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arXiv 2024年

作者： Tang, Hao Lu, Junhao Huang, Guoheng Li, Ming Chen, Xuhang Zhong, Guo Tan, Zhengguang Li, Zinuo School of Computer Science and Technology Guangdong University of Technology Guangdong China School of Computer Science and Engineering Huizhou University Huizhou China Zhejiang Institute of Optoelectronics Jinhua China Zhejiang Key Laboratory of Intelligent Education Technology and Application Jinhua China School of Information Science and Technology Guangdong University of Foreign Studies Guangzhou China School of Physics Maths and Computing University of Western Australia WA Australia

In Few-Shot Learning (FSL), traditional metric-based approaches often rely on global metrics to compute similarity. However, in natural scenes, the spatial arrangement of key instances is often inconsistent across images. This spatial misalignment can result in mismatched semantic pixels, leading to inaccurate similarity measurements. To address this issue, we propose a novel method called the Layer-Wise Features Metric of Semantic-Pixel Matching (LWFM-SPM) to make finer comparisons. Our method enhances model performance through two key modules: (1) the Layer-Wise Embedding (LWE) Module, which refines the cross-correlation of image pairs to generate well-focused feature maps for each layer;(2)the Semantic-Pixel Matching (SPM) Module, which aligns critical pixels based on semantic embeddings using an assignment algorithm. We conducted extensive experiments to evaluate our method on four widely used few-shot classification benchmarks: miniImageNet, tieredImageNet, CUB-200-2011, and CIFAR-FS. The results indicate that LWFM-SPM achieves competitive performance across these benchmarks. Our code will be publicly available on https://***/Halo2Tang/Code-for-LWFM-SPM. Copyright © 2024, The Authors. All rights reserved.

关键词： Zero-shot learning

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A Proposed Radio Environment Monitoring System of Ali Observatory 35

A Proposed Radio Environment Monitoring System of Ali Observ...

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35th General Assembly and Scientific Symposium of the International Union of Radio Science, URSI GASS 2023

作者： Zhang, Haiyan Wang, Yu Huang, Shijie Hu, Hao Zhao, Chuanjiang Wu, Guifang National Astronomical Observatories Chinese Academy of Sciences 20A Datun Road Chaoyang District Beijing100101 China Hebei Key Laboratory of Radio Astronomy Technology Hebei Shijiazhuang050081 China Research Center for Intelligent Computing Platforms Zhejiang Laboratory Zhongtai Street Yuhang District Zhejiang Hangzhou311100 China Beijing Boer Communication Technology Co. Ltd Zhongguancun Software Park No. 8 Dongbei Wangxi Road Haidian District Beijing100089 China China Electric Power Research Institute Co. Ltd 15 Qinghe Xiaoying East Road Haidian District Beijing100192 China

ISBN: (纸本)9789463968096

With its extremely dry air and high altitude above 5000 meters, the Ail Observatory located in the Tibet plateau has been a promising site for several astronomical instruments working in the optical or millimeter wave bands. In addition to protect the dark sky around the Ali Observatory, the measures to protect local radio environment have also been discussed. Based on the requirements of radio astronomical instruments, a radio environment monitoring system has been proposed. This paper describes the preliminary design of the system, including the technical specification and the system composition. In the near future, this system will be put into use. © 2023 International Union of Radio Science.

关键词： Observatories

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Hydrogel Immobilized Papain-Cuso4@Sio2 Biomimetic Nanozyme for Real-Time Colorimetric Detection

SSRN

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SSRN 2025年

作者： Hu, Qiannan Zhang, Yaoqi Yang, Xuxu Dong, Yingying Xiang, Yizhou Wong, Tuck-Whye Luo, Zisheng Ban, Zhaojun Xuan, Jinghong Li, Li College of Biosystems Engineering and Food Science Fuli Institute of Food Science Key Laboratory of Agro-Products Postharvest Handling Ministry of Agriculture and Rural Affairs Zhejiang University Hangzhou310023 China National-Local Joint Engineering Laboratory of Intelligent Food Technology and Equipment Zhejiang Key Laboratory for Agro-Food Processing Zhejiang Engineering Laboratory of Food Technology and Equipment Zhejiang University Hangzhou310023 China Center for X-Mechanics Department of Engineering Mechanics Zhejiang University Hangzhou310027 China Sustainable and Smart Materials Laboratory Department of Biomedical Engineering and Health Sciences Universiti Teknologi Malaysia Johor Johor Bahru81310 Malaysia School of Biological and Chemical Engineering Zhejiang University of Science and Technology Hangzhou310023 China Agricultural and Rural Development Service Center of Liaoning Province Shenyang110032 China

