Article (Scientific journals)
Principal-component analysis of particle motion
Chen, Hui Yao; Liegeois, Raphaël; de Bruyn, John et al.
2015In Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
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Keywords :
Particle motion; Component analysis
Abstract :
[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.
Disciplines :
Physical, chemical, mathematical & earth Sciences: Multidisciplinary, general & others
Author, co-author :
Chen, Hui Yao
Liegeois, Raphaël ;  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
Language :
English
Title :
Principal-component analysis of particle motion
Publication date :
2015
Journal title :
Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
ISSN :
1539-3755
eISSN :
1550-2376
Publisher :
American Physical Society, United States - Maryland
Peer reviewed :
Peer Reviewed verified by ORBi
Available on ORBi :
since 02 September 2015

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