ArticleSensors (Basel, Switzerland)2023
AI-Assisted Ultra-High-Sensitivity/Resolution Active-Coupled CSRR-Based Sensor with Embedded Selectivity.
Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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12 citing papers in PubMed.
- Research on the Dielectric Constant Sensor System Based on the Short-Ended Transmission Line.Sensors (Basel, Switzerland) · 2026Article
- Design and analysis of a terahertz metamaterial sensor with cross-shaped resonators for alkene detection.Scientific reports · 2026Article
- Microwave technologies for biomedical diagnosis and therapy: advances, challenges, and perspectives.Mikrochimica acta · 2026Review
- Through-Hole Spiral Microstrip Resonator for Inline Dielectric Characterization of Liquids.Sensors (Basel, Switzerland) · 2026Article
- Detection of fasting blood sugar using a microwave sensor and convolutional neural network.Scientific reports · 2025Article
- Unsupervised design and geometry optimization of high-sensitivity ring-resonator-based sensors.Scientific reports · 2025Article
- Quad-band split ring resonator-based sensor for microwave sensing application.Scientific reports · 2025Article
- Research and Application of Microwave Microstrip Transmission Line-Based Icing Detection Methods for Wind Turbine Blades.Sensors (Basel, Switzerland) · 2025Article
- Highly sensitive split ring resonator-based sensor for quality monitoring of edible oils.Scientific reports · 2025Article
- Neural Network Methods in the Development of MEMS Sensors.Micromachines · 2024Review
- Multi-parameter simultaneous extraction with a novel microwave sensor based on coupled resonators.Scientific reports · 2024Article
- Dynamic analysis of geomaterials using microwave sensing.Scientific reports · 2024Article
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Authors and funding
4 authors.
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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).
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