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2024 Photonics and Electromagnetics Research Symposium, PIERS 2024

作者： Lv, Yanfang Zhang, Lanjie Zhang, Yifan Zhang, Biao Key Laboratory of Information and Communication Systems Ministry of Information Industry Beijing Information Science and Technology University Beijing100101 China Key Laboratory of Modern Measurement and Control Technology Ministry of Education Beijing Information Science and Technology University Beijing100101 China

ISBN: (纸本)9798350375909

As an essential component of the earth system, precipitation plays a crucial role in the regional and global water cycle. This paper uses observations from the Fengyun-3D microwave humidity and temperature sounder and microwave radiation imager, as well as Integrated Multi-satellite Retrievals for GPM (IMERG), to establish a precipitation retrieval model based on the light gradient boosting model (LGBM). Using IMERG as the comparison data, the performance of precipitation identification with the LGBM-based precipitation retrieval model is as follows: accuracy is 0.9302, probability of detection is 0.7683, false alarm rate is 0.3529, and critical success index is 0.5414. The performance of precipitation estimation is also evaluated, with a correlation coefficient of 0.7536, a mean absolute error of 0.9119, and a mean squared error of 3.9506. In summary, this study achieves good performance in precipitation identification and estimation by the LGBM model. © 2024 IEEE.

关键词： Microwave acoustic devices

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Typhoon Wind Speed Retrieval from HY-2C Microwave Radiometer Based on LightGBM

Typhoon Wind Speed Retrieval from HY-2C Microwave Radiometer...

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2024 Photonics and Electromagnetics Research Symposium, PIERS 2024

作者： Dai, Xuchen Zhang, Biao Zhang, Yifan Zhang, Lanjie Key Laboratory of Information and Communication Systems Ministry of Information Industry Beijing Information Science and Technology University Beijing100101 China Ministry of Education Beijing Information Science and Technology University Key Laboratory of Modern Measurement and Control Technology Beijing100101 China

ISBN: (纸本)9798350375909

In this paper, the sea surface wind speed (SSWS) retrieval accuracy for HaiYang-2C (HY-2C) passive microwave radiometer under the severe weather such as typhoon is evaluated. Based on the observation data of HY-2C, this study explores the influence of brightness temperature (TB) observed by microwave radiometer on sea surface wind speed retrieval under typhoon conditions. In the case of typhoon precipitation, the attenuation of the atmosphere to the high frequency channel is stronger, which makes the sea surface information observed by the microwave radiometer very weak. Therefore, we mainly select the TB of horizontal polarization and vertical polarization observation of 6.9GHz and 10.7GHz channels to carry out the study of typhoon wind speed retrieval. This paper constructs a matching data set based on the TB observed by HY-2C passive microwave radiometer, HY-2C L2B wind product and H*wind reanalysis wind field data in October and November 2020. The maximum differences of spatial and temporal were set as 25km and 30minutes, respectively. Then, the typhoon wind speed retrieval model based on LightGBM is established. We use the data of October 2020 to train the model and the data of November 2020 to test model performance. Comparing with the H*wind reanalysis wind field data, the bias and root-mean-square error of retrieved wind speed are 0.15m/s and 1.26m/s, respectively. In addition, the correlation coefficient is 0.90, indicating that the retrieved wind speed has a good consistency with H*wind data. © 2024 IEEE.

关键词： Hurricanes

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Target-Before-Detect Algorithm for Passive MIMO Radar Based on Improved Particle Filter

Target-Before-Detect Algorithm for Passive MIMO Radar Based ...

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2022 Global Conference on Robotics, Artificial Intelligence and information technology, GCRAIT 2022

作者： Zhou, Yawen Zhu, Jiawei Song, Tingsong Yao, Siyi Lai, Rui Guo, Yuning School of Electronic Science and Technology University Chengdu China Information Security Control Laboratory Science and Technology on Communication Jiaxing China

ISBN: (数字)9781665481922

ISBN: (纸本)9781665481922

In order to improve the target detection performance of passive multiple-input-multiple-output (PMR) radar system, a Target-Before-Detect (TBD) algorithm based on improved particle filter is proposed in this paper. As a classical TBD algorithm, particle filter algorithm can realize the gain accumulation of target signal in time domain and effectively improve the performance of target detection. Although this algorithm has performed well in passive single-input-single-output (SISO) radar (PSR) systems, it cannot be directly applied to PMR systems. Therefore, an improved particle filtering algorithm based on passive MIMO radar is proposed in this paper. This method takes advantage of the PMR spatial accumulation and proposes a novel particle weight calculation method, which uses the false alarm probability distribution of detector to calculate the weight of the particle, so as to obtain the gain accumulation of the target in time and space. The simulation results show that the detection probability of the proposed algorithm can reach 90% even when the target-path signal-To-noise ratio (SNR) is low. Meanwhile, the good detection performance of the proposed algorithm is verified by comparing with the single frame detection results. © 2022 IEEE.

