Table of Content

25 January 2015, Volume 43 Issue 1

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Electronics, Communication & Automation Technology

Adaptive Intelligent Detection Technology for Digital Products’ Shell Surface

Kuang Yong-cong Zhang Kun Xie Hong-wei

2015, 43(1):  1-8.  doi:10.3969/j.issn.1000-565X.2015.01.001

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As different types of digital products have different superficial optical characteristics，a visual detection method adaptive to various surface types is proposed to improve the reliability of defect detection.Firstly，after the image collection under different light sources,materials are classified according to the recognition results of gray statistic analysis. Secondly，a hybrid threshold segmentation algorithm，which is on the basis of global and dynamic threshold segmentation techniques ，as well as an improved curve detector ， which uses Gaussian filter and partial derivative feature to find out the curve ’ s key points and then connects the key points into a line through the “ relaxation ” algorithm ， is used to detect different given surfaces. Experimental results show that the proposed algorithm is highly robust and resistive to external disturbances. Moreover ，comprehensive performance analysis indicates that the proposed algorithm produces a false alarm rate lower than 5% and an accuracy rate higher than 93%. Besides ， the high detection speed makes the algorithm possible to be applied to actual production.


Finite-Time Stability and Stabilization for a Class of Quadratic Discrete-Time Systems

Feng Zhi-hui Deng Fei-qi Liu Wen-hui

2015, 43(1):  9-14.  doi:10.3969/j.issn.1000-565X.2015.01.002

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In practice ， finite-time stability of systems is more practical and economical than the traditional stability due to costs and other constraints. This paper studies the finite-time stability and stabilization for a class of quadratic discrete-time systems. By means of linear matrix inequality ， the design of feedback control gain matrix is converted into the solutions for linear matrix inequalities. Moreover ， sufficient conditions for the finite-time stability of closed-loop systems with state feedback are determined ， and a method to design the feedback control gain matrix is proposed to achieve the finite-time stability of systems. This method is also suitable to deal with the finite-time boundedness of systems with exogenous disturbance. Finally ， numerical examples are used for validation. The consistency of simulation and theoretical analysis results proves the feasibility of the proposed method.


Implantable Analog Front-End with High PSRR and CMRR for Neural Signal Acquisition

Wu Zhao-hui Xie Yu-zhi Zhao Ming-jian Li Bin

2015, 43(1):  15-20.  doi:10.3969/j.issn.1000-565X.2015.01.003

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As the in vivo electroneurographic signals are weak with high noise and strong environment interference ， an implantable low-noise analog front-end with high power supply rejection ratio （ PSRR ） and com-mon mode rejection ratio （ CMRR ） for neural signal acquisition is proposed. Fully differential structure is used to design the preamplifier ， switch capacitor filter and variable gain amplifier of the analog front-end. Chopper stabilization technology is used to reduce the low-frequency noise and a ripple reduction loop with current DAC is used to reduce the output ripple. In this way ， the proposed analog front-end can achieve high PSRR and CMRR without increasing the original low noise. Moreover ， a front-end chip is designed via 0.18μm CMOS technology and is used for simulation. The results of layout show that （ 1 ） input-referred noise of the analog front-end is 2.6μV at the interval of 0.1Hz~10kHz ；（ 2 ） the gain of the analog front-end can switch among 46.35 ， 52.18 ， 60.02 and 66.95dB ； and （ 3 ） the CMRR and PSRR of the analog front-end are respectively 146 and 108 dB. Thus ， it draws the conclusion that the proposed analog front-end is suitable for neural signal acquisition.


A Clustering Method for Multiple Speaker Roles

Li Wei He Qian-hua Li Yan-xiong

2015, 43(1):  21-27,33.  doi:10.3969/j.issn.1000-565X.2015.01.004

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In order to find the number of speaker roles and the corresponding speakers ’ speech in meeting speeches ， a clustering method for multiple speaker roles is proposed. Firstly ， features for speaker role clustering are defined. Secondly ， geodesic distance is used to measure the similarities among features. Then ， inner-class distance is used to control inter-class mergence to form the clustering method. Finally ， four different types of meeting speech corpora are used to validate the effectiveness of the proposed method. The results indicate that ，for the meeting speeches obtained by both manual and automatic segmentation ， the clustering performance using geodesic distance is superior to that using traditional distance when the same clustering algorithm is used in all cases ， and that the proposed method performs better than the traditional hierarchical clustering method when the same measuring distance is used.

