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3rd International Conference on Artificial Intelligence, Automation, and High-Performance Computing, AIAHPC 2023

作者： Wang, Ying Gao, Bo Li, Jinze Hao, Yibo Nation’s Research Laboratory of Computer Application Engineering Xi’an Aeronautics Computing Technique Research Institute AVIC Xi’an China Xi’an XinHang Talent Service Co. Ltd. Xi’an China

ISBN: (数字)9781510666580

ISBN: (纸本)9781510666573

As a special I/O channel in the control system, the eTPU (Enhanced Time Processing Unit) cooperates with the host computer to complete the corresponding functions. As the most foundational and important function in the eTPU, which is completed through I/O channel. This paper makes a comprehensive analysis of the thread entry and the pre-defined channel mode in the process of implementing the I/O function. Through the design of thread entry and the optimal selection of the pre-defined channel mode, the I/O function can be efficiently completed. By adopting clever design ideas and optimization, eTPU can provide more accurate and efficient functions. Especially through the implementation part of this article, the usage method of eTPU is more convenient to master. © 2023 sPIE.

关键词： computer control systems

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3D Point Cloud Classification Model Based on Local Location Information 5

3D Point Cloud Classification Model Based on Local Location ...

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5th International Conference on computer Information science and application Technology, CIsAT 2022

作者： Chen, Teng Jiang, Lifen Zheng, Fengbo Wu, Panpan Chen, Bowei Li, Kang Nation's Research Laboratory School of Computer and Information Engineering Tianjin Normal University Tianjin300387 China

ISBN: (纸本)9781510660076

For the existing 3D point cloud model classification method PointNet only extracts global feature information, but for the problem of insufficient local feature information extraction, a network that can take into account the combination of global features and local features is proposed, so as to realize 3D point Classification of cloud models. This paper proposes a classification network based on local location information. First, the point cloud model is sampled in multiple groups to obtain the center point of each group of samples, and then the radius is set to obtain its spherical neighborhood, and the points in the neighborhood and their center points are used as local location features. In information fusion, the acquired local information features are used as high-dimensional features of the neighborhood space through feature mapping. By analogy, multiple sets of sampled local features are integrated to realize the classification of the point cloud model. The experimental results show that the classification network can achieve a classification accuracy of 92.5% on the ModelNet40 data set, which is higher than the current mainstream algorithm. © 2022 sPIE.

关键词： Classification (of information)

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Joint Task Partitioning and Resource Allocation in UAV-Enabled Vehicular Edge Computing Based on Deep Reinforcement Learning

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IEEE Internet of Things Journal 2025年第11期12卷 15453-15466页

作者： Liang, Hongbin Zhang, Han Ale, Laha Hong, Xintao Wang, Lei Jia, Qiong Zhao, Dongmei Southwest Jiaotong University School of Transportation and Logistics National United Engineering Laboratory of Integrated and Intelligent Transportation National Engineering Laboratory of Integrated Transportation Big Data Application Technology Chengdu611756 China Southwest Jiaotong University School of Computing and Artificial Intelligence Chengdu611756 China Southwest Jiaotong University School of Information Science and Technology Chengdu611756 China Chinese Academy of Sciences Laboratory of Noise and Vibration Research Institute of Acoustics Beijing100190 China McMaster University Department of Electrical and Computer Engineering HamiltonONL8S 4K1 Canada

Vehicle Edge Computing (VEC) leverages compact cloud computing at the mobile network edge to meet the processing and latency needs of vehicles. By bringing computation closer to the vehicles, VEC reduces data transmission, minimizes latency, and boosts performance for compute-intensive applications. However, during peak hours of urban road traffic, the scarce computational resources available at edge servers could pose challenges in fulfilling the processing needs of vehicles. Introducing Unmanned Aerial Vehicles (UAVs) as supplementary edge computing nodes could significantly mitigate the aforementioned issue. In this paper, we propose a flexible edge computing framework in which a fleet of UAVs function as mobile computational service providers, offering computation offloading services to multiple vehicles. We design and optimize a computation offloading model for the UAV-enabled vehicle edge computing environment. The proposed model tackles the task offloading challenge, aiming to optimize UAV revenue and task processing efficiency while considering the constraints of UAVs' restricted computational power and energy resources. Towards this end, our model jointly considers two key factors: task partitioning and computational resource allocation. To tackle the challenges posed by the aforementioned non-convex optimization problem, we construct a Markov Decision Process (MDP) model for the multi-UAV-enabled mobile edge computing system and introduce an innovative Multi-Agent Deep Reinforcement Learning (MADRL) framework addressing the decision-making challenge represented by MDP model. Comprehensive simulation outcomes illustrate that our devised task offloading technique outperforms other optimization methods. © 2014 IEEE.

