Table of Content

29 November 2013, Volume 31 Issue 6

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Communication Engineering

Analysis of Reciprocal Access Scheme Based on Non-random Access Protocol in Heterogeneous Cognitive Networks

HAN Peng, TIAN Hua, XIE Wei

2013, 31(6):  551-558.  doi:10.3969/j.issn.0255-8297.2013.06.001

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 An opportunistic spectrum access scheme, reciprocal access scheme, based on non-random access protocol for heterogeneous networks is proposed. A case is considered, in which the users of these two heterogeneous reciprocal networks have cognitive functions, and they can access each other dynamically. Users’ behavior is modeled by a continuous-time Markov model. Moreover, probabilities of blocked and forced termination of users in the two heterogeneous cognitive networks are derived as the performance metrics. In addition, throughput of users is evaluated. Performance of the reciprocal access scheme is compared with the traditional non-shared access scheme. The results show that the system performance can benefit from the
proposed scheme.

Construction of High Rate LDPC Codes with Short Block Length

WU Guang-fu, WANG Lin

2013, 31(6):  559-563.  doi:10.3969/j.issn.0255-8297.2013.06.002

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A method of constructing low-density parity-check (LDPC) codes is presented, where an identity matrix is combined with another sub-matrix constructed under the condition of a given girth and fixed weight of column to generate a parity check matrix. Let Wc denote the fixed weight of column, which is usually not
less than 3. Two kinds of check matrices sized 28×76 and 64×328 are constructed. Simulation results over additive white Gaussian noise (AWGN) channels show that performance of the codes is better than that of check matrices sized 42×105, 170×425 and 66×330. Meanwhile, sufficient conditions for the girth of Tanner graph based on Matroid theory are present. The girth conditions can be used to construct LDPC codes with given short girths. High-rate LDPC codes with short block length can be applied to the future digital video broadcast-handsets.

Signal and Information Processing

Multi-feature Fused PSOPF for Underwater Target Tracking

LIU Li-xin, BIAN Hong-yu

2013, 31(6):  564-568.  doi:10.3969/j.issn.0255-8297.2013.06.003

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Particle swarm optimized particle filter (PSOPF) can deal with leanness and divergence of the traditional particle filter to some extent. However, robustness and tracking precision need to be improved as the system observation model is limited to the use of observed data. Based on the imaging mechanism of the
forward-looking sonar, extraction of multiple features and fusion of the features with PSOPF are discussed.An optimized algorithm using adaptive weighted eigenvalues as fitness is proposed. It makes particles move to the high likelihood zone by updating their speed and position. Experiments are performed to track underwater targets from sonar image sequences. The results show that the multi-feature fused PSOPF can improve robustness and decrease risk of leanness and divergence. It can achieve high tracking precision with fewer particles. The advantages make it suitable for underwater target tracking.

An Efficient (k, p) Notational System Transform Algorithm

CHEN Jia-yong1,2,3, ZHANG Wei-ming2, HU Jin-long2, ZHU Yue-fei2, GUO Dong-hui1

2013, 31(6):  569-578.  doi:10.3969/j.issn.0255-8297.2013.06.004

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Muti-ary codes and mix-ary codes are widely used in information hiding, while the transfer efficiency between notational systems is an important factor of coding efficiency. An effective notational system transfer algorithm based on double-coding method is proposed for transforming arbitrary k-ary sequences to
p-ary sequences. It is proved that the proposed notational system transform algorithm is optimal. The method is applied to improve embedding efficiency of several kinds of steganographic algorithms based on mix-ary codes or grouped multi-ary codes, including KT-Lex steganographic system, webpage steganography based on equal tag, grid coloring codes and APPM codes. Both theory analysis and experimental results show that the proposed method is effective.  

Algorithm of Fractal Dimension Based on Neighborhood Extremum Difference Signal Power Spectrum with Application to Low SNR Speech Activity Detection

CHEN Xue-qin1,2, YU Yi-biao1, ZHAO He-ming1

2013, 31(6):  579-584.  doi:10.3969/j.issn.0255-8297.2013.06.005

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In this paper, a fractal dimension algorithm is proposed based on the neighborhood extremum difference signal and its power spectrum. The proposed method is applied to speech activity detection (SAD)in low SNR environments. In the time domain, the extremum difference signal is searched in the neighborhood.The fractal value is then estimated from the power spectrum of the difference signal based on a minimum error criterion. In a quiet environment, performance of the method is similar to the box algorithm and better than entropy algorithm in normal and whispered speech detection, while in several noise environments, it clearly outperforms the entropy algorithm. It is also better than the box algorithm except in a white noise
environment. In addition, the computation load is only 5% of the box algorithm. Experimental results show that the proposed algorithm has a good overall performance in terms of efficiency and SAD.

