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6th International Conference on Blockchain and Trustworthy Systems, BlockSys 2024

作者： Sun, Xueping Liu, Peng Liao, Bingjie He, Qian State and Local Joint Engineering Research Center for Satellite Navigation and Location Service Guilin University of Electronic Technology Guilin541004 China Guangxi Key Laboratory of Cryptography and Information Security Guilin University of Electronic Technology Guilin541004 China

ISBN: (纸本)9789819614103

With the development of communication technology and the prevalence of mobile smart devices, location-based services have been widely applied in various scenarios. However, the data typically provided by location-based services includes sensitive information such as personal identity, posing a risk of privacy leakage. Due to the limited resources of mobile smart devices, data is typically uploaded to semi-trusted cloud servers for storage and sharing, leading to the possibility of malicious access and abuse of data. To address these issues, we propose a location data sharing method based on blockchain and attribute-based encryption scheme. By combining blockchain technology with ciphertext-policy attribute-based encryption technology, the shared data is authenticated with trust while providing fine-grained access control. The immutability and traceability features of blockchain are utilized to enhance the security of the entire system. Furthermore, local differential privacy is employed to locally perturb the data before sharing, ensuring that individual privacy in location data remains uncompromised by untrusted third parties. Security analysis and performance evaluation indicate that our scheme is superior to the existing solutions. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Access Control Attribute-Based Encryption Blockchain local Differential Privacy location-Based service

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Velocity Deambiguation Method for DDMA-MIMO Radar Based on Phase Compensation

Velocity Deambiguation Method for DDMA-MIMO Radar Based on P...

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IEEE Information Technology and Mechatronics engineering Conference (ITOEC)

作者： Hu Haoming Sun Xiyan Li Jingjing Guangxi Key Laboratory of Precision Navigation Technology and Application University of Electronic Technology Guilin China Information and Communicaiton Schnool Guilin University of Electronic Technology Guilin China National & Local Joint Engineering Research Center of Satellite Navigation Positioning and Location Service Guilin China

ISBN: (数字)9798331529482

ISBN: (纸本)9798331529499

In order to address the issues of velocity ambiguity and target energy dispersion in DDMA-MIMO radar, an enhanced velocity defuzzification methodology is presented. Firstly, the repeated distribution of the target's spectrum is exploited to compress the range Doppler spectrum in the Doppler dimension. This results in the conversion n of the two-dimensional detection to the detection of a single distance-dimensional vector. Furthermore, the subarea peak search is employed to replace the Doppler-dimensional CFAR detection within the Doppler dimension, thereby significantly enhancing the detection efficiency. In addition, a DDMA waveform free of empty bands is designed to estimate the target angle while achieving velocity deblurring through phase compensation and digital beam forming algorithms. The efficacy and superiority of the proposed method are demonstrated through a verification process using measured data.

关键词： Radar measurements Radar detection Estimation Transforms Radar antennas Vectors Transceivers Linear antenna arrays Doppler effect Velocity measurement

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Multi-attitude based target localization method

Multi-attitude based target localization method

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2024 International Conference on Mechatronic engineering and Artificial Intelligence, MEAI 2024

作者： Yan, Suqing Xu, Xiaoyue Xiao, Jianming Ji, Yuanfa Hawari Bin Ghazali, Kamarul Guangxi Key Laboratory of Precision Navigation Technology and Application Guilin University of Electronic Technology Guilin China School of Information and Communication Guilin University of Electronic Technology Guilin China Department of Science and Engineering Guilin University Guilin China National & Local Joint Engineering Research Center of Satellite Navigation Localization and Location Service GUET-Nanning E-Tech Research Institute Co. Ltd. Nanning China Centre for Advanced Industrial Technology University of Malaysia Pahang Al Sultan Abdullah Pekan Campus Pahang Pekan Malaysia

