TY - GEN
T1 - Artificial Neural Network Modeling of Microwave Sensors for Dielectric Liquids Characterization
AU - Gugliandolo, Giovanni
AU - Marinković, Zlatica
AU - Bao, Xiue
AU - De Marchis, Cristiano
AU - Battaglia, Filippo
AU - Latino, Mariangela
AU - Campobello, Giuseppe
AU - Crupi, Giovanni
AU - Donato, Nicola
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - The aim of this study is to develop a modeling procedure based on using artificial neural networks (ANNs) for predicting the frequency-dependent behavior of a microwave split-ring resonator (SRR) used for the dielectric characterization of liquid samples. The SRR device was designed and fabricated using the inkjet printing technology and, then, calibrated by means of water/ethanol mixtures with varying concentrations. By observing the variations in the forward transmission coefficient (i.e., S21) of the studied microwave device, a frequency shift of the resonant frequency and variations in the magnitude of S21 were recorded, which were related to the ethanol volume fraction. Using this calibration data, an ANN-based model is developed, which takes the ethanol volume fraction as input feature and, then, predicts the SRR sensor resonant parameters. The accuracy of the ANN-based model is reported and discussed.
AB - The aim of this study is to develop a modeling procedure based on using artificial neural networks (ANNs) for predicting the frequency-dependent behavior of a microwave split-ring resonator (SRR) used for the dielectric characterization of liquid samples. The SRR device was designed and fabricated using the inkjet printing technology and, then, calibrated by means of water/ethanol mixtures with varying concentrations. By observing the variations in the forward transmission coefficient (i.e., S21) of the studied microwave device, a frequency shift of the resonant frequency and variations in the magnitude of S21 were recorded, which were related to the ethanol volume fraction. Using this calibration data, an ANN-based model is developed, which takes the ethanol volume fraction as input feature and, then, predicts the SRR sensor resonant parameters. The accuracy of the ANN-based model is reported and discussed.
KW - ANN
KW - SRR
KW - biological materials
KW - dielectric characterization
KW - liquids
KW - microwave sensors
KW - scattering parameter measurements
UR - https://www.scopus.com/pages/publications/85185786286
U2 - 10.1109/MetroXRAINE58569.2023.10405750
DO - 10.1109/MetroXRAINE58569.2023.10405750
M3 - Conference contribution
AN - SCOPUS:85185786286
T3 - 2023 IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering, MetroXRAINE 2023 - Proceedings
SP - 401
EP - 405
BT - 2023 IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering, MetroXRAINE 2023 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2nd Edition IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering, MetroXRAINE 2023
Y2 - 25 October 2023 through 27 October 2023
ER -