Table of Content

25 May 2015, Volume 43 Issue 5

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Electronics, Communication & Automation Technology

Adaptive Noise Model Estimation Based on Residual Sub-Band Grouping in DVC

Yang Chun-ling Wu Juan Zheng Bo-wei

2015, 43(5):  1-7.  doi:10.3969/j.issn.1000-565X.2015.05.001

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In order to improve the estimation accuracy of noise model and the rate-distortion performance of the sys-tem，an adaptive noise model estimation method on the basis of residual sub-band grouping is proposed． In this method，firstly，residual sub-bands are grouped according to their frequencies． Secondly，feature vectors are generated from the residual coefficients of all sub-bands in the same group． Then，the coefficients in each sub-band are clustered into different classes by means of improved fuzzy c-means clustering． Finally，the noise para-meters of each class of residual coefficients are estimated successfully． Experimental results show that，in compari-son with the method on the basis of adjacent sub-band clustering and variance estimation，the proposed method matches the residual distribution characteristics more accurately，improves the average rate-distortion performance by 0. 60dB，and saves the decoding time by 40. 59%．


Spread Spectrum-Based Multi-Sensor Communication Using Stress Wave for Structural Health Monitoring

Hong Xiao-bin Huang Jing-xiao Xu Wei-ying Liu Gui-xiong

2015, 43(5):  8-16.  doi:10.3969/j.issn.1000-565X.2015.05.002

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In order to meet the requirements of multi-sensor communication for structural health monitoring in spe-cial environments，a new communication method using multi-sensing stress wave，which is on the basis of spread spectrum，is proposed． In this method，stress wave is utilized as a message carrier，all sensor data in the same area are grouped into some sets of dual-channel signals according to their importance，m sequence code is used as a spread spectrum code to broaden the baseband spectrum for distinguishing signals between two different groups，and Walsh code is used as an address code to distinguish different sensors in the same group． Moreover，the change of carrier's relative phase between neighboring symbols is utilized to modulate transporting information，and the received signal is synchronized before the synchronous demodulation of the carrier． Then，the correlation disprea-ding of demodulated signal is executed by the pseudo-noise code of each sensor group． Finally，the original binary signal is recovered after the judgment of each channel to gain the corresponding sensor data． The proposed method is applied to the communication of underwater steel strands． The results show that，when the emission rate changes from 400b/s to 50b/s，the stress wave communication effectiveness (SWCE) increases by 24%，and error-free communication can be realized for 256-bit sensor information; and that，when the PZT transmitter is put in the wa-ter instead of in the air，the received signal amplitudes of single and double channels both increase by 20%，while the SWCE keeps almost invariant．


A New Random Spectrum Access Scheme Based on Cross-Layer Modeling

Zhang Min Li Bin

2015, 43(5):  17-21,29.  doi:10.3969/j.issn.1000-565X.2015.05.003

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In view of different operation mechanisms of call level and packet level，a cross-layer Markov model is established to describe the relationship between call level and packet level，the formulas of call blocking probability and packet delay for secondary users are derived，and a new random spectrum access (RSA) scheme on the basis of cross-layer model is proposed． Furthermore，a system level simulation is conducted for cognitive radio networks．Simulated results show that the proposed model helps evaluate the performance of cognitive radio networks accurate-ly，and that the random spectrum access scheme can guarantee the performance of both the call level and the packet level for secondary users．


Performance Analysis of Multi-Antenna UHF RFID System

Li Ju-hu Zhang Hai-yan

2015, 43(5):  22-29.  doi:10.3969/j.issn.1000-565X.2015.05.004

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The performances of UHF (Ultra-High Frequency) RFID (Radio Frequency Identification) systems are affected by multi-path channels． In order to improve the reliability of UHF RFID systems，this paper gives the probability distribution of SNR (Signal-to-Noise Ratio) under the MRC (Maximal Ratio Combining) combiner for the UHF RFID system with Rician distribution in both forward and backward channels，and presents the theoretical descriptions of both outage probability and bit error rate． Then，a prototype of multi-antenna UHF RFID reader is developed，and the system performance affected by receiving antenna number and Ｒician factor is investigated through numerical simulation and practical test． Experimental results show that multi-antenna technology improves the performance of UHF RFID systems effectively，and that the outage probability and bit error rate of UHF RFID systems both decrease as the receiving antenna number increases．


