Table of Content

30 November 2023, Volume 41 Issue 6

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Communication Engineering

Unfolded Augmented Co-prime Array for MIMO Radar: Low Mutual Coupling and High Degree of Freedom

HAO Honghao, LAI Xin, HAN Shengxinlai, ZHANG Xiaofei

2023, 41(6):  911-925.  doi:10.3969/j.issn.0255-8297.2023.06.001

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Aiming at the problem of high mutual coupling rate and low degree of freedom (DOF) of existing multiple input multiple output (MIMO) radar, an unfolded augmented co-prime MIMO radar is proposed in this paper. First, the unfolded augmented co-prime array is deployed in both transmitter and receiver. Then, by introducing the sparse expansion factor, the inter-element spacing of different subarrays in unfolded augmented co-prime array is extended, and the closed-form expression of the position set for the unfolded augmented co-prime MIMO radar is obtained. Then, the generalized sum and different co-array (GSDC) concept is used to derive the closed-form solutions of continuous DOF and total DOF. Finally, the direction of arrival (DOA) is obtained by spatial smoothing multiple signal classification (MUSIC) method. Compared with other co-prime MIMO radar structures, the proposed MIMO radar offers higher DOF and lower mutual rate. Simulation results verify the effectiveness and advantages of the proposed radar structure in DOA estimation.

One-Dimensional DOA Estimation Based on Unitary Reconstructive Subspace

JIN Yanliang, Lü Rukun, WANG Xiaoyong, ZHENG Guoxin

2023, 41(6):  926-939.  doi:10.3969/j.issn.0255-8297.2023.06.002

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To address the limitations of traditional multiple signal classification (MUSIC) algorithm, such as ineffective performance in low signal to noise ratio (SNR), small snapshots and low array number under small incident angle interval signals, we propose an improved algorithm called unitary reconstructed subspace MUSIC (URS-MUSIC). The proposed algorithm transforms the actual received signal of a uniform linear array from complex to real value using unitary transformation, then reconstructs subspaces and revised matrices to obtain new spatial spectrums based on the size of the subspace eigenvectors. The obtained spectrums are multiplied by the signal subspace projection (SSP) to realize direction of arrival (DOA) estimation. Simulation results demonstrate that URS-MUSIC outperforms both traditional and signal subspace projection algorithms with better resolution performance, especially under challenging conditions such as low SNR, small snapshots, and low array numbers.

Recommendation Algorithm Based on User Similarity Selection and Label Distance

SU Zhan, CHEN Xueqian, AI Jun, HUANG Zhong

2023, 41(6):  940-957.  doi:10.3969/j.issn.0255-8297.2023.06.003

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Neighbor selection and item label information have important influence on rating prediction of recommendation system. To improve the accuracy and scalability of recommendation systems, this paper proposes a distance-model based approach that utilizes user similarity selection and label distance. First, the users with similarity greater than the threshold value are selected as the neighbors of the users to be predicted to deal with insufficient scalability of the algorithm. Second, the user’s rating of the item is mapped to the user’s rating of the item label using the label information to enhance the accuracy. Users’ ratings of movies were predicted by using discount validation in both movie datasets. Experimental results show that the accuracy and scalability of the recommendation algorithm based on user similarity selection and label distance are greatly improved.

D2D Interference Management Scheme Based on K-means and Gale-Shapley Algorithm

CHEN Fatang, CHEN Yongtai, CHEN Feng, WANG Dan

2023, 41(6):  958-966.  doi:10.3969/j.issn.0255-8297.2023.06.004

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In this paper, we propose a resource allocation scheme for managing interference in device to device (D2D) communication, aiming to address the issue of inter-user interference caused by the multiplexing of cellular network resources. The proposed scheme is based on K-means and Gale-Shapley stable matching algorithm. By analyzing the signal to interference plus noise ratio (SINR) formula, K-means clustering algorithm is used to group users, reduce the interference between users, and achieve multiple to one resource reuse. To improve communication system capacity and ensure fairness among users, GaleShapley stable matching algorithm is used to realize channel resource sharing within the user groups. Simulation results show that the system interference is reduced by 10% to 30% compared to the greedy graphical coloring resource allocation algorithm, while maintaining stable system capacity.

Signal and Information Processing

Clothing Image Recognition Method Based on SASK and Double Branch Structure

ZHOU Xiaohui, YU Lei, ZHANG Ruiting, XIONG Bangshu, OU Qiaofeng

2023, 41(6):  967-977.  doi:10.3969/j.issn.0255-8297.2023.06.005

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Due to the limited performance of the existing recognition methods for clothing images with varying brightness and scales, we propose a neural network model based on spatial attention selective kernel（SASK） and double branch structure in this paper.Firstly, a double branch neural network that incorporates jump connection, dense connection, multi-scale and channel splitting is established to fully extract the overall features of clothing images. Secondly, the SASK module based on the spatial attention mechanism is designed to enable the network to focus on the morphological feature information of clothing images for accurate recognition. Experimental results show that our method not only improves the recognition accuracy of typical clothing datasets compared to existing mainstream methods, but also performs effectively on image datasets with different brightness and scales.