关键词： Convex optimization

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A weakly supervised pavement crack segmentation based on adversarial learning and transformers

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Multimedia systems 2025年第3期31卷 1-21页

作者： Apedo, Yvon Tao, Huanjie School of Computer Science Northwestern Polytechnical University Xi’an People’s Republic of China Engineering and Research Center of Embedded Systems Integration (Northwestern Polytechnical University) Ministry of Education Xi’an People’s Republic of China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Xi’an People’s Republic of China

While pixel-level crack segmentation has demonstrated significant potential in infrastructure inspections, its reliance on detailed annotations poses challenges for widespread adoption due to the time-consuming and expensive nature of such labeling. In response, weakly supervised crack segmentation has garnered attention as it eliminates the need for pixel-level annotations. However, existing methods, primarily based on class activation maps (CAM), involve complex training processes and suffer from poor performance due to misalignment between CAM-generated labels and the true target in the image. To address these challenges, we propose a weakly supervised approach for crack segmentation that simultaneously generates synthetic crack images and performs segmentation through adversarial learning. Unlike traditional methods that rely on coarse labels, our approach leverages synthetic crack images with corresponding labels, effectively eliminating issues of misalignment and noisy pseudo-labels. Our method introduces an encoder-decoder architecture for the segmentation model, incorporating a Transformer-based Feature Enhancement module (TFE) in the encoder. This module is specifically designed to capture long-range dependencies and efficiently integrate both high- and low-level features. Additionally, the model includes a Hilo block to extract both high- and low-frequency information from the images, along with a Progressive shrinking Decoder (PsD) to aggregate and refine adjacent feature maps. Extensive experiments were conducted, and our model achieved an ODs of 67.12% on the CrackForest dataset, 60.11% on the Crack500 dataset, and 44.80% on the AEL dataset. These results demonstrate that our model outperforms several existing weakly supervised pavement segmentation methods across these datasets.

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WheatNet: Attentional Path Aggregation Feature Pyramid Network for Precise Detection and Counting of Dense and Arbitrary-Oriented Wheat spikes

IEEE Transactions on AgriFood Electronics

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IEEE Transactions on AgriFood Electronics 2024年第2期2卷 606-616页

作者： Jiao, Lin Liu, Qihuang Liu, Haiyun Chen, Peng Wang, Rujing Liu, Kang Dong, shifeng Anhui University School of Internet Hefei230031 China Chinese Academy of Sciences National Engineering Research Center for Agro-Ecological Big Data Analysis and Application Hefei230031 China Chinese Academy of Sciences Institute of Intelligent Machines Hefei Institutes of Physical Science Hefei230031 China University of Science and Technology of China Hefei230031 China Anhui University National Engineering Research Center for Agro-Ecological Big Data Analysis and Application Information Materials and Intelligent Sensing Laboratory of Anhui Province School of Internet Hefei230601 China Anhui University Institutes of Physical Science and Information Technology Hefei230601 China Chinese Academy of Science Institute of Intelligent Machines Hefei Institutes of Physical Science Hefei230031 China University of Sheffield Department of Computer Science SheffieldS10 2TN United Kingdom Institute of Dataspace Hefei230093 China

Achieving the precise and real-time detection of wheat spikes play a crucial role in wheat growth monitoring for precision agriculture community. Machine-learning methods are commonly introduced to automatically detect and count the wheat spikes, which need carefully selected hand-crafted feature descriptors, leading to time-consuming and poor performance. The deep learning has become a promising technology for the accurate detection wheat spikes, owing to its powerful ability of feature extraction. However, the obtained wheat spike images from UAV still have serious overlap, dense distribution, various orientations, and large aspect ratios, leading to poor performance of recent wheat spike detection method. To address the demand of precise and fast detection and counting of wheat spike with dense distribution and arbitrary-orientation, a novel deep learning-based method, WheatNet, has been proposed. The attention mechanism has been introduced the process of feature fusing to highlight the important features of wheat spike as well as inhibit the useless information. Additionally, to optimize the parameters of the network, a loss function with soft dynamic label assignment is adopted to reduce the number of low-quality matches, which provides significant performance gains over other wheat spike detectors. Furthermore, to achieve the precise detection of wheat spike with multi-orientations, a large-scale oriented wheat spike dataset has been constructed, named RoWheat, including 900 images and 50419 annotations with dense distribution and various orientation. Experimental studies demonstrate that the proposed WheatNet achieves a recall of 99.7% and mAP of 91.8%, showing its promising performance gain compared to other state-of-the-art methods. © 2023 IEEE.

