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    20 April 2020 Volume 47 Issue 2
      
    360-degree real-time stitching technology for multi-channel video
    HUANG Zhangqin,MU Zhao,CEN Chen,GAO Han
    Journal of Xidian University. 2020, 47(2):  1-8.  doi:10.19665/j.issn1001-2400.2020.02.001
    Abstract ( 594 )   HTML ( 136 )   PDF (2402KB) ( 382 )   Save
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    Aiming at the time-consuming problem of image registration in video mosaic, a combined algorithm based on the BRISK and GMS is designed and optimized. Based on the fast feature extraction feature of the BRISK, the grid image method is used to divide the overlap region image to make the feature point distribution more uniform, thus reducing the number of feature points that need to be operated. In the feature matching stage after feature extraction, the GMS is used to eliminate the mismatching pair, and improves the matching accuracy according to the two-way matching strategy. In order to make the image fusion more natural and smooth, this paper designs a regionalized feather blending weighted fusion algorithm. To reduce the error in solving the overlapping area, the fusion area is constructed and divided into regions. The best suture method is used to get the seam. The gradual in and out weighting algorithm is used in different regions to achieve image fusion, so as to obtain the panoramic image with higher fusion quality. Finally, a complete prototype of the panoramic video stitching system is designed. The feasibility and practicability of the system scheme are verified by experiments which show that, compared with the traditional video splicing technology, this algorithm ensures the real-time performance of video splicing while eliminating the ghosting and seaming problems that occur during splicing.

    Classifying health questions with local semantic and global structural information
    ZHANG Zhichang,ZHANG Zhiman,ZHANG Zhenwen
    Journal of Xidian University. 2020, 47(2):  9-15.  doi:10.19665/j.issn1001-2400.2020.02.002
    Abstract ( 221 )   HTML ( 32 )   PDF (1328KB) ( 104 )   Save
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    Considering the shortcomings of existing research methods in the Chinese medical health questions classification task, this paper proposes a new health questions classification method that incorporates the health questions’ local semantic information and global structural information. We first obtain the questions’ local semantic representation and global structural representation by the convolutional neural network (CNN) and independent recurrent neural network (IndRNN). Then, we extract the questions’ semantic representation, and especially we get the questions’ semantic representation by fusing the local semantic representation and global structural representation using a self-attention mechanism. Finally, we classify the semantic representation of the medical health question through the softmax layer and output classification result. Experimental results show that this method leads to a good performance in the Chinese medical health questions dataset, and that it effectively improves the semantic representation ability of the model and significantly resolves the gradient vanishing and gradient explosion problems.

    Algorithm for extraction of features of robot speech control in the factory environment
    WANG Xiaohua,YAO Pengchao,MA Liping,WANG Wenjie,ZHANG Lei
    Journal of Xidian University. 2020, 47(2):  16-22.  doi:10.19665/j.issn1001-2400.2020.02.003
    Abstract ( 217 )   HTML ( 34 )   PDF (1670KB) ( 93 )   Save
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    In the real working environment,the mobile robots have a poor recognition performance to speech control commands due to the noise effect. Aiming at this issue,this paper proposes a new algorithm based on the gammatone frequency cepstral coefficient and the mixed feature extraction of the Teager energy operator. This algorithm replaces the common Mel filter with the Gammatone filter. In the process of extracting gammatone frequency cepstral coefficients,the Teager energy operator reflecting the energy of speech signal is added to form a new feature, with the dynamic characteristics of the speech signal considered. It is combined with the first-order difference parameters to form a mixed feature. And the principal component analysis is made to reduce the dimension,and the final mixed features are used to the speech recognition system for control command of the mobile robot. Experimental results show that,in the environment of the workshop noise and signal-to-noise ratio of 10dB,the recognition rate of mixed features is improved by 12.20% compared with the mel frequency cepstrum coefficient. The recognition rate of the mixed feature is increased by 1.02% when the dimension is reduced by principal component analysis.

