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Transient multiexponential signals analysis using Bayesian deconvolution

Author

Listed:
  • Lai, Wei
  • Liu, Xianming
  • Chen, Weimin
  • Lei, Xiaohua
  • Tang, Xiaosheng
  • Zang, Zhigang

Abstract

A new method based on Bayesian deconvolution is proposed for multiexponential transient signal analysis. The multiexponential signal is initially converted to a convolution model using logarithmic and differential transformation after which the Bayesian iteration is used to deconvolve the data. The numerical simulation is applied on four different multiexponential signals with different levels of noise. Thermal transient experiment data of the high power light emitting diodes are also analyzed using the proposed method. Simulation and experimental results indicate that the present method performs efficiently in accurately estimating the decay rates except at low SNR case.

Suggested Citation

  • Lai, Wei & Liu, Xianming & Chen, Weimin & Lei, Xiaohua & Tang, Xiaosheng & Zang, Zhigang, 2015. "Transient multiexponential signals analysis using Bayesian deconvolution," Applied Mathematics and Computation, Elsevier, vol. 265(C), pages 486-493.
    • Handle: RePEc:eee:apmaco:v:265:y:2015:i:c:p:486-493
    DOI: 10.1016/j.amc.2015.05.032
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    Cited by:

    1. Valentin Bissuel & Frédéric Joly & Eric Monier-Vinard & Alain Neveu & Olivier Daniel, 2020. "Thermo-Fluidic Characterizations of Multi-Port Compact Thermal Model of Ball-Grid-Array Electronic Package," Energies, MDPI, vol. 13(11), pages 1-17, June.
    2. Karban, Pavel & Kropík, Petr & Kotlan, Václav & Doležel, Ivo, 2018. "Bayes approach to solving T.E.A.M. benchmark problems 22 and 25 and its comparison with other optimization techniques," Applied Mathematics and Computation, Elsevier, vol. 319(C), pages 681-692.

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