2020 IEEE USNC-CNC-URSI North American Radio Science Meeting (Joint with AP-S Symposium)最新文献_第3页

Occluded Scatterers and the Urban Ground-to-ground Channel at Low UHF 低UHF时城市地对地信道的遮挡散射体

2020 IEEE USNC-CNC-URSI North American Radio Science Meeting (Joint with AP-S Symposium) Pub Date : 2020-07-05 DOI: 10.23919/USNC/URSI49741.2020.9321618

D. Breton, C. Haedrich

{"title":"Occluded Scatterers and the Urban Ground-to-ground Channel at Low UHF","authors":"D. Breton, C. Haedrich","doi":"10.23919/USNC/URSI49741.2020.9321618","DOIUrl":"https://doi.org/10.23919/USNC/URSI49741.2020.9321618","url":null,"abstract":"Ground-to-ground radio links in urban environments rarely enjoy direct line-of-sight between terminals, and therefore in-canyon, over-rooftop, and scattering from distant structures become primary propagation modes. Because both rooftop diffraction and canyon propagation losses can be severe, and because the walls of deep urban canyons often occlude distant scatterers, the relative importance of these three propagation modes to a given urban channel is unclear. We present results of channel sounding measurements at 437 MHz for ground-to-ground links in Boston, Massachusetts, USA to quantify the importance of each propagation mode. Occupancy curves derived from our measured channels suggest that while canyon-mode propagation is dominant for short range urban links, the importance of the distant scatterer propagation mode increases with terminal separation distance, even when those scatterers are occluded from transmitter and/or receiver view. We present an urban channel model which evaluates the vertical profile of incident power on distant scatterers, even those that are occluded, and find that reasonable agreement can be obtained between measured and modeled channels only when occluded buildings are considered.","PeriodicalId":443426,"journal":{"name":"2020 IEEE USNC-CNC-URSI North American Radio Science Meeting (Joint with AP-S Symposium)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128647455","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

P and L Band Coherent Wave Propagation through a Tree Covered Mountainside P和L波段相干波通过树木覆盖的山腰的传播

2020 IEEE USNC-CNC-URSI North American Radio Science Meeting (Joint with AP-S Symposium) Pub Date : 2020-07-05 DOI: 10.23919/USNC/URSI49741.2020.9321656

C. Suer, Y. Park, R. Lang, C. Haedrich, D. Breton

引用次数: 1

Improving the Efficiency of Maxwell’s Equations FDTD Modeling for Space Weather Applications by Scaling the Speed of Light 以光速为尺度提高空间气象应用麦克斯韦方程FDTD建模效率

2020 IEEE USNC-CNC-URSI North American Radio Science Meeting (Joint with AP-S Symposium) Pub Date : 2020-07-05 DOI: 10.23919/USNC/URSI49741.2020.9321624

Yisong Zhang, J. Simpson, D. Welling, M. Liemohn

引用次数: 1

A Deep Learning-Based Methodology for Rapidly Detecting the Defects inside Tree Trunks via GPR 基于深度学习的GPR树干内部缺陷快速检测方法

2020 IEEE USNC-CNC-URSI North American Radio Science Meeting (Joint with AP-S Symposium) Pub Date : 2020-07-05 DOI: 10.23919/USNC/URSI49741.2020.9321692