    Optimization algorithm for estimating the human pose by using the morphable model
    LI Jian,ZHANG Haoruo,HE Bin
    Journal of Xidian University. 2020, 47(2):  23-31.  doi:10.19665/j.issn1001-2400.2020.02.004
    Abstract ( 122 )   HTML ( 13 )   PDF (5771KB) ( 68 )   Save
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    An optimization algorithm is proposed utilizing the video data and point cloud data captured by the depth camera to solve the problems such as error-proneness and incoherence of motion sequence caused by the existing human pose estimation algorithms based on the morphable model. For video data, the neural network is first used in extracting the model parameters from each color image frame. Next, the human key-points and contour constraint are considered to optimize the above parameters. Then the coherence between every two consecutive frames is utilized to correct the error of pose estimation, thus making the resulting motion sequence smoother. In addition, the point cloud and the model obtained from the corresponding color image frame are used as the joint input to further improve the estimation accuracy. Finally, the distance between the point cloud and the corresponding point of the model is constrained to be as small as possible to obtain a more reasonable solution. The proposed algorithm and the state-of-the-art algorithms are compared qualitatively and quantitatively on the data set and real video set. Experimental results show that the algorithm can effectively correct the error and incoherence in the single-frame pose estimation results and greatly improve the accuracy when using point cloud data optimization.

    Simulation analysis of directivity and optimization of the array of the AlN thin film PMUT array
    LOU Lifei,AN Zaifang,LI Yining,ZHAO Mingyang,ZHAO Jianxin
    Journal of Xidian University. 2020, 47(2):  32-38.  doi:10.19665/j.issn1001-2400.2020.02.005
    Abstract ( 137 )   HTML ( 14 )   PDF (3196KB) ( 41 )   Save
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    The relatively low piezoelectric constant of the aluminum nitride piezoelectric film limits the development and application of the piezoelectric micro-machined ultrasonic transducer based on the aluminum nitride. Therefore, the directivity of the piezoelectric micro-machined ultrasonic transducer array is studied for this problem. First, the far-field sound pressure and normalized directivity function of the transducer array are calculated according to the Rayleigh principle. Furthermore, the effects of array element radius, array element spacing, array element number and operating frequency on the beam width, direction sharpness angle and sidelobe level of the array are analyzed, and the transducer array is optimized. Finally, based on the optimization results, area and filling efficiency, the final structure size of the transducer array is determined and the sound pressure distribution visually simulated. The results show that the optimized transducer array has an ideal sound pressure distribution, good directivity and sharp main lobe. The-3dB beamwidth of the main lobe is about 9 ° and the sidelobe level is about 0.228.

    Urine cell image classification algorithm based on the squeeze and excitation mechanism
    SONG Jianfeng,WEI Yue,MIAO Qiguang,QUAN Yining,CHEN Yusheng
    Journal of Xidian University. 2020, 47(2):  39-45.  doi:10.19665/j.issn1001-2400.2020.02.006
    Abstract ( 111 )   HTML ( 11 )   PDF (1438KB) ( 40 )   Save
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    In order to solve the classification problem on the urine-forming sub-cell images, a urine cell image classification algorithm based on the Squeeze-and-Excitation GoogLeNet is proposed. The algorithm uses the feature recalibration mechanism and brings about significant improvement in the useful feature for the current task through squeeze and excitation operations, which explicitly models interdependencies between cell feature channels learned by the Inception architecture during the training process. On the urine cell datasets, comparative experimental results show that the algorithm provides a better classification effect, which improves the accuracy of classification by 3% and the recall rate by 1% at the similar speed of the GoogLeNet network.

    Lightweight deep neural network for point cloud classification
    YAN Lin,LIU Kai,DUAN Meiyu
    Journal of Xidian University. 2020, 47(2):  46-53.  doi:10.19665/j.issn1001-2400.2020.02.007
    Abstract ( 151 )   HTML ( 15 )   PDF (1824KB) ( 71 )   Save
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    For point cloud classification, deep learning based methods use operations like voxelization to generate regular 3D grids or render the 3D mesh into a collection of images from multiple angles. However, the conversion will introduce additional computing and storage consumption. Some methods directly consume the raw point cloud. But their network scale and computational complexity make it difficult for them to deploy in embedded environments. On the basis of intensive studies of these algorithms, a novel lightweight dual path way network is proposed in this paper. Without additional conversion, our network attains a comparable performance but has 0.8 million floating parameters only. With point-wise and neighbor-wise representations, our approach incorporates global and local features of the point cloud. Experimental results on ModelNet40 and MNIST data-set demonstrate that our method achieves a good accuracy, and prove the effectiveness of our design.

