Journal of Xidian University 2019 Vol.46
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Hybrid location algorithm for the acoustic source based on error correction
QI Xiaogang, YUAN Lieping, LIU Lifang
Journal of Xidian University    2019, 46 (1): 1-7.   DOI: 10.19665/j.issn1001-2400.2019.01.001
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Aiming at the problem that due to the influences of non line-of-sight (NLOS) propagation, the multipath effect and other factors, the location system is liable to low accuracy with large errors. A hybrid location algorithm for the acoustic source based on error correction (ECHL) and time difference of arrival (TDOA) is proposed. First, by correcting the weighted matrix of weighted least square (WLS) dynamically this algorithm can be used to obtain the initial value of Taylor series iteration, so as to achieve the purpose of reducing the number of times of iteration. Second, the influence of the time delay with larger errors on the positioning accuracy is eliminated by combining the algorithm for latter selection time delay with the standardized residual function. Then the optical positioning solution of TDOA integration is chosen. Finally, the target position is determined by the standardized residual weighting. Simulation results show that the proposed method can considerably outperform the single method, effectively restrain the NLOS error and improve the positioning accuracy compared with reference methods.

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Method for Secret key generation using k-means clustering
LIU Jingmei,HAN Qingqing,SHEN Zhiwei,LIU Jingwei
Journal of Xidian University    2019, 46 (1): 8-13.   DOI: 10.19665/j.issn1001-2400.2019.01.002
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Due to the fact that traditional key generation algorithms cannot make full use of the channel information, a key generation method based on k-means clustering is proposed. By using this method, both phase information and amplitude information can be fully utilized, and the consistency of the key can be improved effectively. In the key generation process, the two sides can classify the measured values by transferring the location of the cluster center, and will not cause leakage of key information. In order to improve the performance further, an improved method named k-means two dimensional compensation is proposed which combines the random pilot and two-dimensional compensation. Simulation result shows, compared with other key generation methods, the two schemes proposed in this paper can effectively improve the consistency and randomness of the secret key.

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Cascade residual learning method for infrared image nonuniformity correction
LAI Rui,GUAN Juntao,XU Kunran,XIONG Ai,YANG Yintang
Journal of Xidian University    2019, 46 (1): 14-19.   DOI: 10.19665/j.issn1001-2400.2019.01.003
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Traditional scene adaptive nonuniformity correction methods generally suffer from the over smooth and residual nonuniformity in the corrected results. In view of this, a cascade residual learning based nonuniformity correction method is presented. This method uses the multiscale feature extraction unit to fuse the extracted features and employs the residual learning strategy to deal with the overfitting problem. Experimental results validate that the proposed method yields nearly 5dB improvement in the average peak signal-to-noise ratio (PSNR) as compared to the traditional scene adaptive correction methods. Moreover, its visual effects are clearer and sharper.

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Traffic flow cycle prediction based on the PCA-ESN model
LI Hui,XI Yuanyuan,MA Yuxin,ZHANG Ruimei
Journal of Xidian University    2019, 46 (1): 20-26.   DOI: 10.19665/j.issn1001-2400.2019.01.004
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Aim

ing at the problem of low precision of multi-step traffic flow prediction, a cycle prediction model for traffic flow forecasting is presented. First, the time series is reconstructed by considering the periodicity of traffic flow in our model, and Principal Component Analysis (PCA) is explored as a dimensionality reduction method. Then the Echo State Network (ESN) model is used to predict the traffic flow time series. Meanwhile, an adaptive disturbance particle swarm optimization algorithm is used to optimize the parameters of the model. The availability of the proposed model is proved by predicting the time series of real traffic flow. The Mean Absolute Percentage Error (MAPE) of the prediction results is 9.8%, which is 12.7% lower than that of the traditional ESN multi-step prediction model. Experiments demonstrate that the proposed model can effectively prevent the delay of prediction results and greatly improve the precision of multi-step prediction.

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Fusion of infrared and visual images guided by visual saliency
YI Xiang,WANG Bingjian
Journal of Xidian University    2019, 46 (1): 27-32.   DOI: 10.19665/j.issn1001-2400.2019.01.005
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To obtain a high quality fused image consistent with characteristics of human vision, a novel image fusion method for infrared and visual images guided by visual saliency is proposed. First of all, for the given infrared and visible images, the modified Manifold Ranking algorithm is utilized to extract their visual salient areas respectively. Then, source images are decomposed in different scales and directions by Non-subsampled Contourlet Transform to obtain low frequency information and high frequency information. And results of visual saliency detection are used to guide the fusion rule of low frequency subband coefficients. Besides, the high frequency subband coefficients are fused owing to the local standard deviation criterion. Finally, the fused image is obtained by performing inverse Non-subsampled Contourlet Transform. Experimental results demonstrate that the proposed algorithm can not only assure the final fused images with clear detail information, but also highlight the infrared objects accurately, which presents a good vision effect and effectively enhances recognition probability of infrared and visible compound systems.

