AI-Assisted Ultra-High-Sensitivity/Resolution Active-Coupled CSRR-Based Sensor with Embedded Selectivity
Abdolrazzaghi, Mohammad (University of Toronto. Electrical and Computer Engineering Department)
Kazemi, Nazli (University of Alberta. Electrical and Computer Engineering Department)
Nayyeri, Vahid (Iran University of Science and Technology. School of Advanced Technologies)
Martín, Ferran (Universitat Autònoma de Barcelona. Departament d'Enginyeria Electrònica)

Date:	2023
Abstract:	This research explores the application of an artificial intelligence (AI)-assisted approach to enhance the selectivity of microwave sensors used for liquid mixture sensing. We utilized a planar microwave sensor comprising two coupled rectangular complementary split-ring resonators operating at 2. 45 GHz to establish a highly sensitive capacitive region. The sensor's quality factor was markedly improved from 70 to approximately 2700 through the incorporation of a regenerative amplifier to compensate for losses. A deep neural network (DNN) technique is employed to characterize mixtures of methanol, ethanol, and water, using the frequency, amplitude, and quality factor as inputs. However, the DNN approach is found to be effective solely for binary mixtures, with a maximum concentration error of 4. 3%. To improve selectivity for ternary mixtures, we employed a more sophisticated machine learning algorithm, the convolutional neural network (CNN), using the entire transmission response as the 1-D input. This resulted in a significant improvement in selectivity, limiting the maximum percentage error to just 0. 7% (≈6-fold accuracy enhancement).
Grants:	Agencia Estatal de Investigación PID2019-103904RB-I00 Agencia Estatal de Investigación PDC2021-121085-I00 Agència de Gestió d'Ajuts Universitaris i de Recerca 2017/SGR-1159
Rights:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original.
Language:	Anglès
Document:	Article ; recerca ; Versió publicada
Subject:	Microwave sensor ; Coupled CSRR ; Active sensor ; Deep neural network ; Convolutional neural network ; Selectivity ; Material characterization ; Mixture sensing
Published in:	Sensors (Basel, Switzerland), Vol. 23, Issue 13 (July 2023) , art. 6236, ISSN 1424-8220

DOI: 10.3390/s23136236
PMID: 37448086

20 p, 3.6 MB

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