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    25 December 2017, Volume 38 Issue 12
    Papers
    Reconstruction algorithm based on multi-reference frames hypothesis optimization for compressive sensing
    Yong-hong KUO,Ru-quan WANG,Jian CHEN
    2017, 38(12):  1-9.  doi:10.11959/j.issn.1000-436x.2017297
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    In multi-hypothesis based distributed compressed video sensing systems,the quality of the multi-hypothesis set has important influence on the reconstruction performance of decoder.However,the acquiring of the hypothesis set has not been concerned in existing works.A reconstruction algorithm based on multi-reference frames hypothesis optimization (MRHO) was proposed.This algorithm expanded the selection of hypothesis vectors by increasing the number of reference frames.The quality of the prediction set was improved by hypotheses optimization selection under the same size with the original hypothesis set.Simulation results show that the proposed MRHO algorithm effectively improves the reconstructed quality of the distributed compressed video sensing scheme.

    Virtual machine scheduling strategy based on dual-speed and work vacation mode and its parameter optimization
    Shun-fu JIN,Shan-shan HAO,Bao-shuai WANG
    2017, 38(12):  10-20.  doi:10.11959/j.issn.1000-436x.2017298
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    Due to the increasingly strict environmental standards,high pollution and high energy consumption have become the significant factors restricting the development of cloud data centers (CDC).Under the premise of guaranteeing the quality of service (QoS) of CDC,dynamic power management (DPM) technology was applied,synchronous multiple working sleep mode was introduced,and a novel virtual machine (VM) scheduling strategy was proposed.By establishing a two-dimensional continuous-time Markov stochastic model with adaptive service rate and synchronous multiple work vacations,and using the method of a matrix geometric solution,the performance of the VM scheduling strategy was evaluated in terms of energy saving level and average delay of requests.Numerical results with analysis and simulation verify the energy saving effectiveness of the VM scheduling strategy.In order to achieve a reasonable balance between the response performance and the energy-saving effect,a system utility function was constructed from the perspective of economics and design a researching algorithm of the sleep parameter based on the firefly algorithm(FA).

    Abnormal trajectory detection method based on enhanced density clustering and abnormal information mining
    Ming HE,Gong-da QIU,Bo ZHOU,Qiang LIU,Yu-ting CAO
    2017, 38(12):  21-33.  doi:10.11959/j.issn.1000-436x.2017287
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    Aiming at problems of low accuracy in the recognition and difficulty in enriching the information of abnormal behavior in the social security incidents,an abnormal trajectory detection method based on enhanced density clustering and abnormal information mining was proposed.Firstly,combined with Hausdorff distance,an enhanced DTW distance aiming at the problem of sampling to describe the behavior in detail was proposed.And based on the MBR distance, some definitions to describe the geographical distribution of trajectory were proposed.Secondly,with the density-distance decision model of CFSFDP algorithm,intelligent recognition of cluster was realized by using the difference of SSVR which was proposed based on SVR.Finally,based on the analysis of distribution under the two kinds of density,more abnormal information could be mined,three kinds of abnormal trajectories would be recognized.And the simulation results on trajectory data of Shanghai and Beijing verify that the algorithm is objective and efficient.Comparing to existing method,accuracy in the clustering is promoted by 10%,and the abnormal trajectories are sorted, abnormal information is enriched.

