Frequency Components of Signals Producing the Upper Bound of Absolute Error Generated by the Charge Output Accelerometers

  • Krzysztof TomczykEmail author
  • Marek Sieja
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 548)


The paper presents an assessment of frequency components by the time-frequency representation of signals with one constrain producing the upper bound of the absolute error generated by charge output accelerometers. The constraint concerns the amplitude resulting from the measuring range of an accelerometer. This assessment was carried out by using a wavelet analysis implemented in MATLAB. Mathematical basis regarding both modeling charge output accelerometers and determining the absolute error were presented. Shapes of signals producing the upper bound of error and results of analysis for selected parameters of the accelerometer model are also presented and discussed.


Frequency component Upper bound of error Charge output Accelerometer 


  1. 1.
    Layer, E., Gawedzki, W.: Dynamics of Measurement Systems: Investigation and Estimation. Polish Scientific Publisher, Warsaw (1991)zbMATHGoogle Scholar
  2. 2.
    Rutland, N.K.: The principle of matching: practical conditions for systems with inputs restricted in magnitude and rate of change. IEEE Trans. Autom. Control 39, 550–553 (1994)CrossRefGoogle Scholar
  3. 3.
    Hessling, J.P.: A novel method of estimating dynamic measurement error. Meas. Sci. Technol. 17, 2740–2750 (2006)CrossRefGoogle Scholar
  4. 4.
    Layer, E., Gawedzki, W.: Time-frequency properties of signals maximizing the dynamic errors. SAMS 11, 73–77 (1993)zbMATHGoogle Scholar
  5. 5.
    Tomczyk, K., Layer, E.: Energy density for signals maximizing the integral-square error. Measurement 90, 224–232 (2016)CrossRefGoogle Scholar
  6. 6.
    Goldberg, D.E.: Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, Reading, MA (1989)zbMATHGoogle Scholar
  7. 7.
    Booker, L.B., Goldberg, D.E., Holland, J.H.: Classifier systems and genetic algorithms. Artif. Intell. 40, 235–282 (1989)CrossRefGoogle Scholar
  8. 8.
    Tomczyk, K.: Application of Genetic Algorithm to Measurement System Calibration Intended for Dynamic Measurement. Metrology and Measurement Systems, vol. XIII, pp. 93–103 (2006)Google Scholar
  9. 9.
    Mallat, S.A.: Tour of Signal Processing. Academic Press, New York (1998)zbMATHGoogle Scholar
  10. 10.
    Huang, M.C.: Wave parameters and functions in wavelet analysis. Ocean Eng. 31, 111–125 (2005)CrossRefGoogle Scholar
  11. 11.
    Xiangcheng, M., Haibao, R., Zisheng, O., Wei, W., Keping, M.: The Use of the Mexican Hat and the Morlet Wavelets for Detection of Ecological Patterns. Plant Ecology, vol. 179, pp. 1–19. Springer (2005)Google Scholar
  12. 12.
    Bialasiewicz, J.T.: Wavelet-based approach to evaluation of signal integrity. IEEE Trans. Ind. Electron. 60, 4590–4598 (2013)CrossRefGoogle Scholar
  13. 13.
    Tomczyk, K.: Problems in modelling of charge output accelerometers. Metrol. Meas. Syst. 23(4), 645–659 (2016)CrossRefGoogle Scholar
  14. 14.
    Jyh-Cheng, Y., Ching-Bin, L.: System modeling and robust design of microaccelerometer using piezoelectric thin film. In: Proceedings of the IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems. Taiwan, pp. 99–104 (1999)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Faculty of Electrical and Computer EngineeringCracow University of TechnologyKrakowPoland

Personalised recommendations