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IEEE Annual Joint Conference: INFOCOM, IEEE computer and Communications Societies

作者： Xiulong Liu Keqiu Li Jie Wu Alex X. Liu Xin Xie Chunsheng Zhu Weilian Xue School of Computer Science and Technology Dalian University of Technology China Department of Computer and Information Sciences Temple University USA State Key Laboratory for Novel Software Technology Nanjing University China Department of Electrical and Computer Engineering The University of British Columbia Canada School of Management Liaoning Normal University China

ISBN: (纸本)9781467399548

This paper studies the practically important problem of top-k queries, which is to find the top k largest categories and their corresponding sizes. In this paper, we propose a Top-k Query (TKQ) protocol and a technique that we call Segmented Perfect Hashing (SPH) for optimizing TKQ. Specifically, TKQ is based on the framed slotted Aloha protocol. Each tag responds to the reader with a Single-One Geometric (SOG) string using the ON-OFF keying modulation. TKQ leverages the length of continuous leading 1s in the combined signal to estimate the corresponding category size. TKQ can quickly eliminate the sufficiently small categories, and only needs to focus on a limited number of large-size categories that require more accurate estimation. We conduct rigorous analysis to guarantee the predefined accuracy constraints. To further improve time-efficiency, we propose the SPH scheme, which improves the average frame utilization of TKQ from 36.8% to nearly 100% by establishing a bijective mapping between tag categories and slots. To minimize the overall time cost, we optimize the key parameter that trades off between communication cost and computation cost. Experimental results show that our TKQ+SPH protocol not only achieves the required accuracy constraints, but also achieves a 2.6~7x faster speed than the existing protocols.

关键词： Protocols Radiofrequency identification Estimation computers Servers Sociology Statistics

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A hierarchical image matting model for blood vessel segmentation in fundus images

arXiv

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arXiv 2017年

作者： Fan, Zhun Lu, Jiewei Li, Wenji Wei, Caimin Huang, Han Cai, Xinye Chen, Xinjian Guangdong Provincial Key Laboratory of Digital Signal and Image Processing College of Engineering Shantou University Shan'tou515063 China Department of Mathematics Shantou University Shan'tou515063 China School of Software Engineering South China University of Technology Guang'zhou510006 China College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Jiang'su210016 China Medical Image Processing and Analysis Lab School of Electronics and Information Engineering Soochow University Su'zhou215006 China

In this paper, a hierarchical image matting model is proposed to extract blood vessels from fundus images. More specifically, a hierarchical strategy utilizing the continuity and extendibility of retinal blood vessels is integrated into the image matting model for blood vessel segmentation. Normally the matting models require the user specified trimap, which separates the input image into three regions manually: the foreground, background and unknown regions. However, since creating a user specified trimap is a tedious and time-consuming task, region features of blood vessels are used to generate the trimap automatically in this paper. The proposed model has low computational complexity and outperforms many other state-of-art supervised and unsupervised methods in terms of accuracy, which achieves a vessel segmentation accuracy of 96.0%, 95.7% and 95.1% in an average time of 10.72s, 15.74s and 50.71s on images from three publicly available fundus image datasets DRIVE, STARE, and CHASE_DB1, respectively. Copyright © 2017, The Authors. All rights reserved.

关键词： Blood vessels

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A new data replication scheme for PVFS2 15th

A new data replication scheme for PVFS2

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15th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2015

作者： Bao, Nianyuan Tang, Jie Zhang, Xiaoyu Wu, Gangshan State Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing University Nanjing210046 China

ISBN: (纸本)9783319271361

PVFS is one of the most popular distributed file systems with parallelism, which is still widely used today. Now PVFS is in its version 2, called PVFS2. PVFS2 has a leading performance on I/O operations, but the reliability and stability are not as good. One of the reasons is the lack of data replication. This paper presents a new data replication scheme in PVFS2. In our approach, the backup operation is done on the servers, therefore the user experience is not affected while creating copies of files. In addition, we optimized the read operation of PVFS2. With copies, we can choose the servers to read from, so we can maintain parallelism of read operation under complex conditions such as a server is down or the load of some servers are obviously higher than others. Experimental results verify the effectiveness and efficiency of our method. © Springer International Publishing Switzerland 2015.

关键词： File organization

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The Cross-Network Energy Efficient Tradeoff: From Wavelength Division Multiplexing Wired Networks to 5G Wireless Networks

The Cross-Network Energy Efficient Tradeoff: From Wavelength...

