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15th International Conference on computer Research and Development, ICCRD 2023

作者： Yu, Jiyang Huang, Dan Li, Jinyang Li, Wenjie Wang, Xianjie Shi, Xiaolong China Academy of Space Technology Beijing Institute of Spacecraft System Engineering Beijing China China Research and Development Academy of Machinery Equipment Beijing Chongqing China Chongqing University of Technology Chongqing Beijing China Chongqing Feima Innovation Research Institute Intelligent Equipment Research Department Chongqing China College of Computer Science and Engineering Chongqing University of Technology Chongqing China

ISBN: (纸本)9781665487504

The design architecture of backtracking calculation control processing for space large manipulator control is proposed. SRAM-Type FPGA is used to build the hardware full three-mode computing core, and the processor architecture is designed based on FPGA. The register value and cache access value of the single instruction calculation process of the three computing processors are used to compare the three modes in the anti-fuse FPGA. At the same time, the results of three computing processors were compared by SPARC CPU. Fault backtracking calculation control, when a single computing module occurs an error, first detect the instruction execution position of the faulty module, and then carry out register-level data recovery for the faulty module, and restore to the instruction before the fault to accelerate the operation to complete the 'backtracking' process, until the other two modules complete the calculation synchronization. According to the theoretical derivation and analysis, it is clear that under the linear increasing detection window, the maximum time of fault recovery is only related to the three-mode ratio transmission rate. According to this conclusion, the hardware digital logic design of the key modules of fault detection and recovery is carried out. The overall structure is simple and easy to implement, digital logic only occupies less than 100,000 logic gates. Compared with traditional methods, the fault recovery efficiency is improved by at least 90%. © 2023 IEEE.

关键词： Field programmable gate arrays (FPGA)

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Compressed VGGNet for Automatic COVID-19 Disease Detection from CT Scan Images 8th

Compressed VGGNet for Automatic COVID-19 Disease Detection f...

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8th IFIP TC 12 International Conference on computer, Communication and Signal Processing, ICCCSP 2024

作者： Vinothini, R. Niranjana, G. Dhamanti, Inge Lailiyah, Syifa’ul Yakub, Fitri Department of Computer Science and Engineering SRM Institute of Science and Technology Kattankulathur 603203 India Department of Computing Technologies School of Computing SRM Institute of Science and Technology Kattankulathur 603203 India Department of Health Policy and Administration Faculty of Public Health Universitas Airlangga Surabaya Indonesia Faculty of Health Medicine and Life Sciences Universitas Airlangga East Java Indonesia Electronic System Engineering Department Malaysia-Japan International Institute of Technology Kuala Lumpur Malaysia

ISBN: (纸本)9783031736162

Millions of people across the globe were affected by this rapidly spreading disease by the year 2020. Contamination of the respiratory system results from contracting COVID-19. Nonetheless, establishing a conclusive diagnosis of COVID-19 may prove difficult due to the subtle differences it shares with conventional pneumonia and the complexities involved in identifying areas of infection. A growing number of approaches based on deep learning (DL) are being proposed for the automated detection of COVID-19 from CT scan images. By employing the data pretreatment methodology, unprocessed image data becomes prepared for further analysis. The proposed framework utilised transfer learning to construct VGGNet, which is capable of discerning the Covid-19 disease. The model was subsequently assessed in comparison to the most sophisticated models, VGG16 and VGG19, using the SARS-COV2 CT scan dataset. This dataset contains 1230 CT scans of non-infected images and 1252 CT scans of Covid-19-infected images. The model has achieved 99% accuracy, 98% precision, 97% recall, and 98% F1-score, among other performance metrics. © IFIP International Federation for Information Processing 2025.

关键词： COVID-19

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Advanced Real-Time IoMT system for Early Gastric Cancer Detection through Integrated Grid-Search Multimodal Gating Network and Robust Embedded technology

Advanced Real-Time IoMT System for Early Gastric Cancer Dete...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Liu, Xian-Xian Xu, Mingkun Wei, Yuanyuan Yin, Huifeng Fong, Simon Luo, Wei Gao, Juntao University of Macau The Department of Computer and Information Science China Guangdong Institute of Intelligence Science and Technology Zhuhai China The Chinese University of Hong Kong The Department of Biomedical Engineering Hong Kong Beijing China University of Macau The Department of Computer and Information Science China The First People's Hospital of Foshan The Institute of Clinical Medicine Guangzhou China Beijing China

