Loading...
Office

Table of Content

    20 February 2023 Volume 50 Issue 1
      
    Challenges of and key technologies for the air-space-ground integrated network
    CUI Xinyu, WU Jie, ZHOU Yiqing, LIU Ling, PAN Zhengang
    Journal of Xidian University. 2023, 50(1):  1-11.  doi:10.19665/j.issn1001-2400.2023.01.001
    Abstract ( 1885 )   HTML ( 653 )   PDF (4215KB) ( 681 )   Save
    Figures and Tables | References | Related Articles | Metrics

    The air-space-ground integrated network has been regarded as one of the enable technologies in the future to provide consistent communication experience all over the world.Based on the integration of the air,space and ground network,the area with no communication coverage can be eliminated and interconnection between remote areas can be strengthened.However,the current air-space-ground integrated network has only achieved the preliminary application in some special scenarios,and there are still many challenges to achieve the goal of eliminating the blind area and strengthening the connection.This paper first outlines the structure of the future air-space-ground integrated network.Then,four major challenges encountered in the network integration process are analyzed from the perspectives of hardware,protocols,node deployment and service assurance,including the difficulty of network function evolution raised by dedicated hardware,the low performance of current satellite communication protocol,the deployment of the flying base station in a broad 3D area and the differentiate quality guarantee of various services.Finally,four typical solutions to overcoming the above challenges are summarized to provide reference for the future air-space-ground integrated network,including light-weighted network function virtualization in the satellite,5G based satellite communication system,deployment of the flying base station based on horizontal and vertical decoupling and end-to-end integrated network slicing.

    Virtual signal decomposition based multiple access method
    LI Zhao,HU Jiaojiao
    Journal of Xidian University. 2023, 50(1):  12-18.  doi:10.19665/j.issn1001-2400.2023.01.002
    Abstract ( 532 )   HTML ( 68 )   PDF (1629KB) ( 154 )   Save
    Figures and Tables | References | Related Articles | Metrics

    Co-channel interference (CCI) is a key factor that impedes the improvement of a wireless communication system’s performance.Existing signal processing based multi-user transmission schemes avoid CCI by adjusting the interfering signals.However,they may degrade the performance of data transmissions carried in the adjusted signals.By exploiting interactions among wireless signals,a virtual signal decomposition based multiple access (VSDMA) method is proposed.By employing a coordinate transmitter (Tx) and letting it send a coordinate signal to interact with signal components originating from multiple interfering Txs at the common receiver (Rx),the Rx can recover multiple orthogonal desired signals without interference.The proposed method can not only avoid the degradation of desired transmission’s quality incurred by adjusting the transmitted signal at the Tx but also circumvent the problem of the desired signal’s power loss at the common Rx while it suppresses interference.Simulation results have shown that compared to existing schemes,given a signal-to-noise ratio greater than 0dB,our method can improve the spectral efficiency (SE) and power normalized SE of the multi-user communication system by over 10%.

    TDOA-FDOA passive location algorithm using gauss-newton iteration
    TANG Jianlong, XIE Jialong, XUE Chengjun
    Journal of Xidian University. 2023, 50(1):  19-28.  doi:10.19665/j.issn1001-2400.2023.01.003
    Abstract ( 601 )   HTML ( 41 )   PDF (2883KB) ( 134 )   Save
    Figures and Tables | References | Related Articles | Metrics

    To address the non-convergence problem of the traditional Gauss-Newton iterative method in time difference of arrival (TDOA) and frequency difference of arrival (FDOA) location due to the inaccurate iterative initial value,a Gauss-Newton iterative algorithm based on the constrained weighted least square (CWLS) is proposed.First,the nonlinear positioning equation in the positioning problem is transformed into a set of pseudo-linear equations about the target position and velocity.Initial values of the target position and velocity are estimated step by step.In order to realize the accurate estimation of the initial value,the equality constraint relationship between the target position and the auxiliary variable are relaxed to the second-order cone programming (SOCP) condition.The stochastic robust least square (SRLS) is introduced to construct a new linear relation.When the weighted least square solution does not meet the SOCP condition,the semi-definite programming (SDP) is used to solve the estimated solution of the target position.The target velocity is solved by the obtained target position.After obtaining the initial values of the target parameters,the Gaussian Newton iterative equations for the target position and velocity in the TDOA-FDOA localization system are established.Target parameters are solved by the Gaussian Newton iterative process,which does not require the introduction of auxiliary parameters and can directly obtain the target parameters.Simulation experiments show that the proposed algorithm has a good localization effect on both near-field and far-field targets,and that its robustness and high localization accuracy are better than those of the existing classical two-stage weighted algorithm.At the same time,simulation results show the necessity of optimizing the initial values when the Newton iterative equations are used.

