Faint and clustered components in exponential analysis

Annie Cuyt, Min-nan Tsai, Marleen Verhoye and Wen-shin Lee

Applied Mathematics and Computation, 2018, vol. 327, issue C, 93-103

Abstract: An important hurdle in multi-exponential analysis is the correct detection of the number of components in a multi-exponential signal and their subsequent identification. This is especially difficult if one or more of these terms are faint and/or covered by noise. We present an approach to tackle this problem and illustrate its usefulness in motor current signature analysis (MCSA), relaxometry (in FLIM and MRI) and magnetic resonance spectroscopy (MRS).

Keywords: Multi-exponential analysis; Padé approximation; Spectral analysis (search for similar items in EconPapers)
Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:eee:apmaco:v:327:y:2018:i:c:p:93-103

DOI: 10.1016/j.amc.2017.11.007

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