    Optical fiber relative humidity sensor using the hydroxyethyl cellulose hydrogel film
    LI Jinze,ZHANG Jianqi,Sun Hao
    Journal of Xidian University. 2020, 47(2):  54-59.  doi:10.19665/j.issn1001-2400.2020.02.008
    Abstract ( 73 )   HTML ( 8 )   PDF (1233KB) ( 22 )   Save
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    In order to demonstrate the feasibility of hydroxyethyl cellulose hydrogel as an optical fiber sensor moisture sensitive material, a new optical fiber humidity sensor based on hydroxyethyl cellulose is proposed. First, a hollow-core fiber(150μm) is fused to a single mode fiber. Second, a 10μm thick hydroxyethyl cellulose hydrogel film is coated on the end face of the hollow-core fiber. Finally, the proposed humidity sensor is placed in a constant temperature and humidity chamber for a humidity response test. Experimental results show that this sensor has good humidity response characteristics and only needs 2.75s from 35%-85% RH, with the RH sensitivity being 224.5pm/%RH. As a good moisture sensitive material, hydroxyethyl cellulose has a good application prospect in various humidity sensors.

    Fast algorithm for intra prediction in quality SHVC
    LI Qiang,ZUO Jing,WANG Haining
    Journal of Xidian University. 2020, 47(2):  60-66.  doi:10.19665/j.issn1001-2400.2020.02.009
    Abstract ( 82 )   HTML ( 9 )   PDF (829KB) ( 21 )   Save
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    In order to reduce the coding complexity of quality Scalable High efficiency Video Coding (SHVC), a fast algorithm for intra prediction is proposed. First, inter-layer correlation and spatial correlation are combined to predict the coding unit depth range of the enhancement layer. Then, at each depth level, the residual coefficients are tested by Jarque-Bera to determine whether the inter layer reference mode is optimal. If the inter layer reference mode is optimal, the high-complexity intra prediction can be skipped directly. Finally, in order to terminate the depth division ahead of time, hypothesis tests are carried out to determine whether the residual coefficients in the coding unit show significant differences. Experimental results demonstrate that compared with standard SHVC encoder, the proposed algorithm reduces the coding time of the enhancement layer by 79% on average with almost the same coding efficiency.

    Highly concurrent NVM storage system based on the asymmetric lock
    CAI Tao,LIU Peiyao,WANG Jie,NIU Dejiao,HE Qingjian,CHEN Zhipeng
    Journal of Xidian University. 2020, 47(2):  67-74.  doi:10.19665/j.issn1001-2400.2020.02.010
    Abstract ( 77 )   HTML ( 6 )   PDF (2552KB) ( 15 )   Save
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    In order to improve the ability of the non-volatile memory storage device system to concurrently execute access requests, aiming at the diverse nature between read and write access requests and the different properties of file data and metadata in the storage device, we have designed a file-based parallel write-based file data concurrent write strategy, RCU based file data read and write concurrency strategy and a minimum spin lock-based metadata synchronization strategy to improve the degree of concurrency of requests execution. And then we have implemented a prototype of the asymmetric lock-based high concurrent non-volatile memory storage system, which has been tested and analyzed by common test tools and methods, the result shows that compared with the PMFS, the prototype system can increase the throughput by 40%~162% and input/output operations per second by 61%~159%.

    Aircraft reinforcement learning multi-mode control in orbit
    ZHANG Ying,WEI Minfeng,WANG Shihui,TAO Leiyan,CAO Jian,ZHANG Xing
    Journal of Xidian University. 2020, 47(2):  75-82.  doi:10.19665/j.issn1001-2400.2020.02.011
    Abstract ( 89 )   HTML ( 9 )   PDF (1779KB) ( 22 )   Save
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    In order to improve the long-term in orbit flight reliability of the aircraft control system, a multi-mode control scheme is proposed based on reinforcement learning. This system includes a sensor module, a control module and an execution module. The sensor module is used to input the sensitive flight data of the aircraft to the control module in real time. This data is divided into multidimensional structured floating point data with historical relevance that can be directly used for aircraft control and the unique physical representation quantity of a particular sensor. The control module is divided into an input layer, a feature extraction layer and a full connection layer. The execution module is used to receive the driving data from the control module in real time, which includes the optimal state value for decision-making and the action output value for evaluation. The system decides which specific execution modules to use based on the optimal return value for decision making, with the output value of a selected specific execution module depending on the output value of the action used for evaluation. The system enables the aircraft to complete a long-term orbit operation in the multi-mode input and output state with 15ms fast response and 5.23GOP/s/W Performance per Watt.

