Advantages of Fuzzy and Anytime Signal- and Image Processing Techniques - A Case Study

  • Teréz A. Várkonyi
Part of the Studies in Computational Intelligence book series (SCI, volume 378)


Nowadays practical solutions of engineering problems always involve some kind of, preferably model-integrated, information processing task. Unfortunately, however, the available knowledge about the information to be processed is usually incomplete, ambiguous, noisy, or totally missing. Furthermore, the available time and resources for fulfilling the task are often not only limited, but can change during the operation of the system. All these facts seriously limit the effective usability of classical information processing algorithms which pressed researchers and engineers to turn towards non-classical methods and these approaches proved to be very advantageous. In this chapter, a brief overview is given about various imprecise, fuzzy and anytime, signal- and image processing methods and their applicability is discussed in treating the insufficiency of knowledge of the information necessary for handling, analyzing, modeling, identifying, and controlling of complex engineering problems.


Membership Function Soft Computing Corner Detection Fuzzy Technique Information Processing Task 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Andoga, R., Madarász, L., Karas, M.: The Proposal of Use of Hybrid Systems in Situational Control of Jet Turbo-compressor Engines. In: Proceedings of the 3rd Slovakian-Hungarian Joint Symposium on Applied Machine Intelligence, pp. 93–106 (2005)Google Scholar
  2. 2.
    Catté, F., Lions, P.-L., Morel, J.-M., Coll, T.: Image selective smoothing and edge detection by nonlinear diffusion. SIAM Journal on Numerical Analysis 29(1), 182–193 (1992)MathSciNetzbMATHCrossRefGoogle Scholar
  3. 3.
    Crochiere, R.E., Rabiner, L.R.: Multirate Digital Signal Processing. Prentice-Hall, Inc., Englewood Cliffs (1983)Google Scholar
  4. 4.
    Förstner, W.: A feature based correspondence algorithm for image matching. Int. Arch. Photogramm Remote Sensing 26, 150–166 (1986)Google Scholar
  5. 5.
    Klir, G.J., Folger, T.A.: Fuzzy Sets, Uncertainty, and Information. Prentice Hall Intg. Inc., Englewood Cliffs (1988)zbMATHGoogle Scholar
  6. 6.
    Liu, J.W.S., et al.: Imprecise Computations. Proceedings of the IEEE 82(1), 83–93 (1994)CrossRefGoogle Scholar
  7. 7.
    Madarász, L.: Intelligent technologies and their applications in complex systems, p. 348. University Press, Slovakia (2004)Google Scholar
  8. 8.
    Madarász, L., Andoga, R., Fözö, L., Lazar, T.: Situational control, modeling and diagnostics of large scale systems. In: Rudas, I.J., Fodor, J., Kacprzyk, J. (eds.) Towards Intelligent Engineering and Information Technology. SCI, vol. 243, pp. 153–164. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  9. 9.
    Melin, P., Castillo, O.: Adaptive Intelligent Control of Aircraft Systems with Hybrid Approach Combining Neural Networks, Fuzzy Logic, and Fractal Theory. Applied Soft Computing 3, 353–362 (2003)CrossRefGoogle Scholar
  10. 10.
    Rojas, R.: Neural Networks, A Systematic Introduction. Springer, Berlin (1996)zbMATHGoogle Scholar
  11. 11.
    Rudas, I.J., Kaynak, M.O., Bitó, J.F., Szeghegyi, Á.: New Possibilities in Fuzzy Controllers Design Using Generalized Operators. In: Proceedings of the 5th International Conference on Emerging Technologies and Factory Automation, pp. 513–517 (1996)Google Scholar
  12. 12.
