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WPANFIS:combine fuzzy neural network with multiresolution for network traffic prediction

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The Journal of china Universities of posts and telecommunications 2010年第4期17卷 88-93页

作者： LI Rui chen jian-ya liu {4.-jie wang zhen-kai school of information and Telecommunication engineering Beijing University of Posts and Telecommunications Beijing 100876 China china Unicorn Beijing 100140 China

A novel methodology for prediction of network traffic, WPANFIS, which relies on wavelet packet transform （WPT） for multi-resolution analysis and adaptive neuro-fuzzy inference system （ANFIS） is proposed in this article. The widespread existence of self-similarity in network traffic has been demonstrated in earlier studies, which exhibits both long range dependence （LRD） and short range dependence （SRD）. Also, it has been shown that wavelet decomposition is an effective tool for LRD decorrelation. The new method uses WPT as extension of wavelet transform which can decoorrelate LRD and make more precisely partition in the high-frequency section of the original traffic. Then ANFIS which can extract useful information from the original traffic is implemented in this study for better prediction performance of each decomposed non-stationary wavelet coefficients. Simulation results show that the proposed WPANFIS can achieve high prediction accuracy in real network traffic environment.

关键词： network traffic prediction WPT ANFIS

Modeling in-network caching and bandwidth sharing performance in information-centric networking

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The Journal of china Universities of posts and telecommunications 2013年第2期20卷 99-105页

作者： wang Guo-qing HUANG Tao liu jiang chen jian-ya liu yun-jie Key Laboratory of Universal Wireless communications of Ministry of Education Beijing University of Posts and Telecommunications school of Telecommunication engineering Beijing University of Posts and Telecommunications

information-centric networking （ICN） proposes a content-centric paradigm which has some attractive advantages, such as network load reduction, low dissemination latency, and energy efficiency. In this paper, based on the analytical model of ICN with receiver-driven transport protocol employing least-recently used （LRU） replacement policy, we derive expressions to compute the average content delivery time of the requests＇ arrival sequence of a single cache, and then we extend the expressions to a cascade of caches＇ scenario. From the expressions, we know the quantitative relationship among the delivery time, cache size and bandwidth. Our results, analyzing the trade-offs between performance and resources in ICN, can be used as a guide to design ICN and to evaluation its performance.

关键词： ICN LRU miss probability content delivery time

Dynamic recovery for survivable virtual network embedding

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The Journal of china Universities of posts and telecommunications 2014年第3期21卷 77-84页

作者： LU Bo HUANG Tao SUN Xiao-chuan chen jian-ya liu {4.-jie beijing Key Laboratory of Network System Architecture and Convergence Beijing University of Posts and Telecommunications school of information engineering Hebei United University

Network virtualization is a promising way to overcome the current ossification of the Intemet. It is essential challenge to find effective, efficient and robust embedding algorithms for recovering virtual network. The virtual network mapping algorithm based on integer programming which was proposed months ago. But it did consider the faults of physical network resources, which is so called survivable virtual network embedding （VNE） problem. Previous strategies for enabling survivability in network virtualization focused on providing protection for the physical network or enhancing the virtual networks by providing backup physical resources in advance, and treated all the physical failures as link failures. In the article, a dynamic recovery method is proposed to solve the survivable virtual network embedding problem based on the integer programming VNE algorithm. The dynamic recovery method doesn＇t need to backup physical resources and it makes more substrate resources which can be used in the embedding. The dynamic recovery process will be activated only when physical failures occur. Different algorithms are used to recovery node and link failures. Simulations show that the method helps to recover almost all of physical failures by finding the substitute nodes and paths, and its performance is very close to that of pure VNE method without considering physical failures.