Compressed Sensing Reconstruction Algorithm Based on DCT Band Energy

Liu Jie-ping Fang Jie Wei Gang

2015, 43(1):  28-33.  doi:10.3969/j.issn.1000-565X.2015.01.005

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In order to improve the reconstruction quality of compressed sensing images ， a band-CS (Compressed Sensing) algorithm with smooth projection Landweber is proposed on the basis of discrete cosine transform (DCT). Since different DCT coefficient bands have different effects on the final image reconstruction quality ，first ， this algorithm divides an image into several blocks and carries out DCT for each block. Secondly ， DCT coefficients are regrouped according to the band energy ， and the band with higher energy is sampled by random matrix with greater sampling rate. Then ， the blocking effect is eliminated by using a smoothing filter. Finally ，the compressed sensing reconstruction of images is implemented by means of smooth projection Landweber.Experimental results show that ， in comparison with BCS-SPL and MS-BCS-SPL algorithms ， the proposed algorithm improves the peak signal-to-noise ratio of reconstructed images significantly.

Situation Assessment Algorithm for Online Parameters Learning in Dynamic Bayesian Networks

Yuan De - ping Zheng Juan - yi Shi Hao - shan

2015, 43(1):  34-40.  doi:10.3969/j.issn.1000-565X.2015.01.006

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In order to assess battlefield situation accurately and quickly ， an algorithm for online parameters learning is proposed on the basis of dynamitic Bayesian networks （ DBN ） . Forward recursion algorithm is used to estimate the parameters of network model after the structure model of dynamic Bayesian network is confirmed by expert knowledge. Dirichlet distribution is used as the prior distribution of samples according to the characteristics of small samples for the observation value of battlefield situation model ， and moment estimation is adopt to estimate the hyper parameters of the prior distribution. Then ， in combination with the equivalent samples value from the prior distribution ， the observation value can be used to implement parameters learning and battlefield situation assessment. Simulated results indicate that the proposed algorithm is of good real-time performance and high accuracy for situation assessment.

Potential Game Theory-Based Resource Allocation Algorithm of High-Density D2D Users

Lan Bing Li Bing-bing Liu Jia Chang Jun-ren

2015, 43(1):  41-46,52.  doi:10.3969/j.issn.1000-565X.2015.01.007

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In order to solve the contesting among multiple device-to-device (D2D) users reusing the resource of one cellular user in high-density user scenarios, a D2 D resource allocation algorithm on the basis of game theory is proposed.Firstly, a utility function minimizing the system interference is proposed, which considers both the interference among D2 D users and the interference between D2 D users and cellular users.Secondly, a potential function of this game is designed.Then, the potential game nature of utility function as well as the existence of Nash equilibrium is proved.Simulated results show that the proposed algorithm possesses better system level fairness and convergence, improves system throughput, and reduces the interference to D2 D users.

Iterative Dimensionality-Reduction Parallel Detection Algorithm for MIMO System

Zhang Qi Ge Jian-hua Li Jing

2015, 43(1):  47-52.  doi:10.3969/j.issn.1000-565X.2015.01.008

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An improved iterative dimensionality-reduction parallel detection algorithm is proposed to mitigate the effects of the low diversity gain of the first detective sub-streams and the error propagation in traditional Multiple Input Multiple Output (MIMO) detection algorithm.In each iteration of the algorithm, the first substream is found by exhaustive search while other sub-streams are detected in parallel through ordered successive interference cancellation (OSIC) , and only the estimates of the first sub-stream with the highest diversity order can be obtained at the end of each iteration.Furthermore, between two different iterations, interference cancellation is employed to reduce the dimension of sub-streams.Simulated results indicate that, only with marginal complexity cost, the proposed algorithm helps obtain BER (Bit Error Rate) performance approaching maximum likelihood detection algorithm.Particularly, in a 4×4 QPSK modulation MIMO system, the performance gain of the proposed algorithm over OSIC is 9.3dB at a BER of 10^-3.