Energy Efficiency-Based Cell Switch On-Off Strategy Under Heterogeneous Services

Pan Wei-qiang Hu Shao-dong Liu Jing

2015, 43(5):  30-34.  doi:10.3969/j.issn.1000-565X.2015.05.005

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In order to improve the energy efficiency of wireless communication systems，by taking into consideration the tidal property of user demands，a strategy to improve energy efficiency for cellular system is proposed on the ba-sis of cell switch on-off． In this strategy，the demands of users are divided into constant-bit-rate (CBR) and varia-ble-bit-rate (VBR) demands to maximize the energy efficiency under the constraint that CBR demands are ful-filled． Moreover，an improved genetic algorithm (GA) is employed to solve the constrained optimization problem，and the switch on-off states of base stations are encoded with binary sequence，followed with specifically-designed selection，crossover and mutation operators． Simulated results show that the proposed GA algorithm achieves fast convergence and prevents prematurity of chromosome population; and that the proposed cell switch on-off strategy effectively improves system's energy efficiency and guarantees the demand of CBR service，so that it is of high per-formance close to the exhaustive searching algorithm．


Channel-Hopping Algorithm for Rendezvous Based on Asymmetric Model in Cognitive Networks

Zhong Ming Zhang Hai-lin Ma Bei Lan Bing

2015, 43(5):  35-39.  doi:10.3969/j.issn.1000-565X.2015.05.006

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Proposed in this paper is a novel channel-hopping algorithm for rendezvous on the basis of asymmetric model in cognitive networks (named AMCH)． According to the available channel set sensed by each secondary user，the lengths of channel hopping sequences for transmitters and receivers are selected in two disjoint sets of prime numbers，and two channel hopping sequences are respectively constructed． Without any other auxiliary infor-mation (such as the synchronization information and the global channel label information，etc． )，any two secondary users within the communication scope can rendezvous in a short time and establish a link in their common channels．In comparison with the existing algorithms，AMCH algorithm diminishes the redundancies of channel-hopping se-quences and improves the efficiency of rendezvous． Simulated results show that AMCH algorithm outperforms the existing algorithms in terms of time-to-rendezvous upper boundary and rendezvous performance．


Noise Suppression Algorithm for Chaotic Mapping Based on Nonlocal Mean

Wang Meng-jiao Feng Jiu-chao Wu Zhong-tang Fang Jie Wang Qian

2015, 43(5):  40-44,50.  doi:10.3969/j.issn.1000-565X.2015.05.007

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Proposed in this paper is a noise suppression algorithm for chaotic mapping on the basis of nonlocal mean． According to the characteristics of chaotic mappings，this algorithm obtains the optimal filtering parameters (including patch size，search neighborhood and bandwidth) of the nonlocal mean applied to the noise suppression of chaotic mappings via experimental analysis． Simulated results show that the proposed algorithm outperforms such existing methods as phase space estimating projection，extended Kalman filtering and unscented Kalman filtering in terms of Gaussian noise suppression; and that it effectively suppresses chaotic mappings at different noise levels．


Design of Cyclic Delay Diversity Receiving Scheme in LTE Uplink System

Han Wei-liang Ge Jian-hua Bu Qi-fei Shi Xiao-ye Ji Yan-cheng

2015, 43(5):  45-50.  doi:10.3969/j.issn.1000-565X.2015.05.008

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For the lack of transmitting antenna in long-term evolution (LTE) uplink system，base stations cannot obtain a good diversity gain and system performance． In order to solve this problem，a novel scheme using cyclic delay combining technique at the receiver is proposed to achieve full spatial-multipath diversity． In this scheme，cyclic delayed copy signals at different receiving antennas are combined． Then，the minimum mean square error equalization in frequency domain is made and the signal is detected symbol by symbol． When an appropriate cyclic delay shift is chosen，the system can obtain a maximum spatial-multipath diversity gain with low demodulation com-plexity． Simulated results indicate that，in comparison with the existing antenna selection schemes in LTE uplink system，the proposed scheme helps acquire the same diversity gain and a higher coding gain without additional de-modulation complexity; and that，when outage probability reaches 10^-3，the proposed scheme leads to 2 ～4dB of gain under Rayleigh channel and at least 1. 5dB of gain under extended pedestrian channel．