关键词： Image annotation

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seaConvNeXt:A Lightweight Two-Branch Network Architecture for Efficient Prediction of specific IHC Proteins and Antigens on Hematoxylin and Eosin(H&E)Images

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Big Data Mining and Analytics 2024年第4期7卷 1212-1236页

作者： Yuli Chen Guoping Chen Guoying shi Yao Zhou Jiayang Bai Germán Corredor Cheng Lu Xiujuan Lei School of Computer Science Shaanxi Normal UniversityXi’an 710119China Department of Pathology Shaanxi Provincial Hospital of Traditional Chinese MedicineXi’an 710003China Wallace H.Coulter Department of Biomedical Engineering Emory University and Georgia Institute of TechnologyAtlanta 30322GAUSAand also with Joseph Maxwell Cleland Atlanta VA Medical CenterDecatur 30033GAUSA Department of Radiology and Medical Research Institute Guangdong Provincial People’s Hospital(Guangdong Academy of Medical Sciences)Southern Medical UniversityGuangzhou 510080Chinaand also with Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and ApplicationGuangzhou 510080China

Immunohistochemistry(IHC)is a vital technique for detecting specific proteins and antigens in tissue sections using antibodies,aiding in the analysis of tumor growth and ***,IHC is costly and time-consuming,making it challenging to implement on a large *** address this issue,we introduce a method that enables virtual IHC staining directly on Hematoxylin and Eosin(H&E)***,we have developed a novel registration technique,called Bi-stage Registration based on density Clustering(BiReC),to enhance the registration efficiency between H&E and IHC *** method involves automatically generating numerous Regions Of Interest(ROI)labels on the H&E image for model training,with the labels being determined by the intensity of IHC ***,we propose a novel two-branch network architecture,called seaConvNeXt,which integrates a lightweight squeeze-Enhanced Axial(sEA)attention mechanism to efficiently extract and fuse multi-level local and global features from H&E images for direct prediction of specific proteins and *** seaConvNeXt consists of a ConvNeXt branch and a global fusion *** ConvNeXt branch extracts multi-level local features at four stages,while the global fusion branch,including an sEA Transformer module and three global blocks,is designed for global feature extraction and multiple feature *** experiments demonstrate that seaConvNeXt outperforms current state-of-the-art methods on two public datasets with corresponding IHC and H&E images,achieving an AUC of 90.7%on the HER2sC dataset and 82.5%on the CRC *** results suggest that seaConvNeXt has great potential for predicting virtual IHC staining on H&E images.

关键词： Immunohistochemistry(IHC) Bi-stage Registration based on density Clustering(BiReC) automatic label generation seaConvNeXt attention mechanism multi-level local and global features virtual IHC staning prediction

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Attention-based unsupervised prompt learning for sAM in leaf disease segmentation

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Knowledge-Based systems 2025年 320卷

作者： Tian, Luda Yuan, Yingchun En, Qing Ma, Wei Zhang, Guanghui Liang, Fangfang School of Information Science and Technology Hebei Agricultural University Baoding071001 China Hebei Key Laboratory of Agricultural Big Data Baoding071001 China Hebei Engineering Research Center for Agricultural Remote Sensing Application Baoding071001 China Lab School of Computer Science Carleton University OttawaK1S 5B6 Canada Faculty of Information Technology Beijing University of Technology Beijing100124 China

