  The intrinsic mode function (IMF) provides adaptive function bases for
nonlinear and non-stationary time series data. A fast convergent iterative
method is introduced in this paper to find the IMF components of the data, the
method is faster and more predictable than the Empirical Mode Decomposition
method devised by the author of Hilbert Huang Transform. The approach is to
iteratively adjust the control points on the data function corresponding to the
extrema of the refining IMF, the control points of the residue function are
calculated as the median of the straight line segments passing through the data
control points, the residue function is then constructed as the cubic spline
function of the median points. The initial residue function is simply
constructed as the straight line segments passing through the extrema of the
first derivative of the data function. The refining IMF is the difference
between the data function and the improved residue function. The IMF found
reveals all the riding waves in the whole data set. A new data filtering method
on frequency and amplitude of IMF is also presented with the similar approach
of finding the residue on the part to be filtered out. The program to
demonstrate the method is distributed under BSD open source license.
