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Channel Estimation and Throughput Evaluation for 5G Wireless Communication Systems in Various Scenarios on High Speed Railways

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China Communications 2018年第4期15卷 86-97页

作者： yanrong zhao xiyu wang gongpu wang ruisi he yulong zou zhuyan zhao school of computer and information technology beijing jiaotong universityBeijing 100044China the state key laboratory of rail traffic control and safety beijing jiaotong universityBeijing 100044China the key laboratory of broadband wireless communication and sensor network technology of ministry of education nanjing university of posts and telecommunicationsNanjing 210003China nokia bell labs beijing Beijing 100044China

The fifth generation （5G） wireless communication is currently a hot research topic and wireless communication systems on high speed railways （HSR） are important applications of 5G technologies. Existing stud- ies about 5G wireless systems on high speed railways （HSR） often utilize ideal channel parameters and are usually based on simple scenarios. In this paper, we evaluate the down- link throughput of 5G HSR communication systems on three typical scenarios including urban, cutting and viaduct with three different channel estimators. The channel parameters of each scenario are generated with tapped delay line （TDL） models through ray-tracing sim- ulations, which can be considered as a good match to practical situations. The channel estimators including least square （LS）, linear minimum mean square error （LMMSE）, and our proposed historical information based ba- sis expansion model （HiBEM）. We analyze the performance of the HiBEM estimator in terms of mean square error （MSE） and evaluate the system throughputs with different channel estimates over each scenario. Simulation results are then provided to corroborate our proposed studies. It is shown that our HiBEM estimator outperforms other estimators and that the sys-tem throughput can reach the highest point in the viaduct scenario.

关键词： 5G channel estimation HSR tapped delay line throughput pertbrmanceanalysis wireless communication.

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Visual Tracking Algorithm Based on Color Name Histogram

Visual Tracking Algorithm Based on Color Name Histogram

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作者： Chenchen Yue Wangsheng Yu Zhiqiang Hou Sugang Ma College of Computer Science and Technology Xi'an University of Posts and Telecommunications Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an University of Posts and Telecommunications Information and Navigation Institute of Air Force Engineering University

Aimed at the problem that traditional histogram is sensitive to illumination changes in visual tracking,combined with the CN(Color Name) feature,we proposed a new feature(denotes CNH,Color Name Histogram) based on color ***,the method projected the original RGB image to CN space to obtain robust 11 feature ***,we counted the each pixel numbers of feature ***,normalizing the amount of pixels in each *** addition,we adopted a feature adaptive fusion method to combine CNH and HOG(Histogram of Oriented Gradient).In order to prove validity of the proposed algorithm,we use Staple(Sum of Template And Pixel-wise Learners) algorithm frame to make a controlled *** contrast with the reference algorithms,the success of our algorithm increases by 1.5% and the precision increases by 1.7%.The results show that this method retains the advantages of traditional histogram which is insensitive to target deformation,but also enhances the robustness to illumination change.

关键词： Visual tracking Color histogram Color name Feature fusion

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Beyond Inferring Class Representatives: User-Level Privacy Leakage From Federated Learning

Beyond Inferring Class Representatives: User-Level Privacy L...

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IEEE Conference on computer Communications

作者： Zhibo Wang Mengkai Song Zhifei Zhang Yang Song Qian Wang Hairong Qi Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan University P. R. China Deptment of Electrical Engineering and Computer Science University of Tennessee Knoxville USA

ISBN: (纸本)9781728105161

Federated learning, i.e., a mobile edge computing framework for deep learning, is a recent advance in privacy-preserving machine learning, where the model is trained in a decentralized manner by the clients, i.e., data curators, preventing the server from directly accessing those private data from the clients. This learning mechanism significantly challenges the attack from the server side. Although the state-of-the-art attacking techniques that incorporated the advance of Generative adversarial networks (GANs) could construct class representatives of the global data distribution among all clients, it is still challenging to distinguishably attack a specific client (i.e., user-level privacy leakage), which is a stronger privacy threat to precisely recover the private data from a specific client. This paper gives the first attempt to explore user-level privacy leakage against the federated learning by the attack from a malicious server. We propose a framework incorporating GAN with a multi-task discriminator, which simultaneously discriminates category, reality, and client identity of input samples. The novel discrimination on client identity enables the generator to recover user specified private data. Unlike existing works that tend to interfere the training process of the federated learning, the proposed method works "invisibly" on the server side. The experimental results demonstrate the effectiveness of the proposed attacking approach and the superior to the state-of-the-art.