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Dynamic program verification via a CPAChecker
DUAN Zhao, LIU Kunlong
Journal of Xidian University    2019, 46 (1): 33-38.   DOI: 10.19665/j.issn1001-2400.2019.01.006
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To overcome the state space explosion problem in model checking, a CPAChecker based dynamic program verification approach is proposed. The proposed approach first verifies the program statically by unwinding the control flow chart. In the process, dynamic execution is applied to accelerate the verification on the basis of the determinism of branch statements. Specifically, abstract verification effectively reduces the size of the system models, while dynamic detection not only effectively reduces false positives, but also guides the construction of more accurate system models. As a result, the proposed approach makes counterexample-guided abstraction refinement more efficient and accurate in practice.

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Anti-fuzzy local feature descriptor on images
TANG Guoliang
Journal of Xidian University    2019, 46 (1): 39-45.   DOI: 10.19665/j.issn1001-2400.2019.01.007
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The SIFT descriptor is only partially invariant to illumination when extracting the local features of the image. In particular, the SIFT descriptor is not invariant to non-linear illumination changes and cannot accurately extract the feature points or few of them can be extracted from the fuzzy object image. In order to solve these problems, a new anti-fuzzy local feature descriptor is proposed that is consistent with the visual cognition process of the human visual system from bottom-top and top-down. Experimental results suggest that the proposed operator is robust to the changes of illumination conditions, and more feature points can be extracted accurately from the fuzzy object image. The proposed operator retains the advantages of SIFT descriptors such as invariance of scaling, rotation and compression, and can significantly improve the matching rate on fuzzy images.

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Method for robot obstacle avoidance based on the improved dueling network
ZHOU Yi,CHEN Bo
Journal of Xidian University    2019, 46 (1): 46-50.   DOI: 10.19665/j.issn1001-2400.2019.01.008
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In view of the disadvantages of traditional reinforcement learning methods in motion planning, especially the problem of robot obstacle avoidance, it is easy to have overestimation and difficult to adapt to complex environment. A new model based on deep reinforcement learning is proposed to improve the obstacle avoidance performance of robots. The model combines dueling networks with Q-learning which is the traditional reinforcement learning method, and using two independent trained dueling networks to deal with environmental data and predict the action value. In the output layer, the state value and the action advantage are output respectively, with both values combined as the final action value. The model can process high dimension data to adapt to complex and changeable environment, and output advantageous actions for robot selection to get a higher accumulative reward. It can effectively improve the obstacle avoidance performance of a robot.

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Method for IF estimation of multicomponent FM signals
SU Xiaofan,XIAO Rui,ZHU Mingzhe
Journal of Xidian University    2019, 46 (1): 51-56.   DOI: 10.19665/j.issn1001-2400.2019.01.009
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Aiming at the key problem on the phase structure analysis for multicomponent FM signals, an IF(Instantaneous Frequency) estimation method based on the Viterbi algorithm is proposed. Inspired by the idea of track association in multi-target tracking, combining IF trajectory tracking with multi-target tracking, a new membership penalty function is introduced to broaden adaptability and the proposed method performs better than the original VA which is only applicable to single-component signals. Furthermore, a new TF(Time-Frequency) cross-point processing method is proposed to improve the estimation accuracy of multicomponent signals. According to simulation results, the proposed method can obtain accurate IF information on multicomponent signals with a complex phase structure. Compared to the existing algorithms, it is shown that the proposed algorithm based on the VA improves effectively the applicability and robustness for IF estimation of multicomponent signals.

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Robust target tracking algorithm based on the ELM and discriminative correlation filter
WANG Xinyuan,XIAO Song,LI Lei,JIAO Lingling
Journal of Xidian University    2019, 46 (1): 57-63.   DOI: 10.19665/j.issn1001-2400.2019.01.010
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In order to solve the problem that the tracking results fall into the local minimum easily and the feature extraction process is too slow due to the utilization of deep learning, we study the robust object tracking algorithm based on the Extreme Learning Machine (ELM) and Discriminative Correlation Filter(DCF). Based on the C-COT algorithm, our method improves its feature extraction way and the optimization method for the confidence map. First, a new feature extraction model is designed by using the multi-layer ELM sparse autoencoders to extract the image features efficiently and replacing the original Convolutional Neural Network(CNN). Second, after the feature extraction model, an Online Sequential Extreme Learning Machine(OS-ELM) is used to construct the target rough location estimation model and the multi-peak detection method is used to get the predicted rough location of the target. Third, the search area of the confidence map is determined according to the preliminary target location to avoid the tracking result getting into the local minimum. Finally, the effectiveness of the proposed algorithm is tested on three visual tracking benchmarks. Experimental results show that the proposed algorithm is robust to occlusion, motion blur and similar targets and has a tracking speed of 12.9 times that of the C-COT, effectively improving the tracking accuracy and speed.

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Robust support vector machines and their sparse algorithms
AN Yali,ZHOU Shuisheng,CHEN Li,WANG Baojun
Journal of Xidian University    2019, 46 (1): 64-72.   DOI: 10.19665/j.issn1001-2400.2019.01.011
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Based on nonconvex and smooth loss, the robust support vector machine (RSVM) is insenstive to outliers for classification problems. However, the existing algorithms for RSVM are not suitable for dealing with large-scale problems, because they need to iteratively solve quadratic programmings, which leads to a large amount of calculation and slow convergence. To overcome this drawback, the method with a faster convergence rate is used to solve the RSVM. Then, by using the idea of least square, a generalized exponentially robust LSSVM (ER-LSSVM) model is proposed, which is solved by the algorithm with a faster convergence rate. Moreover, the robustness of the ER-LSSVM is interpreted theoretically. Finally, ultilizing low-rank approximation of the kernel matrix, the sparse RSVM algorithm (SR-SVM) and sparse ER-LSSVM algorithm (SER-LSSVM) are proposed for handing large-scale problems. Many experimental results illustrate that the proposed algorithm outperforms the related algorithms in terms of convergence speed, test accuracy and training time.

