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arXiv 2017年

作者： Lv, Shaohe Lu, Yong Dong, Mianxiong Wang, Xiaodong Dou, Yong Zhuang, Weihua National Laboratory of Parallel and Distributed Processing National University of Defense Technology ChangshaHunan China Department of Information and Electronic Engineering Muroran Institute of Technology 27-1 Mizumoto-cho Muroran Hokkaido050-8585 Japan Department of Electrical and Computer Engineering University of Waterloo WaterlooON Canada

—Human-machine systems required a deep understanding of human behaviors. Most existing research on action recognition has focused on discriminating between different actions, however, the quality of executing an action has received little attention thus far. In this paper, we study the quality assessment of driving behaviors and present WiQ, a system to assess the quality of actions based on radio signals. This system includes three key components, a deep neural network based learning engine to extract the quality information from the changes of signal strength, a gradient based method to detect the signal boundary for an individual action, and an activity-based fusion policy to improve the recognition performance in a noisy environment. By using the quality information, WiQ can differentiate a triple body status with an accuracy of 97%, while for identification among 15 drivers, the average accuracy is 88%. Our results show that, via dedicated analysis of radio signals, a fine-grained action characterization can be achieved, which can facilitate a large variety of applications, such as smart driving assistants. Copyright © 2017, The Authors. All rights reserved.

关键词： Man machine systems

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HPDedup: A hybrid prioritized data deduplication mechanism for primary storage in the cloud

arXiv

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arXiv 2017年

作者： Wu, Huijun Wang, Chen Fu, Yinjin Sakr, Sherif Zhu, Liming Lu, Kai Data61 CSIRO University of New South Wales Australia PLA University of Science and Technology China Science and Technology on Parallel and Distributed Laboratory State Key Laboratory of High Performance Computing State Key Laboratory of High-end Server & Storage Technology College of Computer National University of Defense Technology Changsha China

—Eliminating duplicate data in primary storage of clouds increases the cost-efficiency of cloud service providers as well as reduces the cost of users for using cloud services. Most existing primary deduplication techniques either use inline caching to exploit locality in primary workloads or use post-processing deduplication running in system idle time to avoid the negative impact on I/O performance. However, neither of them works well in the cloud servers running multiple services or applications for the following two reasons: Firstly, the temporal locality of duplicate data writes may not exist in some primary storage workloads thus inline caching often fails to achieve good deduplication ratio. Secondly, the post-processing deduplication allows duplicate data to be written into disks, therefore does not provide the benefit of I/O deduplication and requires high peak storage capacity. This paper presents HPDedup, a Hybrid Prioritized data Deduplication mechanism to deal with the storage system shared by applications running in co-located virtual machines or containers by fusing an inline and a post-processing process for exact deduplication. In the inline deduplication phase, HPDedup gives a fingerprint caching mechanism that estimates the temporal locality of duplicates in data streams from different VMs or applications and prioritizes the cache allocation for these streams based on the estimation. HPDedup also allows different deduplication threshold for streams based on their spatial locality to reduce the disk fragmentation. The post-processing phase removes duplicates whose fingerprints are not able to be cached due to weak temporal locality from disks. The hybrid deduplication mechanism significantly reduces the amount of redundant data written to the storage system while maintaining inline data writing performance. Our experimental results show that HPDedup clearly outperforms the state-of-the-art primary storage deduplication techniques in terms of inline

关键词： Efficiency

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A Soft Multi-core Architecture for THP of MIMO-OFDM Systems

A Soft Multi-core Architecture for THP of MIMO-OFDM Systems

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International Conference on Network and Information Systems for Computers (ICNISC)

作者： Lirui Chen Yang Zhang Cang Liu Zuocheng Xing National Laboratory for Parallel and Distributed Processing National University of Defense Technology Changsha China

ISBN: (纸本)9781467388399

MIMO system is widely studied for its high performance in wireless communication, of which the THP algorithm is a bottleneck of performance. Generally the THP is implemented in ASIC for high performance, which unfortunately makes it a difficulty for system updating. Compared to the ASIC solution, configurable processing, as its inherent flexibility and extension, becomes a promising solution for this difficulty, especially in the case of FPGA based systems where multi-core method is utilized to make up for the performance deficiency of soft cores. This paper explores a micro blaze based-multi-core system to increase the performance of THP algorithm while keeping the system flexibility. In support of the flexibility to update the algorithm, the effective adaption of this approach is demonstrated. Moreover, optimized application specific hardware is combined with data-level paralleled software modules in the multi-core embedded system, which increases the performance of THP algorithm to a 6× speed over all-soft single-core solution.

