Ibrahim Mona, Parrish Dan J, Brown Tim W C, McDonald Peter J
Department of Physics, University of Surrey, Guildford GU2 7XH, UK.
Institute for Communication Systems, University of Surrey, Guildford GU2 7XH, UK.
Sensors (Basel). 2019 Jul 17;19(14):3153. doi: 10.3390/s19143153.
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.
射频干扰严重限制了在工业环境中使用未屏蔽的核四极或核磁共振方法进行质量控制和保证应用的现场使用。在这项工作中，我们将机场安检类应用中违禁品的检测作为测试案例，该应用会受到突发模式射频干扰。我们表明，机器学习决策树模型非常适合自动识别干扰突发情况，并可用于支持自动干扰抑制算法。在旨在模拟安全违禁品检测应用的验证试验的接收器操作特性（ROC）分析中，展示了与传统处理相比，新算法额外处理的数据的有用性。结果表明，对于正确识别因干扰突发而失真的恢复数据，ROC曲线下的面积从0.580显著增加到0.906。