Iterative Frequency Offset Estimation for High-Frequency Channel Based on Interpolation Using Fourier Coefficients

You Xing-yuan Yang Ping Xu Bin-bin

2015, 43(1):  53-58.  doi:10.3969/j.issn.1000-565X.2015.01.009

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In order to overcome the frequency offset existing in short-wave （ namely high-frequency ， HF ） burst-mode communication ， an iterative frequency offset estimation algorithm on the basis of interpolation using Fourier coefficients is proposed ， which takes into consideration the waveform of HF modem under low signal-to-noise ratio （SNR）.Theproposedestimatoristackledinseveralstages.Firstly ， acoarseestimationofpeakfrequencyismade bythe application ofdiscrete Fourier transform （ DFT ） . Secondly ， Jacobsen ’ s method is used to interpolate DFT sam-ples ， and a bias analysis of the interpolating results is carried out. Then ， variance as a function of SNR and training sequence length is obtained ， and a confidence interval with a confidence coefficient of 0.99999 ， which is used as the searching interval of frequency offset estimation ， is adaptively adjusted. Finally ， a combination of parabolic interpolation is made with the iterative method to obtain an accurate result through the interval.Simulated results show that the proposed algorithm helps achieve Cramer-Rao low bound （ CRLB ） at low SNR with the training sequences equal to 128 ， 256 and 512. Especially ， at 0dB ， the estimator ’ s mean square error is about 1.005 times that of CRLB only with two iterations.


Image Enhancement in NSCT Domain Based on Fuzzy Sets and Artificial Bee Colony Optimization

Wu Yi - quan Yin Jun Dai Yi - mian

2015, 43(1):  59-65.  doi:10.3969/j.issn.1000-565X.2015.01.010

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Proposed in this paper is an adaptive image enhancement method on the basis of nonsubsampled Contourlet transform (NSCT) ， fuzzy sets and artificial bee colony (ABC) optimization ， which helps improve the low contrast and definition of the image acquired in practical applications. In this method ， first ， an input image is decomposed into a low-frequency sub-band and several high-frequency sub-bands through NSCT. Secondly ，the coefficients of high-frequency sub-bands are enhanced according to Bayesian shrinkage threshold and nonlinear gain function ， while that of the low-frequency sub-band is enhanced by using the fuzzy enhancement method with its adaptability improved by fuzzy parameter optimization via ABC algorithm. Then ， for the purpose of reducing running time ， the entropy of low-frequency sub-band image is used as the fitness function of ABC algorithm and a random initializing strategy of inferior populations is introduced to improve ABC algorithm. The proposed enhancement method is finally employed to process three kinds of images of freshwater fish ， rail surface and grain pest ， and a comparison is made between the proposed method and three other similar enhancement methods in terms of subjective visual effect and such objective quantitative evaluation indices as contrast gain ， definition gain and entropy. Experimental results show that the proposed method is of the most
excellent visual effect because it helps obtain images with improved contrast and definition ， smooth edge and greater information amount ， which benefits further accurate image detection and recognition.


Computer Science & Technology

Virtual Network Mapping Algorithm Based on Graph Adjacency Segmentation

Peng Li - min

2015, 43(1):  66-71,78.  doi:10.3969/j.issn.1000-565X.2015.01.011

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As the existing mapping algorithms of large-scale virtual networks are of low execution efficiency and are prone to network partition when mapping virtual nodes and links ， a method of graph adjacency segmentation is proposed ， which decomposes a virtual network into several adjacent star configurations and reduces the size of large-scale virtual network evidently. At the same time ， a resource allocation model matching nodes and their adjacency links is proposed ， which enables nodes mapping to adapt links ’ resource state and makes links mapping match with the resource size of related nodes in a coordinated way. Thus ， the inharmonious operation of mapping nodes and links ， as well as the mismatching of allocating network resources ， is solved effectively.Simulated results show that the proposed algorithm reduces the mapping path length of virtual links and improves the mapping efficiency of virtual networks as well as the load-balancing performance ， and thus high acceptance ratio of virtual network requests can be achieved.


Improved Evolution of Geodesic Active Contour and its Application to Target Tracking

Song Jia-sheng Hu Guo-qing Jiao Liang

2015, 43(1):  72-78.  doi:10.3969/j.issn.1000-565X.2015.01.012

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In order to improve the efficiency and accuracy of geometric active contour model-based segmentation algorithm, a novel edge detection and tracking algorithm is presented. Firstly, the gradient of an image is calculated according to vector image, and an edge indicator with adaptive threshold is proposed. Secondly, an improved evolution model using variational level set is put forward. Then, on the basis of this model, an improved edge detection algorithm is proposed, and a target tracking algorithm is designed in the framework of unscented Kalman filter. Experimental results demonstrate that the proposed algorithm not only increases the convergence rate and flexibility of active contour evolution model significantly but also possesses strong robustness to such interferences as shadow, occlusion, object deformation and background interference.