    Multiple access method using a relaxed orthogonal precoder
    LIU Chengyu, ZHANG Bigui, LI Zhao
    Journal of Xidian University. 2023, 50(1):  29-35.  doi:10.19665/j.issn1001-2400.2023.01.004
    Abstract ( 433 )   HTML ( 20 )   PDF (2107KB) ( 99 )   Save
    Figures and Tables | References | Related Articles | Metrics

    In medium access control (MAC) method design,the effective management of co-channel interference (CCI) among multiple concurrent transmissions is critical to enhancing a system’s spectrum efficiency (SE).The main idea of existing interference management (IM) methods is to make multiple signals orthogonal to each other during their transmissions.However,in the multi-user multiple input multiple output (MU-MIMO) system,the numbers of transmitters and receivers and their antenna settings highly restrict the number of orthogonal concurrent data transmissions that the system can support.To remedy such deficiency,this paper proposes a relaxed orthogonal precoder based multiple access (ROPMA) method for MU-MIMO downlink transmission.By introducing an orthogonal amplitude tuning function to adjust the precoder’s amplitude in a symbol duration,and then using the adjusted precoder for signal processing and transmission,the requirement of strict orthogonality of multi-user signals in the spatial domain can be relaxed to the orthogonality in the sense of correlation.Accordingly,the receiver employs correlation based desired signal detection;by exploiting the orthogonality of amplitude tuning functions,the influence of the CCI can be eliminated at the decision time.Our simulation results have shown that the proposed method can significantly increase the number of users that the system can accommodate and improve the system’s SE without consuming the extra spectrum resource.

    Vehicle-target detection network for SAR images based on the attention mechanism
    ZHANG Qiang, YANG Xinpeng, ZHAO Shixiang, WEI Dongdong, HAN Zhen
    Journal of Xidian University. 2023, 50(1):  36-47.  doi:10.19665/j.issn1001-2400.2023.01.005
    Abstract ( 667 )   HTML ( 43 )   PDF (4278KB) ( 171 )   Save
    Figures and Tables | References | Related Articles | Metrics

    In the processing of vehicle-target detection in synthetic aperture radar (SAR) images,the contours of vehicles not only provide their position but also represent their condition,which is a key to SAR image understanding.But the multiplicative speckle noise in SAR images interferes with the border positioning of vehicles,resulting in difficulties for vehicle-target detection.To solve this problem,the present paper proposes an attention-mechanism-based neural network for pixel level vehicle detection,which consists of a target filtering module,a target locating module and a contour refining module.The target filtering module contains a lightweight feature extraction network with a channel-attention and self-attention mechanism to enhance feature expression.This module can decrease the effect of the speckle on features to select images containing the target quickly and precisely,and provide the output stable location heat map for the next module.The target locating module uses the foreground-background cross-attention mechanism to refine the coarse-scale features in accordance with the location heat map and refine the target location.Furthermore,the module adopts the fine-scale information to improve the details of the target contour.The contour refining module eliminates the contour uncertain points caused by upsampling and speckle noise to obtain accurate contour pixel confidence.For testing this network,a target image dataset and a large-scene image dataset are built with the pixel-level vehicle annotation of the dataset labeled by ourselves.The result of testing indicates that the network has a good pixel-level detection performance and can detect vehicle targets in large SAR images rapidly and accurately.