关键词： Probability distributions

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General Simultaneous Localization and Mapping Scheme for mmWave communication Systems

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IEEE Internet of Things Journal 2024年第12期11卷 22521-22536页

作者： Du, Tao Yang, Jie Wen, Chao-Kai Xia, Shuqiang Jin, Shi Southeast University National Mobile Communications Research Laboratory Nanjing210096 China Southeast University Key Laboratory Of Measurement And Control Of Complex Systems Of Engineering Ministry Of Education The Frontiers Science Center For Mobile Information Communication And Security Nanjing210096 China Institute Of Communications Engineering National Sun Yat-sen University Kaohsiung80424 Taiwan Zte Corporation The State Key Laboratory Of Mobile Network And Mobile Multimedia Technology Shenzhen518055 China Southeast University National Mobile Communications Research Laboratory The Frontiers Science Center For Mobile Information Communication And Security Nanjing210096 China

Utilizing high-resolution antenna arrays and wide bandwidth of the millimeter-wave (mmWave) spectrum in 5G new radio (NR) mmWave communication systems holds the potential for high-throughput data transmission while enabling user localization and environmental mapping. However, the majority of existing simultaneous localization and mapping (SLAM) algorithms rely on methods akin to the extended Kalman filter for generating initial map features. These methods prove ineffective when the measurement dimension is insufficient. Furthermore, there is a notable absence of research exploring mmWave prototype systems to evaluate and compare the performance and viability of various SLAM algorithms. To address these challenges, we propose an innovative probability hypothesis density (PHD) generation scheme for birth events and have developed a prototype system. Our approach, referred to as PHD-SLAM, exhibits remarkable effectiveness even in scenarios where the measurement dimension falls short of map features. This means it can function seamlessly with only delay or angle information available. Additionally, we have designed a 28GHz mmWave beam scanning prototype system that leverages the 5G NR frame for accomplishing SLAM algorithms. Following this, we conducted extensive simulations and experimental evaluations to gauge the performance of several leading-edge SLAM algorithms under diverse mmWave circumstances, encompassing PHD-based and belief propagation (BP) SLAM algorithms. Our analysis reveals that both PHD and BP SLAM can achieve agent localization precision within a decimeter and mapping precision within a meter, capitalizing on the angle parameters of mmWave signals. While BP SLAM showcases reduced computational demand, its estimation precision is marginally inferior to that of PHD-SLAM. © 2014 IEEE.

关键词： Belief propagation

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Multi-scale network for single image deblurring based on ensemble learning module

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Multimedia Tools and Applications 2025年第11期84卷 9045-9064页

作者： Wu, WenBo Pan, Yun Su, Na Wang, JingTao Wu, ShaoChuan Xu, ZeKun Yu, YouJian Liu, YaPeng State Key Laboratory of Media Convergence and Communication Communication University of China Beijing100024 China School of Computer and Cyberspace Security Communication University of China Beijing100024 China School of Electronics and Information Engineering Harbin Institute of Technology Harbin150080 China College of Computer and Control Engineering Northeast Forestry University Harbin150040 China School of Computer and Information Engineering Tianjin Chengjian University Tianjin300384 China School of Computer Science and Technology Anhui University Hefei230601 China

In this paper, we have identified two primary issues with current multi-scale image deblurring methods. On the one hand, the blurring scale is ignored. On the other hand, the context information of images is not fully utilized, and the spatial detail information of images is lost. To better solve the above problems, on top of that, this paper proposes a multi-scale network for single image deblurring. The designed network contains two parts: 1) Ensemble Learning Model: The Weak Learning Model in this module connects multiple weak learners in series to increase network depth. Moreover, through multi-scale feature ensemble module, the output features and intermediate features of different weak learner are integrated, and finally strong learner is used to enhance the features with semantic information at different scales;2) Spatial Feature Enhancement Module: This module can preserve spatial detail information of images and compensate for the disadvantages of losing image spatial details due to continuous downsampling in the Ensemble Learning Model structure. Our proposed deblurring model has been extensively evaluated on several benchmark datasets, and the model achieves superior performance compared to the most advanced deblurring methods. In particular, the peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) values are 33.57 dB and 0.965 on the GoPro dataset, respectively, with PSNR and SSIM improving by 0.49 dB and 0.002 over NAFNet, and 0.65 dB and 0.004 over Restormer. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Learning systems

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Scale Normalization and Lightweight FPN for Small Object Detection in Aerial Images 3

Scale Normalization and Lightweight FPN for Small Object Det...

