Direct Permittivity Reconstruction From Power Measurements Using a Machine Learning Aided Method-Reference-Cited by-同舟云学术

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Direct Permittivity Reconstruction From Power Measurements Using a Machine Learning Aided Method

Published:2023-10 Issue:10 Volume:71 Page:4437-4448
ISSN:0018-9480
Container-title:IEEE Transactions on Microwave Theory and Techniques
language:
Short-container-title:IEEE Trans. Microwave Theory Techn.

Author:

Mosavirik Tahoura¹,Nayyeri Vahid²^ORCID,Hashemi Mohammad³,Soleimani Mohammad¹^ORCID,Ramahi Omar M.⁴^ORCID

Affiliation:

1. School of Electrical Engineering, Iran, University of Science and Technology, Tehran, Iran

2. School of Electrical Engineering, Iran University of Science and Technology, Tehran, Iran

3. Department of Electrical and Computer Engineering, Worcester Polytechnic Institute (WPI), Worcester, MA, USA

4. Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, Canada

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Condensed Matter Physics,Radiation

Link

http://xplorestaging.ieee.org/ielx7/22/10273174/10111003.pdf?arnumber=10111003

Reference42 articles.

1. Permittivity Reconstruction of Nondispersive Materials Using Transmitted Power at Microwave Frequencies

2. A simple method to derive bounds on the size and to train multilayer neural networks

3. Elimination of the multiple-solutions ambiguity in permittivity extraction from transmission-only measurements of lossy materials

4. Accuracy-Improved and Low-Cost Material Characterization Using Power Measurement and Artificial Neural Network

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