Enhancing deep learning nuclear quadrupole resonance detection using transfer learning and autoencoders

Abstract

Nuclear quadrupole resonance is a highly specific spectroscopy technique for analyzing solid substances with applications ranging from laboratory analysis to security screening screening for prohibited substances. The technique has the drawback of a very low signal-to-noise ratio and multiple signal processing and analysis solutions have been proposed for noise rejection and detection. Among these, the deep learning approach using the AlexNet network was recently shown to outperform previous solutions. This paper proposes the enhancement of deep learning detection using transfer learning to extend the applicability of the detection algorithm to other spectrometers and denoising autoencoders to improve its performance at very low signal-to-noise ratios. The transfer learning technique is demonstrated by training the AlexNet network on a simulated data set and transferring the gained knowledge to a real data set. The resulting model achieves a detection accuracy of 98%, close to that obtained by the initial model trained on the real data. Two denoising architectures are proposed, such as deep neural network-based autoencoder and convolutional autoencoder. A comparative evaluation is performed at multiple signal-to-noise ratio conditions in the range [-30, 20] dB, and the convolutional autoencoder is shown to provide the best results, by significantly increasing the detection accuracy by approx. 20% at −30 dB.