Nanozyme-based biosensors hold great promise for hazardous substance detection but face challenges in scalability, stability, and real-time application. This study presents a biomimetic papain-CuSO4 nanozyme synthesized via a simple, one-step hydrothermal process. Inspired by natural copper-containing enzymes, the nanozyme exhibits excellent peroxidase-like activity, driven by a catalytic mechanism involving electron transfer from cysteine residues to Cu2+ ions, forming Cu+ and Cu-S catalytic centers. These centers mediate Fenton-like reactions, generating hydroxyl radicals (⋅OH) that oxidize 3,3',5,5'-tetramethylbenzidine (TMB), producing a colorimetric signal. To further enhance stability and catalytic performance, SiO2 was incorporated to obtain papain-CuSO4@SiO2 nanozyme, which prevents aggregation, promotes Cu+ regeneration, and exposes more active sites. To demonstrate its practical utility, the nanozyme was immobilized in a hydrogel matrix, leveraging the confinement effect to amplify activity and stability. Integrated into a multi-enzyme cascade, the system demonstrated rapid, sensitive solanine detection, utilizing acetylcholinesterase (ACHE) inhibition as the sensing mechanism. A smartphone-based platform enabled detection within 40 minutes, with a limit of detection (LOD) of 0.0335 mg L−1. The system exhibited high specificity and stability in complex matrices, validated through tests on spiked potato and lake water samples. This study introduces a portable, cost-effective nanozyme-hydrogel system for real-time toxic compound detection. Its practical applicability and mechanistic insights provide a foundation for advancing nanozyme-based biosensors in food safety and environmental monitoring. © 2025, The Authors. All rights reserved.

关键词： Papain

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Privacy-Preserving Federated Learning Framework with Resisting Source Inference Attacks in Smart Healthcare

SSRN

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SSRN 2024年

作者： Chenghe, Dong Xu, Guangquan Zhang, Jianhong Bai, Hongpeng Hao, Peng College of Intelligence and Computing Tianjin University Tianjin300354 China School of Big Data Qingdao Huanghai University Qingdao China Tianjin300350 China Department of Electrical and Computer Engineering North China University of Technology Beijing100144 China Key Laboratory of Intelligent Education Technology Application of Zhejiang Province School of Computer Science and Technology Zhejiang Normal University Jinhua321004 China

Federated Learning (FL) is a distributed machine learning technology that is extensive applications in smart healthcare, since it allows multiple medical institutions to jointly train medical diagnostic models without sharing their local sensitive dataset with others. Despite its benefits, FL is susceptible to source inference attacks and model poisoning. To avoid these issues, we propose a federated learning framework for smart healthcare that preserves privacy and employs certificateless ring signatures to counteract potential attacks. During the collaborative training process of the FL model, certificateless ring signatures are used to obscure the origin of parameter updates, ensuring the anonymity of the parameter sources. This approach has the advantage of being computationally efficient due to no complicated pairing operations. Furthermore, compared with the previous certificateless ring signature schemes, our proposed scheme is more effective in reducing the success rate of source inference attacks, and is efficient in terms of computation and communication overhead. © 2024, The Authors. All rights reserved.

关键词： Federated learning

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Intracardiac Electrogram Signals key Feature Points Recognition Using Dense Convolutional Network

Intracardiac Electrogram Signals Key Feature Points Recognit...

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Pattern Recognition and Machine Learning (PRML), IEEE International Conference on

作者： Jiang Yihang Gu Kaihao Wu Xiaomei Department of Biomedical Engineering School of information Science and Technology Fudan University Shanghai China Department of Biomedical Engineering Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention (MICCAI) of Shanghai School of information Science and Technology Fudan University Academy for Engineering and Technology Shanghai Engineering Research Center of Assistive Devices Shanghai China Yiwu Research Institute of Fudan University Zhejiang China