Aesthetic Enhancement of Images Based on Photography Composition Guidelines

Wang Wei-ning Liu Jian-cong Xu Xiang-min Jiang Yi-zi Wang Li

2015, 43(5):  51-58.  doi:10.3969/j.issn.1000-565X.2015.05.009

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With the prevalence of digital cameras and smart phones，people can acquire digital images more and more conveniently． However，normal users are not satisfied with their photos constantly because they are not fami-liar with the professional knowledge of photography． In order to solve this problem，an approach of image aesthetic enhancement，which takes into consideration both the subject and the background layout of an image，is presented．
According to the rules of trisection and visual balance，this approach retargets the subject and resizes the certain part of background in an image to enhance image aesthetic quality by means of subject extraction，region borderline detection，exemplar-based image inpainting，seam carving and subject retargeting． Experimental results show that the proposed approach can enhance the aesthetics of image composition without deforming the salient objects in an image and without losing any image information．


Edge Detection of Images Based on NSCT and KFCM

Wu Yi-quan Zhu Li Li Li

2015, 43(5):  59-65.  doi:10.3969/j.issn.1000-565X.2015.05.010

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In order to improve the performance of existing image edge detection methods，a novel edge detection method on the basis of nonsubsampled contourlet transform (NSCT) and kernel fuzzy c-means clustering (KFCM) is proposed． In this method，firstly，an original image is decomposed into a low-frequency component and some high-frequency components via NSCT． Secondly，edge information is extracted from the low-frequency component with less noise and is clustered via KFCM to obtain low-frequency edge image． As a result，the accuracy of edge localization is improved． Then，in order to decrease pseudo-edges and richen image details，the method of modulus maxima is applied to high-frequency components with more edges and details． Finally，the whole image edge is obtained by fusing the edge images of low-frequency component and high-frequency components． Experimental results show that，in comparison with the Canny method，the method on the basis of edge detection operator and fuzzy clustering，the method on the basis of edge information and fuzzy c-means algorithm optimized by chaotic par-ticle swarm，as well as the method of modulus maxima in NSCT domain，the proposed method helps obtain better edge detection effect with accurate edge localization，continuous and complete edges，as well as abundant details．


Realization of Human Oocytes Segmentation in the Process of Optical Micro-Injection

Qin Chuan-bo Tian Lian-fang Du Qi-liang

2015, 43(5):  66-72.  doi:10.3969/j.issn.1000-565X.2015.05.011

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Aiming at the segmentation of human cytoplasm，injection pipette and egg polar body in the process of intra-cytoplasmic sperm injection (ICSI)，firstly，the operator of mathematical morphology is adopted to detect the region of interest according to the difference in shapes of segmentation targets． Secondly，according to local gray level and local variance，an improved fuzzy clustering method is used to implement the rough classification of pre-processing images with fuzzy boundaries and noises． Then，the level set algorithm is used to carry out the segmen-tation of cytoplasm，polar body and injection pipette，with its initialization location and controlling parameters be-ing set according to fuzzy clustering results． Finally，line fitting is employed to mark the whole injection pipette from the local images of injection pipette inserted in cytoplasm． Experimental results show that the proposed algo-rithm can implement the segmentation of human oocyte cytoplasm，polar body and injection pipette correctly．


Identification of Damped and Driven Sine-Gordon Equation Based on Deterministic Learning

Dong Xun-de Wang Cong

2015, 43(5):  73-77,113.  doi:10.3969/j.issn.1000-565X.2015.05.012

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Discussed in this paper is the identification of dynamics of a class of damped and driven Sine-Gordon (SG) equation． Firstly，SG equation described by partial differential equation (PDE)，which is infinite dimen-sional，is approximated by a set of ordinary differential equation with finite dimension by means of finite difference method． Then，the existence，uniqueness and convergence of the solution of the approximated system are proofed．Finally，the dynamics of the approximated system is identified on the basis of deterministic learning． Experimental results show that the proposed method helps achieve locally accurate identification of SG equation dynamics．


