Artificial neural network-based sodium nitrite NQR analysis in an urban noisy environment

Abstract

Using the nuclear quadrupole resonance procedure in non-shielded environments requires special measures. For this purpose, noise reduction and interference suppression algorithms have been used to increase signal-to-noise and interference ratio or SNIR. For this aim, two types of antennas are considered, the ferrite core coil antenna as the main antenna receives the free induction decay or FID signal, radio frequency interference or RFI, and noise, and the omnidirectional auxiliary antenna receives the RFI and noise as the algorithm reference noise. To perform the noise and interference cancelation, the weighting factors in auxiliary antenna data are so important, that an artificial neural network or ANN model has been used to increase the SNIR. In this research, sodium nitrite has been used as a sample, then algorithms have been tested in a non-shielded environment. The resonant frequency of the 200 g sample, by the signal-to-noise ratio improvement of 18.889 dB, the signal-to-interference ratio improvement of 24.819 dB, and the FID signal amplification of 16.925 dB, were measured at 4.649 MHz. The main technique in this study was to use an auxiliary antenna to estimate the noise and interference and compute the learned weighting factor before sending the NQR pulse. © 2023, The Korean Physical Society.