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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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Temperature Self-Reference Gas Pressure Sensor Based on Intracavity Sensing of Fiber Ring Laser

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Journal of Lightwave technology 2025年第9期43卷 4259-4265页

作者： Shi, Boya Huo, Xinjuan Wang, Shaona Niu, Pingjuan Tang, Longhuang Li, Hongmei Yao, Jianquan Shi, Jia Tiangong University Tianjin Key Laboratory of Optoelectronic Detection Technology and System School of Electronics and Information Engineering Tianjin300387 China China Academy of Engineering Physics Institute of Fluid Physics Mianyang621900 China Aero Engine Corporation of China Commercial Aircraft Engine Company Limited Shanghai200241 China Ministry of Education Tianjin University Key laboratory of Opto-Electronic Information Technology Tianjin300072 China

The temperature cross-sensitivity is always challenging for the design and application of fiber-optic gas pressure sensors. Here, a temperature self-reference gas pressure sensor based on intracavity sensing of fiber ring laser has been demonstrated. The Fabry-Perot interferometer and fiber Bragg grating are cascaded as reflection sensing head and coupled into a fiber ring laser. The Fabry-Perot interferometer shows high sensitivities of gas pressure and temperature because of its specific fiber micro cavity structure. The fiber Bragg grating has narrow-band filtering characteristics at a certain wavelength, which ensures the desired accuracy for intracavity sensing, and has good temperature response characteristics. In the temperature self-reference sensing system, the output wavelength of the fiber ring laser is only modulated by the sensor temperature, while the output intensity is simultaneously modulated by the sensor temperature and gas pressure. The sensing model of the temperature self-reference gas pressure sensor has been presented. The experiment verifies that the scheme can measure gas pressure and temperature simultaneously. The stability and measurement errors have been analyzed. The proposed sensing system provides a potential solution for solving temperature cross-sensitivity in gas pressure measurement and can be extensible to similar fiber-optic sensors. © 1983-2012 IEEE.

关键词： Fabry-Perot interferometers

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Ce-UiO-66 nanozymes with charge-regulated uricase-like activity for enzyme/reagent-free detection of uric acid

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Nano Research 2025年第2期18卷 675-686页

作者： Zhixiong Guo Da Chen Wei Gao Yongquan Qu Zhimin Tian Key Laboratory of Special Functional and Smart Polymer Materials of Ministry of Industry and Information Technology School of Chemistry and Chemical EngineeringNorthwestern Polytechnical UniversityXi’an 710054China State Key Laboratory of Solidification Processing Center for Nano Energy MaterialsSchool of Materials Science and EngineeringNorthwestern Polytechnical UniversityXi’an 710072China

Designing highly performed uricase-like nanozymes through enzymatic-like colorimetric analysis system is of vital significance for the quantitative detection of uric acid(UA).Herein,series of Ce-UiO-66 nanozymes with different surface charges through the ligand engineering strategy were rationally designed and synthesized as efficient uricase mimics with tailorable uricase-like activities to catalytically convert UA into allantoin and ***,by tuning the functional groups of 1,4-benzoic acid ligands,we explored the relationships between the surface charges of Ce-UiO-66-X(X=H,NO_(2),Br,CH_(3),and OH)nanozymes and their uricase-like *** them,Ce UiO-66-CH_(3) with the moderate surface charge exhibited optimal substrate adsorption and product desorption,displaying the highest uricase-like ***,H2O2,as the product of UA oxidation,enabled Ce-UiO-66-CH_(3) itself as a dose-dependent chromogenic substrate of H_(2)O_(2),giving a white-to-orange color evolution due to the Ce-UiO-66-CH_(3)-to-CeO_(2) phase ***,a smartphone-assisted all-in-one enzyme/reagent-free biosensor based on Ce-UiO-66-CH_(3) was established for the precise visual detection of UA analysis,which was featured by a wide detection range(31–4000µM),a high sensitivity(limit of detection:8.9µM),a rapid response(-3 min),a high structural stability,and a high anti-interference ability.