关键词： network virtualization integer programming survivable virtual network embedding dynamic recovery

Analysis of prediction performance in wavelet minimum complexity echo state network

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The Journal of china Universities of posts and telecommunications 2013年第4期20卷 59-66页

作者： CUI Hong-yan FENG chen liu yun-{4. Key Laboratory of Network System Architecture and Convergence Beijing University of Posts and Telecommunications school of information and communication engineering Beijing University of Posts and Telecommunications

Echo state network （ESN） has become one of the most popular recurrent neural networks （RNN） for its good prediction performance of non-linear time series and simple training process. But several problems still prevent ESN from becoming a widely used tool. The most prominent problem is its high complexity with lots of random parameters. Aiming at this problem, a minimum complexity ESN model （MCESN） was proposed. In this paper, we proposed a new wavelet minimum complexity ESN model （WMCESN） to improve the prediction accuracy and increase the practical applicability. Our new model inherits the characters of minimum complexity ESN model using the fixed parameters and simple circle topology. We injected wavelet neurons to replace the original neurons in internal reservoir and designed a wavelet parameter matrix to reduce the computing time. By using different datasets, our new model performed better than the minimum complexity ESN model with normal neurons, but only utilized tiny time cost. We also used our own packets of transmission control protocol （TCP） and user datagram protocol （UDP） dataset to prove that our model can deal with the data packet bit prediction problem well.

关键词： wavelet minimum complexity echo state network echo state network wavelet parameter matrix practical applicability

Incomplete fingerprint recognition based on feature fusion and pattern entropy

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The Journal of china Universities of posts and telecommunications 2013年第3期20卷 121-128页

作者： ZHANG jie JING Xiao-jun chen Na wang jian-li school of information and communication engineering Beijing University of Posts and Telecommunications Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education Beijing University of Posts and Telecommunications

Considering the inherent characteristics of incomplete fingerprint： local feature loss and global information distortion, the recognition progress has been mainly restricted by two critical problems： how to precisely extract informative features and still with compact representation of the incomplete fingerprint; and how to effectively measure the similarity between fingerprint images. In this paper, to handle the first problem, both the minutiae and orientation field feature are extracted and then fused to get a more comprehensive feature with scale and rotation invariability. Dealing with the second one, the pattern entropy is introduced to robustly measure the similarity of two incomplete fingerprints. Extensive experiments have been conducted on both those popular fingerprint databases and our extended databases containing more incomplete fingerprints. Meanwhile, thorough performance comparisons have been made with existing approaches. Experimental results show that our approach has more efficient ability especially in incomplete fingerprint recognition, and also performs well in both accuracy and efficiency.

关键词： incomplete fingerprint recognition feature fusion pattern entropy similarity measurement

EyeHealer:A large-scale anterior eye segment dataset with eye structure and lesion annotations

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Precision Clinical Medicine 2021年第2期4卷 85-92页

作者： Wenjia Cai jie Xu Ke wang Xiaohong liu Wenqin Xu Huimin Cai Yuanxu Gao Yuandong Su Meixia Zhang jie Zhu Charlotte L.Zhang Edward E.Zhang {4.gfei wang yun Yin Iat {4. Lai Guangyu wang Kang Zhang Yingfeng zheng State Key Laboratory of Ophthalmology Zhongshan Ophthalmic CenterSun Yat-sen UniversityGuangzhou 510060China beijing Institute of Ophthalmology Capital Medical UniversityBeijing Tongren HospitalBeijing 100730China Department of Computer Science and Technology&BNRist Tsinghua UniversityBeijing 100084China Center for Biomedicine and Innovations Faculty of MedicineMacao University of Science and Technology and University HospitalMacao 999078China Guangzhou Women and Children’s Medical Center Guangzhou Medical UniversityGuangzhou 510623China Center for Translational Innovations West China Hospital and Sichuan UniversityChengdu 610041China Bioland Laboratory Guangzhou 510005China school of Business Macao University of Science and TechnologyMacao 999078China Ophthalmic Center Kiang Wu HospitalMacaoChina school of information and communication engineering Beijing University of Posts and TelecommunicationsBeijing 100876China

Anterior segment eye diseases account for a significant proportion of presentations to eye clinics worldwide,including diseases associated with corneal pathologies,anterior chamber abnormalities(*** or inflammation),and lens *** construction of an automatic tool for segmentation of anterior segment eye lesions would greatly improve the efficiency of clinical *** research on artificial intelligence progressing in recent years,deep learning models have shown their superiority in image classification and *** training and evaluation of deep learning models should be based on a large amount of data annotated with expertise;however,such data are relatively scarce in the domain of ***,the authors developed a new medical image annotation system,called *** is a large-scale anterior eye segment dataset with both eye structures and lesions annotated at the pixel *** experiments were conducted to verify its performance in disease classification and eye lesion *** results showed that semantic segmentation models outperformed medical segmentation *** paper describes the establishment of the system for automated classification and segmentation *** dataset will be made publicly available to encourage future research in this area.

