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A characterization of solvability for a class of networks

science china(information sciences) 2012年第4期55卷 747-754页

作者： yuan chen & kan haibin shanghai key lab of intelligent information processing,school of computer science,fudan university,shanghai 2.04.3,china 1. shanghai key lab of intelligent information processing School of Computer Science Fudan University Shanghai 200433 China

In this paper,we find that Property P can be generalized to characterize the solvability of a kind of networks with any number of sources,thus partially answering the open problem as to whether there are properties similar to Property P to characterize the solvability of some *** an application,for a given integer n,we construct such a solvable network that has no solvable solution if its alphabet size is less than n.

关键词： capacity coding capacity network coding solvability

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The maximal rates and minimal decoding delay of more general complex orthogonal designs

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science china(information sciences) 2010年第9期53卷 1826-1832页

作者： LI yuan,kan haibin*,yuan chen & MA HuanFei The shanghai key lab of intelligent information processing,school of computer science,fudan university,shanghai 2.04.3,china 1. The shanghai key lab of intelligent information processing School of Computer Science Fudan University Shanghai 200433 China

Space-time block codes based on complex orthogonal design have been widely investigated for their remarkable *** this paper,we propose a definition of more general complex orthogonal designs,which permits arbitrary complex factor with unit modulus in each *** prove that the maximal rate is still (m+1)/2m for n = 2m or 2m-1 antennas and minimal delay is also lower-bounded by(2m/(m-1)).Thus,allowing complex factor with unit modulus in each entry will not improve the main performance criterion of space-time block codes.

关键词： space-time block codes complex orthogonal design more general complex orthogonal design maximal rates minimal delays

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基于区块链和去中心不可否认属性签名的分布式公钥基础设施方案

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中国科学：信息科学 2022年第6期52卷 1135-1148页

作者：袁和昕刘百祥阚海斌陈泽宁复旦大学计算机科学技术学院上海市智能信息处理重点实验室上海200433 上海市区块链工程技术研究中心上海200433 电磁波信息科学教育部重点实验室上海200433

灵活有效的身份体制方案一直是信息时代的核心需求之一.传统的中心化公钥基础设施存在诸多缺陷,而已有的运行在区块链上的分布式的公钥基础设施同样存在诸如性能、鲁棒性、不可否认性、身份灵活性等方面的问题.本文创新地将区块链与去... 详细信息

灵活有效的身份体制方案一直是信息时代的核心需求之一.传统的中心化公钥基础设施存在诸多缺陷,而已有的运行在区块链上的分布式的公钥基础设施同样存在诸如性能、鲁棒性、不可否认性、身份灵活性等方面的问题.本文创新地将区块链与去中心不可否认属性签名结合,提出一种新型分布式公钥基础设施方案,方案利用门限算法和属性签名对身份进行细粒度的管理,并引入非交互式零知识证明使得证书具有不可否认的性质,利用区块链的共识机制进行证书库的同步以实现分布式的身份认证.本文通过实验仿真和分析并结合具体场景,证明了该方案在安全性和可用性上都满足实际落地的需求.

关键词：区块链公钥基础设施属性签名门限算法零知识证明身份认证

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Development and Validation of an Automatic Ultrawide-Field Fundus Imaging Enhancement System for Facilitating Clinical Diagnosis:A Cross-Sectional Multicenter Study

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Engineering 2024年第10期41卷 179-188页

作者： Qiaoling Wei Zhuoyao Gu Weimin Tan Hongyu Kong Hao Fu Qin Jiang Wenjuan Zhuang Shaochi Zhang Lixia Feng Yong Liu Suyan Li Bing Qin Peirong Lu Jiangyue Zhao Zhigang Li Songtao yuan Hong Yan Shujie Zhang Xiangjia Zhu Jiaxu Hong chen Zhao Bo Yan Department of Ophthalmology Eye&ENT Hospital of Fudan UniversityShanghai 200231China school of computer science Shanghai Key Laboratory of Intelligent Information ProcessingFudan UniversityShanghai 200433China The Affiliated Eye Hospital of Nanjing Medical university Nanjing 210029China Ningxia Eye Hospital People’s Hospital of Ningxia Hui Autonomous RegionThird Clinical Medical College of Ningxia Medical UniversityYinchuan 750002China Department of Ophthalmology The First Affiliated Hospital of Anhui Medical UniversityHefei 230022China Southwest Hospital/Southwest Eye Hospital Third Military Medical University(Army Medical University)Chongqing 400038China key lab of Visual Damage and Regeneration Restoration of Chongqing Chongqing 400038China Department of Ophthalmology The Affiliated Xuzhou Municipal Hospital of Xuzhou Medical UniversityXuzhou 221000China Department of Ophthalmology Suqian First Hospital of Jiangsu Province HospitalSuqian 223800China Department of Ophthalmology The First Affiliated Hospital of Soochow UniversitySuzhou 215006China Department of Ophthalmology The Fourth Affiliated Hospital of China Medical UniversityEye Hospital of China Medical UniversityShenyang 110000China Department of Ophthalmology The First Affiliated Hospital of Zhengzhou UniversityZhengzhou 450052China Department of Ophthalmology Jiangsu Province HospitalNanjing 210029China Shaanxi Eye Hospital Xi’an People’s Hospital(Xi’an Fourth Hospital)The Affiliated People’s Hospital of Northwest UniversityXi’an 710004China

In ophthalmology,the quality of fundus images is critical for accurate diagnosis,both in clinical practice and in artificial intelligence(AI)-assisted *** the broad view provided by ultrawide-field(UWF)imaging,pseudocolor images may conceal critical lesions necessary for precise *** address this,we introduce UWF-Net,a sophisticated image enhancement algorithm that takes disease characteristics into *** the fudan university ultra-wide-field image(FDUWI)dataset,which includes 11294 Optos pseudocolor and 2415 Zeiss true-color UWF images,each of which is rigorously annotated,UWF-Net combines global style modeling with feature-level lesion *** consistency loss is also applied to maintain fundus feature integrity,significantly improving image *** and qualitative evaluations demonstrated that UWF-Net outperforms existing methods such as contrast limited adaptive histogram equalization(CLAHE)and structure and illumination constrained generative adversarial network(StillGAN),delivering superior retinal image quality,higher quality scores,and preserved feature details after *** disease classification tasks,images enhanced by UWF-Net showed notable improvements when processed with existing classification systems over those enhanced by StillGAN,demonstrating a 4.62%increase in sensitivity(SEN)and a 3.97%increase in accuracy(ACC).In a multicenter clinical setting,UWF-Net-enhanced images were preferred by ophthalmologic technicians and doctors,and yielded a significant reduction in diagnostic time((13.17±8.40)s for UWF-Net enhanced images vs(19.54±12.40)s for original images)and an increase in diagnostic accuracy(87.71%for UWF-Net enhanced images vs 80.40%for original images).Our research verifies that UWF-Net markedly improves the quality of UWF imaging,facilitating better clinical outcomes and more reliable AI-assisted disease *** clinical integration of UWF-Net holds great

关键词： Ultrawide-field imaging Fundus photography Image enhancement algorithm Artificial intelligence Multicenter study Artificial intelligence-assisted diagnostics Diagnostic accuracy

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