    Weakly-supervised salient object detection with the multi-scale progressive network
    LIU Xiaowen, GUO Jichang, ZHENG Sida
    Journal of Xidian University. 2023, 50(1):  48-57.  doi:10.19665/j.issn1001-2400.2023.01.006
    Abstract ( 432 )   HTML ( 20 )   PDF (4648KB) ( 120 )   Save
    Figures and Tables | References | Related Articles | Metrics

    Existing weakly-supervised salient object detection methods often suffer from problems such as false positive,low recall rate,and unclear edges.To address the above issues,a weakly-supervised salient object detection with the multi-scale progressive network is proposed,which divides salient object detection into three sub-tasks:object localization,saliency region improvement and edge refinement.First,the input image is sampled into three images of different scales,which are respectively fed into the three stages of the multi-scale progressive network for learning.Second,in order to better locate the salient objects,a nested shift multi-layer perceptron is proposed in the object localization stage,which can balance the global feature and local feature extraction ability of the network.Finally,according to the characteristic that the structure of saliency maps is not affected by scale changes,a multi-scale self-supervision module and an object consistency loss are designed to build a self-supervision mechanism,so that the network can output a saliency map with complete regions and sharp edges.The proposed method is tested on five datasets,and outperforms the recent weakly-supervised methods in both quantitative and qualitative comparisons,and can reach 89% of the performance of the related fully-supervised methods on the F-measure index.Experimental results show that the proposed algorithm can generate saliency maps with complete saliency regions and sharp edges,and has good robustness.

    Performance of APD-based OFDM-UWOC system with partially coherent beams
    SONG Yatong, LI Shuang, LI Ganggang, GU Shizhong, WANG Ping, GUO Lixin
    Journal of Xidian University. 2023, 50(1):  58-65.  doi:10.19665/j.issn1001-2400.2023.01.007
    Abstract ( 202 )   HTML ( 13 )   PDF (927KB) ( 68 )   Save
    Figures and Tables | References | Related Articles | Metrics

    In practical underwater wireless optical communication (UWOC) system,turbulence and receiver noise are two important factors that limit the system performance.In this paper,with shot and thermal noise,the average bit error rate (ABER) performance of orthogonal frequency division multiplexing (OFDM) assisted UWOC system based on the avalanche photodiodes (APD) over the weak oceanic turbulence condition has been investigated.To effectively characterize the impacts of absorption,scattering and misalignment as well as underwater optical turbulence,a composite fading model has been adopted in this work where beam spread function (BSF) is utilized.After that,the analytical expressions of ABER in shot-noise-limited and thermal-noise-limited conditions are derived,respectively.Under shot-noise-limited condition,in this paper we analyze the ABER performance affected by various parameters,specifically,receiver noise temperature,average optical signal intensity and APD gain.Similarly,under thermal-noise-limited condition with partial coherent beams (PCB) being adopted,the specific impact of underwater optical turbulence on the reliability of the considered UWOC system is also illustrated.Numerical results reveal that,for the internal factors of the photodetector,the receiver temperature and average optical signal intensity will affect the ABER performance of the system.Furthermore,choosing an appropriate APD gain can obtain a better ABER performance of the UWOC system.Moreover,PCB and the underwater turbulence will affect the ABER performance.

    Trajectory optimization method of the DF-UAV relay broadcast communication system
    LI Dongxia,SONG Siyu,LIU Haitao
    Journal of Xidian University. 2023, 50(1):  66-75.  doi:10.19665/j.issn1001-2400.2023.01.008
    Abstract ( 210 )   HTML ( 9 )   PDF (1334KB) ( 83 )   Save
    Figures and Tables | References | Related Articles | Metrics

    An unmanned Aerial Vehicle(UAV) relay broadcast communication system can provide a high quality service and multiple business assistances for users.UAV trajectory optimization is one of the critical technical problems of the UAV relay communication system.The trajectory optimization methods are investigated to improve the link transmission performance of the UAV relay broadcast communication system with the decode-and-forward protocol.First,the UAV relay broadcast communication system model is established.The calculation formula for the interruption probability of a user link and that for the average interruption probability of the communication system are given.After that,a trajectory optimization method based on the criterion of minimizing the maximum outage probability for all users is proposed,and the mathematical expression for the accurate ergodic capacity of the UAV relay broadcast communication system is theoretically derived.Finally,the influence of system characteristic parameters such as service area radius,maximum turning angle,path loss factor and transmitting power on the optimal track,system interruption performance and system capacity performance of the relay UAV is verified by computer simulation.Meanwhile,the system interruption performance and system capacity performance of two relay forwarding protocols are compared under the same optimization criteria.Simulation results show that the outage performance and capacity performance of the decode-and-forward protocol are significantly improved compared with the amplify-and-forward protocol,which verifies the effectiveness of the proposed trajectory optimization criterion.

