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• 2020, No. 05　Published:25 May 2020 Last issue    Next issue
  Columns in this issue:

• A parallel Turbo product decoder

  on graphics processing unit

  LI Rong-chun,ZHOU Xin,PAN Heng-yue,NIU Xin,GAO Lei,DOU Yong

  2020, 42(05): 761-769. doi:

  Abstract ( 227 )   PDF (711KB) ( 282 )   　　 Review attachment

  Turbo Product Code (TPC) is a class of Forward Error Correction (FEC) codes that have good Bit Error Rate (BER) performance at high code rate. TPC is widely used in a variety of scenarios, such as satellite communication systems and data storage systems. This paper proposes a GPU-based parallel TPC decoder. In it, all rows or columns of the two-dimensional product code matrix can be translated at the same time. A parallel basic decoder is designed to simplify the decoding process of TPC consisting of extended Hamming code. The parallelization of test sample and effective code word calculation is realized, and the decoding delay is reduced. In order to further improve the decoding throughput, we propose a multi-channel TPC decoder. In addition, the performance of parallel decoders is measured on different GPUs. The experimental results show that the decoding delay of the GPU-based parallel decoder is significantly reduced compared with the CPU-based TPC decoder. In addition, the throughput of the GPU decoder reaches 30 Mbps on the Nvidia RTX 2080 Ti and 38 Mbps on the NVIDIA GTX Titan V, which is 44 times and 54 times the performance of the CPU-based decoder.

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  Analysis of cloud server fault data based

   on improved FP-Growth algorithm

  HE Wang1,2,LIN Guo-yuan1,2

  2020, 42(05): 770-775. doi:

  Abstract ( 101 )   PDF (563KB) ( 135 )   　　

  In order to analyze the problem of abnormal parameters in the process of using the cloud server, the process of parameter data acquisition, data cleaning, and effective analysis of the cloud server is introduced. Aiming at the problems that the conditional FP-tree construction process is too redundant and the larger amount of data causes lower processing efficiency in the existing FP-Growth algorithm, an improved FP-Growth algorithm is proposed. It introduces the array tagging strategy, and each FP-tree node retains only pointers to the parent node. It does not need to generate a conditional FP-tree during the mining process, thus reducing time and space consumption. Experimental results show that the improved FP-Growth parallel algorithm can effectively improve the correlation analysis efficiency of abnormal data of cloud platform virtual machines, and is also suitable for data mining of large-scale data sets.

   

  Memory leak mechanism analysis

  and detection of C Programs

  ZHANG Jing1,HUANG Zhi-qiu1,2,SHEN Guo-hua1,2,YU Yao-shen1,AI Lei1

  2020, 42(05): 776-787. doi:

  Abstract ( 138 )   PDF (854KB) ( 186 )   　　 Review attachment

  As the main implementation language of safety-critical software, memory leak defects of C language are highly concealed and harmful. How to ensure the accuracy and efficiency of memory leak detection is a big challenge. Static analysis has the advantage of directly analyzing the source code, detecting software errors early, so it can reduce the cost of repairs. Based on static analysis technology, the paper proposes a memory leak detection method based on path-sensitive value-flow analysis. Firstly, pointer analysis is performed to generate precise point-to information. Secondly, based on the point-to information, value-flow constraints are constructed, and reachability analysis is performed to identify the memory leak paths in the program. Finally, the memory leak paths are verified by the effective life cycle of pointers and memory addresses. Experimental results on typical benchmark C programs show that the proposed method can improve the efficiency and accuracy compared with the existing technology.

  
   

   

  A vehicular edge computing handoff strategy

  based on Markov decision process

  LI Bo，NIU Li，PENG Zi-yi，HUANG Xin，DING Hong-wei

  2020, 42(05): 788-794. doi:

  Abstract ( 155 )   PDF (666KB) ( 158 )   　　 Review attachment

  Aiming at the influence of the dynamic change of the offloading environment in the vehicle edge computing on the computing offloading, this paper proposes a computing handoff strategy based on Markov decision process. While ensuring the task completion time, the strategy analyzes the overall process of computing offload to further reduce the influence of the handoff strategy on the offloading effect. Simulation experiments are carried out to show that whether the introduction of the switch can improve the computing offloading effect and how the strategy further reduces the influence of the handoff policy strategy on the offloading effect. The experimental results show that, compared with, the proposed handoff strategy can improve the efficiency of computing offloading and ensure the user service experience.

