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检索条件"机构=Parallel & Distributed Processing Laboratory"

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Maximizing Uniform Multicast Throughput in Multi-Channel Dense Wireless Sensor Networks 12

Maximizing Uniform Multicast Throughput in Multi-Channel Den...

引用

12th International Conference on Mobile Ad-Hoc and Sensor Networks, MSN 2016

作者： Jiao, Xianlong Chen, Guirong Wang, Xiaodong Chen, Yuli Yang, Li Information and Navigation College Air Force Engineering University Xi'an710077 China College of Information System and Management National University of Defense Technology Changsha410073 China Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha410073 China Chongqing Guanyinqiao Elementary School Chongqing400020 China Chongqing Liangjiangxinqu Renhe Experimental School Chongqing400021 China

ISBN: (纸本)9781509056965

This paper investigates the problem of maximizing uniform multicast throughput (MUMT) for multi-channel dense wireless sensor networks, where all nodes locate within one-hop transmission range and can communicate with each other on multiple orthogonal channels. This kind of networks show wide application in the real world, and maximizing uniform multicast throughput for these networks is worth deep studying. Previous researches have proved MUMT problem is NP-hard. However, previous researches are either hard to implement, or use too many relay nodes to complete the multicast task, and thus incur high overhead or poor performance. To efficiently solve MUMT problem, we adopt the concept of the maximum independent set with the size constraint, and present one novel Single-Broadcast based Multicast algorithm called SBM based on the concept. We prove that SBM algorithm achieves a constant ratio to the theoretical throughput upper bound. Extensive experimental results demonstrate that, SBM performs better than existing work in terms of both the uniform multicast throughput and the total number of transmissions. © 2016 IEEE.

关键词： Throughput

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Labelwalking nonnegative matrix factorization 40

Labelwalking nonnegative matrix factorization

引用

40th IEEE International Conference on Acoustics, Speech, and Signal processing, ICASSP 2015

作者： Lan, Long Guan, Naiyang Zhang, Xiang Huang, Xuhui Luo, Zhigang Science and Technology on Parallel and Distributed Processing Laboratory College of Computer National University of Defense Technology Changsha China

ISBN: (纸本)9781467369978

Semi-supervised learning (SSL) utilizes plenty of unlabeled examples to boost the performance of learning from limited labeled examples. Due to its great discriminant power, SSL has been widely applied to various real-world tasks such as information retrieval, pattern recognition, and speech separa- tion. Label propagation (LP) is a popular SSL method which propagates labels through the dataset along high density areas defined by unlabeled examples, LP assumes nearby examples should share the same label, thus, it unavoidably pushes the labels to the wrong examples, especially when different la- beled examples are not strictly separated. Seed K-means uses labeled examples to initialize class centers, and avoid getting stuck in poor local optima comparing to traditional K-means, however the hard constraint of each example's membership makes Seed K-means failed in many real world applications. This paper proposes a novel label walking nonnegative matrix factorization method (LWNMF) to handle labeled examples in SSL based on the framework of NMF. LWNMF decomposes the whole dataset into the product of a basis matrix and a coefficient matrix, and to travel labels to unlabeled examples, LWNMF regards the class indicators of labeled examples as their coefficients and iteratively updates both basis matrix and coefficients of unlabeled examples. Since LWNMF learns comprehensive class centroids, labels iteratively walk to unlabeled examples through these significant centroids. © 2015 IEEE.

关键词： K-means Label propagation Nonnegative matrix factorization Semisupervised learning

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Hyperspectral image classification via kernel extreme learning machine using local receptive fields

Hyperspectral image classification via kernel extreme learni...

引用

IEEE International Conference on Image processing

作者： Qi Lv Xin Niu Yong Dou Yueqing Wang Jiaqing Xu Jie Zhou College of Computer National University of Defense Technology Changsha China National Laboratory for Parallel and Distributed Processing National University of Defense Technology Changsha China National University of Defense Technology Changsha Hunan CN

This paper proposes a classification approach for hyperspectral image (HSI) using the local receptive fields based kernel extreme learning machine. Extreme learning machine (ELM) has drawn increasing attention in the pattern recognition filed due to its simpleness, speediness and good generalization ability. A kernel method is often used to promote ELM's performance, which is known as kernel ELM. The local receptive field concept originates from research in neuroscience. Considering the local correlations of spectral features, it is promising to improve the performance of HSI classification by combining local receptive fields with kernel ELM. Experimental results on the Pavia University dataset confirm the effectiveness of the proposed HSI classification method.

关键词： Kernel Training Hyperspectral imaging Convolution Neurons Feature extraction

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Identifying repeated interleavings to improve the efficiency of concurrency bug detection 15th

Identifying repeated interleavings to improve the efficiency...

