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Decision Tree Pattern Recognition Model for Radio Frequency Interference Suppression in NQR Experiments.

Mona Ibrahim1, Dan J Parrish2, Tim W C Brown3

  • 1Department of Physics, University of Surrey, Guildford GU2 7XH, UK. mona.ibrahim@surrey.ac.uk.

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|July 20, 2019
PubMed
Summary
This summary is machine-generated.

Radio frequency interference hinders nuclear quadrupole resonance (NQR) and nuclear magnetic resonance (NMR) applications. A machine learning decision tree model effectively identifies and suppresses interference bursts, significantly improving contraband detection accuracy.

Keywords:
machine learning classificationnuclear magnetic resonancenuclear quadrupole resonanceradio frequency interference

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Area of Science:

  • Nuclear Magnetic Resonance Spectroscopy
  • Nuclear Quadrupole Resonance Spectroscopy
  • Machine Learning Applications
  • Signal Processing

Background:

  • In-situ applications of unshielded nuclear quadrupole resonance (NQR) and nuclear magnetic resonance (NMR) are limited by radio frequency (RF) interference in industrial settings.
  • Quality control and assurance applications require robust methods resistant to environmental electromagnetic noise.
  • Burst-mode RF interference poses a significant challenge for sensitive spectroscopic techniques.

Purpose of the Study:

  • To investigate the effectiveness of machine learning for identifying and mitigating radio frequency interference in NQR/NMR applications.
  • To develop and validate an automated interference suppression algorithm for quality control and assurance.
  • To assess the performance improvement in a contraband detection scenario using the proposed method.

Main Methods:

  • A machine learning decision tree model was developed for automated identification of RF interference bursts.
  • The developed algorithm was integrated to support automated interference suppression techniques.
  • Receiver Operating Characteristic (ROC) analysis was employed to evaluate the performance of the new processing method against traditional approaches.

Main Results:

  • The machine learning decision tree model demonstrated suitability for automated identification of interference bursts.
  • Data processed with the new algorithm showed a significant improvement in accuracy compared to traditional methods.
  • The area under the ROC curve increased from 0.580 to 0.906, indicating highly significant improvement in identifying data distorted by interference.

Conclusions:

  • Machine learning, specifically decision tree models, offers a viable solution for automated detection and suppression of RF interference in NQR/NMR.
  • The developed algorithm significantly enhances the reliability of NQR/NMR data analysis in noisy industrial environments.
  • This approach shows promise for improving the performance of security applications, such as contraband detection, by overcoming RF interference limitations.