ISBN: (纸本)9781510689176

In recent years, location-based services have gradually penetrated all aspects of people's daily life, bringing great convenience to people's work and life. Pedestrian Dead Reckoning (PDR) is a positioning method that can derive a pedestrian's movement trajectory by relying on sensors information, with a high degree of autonomy and continuity. However, existing PDR tends to restrict the target posture to remain steadily pointing in the direction of pedestrian movement throughout the localization process. In addition, although it can achieve a high degree of positioning accuracy in a short period of time, it generates a cumulative error over time. To these problems, this paper proposes a fusion localization method that combines PDR based on multiple smartphones pose recognition and acoustic localization. In this method, we first employ a GWO-ELM based target posture recognition model. Then based on the posture recognition result for PDR localization, Time Difference of Arrival (TDOA) technique is used for acoustic localization. Finally, particle filtering is utilized to combine the two methods. To demonstrate the validity of the method, we performed several experiments on a motion trajectory with a length of 97.2 meters. The results show that the method has better localization performance than the traditional method. In addition, it can realize high-precision localization under different devices with universality and stability. © 2025 SPIE.

关键词： Acoustics Target recognition Detection and tracking algorithms Particles Sensors Education and training Gesture recognition satellite navigation systems Tunable filters Machine learning

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Stereo matching algorithm based on binocular vision 7

Stereo matching algorithm based on binocular vision

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7th International Forum on Electrical engineering and Automation, IFEEA 2020

作者： Ji, Yuanfa Li, Yongchun Sun, Xiyan Yan, Suqing Guo, Ning National Local Joint Engineering Research Center of Satellite Navigation and Location Service Guilin541004 China

ISBN: (纸本)9781728196275

Binocular stereo vision technology has been widely used in target detection, unmanned driving and other aspects due to its advantages of low cost and wide range of application. It is a research hotspot in the field of computer vision. Binocular stereo vision is mainly to obtain 3D depth information of the scene by analyzing two or more views of the scene. Stereo matching is the core part of the scene, and the performance of the matching algorithm directly affects the effect of 3D reconstruction. This paper mainly analyzes and summarizes the local stereo matching algorithm, global stereo matching algorithm and semi-global stereo matching algorithm by studying stereo matching algorithms at home and abroad, expounds the characteristics and performance of various matching algorithms, and makes a comparative analysis of various algorithms. © 2020 IEEE.

关键词： Binocular vision

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Modeling of Total Electron Content in Regional Ionosphere Based on GPS 7

Modeling of Total Electron Content in Regional Ionosphere Ba...

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7th International Forum on Electrical engineering and Automation, IFEEA 2020

作者： Sun, Xiyan You, Menglin Ji, Yuanfa Yan, Suqing Fu, Wentao Liang, Weibin National Local Joint Engineering Research Center of Satellite Navigation and Location Service Guilin541004 China

ISBN: (纸本)9781728196275

Ionospheric delay can cause Global navigation satellite System (GNSS) deviations of several meters or even hundreds of meters in the positioning process. The Total Electron Content (TEC) is one of the important parameters in ionosphere. Starting from the basic principle of using GNSS to extract TEC of ionosphere, we analyze the shortcomings of pseudorange method and carrier phase method in the calculation of ionospheric TEC. And a method of calculating TEC by using smoothing algorithm is proposed. A polynomial function model combined with the smoothing algorithm is used to model TEC. The results show that the accuracy of TEC extraction and TEC model is higher than the previous two methods. © 2020 IEEE.

关键词： Ionosphere

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Distributed Computing method for Synthetic Aperture Radar Compressed Sensing Imaging based on MapReduce 3

Distributed Computing method for Synthetic Aperture Radar Co...

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3rd IEEE International Conference on Electronic Information and Communication Technology, ICEICT 2020

作者： Zheng, Can Liao, Kefei Ouyan, Shan Li, Changshu State and Local Joint Engineering Research Center for Satellite Navigation and Location Service Guilin University of Electronic Technology Guilin541004 China

ISBN: (纸本)9781728190457

When using the compressed sensing method in Synthetic Aperture Radar(SAR) imaging, there are two major problems: long calculation time and insufficient scalability of t calculation ability. In order to solve the above problems, this paper proposes a distributed imaging method for SAR compressed sensing imaging based on MapReduce. First, the sparse data is labeled, then the range and azimuth image are reconstructed by two MapReduce calculation processes. With parallel computing advantages, the acceleration of SAR compressed sensing imaging is realized. © 2020 IEEE.