Boundary-Aware Deeply Residual Network for Salient Object Detection of Strip Steel Surface Defects

SHEN Kunye, ZHOU Xiaofei, FEI Xiaobo, CHEN Yuzhong, ZHANG Jiyong, YAN Chenggang

2023, 41(6):  978-988.  doi:10.3969/j.issn.0255-8297.2023.06.006

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Deep learning-based salient object detection has been used in strip steel surface defects, but there are still some problems such as slow model training speed and unclear boundary of detection results. To address these issues, we proposed a boundary-aware deeply residual network (BADRNet) for salient object detection of strip steel surface defects. Boundary features are introduced into the steel surface defects to solve the problem of unclear boundary of detection results caused by varying object sizes. Three convolution layers with residual structure are used as basic blocks for boundary extraction and salient feature aggregation, improving training efficiency while maintaining original detection accuracy. Experimental results on the public strip steel benchmark dataset, SD-saliency-900, show that our model outperforms existing models in all six evaluation indicators. The proposed BADRNet improves the S-measure performance by 1.6%, and significantly enhances the detection effect on the defect area.

Remote Sensing Image Object Detection Based on CAFPN and Refinement Double-Head Decoupling

XIONG Juan, ZHANG Sunjie, KAN Yaya, CHEN Jiahao

2023, 41(6):  989-1003.  doi:10.3969/j.issn.0255-8297.2023.06.007

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In order to address the challenges posed by complex backgrounds, small object sizes, and arbitrary directions in remote sensing images, this paper presents a novel object detection algorithm. The proposed algorithm consists of several key components.Firstly, a context augmentation feature pyramid network（CAFPN） is introduced. In the feature extraction stage, it integrates adaptively with the dilated convolution to obtain features with rich semantic information and reduce information loss of small objects. Then,midpoint-offset regression（MOR） is employed to detect oriented box in the regression network to reduce the computational complexity caused by redundant anchors. Finally, a double-head network decouples classification and regression features, and incorporates a feature refinement module guided by attention mechanism and polarization functions, enabling the construction of task-specific features that facilitate accurate object detection.Experimental results on public remote sensing datasets, including DOTA, HRSC2016, and UCAS＿AOD, demonstrate the effectiveness of the proposed algorithm. Compared to the Faster RCNN algorithm, the proposed method achieves accuracy improvements of 8.48%,7.60%, and 3.10% on the three datasets, respectively. The proposed method enables high-performance object detection in remote sensing images.

Multi-modal Diagnosis Method of Alzheimer’s Disease

LI Weihan, HOU Beiping, HU Feiyang, ZHU Bihong

2023, 41(6):  1004-1018.  doi:10.3969/j.issn.0255-8297.2023.06.008

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The current grading methods for Alzheimer’s disease（AD）, Early Mild Cognitive Impairment（EMCI）, and Normal Control（NC） suffer from difficulties recognizing EMCI and low multi-classification accuracy. To address these issues, a brain region feature extraction method is proposed, and an AD multi-modal classification model is designed with a fusion of ResNet network. Brain MRI images are spatially registered, segmented by Bayesian and Gaussian mixture models to obtain gray matter, the regions with the greatest difference are selected as the feature image area, and images and biomarkers are processed by the classification model. The proposed method improves performance by at least 5% and achieves an accuracy of 95.5%, 93.5%, and 86.3% for AD＆NC, AD＆EMCI,and AD＆EMCI＆NC classification, respectively, surpassing any single-modal network and verifying the effectiveness of this method.

Study on the Accessibility of Nucleic Acid Sampling Sites in Core City of Nanchang

MA Feihu, WU Yongheng, HU Yun

2023, 41(6):  1019-1030.  doi:10.3969/j.issn.0255-8297.2023.06.009

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This paper explores the accessibility and equity of nucleic acid sampling points in central city communities. By analyzing the spatial distribution and resource allocation of nucleic acid sampling points, this paper aims to address the growing demand for nucleic acid sampling, and improve the efficiency and equity of nucleic acid sampling services. The study focuses on the central urban area of Nanchang City and proposes a research method to assess the comprehensive accessibility of nucleic acid sampling points under various travel modes. The analysis includes matching nucleic acid sampling points with the population and evaluating the level of parity using non-spatial analysis indexes such as the Lorentz curve and Gini coefficient. The results indicate that while the accessibility of nucleic acid sampling sites in Nanchang is generally good, there is still an unfair distribution in terms of equity.

