Enhancing Microwave Sensor Performance With Ultrahigh Q Features Using CycleGAN

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18470%2F22%3A50019993" target="_blank" >RIV/62690094:18470/22:50019993 - isvavai.cz</a>

Výsledek na webu

<a href="https://ieeexplore.ieee.org/document/9943996" target="_blank" >https://ieeexplore.ieee.org/document/9943996</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/TMTT.2022.3218015" target="_blank" >10.1109/TMTT.2022.3218015</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Enhancing Microwave Sensor Performance With Ultrahigh Q Features Using CycleGAN

Popis výsledku v původním jazyce

In this work, a microwave planar sensor is used for liquid material characterization. Two identical complementary split ring resonators (CSRRs) operating at 3 GHz are coupled to create a highly sensitive capacitive region. The moderate quality factor of the sensor approximate to 230 is significantly improved up to approximate to 5040 with loss compensation using a regenerative amplifier. The moderate quality factor restrains the passive mode sensor from distinguishing low concentrations of 1%-4% water in ethanol, while considerably distinct profiles are achievable using the active-mode sensor. The measured passive mode sensor response is then processed using CycleGAN, a machine-learning algorithm conventionally used for image-to-image translation. This strongly enhances the quality factor of the responses, effectively translating them to the active domain. This improvement reduces the limit of water detection down to 1% for the water-inethanol mixture. In addition, the sensor is used for noninvasive monitoring of glucose levels, in both passive and active modes. The resolution of the CycleGAN-boosted response approaches that of the active sensor (approximate to 20 mg/dL), showing a considerable enhancement when compared to the resolution of the passive sensor (approximate to 70 mg/dL).

Název v anglickém jazyce

Enhancing Microwave Sensor Performance With Ultrahigh Q Features Using CycleGAN

Popis výsledku anglicky

In this work, a microwave planar sensor is used for liquid material characterization. Two identical complementary split ring resonators (CSRRs) operating at 3 GHz are coupled to create a highly sensitive capacitive region. The moderate quality factor of the sensor approximate to 230 is significantly improved up to approximate to 5040 with loss compensation using a regenerative amplifier. The moderate quality factor restrains the passive mode sensor from distinguishing low concentrations of 1%-4% water in ethanol, while considerably distinct profiles are achievable using the active-mode sensor. The measured passive mode sensor response is then processed using CycleGAN, a machine-learning algorithm conventionally used for image-to-image translation. This strongly enhances the quality factor of the responses, effectively translating them to the active domain. This improvement reduces the limit of water detection down to 1% for the water-inethanol mixture. In addition, the sensor is used for noninvasive monitoring of glucose levels, in both passive and active modes. The resolution of the CycleGAN-boosted response approaches that of the active sensor (approximate to 20 mg/dL), showing a considerable enhancement when compared to the resolution of the passive sensor (approximate to 70 mg/dL).

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

2022

Kód důvěrnosti údajů

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

Název periodika

IEEE TRANSACTIONS ON MICROWAVE THEORY AND TECHNIQUES

ISSN

0018-9480

e-ISSN

1557-9670

Svazek periodika

70

Číslo periodika v rámci svazku

12

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

14

Strana od-do

5369-5382

Kód UT WoS článku

000881963500001

EID výsledku v databázi Scopus

2-s2.0-85141613235