关键词： MapReduce

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Reconstruction of sub cross-correlation cancellation technique for unambiguous acquisition of BOC(kn, n) signals

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Journal of Systems engineering and Electronics 2019年第5期30卷 852-860页

作者： JI Yuanfa CHEN Xiaoqian FU Qiang SUN Xiyan ZHEN Weimin Guangxi Key Laboratory of Precision Navigation Technology and Application Guilin University of Electronic TechnologyGuilin 541004China National&Local Joint Engineering Research Center of Satellite Navigation Positioning and Location Service Guilin 541004China Key Laboratory of Radio Wave Environment and Modeling Technology Qingdao 266000China

In order to solve the problem of ambiguous acquisition of BOC signals caused by its property of multiple peaks,an unambiguous acquisition algorithm named reconstruction of sub cross-correlation cancellation technique(RSCCT)for BOC(kn,n)signals is *** this paper,the principle of signal decomposition is combined with the traditional acquisition algorithm structure,and then based on the method of reconstructing the correlation *** method firstly gets the sub-pseudorandom noise(PRN)code by decomposing the local PRN code,then uses BOC(kn,n)and the sub-PRN code cross-correlation to get the sub cross-correlation ***,the correlation peak with a single peak is obtained by reconstructing the sub cross-correlation function so that the ambiguities of BOC acquisition are *** simulation shows that RSCCT can completely eliminate the side peaks of BOC(kn,n)group signals while maintaining the narrow correlation of BOC,and its computational complexity is equivalent to sub carrier phase cancellation(SCPC)and autocorrelation side-peak cancellation technique(ASPeCT),and it reduces the computational complexity relative to *** BOC(n,n),the acquisition sensitivity of RSCCT is 3.25 dB,0.81 dB and 0.25 dB higher than binary phase shift keying(BPSK)-like,SCPC and ASPeCT at the acquisition probability of 90%,*** peak to average power ratio is 1.91,3.0 and 3.7 times higher than ASPeCT,SCPC and BPSK-like at SNR=–20 dB,*** BOC(2n,n),the acquisition sensitivity of RSCCT is 5.5 dB,1.25 dB and 2.69 dB higher than BPSK-like,SCPC and ASPeCT at the acquisition probability of 90%,*** peak to average power ratio is 1.02,1.68 and 2.12 times higher than ASPeCT,SCPC and BPSK-like at SNR=–20 dB,respectively.

关键词： signal decomposition sub cross-correlation reconstructingcorrelation function acquisition performance

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LEBFL: Lightweight authentication and efficient consensus for blockchained federated learning in vehicle–road cooperation systems with AIoT

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Computer Communications 2025年 239卷

作者： Liu, Peng He, Qian Li, Sen Chen, Yiting Liu, Anfeng The State and Local Joint Engineering Research Center for Satellite Navigation and Location Service Guilin University of Electronic Technology Guangxi Guilin541004 China Guangxi Key Laboratory of Digital Infrastructure Guangxi Zhuang Autonomous Region Information Center Guagnxi Nanning530022 China School of Electronic Information Central South University Hunan Changsha410073 China