引用

15th International Conference on Algorithms and Architectures for parallel processing, ICA3PP 2015

作者： Wu, Zhendong Lu, Kai Wang, Xiaoping Science and Technology on Parallel and Distributed Processing Laboratory College of Computer National University of Defense Technology Changsha410073 China

ISBN: (纸本)9783319271392

Detecting concurrency bugs is becoming increasingly important. Many pattern-based concurrency bug detectors focus on the specific types of interleavings that are correlated to concurrency bugs. To detect multiple types of concurrency bugs, general detectors focus on multiple types of interleaving patterns, including data race pattern, atomicity violation pattern, and atomic-set violation pattern. Unfortunately, they suffer from redundant analysis due to repeated interleavings, which may affect the efficiency of concurrency bug detection. Hence, we propose an approach to identify the repeated interleavings. To the best of our knowledge, this is the first approach that can prune repeated interleavings to improve the efficiency of concurrency bug detection. We apply our approach to existing general detectors (PECAN and Maple) to avoid analyzing repeated interleavings. We evaluated the general detectors with and without our approach, respectively. The experimental results show that the bug detection results are not affected. With our approach, the bug detection time of PECAN and Maple are reduced by 40.0% and 44.4%, respectively. Additionally, our approach does not affect the overhead of bug detection, and consumes only a little memory. © Springer International Publishing Switzerland 2015.

关键词： Efficiency

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parallel pipeline implementation for moving objects detection in traffic video analysis on a heterogeneous platform 4th

Parallel pipeline implementation for moving objects detectio...

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4th International Conference on Computer Engineering and Networks, CENet2014

作者： Li, Teng Dou, Yong Jiang, Jingfei Qiao, Peng Department of Computer Science National Laboratory for Parallel and Distributed Processing National University of Defense Technology Changsha410073 China

ISBN: (纸本)9783319111032

Moving objects detection is important in traffic video analysis, and many algorithms are being increasingly applied to moving objects detection. Most of these algorithms are time-consuming and cannot satisfy real-time demand in traffic video analysis by using a conventional central processing unit (CPU) sequential method. The emergence of the graphics processing unit (GPU) and multi-core CPU provides a method to accelerate the aforementioned algorithms to meet demand. In this study, we provide a GPU-accelerated implementation of the background subtraction algorithm and the morphological operation algorithm. Then, the connected component labeling algorithm is parallelized on a multi-core CPU with Open Multi-processing (OpenMP). Furthermore, parallel pipeline implementation on a heterogeneous platform is proposed by integrating the aforementioned algorithms. Experimental results show that the proposed implementation achieves a significant speedup of up to 5×, compared with sequential implementation on a CPU. © Springer International Publishing Switzerland 2015.

关键词： Program processors

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Deadline-oriented task scheduling for mapreduce environments 15th

Deadline-oriented task scheduling for mapreduce environments

引用

15th International Conference on Algorithms and Architectures for parallel processing, ICA3PP 2015

作者： Hu, Minghao Wang, Changjian You, Pengfei Huang, Zhen Peng, Yuxing National Laboratory for Parallel and Distributed Processing School of Computer Science National University of Defense Technology Changsha410072 China

ISBN: (纸本)9783319271217

To provide timely results for ‘Big Data Analytics’, it is crucial to satisfy deadline requirements for MapReduce jobs in production environments. In this paper, we propose a deadline-oriented task scheduling approach, named Dart, to meet the given deadline and maximize the input size if only part of the dataset can be processed before the time limit. Dart uses an iterative estimation method which is based on both historical data and job running status to precisely estimate the realtime job completion time. By comparing the estimated time with the deadline constraint, a YARN-based task scheduler dynamically decides whether continuing or terminating the map *** have validated our approach using workloads from OpenCloud and Facebook on a cluster of 60 virtual machines. The results show that Dart can not only effectively meet the deadline but also process near-maximal data volumes even when the deadline is set to be extremely small and limited resources are allocated. © Springer International Publishing Switzerland 2015.

关键词： MapReduce

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DFIS: A scalable distributed fingerprint identification system 15th

DFIS: A scalable distributed fingerprint identification syst...

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15th International Conference on Algorithms and Architectures for parallel processing, ICA3PP 2015

作者： Zhao, Yunxiang Zhang, Wanxin Li, Dongsheng Huang, Zhen National Laboratory for Parallel and Distributed Processing College of Computer National University of Defence Technology ChangshaHunan410073 China

ISBN: (纸本)9783319271361

Fingerprint has been widely used in a variety of biometric identification systems. However, with the rapid development of fingerprint identification systems, the amount of fingerprints information stored in systems has been rising sharply, making it challenging to process and store fingerprints efficiently and robustly with traditional standalone systems and relational databases. In this paper, we propose a scalable distributed fingerprint identification system, named DFIS. It combines the feature extraction procedure with HIPI library and optimizes the load balance strategy of MongoDB to construct a much more robust and stable system. Related experiments and simulations have been carried out and results show that DFIS can reduce the time expense by 70% during the features extraction procedural. For load balance of MongoDB, DFIS can decrease the difference of access load to less than 5% and meanwhile decrease 50% data migration to gain more reasonable distribution of operation load and data load among shards compared with the default load balance strategy in MongoDB. © Springer International Publishing Switzerland 2015.