关键词： Ce-UiO-66 uricase-like nanozyme colorimetric uric acid analysis enzyme/reagent-free detection smartphone

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Facile formation of Mg-containing interphase on nanosilicon from Agar/MgCO3 precursor for lithium-ion battery anodes

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Progress in Natural science:Materials International 2025年第1期35卷 177-186页

作者： Ke Zhang Jianhua Zhou Haoran Liu Xiaoyang Wang Jie Gao Jun-Liang Chen Haiqing Qin Wenping Liu Xiaoxu Lei Lei Miao Guangxi Key Laboratory of Information Materials Guangxi Collaborative Innovation Center of Structure and Property for New Energy Materials School of Material Science and Engineering Guilin University of Electronic Technology Novel Battery Materials Research Center of Engineering Technology State Key Laboratory of Featured Metal Materials and Life-cycle Safety for Composite StructuresSchool of Physical Science and Technology Guangxi University Guangxi Key Laboratory of Superhard Material Guangxi Technology Innovation Center for Special Mineral Material China Nonferrous Metal (Guilin) Geology and Mining Co. Ltd.

The ultra-high theoretical capacity(4200 mAh g-1) of Silicon anode materials for lithium-ion batteries while which is one of the ideal replacement materials for graphite anodes. However, the poor electrical conductivity and greatly reduces the cycle life of the battery of Silicon material, which suffers from severe volume expansion during charge/discharge cycling leading to electrode pulverization. In this work, Agar/magnesium carbonate-coated silicon nanocomposites were synthesized by utilizing the "self-solidifying" property of agar. The magnesium oxide formed by the decomposition of magnesium carbonate at high temperatures mitigates the volume expansion of nanosilicon together with the derived carbon layer. Moreover, the lithium-magnesium alloy electronconducting interface layer generated by the reaction between lithium ions and magnesium oxide, which greatly shortens the diffusion length of lithium ions and electrons inside the electrode during the charge-discharge cycle. The first discharge specific capacity was 2060.2 mAh g-1, the Coulombic efficiency as high as 86.28 %, and still possesses a reversible specific capacity of 801.5 mAh g-1after cycling 200 cycles at a high current of 500 mA g-1of Agar/magnesium@Si with optimal ratio composite. The composite material used in this work largely suppresses the volume expansion of the silicon anode and contributes to the design of low-cost and high Coulombic efficiency lithium-ion batteries.

关键词： Agar-coated Lithium-ion battery Lithium-magnesium conductive layer Silicon anode

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Convolutional Neural Networks Aided Reinforcement Learning for Accelerated Optimization of Antenna Topology

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Applied Computational Electromagnetics Society Journal 2025年第1期40卷 35-41页

作者： Dou, Jiangling Gong, Hao Wei, Shuaibing Chen, Haokang Chen, Yujie Shen, Tao Song, Jian Yunnan Key Laboratory of Computer Technologies Application Kunming University of Science and Technology Kunming650500 China School of Information Engineering and Automation Kunming University of Science and Technology Kunming650500 China College of Mechanical and Electrical Engineering Yunnan Electromechanical Vocational and Technical College Kunming650500 China

A machine learning (ML) framework is proposed to achieve the automatic and rapid optimization of antenna topologies. A convolutional neural network (CNN) is utilized as a surrogate model (SM) and is combined with reinforcement learning (RL) algorithms. Specifically, the RL agent interacts with simulation software to learn. Data accumulated from electromagnetic (EM) simulations are used to train the SM. The CNN-based SM predicts antenna performance based on the topology of the antenna. Subsequently, the SM replaces EM simulations within the RL training environment. The RL agent interacts with the CNN-based SM to search for the optimal topology. This approach significantly reduces dependence on time-consuming EM simulations. To validate the effectiveness of the optimization method, a center-fed microstrip patch antenna is optimized. Simulation results demonstrate that, compared to other optimization methods, impedance bandwidth is improved, while the number of simulation samples and optimization time are significantly reduced. © ACES.

关键词： Convolutional neural networks

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Compression Format and Systolic Array Structure Co-design for Accelerating Sparse Matrix Multiplication in DNNs 24th

Compression Format and Systolic Array Structure Co-design f...

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24th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2024

作者： Cao, Yongxiang Jiang, Jixiang Zhao, Guocheng Wang, Wei Jiang, Hongxu Song, Yanfei School of Computer Science and Engineering Beihang University Beijing China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing China

