2019 IEEE International Conference on Computational Electromagnetics (ICCEM)最新文献_第2页

Computational Electromagnetics Performances of Magnetic Microrobotics Systems 磁性微型机器人系统的计算电磁学性能

2019 IEEE International Conference on Computational Electromagnetics (ICCEM) Pub Date : 2019-03-20 DOI: 10.1109/COMPEM.2019.8778897

Ruipeng Chen, D. Folio, Antoine Ferreira

引用次数: 0

Application of Artificial Neural Network for Electromagnetic Source Reconstruction 人工神经网络在电磁源重构中的应用

2019 IEEE International Conference on Computational Electromagnetics (ICCEM) Pub Date : 2019-03-20 DOI: 10.1109/COMPEM.2019.8779236

Z. Hu, Y. Zhong, Yi-Wen Wang, Y. Shu, Xing-Chang Wei

引用次数: 3

Electronic Beam-steering Using 1-Bit Digital Rflective Metasurface at Ka Band Ka波段1位数字反射超表面电子波束导向

2019 IEEE International Conference on Computational Electromagnetics (ICCEM) Pub Date : 2019-03-20 DOI: 10.1109/COMPEM.2019.8779030

Jiaqi Han, Guangyao Liu, Yan Shi, Long Li

引用次数: 0

High-efficiency Dual-band Bifocal Metalens Based on Reflective Metasurface 基于反射超表面的高效双频双焦点超透镜

2019 IEEE International Conference on Computational Electromagnetics (ICCEM) Pub Date : 2019-03-20 DOI: 10.1109/COMPEM.2019.8778990

Rensheng Xie, Yiting Liu, Tailei Wang, Guohua Zhai, Jianjun Gao, Jun Ding

引用次数: 4

Solving Full-Wave Nonlinear Inverse Scattering Problems by Deep Learning Schemes 用深度学习方法求解全波非线性逆散射问题

2019 IEEE International Conference on Computational Electromagnetics (ICCEM) Pub Date : 2019-03-20 DOI: 10.1109/COMPEM.2019.8779092

Zhun Wei, Xudong Chen

引用次数: 2

A Wideband Substrate Integrated Waveguide Slot Antenna with Quad Modes 四模宽带基板集成波导缝隙天线

2019 IEEE International Conference on Computational Electromagnetics (ICCEM) Pub Date : 2019-03-20 DOI: 10.1109/COMPEM.2019.8779212

Yun-Yong Zou, Yong-Dan Kong

引用次数: 1

Efficient Evaluations of Periodic Green’s Functions Through Imaginary Wavenumber Cancellations 虚波数对消周期格林函数的有效求值

2019 IEEE International Conference on Computational Electromagnetics (ICCEM) Pub Date : 2019-03-20 DOI: 10.1109/COMPEM.2019.8778978

Shurun Tan, L. Tsang

{"title":"Efficient Evaluations of Periodic Green’s Functions Through Imaginary Wavenumber Cancellations","authors":"Shurun Tan, L. Tsang","doi":"10.1109/COMPEM.2019.8778978","DOIUrl":"https://doi.org/10.1109/COMPEM.2019.8778978","url":null,"abstract":"We report an interesting and newly invented technique to efficiently evaluate periodic Green’s functions that are widely used for analyzing periodic structure scattering using integral equations. The Green’s function at an arbitrary wavenumber is evaluated through subtracting out the Green’s function evaluated at a specified imaginary wavenumber and then adding back the same component. The extracted terms are effectively evaluated in terms of spatial series which decay exponentially at imaginary wavenumbers. On the other hand, the terms being added back are represented in spectral series, which not only cancel out the singularity of the periodic Green’s function, but also significantly improve the spectral convergence rate of the Green’s function. Such a technique constitutes a self-consistent methodology to effectively evaluate periodic Green’s functions using ordinary representations without involving complicated integral transformations or transcendental functions. The technique is widely applicable to periodic Green’s functions that share the same dimensionality between the problem space and periodicity, which occur frequently in metamaterials, photonic crystals, phononic crystals, and atomic structures. It is also applicable to periodic Green’s function where the dimensionality of periodicity is less than the problem space, well representative of gratings and metasurfaces. In the former case, the new representation of the Green’s function has very simple wavenumber dependences in a rational multiplicative factor, so it is especially effective to evaluate the Green’s function over a broadband of wavenumbers. In the latter case, computing the Green’s function when both the field point and source point are located in a plane parallel to the lattice vectors is deemed difficult. We demonstrate the effectiveness of the proposed technique using two-dimensional problems with both two-dimensional and one-dimensional periodicities.","PeriodicalId":342849,"journal":{"name":"2019 IEEE International Conference on Computational Electromagnetics (ICCEM)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129074645","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: 1

An Ultra Wideband FSS Operating at Ka Band 工作在Ka波段的超宽带FSS

2019 IEEE International Conference on Computational Electromagnetics (ICCEM) Pub Date : 2019-03-20 DOI: 10.1109/COMPEM.2019.8778845

Yinfang Xu, Shengjun Zhang

引用次数: 2

End-to-End Target Detection and Classification with Data Augmentation in SAR Images 基于数据增强的SAR图像端到端目标检测与分类

2019 IEEE International Conference on Computational Electromagnetics (ICCEM) Pub Date : 2019-03-20 DOI: 10.1109/COMPEM.2019.8779096

Mingtao Dong, Yuanhao Cui, Xiaojun Jing, Xiaokang Liu, Jianquan Li

{"title":"End-to-End Target Detection and Classification with Data Augmentation in SAR Images","authors":"Mingtao Dong, Yuanhao Cui, Xiaojun Jing, Xiaokang Liu, Jianquan Li","doi":"10.1109/COMPEM.2019.8779096","DOIUrl":"https://doi.org/10.1109/COMPEM.2019.8779096","url":null,"abstract":"While applying traditional algorithm to synthetic aperture radar automatic target recognition (SAR-ATR) is facing difficulties, deep learning-based end-to-end object detection algorithms are becoming better options due to the automatic feature extraction and availability of high-quality data. In this paper, both single-staged and two-staged end-to-end models are experimented. We proposed modified Faster R-CNN models and SSD models to address SAR-ATR. Data augmentation techniques including random flipping, multiplying, rotation, translation, and flipping are applied to MSTAR SAR dataset to solve problems related to limited training samples. Transfer learning of SSD models and Faster R-CNN models on COCO dataset are utilized. Both existing algorithms and proposed algorithms are tested in ten-class MSTAR dataset. Experimental results show that SSD-Inception with widened network and MobileNet-SSD with light weight structure perform with much faster speed and cheaper computational cost, hundreds of times faster than Faster R-CNNs. MobileNet-SSD is especially suitable for mobile devices with 0.028 second per batch*step. Faster R-CNN with ResNet-101 and Inception ResNet perform in slightly higher accuracy than SSDs, reaching 99.4% mAP. MobileNet-SSD and SSD-Inception reach 96.79% and 99.16% mAP respectively.","PeriodicalId":342849,"journal":{"name":"2019 IEEE International Conference on Computational Electromagnetics (ICCEM)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128079402","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

Dual-Band and Tri-Band Parasitic Dipole Antennas for Wearable Applications 可穿戴应用的双频和三频寄生偶极子天线

2019 IEEE International Conference on Computational Electromagnetics (ICCEM) Pub Date : 2019-03-20 DOI: 10.1109/COMPEM.2019.8778873

Ning Luo, Long Zhang, Sai‐Wai Wong, Wei He, Yejun He

引用次数: 5