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Dynamic Speckle Analysis using Multivariate Techniques

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Publication Date
2015-03-01
Authors
Alda, Javier
Rabal, Héctor
Grumel, Eduardo
Trivi, Marcelo
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IOP Publishing Group
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Abstract
In this work we use principal components analysis to characterize dynamic speckle patterns. This analysis quantitatively identifies different dynamics that could be associated to physical phenomena occurring in the sample. We also found the contribution explained by each principal component, or by a group of them. The method analyzes the paint drying process over a hidden topography. It can be used for fast screening and identification of different dynamics in biological or industrial samples by means of dynamic speckle interferometry.
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[1] Rabal H J and Braga R A Jr (ed) 2008 Dynamic Laser Speckle and Applications (Boca Raton, FL, London: CRC Press, Taylor and Francis) pp 14–18 [2] Passoni I, Rabal H, Meschino G and Trivi M 2013 Probability mapping images in dynamic speckle classification Appl. Opt. 52 726–33 [3] Passoni L, Dai Pra A L, Scandurra A, Meschino G, Weber C, Guzman M, Rabal H, Trivi M and Estévez P A 2013 Improvements in the visualization of segmented areas of patterns of dynamic laser speckle Advances in Self-Organizing Maps (AISC vol 198) (Berlin: Springer) pp 163–71 [4] Jolliffe I T 2002 Principal component analysis Springer Series in Statistics (Berlin: Springer) [5] Sendra G H, Dai Pra A L, Passoni I L, Arizaga R, Rabal H and Trivi M 2010 Biospeckle signal descriptor: a performance comparison Proc. SPIE 7387 73871K-1–5 [6] Faccia P A, Pardini O R, Amalvy J I, Cap N, Grumel E E, Arizaga R and Trivi M 2009 Differentiation of the drying time of paints by dynamic speckle interferometry Prog. Org. Coat. 64 350–5 [7] Cattell R B 1966 The scree test for the number of factors J. Multivariate Behav. Res. 1 245–76 [8] López-Alonso J M, Alda J and Bernabeu E 2002 Principal components characterization of noise for infrared images Appl. Opt. 41 320–31 [9] North G R, Bell T L, Cahalan R F and Moeng F J 1982 Sampling errors in the estimation of empirical orthogonal functions Mon. Weather Rev. 19 699–706 [10] López-Alonso J M and Alda J 2005 Characterization of dynamic sea scenarios with infrared imagers Infrared Phys. Technol. 46 355–63 [11] López-Alonso J M and Alda J 2003 Operational parametrization of the 1/f noise of a sequence of frames by means of the principal component analysis in focal plane arrays Opt. Eng. 42 1915–22 [12] López-Alonso J M, Rico-Garcia J M and Alda J 2004 Photonic crystal characterization by FDTD and principal component analysis Opt. Express 12 2176–86 [13] López-Alonso J M and Alda J 2004 Correlation in finance: statistical approach Proc. SPIE 5471 311–21 [14] López-Alonso J M and Alda J 2004 Identification of weak faint point sources by using principal component analysis Proc. SPIE 5238 142–52 [15] López-Alonso J M, Monacelli B, Alda J and Boreman G D 2005 Infrared laser beam temporal fluctuations: characterization and filtering Opt. Eng. 44 054203 [16] du Bosq T, López-Alonso J and Boreman G 2006 Millimeter wave imaging system for landmine detection Appl. Opt. 45 5686–92 [17] Blandin H P, David J C, Vergnaud J M, Illien J P and Malizewicz M 1987 Modelling of drying of coatings: effect of the thickness, temperature and concentration of solvent Prog. Org. Coat. 15 163–72 [18] Amalvy J I, Lasquibar C A, Arizaga R, Rabal H and Trivi M,2001 Application of dynamic speckle interferometry to the drying of coatings Prog. Org. Coat. 42 89–99
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