关键词： Servers private data customers Clients data distribution Privacy Extravasation Machine Learning Legislators advance

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Superpixel segmentation based on spatially constrained subspace clustering

arXiv

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arXiv 2020年

作者： Li, Hua Jia, Yuheng Cong, Runmin Wu, Wenhui Kwong, Sam Chen, Chuanbo School of Software Engineering Huazhong University of Science and Technology Wuhan430074 China Department of Computer Science City University of Hong Kong Kowloon Hong Kong School of Computer Science and Engineering Southeast University Nanjing211189 China Institute of Information Science Beijing Jiaotong University Beijing100044 China Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing Jiaotong University Beijing100044 China College of Electronics and Information Engineering Shenzhen University Shenzhen518060 China Department of Computer Science City University of Hong Kong Kowloon Hong Kong City University of Hong Kong Shenzhen Research Institute Shenzhen518057 China School of Software Engineering Huazhong University of Science and Technology Wuhan430074 China

Superpixel segmentation aims at dividing the input image into some representative regions containing pixels with similar and consistent intrinsic properties, without any prior knowledge about the shape and size of each superpixel. In this paper, to alleviate the limitation of superpixel segmentation applied in practical industrial tasks that detailed boundaries are difficult to be kept, we regard each representative region with independent semantic information as a subspace, and correspondingly formulate superpixel segmentation as a subspace clustering problem to preserve more detailed content boundaries. We show that a simple integration of superpixel segmentation with the conventional subspace clustering does not effectively work due to the spatial correlation of the pixels within a superpixel, which may lead to boundary confusion and segmentation error when the correlation is ignored. Consequently, we devise a spatial regularization and propose a novel convex locality-constrained subspace clustering model that is able to constrain the spatial adjacent pixels with similar attributes to be clustered into a superpixel and generate the content-aware superpixels with more detailed boundaries. Finally, the proposed model is solved by an efficient alternating direction method of multipliers (ADMM) solver. Experiments on different standard datasets demonstrate that the proposed method achieves superior performance both quantitatively and qualitatively compared with some state-of-the-art methods. © 2020, CC BY-NC-ND.

关键词： Clustering algorithms

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Direct constructions and proofs for CCA secure(LR)IBE with dual system encryption

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Science China(information Sciences) 2017年第11期60卷 306-308页

作者： Yi ZHAO Bo YANG School of Computer Science Shaanxi Normal University State Key Laboratory of Information Security Institute of Information EngineeringChinese Academy of Sciences

Dear editor,In the past decade,the development of identitybased cryptography has been highly *** to its superiority in the task of key distribution as well as its expressive ability,many related techniques have been made industrial *** and Boyen[1]developed the first secure construction(BB1)in the standard model under a weakened adaptive notion called selective-ID *** then,ongoing research has contributed to enhancing the performance of IBE schemes in the standard model.