关键词： Program processors Algorithm design and analysis MIMO Hardware Matrix decomposition Field programmable gate arrays Computer architecture

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Comparative Studies of Sampling for Analytics on Massive Data

Comparative Studies of Sampling for Analytics on Massive Dat...

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International Conference on Systems and Informatics

作者： Xuan Zhang Dongsheng Li National Laboratory for Parallel and Distributed Processing National University of Defense Technology Changsha China

ISBN: (纸本)9781509055227

Groupwise analytics on big data have been widely used in statistics, computer science, parallel computing and many other fields in recent years. At The same time, Aggregation queries is one of the most important analytics techniques. In big data eras, the aggregation queries on the ever-increasing data volumes will consumes much time, the traditional methods of traversing the entire dataset is not acceptable to users. Data sampling is a technique that only process a part of data to get an approximate result, the technique can save a lot of time when dealing with a vast amount of data with the sacrifice of accuracy. This paper will introduce several data sampling algorithms for approximate aggregation queries for big data, and analyze the shortcomings and advantages of each methods. Including the technique apply to the sparse data which meaning data has a limited population but a wide range.

关键词： Groupwise analytics Aggregation Approximate result Data sampling Sparse data

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DISVMs: Fast SVMs Training on Large-scale Data Sets

DISVMs: Fast SVMs Training on Large-scale Data Sets

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International Conference on Tools with Artificial Intelligence

作者： Lijuan Cui Changjian Wang Ziyang Li Yuxing Peng Science and Technology on Parallel and distributed Processing Laboratory National University of Defence Technology

ISBN: (纸本)9781509044603

Support Vector Machines (SVMs) are powerful classification tools. However, the model training is very time-consuming when meeting large scale data sets. Some efforts have been devoted to screening out non-support vectors (non-SVs) to accelerate the training. But their processes rely on prior knowledge of other classifiers with different parameters to screen out non-SVs. In this paper, we propose Directional Indicator Support Vector Machines (DISVMs) to efficiently identify non-SVs. DISVMs employs a directional indicator, which points to the approximately orthogonal direction of the separating hyperplane, to qualitatively define the location of different samples and thus identify non-SVs. Furthermore, DISVMs leverages a two-stage algorithm: the first stage is to compute the directional indicator. The second stage is to identify non-SVs using the indicator. To avoid misjudgement, we propose CnSV method for non-SVs based on the majority rule. DISVMs screens out non-SVs with light computation and little accuracy loss. Experiments show that our approach significantly reduces the total computation cost.

关键词： Support vector machines Non-support vector Directional indicator Computation efficiency Data preprocessing

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Combining FFT and Spectral-Pooling for Efficient Convolution Neural Network Model

Combining FFT and Spectral-Pooling for Efficient Convolution...

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2016 2nd International Conference on Artificial Intelligence and Industrial Engineering (AIIE2016)

作者： Zelong Wang Qiang Lan Dafei Huang Mei Wen Department of Compute National University of Technology Defense National Key Laboratory of Parallel and Distributed Processing National University of Defense Technology

ISBN: (纸本)9781510835368

Convolution operation is the most important and time consuming step in a convolution neural network *** this work,we analyze the computing complexity of direct convolution and fast-Fourier-transform-based(FFT-based) *** creatively propose CS-unit,which is equivalent to a combination of a convolutional layer and a pooling layer but more *** computing complexity of and some other similar operation is demonstrated,revealing an advantage on computation of ***,practical experiments are also performed and the result shows that CS-unit holds a real superiority on run time.

关键词： computing complexity FFT-based convolution CS-unit

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Fin width and height dependence of bipolar amplification in bulk FinFETs submitted to heavy ion irradiation

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Chinese Physics B 2015年第11期24卷 650-655页

作者：于俊庭陈书明陈建军黄鹏程 College of Computer National University of Defense Technology National Laboratory for Parallel and Distributed Processing National University of Defense Technology

FinFET technologies are becoming the mainstream process as technology scales down. Based on a 28-nm bulk p- FinFET device, we have investigated the fin width and height dependence of bipolar amplification for heavy-ion-irradiated FinFETs by 3D TCAD numerical simulation. Simulation results show that due to a well bipolar conduction mechanism rather than a channel （fin） conduction path, the transistors with narrower fins exhibit a diminished bipolar amplification effect, while the fin height presents a trivial effect on the bipolar amplification and charge collection. The results also indicate that the single event transient （SET） pulse width can be mitigated about 35% at least by optimizing the ratio of fin width and height, which can provide guidance for radiation-hardened applications in bulk FinFET technology.