ISBN: (纸本)9798350351255

Achieving real-time, remote control and precise localization of early gastric cancer (EGC) lesions in endoscopic capsules is a significant obstacle in biomedical imaging development. This work outlines an innovative integrated system that uses an effective combination of Zynq UltraScale+ and Gizwits IoT to overcome this challenge. Our work employs a fully convolutional neural network underpinning grid-search clustering-driven multi-modals gating information local patch learning (GS-MGIF-LPLs). This system, designed as an adaptive location computing acceleration platform (ACAP), elegantly marries a double threshold fast search strategy with patch-based FCNN, fueling efficient training, testing, and performance metrics with an accuracy of 99.53%, precision coefficient of 86.06%, and an IoU of 84.26%. Upon benchmarking against four EGC types, our GS-MGIF-LPLs model demonstrates exceptional superiority against five established methods, providing a significant stride in computational efficiency and diagnostic advancements for gastrointestinal diseases. © 2024 IEEE.

关键词： Edge computing

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An Isomerism Learning Model to Solve Time-Varying Problems Through Intelligent Collaboration

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IEEE/CAA Journal of Automatica Sinica 2023年第8期10卷 1772-1774页

作者： Zhihao Hao Guancheng Wang Bob Zhang Leyuan Fang Haisheng Li the Department of Computer and Information Science University of MacaoMacao 999078China the School of Data Science the Chinese University of Hong KongShenzhen 518172 Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen 518000 China Industrial Control Systems Cyber Emergency Response Team Beijing 100040China IEEE the College of Electrical and Information Engineering Hunan UniversityChangsha 410082 the Peng Cheng Laboratory Shenzhen 518000China the Beijing Key Laboratory of Big Data Technology for Food Safety Beijing Technology and Business UniversityBeijing 100048 the School of Computer Science and Engineering Beijing Technology and Business UniversityBeijing 100048China

Dear Editor,This letter deals with a solution for time-varying problems using an intelligent computational(IC)algorithm driven by a novel decentralized machine learning approach called isomerism *** order to meet the challenges of the model’s privacy and security brought by traditional centralized learning models,a private permissioned blockchain is utilized to decentralize the model in order to achieve an effective coordination,thereby ensuring the credibility of the overall model without exposing the specific parameters and solution process.

关键词： ism thereby letter

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First wetland mapping at 10-m spatial resolution in South America using multi-source and multi-feature remote sensing data

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science China Earth sciences 2024年第10期67卷 3252-3269页

作者： Weiwei SUN Gang YANG Yuling HUANG Dehua MAO Ke HUANG Lin ZHU Xiangchao MENG Tian FENG Chao CHEN Yong GE Department of Geography and Spatial Information Techniques Ningbo UniversityNingbo 315211China State Key Laboratory of Resources and Environmental Information System Institute of Geographic Sciences&Natural Resources ResearchChinese Academy of SciencesBeijing 100101China Natural Resources and Planning Bureau Shanghai Pudong New DistrictShanghai 200100China Key Laboratory of Wetland Ecology and Environment Northeast Institute of Geography and AgroecologyChinese Academy of SciencesChangchun 130102China Faculty of Electrical Engineering and Computer Science Ningbo UniversityNingbo 315211China School of Geography Science and Geomatics Engineering Suzhou University of Science and TechnologySuzhou 215009China

Wetland degradation has been accelerating in recent years globally. Accurate information on the geographic distribution and categories of wetlands is essential for their conservation and management. Despite being the world′s fourth largest continent, South America has limited research on wetland mapping, and there is currently no available map that provides comprehensive information on wetland distribution and categories in the region. To address this issue, we used Sentinel-1, Sentinel-2 and SRTM data, developed a sample collection method and a wetland mapping method with a collection of multi-source features such as optical features, polarization features and shape features for South American wetlands. We produced a 10-m resolution wetland map based on the Google Earth Engine(GEE) platform. Our Level-1 wetland cover map accurately captured six wetland sub-categories with an overall accuracy of 96.24% and a kappa coefficient of 0.8649, while our Level-2 water cover map included five sub-categories with an overall accuracy of 97.23% and a kappa coefficient of0.9368. The results show that the total area of existing wetlands in South America is approximately 1,737,000 km~2, which is6.8% of the total land area. Among the ten wetland categories, shallow sea had the largest area(960,527.4 km~2), while aquaculture ponds had the smallest area 1513.6 km~2. Swamp had the second largest area(306,240.1 km~2). Brazil, Argentina,Venezuela, Bolivia, and Colombia were found to have the largest wetland areas, with Brazil and Colombia having the most diverse wetland categories. This product can serve as baseline data for subsequent monitoring, management, and conservation of South American wetlands.