Generalized Approximate Maximum Likelihood Estimation of Covariance Matrix Structure

GU Xin-feng1,2, JIAN Tao1, HE You1, HAO Xiao-lin3

2013, 31(6):  585-592.  doi:10.3969/j.issn.0255-8297.2013.06.006

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By generalizing the clutter-clustered estimation method and considering the normalized sample covariance matrix (NSCM), a generalized NSCM(GNSCM) is proposed for covariance matrix structure estimation in correlated compound-Gaussian clutter. A maximum likelihood recursive estimation process of covariance matrix structure is derived in generalized clutter-clustered background. A generalized approximate maximum likelihood (GAML) estimator is then obtained by using GNSCM as the initialized estimation estimated matrix to recursive. GAML is an extension of the existing methods the approximate maximum likelihood (AML) and the constrained recursive clutter-clustered estimator (CRCCE). Simulation results show that, compared with
the two previous methods, GAML has higher estimation accuracy, and the corresponding adaptive detector has better constant false alarm ratio (CFAR) property and detection performance.

Modulation Classification Using Cyclostationarity Test and Support Vector Machine

2013, 31(6):  593-600.  doi:10.3969/j.issn.0255-8297.2013.06.007

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Modulation recognition under non-cooperative reception conditions generally requires sophisticated preprocessing and has limited classification set. In the present paper, a modulation classification scheme based on cyclic frequency features and support vector machine classifier is proposed to improve the classification performance and expand the recognition set under blind reception conditions. The signal’s cyclic frequency features of the cyclic cumulants are used to discriminate digital modulation signals including FSK, PSK, QAM, OQAM, CPOFDM, ZPOFDM, etc. The proposed method alleviates the preprocessing needs such as estimation of parameters and synchronization. Theoretical derivation is presented and simulations performed, showing
effectiveness of the method.

Translational Motion Estimation Combined with Improved C-1BT Transform in Global Motion

XI Zhi-hong, CHU Shou-yan

2013, 31(6):  601-606.  doi:10.3969/j.issn.0255-8297.2013.06.008

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 Considering the difficulty in achieving effectiveness and real-time property simultaneously in the global motion estimation, and combining the bit-plane method and ARPS, an accurate approach for estimating the global translational motion is proposed. It improves the traditional C-1BT method by setting an adaptive threshold to construct the mask image. Early determination of zero motion vector areas is achieved, along with twice extraction of low bit planes. The similarity measures between images can represent motion intensity.As such, by selecting a sub-region of the similarity measures within a certain range, these regions can better represent the global motion trends as compared with other regions. Finally, the improved ARPS approach takes full advantage of space-time correlation. Simulation results show that the proposed approach can increases accuracy and reduces the amount of computation.  

SAR Image Target Detection Based on Multi-scale Auto-convolution Variance Saliency

WANG Guo-li, ZHOU Wei, CONG Yu, GUAN Jian

2013, 31(6):  607-6.  doi:10.3969/j.issn.0255-8297.2013.06.009

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 To detect salient objects in SAR image, an adaptive detection method is proposed based on multi-scale auto-convolution variance (MSAV) saliency. With multi-scale auto-convolution operation in SAR image and by calculating MSAV, a variance saliency map is obtained. An auto-threshold-selecting detector is
constructed and salient object detection from the SAR image is achieved. Experimental results show that, by applying the proposed algorithm to a complex scene, salient objects consistent with human visual sense can be effectively detected.

Shadow Detection for PV Array Using Improved PCNN and Two-Dimensional Otsu Algorithm

HU Bei1, LONG Xia1, HU Chao1, DUAN Pan2, TANG Ruo-li1, DUAN Qi-chang1

2013, 31(6):  613-618.  doi:10.3969/j.issn.0255-8297.2013.06.010

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Shadows cause serious reduction of power generation in a photovoltaic (PV) power plant. This paper proposes a shadow detection method based on improved pulse coupled neural networks (PCNN) for partially shaded PV module images. Suitable initial parameters of unit-link PCNN are set. The ULPCNN is applied to the gray shading images, and the two-dimensional Otsu method is used to automatically determine the numbers of iterations. The segmentation that achieves the best threshold in the iteration is selected as an optimal result. Simulations verify that the test images are well segmented, and the method has better performance compared to the conventional PCNN and ULPCNN.

Multi-grid Lattice Boltzmann Method for Anisotropic Image Diffusion

HUANG Bin1, YAN Zhuang-zhi1,2, ZHOU Ming1

2013, 31(6):  619-627.  doi:10.3969/j.issn.0255-8297.2013.06.011

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To augment precision and computing efficiency of traditional Lattice Boltzmann (LB) methods in speckle reduction, this paper proposes a multi-grid LB (M-LB) method. The method automatically realizes anisotropic image diffusion of LB by applying different scales of grid: fine-grids are applied to regions with
obvious gradient change, and coarse-grids applied to regions with tiny gradient change. Experiments were carried out to test the method for speckle noise reduction. The proposed M-LB method was compared to an existing multi-grid method and two traditional LB methods. Natural images, composite images and medical ultrasound images were used. The experiments show that the M-LB method can achieve better denoising results and higher computation efficiency.

