AI-Assisted Ultra-High-Sensitivity/Resolution Active-Coupled CSRR-Based Sensor with Embedded Selectivity

• Published in: Sensors (Basel, Switzerland) Vol. 23; no. 13; p. 6236
• Main Authors: Abdolrazzaghi, Mohammad, Kazemi, Nazli, Nayyeri, Vahid, Martin, Ferran
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 07.07.2023; MDPI
• Subjects: Accuracy; active sensor; Algorithms; Amplifiers, Electronic; Artificial Intelligence; convolutional neural network; coupled CSRR; Data mining; Data processing; deep neural network; Design; Dielectric properties; Machine Learning; microwave sensor; Neural networks; Neural Networks, Computer; selectivity; Sensors; selectivity; coupled CSRR; active sensor; convolutional neural network; microwave sensor; material characterization; mixture sensing; deep neural network
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s23136236

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).