ISBN: (数字)9798350355925

ISBN: (纸本)9798350355932

During radiofrequency ablation procedures for atrial fibrillation patients, physicians can assist in locating ablation targets by analyzing the conduction pathways and low-voltage areas of the intracardiac electrogram signals. The feature points of intracardiac electrogram signals serve as criteria for assessing excitation patterns and low-voltage areas. Therefore, extracting feature points from intracardiac signals is of significant value. To identify key points in intracardiac electrogram signals through deep learning, this experiment collected intracardiac electrogram signals from 21 clinical patients, which were then filtered using wavelet transformation. Subsequently, each excitation was treated as a signal group, indicating the key feature points within. Finally, a total of 36,183 groups of intracardiac electrogram signals annotated with key points were obtained. The dataset consisted of data from 17 patients allocated for training, data from 2 patients reserved for testing, and data from the remaining 2 patients designated for validation purposes. Training was conducted using a DenseNet network incorporating both convolutional attention and feature pyramid modules. The final network successfully identifies the feature points of intracardiac electrogram signals. It outputs six feature points, including the starting point, first rising point, first falling point, peak point, valley point, and termination point, with average absolute errors of only 0.34, 0.27, 0.31, 0.01, 0.27, and 0.42 ms, respectively. The final detection results are highly satisfactory, indicating the potential utility of the findings in assisting the analysis of intracardiac electrogram signals. The research outcomes presented in this paper lay a foundation for the analysis of intracardiac electrogram signals.

关键词： Training Wavelet transforms Radio frequency Low voltage Training data Medical services Myocardium Feature extraction Pattern recognition Testing

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ASRNet: Adversarial Segmentation and Registration Networks for Multispectral Fundus Images

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Computer Systems Science & Engineering 2021年第3期36卷 537-549页

作者： Yanyun Jiang Yuanjie Zheng Xiaodan Sui Wanzhen Jiao Yunlong He Weikuan Jia School of Information Science and Engineering Shandong Normal UniversityJinan250358China Key Laboratory of Intelligent Computing&Information Security in Universities of Shandong Shandong Provincial Key Laboratory for Novel Distributed Computer Software TechnologyInstitute of Biomedical SciencesShandong Normal UniversityJinan250358China Shandong Provincial Hospital Affiliated to Shandong University Jinan250021China INSA Lyon University of LyonCNRSInsermVilleurbanne69621CedexFrance

Multispectral imaging (MSI) technique is often used to capture imagesof the fundus by illuminating it with different wavelengths of light. However,these images are taken at different points in time such that eyeball movementscan cause misalignment between consecutive images. The multispectral imagesequence reveals important information in the form of retinal and choroidal bloodvessel maps, which can help ophthalmologists to analyze the morphology of theseblood vessels in detail. This in turn can lead to a high diagnostic accuracy of several diseases. In this paper, we propose a novel semi-supervised end-to-end deeplearning framework called “Adversarial Segmentation and Registration Nets”(ASRNet) for the simultaneous estimation of the blood vessel segmentation andthe registration of multispectral images via an adversarial learning process. ASRNet consists of two subnetworks: (i) A segmentation module S that fulfills theblood vessel segmentation task, and (ii) A registration module R that estimatesthe spatial correspondence of an image pair. Based on the segmention-drivenregistration network, we train the segmentation network using a semi-supervisedadversarial learning strategy. Our experimental results show that the proposedASRNet can achieve state-of-the-art accuracy in segmentation and registrationtasks performed with real MSI datasets.

关键词： Deep learning deformable image registration image segmentation multispectral imaging(MSI)

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AID-DTI: ACCELERATING HIGH-FIDELITY DIFFUSION TENSOR IMAGING WITH DETAIL-PRESERVING MODEL-BASED DEEP LEARNING

arXiv

引用

arXiv 2024年

作者： Fan, Wenxin Cheng, Jian Li, Cheng Ma, Xinrui Yang, Jing Zou, Juan Wu, Ruoyou Liu, Qiegen Wang, Shanshan Paul C. Lauterbur Research Center for Biomedical Imaging Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Shenzhen China University of Chinese Academy of Sciences Beijing China State Key Laboratory of Software Development Environment Beihang University Beijing China Key Laboratory of Data Science and Intelligent Computing Institute of International Innovation Beihang University Zhejiang Hangzhou China Peng Cheng Laboratory Guangdong Shenzhen China Nan Chang University Jiang Xi Nan Chang China

Deep learning has shown great potential in accelerating diffusion tensor imaging (DTI). Nevertheless, existing methods tend to suffer from Rician noise and detail loss in reconstructing the DTI-derived parametric maps especially when sparsely sampled q-space data are used. This paper proposes a novel method, AID-DTI (Accelerating hIgh fiDelity Diffusion Tensor Imaging), to facilitate fast and accurate DTI with only six measurements. AID-DTI is equipped with a newly designed Singular Value Decomposition (SVD)-based regularizer, which can effectively capture fine details while suppressing noise during network training. Experimental results on Human Connectome Project (HCP) data consistently demonstrate that the proposed method estimates DTI parameter maps with fine-grained details and outperforms three state-of-the-art methods both quantitatively and qualitatively. Copyright © 2024, The Authors. All rights reserved.

