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Spectral Methods for Response Enhancement of Microwave Resonant Sensors in Continuous Non-Invasive Blood Glucose Monitoring.

Giovanni BuonannoAdriana BrancaccioSandra CostanzoRaffaele Solimene
Published in: Bioengineering (Basel, Switzerland) (2022)
In this paper, the performance of three recent algorithms for the frequency-response enhancement of microwave resonant sensors are compared. The first one, a single-step algorithm, is based on a couple of direct-inverse Fourier transforms, giving a densely sampled response as a result. The second algorithm exploits an iterative procedure to progressively restricts the frequency response. The final one is based on the super-resolution MUSIC algorithm. The comparison is carried out through a Monte Carlo analysis. In particular, synthetic signals are firstly exploited to mimic the frequency response of a resonant microwave sensor. Then, experimental data collected from water-glucose solutions are adopted as validation test for potential applications in noninvasive blood-glucose monitoring.
Keyphrases
  • blood glucose
  • machine learning
  • deep learning
  • type diabetes
  • monte carlo
  • magnetic resonance
  • metabolic syndrome
  • big data
  • electronic health record
  • image quality