    Improved LDPC-based short-range frequency-hopping wireless communication system
    Wei-dong FANG,Wu-xiong ZHANG,Ming-ming HU,Wei CHEN,Yang YANG
    2017, 38(12):  34-47.  doi:10.11959/j.issn.1000-436x.2017284
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    In short-range wireless communication system(SWCS),the complex scenarios make its transmission face the diverse interference,and lower hardware resource leads to its limited anti-interference capability.To achieve the reliable transmission,a frequency-hopping-based SWCS (FH-SWCS)with improved LDPC (I-LDPC) code was constructed.In FH-SWCS,a low-complexity check-sum updating(LCU)algorithm was proposed to reduce the amount of computation.LCU-based multi-threshold bit flipping (LCU-MTBF) algorithm was given to increase the reliability of bit-flipping, improving decoding performance and reduce the complexity of decoding.The simulation results demonstrate that LCU is suitable for multiple hard decisions decoding algorithm,and it can reduce the computational complexity of original decoding algorithm without affecting its performance.When BER is 10?5,and iterations number is 5,0.15dB performance gain can be achieved,and the number of additions algorithm can be reduced about 40% in LCU-MTBF.In FH-SWCS with I-LDPC,when BER is 10?4,and SNR is 15dB,the performance gain about 7dB can be achieved to improve the anti-interference of the system effectively.

    Blind parameter estimation algorithm for frequency-hopping signals based on compressed sensing
    Yun-hong FU,Yun-fei ZHANG,Juan WEI,Nai-an LIU
    2017, 38(12):  48-56.  doi:10.11959/j.issn.1000-436x.2017288
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    A blind parameter estimation algorithm for frequency-hopping signals based on compressed sensing was proposed,in order to solve the problem that the existing parameter estimation algorithms did not take into account the sparse structural characteristics of the signals in frequency domain.Firstly,the maximum cosine method was used to process the segmented compressed sampling signals,and the hopping frequency was estimated.Then,the atom matching algorithm was used to process the signal with the hopping point,and the frequency hopping instance time was estimated accurately.Then the hopping speed and hopping time were estimated.The experimental results show that the algorithm can significantly reduce the sampling data and computational complexity,while improving estimation accuracy.

    Sparsity adaptive channel estimation algorithm based on compressive sensing for massive MIMO systems
    Li-jun GE,Hui GUO,Yue LI,Lan ZHAO
    2017, 38(12):  57-62.  doi:10.11959/j.issn.1000-436x.2017291
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    A sparsity-adaptive channel estimation algorithm based on compressive sensing was proposed for massive MIMO systems when the number of channel multi-paths was unknown.By exploiting the joint sparsity characteristics of the sub-channels,the proposed block sparsity adaptive matching pursuit (BSAMP) algorithm first selected atoms by setting a threshold and finding the position of the maximum backward difference,which reduces the energy dispersion caused by the non-orthogonality of the observation matrix and improves the performance of the algorithm.Then a regularization method was utilized to improve the stability of the algorithm.Simulation results demonstrate that the proposed algorithm recovers the channel state information accurately and shows a high computational efficiency.

    Spectral-clustering-based abnormal permission assignments hunting framework
    Liang FANG,Li-hua YIN,Feng-hua LI,Bin-xing FANG
    2017, 38(12):  63-72.  doi:10.11959/j.issn.1000-436x.2017285
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    Migrating traditional access control,such as mandatory and discretionary access control,into role-based access control(RBAC)lightens a practical way to improve the user-permission management efficiency.To guarantee the security of RBAC system,it is important to generate proper roles during the migration.However,abnormal user-permission configurations lead to wrong roles and cause tremendous security risks.To hunt the potential abnormal user-permission configurations,a novel spectral clustering based abnormal configuration hunting framework was proposed and recommendations were given to correct these configurations.Experimental results show its performance over existing solutions.

    Research on the backbone network cache method based on application-awareness
    Chun-jing HAN,Ye YANG,Hong-lei LYU,Jing-guo GE,Tong LI,Yun-jie LIU
    2017, 38(12):  73-85.  doi:10.11959/j.issn.1000-436x.2017226
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    Based on over 900 million HTTP requests from an ISP backbone network,the HTTP characteristic of different content types from a wide range of perspectives was analyzed,including request composition,size,popularity and temporal dynamics,and an application-aware backbone network cache method was proposed,referred to as AACM.The experiment results show that in the case of a slight increase in cache space,the hit ratio of the online-video increases 15%, and the total source traffic and cache I/O load reduced by about half.

