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Machine Learning for Error Estimation and Compensation for ‎Microwave Sensors ‎

  • Author / Creator
    Nazli Kazemi
  • Microwave planar resonators have been widely used in material characterization, environmental ‎monitoring, proximity sensing, mechanical motion detection, etc. In general, non-contact sensing, ‎real-time measurement, and CMOS compatibility, makes them interesting for chemical sensing, ‎especially in harsh environments. However, the planar nature leaves them vulnerable to the ambient ‎changes with potential impact on the perception of the material under test. Temperature, as one of ‎the most important factors that affects the sensor response, is considered an undesired ‎environmental impact and relevant methods are introduced to tackle it. Even though many types of ‎human- or environment-centric errors such as material displacement, uncontrolled pressure, and ‎relative humidity could be easily removed from the sensor response using conventional methods; ‎compensation of temperature is complicated enough to look for machine learning algorithms as the ‎main focus of this thesis. ‎In this study, a split ring resonator (SRR) operating @ 1 GHz is designed to measure ‎methanol/acetone content level in water. The planar sensor holding the material inside a tube and a ‎commercial temperature sensor are located inside a chamber. LabView is employed to record ‎scattering parameters during a temperature cycle from room temperature up to 50 ℃.‎Simulation results suggest the dependency of both material- and sensor-properties on the ‎temperature. It is also shown that an uncontrolled environmental situation can deviate the sensor ‎response into incorrect or meaningless results, based on which our decision is made in industrial ‎application. Transmission/Reflection response of the sensor with all features including resonance ‎frequency, magnitude, and quality factor are examined with the aim of reliable network input data. ‎Various machine learning algorithms, including Decision Tree, KNN, Logistic Regression, and MLP ‎are examined to remove the effect of temperature on sensor response, whose performance ‎comparison for classifying 10 materials concluded MLP as the best classifier with 100 % accuracy.‎But for all that, eliciting environmental status, besides recognizing the material type, is lucrative ‎information to be utilized in further data-processing. Temperature of the measurement, imbued in ‎the recorded transmission profiles, is extracted using a novel technique of cascading a primary ‎classifier with linear regression.‎Finally, in pursuit of improving the limit of detection in the microwave sensor, MLP algorithm is ‎significantly scrutinized with the help of hyperparameter optimization, wherein concentrations of ‎methanol-in-water are discriminated with increments as low as 1 % with accuracy of >95%‎

  • Subjects / Keywords
  • Graduation date
    Spring 2021
  • Type of Item
    Thesis
  • Degree
    Master of Science
  • DOI
    https://doi.org/10.7939/r3-hhhc-1c94
  • License
    This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for non-commercial purposes. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.