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Design of sparse MIMO equalizers using least angle regression
YU Lihong,ZHAO Jiaxiang
Journal of Xidian University    2019, 46 (1): 73-78.   DOI: 10.19665/j.issn1001-2400.2019.01.012
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A new scheme for designing sparse finite impulse response (FIR) decision feedback equalizers(DFE) in multiple input multiple output(MIMO) systems based on the Least Angle Regression(LARS) algorithm is proposed. To decrease the number of nonzero taps for FIR DFE and reduce computational complexity, the problem of designing sparse FIR DFE is transformed into an l1-norm minimization approach, and the proposed design scheme is applied to compute the locations and weights of the nonzero taps for sparse FIR DFE iteratively. Simulation results show that when compared with the optimum Minimum Mean Square Error(MMSE) non-sparse solution for a small given performance loss, the number of nonzero taps for the proposed sparse equalizer design is reduced by more than 70%, while the maximum SNR loss for the proposed sparse equalizer is just about 0.3dB in the Vehicular A channel, which results in an effective trade-off between performance and computational complexity.

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Image steganalysis based on the modularized residual network
GUO Jichang,HE Yanhong,WEI Huiwen
Journal of Xidian University    2019, 46 (1): 79-85.   DOI: 10.19665/j.issn1001-2400.2019.01.013
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In order to improve the detection accuracy of small embedding rate steganography, an image steganalysis method based on the highly modularized convolutional neural network is proposed. First, the fundamental network is built by repeating residual network units to extract the complex statistical properties of digital images. Then, extracting the channel information on the residual image by adding the group convolution, it is very good to strengthen the signal characteristics from the hidden information. Finally, a large number of datasets are used to train the network, and the image steganalysis method based on the modularized residual network is obtained. Experimental results show that compared with the existing methods, the proposed method has a better performance, and extracts more effective image features. Meanwhile, using the residual network module as the template, the network model can be easily built to facilitate adjustment and training.

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CFCC feature extraction for fusion of the power-law nonlinearity function and spectral subtraction
BAI Jing,SHI Yanyan,XUE Peiyun,GUO Qianyan
Journal of Xidian University    2019, 46 (1): 86-92.   DOI: 10.19665/j.issn1001-2400.2019.01.014
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This paper presents an improved speech feature extraction algorithm for improving the accuracy of speech recognition in noisy environment. A New Cochlear Filter Cepstral Coefficient(NCFCC) is extracted by the power-law nonlinear function which can simulate the auditory characteristics of the human ear. Then, the spectral subtraction is introduced in the feature extraction front end to enhance the signal, and the new feature and the first order difference are composed of a mixed feature parameter, after which the combined principal component analysis is made to reduce the dimension of the hybrid feature. The final feature is used in a non-specific persons, isolated words, and small-vocabulary speech recognition system. Experimental results show that, compared with the traditional Cochlear Filter Cepstral Coefficients(CFCC) feature, the Cochlear Filter Cepstral Coefficients extracted from the power-law nonlinear function significantly improve the accuracy of speech recognition. The mixed feature parameter can achieve a better speech recognition performance than a single feature. Combined with the feature set of the principal component analysis(PCA) ,the recognition accuracy can reach up to 88.10% when the signal to noise ratio(SNR) is 0 dB.

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Fence human activity recognition optimized algorithm
HU Kelu,WANG Yingguan
Journal of Xidian University    2019, 46 (1): 93-97.   DOI: 10.19665/j.issn1001-2400.2019.01.015
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In order to effectively recognize the human activities on a fence, we optimize the original human activity recognition system by proposing two algorithms: multi-level classification and multi-node fusion. The multi-level classification algorithm eliminates background data without activities in first level classification, reduces the amount of transferred data, and improves the accuracy of the next level. The multi-node fusion algorithm improves the reliability of the results by merging the recognition results of the neighboring nodes. Based on the data of experimental environment, the effectiveness of the algorithm is verified. The data transfer rate and data transmission amount of the multi-level classification algorithm are much lower than those of the benchmark algorithm. The multi-node fusion algorithm removes the redundancy recognition results by up to 67.7%.

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Improved generalized sidelobe cancellation algorithm in ELF communication
LI Chunteng,JIANG Yuzhong,ZHANG Ning,LIU Fangjun
Journal of Xidian University    2019, 46 (1): 98-105.   DOI: 10.19665/j.issn1001-2400.2019.01.016
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In order to improve the communication quality in extremely-low-frequency(ELF) communication, based on the generalized sidelobe cancellation(GSC) method, an improved GSC method is proposed and constructed. First, the delay summation in the main channel is replaced by the linear filtering algorithm, which is beneficial to further improving the suppression ability of incoherent noise. Second, by considering the difference in signal energy among channels, using the optimized blocking matrix can reduce the amplitude of the desired signal and improve the performance, comparing to the original blocking matrix obtained by simple subtraction among the main channels. Finally, the method using linear filtering instead of the original adaptive algorithm can achieve noise cancellation without reducing the sensitivity of the main antenna and improve the algorithm’s operating speed. In order to verify the effectiveness of the proposed algorithm, an experimental platform is set up in laboratory environment and a series of control experiments are designed. Experimental results show that the designed analog circuits can suppress 50 Hz and its harmonic components and that the improved GSC algorithm is better than the original algorithm in terms of improvement of the signal-to-noise ratio(SNR) and the noise floor.

