Anytime Fuzzy Supervisory System for Signal Auto-Healing

Abstract:

This paper proposes a novel anytime fuzzy supervisory expert system for online signal processing. We demonstrate via simulations that this system is able to follow slowly varying signals and heal the signal in case of missing input data. In the presence of contaminating noise, the supervisory system performs the automatic wavelet shrinkage procedure selection, which ensures to pick the proper algorithm that is the most efficient in the given scenario. The necessary level of wavelet decomposition is determined online by the fuzzy supervisory expert. The system applies orthogonal wavelet functions in order to reduce significantly the processing time of reconstruction. The paper also shows how the online threshold estimator selection module ensures the highest denoising efficiency by selecting the most suitable algorithm.

• Periodical: Advanced Materials Research (Volume 1117)
• Pages: 269-272
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.1117.269
• Online since: July 2015
• Authors: Adrienn Dineva*, Annamária R. Várkonyi-Kóczy, József Kázmér Tar
• Keywords: Anytime System, Denoising, Expert System, Fuzzy Supervisor, Signal Recovery, Wavelet Thresholding

References

[1] S. Mallat, Multiresolution Approximations and Wavelet Orthonormal Bases of L2, Trans. Am. Math. Soc., (1989), pp.69-78.

DOI: 10.1090/s0002-9947-1989-1008470-5

[2] I. Daubechies, Orthonormal Bases of Compactly Supported Wavelets, Comm. Pure & Appl. Math., 41(1988) 909-996.

DOI: 10.1002/cpa.3160410705

[3] S. Zilberstein, S., Operational Rationality through Compilation of Anytime Algorithms, PhD Thesis (1993).

[4] S. Zilberstein, Using Anytime Algorithms in Intelligent Systems, AI Magazine, Vol. 17, No. 3, (1996) 73-83.

[5] L. Justen and R. Ramlau, A General Framework for Soft–Shrinkage with Applications to Blind Deconvolution and Wavelet Denoising, Appl. Comp. Harmon. Anal.  Vol. 26 (1), (2009) 43 – 63.

DOI: 10.1016/j.acha.2008.02.002

[6] D.L. Donoho, I.M. Johnstone, Adapting to Unknown Smoothness via Wavelet Shrinkage, Journal of the American Statistical Association 90 (1995) 1200-1224.

DOI: 10.1080/01621459.1995.10476626

[7] C.M. Stein, Estimation of the Mean of a Multivariate Normal Distribution, The Annals of Statistics 9 (6), (1981) 1135–1151.

[8] D.L. Donoho, Progress in wavelet analysis and WVD: a ten minute tour, in Progress in Wavelet Analysis and Applications, Y. Meyer, S. Roques, Frontières Ed. 1993, p.109–128.

[9] D.L. Donoho, De-noising by Soft-Thresholding, IEEE Trans. on Inf. Theory, 41, 3, (1995) 613–627.

DOI: 10.1109/18.382009

[10] D.L. Donoho, I.M. Johnstone, Minimax Estimation via Wavelet Shrinkage, Annals of Statistics 26, (1998) 879-921.

DOI: 10.1214/aos/1024691081

[11] A. Dineva, A.R. Várkonyi-Kóczy, J.K. Tar, Fuzzy Expert System for Automatic Wavelet Shrinkage Procedure Selection for Noise Suppression, IEEE 18th International Conference on Intelligent Engineering Systems (INES 2014), July 3-5, 2014 Tihany, Hungary, ISBN: 978-1-4799-4616-7, pp.163-168.

DOI: 10.1109/ines.2014.6909361

[12] E.H. Mamdani,S. Assilian, An Experiment in Linguistic Synthesis with Fuzzy Logic Controller, Int. Journal of Men-Machine Studies, Vol. 7., No. 1, (1975), 1-13.

DOI: 10.1016/s0020-7373(75)80002-2