Computer Science & Technology

A Non-Sparse Multi-Kernel Learning Method Based on Primal Problem

Hu Qing-hui Ding Li-xin Liu Xiao-gang Li Zhao-kui

2015, 43(5):  78-85.  doi:10.3969/j.issn.1000-565X.2015.05.013

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Traditional multi-kernel learning (MKL) methods mainly solve primal problems in the dual． However，the solving in the primal may result in better convergence property． In this paper，a novel L p -norm-constraint non-sparse MKL method，which optimizes the modal in the primal，is proposed． In this method，firstly，support vector machine (SVM) is solved by means of subgradient and improved quasi-Newton method． Then，basic kernel weights are obtained via simple calculations． As quasi-Newton method is of second-order convergence property and acquires inverse Hessian matrix without computing the second-order derivative，the proposed method is of higher convergence speed than that of conventional ones． Simulated results show that the proposed method is of comparable classifica-tion accuracy，strong generalization capability，high convergence speed and good scalability．


Selection and Recognition of 3D Facial Expression Feature Based on Bi-Objective Elitist Strategy

Hu Bu-fa Huang Shou-ning

2015, 43(5):  86-91.  doi:10.3969/j.issn.1000-565X.2015.05.014

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Non-dominated sorting genetic algorithm Ⅱ (NSGA-Ⅱ) works effectively in selecting bi-objective fea-tures，but may result in local convergence and prematurity in the process of optimization． In order to solve this problem，an improved NSGA-Ⅱ feature selection algorithm is proposed． In this algorithm，firstly，the first elite strategy is operated to select the elite population from parent population． Secondly，the selected parent elite popu-lation is combined with the offspring population to form a combined population． Finally，the second elite strategy is executed to obtain the next parent population． After the selection of 3D face expression candidate features，the selected features are classified by means of probabilistic neural network． Experimental results show that the pro-posed algorithm improves the performance of NSGA-Ⅱ with local convergence and prematurity problems greatly and increases the accuracy of facial expression recognition effectively．


Implementation and Acceleration of Object-Based Video Synopsis Algorithm

Lin Long-xin Liu Xiao-li Quan Yu-juan Lin Wei-wei

2015, 43(5):  92-99.  doi:10.3969/j.issn.1000-565X.2015.05.015

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As the existing object-based video synopsis algorithms cannot meet the actual demands in large-scale sur-veillance field due to the ignorance of computation efficiency，an improved algorithm，which improves the computa-tion efficiency by reducing frame rate and resolution，detecting motion segments and tracking objects on the basis of gravity center，is proposed． Furthermore，in order to utilize the computing power of CPU and GPU fully，a multi-thread strategy and a GPU programming are conducted to accelerate the execution of the algorithm． Experimental results show that the improved algorithm and the proposed acceleration strategy both improve the computation effi-ciency of video synopsis greatly．


Logistic Kernel Function and its Application to Speech Recognition

Liu Xiao-feng Zhang Xue-ying Zizhong John Wang

2015, 43(5):  100-106.  doi:10.3969/j.issn.1000-565X.2015.05.016

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Kernel function is the core of support vector machine (SVM) and directly affects the performance of SVM． In order to improve the learning ability and generalization ability of SVM for speech recognition，a Logistic kernel function，which is proved to be a Mercer kernel function，is presented． Experimental results on bi-spiral and speech recognition problems show that the presented Logistic kernel function is effective and performs better than linear，polynomial，radial basis and exponential radial basis kernel functions，especially in the case of speech rec-ognition．


Multi-Scale Structure Tensor Image Retrieval Algorithm Based on Edge Tangent Flow Field

2015, 43(5):  107-113.  doi:10.3969/j.issn.1000-565X.2015.05.017

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In order to reduce the effects of weak edges and reinforce the shift invariance in sketch-based image re-trieval (SBIＲ)，a multi-scale structure tensor retrieval algorithm on the basis of edge tangent flow field is pro-posed． In this algorithm，edge tangent flow field is used as a substitute for the gradient map of image，and the structure tensor is calculated directly on remarkable strong edges to suppress the effects of weak edges． Besides，structure tensor feature is extracted in the form of multi-scale partition to enhance the shift invariance． Experimental results indicate that the proposed algorithm is superior to the existing structure tensor method because it helps sup-press the effects of weak edges effectively，avoid instable significant edge of image described by image gradient di-rection，enhance the shift invariance of structure，and，thereby，improve the retrieval efficiency．


