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1. Introduction
Recovery of a signal from observed noisy data is usually regarded as an important preprocessing and has been an area of research for decades. Many algorithms for noise reduction have been reported so far in the literature including traditional linear filter, such as Butterworth low pass filter, Wiener filter, and wavelet based thresholding filter [1]. Most of them have been proved to be effective in removing the unwanted components. For example, Hsu et al. succeeded in removing the aliasing on the original step-edge response curve (SRC) caused by the binning of Moire patterns [2]. However, the linear filtering methods are not very effective when the signals contain sharp edges and impulses of short duration [3]. As for wavelet based denoising methods, it’s difficult to select the wavelet base, scale, threshold function, and optimal threshold value.
In 1998, empirical mode decomposition (EMD) was designed by Wu and Huang primarily for decomposing the nonlinear and nonstationary signals into a series of intrinsic mode functions (IMFs) [4]. The main advantage of EMD is that it depends entirely on the data itself. Consequently, the results preserve the full nonstationarity characteristics of the target signals. Seen in this light, the EMD method is superior to the wavelet analysis approach, where the basic functions are fixed and, thus, do not necessarily match all real signals [3]. The property of EMD to behave as a dyadic filter bank resembling those involved in wavelets [5] has been useful in signal denoising and received more and more attention [3, 5–11].
However, the EMDalgorithm may encounter the problem ofmode mixingwhen a signal contains intermittency. Therefore the ensemble empirical mode decomposition (EEMD) was introduced [12]. The algorithm defines the IMF set for an ensemble of trials, each one obtained by applying EMD to the signal of interest with added independent identically distributed white noise of the same standard deviation. Taking into account properties of the white noise, the problem of mode mixing can be overcome [13]. Owing to the impressive performance, EEMD has been used to address several problems in the field of...