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arXiv 2025年

作者： Luo, Gongning Xu, Mingwang Chen, Hongyu Liang, Xinjie Tao, Xing Ni, Dong Jeong, Hyunsu Kim, Chulhong Stock, Raphael Baumgartner, Michael Kirchhoff, Yannick Rokuss, Maximilian Maier-Hein, Klaus Yang, Zhikai Fan, Tianyu Boutry, Nicolas Tereshchenko, Dmitry Moine, Arthur Charmetant, Maximilien Sauer, Jan Du, Hao Bai, Xiang-Hui Raikar, Vipul Pai Montoya-Del-Angel, Ricardo Martí, Robert Luna, Miguel Lee, Dongmin Qayyum, Abdul Mazher, Moona Guo, Qihui Wang, Changyan Awasthi, Navchetan Zhao, Qiaochu Wang, Wei Wang, Kuanquan Wang, Qiucheng Dong, Suyu School of Computer Science and Technology Harbin Institute of Technology Harbin150001 China Department of Mathematics Faculty of Science National University of Singapore Singapore National-Regional Key Technology Engineering Laboratory for Medical Ultrasound School of Biomedical Engineering Shenzhen University Medical School Shenzhen University Shenzhen China Laboratory Shenzhen University Shenzhen China School of Biomedical Engineering and Informatics Nanjing Medical University Nanjing China Pohang Korea Republic of Heidelberg Division of Medical Image Computing Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Germany Heidelberg Germany HIDSS4Health - Helmholtz Information and Data Science School for Health Karlsruhe Heidelberg Germany Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Germany Department of Biomedical Engineering and Health KTH Royal Institute of Technology Stockholm Sweden France FathomX Singapore Saw Swee Hock School of Public Health National University of Singapore Singapore Philips Research University of Girona Spain Department of Robotics and Mechatronics Engineering DGIST Korea Republic of Department of Interdisciplinary Studies of Artificial Intelligence DGIST Korea Republic of National Heart and Lung Institute Faculty of Medicine Imperial College London London United Kingdom Centre for Medical Image Computing Department of Computer Science University College London London United Kingdom Lab School of Communication and Information Engineering Shanghai University Shanghai China Faculty of Science Mathematics and Computer Science Informatics Institute University of Amsterdam Amsterdam1090 GH Netherlands Department of Biomedical Engineering and Physics Amsterdam UMC Amsterdam1081 HV Netherlands Xi’an Jiaotong-Liverpool University China Department of Ultrasound Harbin Medical University Cancer Hospital No. 150 Haping Road Nangang

Breast cancer is one of the most common causes of death among women worldwide. Early detection helps in reducing the number of deaths. Automated 3D Breast Ultrasound (ABUS) is a newer approach for breast screening, which has many advantages over handheld mammography such as safety, speed, and higher detection rate of breast cancer. Tumor detection, segmentation, and classification are key components in the analysis of medical images, especially challenging in the context of 3D ABUS due to the significant variability in tumor size and shape, unclear tumor boundaries, and a low signal-to-noise ratio. The lack of publicly accessible, well-labeled ABUS datasets further hinders the advancement of systems for breast tumor analysis. Addressing this gap, we have organized the inaugural Tumor Detection, Segmentation, and Classification Challenge on Automated 3D Breast Ultrasound 2023 (TDSC-ABUS2023). This initiative aims to spearhead research in this field and create a definitive benchmark for tasks associated with 3D ABUS image analysis. In this paper, we summarize the top-performing algorithms from the challenge and provide critical analysis for ABUS image examination. We offer the TDSC-ABUS challenge as an open-access platform at https://***/ to benchmark and inspire future developments in1algorithmic research. © 2025, CC BY.

关键词： Image segmentation

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Optimizing the teaching efficiency of english classrooms in colleges and universities based on deep learning

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AIP Conference Proceedings 2024年第1期2937卷

作者： Y. Dileep Kumar M. Praveen Kumar A. Mahesh Reddy G. Vamsi Krishna G. V. Sunil Kumar Department of Electronics & Instrumentation Engineering SreeVidyanikethan Engineering College A. Rangampeta Tirupati-517102 India. Department of Control & Automation School of Electrical Engineering VIT University Katpadi Vellore 632014 Tamilnadu India. Department of Electronics &Communication Engineering Sri Venkatesa Perumal College of Engineering & Technology Puttur-517583 India. Department of Electronics & Instrumentation Engineering Vallurupalli Nageswara Rao Vignana Jyothi Institute of Engineering & Technology Chikkadapally Hyderabad500020 India. Project Manager Involute Automation (P) Ltd Balanagar Hyderabad-500037 Telangana India

An accurate assessment of the quality of English classroom instruction is essential for moving English education reform forward while also raising English education standards in Chinese colleges. A comparison model for evaluating the quality of English classroom teaching is built using SVM-LM (Support vector machine-Learning Machine) model together with Particle Swarm Optimization (PSO). The SVM-LM approach reduces the need for experiment samples while enhancing model fit because fewer hidden layer neurons are required for accuracy. According to results from experiments, utilizing the SVM- LM algorithm to assess English classroom instruction is a realistic option. Consequently, the study created English classroom teaching effectiveness index system has been successful and is intended to increase the standard of English language classroom instruction as well as how it is administered. The study results proved that the proposed algorithm works better than PSO.