In modern agriculture, leaf disease segmentation is crucial for crop disease management and yield improvement. since most deep learning-based segmentation models require extensive annotations, pursuing unsupervised methods becomes a practical solution. Although advanced models like the segment Anything Model (sAM) can produce precise class-agnostic masks without annotations, their reliance on human-interactive prompts limits their utility to unsupervised disease segmentation tasks. To address this limitation, we present UPLs, a novel three-stage framework that leverages unsupervised prompt learning to enable sAM for leaf disease segmentation. This method employs unsupervised contrastive learning to progressively segment leaf and disease areas and automatically generate disease prompts for sAM. Concretely, for fine and intricate lesions, we utilize an improved high-frequency attention mechanism to extract high-frequency features from the leaf area, and construct contrastive losses between global priors and foreground/background features. Furthermore, we devise a strategy to automatically generate disease-related prompts from the segmented leaf and initial lesion regions, enabling sAM to refine disease boundaries without human intervention. Experiments on three public plant disease segmentation datasets show that UPLs outperforms existing non-fully supervised segmentation methods in accuracy and robustness. source code is available at https://***/Tianluda/UPLs. © 2025 Elsevier B.V.

关键词： Unsupervised learning

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Meta: Data Compression and Event Detection Grand Challenge 2024 With sPRsound Dataset

IEEE Data Descriptions

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IEEE Data Descriptions 2024年 1卷 122-130页

作者： Qing Zhang Changyan Chen shuhua Yuan Jing Zhang Jiajun Yuan Huajie Huang Yuhang Zhang Rui Pan Xuya Jiang Jian Zhao Yongfu Li Yong Yin Liebin Zhao Guoxing Wang Yong Lian Department of Micro-Nano Electronics and MoE Key Laboratory of Artificial Intelligence Shanghai Jiao Tong University Shanghai China Shanghai Engineering Research Center of Intelligence Pediatrics Shanghai China The Pediatric AI Clinical Application and Research Center Shanghai Children’s Medical Center Shanghai Jiao Tong University School of Medicine Shanghai China Department of Respiratory Medicine Shanghai Children’s Medical Center Shanghai Jiao Tong University School of Medicine Shanghai China Child Health Advocacy Institute China Hospital Development Institute Shanghai Jiao Tong University Shanghai China School of Computer Engineering and Science Shanghai University Shanghai China Shanghai Children’s Medical Center School of Medicine Shanghai Jiao Tong University Sanya China School of Design Jiangnan University Wuxi China

The development of digital stethoscopes and automatic respiratory sound algorithms is crucial for accelerating diagnosis, reducing physician workload, and lowering mortality rates from respiratory diseases. However, transmitting data from digital stethoscopes to cloud or other storage devices require substantial storage and transmission capacities, especially for wearable devices used for long-term monitoring. Besides, current automatic algorithms often predict labels for segmented sound events, lacking precision in detecting the onsets and offsets of respiratory sound events. To address these challenges, we organized a grand challenge inviting the community to develop data compression and event detection algorithms to reduce the storage and transmission burden and event segmentation workload. A new testing set was prepared to evaluate the performance of the submissions. The top teams presented their work at the 20th IEEE Biomedical Circuits and systems Conference (BioCAs) 2024. IEEE sOCIETY/COUNCIL Circuits and systems (CAs) society DATA TYPE/LOCATION sound and Text; Worldwide DATA DOI/PID 10.21227/bhw7-2044; 10.21227/nfkk-1x47

关键词： Event detection Medical diagnostic imaging Predictive models Data compression Training stethoscope Pulmonary diseases Pediatrics Error analysis Circuits and systems

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FedDBL: Communication and Data Efficient Federated Deep-Broad Learning for Histopathological Tissue Classification

arXiv

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arXiv 2023年

作者： Deng, Tianpeng Huang, Yanqi Han, Guoqiang shi, Zhenwei Lin, Jiatai Dou, Qi Liu, Zaiyi Guo, Xiao-Jing Chen, C.L. Philip Han, Chu The School of Computer Science and Engineering South China University of Technology Guangdong Guangzhou510006 China Southern Medical University Guangzhou510080 China Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application Guangzhou510080 China The Department of Computer Science and Engineering The Chinese University of Hong Kong 999077 Hong Kong The Department of Breast Pathology and Lab Tianjin Medical University Cancer Institute and Hospital National Clinical Research Center of Cancer Key Laboratory of Cancer Prevention and Therapy Tianjin Tianjin’s Clinical Research Center for Cancer Key Laboratory of Breast Cancer Prevention and Therapy Tianjin Medical University Ministry of Education Tianjin300060 China

