Electromagnetic Classification

Files

Electromagnetic Classification.pdf (1.72 MB)

Links to Files

https://ieeexplore.ieee.org/document/10687184

Permanent Link

http://hdl.handle.net/11603/35974
 https://doi.org/10.1109/AP-S/INC-USNC-URSI52054.2024.10687184

Collections

UMBC Computer Science and Electrical Engineering Department
UMBC Data Science
UMBC Faculty Collection

Author/Creator

Author/Creator ORCID

https://orcid.org/0000-0001-9075-7071

Date

2024-09-30

Type of Work

conference papers and proceedings
postprints
Text

Department

Program

Citation of Original Publication

Simsek, Ergun. “Electromagnetic Classification.” 2024 IEEE International Symposium on Antennas and Propagation and INC/USNC‐URSI Radio Science Meeting (AP-S/INC-USNC-URSI), July 2024, 899–900. https://doi.org/10.1109/AP-S/INC-USNC-URSI52054.2024.10687184.

Rights

© 2024 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Subjects

Abstract

Rather than reconstructing the properties or parameters of a medium as it is done in electromagnetic inversion, this work aims to classify objects with neural networks that are trained with scattered field data and labels (classes). The study demonstrates the feasibility of achieving an 86% accuracy, showcasing potential applications in robotics and environmental perception.