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Depth-Map-Based Augmented Reality Holographic Stereogram: Method and Verification

SSRN

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SSRN 2022年

作者： Liu, Yunpeng Jing, Tao Lei, Ming Hu, Hairong Chen, Zhuo Lin, Min Li, Pei Jiang, Xiaoyu Yan, Xingpeng Department of Information Communication Army Academy of Armored Forces Beijing100072 China China Defense Science and Technology Information Center Beijing100037 China Department of Basic Education Army Academy of Armored Forces Beijing100072 China R & D Center for Intelligent Control and Advanced Manufacturing Research Institute of Tsinghua University in Shen Zhen Shen Zhen518057 China Department of Artificial Intelligence College of Information Systems Engineering Strategic Support Force Information Engineering University Zhengzhou450001 China

In this paper, a perspective image fusion algorithm of real-world and artificial scenes based on depth maps is proposed and applied to holographic printing to realize augmented reality display of holographic stereograms. The basic principle of the fusion algorithm is introduced, and the perspective image fusion with correct occlusion relation is implemented theoretically. Under the condition of sampling only once, an image generation algorithm with adjustable depth of artificial scenes is proposed and implemented. We use effective perspective image segmentation and mosaicking method for optical experiments, and verify the effectiveness of the proposed method. © 2022, The Authors. All rights reserved.

关键词： Three dimensional displays

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Conditional Adversarial Domain Adaption based on Self-attention

Journal of Network Intelligence

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Journal of Network Intelligence 2022年第1期7卷 175-188页

作者： Zhao, Li-Quan Zhang, Yu-Peng Teng, Zi-Ming Jiang, Zi-Cong Cui, Ying Kan, Zhong-Feng Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education Northeast Electric Power University Jilin132012 China College of information and communication Jilin University Changchun130012 China Graduate school of Information Science and Electrical Engineering Kyushu University Fukuoka819-0395 Japan Zhuhai Power Supply Bureau Guangdong Electric Power Corporation Zhuhai519000 China Jilin Power Supply Bureau Jilin Electric Power Corporation Jilin132012 China

Transfer learning is a deep learning method. It transforms knowledge in the source domain and feeds it to the target domain to improve the performance of the target domain model. The conditional adversarial domain adaption method is one of a number of transfer learning methods. It uses Resnet as the feature extraction network;however, Resnet can only determine the relationship between local features, and so its feature extraction accuracy is limited. To overcome this problem, a self-attention layer has been designed in order to determine the relationship among all features. This layer is added between the fourth layer and fifth layer of Resnet. Besides, a cross-entropy function is introduced into loss function, and a sigmoid layer is designed and used as the out layer in conditional adversarial domain adaption. These produce gradient disappearance in the deep network, and influence the discriminator training effect. To solve that problem, a least squares function is used as to design the loss function. The new type of loss function does not require the sigmoid function. Using theoretical analysis, we explain how this new loss function can be used to solve the gradient disappearance problem, thus rendering the network structure more stable. We compare our proposed method with other datasets, including Joint Adaptation Networks, adversarial domain adaptation, conditional adversarial domain adaption, Resnet-50, domain-adversarial neural network on imageCLEF-DA, Office-Home, and Office-31. Our simulation results show that the proposed conditional adversarial domain adaption based on self-attention method has the fastest convergence speed and highest accuracy the among different datasets. © 2022, Taiwan Ubiquitous Information CO LTD. All rights reserved.