   

  A virtual network embedding algorithm based

  on double priority sorting model

  ZHU Guo-hui，ZHANG Yin，LIU Xiu-xia，SUN Tian-ao

  2020, 42(05): 795-802. doi:

  Abstract ( 118 )   PDF (696KB) ( 141 )   　　 Review attachment

  In order to solve the problem that the existing virtual network embedding algorithm ignores the attributes of the network itself and allocates resources only according to the order of request arrivals, which leads to the low utilization rate of physical resources, this paper proposes a virtual network embedding algorithm based on double priority sorting by using the time window model. In the first sorting, while coarsening the virtual network request, the priority of the request is calculated according to the service type and attribute parameters, and the mapping order of the virtual network in the window is preliminarily determined. In the second sorting, the priority is determined by the link weight, and the best mapping path is calculated by comprehensively considering the link bandwidth resource demand and node path hops. The simulation results show that the algorithm reduces the average waiting time of virtual network requests, and improves the request receiving rate and the revenue-cost ratio.

   

  Social stratification behavior in virtual space

  MA Man-fu1,2,YUN Xin-miao1,2,LI Yong1,2,LIU Yuan-zhe1,2,WANG Chang-qing3

  2020, 42(05): 803-811. doi:

  Abstract ( 134 )   PDF (910KB) ( 218 )   　　 Review attachment

  A large number of human behaviors occur on the Internet, which has become the most important virtual space corresponding to the real space. Social stratification research in the traditional virtual space is based on objective indicators such as the opportunity and ability of network information resources possession, but does not involves the specific behavior of users using network resources and the content and nature of information and other factors. This paper makes use of the big data of users’ online behaviors provided by China Internet Network Information Center, investigates and analyzes the characteristics and differences of users’ online behaviors of different levels in the virtual space from two aspects of online time and content. The study finds that different levels of users’ time spent in the virtual space and their attention focuses are very different. Higher level users can make better use of network resources to work and shopping, and stay in the virtual space has a relatively stable time. However, lower level users spend a lot of attentions on leisure and entertainment applications, and the stay time is not stable. In addition, this paper uses the neural network model (W2V-BP) based on Word2vec to conduct social stratification recognition of users' online behavior data in the virtual space, and the recognition accuracy reaches 90.22%, indicating that there are behavioral characteristics in the virtual space that can distinguish social stratification.

  
  
   

  A robust watermarking algorithm based

  on hyper-chaotic and Slant transform

   

  LI Wei-an,XIONG Xiang-guang,XIA Dao-xun

  2020, 42(05): 812-818. doi:

  Abstract ( 110 )   PDF (1448KB) ( 159 )   　　 Review attachment

  In order to improve the security and anti-attack ability of digital watermarking algorithm, combining the advantages of hyperchaos and Slant transform, a robust watermarking algorithm based on hyper-chaotic and Slant transform is proposed. Firstly, the watermarking image is encrypted by hyper-chaotic, which improves the security of the image to be embedded. Secondly, the original cover image is divided into non-overlapping blocks of size 8×8 and the Slant transform is performed on each block. Finally, the encrypted watermarking image is embedded into the DC coefficient and the middle frequency coefficient after Slant transform. A large number of simulation results show that the proposed algorithm has better transparency and robust performance for JPEG compression, noise, filtering, scaling, contrast adjustment and other attacks. Compared with the similar algorithms, the proposed algorithm has better robust performance.

   

  A software quality evaluation model

  based on hesitant fuzzy set

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  YU Jian-li,LU Jiao,CHEN Hong-gen

  2020, 42(05): 813-819. doi:

  Abstract ( 135 )   PDF (465KB) ( 184 )   　　 Review attachment

  A software quality evaluation model based on hesitant fuzzy set is proposed. Firstly, according to the expert score, hesitant fuzzy set and intuitionistic fuzzy set are combined to obtain a comprehensive hesitant fuzzy evaluation matrix of software quality characteristics. Then, by calculating the hesitant fuzzy generalized comparison table, the scores of each quality attribute of the software are obtained. Finally, the quality order of the software is given according to the score. It better solves the he- sitant problem when experts evaluate software quality indicators. Specific case analysis proves that the proposed software quality evaluation model is feasible and effective.

