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2019第十一届数字图像处理国际会议

作者： Zhuo Su Jiwei Hu Quan Liu Jiamei Deng School of Information Engineering Wuhan University of Technology Key Lab.of Broadband Wireless Communication and Sensor Network

The development of convolutional neural network has brought great achievements to image classification in recent ***,the classification performance is good only for natural images rather than medical *** important reason is that the medical image database used for training is always *** how to use these limited data to acquire more extensive features has become a hot research *** this paper,we first update the order and number of the whole training data every time in active and incremental *** we set different contribution rate for the data selected in our model,which based on the information quantity of the data in training stage and make our model converge *** that,a pre-trained model and our preprocessed datasets are employed,which allows us to further fine-tune our *** experiments evaluated on two different biomedical datasets shows that our model can achieve promising results.

关键词： Grading learning medical image multivariate training datasets

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New Evaluation Model on Web Site Credibility

New Evaluation Model on Web Site Credibility

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2015 International Conference on Computer Information Systems and Industrial Applications(CISIA2015)

作者： Y.Y.Ma F.Chen Z.X.Sun Key Lab of Broadband Wireless Communication and Sensor Network Technology Ministry of Education Nanjing University of Posts and Telecommunications

ISBN: (纸本)9781510805750

In view of the existing web site credibility evaluation models failing to comprehensively consider the multiple attributes of trust,after learning the D- S evidence theory and fuzzy theory,a new web site credibility evaluation model is put forward in which the trust value is divided into historical trust value and the current trust *** study shows that the model can track the change of user preferences in real-time as well as can meet the dynamic nature of universal environment,so the trust assessment is more reasonable and accurate.

关键词： evaluation model d-s evidence theor fuzzy theory

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Performance analysis of two-hop-relay cellular systems with queueing capability

Performance analysis of two-hop-relay cellular systems with ...

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2015 International Conference on Information and communications Technologies, ICT 2015

作者： Lu, Weifeng Gu, Mengmeng Yang, Luxi College of Computer Nanjing University of Posts and Telecommunications Nanjing210023 China School of Information Science and Engineering SouthEast University Nanjing210096 China Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks Nanjing Jiangsu210023 China Key Lab of Broadband Wireless Communication and Sensor Network Technology Nanjing University of Posts and Telecommunications Ministry of Education Jiangsu Province Nanjing Jiangsu210023 China

ISBN: (纸本)9781849199940

In this paper, we present a two-hop-relay WCDMA cellular system with queueing capability, where a call can wait in the incoming buffer at the congested Base Transceiver Station (BTS) if no channel is immediately available, so as to reduce the call blocking rate. We develop an analytic model based on Multidimensional Markov chains. Then based on this model, we can obtain the average transmission rate and the queuing delay performance of the system respectively. Finally, through numerical calculation, we analyze the impact of the various parameters on the performance of the two-hop-relay WCDMA cellular system with queueing capability.

关键词： Queueing theory

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Real-time Hand Tracking Using Kinect

Real-time Hand Tracking Using Kinect

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作者： Chenxuan Xi Jianxin Chen Chenxue Zhao Qicheng Pei Lizheng Liu Key Lab of Broadband Wireless Communication and Sensor Network Technology(Nanjing University of Posts and Telecommunications) Ministry of Education

Real-time hand tracking is fundamental to human gesture recognition. However, due to the huge computation, previous studies are either off-line or limited to given poses. In order to satisfy the requirement of real-time hand tracking, in this paper we propose a real time hand tracking method using Kinect. Firstly we extract the hand region from the depth image output from Kinect. Then we achieve the hand parameters. During the procedure of hand region extraction, we propose a cascade structure with recursive connected component algorithm to improve the efficiency and reserve connection relationships in 3D space. To determine the fingertips, we use the former 3D connections and geodesic distance over hand skeleton pixels to guarantee the accuracy and robustness, with acceptable loss. Experimental results show that our proposed solution can significantly improve the quality of real-time hand tracking.

