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

Sensors ◽  
2019 ◽  
Vol 19 (14) ◽  
pp. 3153 ◽  
Author(s):  
Ibrahim ◽  
Parrish ◽  
Brown ◽  
McDonald
Keyword(s):  
Decision Tree ◽  
Radio Frequency ◽  
Interference Suppression ◽  
Airport Security ◽  
Test Case ◽  
Radio Frequency Interference ◽  
Automated Identification ◽  
Tree Model ◽  
Radio Frequency Interference Suppression ◽  
Traditional Processing

Radio frequency interference places a major limitation on the in-situ use of unshielded nuclear quadrupole or nuclear magnetic resonance methods in industrial environments for quality control and assurance applications. In this work, we take the detection of contraband in an airport security-type application that is subject to burst mode radio frequency interference as a test case. We show that a machine learning decision tree model is ideally suited to the automated identification of interference bursts, and can be used in support of automated interference suppression algorithms. The usefulness of the data processed additionally by the new algorithm compared to traditional processing is shown in a receiver operating characteristic (ROC) analysis of a validation trial designed to mimic a security contraband detection application. The results show a highly significant increase in the area under the ROC curve from 0.580 to 0.906 for the proper identification of recovered data distorted by interfering bursts.

SVD-based Method for Radio Frequency Interference Suppression Applied to SAR

2012 ◽  
Vol 62 (2) ◽  
pp. 132-136 ◽  
Author(s):  
Yu Chunrui ◽  
Zhang Yongsheng ◽  
Dong Zhen ◽  
Liang Diannong
Keyword(s):  
Radio Frequency ◽  
Interference Suppression ◽  
Radio Frequency Interference ◽  
Radio Frequency Interference Suppression
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