关键词： Computer vision

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SPAHN novel approach for PL-AG gateway discovery for internet connectivity

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Peer-to-Peer Networking and Applications 2020年第4期14卷 2275-2284页

作者： Kannan, K. Sivaranjani, P. Sathish Kumar, S. Nalini, M. Balaji, V. R. Sanjeevi, P. Department of ECE R.M.K. College of Engineering and Technology Chennai India Department of Electronics and communication Engineering Kongu Engineering College Erode India Dept of Electrical and Electronics Engineering M. Kumarasamy College of Engineering Thalavapalayam India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of ECE Sri Krishna College of Engineering and Technology Coimbatore India Key Laboratory of Advanced Process Control for Light Industry Ministry of Education Jiangnan University Wuxi China

Internet Gateway(IGW) main role is detecting availability nodes and providing internet to Mobile Ad Hoc Network(MANET) have whenever connected to internet. Discovery time of gateway is changed based on the throughput and packet delay. Many of the situations the mobile nodes are have fixed host connection to the internet using minimum hop path, it is not good for waiting packets because of that packets are have longer path interface queue. This research paper object is avoided above the problem mentioning using a novel approach of SPAHN (Solving Problem of Ad-Hoc Network). This paper mainly focus is classify the routing protocols of load-aware in MANET and from this classify discover the proactive load-aware gateway (PL-AG) from a device into interface queue size and min-hop-metric. This novel approach has been allowing and gives better handoff between two internet gateways for fixed host seamless connectivity. We justify the performance of the SPAHN approach using two metrics like average end-to-end delay and throughput based on this examination the SPAHN system yield good simulation results comparably existing systems.

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Author Correction: Inferring time-varying generation time, serial interval, and incubation period distributions for COVID-19 ( doi 10.1038/s41467-022-35496-8, 13 dec 2022 )

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NATURE communicationS 2023年第1期14卷 1-1页

作者： Chen, Dongxuan Lau, Yiu-Chung Xu, Xiao-Ke Wang, Lin Du, Zhanwei Tsang, Tim K. Wu, Peng Lau, Eric H. Y. Wallinga, Jacco Cowling, Benjamin J. Ali, Sheikh Taslim WHO Collaborating Centre for Infectious Disease Epidemiology and Control School of Public Health Li Ka Shing Faculty of Medicine The University of Hong Kong Hong Kong Special Administrative Region China Laboratory of Data Discovery for Health Limited Hong Kong Science and Technology Park New Territories Hong Kong Special Administrative Region China College of Information and Communication Engineering Dalian Minzu University Dalian 116600 China Department of Genetics University of Cambridge Cambridge CB2 3EH UK Center for Infectious Disease Control National Institute for Public Health and the Environment (RIVM) Bilthoven The Netherlands Department of Biomedical Data Sciences Leiden University Medical Center Leiden The Netherlands

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Low Frequency Wide Bandwidth Antenna Design Using the Concept of Lumped Elements Integration

Low Frequency Wide Bandwidth Antenna Design Using the Concep...

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IEEE Asia-Pacific Conference on Antennas and Propagation (APCAP)

作者： Yinfeng Xia Yanyan Wang Yingsong Li Wei Xue Qiubo Ye College of Information and Communication Engineering Harbin Engineering University Harbin China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing China School of Information Engineering Jimei University Xiamen China

ISBN: (数字)9781728198057

ISBN: (纸本)9781728198064

In this paper, we will propose a super wide bandwidth low frequency antenna with a compact size for HF, VHF and UHF applications. The developed low frequency antenna consists of coplanar guide feeding structure, sleeve-like structure with lumped elements, reactance loading in the feeding. The antenna is investigated and evaluated in the HFSS, and the simulation results shows that the proposed antenna has a very wide fractional bandwidth of 191.9% from 10 MHz to 481 MHz for supporting the HF, VHF, UHF and P-band detection system applications in various platform like aircraft, ship and vehicles.

关键词： Simulation Loading UHF antennas Broadband antennas Marine vehicles Loaded antennas Wideband

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Integrating V2X solutions in intelligent green cities: an AI-driven point exchange system approach

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Neural Computing and Applications 2025年

作者： Talaat, Fatma M. Shaban, Warda M. ZainEldin, Hanaa Badawy, Mahmoud Elhosseini, Mostafa Faculty of Artificial Intelligence Kafrelsheikh University Kafrelsheikh Egypt Faculty of Computer Science & Engineering New Mansoura University Gamasa35712 Egypt Department of Communication and Electronics Engineering Nile Higher Institute for Engineering and Technology Mansoura Egypt Faculty of Artificial Intelligence and informatics Horus University New Damietta Egypt Computers and Control Systems Engineering Department Faculty of Engineering Mansoura University Mansoura35516 Egypt Computer Science and Information Department Applied College Taibah University Madinah42353 Saudi Arabia College of Computer Science and Engineering Taibah University Yanbu46421 Saudi Arabia

