Simulation result shows that the empirical mode decomposition method is at an advantage when denoising and improving SNR.
实验结果显示，使用经验模态分解方法在去除噪声，提高信噪比方面具有优越性。
There are two types of end effects in the empirical mode decomposition method: in the spline interpolation and in the Hilbert transform.
经验模态分解方法中，有两种端点效应：在样条插值中以及在希尔伯特变换中。
To deal with this problem, comparison is made between the empirical mode decomposition(EMD) and the wavelet method in terms of signal trend extraction.
研究了强噪声混合条件下微弱信号的经验模式分解（EMD）问题，提出了一种基于随机共振降噪的EMD分解方法。
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