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.
Similar content being viewed by others
Change history
16 December 2023
The author’s names have been corrected
02 January 2024
An Erratum to this paper has been published: https://doi.org/10.1007/s40042-023-00993-6
References
M. Marshall, J.C. Oxley, Aspects of explosives detection (Elsevier, Amsterdam, 2009)
V.K. Jain, Magnetic resonance, in Solid state physics. ed. by V.K. Jain (Springer International Publishing, Cham, 2022), pp.393–444
G.A. Williams, Z.M. Saleh, P. Hari, Nuclear quadrupole resonance as a non-destructive testing tool (Springer US, Boston, 1992). https://doi.org/10.1007/978-1-4615-2848-7_86
C. Monea, N. Bizon, Nuclear quadrupole resonance spectroscopy (Springer International Publishing, Cham, 2022). https://doi.org/10.1007/978-3-030-87861-0_3
A. Weiss, S. Wigand, Correlation of NQR and chemical bond parameter. Zeitschrift für Naturforschung A (1990). https://doi.org/10.1515/zna-1990-3-403
W. Shao, J. Barras, K. Althoefer, P. Kosmas, Detecting NQR signals severely polluted by interference. Signal Process. 138, 256–264 (2017). https://doi.org/10.1016/j.sigpro.2017.03.032
M. Ibrahim, D.J. Parrish, T.W. Brown, P.J. McDonald, decision tree pattern recognition model for radio frequency interference suppression in NQR Experiments. Sensors 19, 1–16 (2019). https://doi.org/10.3390/s19143153
J. Yu, J. Li, B. Sun, J. Chen, C. Li, Multiclass radio frequency interference detection and suppression for SAR based on the single shot MultiBox Detector. Sensors 18, 4034 (2018). https://doi.org/10.3390/s18114034
L. Yang, H. Zheng, J. Feng, N. Li, J. Chen, Detection and suppression of narrow band RFI for synthetic aperture radar imaging. Chin. J. Aeronaut. (2015). https://doi.org/10.1016/j.cja.2015.06.018
J. Jover, S. Aissani, L. Guendouz, A. Thomas, D. Canet, NQR detection of sodium nitrite recrystallized in wood. NATO Sci. Peace Secur. Ser. B (2015). https://doi.org/10.1007/978-94-007-7265-6-7
D. Canet, M. Ferrari, Fundamentals of pulsed nitrogen-14 quadrupole resonance (Springer Netherlands, Dordrecht, 2009). https://doi.org/10.1007/978-90-481-3062-7_1
A. Sarra, L. Guendouz, P.-L. Marande, D. Canet, Toward nitrogen-14 nuclear quadrupole resonance imaging by nutation experiments performed with a radio-frequency field gradient. Solid State Nucl. Magn. Reson. (2016). https://doi.org/10.1016/j.ssnmr.2016.12.007
J. Glickstein, S. Mandal, A cryogenically-cooled high sensitivity nuclear quadrupole resonance spectrometer. arXiv (2022). https://doi.org/10.48550/arXiv.2208.01552
P. Hemnani, A. Rajarajan, G. Joshi, S.V.G. Ravindranath, Design of probe for nqr/nmr detection. Int. J. Electr. Comput. Eng. (IJECE) 10, 3468 (2020). https://doi.org/10.11591/ijece.v10i4.pp3468-3475
M. Mikhemar, H. Darabi and A. Abidi, "A tunable integrated duplexer with 50dB isolation in 40nm CMOS," 2009 IEEE International Solid-State Circuits Conference - Digest of Technical Papers, San Francisco, CA, USA, pp. 386–387,387a, doi: https://doi.org/10.1109/ISSCC.2009.4977470 (2009).
B. van Liempd, B. Hershberg, K. Raczkowski, S. Ariumi, U. Karthaus, K.F. Bink, J. Craninckx, 2.2 a +70dbm iip3 single-ended electrical-balance duplexer in 0.18 um soi cmos, Vol. 58. doi:https://doi.org/10.1109/ISSCC.2015.7062851 (2015).
J.B. Miller, Nuclear quadrupole resonance detection of explosives, in Counterterrorist detection techniques of explosives. ed. by J.B. Miller (Elsevier Science BV, Amsterdam, 2007), pp.157–198
Robert Ayrapetian, Trausti Thor Kristjansson, Philip Ryan Hilmes, Carlo Murgia. Multichannel noise cancellation using frequency-domain spectrum masking. – “United States Patent”, Patent No.: US 10,553,236 B1 (2020).
Zhu, K., Zhao, Z., & Jia, H. Nuclear quadrupole resonance signal detectability enhancement methods—An overview. In 2017 International Conference on Information and Communication Technology Convergence (ICTC) (pp. 278–281). IEEE (2017).
Silani, Y., Smits, J., Fescenko, I., Malone, M. W., McDowell, A. F., Jarmola, A., & Acosta, V. M. Nuclear quadrupole resonance spectroscopy with a femtotesla diamond magnetometer. arXiv preprint arXiv:2302.12401 (2023).
P. Hemnani, A.K. Rajarajan, G. Joshi, S.V.G. Ravindranath, Detection of NQR signals using wavelet transform and adaptive filters. Int. J. Instru. Technol. 2(1), 34–49 (2018)
Oproescu, M., Iana, G. V., & Monea, C. Application of genetic algorithm for optimization of NQR signal detection. In 2019 11th International Conference on Electronics, Computers and Artificial Intelligence (ECAI) (pp. 1–4). IEEE (2019).