    Handover algorithm for a high-speed railway based on the LSTM recurrent neural network
    CHEN Yong,NIU Kaiyu,KANG Jie
    Journal of Xidian University. 2023, 50(1):  76-84.  doi:10.19665/j.issn1001-2400.2023.01.009
    Abstract ( 323 )   HTML ( 18 )   PDF (2790KB) ( 102 )   Save
    Figures and Tables | References | Related Articles | Metrics

    In the process of high-speed railway train operation,in order to maintain uninterrupted communication,the train needs to constantly carry out handover with the base station.As a key technology of LTE-R communication,handover is crucial to ensuring traffic safety.Aiming at the low success rate of handover in the next generation high-speed railway LTE-R wireless communication system due to the fixed hysteresis threshold parameters,a high-speed railway handover algorithm based on the LSTM recurrent neural network is proposed.First,by using the memory characteristics of the LSTM neural network and the temporal and spatial correlation characteristics of high-speed railway handover overlapping area signals,a deep learning network for dynamic prediction of handover hysteresis threshold parameters based on the LSTM recurrent neural network is constructed.Second,through the proposed LSTM deep learning model,the handover hysteresis parameters are trained offline and predicted online to obtain the handover threshold value at the future time,which realizes the adaptive prediction of handover hysteresis parameters during high-speed train driving,and overcomes the disadvantage of fixed hysteresis threshold parameters.Finally,simulation results show that the proposed method can effectively improve the handover success rate and reduce the impact of the ping-pong handover rate compared with the traditional A3 algorithm and other comparison algorithms.Research results provide a certain theoretical reference for high-speed railway traffic safety and LTE-R evolution.

    Automatic frequency-calibrated low-pass filter with DC offset cancellation
    YANG Haohan, QIAO Shushan
    Journal of Xidian University. 2023, 50(1):  85-92.  doi:10.19665/j.issn1001-2400.2023.01.010
    Abstract ( 252 )   HTML ( 9 )   PDF (2013KB) ( 88 )   Save
    Figures and Tables | References | Related Articles | Metrics

    In order to improve the LO leakage caused by the DC offset of the analog baseband signal in the direct conversion transmitter,and the drift of the low-pass filter due to process deviation,temperature change,and chip aging,a method of design and implementation of an automatic frequency-calibrated low-pass filter with DC offset cancellation is proposed.In the SPI(Serial Peripheral Interface) control mode,the filter can configure the cut-off frequency from 324 kHz to 648 kHz through the adjustable capacitor array.In the adaptive tuning mode,a digital-analog hybrid calibration structure based on the latched comparator is proposed.By calibrating the RC time constant of the chip,the cut-off frequency is 373.8~393.3 kHz under different PVT conditions,and the tuning accuracy relative to the nominal value of 383 kHz is -2.4%~2.7%.The chip uses 0.18 CMOS technology for tape-out verification,and the tuning circuit area is 0.045 mm2,which is only 2.2% of the main filter area.Under the 1.8V supply voltage,the power consumption of the whole filter is 6.08 mW,and the input reference noise is 38.49 nV/(Hz)1/2.

    System delay compensation method for PMSM sensorless control
    LI Wenzhen,LIU Jinglin
    Journal of Xidian University. 2023, 50(1):  93-101.  doi:10.19665/j.issn1001-2400.2023.01.011
    Abstract ( 276 )   HTML ( 11 )   PDF (3706KB) ( 90 )   Save
    Figures and Tables | References | Related Articles | Metrics

    A position observation strategy considering system delay compensation is presented to avoid the position estimation error caused by analog and digital delay of the permanent magnet synchronous motor drive.First,a typical sliding-mode observer is established and the accessibility of sliding mode motion is analyzed.Second,the influence of analog circuits on the stator current is studied,and the transfer functions of analog circuits are derived.The total delay phase caused by the sampling circuit on the stator current is calculated.Third,the current error from digital implementation of the sliding-mode observer is analyzed.Fourth,a direct signal compensation method used for eliminating the influences of the current error is presented.The proposed strategy lowers the detection error of the sliding-mode observer and the transient process time in closed-loop control.Finally,the proposed strategy is experimentally verified to confirm its merits on system delay effect elimination and system control performance improvement.