引用

IEEE Conference on computer Communications Workshops

作者： Shu Fu Hong Wen Jinsong Wu Bin Wu National Key Lab on Communications University of Electronic Science and Technology of China Department of Electrical Engineering Universidad de Chile School of Computer Science and Technology Tianjin University

ISBN: (纸本)9781467399562

Low-delay and high energy-efficiency (defined as the transmitted bits per unit energy consumption) are highly expected in next general wireless networks. The existing green technologies of Internet Protocol (IP) have been studied in the wired and wireless networks, respectively. However, the joint optimization of the cross-networks energy-efficiency from the wired wavelength division multiplexing (WDM) networks to the next generation wireless networks remains unexplored. In this paper, we formulate a joint optimization to integrate the energy efficiency across networks. Specifically, each base station (BS) first collects the uplink user data. We then propose a novel joint cross-networks scheduling and routing algorithm according to the wireless channel quality of users, user rate, and the topology constraint. The IP packets are then delivered to the targeted users within the limit of delay. Based on the tradeoff between delay and energy efficiency, we achieve energy efficient and performance guaranteed cross-networks. The effectiveness of the proposed framework is verified by extensive simulations.

关键词： Cross-networks Energy efficiency Wavelength division multiplexing (WDM) networks 5G networks

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Pre-stack kirchhoff time migration on hadoop and spark 15th

Pre-stack kirchhoff time migration on hadoop and spark

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15th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2015

作者： Yang, Chen Tang, Jie Gao, Heng Wu, Gangshan State Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing University Nanjing210046 China

ISBN: (纸本)9783319271361

Pre-stack Kirchhoff time migration (PKTM) is one of the most widely used migration algorithms in seismic imaging area. How- ever, PKTM takes considerable time due to its high computational cost, which greatly affects the working efficiency of oil industry. Due to its high fault tolerance and scalability, Hadoop has become the most popular platform for big data processing. To overcome the shortcoming too much network traffic and disk I/O in Hadoop, there shows up a new distributed framework—Spark. However the behaviour and performance of those two systems when applied to high performance computing are still under investigation. In this paper, we proposed two parallel algorithms of the plrestack Kirchhoff time migration based on Hadoop and Sark respectively. Experiments are carried out to compare the performances of them. The results show that both of implementations are efficient and scalable and our PKTM on Spark exhibits better performance than the one on Hadoop. © Springer International Publishing Switzerland 2015.

关键词： MapReduce

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A dynamic extension and data migration method based on PVFS 1

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15th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2015

作者： Zhang, Xiaoyu Tang, Jie Gao, Heng Wu, Gangshan State Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing University Nanjing210046 China

ISBN: (数字)9783319271224

ISBN: (纸本)9783319271217

With the development of the big data, The traditional file system can no longer meet the demand of High Performance Computing and Big Data. Parallel file systems are getting more and more popular in High Performance Computing. As a typical parallel file system, PVFS has been widely used in big data computing area in recent years. However with the increasing of computing scale, there exist the needs to dynamic extend data nodes, which PVFS does not support at present. This paper put forward a dynamic data node extension method as well as the subsequent data migration algorithm based on PVFS. The algorithm first adds a new data node automatically and transparently. After that, the algorithm finds out the most loaded data node in the original file system using a new load evaluation method and transfer the data into the newly added data node to mitigate the imbalance of the system. The experimental results show that our dynamic data node extension method could improve the performance of PVFS and reduce the probability of hot point effectively. © Springer International Publishing Switzerland 2015.

关键词： Big data

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Multilinear hyperplane hashing

Multilinear hyperplane hashing

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2016 IEEE Conference on computer Vision and Pattern Recognition, CVPR 2016

作者： Liu, Xianglong Fan, Xinjie Deng, Cheng Li, Zhujin Su, Hao Tao, Dacheng State Key Lab of Software Development Environment Beihang University Beijing China School of Electronic Engineering Xidian University Xi'an China Department of Computer Science Stanford University StanfordCA United States QCIS and FEIT University of Technology Sydney Australia

ISBN: (纸本)9781467388511

Hashing has become an increasingly popular technique for fast nearest neighbor search. Despite its successful progress in classic pointtopoint search, there are few studies regarding point-to-hyperplane search, which has strong practical capabilities of scaling up applications like active learning with SVMs. Existing hyperplane hashing methods enable the fast search based on randomly generated hash codes, but still suffer from a low collision probability and thus usually require long codes for a satisfying performance. To overcome this problem, this paper proposes a multilinear hyperplane hashing that generates a hash bit using multiple linear projections. Our theoretical analysis shows that with an even number of random linear projections, the multilinear hash function possesses strong locality sensitivity to hyperplane queries. To leverage its sensitivity to the angle distance, we further introduce an angular quantization based learning framework for compact multilinear hashing, which considerably boosts the search performance with less hash bits. Experiments with applications to large-scale (up to one million) active learning on two datasets demonstrate the overall superiority of the proposed approach.