Two-Stages Pedestrian Detection Algorithm Based on Multi-Component Validation

Tan Fei-gang Liu Wei-ming

2015, 43(1):  79-86.  doi:10.3969/j.issn.1000-565X.2015.01.013

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Inspired by Weber's local descriptor and LBP feature ， this paper proposes a SLBH (Saliency Local Binary Haar) feature in view of the weaknesses that Haar features have high dimension and redundancy. SLBH helps obtain good detection performance when using the overall characteristics of pedestrian ， but the detection performance declines rapidly in occlusion scenes. In order to improve the robustness of overall characteristics to partial occlusion ， a two-stage pedestrian detection algorithm combining multi-component validation and SLBH is proposed ， which takes the advantage of overall feature and local feature simultaneously ， and improves the robustness of the algorithm to partial occlusion. Experimental results on INRIA pedestrian detection dataset show that the proposed algorithm is of strong robustness to noise and partial occlusion.


Pedestrian Detection with Vehicle-Mounted Far-Infrared Monocular Sensor Based on Edge Segmentation

Liu Qiong Wang Guo-hua Shen Min-min

2015, 43(1):  87-91,98.  doi:10.3969/j.issn.1000-565X.2015.01.014

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As the pedestrian detection with vehicle-mounted far-infrared monocular sensor using machine learning is usually poor in real-time performance and precision ， a head-histogram of oriented gradient-support vector machine （ Head-HOG-SVM ） approach based on edge segmentation is proposed. The weighted Sobel operator is adopted to enhance the vertical edges of pedestrians in the regions of interest （ ROIs ） . Several pedestrian detec-tion methods are selected according to the pedestrian appearance in different distance. A head feature is used to detect pedestrians at near and middle distance to improve the real-time performance of the system ， and a HOG-SVM classifier cascading with head recognition is used to detect blurred pedestrians at far distance.Experimental results on the several videos captured from suburb scenes show that ， in comparison with the HOG-SVM classifier based on dual threshold segmentation ， the precision and detection rate of the proposed method are respectively increased by 33% and 200%.


EBSN: a New Model with Full-Structure for Modular Data Center

Han Dong Xiao Wen-jun Li Mei-sheng

2015, 43(1):  92-98.  doi:10.3969/j.issn.1000-565X.2015.01.015

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Modular data center （ MDC ）， which encapsulates thousands of servers and switches into a shipping-container ， is a future development trend of data center networks. This paper proposes a new MDC model with full-structure named EBSN. The logical structure of EBSN is an orthogonal of two Biswapped networks ， and the physical implementation of EBNS needs only a small amount of commercial off-the-shelf ， and thus a good network structure of EBSN is ensured. As for the network architecture of EBSN ， server-centric approach is taken ， and routing protocol is performed only on servers. In EBSN ， source routing and re-routing algorithms are specially designed to ensure the throughput and fault tolerance of EBSN. Simulated results indicate that ， in comparison with BCube ， EBSN is of full-structure ， reasonable throughput and good fault tolerance ， so that it is viable for MDC.


A Novel Front-view Gait Recognition Approach in Real Surveillance Environments

Jiang Ying-jun Wang Jian-xin Guo Ke-hua

2015, 43(1):  99-104.  doi:10.3969/j.issn.1000-565X.2015.01.016

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In real monitoring circumstances ， pedestrians ’ front-view gaits are more common than lateral-view ones. As the existing gait recognition methods are mainly focused on lateral-view gaits instead of front-view gaits ，a new front-view gait recognition method on the basis of automatic perspective conversion is proposed according
to the statistics of pedestrians ’ gait features. Through calculating pedestrians ’ gait energy images ， walking
trajectories and gait view angles ， this method extracts and recognizes gait features under the same view angle
after the transformation of view angles. Experimental results show that the proposed method works well under
real monitoring circumstances when the parameters of monocular camera are unknown ， and that it achieves a
recognition rate for front-view gait of 81% and an identified frame number of 21 per second.


Multi-Task Scheduling Based on Particle Swarm Optimization in Cloud Manufacturing Systems

Wu Shan-yu Zhang Ping Li Fang

2015, 43(1):  105-110.  doi:10.3969/j.issn.1000-565X.2015.01.017

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In order to implement the scheduling of multiple tasks with the same type in cloud manufacturing systems, a mathematical model is established and is solved by using a discrete particle swarm-genetic hybrid algorithm with two objectives, namely the least total completing time and the least cost of all tasks being considered simultaneously. The hybrid algorithm employs integer coding method to establish the mapping between particle location matrix and service allocation scheme, and introduces the crossover and mutation idea of genetic algorithm to update particle swarm position with four formulas being conditionally used in a progressive and overlaying way, and thus the diversity of groups is ensured effectively. Simulated results indicate that the proposed algorithm is of high effectiveness and execution efficiency.


