism model with the data-driven model to achieve high-precision temperature analysis with small number of finite element analysis (FEA) samples. First, an equivalent thermal circuit (ETC) model is established for PMLSMs based on the structural parameters and heat transfer principles. Second, a hybrid model based on transfer learning is developed: the model is pre-trained by a large sample set generated from the ETC in the source field (mechanism analytical model), and then the trained model is transferred to the target field (FEA data-driven model). The parameters of the neural network in the target field are optimized by a few FEA sample sets with higher precision to improve the prediction accuracy of the target model. Experimental results demonstrate that the proposed method significantly boosts the performance of the deep neural network (DNN) model, particularly when training sets are small. The root mean square error (RMSE) of the DNN model decreases by 0.901 in the 5:5 (training/testing) dataset and 1.726 in the 2:8 dataset;and the corresponding R2 and maximal absolute error of motor performances are significantly decreased. The smaller the proportion of the training set, the better performance of the hybrid modeling will achieve. Furthermore, a temperature rise experiment conducted on a prototype motor validates the efficacy and precision of the proposed hybrid modeling methodology. © 2015 IEEE.

关键词： Permanent magnets

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Multi-Modal Multi-Kernel Graph Learning for Autism Prediction and Biomarker Discovery

IEEE Transactions on Computational Biology and Bioinformatic...

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IEEE Transactions on Computational Biology and Bioinformatics 2025年第2期22卷 842-854页

作者： Jin Liu Junbin Mao Hanhe Lin Hulin Kuang Shirui Pan Xusheng Wu Shan Xie Fei Liu Yi Pan Hunan Provincial Key Lab on Bioinformatics School of Computer Science and Engineering Central South University Changsha China Xinjiang Engineering Research Center of Big Data and Intelligent Software School of software Xinjiang University Wulumuqi China Hunan Province Key Lab on Bioinformatics School of Computer Science and Engineering Central South University Changsha China School of Science and Engineering University of Dundee Dundee U.K. School of Information and Communication Technology Griffith University Gold Coast QLD Australia Shenzhen Health Development Research and Data Management Center Shenzhen China Faculty of Computer Science and Control Engineering Shenzhen University of Advanced Technology Shenzhen China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China

Graph learning-based multi-modal integration and classification is one of the most challenging tasks for disease prediction. To effectively offset the negative impact among modalities in the process of multi-modal integration and heterogeneous information extractions from graphs, we propose a novel method called Multi-modal Multi-Kernel Graph Learning (MMKGL). To solve the problem of negative impact among modalities, we propose a multi-modal graph embedding module to construct a multi-modal graph. Different from conventional methods that manually construct static graphs for all modalities, each modality generates a separate graph by adaptive learning, where a function graph and a supervision graph are introduced for optimization during the multi-graph fusion embedding process. We then propose a multi-kernel graph learning module to extract heterogeneous information from the multi-modal graph. The information in the multi-modal graph at different levels is aggregated by convolutional kernels with different receptive field sizes, followed by generating a cross-kernel discovery tensor for disease prediction. Our method is evaluated on the benchmark Autism Brain Imaging Data Exchange (ABIDE) dataset and outperforms the state-of-the-art methods. In addition, discriminative brain regions associated with autism are identified by our model, providing guidance for the study of autism pathology.

关键词： Diseases Autism Random access memory Data mining Kernel Electronic mail Training Neuroimaging Graph convolutional networks Bioinformatics

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Using Corrected ASR Projection to Improve AD Recognition Performance from Spontaneous Speech

Using Corrected ASR Projection to Improve AD Recognition Per...