    GaAs bidirectional true time delay chip design
    HAO Dongning,ZHANG Wei
    Journal of Xidian University. 2023, 50(1):  102-108.  doi:10.19665/j.issn1001-2400.2023.01.012
    Abstract ( 353 )   HTML ( 12 )   PDF (3451KB) ( 118 )   Save
    Figures and Tables | References | Related Articles | Metrics

    An X/Ku-band bi-directional True-time delay is presented in 0.25 μm GaAs pHEMT E/D technology.The bi-directional operation is realized by adding two-way selecting switches to the new trombone structure,and it has the advantage of low insertion loss variation among different delay states.Fourth-order and second-order inductive coupled all-pass networks are adopted to form two delay lines.The delay differences are controlled by selecting the delay path through the bi-directional active switches.The true-time delay operates with the bandwidth of 6~18 GHz,and it realizes a 3-bit delay with a minimal delay step of 15 ps and a maximal delay of 106 ps.Simulation results show insertion loss of 8.1~15 dB and the loss variation with delay is ±2 dB.The group delay Root-Mean-Square error less than 10 ps and return loss more than 15 dB are implemented with the chip size of 1.91 mm2.The direct current consumption is 110 mW and the input P1dB is more than 7 dBm.

    Research on node diagnosis under the Symmetric PMC(SPMC) model
    LIU Sanyang,DANG Tuo,BAI Yiguang
    Journal of Xidian University. 2023, 50(1):  109-117.  doi:10.19665/j.issn1001-2400.2023.01.013
    Abstract ( 228 )   HTML ( 9 )   PDF (919KB) ( 67 )   Save
    Figures and Tables | References | Related Articles | Metrics

    Network diagnosis is one of the most exciting topics in graph theory and network science,which affects the reliability and security of multiprocessor systems.With the rapid growth of the scale of the multiprocessor system,the applicability of the global fault diagnosis mode of the system is reduced.Accordingly,the local fault diagnosis benefits from the lower requirements for the network topology,which can process the network in blocks,greatly improving the diagnosis efficiency,having a stronger applicability,and becoming a new research direction.Under the latest Symmetric PMC(SPMC) model,this paper studies the relevant properties of network node diagnosis(local diagnosis),proposes a new topology structure(extended tree structure),obtains the judgment conditions for diagnosable nodes,the relationship between nodes diagnosis and system diagnosis,gives the judgment theorem whether nodes are poor on the extended tree structure,and gives out the detailed proof.According to this theorem,one novel network local fault diagnosis algorithm ST2_B-FDA with the expanded tree structure is proposed.To validate the effectiveness of this algorithm,this paper applies the Hypercube network for simulation.The time complexity of the algorithm is O(NlogN),which is much lower than that of some traditional fault diagnosis algorithms.This algorithm can effectively reduce the diagnosis cost and greatly improve the diagnosis efficiency.In addition,the proposed algorithm is simple in principle,easy to implement and apply,and can also be used as one of the diagnosis methods for large-scale regular network systems.

    Lightweight semantic segmentation network for autonomous driving scenarios
    LIU Bochong, CAI Huaiyu, YANG Shiyuan, LI Haotian, WANG Yi, CHEN Xiaodong
    Journal of Xidian University. 2023, 50(1):  118-128.  doi:10.19665/j.issn1001-2400.2023.01.014
    Abstract ( 277 )   HTML ( 19 )   PDF (2850KB) ( 120 )   Save
    Figures and Tables | References | Related Articles | Metrics

