Data analysis techniques: Difference between revisions

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* [[:Wikipedia:Bicoherence|Bicoherence]], bispectrum
* [[:Wikipedia:Bicoherence|Bicoherence]], bispectrum
* Chaos analysis ([[:Wikipedia:Attractor|Strange attractor]], [[:Wikipedia:Fractal dimension|Fractal dimension]], [[:Wikipedia:Mutual information|Mutual information]], [[:Wikipedia:Lyapunov exponent|Lyapunov exponent]])
* Chaos analysis ([[:Wikipedia:Attractor|Strange attractor]], [[:Wikipedia:Fractal dimension|Fractal dimension]], [[:Wikipedia:Mutual information|Mutual information]], [[:Wikipedia:Lyapunov exponent|Lyapunov exponent]])
* [[:Wikipedia:Hilbert-Huang transform|Hilbert-Huang transform]]
* [[:Wikipedia:Hilbert-Huang transform|Hilbert-Huang transform]] <ref>N. Huang and S. Shen, ''Hilbert-Huang transform and its applications'' (World Scientific, London, 2005) ISBN 978-9812563767</ref>


=== Self-similarity ===
=== Self-similarity ===

Revision as of 10:40, 18 February 2010

This page collects information on data analysis techniques used in fusion research.

Temporal analysis

Linear analysis

Non-linear analysis

Self-similarity

Spatial analysis

Most of the techniques listed under 'temporal analysis' can of course be applied to spatial data.

Spatio-temporal analysis

Image analysis

  • Twodimensional Fourier analysis
  • Twodimensional wavelet analysis
  • Event detection using thresholding
  • Optical flow (for movies)

Integrated data analysis

The goal of integrated data analysis is to combine the information from a set of diagnostics providing complementary information in order to recover the best possible reconstruction of the actual state of the system subjected to measurement.

See also

Error propagation

References