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New Methods of Complex Systems Inspection: Comparison of the ADC Device in Different Operating Modes

  • Raoul R. Nigmatullin
  • Yury K. Evdokimov
  • Evgeny S. DenisovEmail author
  • Wei Zhang
Chapter
  • 722 Downloads
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 343)

Abstract

The authors suggest a general concept for quantitative inspection of complex systems (when the “best fit” model is absent) data in one unified scheme with the help of the sequence of ranged amplitudes (SRA). Moreover, the “up” and “down” branches of SRA distribution can replace a conventional histogram (having uncontrollable errors) and can be expressed in terms of the fitting parameters that are associated with a combination of power-law functions. As an example of a complex system we considered an analog-to-digital convertor having 16 channels. For four compared different operating modes of this device the calculated SRAs have different behavior and the significant quantitative parameters found enable to differentiate all these regimes from each other. We hope that this new approach will find a proper place in analysis of different complex systems and in different engineering applications, where the urgent necessity in quantitative comparison of complex systems without model exists.

Keywords

Intermediate model Data/signal processing Measurements with/without memory Sequence of the ranged amplitudes 

Notes

Acknowledgements

This paper is stimulated by the R&D project realized in the frame of the JNU-KNRTU(KAI) Joint Laboratory of “Fractal Radio-electronics and Fractal Signal Processing”.

