2026-04-18T00:28:30Zhttps://recercat.cat/oai/request

oai:recercat.cat:2072/4733032024-11-04T07:13:12Zcom_2072_98col_2072_378192

00925njm 22002777a 4500 dc Abdolrazzaghi, Mohammad author Kazemi, Nazli author Nayyeri, Vahid author Martín, Ferran author 2023 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). http://hdl.handle.net/2072/473303 Microwave sensor Coupled CSRR Active sensor Deep neural network Convolutional neural network Selectivity Material characterization Mixture sensing AI-Assisted Ultra-High-Sensitivity/Resolution Active-Coupled CSRR-Based Sensor with Embedded Selectivity