Active elimination of radio frequency interference for improved signal-to-noise ratio for in-situ NMR experiments in strong magnetic field gradients

Ibrahim, Mona; Pardi, C. I.; Brown, Tim; McDonald, P.J.

doi:10.1016/j.jmr.2018.01.002

Cited by 5 publications

(7 citation statements)

References 26 publications

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“…We previously recorded a significant volume of RFI within the environs of our laboratories at frequencies in the range of approximately 0 to 10 MHz [18]. Outdoor measurements were undertaken at our laboratories, where it was found that a significant fraction of the RFI comprised pulsed aircraft communication data.…”

Section: Methodsmentioning

confidence: 99%

“…By removing the singular values corresponding to the RFI and noise, the amplitudes of the interference peaks are suppressed by 10 dB. Finally, the authors of [18] suggested a method based on the linear predictive coding of RFI through NMR signals. This method is especially applicable to measurements in highly inhomogeneous magnetic fields, where the echo duration is extremely short.…”

Section: Introductionmentioning

confidence: 99%

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

Ibrahim

Parrish

Brown

et al. 2019

Sensors

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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.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Ibrahim

Parrish

Brown

et al. 2019

Sensors

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“…where H (μ, ν) is the Fourier transformation of the degradation function; G (μ, ν) is the Fourier transformation of the image function; H* (μ, ν) is the complex conjugate of H (μ, ν); S n (μ, ν) is the power spectrum of the noise; and S f (μ, ν) is the power spectrum of the original image. The result of the recovery is evaluated by the improvement of the signal-to-noise ratio N 0 (Ibrahim et al, 2018):…”

Section: Background Segmentation and Object Extraction For The Granulementioning

confidence: 99%

Design and Testing of an On-Line Omnidirectional Inspection and Sorting System for Soybean Seeds

Quan¹,

Zhang²,

Sun³

et al. 2018

Applied Engineering in Agriculture

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Abstract. At present, the manual grading of soybean seeds is both time consuming and laborious, and detecting the full-surface information of soybean seeds using an existing automatic sorting machine is difficult. To solve this problem, an on-line omnidirectional inspection and sorting system for soybean seeds was developed using embedded image processing technology. According to the principles employed by the system, the surface friction properties and full-surface information such as the shape, texture and color of soybean seeds were adopted in the study. Soybean seeds were inspected and sorted using their full surface information in combination with the embedded image processing technology. Split, worm-eaten, gray-spotted, slightly cracked, moldy and normal soybeans were used to test the system. According to the test results, the optimum design parameters of the preliminary sorting device based on the friction properties were a tilting angle of 12° and a linear velocity of 0.4 m/s. Furthermore, the optimum design parameters of the directional integrated device were a tilting angle of 19° and a linear velocity of 0.45 m/s. The sorting speed was 400 soybeans per minute with 8-channel parallel transmission. The average sorting accuracies were 99.4% for split soybeans, 98.5% for worm-eaten soybeans, 98.5% for gray-spotted soybeans, 97.7% for slightly cracked soybeans, 98.6% for moldy soybeans, and 98.9% for normal soybeans. The overall results suggest that the system can potentially meet the needs of the rapid inspection and automatic sorting of soybean seeds and provide references for research on the alternating rotational motion of granules and on-line collection of full-surface information. Keywords: Embedded image processing technology, Full surface, Granules, Inspection, On-line, Sorting, Soybean seeds.

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“…Other methods for reducing mathematical noise from FID signals focus on the region of interest (ROI), such as reference deconvolution [22], harmonic inversion noise removal (HINR) [23], and complete reduction to amplitude frequency table (CRAFT) [24]. In addition, STFT and wavelet transform [25] have been developed as alternative transformation methods to FT for analyzing the relationship between the time and frequency of FIDs. In principle, the exponential decay constant of the FID obtained by applying a 90 • pulse to create transverse magnetization is the T 2 relaxation time, a physical parameter independent of field inhomogeneity.…”

Section: Introductionmentioning

confidence: 99%

“…In this study, by focusing on acquisition parameters [38][39][40][41][42] and noise [25], we have developed an NMR measurement informatics tool for data cleansing based on FID signal deconvolution and noise factor analysis. Our method for deconvoluting signals and noise factor analysis can be applied to original single FIDs from 1D-NMR and is based on STFT [43] and PMSF [44].…”

Section: Introductionmentioning

confidence: 99%

Signal Deconvolution and Noise Factor Analysis Based on a Combination of Time–Frequency Analysis and Probabilistic Sparse Matrix Factorization

Yamada

Kurotani

Chikayama

et al. 2020

IJMS

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Nuclear magnetic resonance (NMR) spectroscopy is commonly used to characterize molecular complexity because it produces informative atomic-resolution data on the chemical structure and molecular mobility of samples non-invasively by means of various acquisition parameters and pulse programs. However, analyzing the accumulated NMR data of mixtures is challenging due to noise and signal overlap. Therefore, data-cleansing steps, such as quality checking, noise reduction, and signal deconvolution, are important processes before spectrum analysis. Here, we have developed an NMR measurement informatics tool for data cleansing that combines short-time Fourier transform (STFT; a time-frequency analytical method) and probabilistic sparse matrix factorization (PSMF) for signal deconvolution and noise factor analysis. Our tool can be applied to the original free induction decay (FID) signals of a one-dimensional NMR spectrum. We show that the signal deconvolution method reduces the noise of FID signals, increasing the signal-to-noise ratio (SNR) about tenfold, and its application to diffusion-edited spectra allows signals of macromolecules and unsuppressed small molecules to be separated by the length of the T 2 * relaxation time. Noise factor analysis of NMR datasets identified correlations between SNR and acquisition parameters, identifying major experimental factors that can lower SNR.

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Active elimination of radio frequency interference for improved signal-to-noise ratio for in-situ NMR experiments in strong magnetic field gradients

Cited by 5 publications

References 26 publications

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

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

Design and Testing of an On-Line Omnidirectional Inspection and Sorting System for Soybean Seeds

Signal Deconvolution and Noise Factor Analysis Based on a Combination of Time–Frequency Analysis and Probabilistic Sparse Matrix Factorization

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