ISBN: (纸本)9789819615445

The systolic array (SA) architecture is widely used in accelerator/AI chip design due to its excellent matrix multiplication acceleration effect. However, the mismatch between the traditional sparse matrix compression format and the SA architecture results in poor SA performance in computing sparse matrices. To solve the problem, we propose a method for co-designing the sparse compression format and SA architecture. This work proposes the Vector Group Compressed Coordinate (VGCC) data stream compression format and designs a sparse SA architecture based on hardware encoding and decoding. VGCC can handle data streams of any size without increasing the index bit width. The VGCC compression size is significantly smaller than general sparse compression formats such as COO/CSR. In a sparse matrix with a sparsity of 90%, it saves 67.13% of storage space compared to the state-of-the-art ECOO data stream compression format and achieves an average acceleration of 1.54 times. We also propose a VGCC-friendly SA architecture. Our SA architecture engine reduces logic consumption by 51.6% compared to Sparse TPU while achieving a 3.23x speedup. To mitigate the issue of high no-load rates in systolic arrays, we introduce a Data Transmission Priority (DTP) data matching algorithm. With sparsity levels ranging from 50% to 90%, the algorithm’s calculation speed is enhanced by 1.124x to 1.991x. We implemented a sparse DNN accelerator on an FPGA Zynq UltraScale+MPSoC ZCU102, utilizing the VGCC compression format proposed in this study for compressing input data. When compared to the S2 Engine, the accelerator achieved a 3.37x speed improvement in matrix multiplication with 75% sparsity, significantly enhancing the speed of sparse matrix multiplication. The accelerator achieved an average throughput of 1079.33GOP/s when inferencing DNNs. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Systolic arrays

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Wavelet-based intelligent optimization for doppler velocity estimation in the presence of celestial spectral distortion

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Advances in Space Research 2025年第10期75卷 7524-7539页

作者： Zhang, Zijun Liu, Jin Ning, Xiaolin Ma, Xin School of Electronic Information Wuhan University of Science and Technology Wuhan430081 China School of Instrument Science & Opto-electronics Engineering Beihang University Beijing100191 China Hefei National Laboratory Hefei230088 China

The variations in instrument status, along with the absorption and reflectance of planetary bodies, can cause distortions in celestial spectra that affect the accuracy of Doppler velocity estimation. High-precision Doppler velocity estimation can enhance the accuracy of the celestial velocimetry navigation. To address this issue, we propose a wavelet-based intelligent optimization for Doppler velocity estimation (WIODVE), considering that wavelet coefficients encapsulate spectral distortion signals. During the training phase, the WIODVE utilizes a weighted factor set derived from the wavelet coefficients of celestial spectra to construct the position of horned lizards, using the Doppler velocity error as the fitness function. The horned lizard optimization algorithm (HLOA) is employed to optimize the weight factor set, allowing for the reconstruction of spectral distortions. In the testing phase, the optimized weight factor set and wavelet transform are used to dynamically reconstruct the distortion of the celestial spectra. Subsequently, the reconstructed distortions are employed to correct the observed celestial spectra, with Doppler velocity estimated by the Taylor method. Additionally, we derive the Cramér-Rao lower bound (CRLB) for Doppler velocity estimation in the presence of celestial spectral distortion. Experimental results demonstrate that the WIODVE outperforms both the template enhanced radial velocity reanalysis application (TERRA) and the Taylor methods, approaching the CRLB, and exhibits strong robustness to spectral distortions in the estimation of the Doppler velocity. Furthermore, the WIODVE significantly enhances the accuracy of the celestial velocimetry navigation. © 2025 COSPAR

关键词： Cramer-Rao bounds

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Analysis of Strain Distribution and Sensitivity Improvement of Optical Fiber Coils in Disc Optical Fiber Accelerometers

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Journal of Lightwave technology 2025年第11期43卷 5473-5482页

作者： Mo, Haohao Wen, Kunhua Yang, Jun Ping, Xinyang An, Ran Wang, Shun Xu, Pengbai Yu, Zhangjun Wang, Yuncai Qin, Yuwen Guangdong University of Technology School of Physics and Optoelectronic Engineering Guangzhou510006 China Ministry of Education of China Guangdong University of Technology Key Laboratory of Photonic Technology for Integrated Sensing and Communication Guangzhou510006 China Guangdong University of Technology Guangdong Provincial Key Laboratory of Information Photonics Technology Guangzhou510006 China Guangdong University of Technology Guangdong Provincial Key Laboratory of Sensing Physics and System Integration Applications Guangzhou510006 China Guangdong University of Technology Institute of Advanced Photonic Technology the School of Information Engineering Guangzhou510006 China Ministry Education of China Harbin Engineering University Key Lab of In-fiber Integrated Optics Harbin150001 China Harbin Engineering University College of Physics and Optoelectronic Engineering Harbin150001 China

