Table of Content

30 November 2022, Volume 40 Issue 6

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Communication Engineering

Image Compressive Sensing Reconstruction Using Ultra-Deep Residual Channel Attention Network

YUAN Wenjie, TIAN Jinpeng, YANG Jie

2022, 40(6):  887-895.  doi:10.3969/j.issn.0255-8297.2022.06.001

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An image compressive sensing reconstruction method based on ultra-deep residual channel attention networks is proposed. The reconstruction part of the ultra-deep residual channel attention network consists of multiple residual groups, each of which contains a long connection and a set of residual blocks with short connections. The long-connected structure can effectively deliver rich low-frequency information, allowing main network to focus on learning high-frequency information. The channel attention mechanism is introduced into residual blocks. By considering the interdependence between channels, the channel features keep changing adaptively so as to strengthen the important features. Experiments show that this method can effectively improve the reconstruction accuracy of image compressed sensing.

S-Type Fiber Cladding SPR Sensor for Hg²⁺ Concentration Detection

WEI Yong, ZHAO Xiaoling, WANG Rui, LIU Chunlan, ZHANG Yonghui

2022, 40(6):  896-905.  doi:10.3969/j.issn.0255-8297.2022.06.002

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Fiber cladding surface plasmon resonance (SPR) sensors can be employed in biochemical sensing without the requirement of cladding removal, but with the limitation of lack of architectural variety. In this paper, a new type of cladding SPR sensor construction based on S-type fiber is proposed. The S-type fiber is first fabricated by electrofusion technology, and then coated with a 50 nm gold film on its cladding surface. In the S-type fiber, light is coupled from the fiber core to the cladding, and contacted with the gold film on the cladding surface to form a fiber cladding SPR sensor, thus, realizing the detection of Hg²⁺ molar concentration. The resonance valley wavelength of the SPR sensor changes from 633.03 nm to 645.09 nm as the Hg²⁺ molar concentration increasing from 5 pmol/L to 5 mmol/L. The detection sensitivity reaches 1.382 nm/lg(mmol/L). The manufacture of S-type fiber cladding SPR sensor features with simplicity and can be used for single-mode and multi-mode optical fibers to realize cladding SPR sensors which are especially useful in the field of biochemical detection.

Signal and Information Processing

Cloud Detection of Landsat Images Based on Attention U-Net

LIU Fei, LI Xin

2022, 40(6):  906-917.  doi:10.3969/j.issn.0255-8297.2022.06.003

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Cloud detection is an effective measure to improve the utilization rate and application range of remote sensing images. However, most of the existing cloud detection algorithms face with two problems: the difficulty in distinguishing complex underlying surfaces like ice and snow from cloud and the requirement of a large number of manually labeled cloud samples for model training. To improve the performance of cloud recognition, we propose a cloud detection algorithm based on Attention U-Net for Landsat images. Firstly, convolution operation is conducted to extract the shallow features of cloud in coding modules. Then, deconvolution, jump connection and attention mechanism are integrated to mine deeper cloud features in decoding modules. Finally, a small number of public Landsat image cloud samples are used for training to achieve end-to-end pixel-level cloud recognition. Compared with traditional machine learning algorithms, experimental results indicate that the proposed algorithm has higher overall accuracy, lower false detection rate and missed detection rate of thin cloud and shadow.

Research Progress on Frequency Diversity Array Radar: from System Framework to Parameter Estimation

ZHANG Xiaofei, WANG Cheng, LI Jianfeng, WU Qihui

2022, 40(6):  918-940.  doi:10.3969/j.issn.0255-8297.2022.06.004

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The received signal of frequency diversity array (FDA) is a function of the angle and range of target by using little frequency offsets among array units in transmitter, which can be utilized to conduct beamforming, joint angle and range estimation and interference suppression. In this paper, the research background and progress are presented, and the principle and signal model of parameter estimation utilizing FDA-MIMO radar are introduced. After that, three frequency offset frameworks, including unfolded coprime linear array-unfolded coprime frequency offset (UCLA-UCFO), virtual coprime planar arrayunfolded coprime frequency offset (VCPA-UCFO), and synthetic aperture multi-coprime frequency offset (SA-MCFO) frameworks are proposed. In addition, several new works on algorithms, including reduce dimension root multi-signal classification (RD-root-MUSIC), fast convergence-trilinear decomposition (FC-TD), successive iteration (SUIT), twice reduce dimension-MUSIC (TRD-MUSIC) and reduce dimension MUSIC with decoupling (RDMD) algorithms are also presented. Finally, the effectiveness and superiority of the frameworks and algorithms are verified by simulations.

