Artificial Neural Network Model for Evaluating Parameters of Reflection-Asymmetric Samples From Reference-Plane-Invariant Measurements-Reference-Cited by-同舟云学术

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Artificial Neural Network Model for Evaluating Parameters of Reflection-Asymmetric Samples From Reference-Plane-Invariant Measurements

Published:2023 Issue: Volume:72 Page:1-8
ISSN:0018-9456
Container-title:IEEE Transactions on Instrumentation and Measurement
language:
Short-container-title:IEEE Trans. Instrum. Meas.

Author:

Hasar Ugur C.¹^ORCID,Ozturk Hamdullah¹^ORCID,Ertugrul Mehmet²^ORCID,Barroso Joaquim J.³^ORCID,Ramahi Omar M.⁴^ORCID

Affiliation:

1. Department of Electrical and Electronics Engineering, Gaziantep University, Gaziantep, Turkey

2. Department of Electrical and Electronic Engineering, Ataturk University, Erzurum, Turkey

3. Instituto Tecnológico de Aeronáutica, São José dos Campos, São Paulo, Brazil

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

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Instrumentation

Link

http://xplorestaging.ieee.org/ielx7/19/10012124/10121334.pdf?arnumber=10121334

Reference51 articles.

1. Determination of Complex Permittivity of Low-Loss Samples From Position-Invariant Transmission and Shorted-Reflection Measurements

2. Extraction of Stable Complex Permittivity and Permeability of Low-Loss Materials From Transmission/Reflection Measurements

3. Unique Retrieval of Complex Permittivity and Permeability of Dispersive Materials From Reflection and Transmitted Fields by Enforcing Causality

4. Contribution to numerical methods for calculation of complex dielectric permittivities

5. An Improved Two-Port Transmission Line Permittivity and Permeability Determination Method With Shorted Sample

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