关键词： IBE CCA Direct constructions and proofs for CCA secure

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Efficient identity-based threshold decryption scheme from bilinear pairings

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Frontiers of computer Science 2018年第1期12卷 177-189页

作者： Wei GAO Guilin WANG Kefei CHEN Xueli WANG School of Mathematics and Statistics Ludong University Yantai 264025 China School of Computer and Software Nanjing University of Information Science & Technology Nanjing 210044 China Key Laboratory of Information Security Guangzhou University Guangzhou 510006 China Shield Lab Singapore Research Center of Huawei Singapore 117674 Singapore Department of Mathematics Hangzhou Normal University Hangzhou 311121China School of Mathematics South China Normal University Guangzhou 510631 China

Using Shamir＇s secret sharing scheme to indi- rectly share the identity-based private key in the form of a pairing group element, we propose an efficient identity-based threshold decryption scheme from pairings and prove its se- curity in the random oracle model. This new paring-based scheme features a few improvements compared with other schemes in the literature. The two most noticeable features are its efficiency, by drastically reducing the number of pair- ing computations, and the ability it gives the user to share the identity-based private key without requiring any access to a private key generator. With the ability it gives the user to share the identity-based private key, our ID-based threshold decryption （IBTD） scheme, the second of its kind, is signif- icantly more efficient than the first scheme, which was de- veloped by Baek and Zheng, at the expense of a slightly in- creased ciphertext length. In fact, our IBTD scheme tries to use as few bilinear pairings as possible, especially without depending on the suite of Baek-Zheng secret sharing tools based on pairings.

关键词： identity-based cryptography threshold cryptog-raphy provable security random oracle model bilinear pair-ing identity-based threshold decryption

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A novel outer approximation method combined penalty function and for hydrothermal coordination 11

A novel outer approximation method combined penalty function...

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11th IET International Conference on Advances in Power System Control, Operation and Management, APSCOM 2018

作者： Luo, Jiangyao Yang, Linfeng Jian, Jinbao Zhang, Zhenrong Department of Computer Electronics and Information Guangxi University Nanning China Department of Science Guangxi University for Nationalities Nanning China Guangxi Key Laboratory of Multimedia Communications and Network Technology Nanning China

ISBN: (纸本)9781839530043

Hydrothermal Coordination (HTC) is a problem involving a large number of continuous variables, binary variables and constraints that is difficult to solve efficiently. A novel outer approximation method (OAM) combined penalty function (PF) is presented in this paper to solve HTC problem, which is a mixed-integer nonlinear programming (MINLP) problems. The proposed algorithm starts by solving the nonlinear programming (NLP) relaxation problem. If an integer solution is not found, a sequence of iterations consisting of NLP sub-problems and mixed-integer linear programming (MILP) master problems are solved in tradition OAM. However, our proposed method employs scaled quadratic cut (SQCs) as valid underestimation of convex nonlinear function in master problems. So MILP master problems changes to mixed-integer quadratically constrained programming (MIQCP) master problems. The proposed MIQCP master problem is based on the OAM algorithm and features an exact PF that allows violations of linearizations of non-convex constraints. The search proceeds until the gap between lower and upper bounds satisfy the tolerance. We split the NLP relaxation problem into four sub-problems, so that the calculation time through solver IPOPT can be significantly reduced. The proposed method was tested on two different size of cases. The simulation results show that our proposed method has excellent performance and obtains great sub-optimal solutions. © 2018 Institution of Engineering and Technology. All rights reserved.

关键词： Nonlinear programming

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Hierarchical prototype learning for zero-shot recognition

arXiv

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arXiv 2019年

作者： Zhang, Xingxing Gui, Shupeng Zhu, Zhenfeng Zhao, Yao Liu, Ji Institute of Information Science Beijing Jiaotong University Beijing100044 China Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing Jiaotong University Beijing100044 China Department of Computer Science University of Rochester RochesterNY14611 United States