W. Li, Z. Zhao, J. Tang, F. He, Y. Li, H. Xiao, Performance analysis and optimal design of the adaptive interference cancellation system. IEEE Trans. Electromagn. Compat. 55(6), 1068–1075 (2013). https://doi.org/10.1109/TEMC.2013.2265803
A.Balanis, Constantine. Antenna Theory - Analysis and Design. 4th Edition, 2016, P. 270 (2017).
H. Zhou, B. Wen, Wu. Shicai, Dense radio frequency interference suppression in HF radars. IEEE Signal Process. Lett. 12(5), 361–364 (2005). https://doi.org/10.1109/LSP.2005.845603
S.-W. Chou, G.-R. Shiu, H.-C. Chang, W.-P. Peng, Wavelet-based method for time-domain noise analysis and reduction in a frequency-scan ion trap mass spectrometer. J. Am. Soc. Mass Spectrom. 23(11), 1855–1864 (2012). https://doi.org/10.1007/s13361-012-0455-2
H. Qin, F. He, J. Meng, Q. Wang, Analysis and optimal design of radio-frequency interference adaptive cancellation system with delay mismatch. IEEE Trans. Electromagn. Compat. 61(6), 2015–2023 (2019). https://doi.org/10.1109/TEMC.2019.2950718
C. Monea, N. Bizon, Development of signal analysis algorithms for nqr detection, in Signal processing and analysis techniques for nuclear quadrupole resonance spectroscopy. ed. by C. Monea, N. Bizon (Springer International Publishing, Cham, 2022), pp.109–142
B. Vaferi, R. Eslamloueyan, S. Ayatollahi, Automatic recognition of oil reservoir models from well testing data by using multi-layer perceptron networks. J. Pet. Sci. Eng. 77, 254–262 (2011)
A. Canakci, S. Ozsahin, T. Varol, Modeling the influence of a process control agent on the properties of metal matrix composite powders using artificial neural networks. Powder Technol. 228, 26–35 (2012)
B. Vaferi, F. Samimi, E. Pakgohar, D. Mowla, Artificial neural network approach for prediction of thermal behavior of nanofluids flowing through circular tubes. Powder Technol. 267, 1–10 (2014)
A.B. Çolak, Developing optimal artificial neural network (ANN) to predict the specific heat of water based yttrium oxide (Y2O3) nanofluid according to the experimental data and proposing new correlation. Heat Transf. Res. 51, 1565–1586 (2020)
E. Ahmadloo, S. Azizi, Prediction of thermal conductivity of various nanofluids using artificial neural network. Int. Commun. Heat Mass Transf. 74, 69–75 (2016)
A.B. Çolak, A novel comparative investigation of the effect of the number of neurons on the predictive performance of the artificial neural network: an experimental study on the thermal conductivity of ZrO2 nanofluid. Int. J. Energy Res. 45, 18944–18956 (2021)
A. Ali, A. Abdulrahman, S. Garg, K. Maqsood, G. Murshid, Application of artificial neural networks (ANN) for vapor-liquidsolid equilibrium prediction for CH4-CO2 binary mixture. Greenh. Gases Sci. Technol. 9, 67–78 (2019)
F.A. Abdul Kareem, A.M. Shariff, S. Ullah, S. Garg, F. Dreisbach, L.K. Keong, N. Mellon, Experimental and neural network modeling of partial uptake for a carbon dioxide/methane/water ternary mixture on 13X zeolite. Energy Technol. 5, 1373–1391 (2017)
M. Vafaei, M. Afrand, N. Sina, R. Kalbasi, F. Sourani, H. Teimouri, Evaluation of thermal conductivity of MgO-MWCNTs/EG hybrid nanofluids based on experimental data by selecting optimal artificial neural networks. Phys. E Low Dimens. Syst. Nanostruct. 85, 90–96 (2017)
A. Akhgar, D. Toghraie, N. Sina, M. Afrand, Developing dissimilar artificial neural networks (ANNs) to prediction the thermal conductivity of MWCNT-TiO2/Water-ethylene glycol hybrid nanofluid. Powder Technol. 355, 602–610 (2019)
S. Öcal, M. Gökçek, A.B. Çolak, M. Korkanç, A comprehensive and comparative experimental analysis on thermal conductivity of TiO2-CaCO3/Water hybrid nanofluid: proposing new correlation and artificial neural network optimization. Heat Transf. Res. 52, 55–79 (2021)
A.B. Çolak, O. Yıldız, M. Bayrak, B.S. Tezekici, Experimental study for predicting the specific heat of water based Cu-Al2O3 hybrid nanofluid using artificial neural network and proposing new correlation. Int. J. Energy Res. 44, 7198–7215 (2020)
O. Acikgoz, A.B. Çolak, M. Camci, Y. Karakoyun, A.S. Dalkilic, Machine learning approach to predict the heat transfer coefficients pertaining to a radiant cooling system coupled with mixed and forced convection. Int. J. Therm. Sci. 178, 107624 (2022)
O. Kalkan, A.B. Colak, A. Celen, K. Bakirci, A.S. Dalkilic, Prediction of experimental thermal performance of new designed cold plate for electric vehicles’ Li-ion pouch-type battery with artificial neural network. J. Energy Storage 48, 103981 (2022)
Author information
Authors and Affiliations
Corresponding authors
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Sharifi A. M, M.S., Afarideh, H., Ghergherehchi, M. et al. Artificial neural network-based sodium nitrite NQR analysis in an urban noisy environment. J. Korean Phys. Soc. 83, 172–178 (2023). https://doi.org/10.1007/s40042-023-00861-3
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s40042-023-00861-3