    Russel, S., Norvig, P.: Atrifial Intelligence a Modern Approach. Prentice Hall, New Jersey (2003)Google Scholar
  13. 13.
    Russo, F.: Fuzzy Filtering of Noisy Sensor Data. In: Proceedings of the IEEE Instrumentation and Measurement Technology Conference, pp. 1281–1285 (1996)Google Scholar
  14. 14.
    Russo, F.: Recent Advances in Fuzzy Techniques for Image Enhancement. IEEE Transactions on Instrumentation and Measurement 47(6), 1428–1434 (1998)CrossRefGoogle Scholar
  15. 15.
    Shynk, J.J.: Frequency-Domain and Multirate Adaptive Filtering. IEEE Signal Processing Magazine, 15–37 (January 1992)Google Scholar
  16. 16.
    Sinčák, P., et al. (eds.): Intelligent Technologies Theory and Applications. IOS Press, Amsterdam (2002)Google Scholar
  17. 17.
    Yager, R.R.: Fuzzy thinking as quick and efficient. Cybernetica 23, 265–298 (1980)MathSciNetzbMATHGoogle Scholar
  18. 18.
    Várkonyi, T.A.: Soft Computing Based Signal Processing Approaches for Supporting Modeling and Control of Engineering Systems - A Case Study. In: Proceedings of the 14th International Conference on Intelligent Engineering Systems, pp. 102–107 (2010)Google Scholar
  19. 19.
    Várkonyi-Kóczy, A.R.: State Dependant Anytime Control Methodology for Non-linear Systems. International Journal of Advanced Computational Intelligence and Intelligent Informatics (JACIII) 12(2), 198–205 (2008)Google Scholar
  20. 20.
    Várkonyi-Kóczy, A.R.: Fuzzy Logic Supported Corner Detection. Journal of Intelligent and Fuzzy Systems 19(3), 41–50 (2008)zbMATHGoogle Scholar
  21. 21.
    Várkonyi-Kóczy, A.R.: Fast Anytime Fuzzy Fourier Estimation of Multisine Signals. IEEE Trans. on Instrumentation and Measurement 58(5), 1763–1770 (2009)CrossRefGoogle Scholar
  22. 22.
    Várkonyi-Kóczy, A.R., Baranyi, P., Patton, R.J.: Anytime Fuzzy Modeling Approach for Fault Detection Systems. In: Proceedings of the IEEE Instrumentation and Measurement Technology Conference, pp. 1611–1616 (2003)Google Scholar
  23. 23.
    Várkonyi-Kóczy, A.R., Kovácsházy, T.: Anytime Algorithms in Embedded Signal Processing Systems. In: Proceedings of the IX. European Signal Processing Conference, vol. 1, pp. 169–172 (1998)Google Scholar
  24. 24.
    Vaščák, J., Kováčik, P., Hirota, K., Sinčák, P.: Performance-based Adaptive Fuzzy Control of Aircrafts. In: Proceedings of the 10th IEEE International Conference on Fuzzy Systems, vol. 2, pp. 761–764 (2001)Google Scholar
  25. 25.
    Vaščák, J., Mikloš, M.: Hybrid Fuzzy Adaptive Control of LEGO Robots. In: Proceedings of the 2nd International Symposium on Advanced Intelligent Systems, vol. 2, pp. 252–256 (2001)Google Scholar
  26. 26.
    Zadeh, L.: Fuzzy Logic, Neural Networks, and Soft Computing. Communications of the ACM 37(3), 77–83 (1994)MathSciNetCrossRefGoogle Scholar
  27. 27.
    Zilberstein, S.: Using Anytime Algorithms in Intelligent Systems. AI Magazine 17(3), 73–83 (1996)Google Scholar
  28. 28.
    Zilberstein, S., Russel, J.: Reasoning about optimal time allocation using conditional profiles. In: Proceedings of AAAI 1992 Workshop on Implementation of Temporal Reasoning, pp. 191–197 (1992)Google Scholar
  29. 29.
    Zilberstein, S., Russel, J.: Constructing utility-driven real-time systems using anytime algorithms. In: Proceedings of the IEEE Workshop on Imprecise and Approximate Computation, pp. 6–10 (1992)Google Scholar

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© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Teréz A. Várkonyi
    • 1
  1. 1.Óbuda UniversityBudapestHungary

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