  An efficient method is introduced in this paper to find the intrinsic mode
function (IMF) components of time series data. This method is faster and more
predictable than the Empirical Mode Decomposition (EMD) method devised by the
author of Hilbert Huang Transform (HHT). The approach is to transforms the
original data function into a piecewise linear sawtooth function (or triangle
wave function), then directly constructs the upper envelope by connecting the
maxima and construct lower envelope by connecting minima with straight line
segments in the sawtooth space, the IMF is calculated as the difference between
the sawtooth function and the mean of the upper and lower envelopes. The
results found in the sawtooth space are reversely transformed into the original
data space as the required IMF and envelopes mean. This decomposition method
process the data in one pass to obtain a unique IMF component without the time
consuming repetitive sifting process of EMD method. An alternative
decomposition method with sawtooth function expansion is also presented.
