Magneto-Dielectric Composites Characterization Using Resonant Sensor and Neural Network Modeling

被引：1

作者：

Alvarez-Botero, German ^{[1
]}

Lobato-Morales, Humberto ^{[2
]}

Hui, Katherine ^{[3
]}

Tarabay, Naji ^{[3
]}

Sanchez-Vargas, Jeu ^{[2
]}

Velez, Camilo ^{[3
]}

Mendez-Jeronimo, Gabriela ^{[2
]}

机构：

[1] Univ Publ Navarra, Dept Elect Elect & Commun Engn, Pamplona 31006, Spain

[2] CICESE, Ensenada 22860, Mexico

[3] Univ Calif Irvine, Mech & Aerosp Engn Dept, Magnet Microsyst & Microrobot Lab, Irvine, CA 92697 USA

来源：

IEEE MICROWAVE AND WIRELESS TECHNOLOGY LETTERS | 2024年 / 34卷 / 04期

关键词：

Artificial neural networks (ANNs); microwavecharacterization; PDMS-Fe3O4; composite; PERMITTIVITY; PERMEABILITY; ANTENNA; DESIGN;

D O I：

10.1109/LMWT.2024.3356418

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

This article presents a novel way to estimate magnetodielectric composites' complex permittivity (epsilon) and per-meability (mu). A methodology based on artificial neural network(ANN) modeling is proposed to determine epsilon and mu fromS-parameter measurements around 2.45 GHz, obtained using a new microstrip split ring resonator (SRR)-based resonant sensor

引用

页码：447 / 450

页数：4

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