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3rd International Conference on Computer Graphics, Image, and Virtualization, ICCGIV 2023

作者： Wei, Haokun Liu, Jingyi Chen, Jinyong Sun, Yuxin Chu, Boce Zhu, Jin Key Laboratory of Aerospace Information Applications of CETC Shijiazhaung China Science and Technology on Electro-Optical Information Security Control Laboratory Tianjin China

ISBN: (纸本)9781510671720

The detection of small objects by oriented bounding box in aerial images is a recent hot topic. However, since the aerial images are not collected at the same height, the Ground Sample Distance (GSD) is different for each image, so that small objects are easily overlooked. Existing algorithms are designed for multi-scale object detection, and feature fusion is time-consuming, resulting in a large amount of model parameters that is not easy to deploy on embedded devices. We propose three methods to address the above problems. First, we scale the collected aerial images to the same scale according to the GSD value. Second, we change the structure of Feature Pyramid Network (FPN) and only keep the necessary low-level feature maps. Finally, we rescale the anchor for the specific scene. We validate our proposed method on the DOTA dataset. The results show that the modified model using our method can identify more small-scale objects, and the maximum number of model parameters can be reduced by 2.7%, the inference speed can be increased by 13.24%, and the model size was reduced by up to 28% when the detection accuracy is the same as the original algorithm. © 2023 SPIE.

关键词： Object detection

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Linearized microwave downconversion link based on fast and intelligent impairment equalization for noncooperative systems

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Chinese Optics Letters 2023年第2期21卷 143-147页

作者：陈智宇钟欣蒋林徐嘉鑫刘静娴盘艳周涛 Key Laboratory of Science and Technology on Electronic Information Control Chengdu 610029China Center for Information Photonics&Communications School of Information Science&TechnologySouthwest Jiaotong UniversityChengdu 610031China Science and Technology on Communication Security Laboratory Institute of Southwestern CommunicationChengdu 610041China

We experimentally demonstrated the use of intelligent impairment equalization(IIE)for microwave downconversion link linearization in noncooperative *** an equalizer is realized based on an artificial neural network(ANN).Once the training process is completed,the inverse link transfer function can be *** the inverse transformation for the detected signal after transmission,the third-order intermodulation distortion components are suppressed significantly without requiring any prior information from an input RF ***,fast training speed is achieved,since the configuration of ANN-based equalizer is *** results show that the spurious-free dynamic range of the proposed link is improved to 106.5 dB·Hz^(2/3),which is 11.3 dB higher than that of a link without ***,the training epochs reduce to only five,which has the potential to meet the practical engineering requirement.

关键词： microwave photonics linearized downconversion link intelligent impairment equalization artificial neural network

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An Improved YOLOv5s-Based Smoke Detection System for Outdoor Parking Lots

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Computers, Materials & Continua 2024年第8期80卷 3333-3349页

作者： Ruobing Zuo Xiaohan Huang Xuguo Jiao Zhenyong Zhang State Key Laboratory of Public Big Data College of Computer Science and TechnologyGuizhou UniversityGuiyang550025China School of Information and Control Engineering Qingdao University of TechnologyQingdao266520China State Key Laboratory of Industrial Control Technology College of Control Science and EngineeringZhejiang UniversityHangzhou310027China Key Laboratory of Computing Power Network and Information Security Ministry of EducationQilu University of Technology(Shandong Academy of Sciences)Jinan250353China Text Computing&Cognitive Intelligence Engineering Research Center of National Education Ministry Guizhou UniversityGuiyang550025China

In the rapidly evolving urban landscape,outdoor parking lots have become an indispensable part of the city’s transportation *** growth of parking lots has raised the likelihood of spontaneous vehicle combus-tion,a significant safety hazard,making smoke detection an essential preventative ***,the complex environment of outdoor parking lots presents additional challenges for smoke detection,which necessitates the development of more advanced and reliable smoke detection *** paper addresses this concern and presents a novel smoke detection technique designed for the demanding environment of outdoor parking ***,we develop a novel dataset to fill the gap,as there is a lack of publicly available *** dataset encompasses a wide range of smoke and fire scenarios,enhanced with data augmentation to ensure robustness against diverse outdoor ***,we utilize an optimized YOLOv5s model,integrated with the Squeeze-and-Excitation Network(SENet)attention mechanism,to significantly improve detection accuracy while maintaining real-time processing ***,this paper implements an outdoor smoke detection system that is capable of accurately localizing and alerting in real time,enhancing the effectiveness and reliability of emergency *** show that the system has a high accuracy in terms of detecting smoke incidents in outdoor scenarios.

