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Algorithms for reconstruction of impedance spectra from non-uniformly sampled step responses

Y. Zaikou

Y. Zaikou

Institute for Bioprocessing and Analytical Measurement Techniques, Heilbad HeiligenstadtHeilbad, Germany
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Zaikou, Y.
, 
C. Gansauge

C. Gansauge

Institute for Bioprocessing and Analytical Measurement Techniques, Heilbad HeiligenstadtHeilbad, Germany
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Gansauge, C.
, 
D. Echtermeyer

D. Echtermeyer

Institute for Bioprocessing and Analytical Measurement Techniques, Heilbad HeiligenstadtHeilbad, Germany
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Echtermeyer, D.
 et 
U. Pliquett

U. Pliquett

Institute for Bioprocessing and Analytical Measurement Techniques, Heilbad HeiligenstadtHeilbad, Germany
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Pliquett, U.
  
14 janv. 2023
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Publié en ligne: 14 janv. 2023
Pages: 143 - 149
Reçu: 14 déc. 2022
DOI: https://doi.org/10.2478/joeb-2022-0020
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© 2022 Y. Zaikou, C. Gansauge, D. Echtermeyer, U. Pliquett, published by Sciendo
This work is licensed under the Creative Commons Attribution 4.0 International License.

Fig. 1

Example of stimulus and response signals. A: Stimulus B: Step response.
Example of stimulus and response signals. A: Stimulus B: Step response.

Fig. 2

Error of simplistic sampling instant estimation and its correction. A: Dependency on time constant of exponential signal and on sampling interval. B: Example of correcting the error.
Error of simplistic sampling instant estimation and its correction. A: Dependency on time constant of exponential signal and on sampling interval. B: Example of correcting the error.

Fig. 3

Derivatives of the straightforward local approximations.
Derivatives of the straightforward local approximations.

Fig. 4

Local signal approximations used to reconstruct measured signal.
Local signal approximations used to reconstruct measured signal.

Fig. 5

Simulated errors of time rescaling for single exponential signals. Reconstruction of measured signals is done by shape-preserving splines.
Simulated errors of time rescaling for single exponential signals. Reconstruction of measured signals is done by shape-preserving splines.

Fig. 6

Local signal approximations with and without time rescaling for spheroid in nozzle. A: Whole spectrum. B: Zoom at higher frequencies.
Local signal approximations with and without time rescaling for spheroid in nozzle. A: Whole spectrum. B: Zoom at higher frequencies.

Fig. 7

Model parameters after fitting the measured electrical signals for spheroid in the chamber. A: simple fit of the distribution of relaxation times model and B: the same model fit with proposed improvements.
Model parameters after fitting the measured electrical signals for spheroid in the chamber. A: simple fit of the distribution of relaxation times model and B: the same model fit with proposed improvements.

Fig. 8

Local signal approximations for spheroid in nozzle. A: without correction. B: with correction.
Local signal approximations for spheroid in nozzle. A: without correction. B: with correction.
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Sujets de la revue:
Ingénierie, Génie biologique et génie biomédicale, Électronique biomédicale, Sciences de la vie, Biophysique, Médecine, Génie biomédical, Physique, Spectroscopie et métrologie
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