Computer Science and Applications

Research on Hash Algorithm Heterogeneous Reconfigurable High Energy Efficiency Computing System

ZHENG Bowen, NIE Yi, CHAI Zhilei

2023, 41(6):  1031-1045.  doi:10.3969/j.issn.0255-8297.2023.06.010

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To meet the high-speed computing requirements of different hash algorithms and the combination of different hash algorithms in various application scenarios, a highefficiency computing system for Hash algorithm with heterogeneous and reconfigurable acceleration end hardware is presented in this paper. The computing system consists of an algorithm hardware acceleration module, a data transmission module, and a multithread management module. The computing energy efficiency is improved through the dynamically reconfigurable hardware design. Experimental results on the Intel Stratix10FPGA heterogeneous computing platform demonstrate significant performance and energy efficiency improvements. Compared with the Intel Core I7-10700 CPU, the system achieves up to 18.7 times performance improvement and 34 times energy efficiency improvement.Compared with the NVIDIA GTX 1650 SUPER GPU, the system achieves up to 2 times performance improvement and 5.6 times energy efficiency improvement.

Dynamic Byzantine Fault Tolerance Algorithm Based on Reputation and Clustering

WU Guangfu, YANG Zi, HUANG Baozhu

2023, 41(6):  1046-1057.  doi:10.3969/j.issn.0255-8297.2023.06.011

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This paper presents a dynamic Byzantine fault-tolerant consensus algorithm based on reputation and clustering. The existing practical algorithms lack a response mechanism for joining or exiting nodes, leading to decreased consensus efficiency with a large number of nodes. To address this issue, the proposed algorithm utilizes a clustering algorithm to divide nodes into K consensus regions, improving efficiency when more nodes participate in consensus. Additionally, K reliable proxy nodes are selected based on high reputation, while low reputation nodes are eliminated to reduce the probability of Byzantine nodes becoming main nodes. The node classification process combines the reputation evaluation algorithm to select K proxy nodes, enhancing system stability and security. Simulation results demonstrate that compared to PBFT, the proposed algorithm supports dynamic node joining and exiting, with lower communication cost, transaction delay, and higher throughput. It also exhibits better fault tolerance and scalability.

Auto-Checking Stamped Document Image Based on OCR and Image Detection

CAO Jing, CHEN Kang, QI Ning, XIA Pengcheng, QIU Yu

2023, 41(6):  1058-1067.  doi:10.3969/j.issn.0255-8297.2023.06.012

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In this paper, we design and implement an auto-checking method based on OCR and image detection to replace the time-consuming and error-prone manual work. The method consists of three parts: text recognition, seal recognition, and content checking. For text recognition, we utilize the SegLink algorithm for angled text detection and the CRNN algorithm for variable length end-to-end text recognition. For seal recognition, we employ the YOLOv3 algorithm for seal recognition and extraction, along with the polar coordinate transformation method for seal content recognition. The content checking is based on the preset rules to check the completeness and correctness of the content extracted from the form. Experimental result shows that the proposed method achieves high accuracy in checking stamped document image with seals.

Control and System

Design of the Autonomous Navigation Test System for Unmanned Surface Vehicle Combining Virtual and Reality

LIU Hongxiao, YANG Cheng, TAN Aidi, LU Jing, LI You

2023, 41(6):  1068-1077.  doi:10.3969/j.issn.0255-8297.2023.06.013

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The cost of on-site testing for unmanned surface vehicle（USV） autonomous navigation systems is high, and pure virtual simulation tests lack authenticity in marine dynamics simulation. To address these challenges and improve the effectiveness-cost ratio of USV autonomous navigation system testing, a hybrid testing system combining virtual and real elements is proposed. The designed system leverages virtual simulation technology for task scene construction and environmental sensing sensors, while real-in-loop technology is used for the transmission system and marine dynamics of the USV. The system achieves real-time position and attitude synchronization between the digital twins of the USV and its physical entities through virtual and real space registration technology. Simulation results validate the correctness and rationality of the system, demonstrating its effectiveness in testing USV autonomous navigation systems while reducing costs.

GORC-PID Algorithm Wireless Temperature Control System with Packet Loss Compensation

TAN Ping, SHI Huiyuan, SU Chengli, LI Ping

2023, 41(6):  1078-1088.  doi:10.3969/j.issn.0255-8297.2023.06.014

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To address the challenges of wiring difficulty, poor scalability, and high maintenance costs associated with wired control in industrial process control, this paper presents the design and development of a wireless temperature control system. The system utilizes the generalized open-loop response control-PID（GORC-PID） algorithm with packet loss compensation. The system adopts the Industrial WirelessHART standard protocol and deploys wireless hardware equipment to establish a wireless communication network, eliminating the need for wired communication. The MCGS configuration software serves as the control platform. The proposed approach combines the improved generalized open loop response control（GORC） with proportional integration differentiation（PID） control. A Smith predictor with packet loss compensation is constructed to estimate and compensate for packet loss data. The estimated value is then incorporated into the GORC rolling optimization process for real-time temperature monitoring and control. The system is validated using a high-temperature furnace as the research object. Experimental results demonstrate that the designed wireless temperature control system effectively mitigates packet loss and communication delays in wireless communication, showcasing its industrial value and applicability.