关键词： Wetland mapping Google Earth Engine Sentinel imagery South America

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Learning against Non-credible Second-Price Auctions 25

Learning against Non-credible Second-Price Auctions

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34th ACM Web Conference, WWW 2025

作者： Wang, Qian Xia, Xuanzhi Yang, Zongjun Deng, Xiaotie Kong, Yuqing Zhang, Zhilin Wang, Liang Yu, Chuan Xu, Jian Zheng, Bo CFCS School of Computer Science Peking University Beijing China Alibaba Group Beijing China Department of Computer Science and Technology Tsinghua University Beijing China Department of Industrial Engineering and Operations Research Columbia University New York United States CMAR Institute for AI Peking University Beijing China

ISBN: (纸本)9798400712746

The standard framework of online bidding algorithm design assumes that the seller commits himself to faithfully implementing the rules of the adopted auction. However, the seller may attempt to cheat in execution to increase his revenue if the auction belongs to the class of non-credible auctions. For example, in a second-price auction, the seller could create a fake bid between the highest bid and the second highest bid. This paper focuses on one such case of online bidding in repeated second-price auctions. At each time t, the winner with bid bt is charged not the highest competing bid dt but a manipulated price pt = α0dt + (1 − α0)bt, where the parameter α0 ∈ [0, 1] in essence measures the seller’s credibility. Unlike classic repeated-auction settings where the bidder has access to samples (ds)st=−11, she can only receive mixed signals of (bs)st=−11, (ds)st=−11 and α0 in this problem. The task for the bidder is to learn not only the bid distributions of her competitors but also the seller’s credibility. We establish regret lower bounds in various information models and provide corresponding online bidding algorithms that can achieve near-optimal performance. Specifically, we consider three cases of prior information based on whether the credibility α0 and the distribution of the highest competing bids are known. Our goal is to characterize the landscape of online bidding in non-credible second-price auctions and understand the impact of the seller’s credibility on online bidding algorithm design under different information structures. © 2025 Copyright held by the owner/author(s).

关键词： bidding algorithms

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A new symbol timing and carrier frequency offset estimation algorithm for OTFS for B5G 2

A new symbol timing and carrier frequency offset estimation ...

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2nd IEEE International Conference on Emerging Research in Computational science, ICERCS 2024

作者： Thangavel, Gunasekaran Rajan, Sheeba Subbulekshmi, S. Anthonisamy, Arun Ganapathi, Rohini Velankanni, Gowri University of Technology and Applied Sciences Department of Electronics and Communication Engineering Muscat Oman SRM Institute of Science and Technology School of Computing Tiruchirapalli India Prathyusha Engineering College Department of Electronics and Communication Engineering Chennai India Panimalar Engineering College Department of Computer Science and Business System Chennai India St. Joseph's Institute of Technology Department of Electronics and Communication Engineering Chennai India K. Ramakrishnan College of Technology Department of Electronics and Communication Engineering Trichy India

ISBN: (纸本)9798331534950

In B5G networks, accurate symbol timing estimate and estimation of the carrier frequency offset are both absolutely necessary. Particularly in highly mobile and interference-ridden settings, these factors are of paramount importance for sustaining high-throughput, dependable wireless communication. Estimating symbol timing and carrier frequency offset in OTFS can be difficult due to factors including the time-varying nature of Doppler shifts and interference from surrounding cells as well as the non-stationary nature of wireless channels. In such changing settings, traditional algorithms often fail to produce reliable estimations. To account for frequency changes caused by things like Doppler effects and drift, this research uses Machine Learning-Based Carrier Frequency Prediction Analysis (ML-CFPA) to implement a dynamic carrier frequency offset estimation and correction system. To preserve signal coherence and lower interference in B5G settings, carrier frequency offset adjustment is essential. Communication solutions with high performance are required for these applications to ensure they can function normally despite the complexity and changeability of their contexts. The outcomes demonstrate the higher performance of the algorithm in contrast to conventional approaches, highlighting its applicability to B5G networks. Its potential as a crucial enabling technology for the B5G future has been solidified by these simulations, which verify its capacity to maintain reliable communication under a variety of real-world conditions. © 2024 IEEE.