    In the autonomous driving scenario,aiming at the problem of limited memory and insufficient computing power when the semantic segmentation model is deployed in vehicle hardware devices,it is necessary to design a semantic segmentation model that can balance efficiency and accuracy.In this paper,a single-branch network structure is used to design a lightweight multi-scale bidirectional attention network.To achieve efficient feature extraction,a lightweight convolutional unit is designed to form the feature extraction backbone of the network.In order to better locate and segment objects with large scale differences in road scenes,a multi-scale bidirectional attention module is proposed which has a global multi-scale receptive field and encodes channel attention in one direction while preserving spatial location information in the other.Based on this attention module,a skip attention connection module and a feature attention fusion module are designed,so that the output features have both detailed information and semantic information.On the Cityscapes dataset,the model in this paper achieves an MIoU(Mean Intersection over Union) of 71.86% with a parameter size of 0.9M,and achieves an inference speed of 88FPS(Frames Per Second) under a single RTX2080Ti GPU.The test results on public datasets show that the model achieves a high segmentation accuracy and is suitable for deployment and application under in-vehicle hardware,which is of certain practical value.

    Method for enhancement of the multi-scale low-light image by combining an attention guidance
    ZHANG Yali, LI Wenyuan, LI Changlu, DING Shaobo
    Journal of Xidian University. 2023, 50(1):  129-136.  doi:10.19665/j.issn1001-2400.2023.01.015
    Abstract ( 266 )   HTML ( 16 )   PDF (3421KB) ( 96 )   Save
    Figures and Tables | References | Related Articles | Metrics

    The low light environment affects the image capture equipment,resulting in low contrast,low brightness,and difficulty in distinguishing objects.In order to improve image quality,a method for enhancement of the multi-scale low-light image by combining an attention guidance is proposed.First,a dense residual network is constructed as a multi-scale feature extractor to extract feature maps at different scales in low light images,and the extracted feature maps are fused by using a modified RefineNet,which makes full use of the feature information in the image.Meanwhile,an interpretable attention mechanism is designed to generate an attention graph based on the results of edge detection.Then by combining a loss function the network is guided through training.The purpose is to enhance edge detail information hidden in the dark without increasing the network’s inference burden.Finally,experiments are completed on synthetic images and SID(See-in-the-Dark) datasets,with the results showing that the proposed method can effectively improve brightness and contrast,restore image edge details as well as improve subjective visual effects.Compared to the contrast algorithm,the PSNR and SSIM are improved by at least 0.79dB and 0.119 on average,respectively.

    Dual graph attention networks model for target sentiment analysis
    CUI Shaoguo,CHEN Siqi,DU Xing
    Journal of Xidian University. 2023, 50(1):  137-148.  doi:10.19665/j.issn1001-2400.2023.01.016
    Abstract ( 382 )   HTML ( 19 )   PDF (3003KB) ( 96 )   Save
    Figures and Tables | References | Related Articles | Metrics

    Target sentiment analysis aims to analyze the sentiment tendency corresponding to different targets in the review text.At present,graph neural network based methods use the dependency syntactic tree to incorporate dependency syntactic relations.On the one hand,these methods mostly ignore the fact that dependency relations lack distinction.On the other hand,without considering the dependency relations provided by the dependency syntactic tree,there is a lack of relations between target and sentiment words.Therefore,a dual graph attention network(DGAT) model is proposed.First,the model uses a bidirectional long short-term memory network to obtain word node representation with semantic information,and then constructs a syntactic graph attention network based on the word node representation according to the dependency syntactic tree,so as to distinguish the importance of dependency syntactic relations,more effectively establish the relation between target and sentiment words,and obtain a more accurate representation of target sentiment features.At the same time,according to the undirected complete graph of sentences,a global graph attention network is used to mine lacking relations between target and sentiment words,so as to further improve the performance of the model.Experimental results show that compared with existing models,the DGAT model has a better accuracy and macro-average F1 value on different datasets.