    Adaptive noise mitigation based on peak estimate and feedback compensation in power line communication
    Zhou-wen TAN,Hong-li LIU,Jie ZHAN,Zi-ji MA,Shu-gang LIU
    2017, 38(12):  86-97.  doi:10.11959/j.issn.1000-436x.2017283
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    According to the problem that the threshold for traditional blanking depends on the characteristic parameters of noise and exist large deviation,an adaptive noise mitigation algorithm based on peak estimate and feedback compensation(ANMPEFC)in power line communication was proposed.First,SLM mapping preprocessing method was employed to reduce the PAPR of emission signal,peak information was used to estimate the frequency of the received signal and the relationship between peak frequency and impulse characteristics was established.Next,received signal was processed by blanking block and feedback compensation block adaptively according to estimated pulse frequency.Finally, performance of proposed algorithm and existing algorithms were analyzed based on threshold deviation.According to the results from simulation,it is clearly demonstrated that the proposed ANMPEFC can work in power line impulse noise environment without knowing the noise characteristics and has better performance in contrast to existing impulse noise suppression algorithm.

    Data stream prediction based on rule antecedent occurrence tree matching
    Tao YOU,Ting-feng LI,Cheng-lie DU,Dong ZHONG,Yi-an ZHU
    2017, 38(12):  98-108.  doi:10.11959/j.issn.1000-436x.2017286
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    There are some shortages in the existing rule-based data stream prediction algorithm,such as inaccurate definition of antecedent occurrence,ignoring the correlation between rules and imprecise description of prediction accuracy.These make low forecasting process efficiency and low prediction accuracy.The superposed prediction algorithm was proposed based on antecedent occurrence tree,and interval minimal non-overlapping occurrence was defined to avoid the problem of excessive matching antecedent.The efficiency was improved for searching antecedent’s occurrence by merging rule’s antecedents in antecedent occurrence tree,and the succedent occurrence based on superposed probability was predicted to enhance prediction accuracy.The theoretical analysis and experimental evaluation demonstrate the algorithm is superior to the existing prediction algorithms in terms of time and space efficiency and prediction accuracy.

    Study of emotion recognition based on fusion multi-modal bio-signal with SAE and LSTM recurrent neural network
    You-jun LI,Jia-jin HUANG,Hai-yuan WANG,Ning ZHONG
    2017, 38(12):  109-120.  doi:10.11959/j.issn.1000-436x.2017294
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    In order to achieve more accurate emotion recognition accuracy from multi-modal bio-signal features,a novel method to extract and fuse the signal with the stacked auto-encoder and LSTM recurrent neural networks was proposed.The stacked auto-encoder neural network was used to compress and fuse the features.The deep LSTM recurrent neural network was employed to classify the emotion states.The results present that the fused multi-modal features provide more useful information than single-modal features.The deep LSTM recurrent neural network achieves more accurate emotion classification results than other method.The highest accuracy rate is 0.792 6

    Research on full-field optical coherence tomography
    Ning MU,Wan-rong GAO,Zhe-qiang WEI
    2017, 38(12):  121-127.  doi:10.11959/j.issn.1000-436x.2017293
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    Full-field optical coherence tomography(FFOCT)was used for imaging the interior part of an object.Firstly,the mathematical modeling and performance analysis of the FFOCT system mounted was provided.Secondly,images of several different human tissues were obtained by FFOCT system,including esophagus,uterus,etc.The images of different depths of the same tissue were shown and compared.It was the first time in China that FFOCT was used for generating depth images on human esophagus and uterus;high-resolution images had been obtained for different depths without cutting the tissue sample,in which intercellular substance and myofibril structure could be clearly identified.As the in-depth structure could be imaged without the process needed for frozen and paraffin sectioning methods,the technology could been highly valuable for early cancer diagnosis and pathological analysis in tumor research.