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Estimation algorithm for an underdetermined mixing matrix based on maximum density point searching
WANG Chuanchuan,ZENG Yonghu,FU Weihong,WANG Liandong
Journal of Xidian University    2019, 46 (1): 106-111.   DOI: 10.19665/j.issn1001-2400.2019.01.017
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Aiming at mixing matrix estimation when the source number is unknown for underdetermined blind source separation (UBSS), a mixing matrix estimation method based on maximum density point searching is proposed. Based on sparse representation of observed signals, for the proposed algorithm, preprocessing of observed signals is processed first, and then the maximum density point of each observed signal is searched, after which the effective sample points are assembled, and then the source number and mixing matrix are estimated by the clustering method. For validation of the proposed algorithm, the simulations are developed by employing two sparse representation methods, which are single source point detection in the time-frequency domain and wavelet transform. Results show that the source number and the mixing matrix effect of the proposed algorithm are better than those of the reference algorithm, and that the calculation complexity of the proposed algorithm is much less than that of the reference algorithm. Further tests show that the proposed algorithm is applicable for mixing matrix estimation of positive-determined, overdetermined and underdetermined blind source separation models.

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Design scheme for an all-digital phase locked loop with a high performance
QU Bayi,CHENG Teng,YU Dongsong,LI Zhiqi,ZHOU Wei,LI Shanshan,LIU Lidong
Journal of Xidian University    2019, 46 (1): 112-116.   DOI: 10.19665/j.issn1001-2400.2019.01.018
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Aiming at the fact that a complex scheme is needed when the two frequencies in the phase locked loop are close to each other or have an approximate integer multiple relationship and the traditional analog phase locked loop is unsuitable for integration and on chip implementation, an all-digital phase locked loop is proposed, which is mainly composed of analog to digital converters, an all-digital phase detector, a digital low pass filter and a digitally controlled oscillator. The analog to digital converters’ quantization errors have been greatly suppressed by using the clock cursor effect and digital edge effect and an all-digital phase locked loop with a high performance is achieved. Experiment indicates the correctness of the design scheme and shows that the proposed loop has characteristics of high precision and low noise.

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Robust object tracking via adaptive weight convolutional features
WANG Haijun,ZHANG Shengyan
Journal of Xidian University    2019, 46 (1): 117-123.   DOI: 10.19665/j.issn1001-2400.2019.01.019
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To solve the tracking failure problem in some videos caused by traditional deep learning tracking algorithms with fixed weight convolutional features, this paper proposes a novel tracking method combing the response map and the entropy function which considers the performance of each layer of convolutional neural networks and automatically adjusts the weight parameters. At the same time, an EdgeBoxes detection scheme is introduced when the maximum value of tracking response is less than a given threshold. A great number of bounding boxes are extracted by a sliding window and are evaluated by the EdgeBoxes detection scheme which generates the original proposal bounding boxes. Finally, the tracking method based on the correlation filter are conducted on the original proposal bounding boxes with the update scheme given. We have tested the proposed algorithm and nine state-of-the-art approaches on OTB-2013 video databases. Experimental results demonstrate that the proposed method has a higher precision and overlap rate.

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Constrained weighted least squares algorithm based on the MIMO radar system for target localization
ZHOU Cheng,MAN Xin,ZHOU Zhiwen
Journal of Xidian University    2019, 46 (1): 124-129.   DOI: 10.19665/j.issn1001-2400.2019.01.020
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For the separated MIMO radar system, a constrained weighted least squares algorithm is proposed by using time delay measurements. By introducing the variable which is the distance between the target and the reference station, the target location equation is pseudolinearized and the cost function is derived. And then, by further study, the relationship between the variable and the target position is explored and used as the constraint condition. Finally, the problem of locating the target is transformed from the nonlinear equation into the quadratic program problems with quadratic constraints, and by use of the Lagrange multiplier method, the target position is solved in a closed-form. Simulation results verify that the proposed algorithm can attain the Cramer-Rao Lower Bound in a relatively high level of noise and that it is robust.

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Enhanced multi-objective evolutionary algorithm for workflow scheduling on the cloud platform
WANG Yan
Journal of Xidian University    2019, 46 (1): 130-136.   DOI: 10.19665/j.issn1001-2400.2019.01.021
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The complex and dynamic pricing mechanism raises big challenges to the workflow scheduling on the cloud platform. Considering the prices of the virtualized computing and storage resources, a multi-objective optimization model is developed for the workflow running on a cloud platform. Based on the character of the target problem, a real-coding mechanism is developed for the workflow scheduling problem, so that the crossover operators in a real-coded evolutionary based optimizer can be conveniently employed and the solution repairing step in combinatorial optimization algorithms can be skipped. Following the algorithm framework of the MOEA/D, a local search strategy is designed, and a new multi-objective workflow scheduling algorithm is proposed. Experimental studies have illustrated that the proposed algorithm can obtain Pareto optimal solution sets with better coverage and uniformity than the compared algorithms, which will contribute to improving the utilization of the resources on the cloud platform.

