Empirical Mode Decomposition, Lifting and Block Wavelet Transform - - PowerPoint PPT Presentation

Empirical Mode Decomposition, Lifting and Block Wavelet Transform

April 9

Empirical Mode Decomposition (EMD)

• Given x(t), decompose it as follows
• Where cj(t) are the intrinsic mode functions

(IMF) and rn(t) is the residual

• Throughout the whole length of a single IMF,

the number of extrema and the number of zero- crossings must either be equal or differ at most by one

EMD Sifting Process

(1) identify all the local extrema (the combination of both maxima and minima) and connect allthese local maxima (minima) with a cubic spline as the upper (lower) envelope; (2) obtain the first component h by taking the difference between the data and the local mean of the two envelopes; and (3) Treat h as the data and repeat steps 1 and 2 as many times as is required until the envelopes are symmetric with respect to zero mean under certain criteria. The final h is designated as cj(t) . Stoppage criterion: A complete sifting process stops when the residue, rn(t), becomes a monotonic function from which no more IMFs can be extracted.

Lifting Implementation of Steps (1)-(3)

• Given the discrete time signal x(n)
• The predictor P:

(upper envelope+lower envelope)/2

2nd Hour - BWT

• Block Wavelet Transform (BWT) is a transform

similar to DFT

• N input values -> N output values
• Each sample carries frequency information

Block Wavelet Transform