Hyperspectral Inversion of Soil Heavy Metal Mass Concentration Based on Semi-supervised Regression

MAO Gengxuan, TU Yan, CUI Wenbo, TAO Chao

2022, 40(6):  941-952.  doi:10.3969/j.issn.0255-8297.2022.06.005

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Aiming at training a robust inversion model of soil heavy metal mass concentration with a small number of labeled samples and a large number of unlabeled samples, we took cadmium (Cd) in soil as research object, and experimentally verified the model by using two groups of the spectral data of four different regions (Hengyang-Chenzhou, Yuanping-Baoding). A hyperspectral retrieval model of soil heavy metal mass concentration based on semi-supervised regression was proposed after reducing the spectral distribution differences of different regions by means of transfer component analysis. Experimental results show that compared with traditional fully supervised modeling method, in the group of Hengyang-Chenzhou, the semi-supervised method proposed in this paper can improve the determination coefficient R² to 0.75 and relative percent difference (RPD) to 2.15; In the group of Yuanping-Baoding, R² increases to 0.70, and RPD increases to 1.61. The experiments show that the model inversion accuracy can be effectively improved by introducing a large number of unlabeled samples to semi-supervised regression analysis in the situations of few labeled samples.

Automatic Acquisition Method of Electric Power Engineering Road Cross Section Integrating BeiDou and LiDAR Mobile Measurement

SUN Yixin, LIU Zhanjie, LIU Zhe, TANG Xuehua, ZENG Xiaodong, LI Jing

2022, 40(6):  953-963.  doi:10.3969/j.issn.0255-8297.2022.06.006

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Traditional power engineering road cross-section measurement methods measure the measurement points on each road cross-section one by one in a contact manner, leading to labor-intensive internal and external works. Although existing LiDAR-based measurement methods can improve the efficiency of field data acquisition, internal data processing is still in a low-efficiency manual manner. Aiming at these bottleneck problems, this paper proposes an automatic road cross-section acquisition method that integrates BeiDou navigation satellite system (BDS) technology and the LiDAR measurement method. The new method obtains high-precision 3D laser point cloud data of road environment through the LiDAR terminals on mobile carriers, and uses the instantaneous pose information provided by BDS and inertial measurement unit (IMU) to construct overall environment point cloud from the point cloud data. Based on the location of a road cross-section, the point cloud of the location is automatically extracted, and the point cloud data of the road cross section is extracted and formed into a standardized format file automatically. Compared with existing methods, the proposed method can obtain result datasets in real time by only operating parameter setting of road design elements and the measurement distance of the cross-sections, greatly improving the degree of automation and work efficiency.

An Improved Method for Highway Cross Section Production Using LiDAR Point Cloud

ZHENG Liang, ZHANG Zhiyi, JU Baolin, LI Shengming

2022, 40(6):  964-972.  doi:10.3969/j.issn.0255-8297.2022.06.007

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Traditional point cloud section extraction is carried out on the basis of 3D products. It is necessary to obtain ground points by filtering point cloud firstly and then sampling to obtain sections. The process suffers low work efficiency and unguaranteed quality of the section production, requiring a lot of manual refinement work. This paper proposes a new production process and method for the production demand of highway cross section thematic production. Firstly, a cross section is extracted based on digital surface model (DSM), and the point cloud set of the cross section is two-dimensionally projected to obtain a two-dimensional point array. Then, the point cloud array is filtered based on the skewness-balanced filtering algorithm, and finally a feature preserving cross section point cloud is extracted. Practice has proved that the new process reduces the amount of data processing and effectively improves the efficiency and precision of cross section production.

High Resolution Range Profile Radar Target Recognition Based on Time-Frequency Analysis and Deep Learning

NIE Jianghua, XIAO Yongsheng, HUANG Lizhen, HE Fengshou

2022, 40(6):  973-983.  doi:10.3969/j.issn.0255-8297.2022.06.008

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In view of the problem that radar target recognition methods based on traditional high resolution range profile (HRRP) are susceptible to noise, an HRRP radar target recognition method employing time-frequency analysis and deep learning is proposed. First, low signal-to-noise ratio HRRP data is processed, and gains an improved signal-to-noise ratio by using a generative model which uses deep convolutional generative adversarial network (DCGAN) and constrained naive least squares generative adversarial network (CNLSGAN) proposed in this paper. Second, the processed data is processed with short-time Fourier transform (STFT) and wavelet transform (wavelet transform, WT) respectively to obtain two-dimensional time-frequency data. Finally, the obtained two-dimensional data is recognized by convolutional neural network (CNN). Experimental results show that the proposed CN-LSGAN performs better in improving signal-to-noise ratio compared to DCGAN, and WT can obtain HRRP feature information more efficiently than STFT. Therefore, the HRRP radar target recognition method based on CN-LSGAN, WT and CNN has higher recognition ability.