Computer Science and Applications

Ensemble Pruning Based on Frequent Patterns

ZHOU Hong-fang1, WANG Xiao1, ZHAO Xue-han1, RAO Yuan2

2013, 31(6):  628-632.  doi:10.3969/j.issn.0255-8297.2013.06.012

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Most ensemble learning methods have high computational complexity, excessive base classifiers and unsatisfactory classification accuracy in case of large-scale data sets. This paper proposes an ensemble pruning algorithm based on frequent patterns. Using the theory of frequent patterns mining, the method
maps the un-pruned ensemble classifier and corresponding sample space to a transactional database, and stores the corresponding classification results in a boolean matrix. After extracting frequent base classifiers from the Boolean matrix and composing a pruning ensemble, the algorithm gives the final pruning ensemble.Experimental results show that this algorithm reduces the number of base classifiers, improves classification accuracy and increases classification efficiency compared with ensemble algorithms of Bagging, AdaBoost, WAVE and RFW.

Extended GSPN and Reduction Rules for Web Services Composition System

YANG Huai-zhou, WANG Xue-long

2013, 31(6):  633-642.  doi:10.3969/j.issn.0255-8297.2013.06.013

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The generalized stochastic Petri net (GSPN) model for large scale Web service composition system with complex timing constraints is usually hard to understand. System performance is difficult to analyze because of the explosion of state apace. By associating different kinds of timing constraints to place, transition and arc respectively, an extended GSPN (EGSPN) model is presented to reflect such Web service composition system in a compact and comprehensible manner. Furthermore, to deal with the problem of lacking general reduction methods for GSPN and overcome the difficulty in preserving timing constraints in model reduction process, a set of reduction rules are presented to facilitate the model reduction of EGSPN for some model
structures in common use. The model state space is decreased via model reduction. Examples show that the presented method provides an effective way to reduce complexity of initial model. It can be used to rapidly analyze performance of the most Web service composition systems.

Matching Similar Patterns for Multivariate Time Series

WU Hu-sheng1,2, ZHANG Feng-ming1, ZHANG Chao1, LI Zheng-xin1, DU Ji-yong1

2013, 31(6):  643-649.  doi:10.3969/j.issn.0255-8297.2013.06.014

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 With ordinary methods, it is difficult to take relational information between variables while match the local shape of multivariate time series efficiently. To deal with the problem, we propose a multidimensional fitting piecewise method based on dynamic window to segment multivariate time series. Secondly, the inclination angle and time span of a fitting segment in a certain variable dimension are used to construct a feature pattern matrix. A multivariate pattern distance is used to measure similarity between the series. Finally, by comparison with principal component analysis and the matching method based on point distribution for three different data sets, we obtain preferable results, showing that the proposed method is more efficient, especially
for the medium sized time series with multivariate and varying time span.

New Attacks on a Variant ARIA Cipher

WEI Yong-zhuang 1,2, SU Chong-mao1, MA Chun-bo1

2013, 31(6):  650-655.  doi:10.3969/j.issn.0255-8297.2013.06.015

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ARIA cipher is a standard block cipher published by South Korean. Its block size is 128 bits and supports three key lengths: 128 bits, 192 bits, and 256 bits. In this paper, the security of a variant ARIA cipher using the same S boxes against the meet-in-the-middle attack is examined. Based on the structure of
the cipher, we design 4-round, 5-round, and 6-round distinguishers, respectively. Moreover, some new attacks on 7-round ARIA-192, 8-round ARIA-192, and 9-round ARIA-256 are proposed. It is shown that the security of ARIA will be reduced significantly if the cipher only uses a nonlinear S box. Furthermore, if the linear and nonlinear layers are not properly combined, one can break the equivalent tradeoff between the dada complexity and the preprocess time complexity under a dada-time-memory tradeoff attack. In this case, a more effective attack may be obtained.

Cryptanalysis against an Improved RSA Algorithm

LI Yun-fei1,2,3 , LIU Qing1, LI Tong1, HAO Lin3

2013, 31(6):  655-660.  doi:10.3969/j.issn.0255-8297.2013.06.016

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 Cryptanalysis against an improved RSA algorithm, client assistant RSA algorithm (CA-RSA), is carried out by using the Jochemsz and May’s techniques for finding small roots of polynomials. We perform cryptanalytic attacks based on the LLL Lattice basis reduction algorithm. Security of the CA-RSA algorithm
with two private exponents is analyzed. CA-RSA can effectively be broken when the two decryption exponents are less than modular N to the power of 1/12.