I. Introduction
Wavelet transform is developed rapidly as a new signal processing tool in recent years. Wavelet noise reduction modeling is one of the most effective techniques with respect to complicated signal analysis. As a signal time-scale(time-frequency) analysis method, Wavelet transform has multi-resolution characteristics, and has the ability of denoting signal characteristics in time and frequency domains. Although wavelet transforms are suitable for noise reduction, in other words, for signal trend extraction, its pre-divided frequency feature limits its ability to decompose the signal into different frequencies according to the inherent characteristics of the signal. Empirical Mode Decomposition (EMD) is a new signal analysis technique showing great promise for signal trend extraction [1]. EMD technique has already been successfully applied to several other scientific problems. It provides an adaptive representation of non-linear signals, which ensure the non-linear signal can be converted into an Intrinsic Mode Function (IMF) more easily for wavelet analysis. Based on EMD and the wavelet shrinkage noise reduction model, a new trend extraction model called the EMD-Wavelet model is presented here.