Compensation of temperature effects on spectra through evolutionary rank analysis

Kałka, Andrzej J.; Turek, Andrzej M.

doi:10.1016/j.saa.2020.118770

Cited by 2 publications

(4 citation statements)

References 57 publications

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“…First and the major challenge to be coped with is a so-called “edge effect” or “edge problem”, 21,37,38 commonly encountered in signal and image processing. In principle, this results from a discrepancy between the discrete nature of the processed datasets and the continuous character of the Fourier operations performed on them.…”

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

“…37 In case of the numerical convolution procedure performed purely in the original (real) domain (Eq. 11), surprisingly effective is to expand the signal by duplicating its edge values (i.e., first and last elements) several times, so that the problematic point is simply moved away from the actual spectrum 38 (Data Filtering Through Numerical Convolution, Supplemental Material). However, when the Fourier transform has to be actually computed, more sophisticated type of the “buffer” should be applied.…”

Section: Resultsmentioning

confidence: 99%

“…Consequently, the regions where the spectrum unexpectedly ends, are prone to a various types of numerical instabilities and for this reason are often being distorted during the data-processing procedure. 37,38 A neat solution to overcome this obstacle is to artificially model the near-edge fragments of the datasets (Figure 3). 37 In case of the numerical convolution procedure performed purely in the original (real) domain (Eq.…”

Section: Effect Of the Edgementioning

confidence: 99%

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Searching for Alternatives to the Savitzky–Golay Filter in the Spectral Processing Domain

Kałka

Turek

2023

Appl Spectrosc

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An elegant, well-established effective data filter concept, proposed originally by Abraham Savitzky and Marcel J.E. Golay, is undoubtedly a very effective tool, however not free from limitations and drawbacks. Despite the latter, over the years it has become a "monopolist” in many fields of spectra processing, claiming a "commercial" superiority over alternative approaches, which would potentially allow to obtain equivalent or in some cases even more reliable results. In order to show that basic operations performed on spectral datasets, like smoothing or differentiation, do not have to be equated to the application of the one particular single algorithm, several of such alternatives are briefly presented within this paper and discussed with regard to their practical realization. A special emphasis is put on the fast Fourier methodology (FFT), being widespread in the general domain of signal processing. Finally, a user-friendly Matlab routine, in which the outlined algorithms are implemented, is shared, so that one can select and apply the technique of spectral data processing more adequate for their individual requirements without the need to code it prior to use.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%