Magneto-dielectric composites characterization using resonant sensor and neural network modeling

This article presents a novel way to estimate magnetodielectric composites’ complex permittivity (ε) and permeability (µ). A methodology based on artificial neural network (ANN) modeling is proposed to determine ε and µ from S-parameter measurements around 2.45 GHz, obtained using a new microstrip s...

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Bibliographic Details
Authors: Álvarez Botero, Germán Andrés, Lobato-Morales, Humberto, Hui, Katherine, Tarabay, Naji, Sánchez-Vargas, Jeu, Vélez, Camilo, Méndez-Jerónimo, Gabriela
Format: article
Status:Published version
Publication Date:2024
Country:España
Institution:Universidad Pública de Navarra
Repository:Academica-e. Repositorio Institucional de la Universidad Pública de Navarra
OAI Identifier:oai:academica-e.unavarra.es:2454/48258
Online Access:https://hdl.handle.net/2454/48258
Access Level:Open access
Keyword:Artificial neural networks (ANNs)
Microwave characterization
PDMS-Fe3O4 composite
Description
Summary:This article presents a novel way to estimate magnetodielectric composites’ complex permittivity (ε) and permeability (µ). A methodology based on artificial neural network (ANN) modeling is proposed to determine ε and µ from S-parameter measurements around 2.45 GHz, obtained using a new microstrip split ring resonator (SRR)-based resonant sensor.

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