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High-voltage-tolerant ECAN driver embedded in the DSP chip
DONG Gang,HUANG Songren,CHEN Diping,YANG Cuiling,YI Feng
Journal of Xidian University    2019, 46 (1): 137-142.   DOI: 10.19665/j.issn1001-2400.2019.01.022
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A high-voltage-tolerant ECAN (enhanced controller area network) driver embedded in the DSP(digital signal processor) chip is designed by employing SMIC 0.18μm CMOS technology. Based on the CAN(controller area network) bus, stacked high-voltage-tolerant driving technology and floating substrate effects are applied to achieve a high-voltage-tolerant driver in the standard CMOS process, avoiding the cost of the high-voltage process. The port voltage is introduced to the output-driver-control module, decreasing the electrostatic discharge at the output. Furthermore, in this way the electrostatic protection circuit can be spared, thus saving the chip area. The tape-out outcomes indicate that the proposed structure meets the CAN bus communication requirements. And the result of the port ESD(electrostatic discharge) sensitivity test reaches Class-3B, which satisfies the application demands.

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Multimodal emotion recognition for the fusion of speech and EEG signals
MA Jianghe,SUN Ying,ZHANG Xueying
Journal of Xidian University    2019, 46 (1): 143-150.   DOI: 10.19665/j.issn1001-2400.2019.01.023
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To construct an effective emotion recognition system, the emotions of joy, sadness, anger and neutrality are induced by sound stimulation, and the corresponding speech and EEG signals are collected. First, this paper extracts the nonlinear geometric feature and nonlinear attribute feature of EEG and speech signals by phase space reconstruction respectively, and the emotion recognition is realized by combining the basic features. Then, a feature fusion algorithm based on the Restricted Boltzmann Machine is constructed to realize multimodal emotion recognition from the perspective of feature fusion. Finally, a multimodal emotion recognition system is constructed through decision fusion by using the quadratic decision algorithm. The results show that the overall recognition rate of the multimodal emotion recognition system constructed by feature fusion is 1.08% and 2.75% higher than that of speech signals and that of EEG signals respectively, and that the overall recognition rate of the multimodal emotion recognition system constructed by decision fusion is 6.52% and 8.19% higher than that of speech signals and that of EEG signals respectively. The overall recognition effect of the multimodal emotion recognition system based on decision fusion is better than that of feature fusion. A more effective emotion recognition system can be constructed by combining the emotional data of different channels such as speech signals and EEG signals.

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Study of improving the electrical contact performance of carbon nanotubes by doping Au using the deposition method
SUN Mengmeng,CHANG Chunrui,ZHANG Zhiming,ZHANG Yadong,AN Libao
Journal of Xidian University    2019, 46 (1): 151-157.   DOI: 10.19665/j.issn1001-2400.2019.01.024
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The effect of doping metal gold into carbon nanotubes (CNTs) by the deposition method on the electrical contact characteristics between CNTs and metal electrodes is studied. First, defects and oxygen-containing functional groups are made by acid reflux on the surface of CNTs, and the CNT dispersion is prepared. Then, the gold sol solution is prepared by mixing the sodium citrate and chloroauric acid, and the prepared CNT dispersion is dropped into the gold sol solution to produce the samples of gold nanoparticle-doped CNTs by oscillating deposition. Scanning electron micrographs and infrared absorption spectra reveal that the acid treatment is successful in constructing some defects and hydrophilic functional groups on the walls and ends of CNTs. The morphological characterization and X-ray photoelectron spectroscopy demonstrate that gold nanoparticles are successfully doped on the surface and ends of CNTs. The increase in the G-band wavenumber of the Raman spectrum of the gold-doped CNTs indicates that the doping type is the p-type. Finally, dielectrophoresis is used to assemble the original and gold-doped CNTs between the gold electrodes, and the contact resistance is measured in real time. The results show that Au doping by the deposition method can reduce the contact resistance between CNTs and gold electrodes, and that the average resistance decrease is 71.49%.

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SDN-based optimal security service path construction mechanism
LIU Yicen,CHEN Xingkai,LU Yu,QIAO Wenxin
Journal of Xidian University    2019, 46 (1): 158-165.   DOI: 10.19665/j.issn1001-2400.2019.01.025
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In view of the fact that existing security service path optimization methods lack a comprehensive consideration of the specific security requirements and the underlying resource status, a dynamic construction mechanism of security service path based on the heuristic breadth first search algorithm is proposed. First, the overall structure of the dynamic construction of the security service path based on the software-defined networking is given, and the integer linear programming is introduced to model this problem. Second, a model solving algorithm is proposed, which mainly adopts the "first select after search" method to solve the security service path construction problem which considers both the specific security needs and the underlying resource status. Finally, simulation results show that the proposed construction mechanism is better than the compared method in terms of the performance index.

