Selective Microwave Zeroth-Order Resonator Sensor Aided by Machine Learning
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18470%2F22%3A50019316" target="_blank" >RIV/62690094:18470/22:50019316 - isvavai.cz</a>
Result on the web
<a href="https://www.mdpi.com/1424-8220/22/14/5362" target="_blank" >https://www.mdpi.com/1424-8220/22/14/5362</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.3390/s22145362" target="_blank" >10.3390/s22145362</a>
Alternative languages
Result language
angličtina
Original language name
Selective Microwave Zeroth-Order Resonator Sensor Aided by Machine Learning
Original language description
Microwave sensors are principally sensitive to effective permittivity, and hence not selective to a specific material under test (MUT). In this work, a highly compact microwave planar sensor based on zeroth-order resonance is designed to operate at three distant frequencies of 3.5, 4.3, and 5 GHz, with the size of only lambda(g-min)/8 per resonator. This resonator is deployed to characterize liquid mixtures with one desired MUT (here water) combined with an interfering material (e.g., methanol, ethanol, or acetone) with various concentrations (0%:10%:100 %). To achieve a sensor with selectivity to water, a convolutional neural network (CNN) is used to recognize different concentrations of water regardless of the host medium. To obtain a high accuracy of this classification, Style-GAN is utilized to generate a reliable sensor response for concentrations between water and the host medium (methanol, ethanol, and acetone). A high accuracy of 90.7% is achieved using CNN for selectively discriminating water concentrations.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
20201 - Electrical and electronic engineering
Result continuities
Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
SENSORS
ISSN
1424-8220
e-ISSN
1424-8220
Volume of the periodical
22
Issue of the periodical within the volume
14
Country of publishing house
CH - SWITZERLAND
Number of pages
20
Pages from-to
"Article Number: 5362"
UT code for WoS article
000832411500001
EID of the result in the Scopus database
2-s2.0-85135132936