Artificial Intelligence Internet of Things (AIoT) technology is gradually overcoming challenges related to traffic data transmission and processing in vehicle-road cooperative systems. However, the dynamism and openness of the vehicle-road cooperative networks make it susceptible to potential attacks, where attackers might intercept or tamper with transmitted local model parameters, thereby compromising the integrity of the models and leaking user privacy. Although existing solutions such as differential privacy and encryption can address these issues, they may reduce data availability or increase computational complexity. To tackle these challenges, we propose a lightweight authentication and efficient consensus for blockchained federated learning in vehicle–road cooperation systems(LEBFL), which provides lightweight privacy-enhanced authentication and efficient consensus while ensuring the privacy of local models and datasets. Specifically, we first introduce a blockchain-based federated learning architecture that enhances privacy and efficient consensus, utilizing the consortium blockchain to replace the centralized server. Subsequently, we design a lightweight anonymous authentication and key agreement protocol using efficient cryptographic primitives to establish secure session keys for the transmission of local models. Furthermore, we propose a utility-based Raft consensus algorithm, which selects the optimal fog server as the leader node using a resource matrix and weight vector, and enhances the performance of the blockchain network by leveraging the idle computing resources of fog servers. Security analysis and experimental results confirm that the proposed scheme shows superior performance without sacrificing security. © 2025 Elsevier B.V.

关键词： Federated learning

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Lightweight Image Super-Resolution via Weighted Multi-Scale Residual Network

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IEEE/CAA Journal of Automatica Sinica 2021年第7期8卷 1271-1280页

作者： Long Sun Zhenbing Liu Xiyan Sun Licheng Liu Rushi Lan Xiaonan Luo Guangxi Key Laboratory of Image and Graphic intelligent processing Guilin University of Electronic TechnologyGuilin 541004China National Local Joint Engineering Research Center of Satellite Navigation and Location Service Guilin University of Electronic TechnologyGuilin 541004China the College of Electrical and Information Engineering Hunan UniversityChangsha 410082China

The tradeoff between efficiency and model size of the convolutional neural network(CNN)is an essential issue for applications of CNN-based algorithms to diverse real-world *** deep learning-based methods have achieved significant improvements in image super-resolution(SR),current CNNbased techniques mainly contain massive parameters and a high computational complexity,limiting their practical *** this paper,we present a fast and lightweight framework,named weighted multi-scale residual network(WMRN),for a better tradeoff between SR performance and computational *** the modified residual structure,depthwise separable convolutions(DS Convs)are employed to improve convolutional operations’***,several weighted multi-scale residual blocks(WMRBs)are stacked to enhance the multi-scale representation *** the reconstruction subnetwork,a group of Conv layers are introduced to filter feature maps to reconstruct the final high-quality *** experiments were conducted to evaluate the proposed model,and the comparative results with several state-of-the-art algorithms demonstrate the effectiveness of WMRN.

关键词： Convolutional neural network(CNN) lightweight framework multi-scale super-resolution

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Multi Scale UNet Encoder-Decoder Network for Building Extraction 2021

Multi Scale UNet Encoder-Decoder Network for Building Extrac...

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3rd International Conference on Information Technology and Computer Communications, ITCC 2021

作者： Sun, Xiyan Xiao, Yu Ji, Yuanfa Huang, Jianhua Bai, Yang Guanxi Key Laboratory of Precision Navigation Technology and Application Guilin University of Electronic Technology Guilin China National and Local Joint Engineering Research Center of Satellite Navigation and Location Service Guilin China

ISBN: (纸本)9781450389884

Buildings in remote sensing images have large scale differences and complex shapes. And there are often distractors with visual features similar to buildings in complex scenes. The traditional methods used to extract buildings are limited by the ability of feature representation, resulting in low accuracy and low universality. The semantic segmentation network based on the Encoder-Decoder structure can automatically learn multi-level building feature representation from the data set, and achieve end-to-end building extraction. UNet is a typical semantic segmentation Encoder-Decoder network, but UNet cannot explore enough building information. Small buildings are easy to be missed, large buildings with complex colors and shapes are incompletely extracted, boundary segmentation is inaccurate. And the network is easily affected by roads, trees, shadows and other distractors. Therefore, this article improves UNet and proposes a multi-scale Encoder-Decoder network to learn multi-scale and distinguishable features to better identify buildings and backgrounds. We experiment with the improved network and the classic U-Net on two data sets, and show that the multi-scale Encoder-Decoder network can effectively improve the accuracy of building extraction. © 2021 ACM.

关键词： Buildings

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