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Non-Line-of-Sight Multipath Classification Method for BDS Using Convolutional Sparse Autoencoder with LSTM

Tsinghua Science and technology 2025年第1期30卷 68-86页

作者： Yahang Qin Zhenni Li Shengli Xie Bo Li Ming Liu Victor Kuzin School of Automation Guangdong University of Technologyand also with Guangdong-Hong Kong-Macao Joint Laboratory for Smart Discrete ManufacturingGuangzhou 510006China School of Automation Guangdong University of TechnologyGuangdong Key Laboratory of IoT Information TechnologyGuangzhou 510006China 111 Center for Intelligent Batch Manufacturing Based on IoT Technology Key Laboratory of Intelligent Information Processing and System Integration of IoTMinistry of EducationGuangzhou 510006China Department of Electronic and Computer Engineering Hong Kong University of Science and TechnologHong Kong 999077China Academician of Russian Engineering Academy Moscow Moscow 125009Russia

Multipath signal recognition is crucial to the ability to provide high-precision absolute-position services by the BeiDou Navigation Satellite System(BDS).However,most existing approaches to this issue involve supervised machine learning(ML)methods,and it is difficult to move to unsupervised multipath signal recognition because of the limitations in signal *** by an autoencoder with powerful unsupervised feature extraction,we propose a new deep learning(DL)model for BDS signal recognition that places a long short-term memory(LSTM)module in series with a convolutional sparse autoencoder to create a new autoencoder ***,we propose to capture the temporal correlations in long-duration BeiDou satellite time-series signals by using the LSTM module to mine the temporal change patterns in the time ***,we develop a convolutional sparse autoencoder method that learns a compressed representation of the input data,which then enables downscaled and unsupervised feature extraction from long-duration BeiDou satellite series ***,we add an l_(1/2) regularizer to the objective function of our DL model to remove redundant neurons from the neural network while ensuring recognition *** tested our proposed approach on a real urban canyon dataset,and the results demonstrated that our algorithm could achieve better classification performance than two ML-based methods(e.g.,11%better than a support vector machine)and two existing DL-based methods(e.g.,7.26%better than convolutional neural networks).

关键词： convolutional sparse autoencoder BeiDou Navigation Satellite System(BDS) long short-term memory(LSTM) multipath classification

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A new S-box construction method meeting strict avalanche criterion

A new S-box construction method meeting strict avalanche cri...

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作者： Li, Lang Liu, Jinggen Guo, Ying Liu, Botao College of Computer Science and Technology Hengyang Normal University Hengyang421002 China Hunan Provincial Key Laboratory of Intelligent Information Processing and Application Hengyang421002 China

S-box is the main security component in lightweight block ciphers. Finding new algorithms and technical applications to generate S-box is still a current research topic. We proposed an S-box construction method that satisfies the strict avalanche criterion. It includes a nonlinear layer and a diffusion layer. These layers enable the constructed S-box to maintain good nonlinearity, differential uniformity, strict avalanche criterion, and a high number of algebraic terms. Secondly, we listed 20 existing classic 4 × 4 S-boxes and tested their cryptographic properties such as nonlinearity, difference uniformity, strict avalanche criterion, algebraic degree, algebraic item number, and fixed point. At the same time, the test results are compared with the proposed S-box. The results show that these cryptographic properties of our proposed S-box are superior to other S-boxes. In addition, we evaluated the proposed S-box hardware implementation overhead. To evaluate our S-box more objectively, we tested the above 20 S-boxes hardware implementation overhead under the same conditions, and the results show that the S-box we constructed achieved a good balance between cryptographic properties and hardware overhead. © 2022 Elsevier Ltd

关键词： Algebra

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Hyperchaotic Image Encryption Algorithm Based on BD-Zigzag Transformation and DNA Coding 12th

Hyperchaotic Image Encryption Algorithm Based on BD-Zigzag T...