Estimating Sampling Interval of Terrestrial Laser Scanning Point Cloud with Neighboring Analysis of Randomly Selected Points

CHEN Maolin, ZHANG Xinyi, LIU Xiangjiang, JI Cuicui, ZHAO Lidu

2022, 40(6):  984-995.  doi:10.3969/j.issn.0255-8297.2022.06.009

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This paper focuses on the estimation of sampling interval from terrestrial laser scanning data and presents an estimation method based on the neighboring analysis of randomly selected points. We select n central points from the randomly scanning data and search for k nearest points of each central point. The horizontal and vertical (with Z axis) angles between the laser beam of each central point and corresponding neighboring points are calculated. Then histograms of horizontal and vertical angles are constructed respectively, with interval step △. The average angle of the interval with the second largest number of points is recognized as the corresponding horizontal or vertical sampling interval. Finally, the median value of the horizontal or vertical sampling values generated from a series of △ values is used as the final estimation result. Tests are carried on the data obtained from different scanners and test code is shared on the MathWorks community. The test results show that the error of our method is smaller than 0.002° with good robustness with respect to different types of targets and parameter settings.

Image Deblurring Model Based on Width Residual and Pixel Attention

KUANG Fa, XIONG Bangshu, OU Qiaofeng, YU Lei

2022, 40(6):  996-1005.  doi:10.3969/j.issn.0255-8297.2022.06.010

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In order to solve the problem that existing methods suffer difficulty in quickly recovering high-quality sharp images from blurred images, an image deblurring model based on width residual and pixel attention is proposed. Based on encoder-decoder networks, the model uses wide convolution and multi-order residual method to construct width residual modules, improving the processing speed of the model. At the same time, local average and matrix cross multiplication are used to construct pixel attention modules, which enhance the model deblurring quality. The experimental results on GOPRO datasets show that the structural similarity of the proposed method is 0.922 3, the peak signal-to-noise ratio is 31.74 dB, and the average running time is 0.37 seconds when the model size is 22.24 MB. Compared with the scale-recurrent network method, the peak signal-to-noise ratio of the proposed method improves by 4%, and its performance is better than the other existing deblurring methods.

Computer Science and Applications

An Authentication Protocol with Hierarchical Access Control for Mobile Cloud Services

WANG Jie, LI Jing, LUO Ying

2022, 40(6):  1006-1018.  doi:10.3969/j.issn.0255-8297.2022.06.011

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In order to resolve the problem of limited resources of mobile terminal devices in mobile computing services environment, an improved privacy-preserving authentication scheme with hierarchical access control is proposed based on signcryption technology and multi-server authentication technology. Users can access multiple mobile cloud service providers by only registering arbitrary one of them, and the authentication process does not require the participation of a trusted third party. Besides, mobile terminals do not use the bilinear pairing operation to avoid high computational complexity. Performance analysis results show that the computing efficiency of the proposed scheme in mobile terminals can be improved by about 34% compared with the existing related schemes, providing practical value in improving the access efficiency of cloud services.

Payment Protocol for Outsourcing of Bilinear Pairing Based on Blockchain

YANG Danling, REN Yanli

2022, 40(6):  1019-1033.  doi:10.3969/j.issn.0255-8297.2022.06.012

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In order to solve the problem that existing bilinear outsourcing payment protocols rely heavilyon a trusted third party and lacks of the fairness of participating parties, in this paper, we propose a protocol based on blockchain system to achieve decentralization, realize data privacy by blindly processing the original data, and ensure fair payment by imposing the penalties of deducting the deposits of malicious participants. When users question the correctness of outsourcing results, the blockchain will quickly verify the results using random data and random vectors provided by users, thereby improving payment efficiency. A simulation experiment is conducted in Ethereum, and its results show that the amount of user calculations is greatly reduced, and users can effectively verify the correctness of outsourcing results and achieve payment fairness. Compared with existing protocols, both users and servers have the highest efficiency in payment phase.

Control and System

Fixed-Time Integrated Attitude-Position Control of a Tilt Quadrotor UAV

XIONG Hang, LI Bo, QIN Ke, GONG Wenquan

2022, 40(6):  1034-1046.  doi:10.3969/j.issn.0255-8297.2022.06.013

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A novel fixed-time control method based on non-singular terminal sliding mode and dual quaternion is proposed for the integrated attitude-position control of a tilt quadrotor unmanned aerial vehicle (UAV) in presence of parameter uncertainties and external disturbances. Firstly, considering the tilt quadrotor UAV tracking control system, a dual quaternion-based non-singular terminal sliding mode surface is constructed, and a novel fixed-time integrated attitude-position control law is designed. Furthermore, for the UAV tracking control system under external disturbances and parameter uncertainties, an immersion and invariance manifold-based adaptive law is established to estimate uncertain parameters, which can solve the problem of the estimated value deviating from the true value. And then, a fixed-time control scheme incorporating adaptive law is proposed to achieve UAV trajectory tracking control. Numerical simulation results verify that the proposed control scheme has effective performance and strong robustness to external disturbances and parameter uncertainties.