Qiqi Dai, B. Wen, Y. Lee, A. Yucel, Genevieve Ow, Mohamed Lokman Mohd Yusof

{"title":"A Deep Learning-Based Methodology for Rapidly Detecting the Defects inside Tree Trunks via GPR","authors":"Qiqi Dai, B. Wen, Y. Lee, A. Yucel, Genevieve Ow, Mohamed Lokman Mohd Yusof","doi":"10.23919/USNC/URSI49741.2020.9321692","DOIUrl":"https://doi.org/10.23919/USNC/URSI49741.2020.9321692","url":null,"abstract":"This paper proposes a deep learning-based approach for rapidly detecting the defects inside tree trunks via ground penetrating radar (GPR) technology. In this approach, GPR measurements are performed centimeters-away from the surface of tree trunk on a straight trajectory. The n the B-scans obtained from GPR measurements are processed via a deep learning algorithm to detect the defects inside the tree trunks, classify their types, and estimate their sizes/severities. An open-source finite-difference time-domain (FDTD) simulator is used to produce a large set of B-scans from random realizations of realistic 2D tree trunk cross-sections without and with different size of defects (cavities, decays, and cracks). The data set is then used to train and test a six-layer convolutional neural network (CNN) with drop-out layers and weight regularization to avoid overfitting. Our preliminary results show that the testing accuracy of the CNN algorithm is more than 90%. The testing results demonstrate that the current methodology al lows accurately detecting the types and sizes of defects inside tree trunks to monitor the health condition of trees.","PeriodicalId":443426,"journal":{"name":"2020 IEEE USNC-CNC-URSI North American Radio Science Meeting (Joint with AP-S Symposium)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127302404","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

A terahertz photonic crystal wavelength division multiplexer based on graphene surface plasmon polaritons 基于石墨烯表面等离子激元极化子的太赫兹光子晶体波分复用器

2020 IEEE USNC-CNC-URSI North American Radio Science Meeting (Joint with AP-S Symposium) Pub Date : 2020-07-05 DOI: 10.23919/USNC/URSI49741.2020.9321631

Chenglong Wang, Junchao Ji, Xidong Wu, Jinfang Zhou

引用次数: 0

DOA Estimation with a Rank-deficient Covariance matrix: A Regularized Least-squares approach 秩缺失协方差矩阵的DOA估计:一种正则化最小二乘方法

2020 IEEE USNC-CNC-URSI North American Radio Science Meeting (Joint with AP-S Symposium) Pub Date : 2020-07-05 DOI: 10.23919/USNC/URSI49741.2020.9321628

Hussain Ali, Tarig Ballal, T. Al-Naffouri, M. Sharawi

引用次数: 0

Solving Time Domain Electromagnetic Problems using a Differentiable Programming Platform 利用可微规划平台求解时域电磁问题

2020 IEEE USNC-CNC-URSI North American Radio Science Meeting (Joint with AP-S Symposium) Pub Date : 2020-07-05 DOI: 10.23919/USNC/URSI49741.2020.9321666

Yanyan Hu, Yuchen Jin, Xuqing Wu, Jiefu Chen

引用次数: 0

Analysis of GPS Gradient Parameters for Rainfall Prediction GPS梯度参数在降雨预报中的应用分析

2020 IEEE USNC-CNC-URSI North American Radio Science Meeting (Joint with AP-S Symposium) Pub Date : 2020-07-05 DOI: 10.23919/USNC/URSI49741.2020.9321610

Anik Naha Biswas, Yee Hui Lee, Shilpa Manandhar

引用次数: 2

Development of an Optimized Real-Time Radio Transient Imager for LWA-SV LWA-SV优化实时无线电瞬变成像仪的研制

2020 IEEE USNC-CNC-URSI North American Radio Science Meeting (Joint with AP-S Symposium) Pub Date : 2020-07-05 DOI: 10.23919/USNC/URSI49741.2020.9321611

H. Krishnan, J. Kent, J. Dowell, Adam P Bearsdley, J. Bowman, G. Taylor, Nithyanandhan Thyagarajan, D. Jacobs

引用次数: 0

A UAV Path Planning Method Based on Deep Reinforcement Learning 基于深度强化学习的无人机路径规划方法

2020 IEEE USNC-CNC-URSI North American Radio Science Meeting (Joint with AP-S Symposium) Pub Date : 2020-07-05 DOI: 10.23919/USNC/URSI49741.2020.9321625

Yibing Li, Sitong Zhang, Fang Ye, T. Jiang, Yingsong Li

引用次数: 12