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12th International Conference on computer Engineering and Networks, CENet 2022

作者： Wang, Lujie Chen, Zhong College of Computer Science and Technology Hengyang Normal University Hengyang421002 China Hunan Provincial Key Laboratory of Intelligent Information Processing and Application Hengyang421002 China

ISBN: (纸本)9789811969003

For the features of Zigzag transformation, a Bidirectional Deformed Zigzag (BD-Zigzag) transformation is proposed in this paper, which starts from both sides of the diagonal and crosses in both directions. This method is more complex than the traditional Zigzag transformation method and has better dislocation effect. Then, a Hyperchaotic image encryption algorithm based on BD-Zigzag transformation and DNA coding is proposed. First, the images are processed with multiple rounds of permutations using the BD-Zigzag transformation. Second, a 4D-hyperchaotic Lorenz system is used to produce random numbers to permute the images at the bit level. Finally, DNA encoding is used to diffuse the pixels in the image. The experimental simulations and performance analysis data can prove that the encryption algorithm has high-level security capability. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： DNA

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A New Encryption Scheme Based on RSA Encryption Algorithm and Chen Chaotic System 12th

A New Encryption Scheme Based on RSA Encryption Algorithm an...

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12th International Conference on computer Engineering and Networks, CENet 2022

作者： He, Chenchen Chen, Zhong College of Computer Science and Technology Hengyang Normal University Hengyang421002 China Hunan Provincial Key Laboratory of Intelligent Information Processing and Application Hengyang421002 China

ISBN: (纸本)9789811969003

Nowadays the security of images is obviously more important. Some scholars have applied Rivest-Shamir-Adleman (RSA) algorithm and Chen chaotic system to image encryption. However, the application of the two methods in image encryption can still be explored. In this paper, it proposes an encryption scheme which uses asymmetric cipher RSA encryption algorithm and Chen chaotic system to scramble and diffuse the image. In this encryption scheme, firstly, it applies the RSA algorithm to obtain the public key and uses the specified formula to calculate the initial value of the Chen chaotic system. Secondly, the four chaotic sequences generated by Chen chaotic system are applied to the scrambling stage and the diffusion stage of the image process respectively. In the scrambling phase, it uses Arnold scrambling. It performs forward XOR diffusion and then performs reverse XOR diffusion in the diffusion stage. Finally, the encryption process is all completed and the encrypted image is obtained. Moreover, after appropriate experiments, the encryption algorithm is proved to have a good guarantee for the security of the image. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Diffusion

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SSD object detection algorithm based on knowledge map

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International Journal of Wireless and Mobile Computing 2024年第2期27卷 152-160页

作者： Huang, Li Wang, Xiaofeng Lu, Jianhua Hu, Wei Zhang, Changrong College of Computer Science and Technology Wuhan University of Science and Technology Hubei Wuhan China China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan University of Science and Technology Hubei Wuhan China Key Laboratory of Metallurgical Equipment and Control Technology of the Ministry of Education Wuhan University of Science and Technology Hubei Wuhan China Precision Manufacturing Research Institute Research Centre for Biomimetic Robot and Intelligent Measurement and Control Wuhan University of Science and Technology Hubei Wuhan China

With the pervasive integration of artificial intelligence into all aspects of human life, talent emerges as a primary resource. Upon analysing the current state of talent training in higher education institutions, issues such as dispersed knowledge points, overlapping content, a singular practice approach and ineffective evaluation have been identified. In response to these challenges, this paper proposes a multidisciplinary and comprehensive practical teaching methodology grounded in the knowledge graph framework. It delves into diverse paths for practical teaching and assessment, including aspects like teaching objectives, problem decomposition, resource integration, implementation methods and performance evaluation. The practical application of the SSD algorithm in researching service robot indoor object detection serves as an illustrative example. Employing the holistic practical teaching approach facilitated by the knowledge graph, this model guides students in acquiring object detection expertise, thereby enhancing their comprehensive development. Copyright © 2024 Inderscience Enterprises Ltd.

关键词： Object detection

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A Multi-emitter Localization Method Based on Improved Sparse Bayesian Learning Under Multi-station Association 12th

A Multi-emitter Localization Method Based on Improved Sparse...