引用

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Yunfan Zhang Yun Jin Guanlin Chen Yong Ma Maoshen Jia Peng Song School of Physics and Electronic Engineering JiangSu Normal University Xuzhou China School of Linguistic Sciences and Arts JiangSu Normal University Xuzhou China Faculty of Information Technology Beijing University of Technology Beijing China School of Computer and Control Engineering Yantai University Yantai China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Alzheimer's Disease patients often exhibit cognitive decline, with language impairment being a prominent biomarker. Spontaneous speech analysis provides a non-invasive screening approach for AD. Large language models, increasingly employed for textual feature extraction, show potential in early AD prediction. However, Automatic Speech Recognition transcription errors, stemming from language impairments in AD and Mild Cognitive Impairment patients, can lead to information loss during feature extraction. To mitigate this, we introduce the Corrected ASR Projecting, CAP model. During training, ASR-transcribed text is manually corrected one by one, and then textual features are extracted independently using BERT, Claude, GLM, and GPT-3. The CAP model is trained by aligning the ASR transcription feature space with the corrected ASR transcription feature space. Experiments on the NCMMSC 2021 dataset demonstrate that the CAP model improves classification performance for AD recognition, with the maximum accuracy improvement reaching 5.55%.

关键词： Training Speech analysis Accuracy Large language models Signal processing Feature extraction Acoustics Speech processing Alzheimer's disease Automatic speech recognition

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Multi-Keypoint Affordance Representation for Functional Dexterous Grasping

arXiv

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arXiv 2025年

作者： Yang, Fan Luo, Dongsheng Chen, Wenrui Lin, Jiacheng Cai, Junjie Yang, Kailun Li, Zhiyong Wang, Yaonan School of Robotics Hunan University China National Engineering Research Center of Robot Visual Perception and Control Technology Hunan University China College of Computer Science and Electronic Engineering Hunan University Changsha China

Functional dexterous grasping requires precise hand-object interaction, going beyond simple gripping. Existing affordance-based methods primarily predict coarse interaction regions and cannot directly constrain the grasping posture, leading to a disconnection between visual perception and manipulation. To address this issue, we propose a multi-keypoint affordance representation for functional dexterous grasping, which directly encodes task-driven grasp configurations by localizing functional contact points. Our method introduces Contact-guided Multi-Keypoint Affordance (CMKA), leveraging human grasping experience images for weak supervision combined with Large Vision Models for fine affordance feature extraction, achieving generalization while avoiding manual keypoint annotations. Additionally, we present a Keypoint-based Grasp matrix Transformation (KGT) method, ensuring spatial consistency between hand keypoints and object contact points, thus providing a direct link between visual perception and dexterous grasping actions. Experiments on public real-world FAH datasets, IsaacGym simulation, and challenging robotic tasks demonstrate that our method significantly improves affordance localization accuracy, grasp consistency, and generalization to unseen tools and tasks, bridging the gap between visual affordance learning and dexterous robotic manipulation. The source code and demo videos will be publicly available at https://***/PopeyePxx/MKA. Copyright © 2025, The Authors. All rights reserved.

关键词： Vision

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An Extended Stochastic Region of Attraction Estimation Method for Power Systems Under Stochastic Sustained Disturbances

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IEEE Transactions on Power Systems 2025年

作者： Yang, Yuerong Lin, Shunjiang Liu, Mingbo Deng, Leyi Xie, Huifan Li, Qifeng South China University of Technology School of Electric Power Engineering Guangzhou510640 China Power Dispatching and Control Center CSG Guangzhou510663 China University of Central Florida Department of Electrical and Computer Engineering OrlandoFL32816 United States

Stochastic sustained disturbances (SSDs) in renewable energy output impact power system transient stability. This study addresses the dynamic performance of power systems under SSDs using Lévy-driven stochastic differential algebraic equations (LDSDAEs). The stochastic region of attraction for LDSDAEs is divided into a stable region and a probabilistically stable region, collectively termed the extended stochastic region of attraction (ESRA). Traditional methods for estimating the deterministic region of attraction (RA) are inadequate. To address this issue, this study proposes an estimation method for the ESRA of LDSDAEs based on escape probability. We first derive an integro-differential equation for escape probabilities of initial system states near the attractor of LDSDAEs. We then explore the connection between escape probability and ESRA. Finally, we propose a wavelet basis functions-based extended projection method to solve the integro-differential equation, providing an analytical expression for escape probabilities and thus determining the ESRA. Case studies on the modified IEEE 39- and 118-bus systems demonstrate the proposed method's high computational accuracy and efficiency for power systems under Lévy disturbances. © 1969-2012 IEEE.