With rapid urbanization, smart cities have become essential for enhancing urban management and sustainability by integrating technological, social, and institutional innovations. Among these innovations, vehicle-to-everything (V2X) communications and electric vehicles (EVs) play a critical role in reducing carbon emissions and optimizing urban mobility. To address the existing gaps in holistic V2X integration, this paper presents a novel eco-assistive fog-based traffic management system (EAFTMS) that leverages a four-tier architecture (IoT, fog, cloud, and application) for scalable, real-time traffic optimization. A key innovation of this system is the AI-driven point exchange system (PES), designed to incentivize sustainable behaviors such as reducing unnecessary vehicle usage and promoting green lifestyle choices. Unlike conventional models, the proposed framework incorporates real-time behavioral monitoring, rewards-based sustainability programs, and V2X-enabled dynamic traffic control. Empirical validation demonstrates that EAFTMS outperforms existing models, including support vector regression (SVR), achieving a 30% reduction in latency, a 40% improvement in response time, a 25% increase in traffic flow efficiency, and a 35% reduction in CO2 emissions. These results highlight the framework’s potential to set new standards in intelligent green cities by offering scalable, practical, and environmentally impactful solutions to urban transportation challenges. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Urban transportation

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Intelligent embedded sensor based accident detection system and prevention system

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AIP Conference Proceedings 2022年第1期2519卷

作者： T. Dineshkumar R. Puviarasi K. Radhika T. Mangayarkarasi 1Department of Electronics and Communication Engineering Kongunadu College of Engineering and Technology Namakkal-Trichy Main Road Thottiam Tamil Nadu India 2Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Thandalam Tamil Nadu India 3Department of Electronics and Communication Engineering Geethanjali Institute of Science and Technology Nellore Andhra Pradesh India 4Department of Instrumentation and Control Engineering Sri Sairam Engineering College Chennai Tamil Nadu India

Many accidents now take place due to over-speed, drowsiness and drunken drivers. Statistics show that every four minutes people die in 2017 because of traffic crashes and thousands of accidents in India, which are higher in numbers than any other accidents, where people have died. But in the case of rescue indicators their lives may have been spared. We are also going to incorporate our thoughts on steps to mitigate the crash and precautionary measures. This proposal immediately identifies the precise location of the crash by GPS and allows GSM to transmit the data to the nearest rescue car, the police department and the individual’s family members. Accident can be detected by the accelerometer sensor and accident magnitude can be detected by the tilt sensor. The tilt angle of the chassis can be monitored. The driver’s sleepiness can be detected with a DMD. The driver’s somnolence is sensed and the driver is alerted by alarm by calculating the percentage of eyelid closing over the period. Through this tool, the speed of a car can be measured, who is responsible for the crash and the direction of GPS can be seen with the mobile theft detection program. Both data can be analyzed using the sensor value in a microcontroller and sent to the cloud site. The main benefits of the device are cost efficient, stable and victim-friendly, fast savings in energy usage, greater precision, time efficiency and the risk of human error are reduced.

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Tigc-Net: Transformer-Improved Graph Convolution Network for Spatio-Temporal Prediction

SSRN

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SSRN 2022年

作者： Chen, Kai Yang, Chunfeng Zhou, Zhengyuan Liu, Yao Ji, Tianjiao Sun, Weiya Chen, Yang School of Cyber Science and Engineering Southeast University Nanjing210096 China Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education Nanjing210096 China The College of Software Engineering Southeast University Nanjing210096 China Laboratory of Image Science and Technology The School of Computer Science and Engineering Southeast University Nanjing210096 China Jiangsu Key Laboratory of Molecular and Functional Imaging Department of Radiology Zhongda Hospital Southeast University Nanjing210009 China Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing School of Computer Science and Engineering Southeast University Nanjing210096 China NHC Key Laboratory of Medical Virology and Viral Diseases National Institute for Viral Disease Control and Prevention Chinese Center for Disease Control and Prevention Beijing China Beijing Institute of Tracking and Communication Technology Beijing100094 China

Modeling spatio-temporal sequences is an important topic yet challenging for existing neural networks. Most of the current spatio-temporal sequence prediction methods usually capture features separately in temporal and spatial dimensions or employ multiple mutually independent local spatio-temporal graphs to represent a spatio-temporal sequence. The first kind of method mentioned above is difficult to mine the complex spatio-temporal correlations, while the other is limited for the accuracy of long-term predictions. To handle these issues, this paper proposes a Transformer-Improved Graph Convolution Network for spatio-temporal prediction. Specifically, the temporal location encoding method is exploited to derive the spatio-temporal characteristics of the sequence utilizing a spatio-temporal feature fusion network. In addition, a spatio-temporal attention network is developed to enhance the spatio-temporal correlation of the sequence, and the dynamic spatial features of sequence are further extracted through the adaptive graph convolution network. A private dataset and a public dataset are employed to demonstrate the performance of the proposed TIGC-Net. The qualitative and quantitative results show that the proposed TIGC-Net can extract dynamic spatiotemporal properties more effectively, enhance the spatio-temporal correlation of sequences and improve the prediction accuracy compared with four stateof-the-art. © 2022, The Authors. All rights reserved.

关键词： Convolution

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