    Improved gravitational search algorithm for shaped beam forming
    SUN Cuizhen,DING Jun,GUO Chenjiang
    Journal of Xidian University. 2020, 47(2):  83-90.  doi:10.19665/j.issn1001-2400.2020.02.012
    Abstract ( 121 )   HTML ( 19 )   PDF (1376KB) ( 34 )   Save
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    In view of the adverse effect of the random initial value on the performance and convergence speed of the gravitation search algorithm, a quasi-oppositional gravity search algorithm (QOGSA) is proposed. The quasi-oppositional based learning OBL is embedded into the GSA algorithm, the number of iteration is divided into multiple learning cycle, the oppositional probability is adjusted according to the success rate of the past learning cycle, and an adjustable oppositional probability is designed to optimize the timing of the mechanism in the evolution, which improves the speed of the algorithm to search for the optimal solution greatly. On this basis, in order to improve the population diversity, elite particles are retained to the next generation population. They replace the particles with a poor fitness value and acquire a higher optimization accuracy. Compared with the existing algorithms in the literature, the optimization accuracy of the QOGSA for the average optimal value of the single-peak and multi-peak test functions can be improved by 1016. For the shaping results of different types of beam, the optimization accuracy of the improved algorithm for the sidelobe can be improved from 1.26dB to 5.99dB. On the premise of the fastest convergence speed, the QOGSA can greatly avoid the problem that other optimization algorithms tend to fall into local optimization, with the overall performance being the best.

    Fast DOA estimation methods for underdetermined wideband signals with a high accuracy
    JIANG Ying,FENG Mingyue,XU Qi,HE Yi
    Journal of Xidian University. 2020, 47(2):  91-97.  doi:10.19665/j.issn1001-2400.2020.02.013
    Abstract ( 84 )   HTML ( 15 )   PDF (1113KB) ( 24 )   Save
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    In order to handle the low accuracy of the algorithm DCS-SOMP, two underdetermined wideband direction of arrival estimation methods with a high accuracy are proposed from the perspective of multiple iteration. First, the wideband signal processing model using the sparse array is established and transformed into a distributed compressive sensing problem through sparse representation. Then, the noises are eliminated through matrix transformation. After that, an algorithm utilizing proximity searching is proposed to improve the estimation accuracy and an algorithm based on the refined grid is proposed to compensate grid mismatch. Simulation results show that the proposed algorithms outperform the DCS-SOMP with a higher estimation accuracy and possess the advantage in computational speed.

    Optimization of memory access for the convolutional neural network training
    WANG Jijun,HAO Ziyu,LI Hongliang
    Journal of Xidian University. 2020, 47(2):  98-107.  doi:10.19665/j.issn1001-2400.2020.02.014
    Abstract ( 121 )   HTML ( 12 )   PDF (2037KB) ( 30 )   Save
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    Batch Normalization (BN) can effectively speed up deep neural network training, while its complex data dependence leads to the serious "memory wall" bottleneck. Aiming at the "memory wall" bottleneck for the training of the convolutional neural network(CNN) with BN layers, an effective memory access optimization method is proposed through BN reconstruction and fused-layers computation. First, through detailed analysis of BN’s data dependence and memory access features during training, some key factors for large amounts of memory access are identified. Second, the “Convolution + BN + ReLU (Rectified Linear Unit)” block is fused as a computational block to reduce memory access with re-computing strategy in training. Besides, the BN layer is split into two sub-layers which are respectively fused with its adjacent layers, and this approach further reduces memory access during training and effectively improves the accelerator’s computational efficiency. Experimental results show that the amount of memory access is decreased by 33%, 22% and 31% respectively, and the actual computing efficiency of the V100 is improved by 20.5%, 18.5% and 18.1% respectively when the ResNet-50, Inception V3 and DenseNet are trained on the NVIDIA TELSA V100 GPU with the optimization method. The proposed method exploits the characteristics of memory access during training, and can be used in conjunction with other optimization methods to further reduce the amount of memory access during training.

