ABSTRACT
Empirical mode decomposition (EMD) is an adaptive signal-dependent decomposition with which any complicated signal can be decomposed into a series of constituents. Adding all the extracted constituents together reconstructs the original signal without information loss or distortion. Many methods exist that analyze signals simultaneously in the time and frequency domains, such as those based on wavelets, short-time Fourier transform (STFT), Wigner-Ville distribution (WVD), reduced interference distributions, and so on. These methods are based on the expansion of the signal into a set of basis functions that are de†ned by the method. The concept of EMD is to expand the signal into a set of functions de†ned by the signal itself. These decomposed constituents are called intrinsic mode functions (IMF). Signal adaptive decomposition by means of principal component analysis (PCA) [1] also expands the signal into a basis de†ned by the signal itself. PCA differs from EMD in that it is based on the signal statistics, while EMD is deterministic and is based on local properties. The EMD process allows time-frequency analysis of transient signals for which Fourier-based methods have been unsuccessful. Whenever we use the Fourier transform to represent frequencies
2.1 Introduction ............................................................................................................................. 21 2.2 Empirical Mode Decomposition .............................................................................................22 2.3 Nyquist Pulse Interpolation .....................................................................................................23 2.4 Raised Cosine Empirical Mode Decomposition .....................................................................25 2.5 Signal Decomposition Quality of RCEMD Algorithm ...........................................................26
