A new method for analysing nonlinear and non-stationary data has been developed. The key part of the method is the `empirical mode decomposition method with which any complicated data set can be decomposed into a nite and often small number of `intrinsic mode functions that admit well-behaved Hilbert transforms. This decomposition method is adaptive, and, therefore, highly ecient.

Matlab runcode
..............\FAacos.m
..............\FAcosfor.m
..............\FAhilbert.m
..............\FAimphilbert.m
..............\FAquadrature.m
..............\FAzc.m
..............\FSPHSP.m
..............\LOD-imf.csv
..............\LOD78.csv
..............\NCU2009V1.txt
..............\blocknormalize.m
..............\confidenceLine.m
..............\dist_value.m
..............\eemd.m
..............\emax.m
..............\emin.m
..............\endprocess1.m
..............\endprocess1.p
..............\ex02d.m
..............\extrema.m
..............\fa.m
..............\findEE.m
..............\findEEfsp.m
..............\findcriticalpoints.m
..............\fspecial.m
..............\hilbert.m
..............\hilbertnormalize.m
..............\hilbtm.m
..............\ifndq.m
..............\linearnormalize.m
..............\local_max.m
..............\medianfilter.m
..............\nnspa.m
..............\nnspe.m
..............\nnspe.m.bak
..............\nspplota.m
..............\nspplote.m
..............\pchipnormalize.m
..............\ratio1.m
..............\ratioa.m
..............\significance.m
..............\significanceIMF.m
..............\signiplotIMF.m
..............\skiphilbt_m.m
..............\splinenormalize.m
..............\splinenormalizeep.m
..............\test.m

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