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12th International Conference on computer Engineering and Networks, CENet 2022

作者： Wang, Zhehao Zhou, Zou Bai, Xiao Liang, Caisheng Provincial Ministry of Education Key Laboratory of Cognitive Radio and Information Processing Guilin University of Electronic Technology Guilin541004 China

ISBN: (纸本)9789811969003

Compressive Sensing (CS) can reduce the target parameter space search complexity and enhance the localization robustness under small-sample noisy observations. Therefore, CS is becoming the mainstream method for multi-emitter localization studies. But in CS-based localization, the real target position may deviate from the preset grid point (off-grid) to form a grid mismatch, which leads to dictionary mismatch. The existing works reduce the localization error due to grid mismatch by dictionary learning but do not fully exploit the spatial diversity of different observation stations. In this paper, based on the uniform grid points division of the sensing region, we consider the sparsity of the distance and angle to form a respective spatial representation dictionary for each observation point. Then, we design a distance-angle off-grid model to propose an improved Sparse Bayesian Learning (SBL) method in the SBL framework. This method combines the multi-station dictionary to simultaneously infer the distance and angle to achieve accurate spatial grid point matching. Simulation results show that the proposed method has better performance in the case of low signal-to-noise ratio (SNR), few array antennas, and many emitters. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Compressed sensing

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Electromigration of Backside Power Delivery Networks for PPA-Reliability Tradeoffs at N2 Node

Electromigration of Backside Power Delivery Networks for PPA...

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2023 International Electron Devices Meeting, IEDM 2023

作者： Cai, Linlin Chen, Yutao Zhang, Haoyu Lin, Jianwen Yin, Binyu Chen, Wangyong Sun Yat-sen University School of Microelectronics Science and Technology Guangzhou510275 China Guangdong Provincial Key Laboratory of Optoelectronic Information Processing Chips and Systems Guangzhou510275 China

ISBN: (纸本)9798350327670

Electromigartion (EM) reliability of backside power delivery network (BSPDN) using three kinds of techniques (BPR, TSVM and BSC) is comprehensively investigated by proposed electro-thermal-stress fully coupled EM simulation method. The physical behaviors of metal atom migration, vacancy generation and void nucleation are reproduced to offer the understanding of microscopic void evolutions. The obtained position of EM failure and resistance degradation show a well agreement with the experiments in different metals. By studying the time to failure (TTF) of various BSPDNs connected to power lines (GND, VDD) with alternative metal materials (W, Ru, Mo), valuable insights can be gained to mitigate potential failure risks and enhance the EM reliability of advanced interconnects technology. The tradeoffs of power-performance-area-reliability (PPAR) are predicted in different BSPDNs at N2 node. © 2023 IEEE.

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Privacy-preserving and efficient user matching based on attribute encryption in mobile social networks

Privacy-preserving and efficient user matching based on attr...

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作者： Wu, Lei Zhao, Shengnan Wei, Xia Meng, Lingzhen School of Information Science and Engineering Shandong Normal University Jinan China Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology Jinan China

With the rapid popularity of social networking platforms, users can be matched when sharing their profiles. However, there is a risk of leakage of sensitive user information during the user matching process, which leads to the lack of user privacy protection. In this paper, we propose a privacy protection scheme based on the encryption of hidden attributes during user matching in mobile social networks, which uses linear secret sharing scheme (LSSS) as the access structure based on ciphertext policy attribute-based encryption (CP-ABE), and the match server can perform friend recommendation by completing bi-directional attribute matching determination without disclosing user attribute information. In addition, the use of selective keywords protects the privacy of requesters and publishers in selecting keywords and selecting plaintext attacks. The scheme reduces the encryption and decryption overhead for users by dividing encryption into a preparation phase and an online phase and shifting most of the decryption overhead from the requester to the match server. The experimental results show that the scheme ensures user privacy while effectively reducing communication overhead. © 2021 John Wiley & Sons, Ltd.

