Reference : Principal-component analysis of particle motion
Scientific journals : Article
Physical, chemical, mathematical & earth Sciences : Multidisciplinary, general & others
http://hdl.handle.net/2268/185442
Principal-component analysis of particle motion
English
Chen, Hui Yao [> >]
Liegeois, Raphaël mailto [Université de Liège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation >]
de Bruyn, John [> >]
Soddu, Andrea [Université de Liège > > Centre de recherches du cyclotron >]
2015
Physical Review. E ,Statistical, Nonlinear, and Soft Matter Physics
Yes (verified by ORBi)
International
1539-3755
1550-2376
[en] Particle motion ; Component analysis
[en] We demonstrate the application of principal-component analysis (PCA) to the analysis of particle motion data in the form of a time series of images. PCA has the ability to resolve and isolate spatiotemporal patterns in the data. Using simulated data, we show that this translates into the ability to separate individual frequency components of the particle motion. We also show that PCA can be used to extract the fluid viscosity from images of particles undergoing Brownian motion. PCA thus provides an efficient alternative to more traditional particle-tracking methods for the analysis of microrheological data.
http://hdl.handle.net/2268/185442

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