关键词： anterior segment eye diseases artificial intelligence annotation

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基于SDN拓扑集中更新的NDN路由策略

北京邮电大学学报 2018年第4期41卷 1-8页

作者：尹弼柏刘江张然黄韬刘韵洁北京邮电大学信息与通信工程学院

基于软件定义网络（SDN）拓扑集中更新的命名数据网络（NDN）路由策略,将路由的2个关键功能分离,拓扑更新功能集中置于SDN控制器中,而路由计算功能分布式地置于NDN内容节点中.同时,对网络拓扑进行负载加权预处理,在考虑网络状态的前提下... 详细信息

基于软件定义网络（SDN）拓扑集中更新的命名数据网络（NDN）路由策略,将路由的2个关键功能分离,拓扑更新功能集中置于SDN控制器中,而路由计算功能分布式地置于NDN内容节点中.同时,对网络拓扑进行负载加权预处理,在考虑网络状态的前提下,可优化路由计算.仿真结果表明,在基于SDN拓扑集中更新的NDN路由策略中,内容请求数据流的首包时延平均降低了90%,SDN控制器的负荷平均降低了10%~30%,全网实现了有效的负载均衡.

关键词：软件定义网络命名数据网络拓扑更新路由计算

MIMO系统中一种基于Tucker-2模型的联合信号检测与信道估计方法

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北京邮电大学学报 2016年第5期39卷 6-10页

作者：杜建和田沛林和昀中国传媒大学信息工程学院北京100024 北京邮电大学信息与通信工程学院.北京100876

针对多输入多输出(MIMO)系统,提出了一种基于Tucker-2模型的联合信号检测与信道估计方法.所提方法在信源端对每个发送信息符号进行三维编码,该编码方式不仅能获得空间和时间分集,并且使得信源发送信号具备盲检测特性.在信宿端,所提方法... 详细信息

针对多输入多输出(MIMO)系统,提出了一种基于Tucker-2模型的联合信号检测与信道估计方法.所提方法在信源端对每个发送信息符号进行三维编码,该编码方式不仅能获得空间和时间分集,并且使得信源发送信号具备盲检测特性.在信宿端,所提方法通过对接收信号构造Tucker-2模型,并设计2种接收算法拟合该模型,从而实现了信息符号和信道的联合估计.理论分析与仿真结果表明,在未知信道状态信息的条件下,所提方法能有效地检测信号,并同时估计出信道状态信息;与传统的导频辅助信道估计方法相比,所提方法具有更好的信道估计精度.

关键词：多输入多输出 Tucker-2模型分集盲检测联合估计

OFDM系统中基于降维PARAFAC模型的信道估计方法

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北京邮电大学学报 2017年第3期40卷 62-66页

作者：林和昀袁超伟杜建和北京邮电大学信息与通信工程学院北京100876 中国传媒大学信息工程学院北京100024

针对采用正交频分复用(OFDM)技术的阵列天线系统,提出了一种基于降维平行因子(PARAFAC)模型的多径信道估计方法.该方案对单输入多输出(SIMO)场景下的接收信号进行建模,构造出具有空-时-频3个维度的PARAFAC信号模型,利用截尾奇异值分解(S... 详细信息

针对采用正交频分复用(OFDM)技术的阵列天线系统,提出了一种基于降维平行因子(PARAFAC)模型的多径信道估计方法.该方案对单输入多输出(SIMO)场景下的接收信号进行建模,构造出具有空-时-频3个维度的PARAFAC信号模型,利用截尾奇异值分解(SVD)法对该模型进行降维,并采用三线性交替最小二乘(TALS)算法对降维后的信号模型进行拟合,实现了信号到达角(AOA)和传播时延的联合估计.与传统PARAFAC分解方法相比,所提方法在拟合过程中占用的存储空间更少,收敛速度更快.仿真结果验证了所提方法的有效性.

关键词：多维矩阵平行因子模型正交频分复用联合估计