   

  Hyperspectral image compression through

  reducing mapping prediction residuals

  LI Jia-ying1,2,ZHU Wen-quan3,MENG Fan-yun1,2

  2020, 42(05): 820-824. doi:

  Abstract ( 142 )   PDF (1733KB) ( 173 )   　　 Review attachment

  CCSDS 123.0-B-1 algorithm is an adaptive lossless compression standard for multispectral and hyperspectral images proposed by the Consultative for Space Data Systems. In order to solve the problems existing in CCSDS 123.0-B-1 algorithm, such as the under-utilization of pixel position information and spectral correlation, and the compression rate to be improved, an RMPR (Reduction of Mapped Prediction Residual) algorithm is proposed to optimize the predictor of the algorithm. The RMPR algorithm can adaptively select the prediction points according to the specific position of the current pixel, remove the spectral correlation of hyperspectral images with bidirectional linear prediction, and use the optimized residual mapper to improve the prediction accuracy and shorten the compression code length. Test on ten hyperspectral images shows that the RMPR algorithm significantly outperforms the original algorithm in terms of compression performance, under the premise of lossless compression and no signi- ficant difference in compression efficiency.

  
  
   

  A visualization method of groundwater

  flow field and its application

   

  HE Liang1,2,3,GAO Mu-chen1,2,3,CHEN Suo-zhong1,2,3,QI Hui1,2,3

  2020, 42(05): 835-842. doi:

  Abstract ( 173 )   PDF (860KB) ( 358 )   　　 Review attachment

  The seepage field is an effective form to reflect the spatial and temporal dynamic characte- ristics of groundwater level and migration. The water level red line is an important indicator for strict control of groundwater exploitation. From the aspects such as streamline drawing of groundwater see- page, flow rate calculation, selection of flow field seed point and termination point, and streamline tracking, a visualization algorithm of pore groundwater seepage field is discussed, and a new seed point layout method is designed to characterize the groundwater seepage field. Moreover, the improved EULER method is used to realize the visual expression of the streamline. Then, the spatial differentiation of the groundwater seepage field and the water level red line are merged and superimposed. Based on the groundwater seepage field, the spatial distribution characteristics of groundwater exploitation intensity and over-exploitation degree are revealed from the 2D perspective, the regional scope of groundwater over-limit mining is automatically delineated, which can provide space-assisted decision-making for the rational exploitation of groundwater and the formulation of geological management protection measures.

   

  A video stabilization algorithm based

  on feature tracking and mesh path motion

  XIONG Wei1,2,WANG Chuan-sheng1,GUAN Lai-fu1,TONG Lei1,LIU Min1,ZENG Chun-yan1

  2020, 42(05): 843-850. doi:

  Abstract ( 149 )   PDF (894KB) ( 151 )   　　

   

   

  
  A video stabilization algorithm based on feature tracking and mesh path motion is proposed to solve the jitter video issues for handheld mobile devices. The algorithm uses SIFT algorithm to extract the feature points of video frames, uses KLT algorithm to track the feature points, uses RANSAC algorithm to estimate the affine transformation matrix between adjacent frames, divides the video frames into uniform grids, calculates motion trajectories of the video, and then optimizes the smoothing of multiple mesh paths by minimizing the energy function. Finally, the compensation matrix between adjacent frames is calculated by the relationship between the original camera path and the smoothed camera path, and then each frame is geometrically transformed by the compensation matrix to obtain a stable video. Experiments show that the proposed algorithm has good results for the jitter video captured by handheld mobile devices. The average PSNR after image stabilization is approximately 11.2 dB higher than that of the original jitter video, and is approximately 2.3 dB higher than the bundled camera path method. The average structural similarity (SSIM) between images is increased by approximately 59%, and is approximately 3.3% higher than the bundled camera path method.

   

  A deep image hole repairing method and a superpixel

  segmentation algorithm based on Kinect camera

   

  LIU Guo-hua1,2,DUAN Jian-chun1

  2020, 42(05): 851-858. doi:

  Abstract ( 199 )   PDF (1080KB) ( 208 )   　　 Review attachment

  Aiming at the problem that there is a hole in the original depth image of Kinect camera, a deep image hole repairing algorithm combining local edge information of color image is proposed. Firs- tly, the smaller holes are repaired by bilateral filtering. Secondly, according to the local edge information of the color image, the larger holes are divided into two types: edgeless and edged. Finally, the average type fill-in repairing is performed on the first type of edgeless holes, and the second type of
  ed- ged holes are segmented based on the local edge features of the color image and then gradually repaired from the outside to the end, so as to complete all hole repairing. After the hole repairing is completed, the fusion depth is fused to re-establish the linear spectral clustering kernel function. Based on this, a linear spectral clustering superpixel segmentation algorithm  (LSC-D) is proposed. The experimental results show that, compared with other methods, the proposed deep image hole repairing algorithm  has higher repairing accuracy, and the proposed superpixel segmentation algorithm  has lower under-segmentation error rate and higher boundary recall rate.