关键词： Nearest neighbor search

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Fast RFID Polling Protocols

Fast RFID Polling Protocols

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International Conference on Parallel Processing (ICPP)

作者： Jia Liu Bin Xiao Xuan Liu Lijun Chen State Key Laboratory for Novel software Technology Nanjing University China Department of Computing The Hong Kong Polytechnic University China College - of Computer Science and Electronic Engineering Hunan University China

ISBN: (纸本)9781509028245

Polling is a widely used anti-collision protocol that interrogates RFID tags in a request-response way. In conventional polling, the reader needs to broadcast 96-bit tag IDs to separate each tag from others, leading to long interrogation delay. This paper takes the first step to design fast polling protocols by shortening the polling vector. We first propose an efficient Hash Polling Protocol (HPP) that uses hash indices rather than tag IDs as the polling vector to query each tag. The length of the polling vector is dropped from 96 bits to no more than log(n) bits (n is the number of tags). We then enhance HPP (EHPP) to make it not only more efficient but also more steady with respect to the number of tags. To avoid redundant transmissions in both HPP and EHPP, we finally propose a Tree-based Polling Protocol (TPP) that reserves the invariant portion of the polling vector while updates only the discrepancy by constructing and broadcasting a polling tree. Theoretical analysis shows that the average length of the polling vector in TPP levels off at only 3.44, 28 times less than 96-bit tag IDs. We also apply our protocols to collect tag information and simulation results demonstrate that our best protocol TPP outperforms the state-of-the-art information collection protocol.

关键词： Protocols Indexes RFID tags Broadcasting Wireless communication Delays

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Support matrix machines 32

Support matrix machines

引用

32nd International Conference on Machine Learning, ICML 2015

作者： Luo, Luo Xie, Yubo Zhang, Zhihua Li, Wu-Jun Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China Institute of Data Science Department of Computer Science and Engineering Shanghai Jiao Tong University China National Key Laboratory for Novel Software Technology Collaborative Innovation Center of Novel Software Technology and Industrialization Department of Computer Science and Technology Nanjing University China

ISBN: (纸本)9781510810587

In many classification problems such as electroencephalogram (EEG) classification and image classification, the input features are naturally represented as matrices rather than vectors or s-calars. In general, the structure information of the original feature matrix is useful and informative for data analysis tasks such as classification. One typical structure information is the correlation between columns or rows in the feature matrix. To leverage this kind of structure information, we propose a new classification method that we call support matrix machine (SMM). Specifically, SMM is defined as a hinge loss plus a so-called spectral elastic net penalty which is a spectral extension of the conventional elastic net over a matrix. The spectral elastic net enjoys a property of grouping effect, i.e., strongly correlated columns or rows tend to be selected altogether or not. Since the optimization problem for SMM is convex, this encourages us to devise an alternating direction method of multipliers (ADMM) algorithm for solving the problem. Experimental results on EEG and image classification data show that our model is more robust and efficient than the state-of-the-art methods. Copyright © 2015 by the author(s).

关键词： Classification (of information)

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3D panorama reconstruction based on sitemap joining

3D panorama reconstruction based on sitemap joining

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Huixuan Wang Yanwen Guo Minh N. Do Caiming Zhang Changhe Tu School of Computer Science and Technology Shandong University Jinan Shandong P.R. China National Key Lab for Novel Software Technology Nanjing University Nanjing Jiangsu P.R. China Coordinated Science Laboratory University of Illinois at Urbana-Champaign Urbana IL U.S.A

We present a new approach for constructing the 3D panorama for an indoor environment by joining together aligned submaps. For each submap, the trajectory of the moving camera is estimated based on the Kanade-Lucas-Tomasi (KLT) features. Our method can update the feature set status by adding new features and removing expiring ones adaptively to accommodate scene changes. The accuracy of the estimated poses is further improved through sparse bundle adjustment. Furthermore, we utilize a linear optimization framework to align all submaps to obtain a consistently extended 3D panorama and to refine the visual odometry at the same time. We evaluated our approach on publicly available benchmark datasets. The experiments demonstrate that the proposed method achieves low translational drift and is robust even when the camera moves very fast.

关键词： Cameras Simultaneous localization and mapping Three-dimensional displays Feature extraction Optimization Visualization Benchmark testing

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