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...
| Autores: | , , , , , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2024 |
| País: | España |
| Institución: | Universidad Pública de Navarra |
| Repositorio: | Academica-e. Repositorio Institucional de la Universidad Pública de Navarra |
| OAI Identifier: | oai:academica-e.unavarra.es:2454/48258 |
| Acceso en línea: | https://hdl.handle.net/2454/48258 |
| Access Level: | acceso abierto |
| Palabra clave: | Artificial neural networks (ANNs) Microwave characterization PDMS-Fe3O4 composite |
| Sumario: | 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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