关键词： Integrodifferential equations

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Convergence of energy-based learning in linear resistive networks

arXiv

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arXiv 2025年

作者： Huijzer, Anne-Men Chaffey, Thomas Besselink, Bart van Waarde, Henk J. Bernoulli Institute for Mathematics Computer Science and Artificial Intelligence University of Groningen Groningen Netherlands CogniGron - Groningen Cognitive Systems and Materials Center Netherlands Control Group Department of Engineering University of Cambridge United Kingdom School of Electrical and Computer Engineering University of Sydney Australia

Energy-based learning algorithms are alternatives to backpropagation and are well-suited to distributed implementations in analog electronic devices. However, a rigorous theory of convergence is lacking. We make a first step in this direction by analysing a particular energy-based learning algorithm, Contrastive Learning, applied to a network of linear adjustable resistors. It is shown that, in this setup, Contrastive Learning is equivalent to projected gradient descent on a convex function, for any step size, giving a guarantee of convergence for the *** Codes 65K10, 68T05, 93B30, 93D99 © 2025, CC BY-SA.

关键词： Contrastive Learning

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Hybrid Routing in VANETs Using OLSR and Q-Learning for Enhanced Performance

Hybrid Routing in VANETs Using OLSR and Q-Learning for Enhan...

引用

IEEE SSD International Multi-Conference on Systems, Signals and Devices

作者： Wafa Akermi Nouha Alyaoui Awatef Benfradj Guiloufi Karim Chabir Computer Science and Telecommunications Department Higher Institute of Applied Science and Technology Gafsa University Gafsa Tunisia Laboratory of Modeling Analysis and Control of Systems (MACS) National Engineering School of Gabes (ENIG) Gabes University Gabes Tunisia Departement of Computer Engineering College of Computer Qassim University Buraidh Saudi Ababia

ISBN: (数字)9798331542726

ISBN: (纸本)9798331542733

High node mobility, rapid topology changes provide specific challenges for vehicular ad hoc networks (VANETs), which have an immediate impact on the routing protocols' performance. Traditional approaches, like the Optimized Link State Routing (OLSR) protocol, provide proactive route management but fail to fully account for critical dynamic parameters like link stability, relative vehicle speed, node distance, and bandwidth availability. This work proposes a hybrid method combining OLSR with Q-learning to facilitate real-time adaptive routing. The model leverages dynamic metrics to proactively evaluate links while employing Q-learning to optimize routing decisions based on rewards computed from performance factors like delay, packet loss rate, and link duration. Simulation results demonstrate that our approach significantly outperforms classic OLSR. The improvements include reduced packet loss rates, increased average throughput, lower average latency, and a reduction in control overhead. These findings confirm that integrating dynamic metrics and adaptive learning effectively addresses the challenges posed by VANETs.

关键词： Q-learning Simulation Vehicular ad hoc networks Packet loss Routing Throughput Routing protocols Stability analysis Topology Vehicle dynamics

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Application of digital twin technology in mining machinery education

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International Journal of Mechanical engineering Education 2025年

作者： Su, Chang Huang, Hao Wei, Zhongliang Xu, Naizhong State Key Laboratory of Mining Response and Disaster Prevention and Control in Deep Coal Mine Anhui University of Science and Technology Huainan China School of Mechanical and Electrical Engineering Anhui University of Science and Technology Huainan China School of Computer Science and Engineering Anhui University of Science and Technology Huainan China School of Miningand Engineering Anhui University of Science and Technology Huainan China

This paper investigates the application of digital twin technology in the teaching of mining machinery. Digital twin technology, an innovative approach that has significantly transformed engineering education and practice, creates dynamic digital replicas of physical entities in virtual environments. This allows for the simulation, prediction, and optimization of system performance without the need for physical intervention. In mechanical engineering education, digital twins provide students with real-time, interactive learning experiences, enabling a more intuitive understanding of complex mechanical structures. Students can conduct experiments and make modifications within a virtual setting, enhancing both learning efficiency and reducing experimental costs. Furthermore, this technology offers a scalable and replicable model for the future of engineering education. © The Author(s) 2025.

关键词： Virtual environments

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