关键词： Cost reduction

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Applying Hybrid Clustering in Pulsar Candidate Sifting with Multi-modality for FAST Survey

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Research in Astronomy and Astrophysics 2024年第3期24卷 283-296页

作者： Zi-Yi You Yun-Rong Pan Zhi Ma Li Zhang Shuo Xiao Dan-Dan Zhang Shi-Jun Dang Ru-Shuang Zhao Pei Wang Ai-Jun Dong Jia-Tao Jiang Ji-Bing Leng Wei-An Li Si-Yao Li School of Physics and Electronic Science Guizhou Normal UniversityGuiyang 550025China Guizhou Provincial Key Laboratory of Radio Astronomy and Data Processing Guizhou Normal UniversityGuiyang 550025China College of Big Data and Information Engineering Guizhou UniversityGuiyang 550025China CAS Key Laboratory of FAST National Astronomical ObservatoriesChinese Academy of SciencesBeijing 100101China Key Laboratory of Information and Computing Guizhou Province Guizhou Normal UniversityGuiyang 550001China School of Big data and Computer Science Guizhou Normal UniversityGuiyang 550025China Guizhou Software Engineering Research Center Guiyang 550000China

Pulsar search is always the basis of pulsar navigation,gravitational wave detection and other research ***,the volume of pulsar candidates collected by the Five-hundred-meter Aperture Spherical radio Telescope(FAST)shows an explosive growth rate that has brought challenges for its pulsar candidate filtering ***,the multi-view heterogeneous data and class imbalance between true pulsars and non-pulsar candidates have negative effects on traditional single-modal supervised classification *** this study,a multi-modal and semi-supervised learning based on a pulsar candidate sifting algorithm is presented,which adopts a hybrid ensemble clustering scheme of density-based and partition-based methods combined with a feature-level fusion strategy for input data and a data partition strategy for *** on both High Time Resolution Universe SurveyⅡ(HTRU2)and actual FAST observation data demonstrate that the proposed algorithm could excellently identify pulsars:On HTRU2,the precision and recall rates of its parallel mode reach0.981 and 0.988 *** FAST data,those of its parallel mode reach 0.891 and 0.961,meanwhile,the running time also significantly decreases with the increment of parallel nodes within ***,we can conclude that our algorithm could be a feasible idea for large scale pulsar candidate sifting for FAST drift scan observation.

关键词： methods data analysis-surveys-methods numerical

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Distributed acoustic sensing signal event recognition and localization based on improved YOLOv7

Distributed acoustic sensing signal event recognition and lo...

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2023 International Congress on Ultrasonics, ICU Beijing 2023

作者： Zhang, Hongyu Wang, Chang Zhang, Faxiang Jiang, Shaodong Sun, Zhihui Wang, Xiaodong Duan, Zhenhui Gao, Fengxia Liu, Zhaoying Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Qilu University of Technology Shandong Academy of Sciences Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

The distributed acoustic sensing (DAS) system based on phase-sensitive optical time domain reflection (Φ -OTDR) technology is widely used in pipeline safety monitoring, perimeter security, structure monitoring, etc. Accurate localization and recognition of multiscene events over long distances has always been a challenge. This paper proposes an improved YOLOv7 algorithm for multi-event real-time detection of DAS system. The algorithm employs space-to-depth Conv(SPD-Conv) to replace the strided convolutions and pooling operations in YOLOv7, reducing fine-grained information loss and learning of inefficient feature representations. In addition, the Convolutional Block Attention Module (CBAM) is introduced in YOLOv7 to improve the model performance. We collected spatial-temporal signal data for six types of pipeline safety events, and passed them into the improved YOLOv7 algorithm in the form of data matrixes for training and *** have shown that the proposed method achieves an mAP@.5 (mean Average Precision) of 99.7% for the identification of six pipeline safety event types. Positioning loss reduced to 0.2%,and detection speed can reach 70 frames per second(FPS). Our scheme achieves significant improvements in localization and classification accuracy compared to Faster R-CNN, etc. The event recognition localization method proposed in this paper has the advantage of fast speed and high accuracy. © 2024 Institute of Physics Publishing. All rights reserved.

关键词： Light reflection

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