关键词： Hand tracking fingertip tracking Kinect connected component analysis geodesic distance

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A Gridless Method for Doa Estimation Under the Coexistence of Mutual Coupling and Unknown Nonuniform Noise

A Gridless Method for Doa Estimation Under the Coexistence o...

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sensor Array and Multichannel Signal Processing Workshop

作者： Dan Li Yuanyuan Jiang Xiaohuan Wu Wei-Ping Zhu Key Lab of Broadband Wireless Communication and Sensor Network Technology Nanjing University of Posts and Telecommunications Nanjing China

ISBN: (数字)9781728119465

ISBN: (纸本)9781728119472

This paper addresses the issue of direction-of-arrival (DOA) estimation under the coexistence of mutual coupling and nonuniform noise. We first construct a selection matrix based on the structural characteristics of the mutual coupling matrix (MCM) to form a new manifold matrix which is not affected by the mutual coupling effect. Then, we employ the covariance fitting criteria to form a semidefinite programming for DOA estimation. Compared to most of existing sparse methods, our method does not require discretization of the angle space, hence is able to achieve super-resolution. Simulations are carried out to verify the effectiveness of the propose method.

关键词： Manifolds Mutual coupling Direction-of-arrival estimation Fitting Superresolution Estimation Signal processing algorithms

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A Review of the Evolutionary Algorithm Based VRP Problem

A Review of the Evolutionary Algorithm Based VRP Problem

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第42届中国控制会议

作者： Lili Pan Bing Hu Zhe Sun Lei Xu Guangfu Gong Xiangpeng Xie Zhixin Sun Key Lab of Broadband Wireless Communication and Sensor Network Technology Ministry of Education Nanjing University of Posts and Telecommunications

The problem of vehicle routing planning(VRP) has far-reaching influence, and is widely used in vehicle scheduling,industrial production, transportation, logistics and distribution. The study of VRP and its associated derivations has grown significantly in recent years. So, starting with the fundamental VRP, this paper categorizes the VRP based on its traits and real-world applications. It then in-depth analyses the capacity constrained VRP, time window VRP, batched delivery VRP,dynamic VRP, and stochastic demand VRP, and provides an update on each type of VRP's research. Additionally, a summary of the VRP problem-solving methods is provided. The study of evolutionary algorithms is the main topic of this paper, and the fundamental solution method is provided in accordance with the development of the algorithms.

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A new variant of sum-product algorithm for sensor self-localization in wireless networks

A new variant of sum-product algorithm for sensor self-local...

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IEEE International Conference on communications in China (ICCC)

作者： Wei Li Zhen Yang Haifeng Hu Key Lab of Broadband Wireless Communication and Sensor Network Technology Nanjing University of Posts and Telecommunications Nanjing China

Graphical models have been widely applied in solving distributed inference problems in wireless sensor networks (WSNs). In this paper, we formulate the sensor self-localization problem in a WSN as an inference problem on a factor graph. Using a sequential schedule of message updates, a sequential uniformly reweighted sum-product algorithm (SURW-SPA) is developed for self-localization problems. The proposed algorithm combines the distributed nature of belief propagation (BP) with the improved performance of sequential tree-reweighted message passing (TRW-S) algorithm. We apply the SURW-SPA to sensor self-localization in large-scale static networks, and evaluate its performance in terms of localization accuracy and convergence speed.