    Comprehensive Review
    Survey on cyber deception
    Zhao-peng JIA,Bin-xing FANG,Chao-ge LIU,Qi-xu LIU,Jian-bao LIN
    2017, 38(12):  128-143.  doi:10.11959/j.issn.1000-436x.2017281
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    The asymmetric situation of network attacks and defenses is one of the key issues of current network security.Cyber deception was a revolutionary technology introduced by defenders to alter the asymmetric situation.By thwarting an attacker's cognitive processes,defenders can mislead attackers,hence causing them to take specific actions that aid network security defenses.In this way,defenders can log attackers'behavior and method,increase cost for the attackers to launch a successful attack,as well as reduce the probability of an attacker's success.Cyber deception formally and classify cyber deception into four classes was defined.Then,the cyber deceptions’development was divided into three stages,and each stage’s character was decided.Next,a hierarchical model to describe the existing work was proposed.At last,the countermeasures in cyber deception and the development trends in this field was discussed.

    Correspondences
    Novel chaotic block encryption scheme for WSN based on dynamic sub key
    Ya-hua WANG,Yu-hua LING,Li-qing LIAO,Ke-hui SUN,Wen-hao LIU
    2017, 38(12):  144-152.  doi:10.11959/j.issn.1000-436x.2017292
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    In view of high efficiency and security requirements in WSN encryption algorithm,a lightweight chaotic block encryption algorithm was designed and a novel scheme of dynamic sub keys extension was proposed.To greatly reduce the computing burden of WSN nodes,this scheme made full use of WSN cloud servers monitoring platform,which was powerful in data computing and processing,and transfered the sub keys synchronization task from nodes to cloud servers.Experimental results and performance analysis show that the scheme has good characteristics of diffusion,confusion and statistical balance,strong key security and high algorithm efficiency.It has a good application prospect in the field of WSN communication encryption.

    Research on network analysis method for development ability of big data industry in underdeveloped area
    Jun-xin SHEN,Ying-qian CHEN
    2017, 38(12):  153-159.  doi:10.11959/j.issn.1000-436x.2017290
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    Traditional evaluation methods of industrial development ability were mostly lack of objectivity.An evaluation model was proposed by using a BP neural network based on entropy weight.Evaluation index system of big data industry development ability in underdeveloped areas was established.Taking Guizhou industrial development data as samples,entropy weight method was used to determine expected output and compared with the actual output .The experimental results show that the proposed entropy weight-BP evaluation model can optimize error of using single BP network and improve the accuracy and objectivity of evaluation.

    Specific emitter identification based on ITD and texture analysis
    Dong-fang REN,Tao ZHANG,Jie HAN,Huan-huan WANG
    2017, 38(12):  160-168.  doi:10.11959/j.issn.1000-436x.2017299
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    To solve the defects of time-frequency analysis and poor separability of extracted features in specific emitter identification (SEI) based on Hilbert-Huang transform (HHT),a novel SEI method based on intrinsic time-scale decomposition(ITD)was proposed.ITD was utilized to decompose the emitter signals and get the time-frequency energy distribution(TFED)firstly,later the TFED spectrum was transformed into gray image and several image texture features through histogram statistic and gray-level co-occurrence matrix was extracted for identification.Measured ship communication signals and simulated emitter signals were used to test the performance of proposed method.Compared with another two SEI methods based on HHT,the proposed method is proved more effective in identification accuracy.

Copyright Information
Authorized by: China Association for Science and Technology
Sponsored by: China Institute of Communications
Editor-in-Chief: Zhang Ping
Associate Editor-in-Chief:
Zhang Yanchuan, Ma Jianfeng, Yang Zhen, Shen Lianfeng, Tao Xiaofeng, Liu Hualu
Editorial Director: Wu Nada, Zhao Li
Address: F2, Beiyang Chenguang Building, Shunbatiao No.1 Courtyard, Fengtai District, Beijing, China
Post: 100079
Tel: 010-53933889、53878169、
53859522、010-53878236
Email: xuebao@ptpress.com.cn
Email: txxb@bjxintong.com.cn
ISSN 1000-436X
CN 11-2102/TN
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