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SMARTIES

Surface Modified Alloy Resource of Ti based Implant with Electromagnetic Structures

This research is funded by a grant from the Romanian Ministry of Education and Research, CNCS-UEFISCDI, project number PN-III-P4-ID-PCE-2020-0404, within PNCDI III

CONFERENCE PUBLICATION

[C4] Key Generation of Biomedical Implanted Antennas Through Artificial Neural Networks

Abstract

This paper presents an accurate and efficient optimization-based approach for modelling and sizing implanted antennas automatically. The proposed method employs the long short-term memory (LSTM) artificial neural network (ANN) for predicting the design specifications in not only one frequency but also in a large frequency band. The entire process is performed in an automated environment that is the combination of electronic design automation (EDA) tools and the numerical analyzer. Based on this intelligent method, the difficulty of designing electromagnetic (EM)-based antennas is solved to the most degrees and the design parameters can be achieved in the easiest way. To validate the efficiency of the presented ANN, two implanted antennas are designed; they and realized on a grounded biocompatible substrate and covered by bone, muscle, fat, and skin tissues, respectively. These implanted antennas are optimized in terms of input scattering parameter, E-plane and H-plane radiation pattern (RP) specifications and the suitable design parameters are provided automatically. The modelled implanted antennas are appropriate to be used at the industrial, scientific, and medical (ISM) frequency band between 2.4 GHz and 2.5 GHz.

Keywords

Automated design, artificial neural network (ANN), bandwidth, implanted antenna, long short-term memory (LSTM) layer, radiation pattern (RP)

Published In: 2021 IEEE/ACM Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE)
Date:

2021-12-16

Location:

Washington, DC, USA

DOI:

10.1109/CHASE52844.2021.00037

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How to cite:

L. Kouhalvandi, L. Matekovits and I. Peter, "Key Generation of Biomedical Implanted Antennas Through Artificial Neural Networks," 2021 IEEE/ACM Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE), 2021, pp. 161-165, doi: 10.1109/CHASE52844.2021.00037.