    Satellite image RPC parameters estimation method using the heteroscedastic errors-in-variables model
    ZHOU Yu,HU Xin,CAO Kailang,ZHOU Yongjun,LI Yunsong
    Journal of Xidian University. 2020, 47(2):  108-117.  doi:10.19665/j.issn1001-2400.2020.02.015
    Abstract ( 124 )   HTML ( 49 )   PDF (1217KB) ( 16 )   Save
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    The positioning accuracy of a satellite image is mainly affected by the estimation accuracy of the rational polynomial coefficients (RPCs). Image point compensation or ground control point correction methods are usually used in the existing algorithms. Because the error characteristics of the design matrix elements are not considered, there are problems such as incomplete systematic error elimination and low parameter estimation accuracy. Considering the influence of the model systematic error, a heteroscedastic estimation method is proposed in this paper. First, the random model of matrix elements is established in the algorithm to describe the system characteristics more accurately. Taking into account the system deviations of the design matrix elements, the least square model is constructed using the Mahalanobis distance as the metric, and parameters are solved using the generalized eigenvalue method. The systematic error can be reduced theoretically. Experiment on different terrain images of TH-1 shows that the image correction accuracy of the proposed method is improved by more than 36 times compared with the traditional method, and the precision consistency is superior, which is of great significance to improving the accuracy of RPC parameters estimation and satellite imagery positioning.

    Unsupervised adversarial learning method for hard disk failure prediction
    JIANG Shaobin,DU Chun,CHEN Hao,LI Jun,WU Jiangjiang
    Journal of Xidian University. 2020, 47(2):  118-125.  doi:10.19665/j.issn1001-2400.2020.02.016
    Abstract ( 96 )   HTML ( 16 )   PDF (1507KB) ( 28 )   Save
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    In order to solve the problem of over-fitting of traditional supervised learning methods in anomaly detection of unbalanced datasets, an unsupervised adversarial learning method is proposed for hard disk failure prediction. This method uses the long short-term memory neural network and fully connected layer to design an Autoencoder that can be used for secondary coding. Only normal samples are used for training. By reducing the reconstruction error and the distance between potential vectors, the model can learn the data distribution of normal samples, thus improving the generalization ability of the model. The model also introduces the generative adversarial network to enhance the effect of unsupervised learning. Experiments on several datasets show that the recall rate and precision of the proposed method are higher than those of traditional supervised learning and semi-supervised learning classifiers, and that its generalization ability is stronger. Therefore, the unsupervised adversarial learning method is effective in hard disk failure prediction.

    Collaborative offloading of overloaded MEC servers in ultra-dense heterogeneous networks
    WANG Ren,WANG Yi,HU Yanjun,JIANG Fang,XU Yaohua
    Journal of Xidian University. 2020, 47(2):  126-134.  doi:10.19665/j.issn1001-2400.2020.02.017
    Abstract ( 108 )   HTML ( 10 )   PDF (1202KB) ( 24 )   Save
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    Mobile Edge Computing (MEC) can perform computational task offloading with the help of edge servers, and is no longer limited by the power of mobile terminals (MTs). When the edge server is overloaded, it often chooses to queue, postpone or reject the MT’s offloading request. QoS (Quality of Service) of users will deteriorate greatly due to service disruption and extended waiting, but the existing research work does not consider how the MEC-BS can relieve load pressure at this time. In this paper, we study how to enhance the computing offloading service of the MEC-BS by offloading the task of the overloaded base station to the other MEC-BS in the same collaboration space. Combining the penalty function with the two-step quasi-newton method, an optimization algorithm is proposed to minimize the joint utility function including the total delay and energy consumption of the edge computing network. Empirical factors are used to adjust the optimization deviation according to the different needs of the optimization target for time delay or energy efficiency. Simulation results show that the proposed scheme is better than two other schemes in improving the system performance and convergence speed.

    Hardware obfuscation design of the RM logical camouflage gate
    WU Qiufeng,ZHANG Yuejun,WANG Pengjun,ZHANG Huihong
    Journal of Xidian University. 2020, 47(2):  135-141.  doi:10.19665/j.issn1001-2400.2020.02.018
    Abstract ( 134 )   HTML ( 9 )   PDF (3887KB) ( 43 )   Save
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    To improve the ability of the integrated circuit to resist reverse engineering, we study the logic obfuscation technology and propose a logic obfuscation scheme based on the Reed-Muller camouflage gate. First, different virtual hole configurations are adopted to realize XOR/AND logical functions on the same layout, and feature information of the logical obfuscating circuit is extracted to make the standard cell physical library. Then, the obfuscation physical library is applied in the circuit netlist by the random insertion algorithm. Finally, the ISCAS benchmark is used to verify the effectiveness of the proposed scheme. Simulation results reveal that the similarity of the Reed-Muller logic camouflage layout is improved by 14.36%, and that the power consumption overhead is about 2.36% under the larger scale benchmark. Experiment indicates that the designed obfuscation gate can effectively resist reverse engineering and improve the hardware security of the circuit.