关键词： YOLOv5s smoke detection deep learning SENet

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Flexible and wearable battery-free backscatter wireless communication system for colour imaging

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npj Flexible Electronics 2024年第1期8卷 761-769页

作者： Jun-Lin Zhan Wei-Bing Lu Cong Ding Zhen Sun Bu-Yun Yu Lu Ju Xin-Hua Liang Zhao-Min Chen Hao Chen Yong-Hao Jia Zhen-Guo Liu Tie-Jun Cui State Key Laboratory of Millimeter Waves School of Information Science and EngineeringSoutheast UniversityNanjing 210096China Center for Flexible RF Technology Frontiers Science Center for Mobile Information Communication and SecuritySoutheast UniversityNanjing 210096China College of Electronic and Information Engineering Nanjing University of Aeronautics and AstronauticsNanjing 211106China

Wireless imaging,equippedwith ultralow power wireless communications and energy harvesting(EH)capabilities,have emerged as battery-free and sustainable ***,the challenge of implementing wireless colour imaging in wearable applications remains,primarily due to high power demands and the need to balance energy harvesting efficiency with device *** address these issues,we propose a flexible and wearable battery-free backscatter wireless communication system specially designed for colour *** system features a hybrid RF-solar EH array that efficiently harvests energy from both ambient RF and visible light energy,ensuring continuous operation in diverse ***,flexible materials allow the working system to conform to the human body,ensuring comfort,user-friendliness,and ***,a compact design utilizing a shared-aperture antenna array for simultaneous wireless information and power transfer(SWIPT),coupled with an optically transparent stacked *** design not only optimizes space but also maintains the performance of both communication and EH *** proposed flexible and wearable systems for colour imaging would have potentially applications in environmental monitoring,object detection,and law enforcement *** approach demonstrates a sustainable and practical solution for the next generation of wearable,power-demanding devices.

关键词： communication antenna battery

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information Freshness-Oriented Trajectory Planning and Resource Allocation for UAV-Assisted Vehicular Networks

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China communications 2023年第5期20卷 244-262页

作者： Hao Gai Haixia Zhang Shuaishuai Guo Dongfeng Yuan School of Control Science and Engineering Shandong UniversityJinan 250061China Shandong Key Laboratory of Wireless Communication Technologies Shandong UniversityJinan 250061China China Academy of Information and Communication Technology(CAICT) Beijing 100191China

In this paper,multi-UAV trajectory planning and resource allocation are jointly investigated to improve the information freshness for vehicular networks,where the vehicles collect time-critical traffic information by on-board sensors and upload to the UAVs through their allocated spectrum *** adopt the expected sum age of information(ESAoI)to measure the network-wide information *** is jointly affected by both the UAVs trajectory and the resource allocation,which are coupled with each other and make the analysis of ESAoI *** tackle this challenge,we introduce a joint trajectory planning and resource allocation procedure,where the UAVs firstly fly to their destinations and then hover to allocate resource blocks(RBs)during a *** on this procedure,we formulate a trajectory planning and resource allocation problem for ESAoI *** solve the mixed integer nonlinear programming(MINLP)problem with hybrid decision variables,we propose a TD3 trajectory planning and Round-robin resource allocation(TTPRRA).Specifically,we exploit the exploration and learning ability of the twin delayed deep deterministic policy gradient algorithm(TD3)for UAVs trajectory planning,and utilize Round Robin rule for the optimal resource *** TTP-RRA,the UAVs obtain their flight velocities by sensing the locations and the age of information(AoI)of the vehicles,then allocate the RBs to the vehicles in a descending order of AoI until the remaining RBs are not sufficient to support another successful *** results demonstrate that TTP-RRA outperforms the baseline approaches in terms of ESAoI and average AoI(AAoI).

关键词： information freshness for vehicular networks multi-UAV trajectory planning resource allocation deep reinforcement learning

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