Extendible Hashing Method Based on GPU

Hu Xue-xuan Xi Jian-qing Lin Miao

2015, 43(1):  111-117.  doi:10.3969/j.issn.1000-565X.2015.01.018

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In order to use an extended hash table for fast data accessing ， an efficient index updating to maintain the hash table is necessary. Proposed in this his paper is a GPU-based extendible hashing algorithm named gEHT ， which takes full advantage of GPU ’ s parallel computing power ， and adopts list reuse as well as pre-split technology to extend and merge lists and to insert and delete data in a lock-free manner. Thus ， high levels of concurrency for hash table creation ， index updating and data retrieval are realized. Experimental results show that gEHT is superior to some other linear hashing and extendible hashing algorithms on the basis of multi-core CPU in terms of querying data ， maintaining hash table and updating index ， especially in the case of heavy load.

Cryptanalysis of an IB-PRE Scheme with Source Hiding Property

Zheng Yu-lin Cai Yi

2015, 43(1):  118-125.  doi:10.3969/j.issn.1000-565X.2015.01.019

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As Emura-IB-PRE ， an identity label-based proxy re-encryption scheme proposed by Emura et al. ， is of source hiding property and chosen ciphertext security in random oracle model ， a chosen ciphertext security method to attack Emura-IB-PRE scheme is proposed ， and Emura-IB-PRE is proved invalid in resisting the chosen ciphertext attack. In order to solve this problem ， an improved scheme named E-SH-IB-PRE is presented and the corresponding security proof is given. The results indicate the presented scheme is secure against chosen ciphertext attack with source hiding property in random oracle model ， and that the principle ， i.e. ， the first level ciphertext can be publicly verified by proxy ， is important for proxy re-encryption schemes.


Discrete Artificial Firefly Algorithm for Solving Traveling Salesman Problems

Yu Hong-tao Gao Li-qun Han Xi-chang

2015, 43(1):  126-131,139.  doi:10.3969/j.issn.1000-565X.2015.01.020

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Proposed in this paper is a discrete artificial firefly algorithm combined with variable neighborhood search algorithm, which is used to solve traveling salesman problems.First, the distance of artificial firefly algorithm is redefined by introducing the concepts of swap operator and swap sequence.Secondly, in order to increase the diversity of firefly swarms and to avoid quick convergence to local optimal solution, a perturbation mechanism is designed on the basis of variable neighborhood search algorithm.Then, several different traveling salesman problems are solved by using the proposed algorithm, and the results finally show that the proposed algorithm is superior to the typical ones in literatures because it helps obtain good solving results.

Question Answering System Based on Social Relationship and Recommendation of the Best Answerer

Du Qing Wang Qi-xuan Huang Dong-ping Cai Yi Wang Tao Min Hua-qing

2015, 43(1):  132-139.  doi:10.3969/j.issn.1000-565X.2015.01.021

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In recent years, community question answering system (CQA) are becoming popular. But with the expansion of the scale of the questions, the proportion of questions that got answered gradually reduced, and the quality of the answers cannot be guaranteed. In order to increase the answering probability of the questions in Q&A system, and enhance the credibility of answer, we put forward Social question answering System based on the social relationship similarity measure. Then we raise a method to find suitable respondents who are willing to answer and are familiar with related field. Experimental results show that the method of this paper can get satisfactory answers faster compared with the traditional Q&A System on the subjectivity or real-time problem sets.

Dynamic Gesture Recognition Based on Gesture Contour Model of Time Series

Pang Hai-bo Li Zhan-bo Ding You-dong

2015, 43(1):  140-146.  doi:10.3969/j.issn.1000-565X.2015.01.022

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Proposed in this paper is a new dynamic gesture recognition framework to solve time-scale variability and complexity of dynamic gestures. This framework constitutes a dynamic gesture contour image by using the gesture contour in time series ， and calculates the mean and variance images of gray-scale images in different time scales. Furthermore ， these mean and variance images are organized into a dynamic gesture contour model library.On this basis ， dynamic gestures are recognized by using correlated information method and improved dynamic time warping method. Experimental results show that the proposed dynamic gesture contour model is of strong robustness to the dynamic gestures in various time scales ， and that the improved dynamic time warping method helps obtain recognition rate higher than that of the traditional method.