Detecting NQR signals severely polluted by interference

Weihang Shao; Jamie Barras; Kaspar Althoefer; Panagiotis Kosmas

doi:10.1016/j.sigpro.2017.03.032

Detecting NQR signals severely polluted by interference

Weihang Shao^*, Jamie Barras, Kaspar Althoefer, Panagiotis Kosmas

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

17 Citations (Scopus)

223 Downloads (Pure)

Abstract

Nuclear Quadrupole Resonance (NQR) signal detection can be severely obstructed by interference in real life settings, especially when the interference is strong, nonstationary, and its frequencies are close to that of the NQR signal. A novel algorithm is proposed to effectively remove (or reduce) interference components in the data and facilitate a valid detection of the NQR signal. The proposed method exhibits better performance compared to the previously proposed ETAML and FETAML algorithms, when applied to both simulated and measured data. Importantly, the present algorithm directly operates on the original primary data, without requiring any secondary data (NQR signal-free data) for acquiring prior knowledge of the interference.

Original language	English
Pages (from-to)	256-264
Number of pages	9
Journal	SIGNAL PROCESSING
Volume	138
Early online date	31 Mar 2017
DOIs	https://doi.org/10.1016/j.sigpro.2017.03.032
Publication status	E-pub ahead of print - 31 Mar 2017

Keywords

Approximate maximum likelihood
Echo train
Frequency selective method
Interference cancellation
Nuclear Quadrupole Resonance (NQR) Signal
Stationary/Nonstationary

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10.1016/j.sigpro.2017.03.032

Detecting NQR signals severely_SHAO_Publishedonline31March2017_GREEN AAMAccepted author manuscript, 1.19 MBLicence: CC BY-NC-ND

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title = "Detecting NQR signals severely polluted by interference",

abstract = "Nuclear Quadrupole Resonance (NQR) signal detection can be severely obstructed by interference in real life settings, especially when the interference is strong, nonstationary, and its frequencies are close to that of the NQR signal. A novel algorithm is proposed to effectively remove (or reduce) interference components in the data and facilitate a valid detection of the NQR signal. The proposed method exhibits better performance compared to the previously proposed ETAML and FETAML algorithms, when applied to both simulated and measured data. Importantly, the present algorithm directly operates on the original primary data, without requiring any secondary data (NQR signal-free data) for acquiring prior knowledge of the interference.",

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AU - Shao, Weihang

AU - Barras, Jamie

AU - Althoefer, Kaspar

AU - Kosmas, Panagiotis

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N2 - Nuclear Quadrupole Resonance (NQR) signal detection can be severely obstructed by interference in real life settings, especially when the interference is strong, nonstationary, and its frequencies are close to that of the NQR signal. A novel algorithm is proposed to effectively remove (or reduce) interference components in the data and facilitate a valid detection of the NQR signal. The proposed method exhibits better performance compared to the previously proposed ETAML and FETAML algorithms, when applied to both simulated and measured data. Importantly, the present algorithm directly operates on the original primary data, without requiring any secondary data (NQR signal-free data) for acquiring prior knowledge of the interference.

AB - Nuclear Quadrupole Resonance (NQR) signal detection can be severely obstructed by interference in real life settings, especially when the interference is strong, nonstationary, and its frequencies are close to that of the NQR signal. A novel algorithm is proposed to effectively remove (or reduce) interference components in the data and facilitate a valid detection of the NQR signal. The proposed method exhibits better performance compared to the previously proposed ETAML and FETAML algorithms, when applied to both simulated and measured data. Importantly, the present algorithm directly operates on the original primary data, without requiring any secondary data (NQR signal-free data) for acquiring prior knowledge of the interference.

KW - Approximate maximum likelihood

KW - Echo train

KW - Frequency selective method

KW - Interference cancellation

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KW - Stationary/Nonstationary

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Detecting NQR signals severely polluted by interference

Abstract

Keywords

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