    Low-power consumption high-precision non-intrusive electrical appliance identification algorithm
    WU Boyun,GU Wenjie,HE Xiandeng
    Journal of Xidian University. 2023, 50(1):  149-157.  doi:10.19665/j.issn1001-2400.2023.01.017
    Abstract ( 268 )   HTML ( 14 )   PDF (1179KB) ( 92 )   Save
    Figures and Tables | References | Related Articles | Metrics

    Non-intrusive electrical appliance identification is the key technology to realize customer-side intelligent sensing in the ubiquitous power Internet of Things.Aiming at the problem that the calculation complexity of the existing non-intrusive electrical appliance identification system is too high and is not conducive to industrialization,a low-cost,low-power consumption,high-precision non-intrusive electrical appliance identification system and the algorithm based on the Long Short-Term Memory(LSTM) network are proposed,and they can be applied to an embedded microcontroller.First,the current on the bus is collected synchronously.Second,the proposed multi-parameter detection method is used to judge the switching event of the electrical appliance.Third,the LSTM network is used to process the data before and after the switching time point,and the type of electrical appliance which is switching is obtained.Finally,the current types and quantities of electrical appliances are judged by the cumulative sum.Simulation and measurement results show that only a small amount of data training for a single electric appliance is needed,and that the recognition accuracy of combined electric appliances can be up to 99.6% in the proposed embedded microcontroller system,in which the power consumption is less than 1.5 watts.The proposed algorithm can be further applied to the statistics of the switching time point,service time and total power consumption of each electrical appliance,which provides refined user power consumption information for the smart grid.and provides an important reference for the energy management and optimization in the smart grid.

    Model for denoising of spatial adaptive directional total variation seismic data
    ZHANG Lili,QIAO Zengqiang,WANG Dehua
    Journal of Xidian University. 2023, 50(1):  158-167.  doi:10.19665/j.issn1001-2400.2023.01.018
    Abstract ( 188 )   HTML ( 14 )   PDF (6903KB) ( 69 )   Save
    Figures and Tables | References | Related Articles | Metrics

    With the continuous advancement of oil and gas exploration in China,seismic exploration is facing great challenges.Affected by the complex exploration environment,acquisition method,detector sensitivity and other factors,the obtained seismic data are often mixed with a large amount of random noise,resulting in the decrease in fidelity,signal-to-noise ratio (SNR) and resolution of subsequent seismic data processing,and the accuracy and reliability of geological interpretation are ultimately affected.In order to break through the limitations of traditional seismic data processing problems,a spatially adaptive directional total variation (SADTV) regularization model for random noise suppression of seismic data is proposed.First,aiming at the problem that the seismic reflection events have the directivity of spatial variation and the poor noise resistance of dip angle calculation,a point by point estimation formula for the spatially varying dip angle based on the gradient structure tensor (GST) is proposed to obtain the direction information on events;Then,the denoising model of SADTV seismic data is established,and the Majorization-Minimization (MM) algorithm for solving the model is derived.Finally,the parameter selection method of the model is discussed,and the denoising results of synthetic and real seismic data are compared with those by similar methods.Experimental results show that the proposed model can not only improve the vertical resolution of the seismic profile and the lateral continuity of the seismic event,but also retain more geological feature information while improving the signal-to-noise ratio.

    Attention spatial-temporal graph neural network for traffic prediction
    GAN Ping, NONG Liping, ZHANG Wenhui, LIN Jiming, WANG Junyi
    Journal of Xidian University. 2023, 50(1):  168-176.  doi:10.19665/j.issn1001-2400.2023.01.019
    Abstract ( 277 )   HTML ( 11 )   PDF (1229KB) ( 93 )   Save
    Figures and Tables | References | Related Articles | Metrics

    With the development of urbanization,traffic prediction plays an important role in the application of traffic planning and urban management.However,in the task of traffic prediction,it is still a great challenge to capture the highly nonlinear and complex spatio-temporal dependencies of traffic data.In order to effectively capture the time dynamics and global spatial correlation of traffic data and satisfy both long-term and short-term prediction tasks,an attention based spatial-temporal graph neural network for traffic prediction is designed.First,the attention mechanism is introduced to adjust the importance of adjacent roads and non-adjacent roads,which is beneficial to integrating global spatial information.Then,the spatial-temporal correlations are captured by graph convolutional networks and gated linear units with extended causal convolution.Experimental results on two real data sets PeMSD7(M) and PEMS-BAY show that the network model can improve the accuracy of both long and short-term traffic prediction.