关键词： wireless sensor networks Message passing Schedules Belief propagation Sum product algorithm wireless networks

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Multidimensional reconciliation protocol for continuous-variable quantum key agreement with polar coding

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中国科学（物理学力学天文学） 2018年第9期61卷 99-102页

作者： ShengMei Zhao ZhiGang Shen Hong Xiao Le Wang Institute of Signal Processing Transmission Nanjing University of Posts and Telecommunications Nanjing 210003 China Key Lab of Broadband Wireless Communication and Sensor Network Technology Ministry of Education Nanjing 210003 China Institute of Signal Processing Transmission Nanjing University of Posts and Telecommunications Nanjing 210003 China

Continuous-variable quantum key distribution (CVQKD) [1-7] allows two remote parties (Alice and Bob) to share a secret key,even in the presence of an eavesdropper (Eve) with unlimited computational power [8-17].While,a highly efficient reconciliation protocol [18-21] is crucial in a CVQKD system,which can not only extract the errorless secret keys,but also provide a promising way to achieve a long distance CVQKD at a low signal-to-noise ratio (SNR).There are some practical reconciliation protocols [18-21] for CVQKD systems,such as,sliced reconciliation protocol (SEC) [18],sign reconciliation protocol [19],and multidimensional reconciliation protocol (MR) [21].Compared with the others,MR utilized the algebraic properties of a multidimensional method,which did not require quantization or any other post-selection technique.

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Scale adaptive block target tracking based on multi-layer convolution features and Kernel correlation filter

Scale adaptive block target tracking based on multi-layer co...

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2020 International Symposium on Automation, Information and Computing, ISAIC 2020

作者： Zhang, Ting Hu, Dong Zhang, Jing Jiangsu Province’s Key Lab of Image Procession and Image Communications Nanjing210003 China Education Ministry’s Key Lab of Broadband Wireless Communication and Sensor Network Technology Nanjing210003 China Education Ministry’s Engineering Research Center of Ubiquitous Network and Heath Service Nanjing University of Posts and Telecommunications Nanjing210003 China

Target tracking is currently a hot research topic in Computer Vision and has a wide range of use in many research fields. However, due to factors such as occlusion, fast motion, blur and scale variation, tracking method still needs to be deeply studied. In this paper, we propose a block target tracking method based on multi-convolutional layer features and Kernel correlation filter. Our method divides the tracking process into two parts: target position estimation and target scale estimation. First, we block the target frame based on the condition number. Second, we extract the features by the convolutional layer and apply it to the kernel correlation filter to get the center position of different block targets. With the reliability of different blocks measured by the Barker coefficient, the overall target position center is obtained. Then, the affine transformation is adopted to achieve the scale adaptation. The algorithm in this paper is evaluated by the public video sequences in OTB-2013. Numerous experimental results demonstrate that the proposed tracking method can achieve target scale adaptation and effectively improve the tracking accuracy. © 2021 Institute of Physics Publishing. All rights reserved.

关键词： Target tracking

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Ghost edge detection based on HED network

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Frontiers of Optoelectronics 2022年第3期15卷 25-33页

作者： Shengmei Zhao Yifang Cui Xing He Le Wang Institute of Signal Processing and Transmission Nanjing University of Posts and Telecommunications(NUPT)Nanjing 210003China Key Lab of Broadband Wireless Communication and Sensor Network Technology(NUPT) Ministry of EducationNanjing 210003China

In this paper,we present an edge detection scheme based on ghost imaging(GI)with a holistically-nested neural *** so-called holistically-nested edge detection(HED)network is adopted to combine the fully convolutional neural network(CNN)with deep supervision to learn image edges *** data are used to train the HED network,and the unknown object’s edge information is reconstructed from the experimental *** experiment results show that,when the compression ratio(CR)is 12.5%,this scheme can obtain a high-quality edge information with a sub-Nyquist sampling ratio and has a better performance than those using speckle-shifting GI(SSGI),compressed ghost edge imaging(CGEI)and subpixel-shifted GI(SPSGI).Indeed,the proposed scheme can have a good signal-to-noise ratio performance even if the sub-Nyquist sampling ratio is greater than 5.45%.Since the HED network is trained by numerical simulations before the experiment,this proposed method provides a promising way for achieving edge detection with small measurement times and low time cost.

关键词： Edge detection Ghost imaging(GI) Holistically-nested neural network Compression ratio(CR) Signal-tonoise ratio(SNR)

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