Multi-turn optical fiber coils, as the key sensing components of Fiber Optic Disc Accelerometers (FODAs), significantly affect the FODA performances due to their layered structure. This paper explores the strain distribution characteristics of the optical fiber coils by utilizing the inherent properties of the FODA, supported by Finite Element Method (FEM) and high-precision Optical Frequency Domain Reflectometry (OFDR). The results indicate that the simulation outcomes are largely consistent with the OFDR measurements, accurately reflecting the strain distribution within the optical fiber coils of the FODA. This analysis dose not requires additional external force application devices and carries no risk of damaging the FODA. Based on FEM, the simulation model is employed to optimize the existing structure, proposing a multi-turn optical fiber coil fabricated from ultra-fine diameter fiber. By altering the structure of optical fiber coil to make the internal strain distribution trend uniform, the sensitivity of the FODA is increased to approximately 150% of its original value. © 2025 IEEE. All rights reserved.

关键词： Frequency domain analysis

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Passive on-chip isolators based on the thin-film lithium niobate platform

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Chinese Physics B 2025年第3期34卷 408-413页

作者： Jiacheng Liu Gongyu Xia Qilin Hong Pingyu Zhu Kai-Kai Zhang Keyu Xia Ping Xu Shiqiao Qin Zhihong Zhu College of Advanced Interdisciplinary Studies&Hunan Provincial Key Laboratory of Novel Nano Optoelectronic Information Materials and Devices National University of Defense TechnologyChangsha 410073China Institute for Quantum Information and State Key Laboratory of High-Performance Computing College of Computer Science and TechnologyNational University of Defense TechnologyChangsha 410073China College of Engineering and Applied Sciences National Laboratory of Solid State Microstructuresand Collaborative Innovation Center of Advanced MicrostructuresNanjing UniversityNanjing 210023China

Optical isolators,the photonic analogs of electronic diodes,are essential for ensuring the unidirectional flow of light in optical systems,thereby mitigating the destabilizing effects of back ***-film lithium niobate(TFLN),hailed as“the silicon of photonics,”has emerged as a pivotal material in the realm of chip-scale nonlinear optics,propelling the demand for compact optical *** report a breakthrough in the development of a fully passive,integrated optical isolator on the TFLN platform,leveraging the Kerr effect to achieve an impressive 10.3 dB of isolation with a minimal insertion loss of 1.87 *** theoretical simulations have demonstrated that our design,when applied to a microring resonator with a Q factor of 5×10^(6),can achieve 20 dB of isolation with an input power of merely 8 *** advancement underscores the immense potential of lithium niobate-based Kerr-effect isolators in propelling the integration and application of high-performance on-chip lasers,heralding a new era in integrated photonics.

关键词： thin-film lithium niobate Kerr effect optical isolator

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Comparison of Machine Learning Methods for Air Quality Prediction Based on Particulate Matter (PM2.5)

Comparison of Machine Learning Methods for Air Quality Predi...

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2025 International Conference on Advancement in Data science, E-learning and information system, ICADEIS 2025

作者： Mar'i, Farhanna Marianingsih, Susi Abdillah, Rifqi Elfaiz, Ersha Aisyah Setyo Prayoga, Riza Akhsani Fahmi, Muhamad Universitas Negeri Surabaya Faculty of Engineering Department of Informatics Engineering Surabaya Indonesia Jayapura University of Science and Technology Department of Information System Jayapura Indonesia Universitas Negeri Surabaya Faculty of Engineering Department of Information Technology Education Surabaya Indonesia Universitas Airlangga Faculty of Science and Technology Department of Biomedical Engineering Surabaya Indonesia

ISBN: (纸本)9798331513320

Particulate PM2.5 has a major effect on human health compared to other types of pollutants such as NO2, 03, CO, and SO2. PM2.5 is the worst type of major pollutant for human health that can cause various types of serious diseases and even death. PM2.5 is defined as an ambient air particle, which is one of the particle elements found in the free air of the earth's surface in the troposphere layer, which is very small in size, reaching 2.5 pollutants. The method of predicting air quality index values can be done using machine learning techniques with time series data analysis. This study will compare machine learning methods with two approaches, namely statistical regression with RF and ARIMA and deep learning method, namely LSTM. Based on the research results, the best machine learning method approach is statistical regression, namely the RF method with accuracy evaluation using RMSE, MAE, and MSE, which are respectively with a minimum value of 0.162, 0.108, and 0.026, followed by the LSTM and ARIMA methods. © 2025 IEEE.

关键词： Polycyclic aromatic hydrocarbons

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