Histopathological tissue classification is a fundamental task in computational pathology. Deep learning-based models have achieved superior performance but centralized training with data centralization suffers from the privacy leakage problem. Federated learning (FL) can safeguard privacy by keeping training samples locally, but existing FL-based frameworks require a large number of well-annotated training samples and numerous rounds of communication which hinder their practicability in the real-world clinical scenario. In this paper, we propose a universal and lightweight federated learning framework, named Federated Deep-Broad Learning (FedDBL), to achieve superior classification performance with limited training samples and only one-round communication. By simply associating a pre-trained deep learning feature extractor, a fast and lightweight broad learning inference system and a classical federated aggregation approach, FedDBL can dramatically reduce data dependency and improve communication efficiency. Five-fold cross-validation demonstrates that FedDBL greatly outperforms the competitors with only one-round communication and limited training samples, while it even achieves comparable performance with the ones under multiple-round communications. Furthermore, due to the lightweight design and one-round communication, FedDBL reduces the communication burden from 4.6GB to only 276.5KB per client using the ResNet-50 backbone at 50-round training. since no data or deep model sharing across different clients, the privacy issue is well-solved and the model security is guaranteed with no model inversion attack risk. Code is available at https://***/tianpeng-deng/FedDBL. © 2023, CC BY.

关键词： Tissue

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Mitigation of allergic asthma in mice: A compound mixture comprising luteolin, arbutin, and marmesin from Gerbera Piloselloides Herba by suppression of PI3K/Akt pathway

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Heliyon 2024年第19期10卷 e37632页

作者： Liu, Chunhua He, Yu Zhou, Kun Wang, Hong Zhou, Meng sun, Jia Lu, Yuan Huang, Yong Wang, Yonglin Liu, Ting Li, Yongjun State Key Laboratory of Functions and Applications of Medicinal Plants Engineering Research Center for the Development and Application of Ethnic Medicine and TCM (Ministry of Education) Guizhou Medical University Guiyang 550004 China Guizhou Provincial Key Laboratory of Pharmaceutics Guizhou Medical University Guiyang 550004 China School of Pharmacy Guizhou Medical University Guiyang 561113 China Department of Pediatrics Guizhou Provincial People's Hospital Guiyang 550002 China Computer Education and Information Technology Center Guizhou Medical University Guiyang 561113 China

Background: Gerberae Piloselloidis Herba (GPH) exhibits notable efficacy in alleviating allergic asthma. Previous studies in our research have identified a mixture of luteolin, arbutin, and marmesin as effective components of GPH in treating allergic asthma. However, the underlying mechanism remains unclear. This study aims to elucidate the molecular mechanism of these active components. Method: Using an ovalbumin (OVA)-induced allergic asthma mouse model, various treatment groups were administered, including GPH, the active component mixture (termed “Mixture”) containing luteolin, arbutin, and marmesin, and a positive drug (dexamethasone, DEX). Relevant indices were assessed, including behavioral characteristics, inflammatory cell counts, cytokine levels, histopathological examination of lung tissue, apoptosis, and expression of key proteins such as Caspase-3, Bax, Bcl-2, PI3K, p-PI3K, Akt, and p-Akt. The effect of the Mixture on the PI3K/Akt signaling pathway was further verified using the PI3K inhibitor LY294002. Results: The Mixture significantly alleviated asthma symptoms, decreased IgE levels, cytokine levels (IL-4, IL-5, IL-13 and TNF-α), and the number of inflammatory cells in serum or bronchoalveolar lavage fluid (BALF), leading to the alleviation of lung pathological lesions. Additionally, the Mixture reduced the expression of Bax and Caspase-3 while increasing Bcl-2 expression, resulting in mitigated apoptosis in lung tissue. Furthermore, there appeared a decrease in the levels of PI3K and p-PI3K, as well as the ratio of p-Akt to Akt in the Mixture group, indicating the suppression of PI3K and Akt phosphorylation. Interestingly, the effects of the Mixture were comparable to those of GPH, LY294002, or the combination of LY294002 with the Mixture. Conclusion: The study confirms that the Mixture containing luteolin, arbutin, and marmesin indeed alleviates allergic asthma induced by OVA in mice by suppressing the PI3K/Akt signaling pathway. These findings hig

关键词： Active compounds Allergic asthma Apoptosis Gerbera Piloselloides Herba Inflammation PI3K/Akt pathway

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