关键词： fin width and height bipolar amplification single event transient bulk FinFET

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Learning from Wireless: a Prospective Approach to Human-Centered Computing

Learning from Wireless: a Prospective Approach to Human-Cent...

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International Joint Conference on Neural Networks

作者： Yong Lu Shaohe Lv Xue Yang Xiaodong Wang Minne Li Xingming Zhou Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology

ISBN: (纸本)9781509006212

One of the most important topics in Human-Centered Computing (HCC) is to recognise human's activities. In this paper, the technology of wireless-based activity recognition is introduced. By using wireless signals, one can achieve Non-Line-Of-Sight (NLOS) recognition without carrying any devices. Also, it is easy to deploy a wireless-based recognition system due to the ubiquity of wireless communication systems. The basic idea is to detect different characteristics of signal propagation that correspond to the distinct human behaviors. As a result, action recognition is performed by analyzing the distinguishable features of signal propagation. This paper introduces the basic principles and applications of wireless-based activity recognition, and discusses the challenges and related performance metrics. Finally, open problems are discussed to point out the future research trends.

关键词： Human-centered computing wireless Wireless communication systems Activity recognition recognition system Performance metrics signals Learning Paper Technology Human behavior

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Auxo: an architecture-centric framework supporting the online tuning of software adaptivity

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Science China(Information Sciences) 2015年第9期58卷 31-45页

作者： WANG HuaiMin DING Bo SHI DianXi CAO JianNong Alvin T.S.Chan National Key Laboratory of Parallel and Distributed Processing College of ComputerNational University of Defense Technology Department of Computing Hong Kong Polytechnic University

Adaptivity is the capacity of software to adjust itself to changes in its environment. A common approach to achieving adaptivity is to introduce dedicated code during software development stage. However,since those code fragments are designed a priori, self-adaptive software cannot handle situations adequately when the contextual changes go beyond those that are originally anticipated. In this case, the original builtin adaptivity should be tuned. For example, new code should be added to provide the capacity to sense the unexpected environment or to replace outdated adaptation decision logic. The technical challenges in this process, especially that of tuning software adaptivity at runtime, cannot be understated. In this paper,we propose an architecture-centric application framework for self-adaptive software named Auxo. Similar to existing work, our framework supports the development and running of self-adaptive software. Furthermore,our framework supports the tuning of software adaptivity without requiring the running self-adaptive software to be terminated. In short, the architecture style that we are introducing can encapsulate not only general functional logic but also the concerns in the self-adaptation loop(such as sensing, decision, and execution)as architecture elements. As a result, a third party, potentially the operator or an augmented software entity equipped with explicit domain knowledge, is able to dynamically and flexibly adjust the self-adaptation concerns through modifying the runtime software architecture. To truly exercise, validate, and evaluate our approach,we describe a self-adaptive application that was deployed on the framework, and conducted several experiments involving self-adaptation and the online tuning of software adaptivity.

关键词： software architecture self-adaptive software architecture style application framework software adaptation

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Flip-flops soft error rate evaluation approach considering internal single-event transient

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Science China(Information Sciences) 2015年第6期58卷 159-170页

作者： SONG RuiQiang CHEN ShuMing HE YiBai DU YanKang College of Computer Science National University of Defense Technology Science and Technology on Parallel and Distributed Processing Laboratory

The internal single-event transient(SET) induced upset in flip-flops is becoming significant with the increase of the operating frequency. However, the conventional soft error rate(SER) evaluation approach could only produce an approximate upset prediction result caused by the internal SET. In this paper, we propose an improved SER evaluation approach based on Monte Carlo simulation. A novel SET-based upset model is implemented in the proposed evaluation approach to accurately predict upsets caused by the internal SET. A test chip was fabricated in a commercial 65 nm bulk process to validate the accuracy of the improved SER evaluation approach. The predicted single-event upset cross-sections are consistent with the experimental data.

关键词： soft error rate Monte Carlo internal SET single-event upset flip-flops

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