Zero-Shot Learning (ZSL) has received extensive attention and successes in recent years especially in areas of fine-grained object recognition, retrieval, and image captioning. key to ZSL is to transfer knowledge from the seen to the unseen classes via auxiliary semantic prototypes (e.g., word or attribute vectors). However, the popularly learned projection functions in previous works cannot generalize well due to non-visual components included in semantic prototypes. Besides, the incompleteness of provided prototypes and captured images has less been considered by the state-of-the-art approaches in ZSL. In this paper, we propose a hierarchical prototype learning formulation to provide a systematical solution (named HPL) for zero-shot recognition. Specifically, HPL is able to obtain discriminability on both seen and unseen class domains by learning visual prototypes respectively under the transductive setting. To narrow the gap of two domains, we further learn the interpretable super-prototypes in both visual and semantic spaces. Meanwhile, the two spaces are further bridged by maximizing their structural consistency. This not only facilitates the representativeness of visual prototypes, but also alleviates the loss of information of semantic prototypes. An extensive group of experiments are then carefully designed and presented, demonstrating that HPL obtains remarkably more favorable efficiency and effectiveness, over currently available alternatives under various settings. Copyright © 2019, The Authors. All rights reserved.

关键词： Semantics

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Robust unsupervised feature selection by nonnegative sparse subspace learning

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Neurocomputing 2019年 334卷 156-171页

作者： Wei Zheng Hui Yan Jian Yang School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing PR China School of Computer Engineering Jinling Institute of Technology Nanjing PR China PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education and Jiangsu Key Lab of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology Key Laboratory of Trusted Cloud Computing and Big Data Analysis Nanjing Xiaozhuang University Nanjing PR China

Sparse subspace learning has been demonstrated to be effective in data mining and machine learning. In this paper, we cast the unsupervised feature selection scenario as a matrix factorization problem from the viewpoint of sparse subspace learning. By minimizing the reconstruction residual, the learned feature weight matrix with the l 2,1 -norm and the non-negative constraints not only removes the irrelevant features, but also captures the underlying low dimensional structure of the data points. Meanwhile in order to enhance the model's robustness, l 1 -norm error function is used to resistant to outliers and sparse noise. An efficient iterative algorithm is introduced to optimize this non-convex and non-smooth objective function and the proof of its convergence is given. Although, there is a subtraction item in our multiplicative update rule, we validate its non-negativity. The superiority of our model is demonstrated by comparative experiments on various original datasets with and without malicious pollution.

关键词： Subspace learning Non-negative matrix factorization Unsupervised feature selection

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Cost Efficient Offloading Strategy for DNN-Based Applications in Edge-Cloud Environment

Cost Efficient Offloading Strategy for DNN-Based Application...

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IEEE International Conference on Big Data and Cloud Computing (BdCloud)

作者： Yinhao Huang Bing Lin Yongjie Zheng Junqin Hu Yuchang Mo Xing Chen College of Mathematics and Computer Science Fuzhou University Fuzhou China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou China College of Physics and Energy Fujian Normal University Fuzhou China Fujian Province University Key Laboratory of Computational Science Huaqiao University Quanzhou China

ISBN: (数字)9781728143286

ISBN: (纸本)9781728143293

Deep Neural networks (DNNs) are pervasively used in a large number of applications, such as Microsoft Cortana, Apple Siri, and Google Now. The traditional method is to offload them in the cloud, which causes serious data transmission delay and high network resource cost, as well as user privacy leakage. The emergence of Mobile Edge Computing(MEC) provides a new solution for the execution of DNN-based applications. With the increase of the computing power of mobile devices and the appearance of edge nodes, offloading partial layers of the DNN-based application to the edge will speed up system response, reduce the burden of cloud center and protect privacy of users. Most of existing researches focus on the optimization of response delay for DNN-based applications in edge computing system, but there is no mature solution to the cost optimization of edge computing system. To effectively reduce the cost of edge computing for offloading DNN-based applications, we propose a offloading strategy based on Discrete Particle Swarm Optimization with Genetic Operators(DPSO-GO). By introducing crossover operator and mutation operator of genetic algorithm, it overcomes the defect that the traditional PSO algorithm is easy to fall into local optimum. The results show that our algorithm can reduce the cost of edge computing system effectively while satisfying the delay requirements of all the DNN-based applications.

关键词： Mobile handsets Task analysis Cloud computing Edge computing Servers Optimization Bandwidth

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