Electrochemical Nois E Signal Processing Using R/S Analysis and Fractional Fourier Transform

Sun, Rongtao; Zaveri, Nikita; Chen, YangQuan; Zhou, Anhong; Zufelt, Nephi A.

doi:10.3182/20060719-3-pt-4902.00033

Cited by 4 publications

(3 citation statements)

References 13 publications

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“…Figures 3 and 4 are wavelet spectrum and FrFT spectrum of the electrochemical noise calculated on scale j , respectively. The testing electrochemical noise is the corrosion potential of the stainless steel electrode with bacteria attached in the artificial saliva called Jenkin's Solution [16]. The electrode is put in the solution for 24 hours at room temperature.…”

Section: Effect Of Nonstationaritymentioning

confidence: 99%

“…A process is said to have long range dependence when 0.5 < H < 1 [12]. Many methods have been proposed for Hurst parameter estimation like wavelet-based [13], local Whittle [14], R/S analysis [15,16], periodogram methods [17], dispersional analysis method [2] and so on. All existing estimation methods have their limitations in terms of estimation accuracy and efficiency.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An improved Hurst parameter estimator based on fractional Fourier transform

Chen

Sun²,

Zhou

2009

Telecommun Syst

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A fractional Fourier transform (FrFT) based estimation method is introduced in this paper to analyze the long range dependence (LRD) in time series. The degree of LRD can be characterized by the Hurst parameter. The FrFTbased estimation of Hurst parameter proposed in this paper can be implemented efficiently allowing very large data set. We used fractional Gaussian noises (FGN) which typically possesses long-range dependence with known Hurst parameters to test the accuracy of the proposed Hurst parameter estimator. For justifying the advantage of the proposed estimator, some other existing Hurst parameter estimation methods, such as wavelet-based method and a global estimator based on dispersional analysis, are compared. The proposed estimator can process the very long experimental time

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Section: Effect Of Nonstationaritymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An improved Hurst parameter estimator based on fractional Fourier transform

Chen

Sun²,

Zhou

2009

Telecommun Syst

Self Cite

View full text Add to dashboard Cite

show abstract

“…0 < < H [2]. Many methods have been proposed for Hurst parameter estimation like wavelet-based [3], local Whittle [4], R/S analysis [5], periodogram methods [6] and so on. On the other hand, Stoev, Taqqu, have shown that non-stationarity effects such as abrupt shifts in the mean and some other contaminations may affect the above methods and result in overestimating the Hurst parameter [7].…”

Section: Introductionmentioning

confidence: 99%

Local Analysis of Long Range Dependence Based on Fractional Fourier Transform

Sun

Chen

Zaveri

et al. 2006

2006 IEEE Mountain Workshop on Adaptive and Learning Systems

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The long range dependence (LRD) of stationary process is characterized by the Hurst parameter. In practice, previous methods for estimation of the Hurst parameter might have poor performance when processing the non-stationary time series or trying to distinguish the slight difference between very long stochastic processes. This paper explores the use of fractional Fourier transform (FrFT) for estimating the Hurst parameter. The time series was processed locally to achieve a reliable local estimation of the Hurst parameter. The biocorrosion signal which is very popular in biological engineering was studied as an example to show the long range dependence properties. After comparing with the commonly used wavelet based method and another method based on Matlab's polyfit, the new Hurst parameter estimator proposed in this paper is proved to be more robust for non-stationarity and can show the slight difference clearly between those very long sets of biocorrosion data.Index Terms-biocorrosion signal, fractional Fourier transform, Hurst parameter, long range dependence, parameter estimation

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