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Algorithm for cooperational localization of the sectional interval and LOS node in a coal mine
ZHAO Tong,LI Xiansheng,ZHANG Lei,DING Enjie,HU Yanjun
Journal of Xidian University    2019, 46 (1): 166-173.   DOI: 10.19665/j.issn1001-2400.2019.01.026
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To overcome the problem of serious signal fluctuation and Non-line-of-sight signal attenuation with long distance positioning in a coal mine, a segmentation node cooperative localization algorithm is proposed. By using the learning vector quantization clustering to segment the long distance transmission interval, the threshold is used to select the range for an unknown node. We think of the unknown node that has been located as the virtual reference node of other unknown nodes, so that all node information can communicate with each other. In the idea of node screening, the multi-path effects are overcome and line-of-sight nodes are searched by channel state information. The reference nodes in the long range are replaced by the optimally located line-of-sight nodes in the close range. Results show that compared with the traditional unsegmented and unfinished line-of-sight path nodes, the positioning error is only 1.5m and the accuracy improvement rate is 85%.

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Improved cuckoo search algorithm for optimizing the beam patterns of linear antenna arrays
LIANG Shuang,SUN Geng,LIU Yanheng
Journal of Xidian University    2019, 46 (1): 174-180.   DOI: 10.19665/j.issn1001-2400.2019.01.027
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To solve the problems of sidelobe level (SLL) suppression and nulls control of the linear antenna arrays (LAA), a spread variation cuckoo search (SVCS) algorithm is proposed. First, the SVCS uses the aggregated diffusion strategy to improve the possibility of obtaining the global optimal solutions of the algorithm. Second, the gene mutation method of the genetic algorithm is introduced to improve the population diversity so as to avoid the algorithm falling into the local optima. Simulation results show that the proposed SVCS has a better performance in terms of the convergence rate and accuracy compared with the firefly algorithm, the particle swarm optimization, the conventional cuckoo search algorithm, the monarch butterfly optimization algorithm and the earthworm optimization algorithm for reducing the SLL of the LAA. Moreover, the SVCS also has a better performance for solving the joint optimization problem of SLL suppression and nulls control compared with the above mentioned benchmark algorithms.

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Design of serial connecting multiple spatially coupled LDPC codes for block-fading channels
SUN Yue,LI Beilei,LIANG Caihong,LI Ying
Journal of Xidian University    2019, 46 (2): 1-5.   DOI: 10.19665/j.issn1001-2400.2019.02.001
Accepted: 20 April 2019
Online available: 18 April 2019

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To improve the performance of spatially coupled low density parity check (SC-LDPC) codes over block-fading channels, a serial connecting multi-SC-LDPC (SC-MSC-LDPC) codes is proposed based on the structure characteristics of the SC-LDPC codes, which can lead to full diversity. By exchanging the edge connections of variable nodes at the same location of each subchain, multiple subchains are coupled to form the codes with a strong correlation between different fading blocks, which can effectively avoid the outage occurrence caused by deep fading blocks. The infinite-length performance is analyzed by utilizing the protograph-based extrinsic information transfer (PEXIT) algorithm. Furthermore, the finite-length performance is obtained via belief propagation (BP) decoding. Simulation results show that the proposed SC-MSC-LDPC codes improve the performance of SC-LDPC codes over the block-fading channels.

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Cross-tier cooperation resource allocation for interference minimization in ultra-dense networks
ZHENG Chuangming,LIU Longwei,ZHANG Hailin,LI Yongzhao
Journal of Xidian University    2019, 46 (2): 6-11.   DOI: 10.19665/j.issn1001-2400.2019.02.002
Accepted: 20 April 2019
Online available: 18 April 2019

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Ultra-dense networks (UDN) can maximize spectrum utilization, Nevertheless, the unpredictable and overwhelming inter-cell interference (ICI) constitutes a new challenge. To tackle this problem, a cross-tier cooperation resource allocation for interference minimization (CCRA-IM) is proposed, which is adapted to the dynamic payload of the UDN. First, every piece of cell sub-band interference information is exchanged by the aid of cross-tier cooperation. Then the CCRA-IM allocates the high-power users to the physical resources with the lower sub-band interference of neighboring cells, which results in a decrease in the ICI. When the cell traffic is partially loaded, the CCRA-IM tries its best to reduce the power spectral density of the high-power users by allocating them more resources, which can further mitigate the ICI. Simulation results show that the CCRA-IM can work well to mitigate the ICI of the network efficiently and improve the energy efficiency in the dynamic payload of the UDN.

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Adaptive target birth δ-generalized labeled multi-Bernoulli filtering algorithm
LI Cuiyun,CHEN Dongwei,SHI Renzheng
Journal of Xidian University    2019, 46 (2): 12-16.   DOI: 10.19665/j.issn1001-2400.2019.02.003
Accepted: 20 April 2019
Online available: 18 April 2019

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Aiming at the problem that the standard δ-generalized labeled multi-Bernoulli (δ-GLMB) filter requires a priori knowledge of target birth distributions, which leads to the reduction of estimation accuracy in a real world scenario, an adaptive target birth δ-GLMB filtering algorithm is proposed. Based on the δ-GLMB filter, the new algorithm approximates the existence probabilities and kinematic states of birth targets using measured data from the previous time, and provides parameterized representations of labeled Bernoulli random finite sets of new birth targets in the current time. Simulation results indicate that the proposed algorithm has a strong robustness, and a better performance in tracking accuracy and time consumption than the standard δ-GLMB filtering algorithm under the unknown priori knowledge of the birth targets complex scenario.

