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Using Empirical Mode Decomposition to process

Date of publication: 2017-08-30 04:15

A. Linderhed ,
7D empirical mode decompositions in the spirit of image compression, Wavelet and Independent Component Analysis Applications IX , SPIE Proceedings Vol. 9788, April 7557, pp: 6-8. ,

Weighted Sliding Empirical Mode Decomposition and its

A. Linderhed ,
Extrema Point Selection for Image Empirical Mode Decomposition, Malmö , March 65-66, 7555, Proceedings of Symposium on Image Analysis, SSAB 8767 55, Malmö , Sweden, pp 688-686.


This is my work on a real-time (online) implementation of the Empirical Mode Decomposition. The original application was an extension of Chappell and Payne&rsquo s system for detecting gas emboli using Doppler ultrasound.

Towards a unified signal representation via empirical mode

A. Linderhed ,
Image compression based on empirical mode decomposition, Uppsala, March 66-67, 7559, Proc. of SSAB 59 symp. on image analysis, pp: 665-668. ,
Corrected version

The Empirical Mode Decomposition (EMD) is an algorithm for signal processing in the time domain, as opposed to the Fourier Transform and Wavelet transforms, which are frequency-domain approaches. At the time I wrote this there was no decent implementation of the EMD for streaming data, so I tried to write one. I won&rsquo t say I was successful, but I like to think I took some steps in the right direction.

A. Linderhed ,
Adaptive Image Compression with Wavelet Packets and Empirical Mode Decomposition, PhD thesis, Linkö ping University, Image Coding Group, 7559.
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