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Issue 11, 2018
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Salient space detection algorithm for signal extraction from contaminated and distorted spectrum

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Abstract

An algorithm for signal extraction from a contaminated and distorted spectrum is proposed. First, this algorithm combines the salient space of the spectrum and the statistical characteristics of the noise to detect signal regions at different scales. Second, it extracts signals by subtracting the baseline from the spectrum in the signal regions. The baseline is fitted by segmented polynomial functions. This algorithm has been applied to simulated and experimental data, and the results show that this algorithm can accurately and automatically extract signals with varying widths from a contaminated spectrum. This method minimizes the influence of baseline distortion and exhibits good anti-noise capability and high real-time performance.

Graphical abstract: Salient space detection algorithm for signal extraction from contaminated and distorted spectrum

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Publication details

The article was received on 01 Dec 2017, accepted on 22 Apr 2018 and first published on 14 May 2018


Article type: Paper
DOI: 10.1039/C7AN01941F
Citation: Analyst, 2018,143, 2656-2664
  • Open access: Creative Commons BY license
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    Salient space detection algorithm for signal extraction from contaminated and distorted spectrum

    Y. W. Jia, S. Y. Sun, L. Yang and D. Wang, Analyst, 2018, 143, 2656
    DOI: 10.1039/C7AN01941F

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