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Improved method for image caption with global attention mechanism
MA Shulei,ZHANG Guobin,JIAO Yang,SHI Guangming
Journal of Xidian University    2019, 46 (2): 17-22.   DOI: 10.19665/j.issn1001-2400.2019.02.004
Accepted: 20 April 2019
Online available: 18 April 2019

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Aiming at the lack of global information in existing attention based image caption methods, we propose an improved image caption method with global attention mechanism. The proposed method mimics the entire human perception process via designing a global feature extraction network to enhance the global context based on visual attention mechanism. This paper compares the proposed method with the existing attention based image caption technique under the same dataset and hyper parameters, and analyzes the influence of introducing the global feature. The results show that our method outperforms the existing technique in objective evaluations with the challenging Chinese caption dataset. In the subjective evaluation, in the meanwhile, the captions generated by the proposed method describes the image more accurately, vividly and diversely, and they are more close to the natural language.

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Array thermal deformation error calibration method for spaceborne radar
HUO Lihuan,LIAO Guisheng,YANG Zhiwei,XIN Jinlong
Journal of Xidian University    2019, 46 (2): 23-28.   DOI: 10.19665/j.issn1001-2400.2019.02.005
Accepted: 20 April 2019
Online available: 18 April 2019

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In order to improve the accuracy of the thermal deformation error estimation for spaceborne phased arrays, an array error estimation method is proposed which combines the measurement and the signal processing. In the proposed method, the deformation order and initial positions are obtained from the measurement. Then the echo data can be obtained from the range-Doppler images. The optimization model is established based on the deformation characteristics. The sensor positions can be corrected with high accuracy in the iteration. Several simulated results show that the proposed method can obtain estimation better than one-fifteenth the wavelength and is still robust under the large deformation error condition. The array pattern is compensated well in the simulation.

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Radar HRRP target recognition by the bidirectional LSTM model
XU Bin,CHEN Bo,LIU Jiaqi,WANG Penghui,LIU Hongwei
Journal of Xidian University    2019, 46 (2): 29-34.   DOI: 10.19665/j.issn1001-2400.2019.02.006
Accepted: 20 April 2019
Online available: 18 April 2019

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As the traditional recognition methods utilize the envelope information of the high resolution range profile (HRRP) without considering the temporal correlation between range cells, a novel recognition method based on the bidirectional long short term memory (BLSTM) is proposed. First, the method extracts the time-shift robust input representation by figuring out the target areas. Then, to consider the bidirectional correlation between range cells, a bidirectional long short term memory is utilized to extract the bidirectional information from the input representations. Finally, the model uses a voting mechanism to combine the bidirectional information and predict the labels. Experimental results based on measured data show that the proposed model is efficient in recognition and robust to the time-shift sensitivity.

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Lossless high compression ratio circuit design
ZHU Jia,LIU Hongxia
Journal of Xidian University    2019, 46 (2): 35-40.   DOI: 10.19665/j.issn1001-2400.2019.02.007
Accepted: 20 April 2019
Online available: 18 April 2019

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In order to save the bandwidth and meet the compression requirement of a real-time system, a novel hardware compression circuit based on the deflate algorithm is proposed. Dual hash functions with four columns parallel match processing and a static Huffman encoder are employed to accelerate the compression speed and improve the compression ratio. The compression circuit is implemented with System Verilog, verified by the FPGA, and applied in the trace module in the baseband chip, with the area of the compression module being 0.022mm 2. Test results show that the compression ratio of the hardware circuit reaches 56.68%. The circuit average bandwidth of compression reaches 1039M bit/s, which can satisfy the real-time compression of the baseband trace system.

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Design and realization of a high-precision and low temperature drift reference circuit
LIU Xiaoxuan,ZHANG Yuming,JI Qingzhou,CAO Tianjiao
Journal of Xidian University    2019, 46 (2): 41-46.   DOI: 10.19665/j.issn1001-2400.2019.02.008
Accepted: 20 April 2019
Online available: 18 April 2019

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A novel bandgap reference circuit with a low temperature coefficient is presented, which compensates the voltage slightly and optimizes the temperature characteristic by setting up a subsection compensation circuit. The circuit and its layout have been done by the 3μm 18V Bipolar process in the No.771 Institute. Simulation and fabrication results show that the temperature coefficient of the voltage reference is 1.7×10 -6~6.0×10 -6/℃ at -55℃~125℃ under the condition of the 2.2V wide input voltage range, and that the circuit possesses the power supply rejected characteristic of 0.03 mV/V. This circuit and its layout have been successfully applied to a low-dropout regulator.

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Algorithm for intelligent and efficient parallel rostering of nurses
WANG Zhi,LI Yanni
Journal of Xidian University    2019, 46 (2): 47-53.   DOI: 10.19665/j.issn1001-2400.2019.02.009
Accepted: 20 April 2019
Online available: 18 April 2019

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Nurse rostering problem, NRP is an NP-hard optimization problem with multiple constraints. Good nurse rostering is of great significance to improve the efficiency of nurses’ work and optimize the allocation of human resources in hospitals. However,most existing NRP algorithms not only suffer from a non-effective balance between computational time and solution quality, but also deal with the large-scale NRP with difficulty. Motivated to overcome the above problem, in this paper, an intelligent and efficient two-steps parallel nurse rostering algorithm IEPNR is presented with some intelligent and efficient optimization strategies. First, the IEPNR adopts a heuristic method to sort initial stochastic solutions to obtain its high-quality initial solution. Then, a novel intelligent parallel diversified variable neighborhood search and incremental computing strategy are used to quickly obtain optimal solutions. Meanwhile, the random disturbances and a taboo list are introduced to efficiently escape from local optimal solutions and to avoid redundant calculations. Extensive experiments on the benchmarks show that the proposed algorithm IEPNR outperforms the state-of-the-art algorithms in the average solution quality and running time, and that it is more suitable for the large-scale NRP.