Image Classification Based on JointBoost I2C Distance Metric

Li Zi-long Liu Wei-ming

2015, 43(5):  114-119.  doi:10.3969/j.issn.1000-565X.2015.05.018

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Image classification on the basis of image-to-class (I2C) distance metric is a novel method． However，its classification performance needs to be further improved． In this paper，a new image classification method on the basis of JointBoost I2C distance metric is proposed． In this method，a prototype feature set with representative sam-ples is generated,which makes the calculation of distance from the test image to the set more effective． Then，on the basis of JointBoost algorithm，multiple I2C distance metrics are combined to generate a strong classifier for in-tegrating spatial information． Experimental results show that the proposed method is of higher performance for image classification．


Spatial Non-Negative Matrix Factorization Based on Max-Margin Coding

Liu Da-kun Tan Xiao-yang

2015, 43(5):  120-125.  doi:10.3969/j.issn.1000-565X.2015.05.019

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Although the parts-based representation results in strong robustness in image processing，the local con-straint in non-negative matrix factorization (NMF) is implicit，which leads to insufficient uniqueness and locality．Meanwhile，as two important property indexes，locality and discriminant in feature extraction are seldom considered in NMF simultaneously． In order to solve this problem，a discriminative NMF on the basis of max-margin coding is pro-posed． In this method，image data are regarded as a 2D network of pixels，and，on the basis of network knowledge，spatial information is embedded into basis images，which not only imposes an explicit local constraint but also com-pensates the spatial information loss caused by data vectorization． Additionally，an extra 1D space learned from max-margin constraint is adopted to balance the effects of reconstruction error and discriminative constraint on basis ima-ges． Experimental results on AR and extended YaleB databases for face recognition show that，in comparison with NMF and some of its variants，the proposed max-margin coding-based spatial NMF is more robust．


Multi-Threshold Image Segmentation Algorithm Based on Improved Quantum-Behaved Particle Swarm Optimization

Yang Zhen-lun Min Hua-qing Luo Rong-hua

2015, 43(5):  126-131,138.  doi:10.3969/j.issn.1000-565X.2015.05.020

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In order to improve the quality of image segmentation in engineering applications，an improved quantum-behaved particle swarm optimization (QPSO) algorithm is proposed on the basis of mutated QPSO，which is then combined with the maximum between-cluster variance method to present a multi-threshold image segmentation algo-rithm． The algorithm is characterized by a memory vector constructed from memory information in the search proce-dure of particles using Bayesian theorem． The memory vector is used to predict the future behaviors of particles and to assign the mutation probability of each particle automatically． In this way，the global search ability is enhanced and the convergence ability is preserved for the algorithm． Experimental results on Berkeley datasets show that the proposed algorithm is superior to two existing PSO-based methods because it helps obtain more stable and clearer image segmentation results．


Parameterized Decomposition Tree-Based Obfuscation Method with Double Flattening Control Flow

Zhou Na-qin Qi De-yu

2015, 43(5):  132-138.  doi:10.3969/j.issn.1000-565X.2015.05.021

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Aiming at the security problem of software white box，an improved parameterized decomposition tree-based obfuscation method with double flattening control flow is put forward． On the basis of given upper bounds of depth，breadth and granularity，the method builds a decomposition tree，coordinates the whole tree with a cycle se-lection structure named while-switch，and then applies double flattering to relevant nodes that satisfy certain condi-tions． Experimental results indicate that，in comparison with the flattening obfuscation method of control flow on the basis of parameterized decomposition tree，the proposed method reduces the execution expense and solves the deep nonfeasance problem; and that，in comparison with the traditional method only with flattening control flow，the pro-posed method increases the difficulty in decompilation and reverse engineering．


An Energy-Efficient Routing Algorithm Based on Context Awareness

Xu Fang Zhang Hu-yin Xu Ning Wang Zhi-yong

2015, 43(5):  139-144.  doi:10.3969/j.issn.1000-565X.2015.05.022

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In order to cope with Ad hoc networks consisting of smart mobile communication devices that support rich media applications with Internet connectivity，a novel context-aware energy-efficient routing (CAER) algo-rithm is proposed． The algorithm introduces a context-aware self-learning solution to monitor context information in the operation process of devices，calculates energy utility imposed by various applications in nodes，and then syn-thetically utilizes the context information containing application-related energy utility，remaining energy and signal strength to make routing decisions in an adaptive way． By using this algorithm，energy is saved for mobile nodes with limited resources． Simulated results show that，in comparison with some routing protocols，CAER performs more effectively on the energy efficiency and lifetime of network，and reduces the delay overhead of network at the same time．