    Lightweight RFID dual-tag authentication protocol using cloud and PUF
    AI Lulin, CHANG Zhengtai, FAN Wenbing, KONG Dehan
    Journal of Xidian University. 2023, 50(1):  177-191.  doi:10.19665/j.issn1001-2400.2023.01.020
    Abstract ( 169 )   HTML ( 12 )   PDF (1854KB) ( 72 )   Save
    Figures and Tables | References | Related Articles | Metrics

    Focusing on the simultaneous authentication of drugs and instructions in the medical system,a rapid dual-tag authentication scheme(CP-LRDP) is proposed,which introduces a cloud server and PUF to ensure the scalability of the RFID system and the unclonability of tags.Aiming at the problem of sequential dual-tag authentication with a low efficiency in a traditional RFID system,a dual-tag response merging process is proposed.For the system error authentication problem caused by the PUF,the optimal authentication threshold of the PUF response is calculated to reduce the authentication error rate of the system.To solve the untrusted problem of the cloud server,three ultra-lightweight bitstream functions are proposed to implement two encryption mechanisms for protecting the forward channel from the threat of cloud server privacy leakage.Security analysis shows that the CP-LRDP not only satisfies the tag anonymity and untraceability,but also can effectively resist cloning attacks,desynchronization attacks,replay attacks and other malicious attacks.In addition,BAN logic analysis and the AVISPA tool are used to further verify the security of the protocol.Compared with recent authentication protocols,the CP-LRDP with the shortest server search time not only meets various security properties,but also realizes achieving rapid dual-tag authentication with resource costs similar to those of single-tag,which is suitable for resource-constrained large-scale dual-tag authentication scenarios.

    Analysis and improvement of the security of the key-nets homomorphic encryption scheme
    LI Wenhua,DONG Lihua,ZENG Yong
    Journal of Xidian University. 2023, 50(1):  192-202.  doi:10.19665/j.issn1001-2400.2023.01.021
    Abstract ( 290 )   HTML ( 10 )   PDF (2510KB) ( 61 )   Save
    Figures and Tables | References | Related Articles | Metrics

    key-nets,as the first optical homomorphic encryption scheme,is used toprotect the privacy of images used for machine learning.However,in the case of the vision sensor being obtained illegally,the author obtained the key used to encrypt the image in the key-nets scheme by solving the system of linear equations.In view of the security risks in this scheme and the difficulty of machine learning model training,this paper proposes a homomorphic encryption scheme that can use different generalized random matrices for each encryption without changing the original convolutional network structure,and further use the Diffie-Hellman key exchange protocol,which improves the security of the encryption key-nets and also improves the security of the convolutional network matching the vision sensor.Through the analysis of the feasibility of the scheme,privacy parameters,forward security,backward security,etc.,it is proved that the improved scheme can still protect the image information even if the attacker illegally obtains the visual sensor.

    Fair redactable blockchain supporting malicious punishment
    REN Yanli,ZHAI Mengjuan,HU Mingqi
    Journal of Xidian University. 2023, 50(1):  203-212.  doi:10.19665/j.issn1001-2400.2023.01.022
    Abstract ( 242 )   HTML ( 9 )   PDF (2728KB) ( 56 )   Save
    Figures and Tables | References | Related Articles | Metrics

    The chameleon hash algorithm that can realize data editing provides a content controllable method for blockchain.However,once a participant obtains permission to edit the data,he can rewrite anything without being punished for his malicious behavior.At present,most redactable blockchain schemes can only punish malicious users without considering the malicious tampering of the blockchain by editors,which cannot realize the fairness of both users and editors at the same time.A fair and redactable blockchain scheme that supports malicious punishment is proposed,which ensures the fairness of the redactable blockchain scheme by effectively restricting the rights of editors and punishing their malicious behaviors.In the proposed scheme,the chameleon hash with a short-term trapdoor is used,so that editors and users must cooperate to complete the editing of the blockchain,which effectively restricts the rights of editors.Based on the secret sharing and signature schemes,a punishment mechanism for malicious editors is proposed to punish the malicious tampering behavior of editors and resist the malicious reports of users.Theoretical and experimental analysis shows that the proposed scheme not only realizes the collision-resistance and semantic security of blockchain data,as well as the fairness of users and editors in the data editing process,but also has a lower computational cost than existing schemes.In addition,the advantages of the proposed scheme become more obvious with the increase in the number of editing times,so it is of more practical application value.