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Fatigue lifetime analysis of the electronic packaging structures under the combined thermal stress and vibration loading conditions
ZHAO Fubin,QIU Yuanying,JIA Fei,Ma Hongbo
Journal of Xidian University    2019, 46 (2): 54-60.   DOI: 10.19665/j.issn1001-2400.2019.02.010
Accepted: 20 April 2019
Online available: 18 April 2019

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The research object is a substrate which includes a plastic ball grid array package structure and single power level conversion chips. In order to calculate its fatigue life under thermal vibration coupling loading conditions, an approach to calculating fatigue lifetime under coupled loads is developed based on the incremental damage superposition theory. This approach adopts the Anand unified viscoplasticity theory to describe the mechanical behavior of the solder material under the thermal cycle load condition, and adopts the Coffin-Manson equation adjusted by the thermal cycle load frequency to calculate the fatigue lifetime. The random vibration damage in different temperature environments is calculated. Taking the time fraction of each temperature in the thermal cycle load as the weight, the random vibration damage is averaged. The total damage is obtained by superposing the average damage of random vibration and thermal cycle damage, and then the fatigue life is obtained. The result shows that the fatigue lifetime of the research object meets the use requirements under the specific combined thermal stress and vibration loading conditions.

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Successive missing completion based on deep fusion from multiple views
MAO Yingchi,ZHANG Jianhua,CHEN Hao
Journal of Xidian University    2019, 46 (2): 61-68.   DOI: 10.19665/j.issn1001-2400.2019.02.011
Accepted: 20 April 2019
Online available: 18 April 2019

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Aiming at the shortcomings of existing successive missing complement methods, a successive missing data completion method for multi-view depth fusion is established. The method adopts inverse distance weighted interpolation, bidirectional simple exponential smoothing, user-based collaborative filtering, the collaborative filtering based on mass diffusion and structure embeddings, to obtain intermediate results of five missing data in spatiotemporal and semantic respectively; then, this method constructs a neural network model that combines complementary heterogeneous information across time and space and semantic views to achieve successive missing completion. Experimental results show that the method is universally applicable to the field of Spatial-Temporal successive missing sequence completion and, that it not only achieves a high efficiency, but also reduces the mean absolute error and the mean relative error by 7% and 22%, respectively, compared with the Spatial-Temporal Multi-view completion method.

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Joint robust design of the space-time code and receive filter for multiple-input multiple-output radar
WANG Hongyan,QIAO Huijiao,PEI Bingnan
Journal of Xidian University    2019, 46 (2): 69-77.   DOI: 10.19665/j.issn1001-2400.2019.02.012
Accepted: 20 April 2019
Online available: 18 April 2019

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For the issue that the detection performance of multiple-input multiple-output (MIMO) radar based space-time adaptive processing (STAP) is sensitive to the parameter estimation error such that the detection probability robustness is rather poor, a joint robust design method for the transmit waveform and receive weight is proposed. The mathematical expression for the output signal-inference-noise ratio (SINR) of MIMO-STAP is firstly derived, and then the estimation error model of the space-time-frequency steering vector of the target can be constructed. With the constraints of the obtained error model, waveform constant modulus property, as well as sidelobe and clutter suppression, under the criterion of maximizing the worst-case output SINR, the joint robust optimization problem of transmit waveform covariance matrix and receive weight is formulated to improve the detection probability of MIMO-STAP. To solve the resultant sophisticated nonlinear optimization issue, an iterative approach is developed here. Each step of the developed method can be recast as a semidefinite programming problem, and hence it can be solved effectively. Compared to the existing state-of-the-art robust and non-robust methods as well as uncorrelated waveforms, the proposed method, it is shown by simulation, can improve the robustness of the detection probability of MIMO-STAP considerably.

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Land earth-pole of the HVDC remote control system
TANG Jie,AO Shaopeng,YUAN Wenjun,LIN Peifei,WANG Yang
Journal of Xidian University    2019, 46 (2): 78-82.   DOI: 10.19665/j.issn1001-2400.2019.02.013
Accepted: 20 April 2019
Online available: 18 April 2019

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With the increasing development of mobile communication technology, the application of remote control systems is constantly creating values for production and life. For the case where the equipment is placed in more remote locations, the application of remote control systems is capable of reducing costs. In this paper, we develop a land earth-pole of the high-voltage direct current (HVDC) remote control system. In particular, through the mobile phone and Global System for Mobile Communications (GSM) module short message communication, we are able to achieve information transmission in the micro-control unit for the legitimacy of the information passed after the resolution of the current command. After that, a pulse signal is generated in order to control the stepper motor by outputting the required torque to achieve the brake opening and closing.

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