关键词： Singular value decomposition

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An intelligent Resource Scheduling Method with Edge Channel Deployment for BPM 19

An Intelligent Resource Scheduling Method with Edge Channel ...

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2022 IEEE SmartWorld, 19th IEEE International Conference on Ubiquitous Intelligence and computing, 2022 IEEE International Conference on Autonomous and Trusted Vehicles Conference, 22nd IEEE International Conference on Scalable computing and Communications, 2022 IEEE International Conference on Digital Twin, 8th IEEE International Conference on Privacy computing and 2022 IEEE International Conference on Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PriComp/Metaverse 2022

作者： Liu, Bowen Dou, Wanchun Zhou, Xiaokang Zhang, Xuyun Qi, Lianyong Dai, Fei Chen, Chaochao Nanjing University State Key Laboratory for Novel Software Technology Nanjing China Shiga University Faculty of Data Science Hikone Japan Macquarie University Department of Computing Sydney Australia College of Computer and Software Qingdao China Southwest Forestry University College of Big Data and Intelligent Engineering Kunming China Zhejiang University College of Computer Science and Technology Hangzhou China

ISBN: (纸本)9798350346558

Edge computing is a novel computing paradigm that offers kinds of resources at the network edge. In edge computing, terminal users are connected to edge servers via the wireless network and there are various channels in each wireless link. These wireless channels are limited resource while different channel has different cost and service ability. The dynamic changes of users' status make it harder to find an appropriate method to satisfy the BPM requirements of channel deployment. With this observation, it is a tricky challenge to make a trade-off between the system cost(rental price) and the service ability(number of users). In view of this challenge, an intelligent resource scheduling method, named EdgeIRS, is proposed in this paper. In the technical sense, the EdgeIRS method can accommodate most users at the edge with a minimum cost of deploying channel resources in an online way. Its performance is analyzed theoretically and the experiments verify the superiority of the method. © 2022 IEEE.

关键词： Cost effectiveness

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A DECOUPLED STRUCTURE PRESERVING SCHEME FOR THE POISSON-NERNST-PLANCK NAVIER-STOKES EQUATIONS AND ITS ERROR ANALYSIS

arXiv

引用

arXiv 2023年

作者： Yu, Ziyao Cheng, Qing Shen, Jie Wang, Changyou Department of Mathematics West LafayetteIN47907 United States Key Laboratory of Intelligent Computing and Application Tongji University Ministry of Education Department of Mathematics Tongji University Shanghai200092 China Eastern Institute of Technology Zhejiang Ningbo315200 China

We consider in this paper a numerical approximation of Poisson-Nernst-Planck-Navier-Stokes (PNP-NS) system. We construct a decoupled semi-discrete and fully discrete scheme that enjoys the properties of positivity preserving, mass conserving, and unconditionally energy stability. Then, we establish the well-posedness and regularity for the initial and (periodic) boundary value problem of the PNP-NS system under suitable assumptions on the initial data, and carry out a rigorous convergence analysis for the fully discretized scheme. We also present some numerical results to validate the positivity preserving property and the accuracy of our scheme. Copyright © 2023, The Authors. All rights reserved.

关键词： Navier Stokes equations

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Detection of Children’s Molars Based on Noise Filter

Research Square

引用

Research Square 2022年

作者： Yan, Zhiwen Chen, Ying Song, Jinlong Zhu, Jia Li, Jianbo School of Computing Science South China Normal University Guangzhou China Key Laboratory of Intelligent Education Technology Application of Zhejiang Province Zhejiang Normal University Zhejiang China Stomatological Hospital Southern Medical University Guangzhou China

Pit and fissure sealant is for children aged seven to twelve years to prevent molars from becoming caries. In this paper, we propose a new detection framework to identify whether children need pit and fissure sealing. We divide the framework into two parts: molar detection and molar classification. According to the characteristics of teeth, we propose to use the clustering method to filter the bounding box in the object detection part. In the region divided by clustering, we only keep one detection frame in the same category. In the classification part, we propose a noise filtering layer based on wavelet transform for feature extraction. We map the training samples to another space in the training process based on metric learning to increase the distance between categories and improve the accuracy of classification. © 2022, CC BY.

关键词： Wavelet transforms

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