Resolution enhancement of microwave sensors using super-resolution generative adversarial network

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18470%2F23%3A50020125" target="_blank" >RIV/62690094:18470/23:50020125 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.sciencedirect.com/science/article/abs/pii/S0957417422022709" target="_blank" >https://www.sciencedirect.com/science/article/abs/pii/S0957417422022709</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.eswa.2022.119252" target="_blank" >10.1016/j.eswa.2022.119252</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Resolution enhancement of microwave sensors using super-resolution generative adversarial network

Popis výsledku v původním jazyce

This article presents an approach to significantly improve the resolution of a highly-sensitive microwave planar sensor response with a super-resolution generative adversarial network (SRGAN). Three identical complementary split-ring resonators are coupled so that the sensitivity is doubled. This highly-sensitive resonator with a deep transmission zero at 4.7 GHz is deployed to measure minute variations of glucose in interstitial fluid. Measuring the sensor response with 1001 frequency-points allows differentiating 10 glucose samples within the range of 40-400 mg/dL. However, in practical readout systems with limited number of frequency-points (here 28), recognizing the deep zero in the S21 response lacks precision. Sensor responses (magnitude vs. frequency and phase vs. frequency) are converted into equivalent 2D images (heatmaps: phase vs. frequency with colored pixels as amplitude) to be compatible as SRGAN input. As a result of 8-fold resolution enhancement using SRGAN, the classification accuracy is substantially improved from 62.1% to 93.3%. The proposed passive sensor followed by an SRGAN unit is shown to be practical as a wearable glucose monitoring sensor due to its high-sensitivity and high resolution features in a low-profile design.

Název v anglickém jazyce

Resolution enhancement of microwave sensors using super-resolution generative adversarial network

Popis výsledku anglicky

This article presents an approach to significantly improve the resolution of a highly-sensitive microwave planar sensor response with a super-resolution generative adversarial network (SRGAN). Three identical complementary split-ring resonators are coupled so that the sensitivity is doubled. This highly-sensitive resonator with a deep transmission zero at 4.7 GHz is deployed to measure minute variations of glucose in interstitial fluid. Measuring the sensor response with 1001 frequency-points allows differentiating 10 glucose samples within the range of 40-400 mg/dL. However, in practical readout systems with limited number of frequency-points (here 28), recognizing the deep zero in the S21 response lacks precision. Sensor responses (magnitude vs. frequency and phase vs. frequency) are converted into equivalent 2D images (heatmaps: phase vs. frequency with colored pixels as amplitude) to be compatible as SRGAN input. As a result of 8-fold resolution enhancement using SRGAN, the classification accuracy is substantially improved from 62.1% to 93.3%. The proposed passive sensor followed by an SRGAN unit is shown to be practical as a wearable glucose monitoring sensor due to its high-sensitivity and high resolution features in a low-profile design.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20201 - Electrical and electronic engineering

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2023

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Expert systems with applications

ISSN

0957-4174

e-ISSN

1873-6793

Svazek periodika

213

Číslo periodika v rámci svazku

March

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

14

Strana od-do

"Article Number: 119252"

Kód UT WoS článku

000913153300003

EID výsledku v databázi Scopus

2-s2.0-85142712880