关键词： Graphical user interfaces

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ME-GraphSAGE: Minority Class Feature Enhanced GraphSAGE for Automatic Labeling of Coronary Arteries 18th

ME-GraphSAGE: Minority Class Feature Enhanced GraphSAGE for ...

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18th Chinese Conference on Image and Graphics technology and Application Conference, IGTA 2023

作者： Ding, Yang Fu, Tianyu Chen, Sigeng Xiao, Deqiang Fan, Jingfan Song, Hong Yu, Yang Yang, Jian Beijing Engineering Research Center of Mixed Reality and Advanced Display School of Optics and Photoics Beijing Institute of Technology Beijing China School of Medical Technology Beijing Institute of Technology Beijing China School of Computer Science and Technology Beijing Institute of Technology Beijing China Department of Cardiac Surgery Beijing Anzhen Hospital Capital Medical University Beijing China

ISBN: (纸本)9789819975488

Automatic labeling of coronary artery segments improves efficiency in the diagnosis and treatment of coronary artery disease, but faces challenges due to the class imbalance between main and side branches. State-of-the-art methods primarily focus on position-direction and pixel features, which leads to suboptimal performance when dealing with bifurcated segments. In this paper, we propose a minority class feature enhanced GraphSAGE (ME-GraphSAGE), which alleviates class imbalance by generating minority class nodes. We extract bifurcation features from Digital Subtraction Angiography (DSA) images taken from four commonly observed views of coronary arteries. These features, along with other relevant ones, are fed into ME-GraphSAGE to enhance the accuracy of segment labeling in bifurcated regions. By combining the results from the four views, a higher-level sixteen-segment-based coronary labeling is obtained. Our method is evaluated on a dataset of 205 coronary DSA sequences. The experimental results show that ME-GraphSAGE significantly outperforms state-of-the-art methods in labeling coronary artery branches. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Bifurcation (mathematics)

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RPF-ELD: Regional Prior Fusion using Early and Late Distillation for Breast Cancer Recognition in Ultrasound Images

RPF-ELD: Regional Prior Fusion using Early and Late Distilla...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Wang, Haosen Zhang, Gengyuan Zhao, Yingnan Lai, Fang Cui, Wenwei Xue, Jiexiao Wang, Qihang Zhang, Hao Lin, Yi Beijing Jiaotong University School of Computer Science and Information Technology Beijing China Sun Yat-Sen University School of Computer Science and Engineering Guangzhou China Harbin Engineering University School of Computer Science and Technology Harbin China University of Denver Department of Computer Science Denver United States University of York Wentworth College York United Kingdom Institute of Heilongjiang The Second Surveying and Mapping Harbin China Southeast University School of Information Science and Engineering Nanjing China Harbin Institute of Technology Faculty of Computing Harbin China Harbin Medical University School of Interdisciplinary Medicine and Engineering Harbin China

ISBN: (纸本)9798350386226

Breast cancer is one of the main factors responsible for the deaths of women worldwide. Ultrasound imaging is a key method for early detection of breast cancer, which can help patients gain valuable treatment time and improve their chances of survival. The computer-aided system of breast cancer recognition has started to receive attention due to the lack of experienced sonographers. Presently, most breast cancer recognition methods typically suffer from uncertain locations and proportions of tumor regions in ultrasound images. In this paper, we propose a novel Regional Prior Fusion framework using Early and Late Distillation (RPF-ELD), inspired by the knowledge distillation of the teacher-student framework, for breast cancer recognition in ultrasound images. Firstly, to enhance the concentration of the tumor regions, a high-performing prior-fused model is trained as the teacher model using ultrasound images with the corresponding regional prior information. Next, a diagnostic model is trained as the student model under the prior-fused model distillation using early and late features to implicitly obtain the regional prior knowledge. Finally, the diagnostic model recognizes the categories of breast cancer from only ultrasound images using the experience from distilled prior knowledge. Two publicly released datasets are used to evaluate the proposed RPF-ELD framework. Experimental results demonstrate that the proposed RPF-ELD surpasses current state-of-the-art methods. © 2024 IEEE.

关键词： Ultrasonic imaging

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

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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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