   

  An underwater image feature registration

  method based on improved CNN-RANSAC

  SHENG Ming-wei，TANG Song-qi，WAN Lei，QIN Hong-de，LI Jun

  2020, 42(05): 859-868. doi:

  Abstract ( 182 )   PDF (1923KB) ( 263 )   　　

  Due to the light absorption and scattering effect underwater, the quality of underwater images is reduced, which greatly limits the visual range of underwater images. The robustness and accuracy of complex underwater scenes make feature extraction and matching a challenging task. In order to get better registration of underwater images, this paper proposes an underwater image feature registration method based on improved CNN-RANSAC. Firstly, the underwater image is enhanced by the image enhancement algorithm based on deep convolutional neural network, and the VGGNet-16 framework is trained by transfer learning through the underwater image classification datasets, and the improved framework is applied to carry out feature extraction and generate robust multi-scale characteristic descriptor and feature points. After coarse feature matching and dynamic interior point selection, the improved RANSAC algorithm is used to eliminate false matching points. The feature extraction and feature matching experiments are carried out on a large number of underwater image datasets. Compared with the traditional SIFT and SURF registration algorithms, the proposed algorithm can detect more feature points and achieve a significant improvement in matching accuracy.

   

  A fractal image compression algorithm based on

  centroid features and important sensitive area classification

  WANG Li,LIU Zeng-li

  2020, 42(05): 869-878. doi:

  Abstract ( 115 )   PDF (763KB) ( 132 )   　　

   Image compression is an indispensable process in data transmission and storage. The fractal image compression method has unique advantages due to its simple compression method, reconstruction at any scale, fast decoding speed and high compression ratio. However, the traditional fractal image compression method has a defect that the encoding time is too long. Aiming at the imbalance between the compression ratio and the recovery effect, it is necessary to solve the problem of too long time in the encoding process under the premise of ensuring the image restoration effect. So a fractal image compression method based on centroid feature and important sensitive area classification is proposed. By constructing the centroid feature, the problem of searching the minimum Mean Square Error (MSE) of the   block in the basic fractal is converted into the problem of searching the best matching block of the   block centroid feature in the corresponding   block centroid feature codebook. It simplifies the block search process, changes a global search to a local search, considers the important sensitive area of the image, and adopts a global search for important sensitive areas, thereby increasing the visual effect of the restored image. Experimental simulation shows that, compared with the basic fractal image compression algorithm, the centroid feature method can effectively shorten the coding time. Under the premise of achieving a satisfactory image restoration effect, this method can save the coding time by about 64% compared with the basic algorithm. This method can achieve better recovery effect than the sum of double cross/eigenvalues methods.

   

   

   

  Pixel attention based siamese convolution

  neural network for stereo matching

  SANG Hai-wei1,3,XU Hai2,XIONG Wei-cheng1,ZUO Yu1,ZHAO Yong1,2

  2020, 42(05): 877-883. doi:

  Abstract ( 132 )   PDF (679KB) ( 180 )   　　 Review attachment

  Aiming at the problem that the existing stereo matching algorithm has high mismatch rate in ill-posed regions such as weak texture, repeated texture and reflective surface, a new pixel attention siamese neural network is proposed. Our method consists of siamese attention hourglass subnetwork and attention U-shaped subnetwork . Firstly, the feature map of the input image is extracted by the siamese attention hourglass subnetwork. Secondly, the cost matrix of the feature graph is obtained through the correlation layer. Finally, the cost matrix is aggregated by the attention U-shaped subnetwork, and the disparity map is output. Experiments on the KITTI dataset demonstrate that the proposed algorithm can effectively solve the ill-posed problem and improve the stereo matching accuracy.

