Material Characterization Using Power Measurements: Miracle of Machine Learning-Reference-Cited by-同舟云学术

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Material Characterization Using Power Measurements: Miracle of Machine Learning

Published:2022-04-04 Issue: Volume: Page:
ISSN:
Container-title:2021 51st European Microwave Conference (EuMC)
language:
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Author:

Mosavirik Tahoura¹,Hashemi Mohammad²,Soleimani Mohammad¹,Nayyeri Vahid³,Ramahi Omar M.⁴

Affiliation:

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

2. School of Electrical and Computer Engineering, University of Tehran,Tehran,Iran

3. School of Advanced Technologies, Iran University of Science and Technology,Tehran,Iran

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

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9784154/9783612/09784321.pdf?arnumber=9784321

Reference17 articles.

1. Intelligent sensing using multiple sensors for material characterization;albishi;SENSORS,2019

2. Artificial Intelligence Assisted Noncontact Microwave Sensor for Multivariable Biofuel Analysis

3. Measurement of Complex Permittivity using Artificial Neural Networks

4. AN ARTIFICIAL NERVE NETWORK REALIZATION IN THE MEASUREMENT OF MATERIAL PERMITTIVITY

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

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1. A General Study for the Complex Refractive Index Extraction Including Noise Effect Using a Machine Learning-Aided Method;IEEE Access;2024

2. Battery-Free, Artificial Neural Network-Assisted Microwave Resonator Array for Ice Detection;IEEE Transactions on Microwave Theory and Techniques;2023-02

3. Enhancing Microwave Sensor Performance With Ultrahigh Q Features Using CycleGAN;IEEE Transactions on Microwave Theory and Techniques;2022-12

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