关键词： distributed computer systems

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An Efficient Algorithm for Tensor Principal Component Analysis via Proximal Linearized Alternating Direction Method of Multipliers

An Efficient Algorithm for Tensor Principal Component Analys...

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International Conference on Advanced Cloud and Big Data (CBD)

作者： Linbo Qiao Bofeng Zhang Lei Zhuang Jinshu Su College of Computer National University of Defense Technology ChanzSha China National Laboratory for Parallel and Distributed Processing National University of Defense Technology ChangSha China Department of Statistics Shanghai University of Finance and Economics Shanghai China

ISBN: (纸本)9781509036783

In paper, we focus on the computation of the principal components for a general tensor, known as the tensor principal component analysis (PCA) problem. It has been proven that the general tensor PCA problem is reducible to its matricization form when the order is even. Usually it is considered as low-rank matrix completion problems theoretically. It is common to consider nuclear norm as a surrogate of the rank operator since it is the tightest convex lower bound of the rank operator under certain condition. However, most nuclear norm minimization based approaches involve numbers of singular value decomposition (SVD) operations. Given a matrix X ∈ R m × n , the time complexity of SVD operation is O(mn 2 ), which brings prohibitive computational burden to apply these methods in real applications. However, the problem is non-convex, and the proximal mapping associated with non-convex regularization is not easy to compute. It is always solved by the Linearized Alternating Direction Method of Multipliers (LADMM). Despite the success of LADMM in practice, it remains unknown if LADMM is convergent in solving such non-convex compositely regularized optimization. In this paper, we firstly present a detailed convergence analysis of the LADMM algorithm for solving non-convex compositely regularized optimization with a large class of non-convex penalties. Furthermore, we propose a new efficient and scalable algorithm for matrix principal component analysis called Proximal Linearized Alternating Direction Method of Multipliers for Principal Component Analysis(PLADMPCA). Different from traditional matrix factorization methods, PLADMPCA utilizes the linearization technique to formulate the matrix as an outer product of vectors, which greatly improves the computational efficacy compared to matrix factorization method. We empirically evaluate the proposed algorithm PLADMPCA on synthetic tensor data with different order. Results have shown that PLADMPCA have much better computational

关键词： Tensile stress Principal component analysis Algorithm design and analysis Convergence Matrix decomposition Optimization Computational efficiency

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Partial clones for stragglers in MapReduce

Partial clones for stragglers in MapReduce

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International Conference of Young Computer Scientists, Engineers and Educators, ICYCSEE 2015

作者： Li, Jia Wang, Changjian Li, Dongsheng Huang, Zhen National Laboratory for Parallel and Distributed Processing School of Computer Science National University of Defense Technology Changsha410073 China

ISBN: (纸本)9783662462478

Stragglers can temporize jobs and reduce cluster efficiency seriously. Many researches have been contributed to the solution, such as Blacklist[8], speculative execution[1, 6], Dolly[8]. In this paper, we put forward a new approach for mitigating stragglers in MapReduce, name Hummer. It starts task clones only for high-risk delaying tasks. Related experiments have been carried and results show that it can decrease the job delaying risk with fewer resources consumption. For small jobs, Hummer also improves job completion time by 48% and 10% compared to LATE and Dolly. © Springer-Verlag Berlin Heidelberg 2015.

关键词： MapReduce

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Towards Robust Ego-Centric Hand Gesture Analysis for Robot Control

Towards Robust Ego-Centric Hand Gesture Analysis for Robot C...

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2016 IEEE International Conference on Signal and Image processing

作者： Hongyong Song Weijiang Feng Naiyang Guan Xuhui Huang Zhigang Luo Institute of Software College of Computer National University of Defense Technology Department of Computer Science and Technology College of Computer National University of Defense Technology Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology

Wearable device with an ego-centric camera would be the next generation device for human-computer interaction such as robot *** gesture is a natural way of egocentric human-computer *** this paper, we present an ego-centric multi-stage hand gesture analysis pipeline for robot control which works robustly in the unconstrained environment with varying *** particular, we first propose an adaptive color and contour based hand segmentation method to segment hand region from the egocentric *** then propose a convex U-shaped curve detection algorithm to precisely detect positions of *** parallelly, we utilize the convolutional neural networks to recognize hand *** on these techniques, we combine most information of hand to control the robot and develop a hand gesture analysis system on an i Phone and a robot arm platform to validate its *** result demonstrates that our method works perfectly on controlling the robot arm by hand gesture in real time.

关键词： ego-centric vision hand detection and segmentation fingertips detection hand gesture recognition robot control human-computer interaction

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