   

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  LIU Hai-yan1,2，YANG Yun-fei1,2，ZHU Jian1,2，LI Xiao-jie1,2

  2020, 42(05): 884-892. doi:

  Abstract ( 106 )   PDF (820KB) ( 133 )   　　

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  A voice conversion algorithm based on multi-spectral

  feature generative adversarial network 

  ZHANG Xiao,ZHANG Wei,WANG Wen-hao,WAN Yong-jing

  2020, 42(05): 893-901. doi:

  Abstract ( 107 )   PDF (1107KB) ( 180 )   　　

  Voice conversion is widely used in education, entertainment, medical and other fields. In order to obtain high-quality converted speech, this paper proposes a voice conversion algorithm based on multi-spectral feature generative adversarial network. It uses generative adversarial network to convert the voiceprint obtained by spectral feature parameters. The feature-level multimodal fusion technique is used to make the network learn multiple spectral feature information from different feature domains, so as to improve the perception of speech signals of the network. Finally, the high-quality converted speech with good definition and intelligibility is obtained. The experimental results show that the proposed algorithm is significantly superior to the traditional algorithms in the subjective and objective evaluation indicators.

   

  A signal modulation recognition method based

  on wavelet feature and depth neural network

  TANG Zuo-dong,GONG Xiao-feng,LUO Rui-sen

  2020, 42(05): 902-909. doi:

  Abstract ( 129 )   PDF (951KB) ( 200 )   　　

  Aiming at the problem of inaccurate recognition of current communication signals in low signal-to-noise ratio (SNR), a recognition algorithm combining wavelet feature and depth neural network is proposed. This method generates 10 kinds of common communication signals with Gauss white noise{MASK, MPSK, MFSK, OFDM, 16QAM, AM, FM}. A new kind of wavelet characteristic parameters are extracted from the signals by using the wavelet decomposition and reconstruction algorithm. The improved BP neural network with plenty hidden layers is studied and tested as classifier. The parameters of the neural network are trained by the elastic back propagation algorithm. The optimal layers of the neural network are determined by the identification results. The simulation results show that the minimum recognition rate of single modulated signals is more than 95% and the average recognition rate is more than 98%, when the signal-to-noise ratio is as low as 0 dB, which greatly improves the recognition rate of standard recognition under low signal-to-noise ratio, thus proving the effectiveness and practicability of this method.

   

  A hesitant fuzzy pros and cons IPA evaluation method for

  the quality of multi-source information cloud service

   

  PENG Ding-hong1,2，CHEN Wen-ni1,2，ZENG Hong-xin3,WU Jin-fu4

  2020, 42(05): 910-922. doi:

  Abstract ( 113 )   PDF (855KB) ( 131 )   　　 Review attachment

  In order to effectively evaluate the quality level of multi-source information cloud service, a hesitant fuzzy method based on the pros and cons of the indicators is proposed under the IPA framework. Firstly, the IPA hierarchical structure framework of multi-source information cloud service quality evaluation is constructed from the perspective of user satisfaction. Secondly, the hesitation fuzzy set is used to express the evaluation information. Based on the reference solution-dependent evaluation rules, the optimal and worst reference solutions in the TOPSIS method and the average reference solution in the EDAS method are fused together to develop a hesitant fuzzy IPA analysis method based on the pros and cons of the indicators for the multi-source information cloud service quality evaluation. In addition, considering that the existing hesitant fuzzy distance measurement method cannot measure the asymmetric information problem, a hesitant fuzzy measurement method based on Squared-χ2  is constructed. Finally, the multi-source information cloud service evaluation of a Dongguan enterprise is taken as an example to verify the feasibility of the proposed method. Comparison with other similar methods shows that the method can effectively evaluate the quality of multi-source information cloud service.

  Dynamic refueling vehicle scheduling

  considering task balance

  HENG Hong-jun, QI Xin-tong

  2020, 42(05): 923-930. doi:

  Abstract ( 132 )   PDF (675KB) ( 194 )   　　

  In order to improve the utilization rate of airport resources and the punctuality rate of flights, it is imperative to reasonably arrange airport refueling trucks to provide fuel refueling services for flights. Considering the unpredictability of the actual flight time, a dynamic planning time window needs to be established, and an airport vehicle scheduling model for is constructed for the window according to the flight captured at the estimated flight time.
  The model is solved by the adaptive branch pricing algorithm to configure vehicles and personnel, plan vehicle routes, and connect tasks, in order to achieve the purpose of minimal vehicle routes and balanced workloads of fuelers. Simulation experiments on the actual data of an airport in north China show that, compared with the saving algorithm, the adaptive branch pricing algorithm reduces the standard difference of travel time and workload by 1.38% and 7.41% respectively. The experiments verify the advantages of the algorithm, and the algorithm is also applicable to planning other airport ground service problems.