References

  1. 1.
    Rabiner, L.R., Gold, B.: Theory and Application of Digital Signal Processing. Prentice-Hall, Englewood Cliffs (1975)Google Scholar
  2. 2.
    Singleton, Jr., Royce, A., Straits, B.C., Straits, M.M.: Approaches to Social Research. Oxford University Press, Oxford (1993)Google Scholar
  3. 3.
    Mendel, J.M.: Lessons in Estimation Theory for Signal Processing, Communications, and Control. Pearson Education, Upper Saddle River (1995)zbMATHGoogle Scholar
  4. 4.
    Hagan, M.T., Demuth, H.B., Beale M.H.: Neural Network Design. PWS Publishing, Boston (1996)Google Scholar
  5. 5.
    Ifeachor, E.C., Jervis, B.W.: Digital Signal Processing: A Practical Approach. Pearson Education, Harlow (2002)Google Scholar
  6. 6.
    Montgomery, D.C., Jennings, C.L., Kulahci, M.: Introduction to Time Series Analysis and Forecasting. Wiley, Hoboken (2011)Google Scholar
  7. 7.
    Bendat, J.S., Piersol, A.G.: Random Data: Analysis and Measurement Procedures. Wiley, New York (2011)Google Scholar
  8. 8.
    Gelman, A., Carlin, J.B., Stern, H.S., Dunson, D.B., Vehtari, A., Rubin, D.B.: Bayesian Data analysis. CRC Press, Boca Raton (2013)Google Scholar
  9. 9.
    Box, G.E.P., Jenkins, G.M., Reinsel, G.C.: Time Series Analysis: Forecasting and Control. Wiley, Hoboken (2008)CrossRefGoogle Scholar
  10. 10.
    Chatfield, C. (ed.): The Analysis of Time Series: An Introduction. CRC Press, New York (2003)Google Scholar
  11. 11.
    Sheng, H., Chen, Y., Qui, T.: Fractal Processes and Fractional-Order Signal Processing. Techniques and Applications. Springer, Berlin (2012)CrossRefGoogle Scholar
  12. 12.
    Baleanu, D., Guvench, Z.B., Tenreiro Machado, J.A.: New trends in Nanotechnology and Fractional Calculus Applications. Springer, Heidelberg (2010)zbMATHCrossRefGoogle Scholar
  13. 13.
    Baleanu, D., Tenreiro Machado, J.A., Luo, A.C.J.: Fractional Dynamics and Control. Springer, New York (2012)zbMATHCrossRefGoogle Scholar
  14. 14.
    Luo, A.C.J., Tenreiro Machado, J.A., Baleanu, D.: Dynamical Systems and Methods. Springer, New York (2012)zbMATHCrossRefGoogle Scholar
  15. 15.
    Ciurea, M.L., Lazanu, S., Stavaracher, I., Lepadatu, A.-M., Iancu, V., Mitroi, M.R., Nigmatullin, R.R., Baleanu, C.M.: Stress-induced traps in multilayered structures. J. Appl. Phys. 169, 013717 (2011)CrossRefGoogle Scholar
  16. 16.
    Nigmatullin, R.R., Baleanu, D., Dinch, E., Ustundag, Z., Solak, A.O., Kargin, R.V.: Analysis of a nanofilm of the mercaptophenyl diazonium modified gold electrode within new statistical parameters. J. Comput. Theor. Nanosci. 7(3), 562–570 (2010)CrossRefGoogle Scholar
  17. 17.
    Nigmatullin, R.R.: New noninvasive methods for ‘reading’ of random sequences and their applications in nanothechnology. In: Baleanu, D., Guvench, Z.B., Tenreiro Machado, J.A. (eds.) New Trends in Nanotechnology and Fractional Calculus Applications, pp. 43–56. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  18. 18.
    Nigmatullin, R.R.: Universal distribution function for the strongly-correlated fluctuations: General way for description of different random sequences. Commun. Nonlinear Sci. Numer. Simul. 15, 637–647 (2010)zbMATHMathSciNetCrossRefGoogle Scholar
  19. 19.
    Nigmatullin, R.R.: The statistics of the fractional moments: Is there any chance to “read quantitatively” any randomness? Signal Process. 86, 2529–2547 (2006)zbMATHCrossRefGoogle Scholar
  20. 20.
    Nigmatullin, R.R., Ionescu, C., Baleanu, D.: NIMRAD: Novel technique for respiratory data treatment. Signal Image Video Process. (2012). doi: 10.1007/s11760-012-0386-1 Google Scholar
  21. 21.
    Nigmatullin, R.R., Striccoli, D., Zhang, W.: General theory for reproducible data processing: Apparatus function and reduction to an “ideal” experiment. In: Books of Abstracts, 2014 International Conference on Mathematics Models and Methods in Applied Sciences (MMMAS 2014), pp. 303–305. S-Petersburg State Politechnical University, Saint-Petersburg (2014)Google Scholar
  22. 22.
    El-Bakry, H.M., Mastorakis, N.: A new fast forecasting technique using high speed neural networks. In: Proceedings of 8th WSEAS International Conference on Signal, Speech and Image Processing (SSIP ’08), pp. 116–138. Santander, Cantabria (2008)Google Scholar
  23. 23.
    Kan, B., Yazici, B.: Comparison of the results of factorial experiments, fractional factorial experiments, regression trees and mars for fuel consumption data. WSEAS Trans. Math. 9(2), 110–119 (2010)Google Scholar
  24. 24.
    Nigmatullin, R.R., Tenreiro Machado, J., Menezes, R.: Self-similarity principle: The reduced description of randomness. Cent. Eur. J. Phys. 11(6), 724–739 (2013)Google Scholar
  25. 25.
    Nigmatullin, R.R., Baleanu, D.: The derivation of the generalized functional equations describing self-similar processes. Fract. Calc. Appl. Anal. 15(4), 718–740 (2012)MathSciNetCrossRefGoogle Scholar
  26. 26.
    Nigmatullin, R.R., Baleanu, D.: New relationships connecting a class of fractal objects and fractional integrals in space. Fract. Calc. Appl. Anal. 16(4), 911–936 (2013)MathSciNetCrossRefGoogle Scholar
  27. 27.
    Nigmatullin, R.R., Khamzin, A.A., Machado, J.T.: Detection of quasi-periodic processes in complex systems: How do we quantitatively describe their properties? Phys. Scr. (2014). doi: 10.1088/0031-8949/89/01/015201 Google Scholar
  28. 28.
    Nigmatullin, R.R., Osokin, S.I., Baleanu, D., Al-Amri, S., Azam, A., Memic, A.: The first observation of memory effects in the infraRed (FT-IR) measurements: Do successive measurements remember each other? PLoS One (2014). doi: 10.1371/journal.pone.0094305 Google Scholar
  29. 29.
    Nigmatullin, R., Rakhmatullin, R.: Detection of quasi-periodic processes in repeated measurements: New approach for the fitting and clusterization of different data. Commun. Nonlinear Sci. Numer. Simul. 19(12), 4080–4093 (2014)CrossRefGoogle Scholar
  30. 30.
    Nigmatullin, R.R.: Eigen-coordinates: New method of analytical functions identification in experimental measurements. Appl. Magn. Reson. 14, 601–633 (1998)CrossRefGoogle Scholar
  31. 31.
    Nigmatullin, R.R.: Recognition of nonextensive statistical distributions by the eigencoordinates method. Phys. A 285, 547–565 (2000)zbMATHCrossRefGoogle Scholar
  32. 32.
    Nigmatullin, R.R., Bras, A.R., Coreia, N.T.: Evidences of the fractional kinetics in temperature region: Evolution of extreme points in ibuprofen. Commun. Nonlinear Sci. Numer. Simul. 15, 2942–2966 (2010)CrossRefGoogle Scholar
  33. 33.
    Nigmatullin, R.R.: Strongly correlated variables and existence of a universal distribution function for relative fluctuations. Phys. Wave Phenom. 16(2), 119–145 (2008)CrossRefGoogle Scholar
  34. 34.
    NI 6366/6368 Specifications. http://www.ni.com/pdf/manuals/370084d.pdf (2013). Accessed 10 Sep 2014

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Raoul R. Nigmatullin
    • 1
  • Yury K. Evdokimov
    • 1
  • Evgeny S. Denisov
    • 1
    Email author
  • Wei Zhang
    • 2
  1. 1.Radioelectronics and Information & Measuring Techniques DepartmentKazan National Research Technical University named after A.N. Tupolev–KAIKazanRussia
  2. 2.Department of Electronic Engineering, College of Information Science and TechnologyJinan University Shi-PaiGuangzhouChina

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