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All issues Volume 9 (2014) J. Eur. Opt. Soc.-Rapid Publ., 9 (2014) 14009 References
Open Access
Issue
J. Eur. Opt. Soc.-Rapid Publ.
Volume 9, 2014
Article Number 14009
Number of page(s) 10
DOI https://doi.org/10.2971/jeos.2014.14009
Published online 05 February 2014
  1. H. J. Rabal, and R. A. Braga (ed.), Dynamic laser speckle and applications (CRC Press, New York, 2008). [CrossRef] [Google Scholar]
  2. I. Passoni, A. Dai Pra, H. Rabal, M. Trivi, and R. Arizaga, “Dynamic speckle processing using wavelets based entropy,” Opt. Commun. 246, 219–228 (2005). [NASA ADS] [CrossRef] [Google Scholar]
  3. R. A. Braga, L. Dupuy, M. Pasqual, and R. R. Cardoso, “Live biospeckle laser imaging of root tissue,” Eur. Biophys. J. 38, 679–686 (2009). [CrossRef] [Google Scholar]
  4. P. Zakharov, A. C. Völker, M. T. Wyss, F. Haiss, N. Calcinaghi, C. Zunzunegui, A. Buck, et al. “Dynamic laser speckle imaging of cerebral blood flow,” Opt. Express 17, 13904–13917 (2009). [NASA ADS] [CrossRef] [Google Scholar]
  5. A. Mavilio, M. Fernández, M. Trivi, H. Rabal, and R. Arizaga, “Characterization of a paint drying process through granulometric analysis of speckle dynamic patterns,” Signal Process. 90, 1623–1630 (2010). [NASA ADS] [CrossRef] [Google Scholar]
  6. M. D. Z. Ansari, and A. K. Nirala, “Biospeckle activity measurement of Indian fruits using the methods of cross-correlation and inertia moments,” Opt. – Int. J. Light Electron Opt. 124, 2180–2186 (2013). [CrossRef] [Google Scholar]
  7. R. R. Cardoso, A. G. Costa, C. M. B. Nobre, and R. A. Braga Jr., “Frequency signature of water activity by biospeckle laser,” Opt. Commun. 284, 2131–2136 (2011). [NASA ADS] [CrossRef] [Google Scholar]
  8. G. H. Sendra, S. Murialdo, and I. Passoni, “Dynamic laser speckle to detect motile bacterial response of Pseudomonas aeruginosa,” J. Phys. Conf. Ser. 90, 012064 (2007). [NASA ADS] [CrossRef] [Google Scholar]
  9. G. H. Sendra, R. Arizaga, H. Rabal, and M. Trivi, “Decomposition of biospeckle images in temporary spectral bands,” Opt. Lett. 30, 1641–1643 (2005). [NASA ADS] [CrossRef] [Google Scholar]
  10. F. I. M. Argoud, F. M. de Azevedo, and J. Mariano Neto, “Comparative study concerning to wavelet functions and its different applicabilities to pattern recognition in electroencephalogram,” Rev. Bras. Eng. Biomédica 20, 49–59 (2004). [Google Scholar]
  11. H. Rabal, N. Cap, M. Trivi, and M. Guzmán, “Q-statistics in dynamic speckle pattern analysis,” Opt. Lasers Eng. 50, 855–861 (2012). [NASA ADS] [CrossRef] [Google Scholar]
  12. B. J. Berne, and R. Pecora, Dynamic light scattering with applications to chemistry, biology and physics (John Wiley & Sons, New York, 1976). [Google Scholar]
  13. A. F. da Silva, V. P. R. Minim, and M. M. Ribeiro, “Sensory evaluation of differents comercial marks of the organic coffee (Coffea arabica l.),” Ciênc. agrotec. 29, 1.224–1.230 (2005). [Google Scholar]
  14. L. Zhang, W. Dong, D. Zhang, and G. Shi, “Two-stage image denoising by principal component analysis with local pixel grouping,” Pattern Recogn. 43, 1531–1549 (2010). [NASA ADS] [CrossRef] [Google Scholar]
  15. S. Jung, A. Sen, and J. S. Marron, “Boundary behavior in high dimension, low sample size asymptotics of PCA,” J. Multivariate Anal. 109, 190–203 (2012). [Google Scholar]
  16. T. O. Nielsen, R. B. West, S. C. Linn, O. Alter, M. A. Knowling, J. X. O’Connell, S. Zhu, et al., “Molecular characterisation of soft tissue tumours: a gene expression stude,” The Lancet 359, 1301–1307 (2002). [CrossRef] [Google Scholar]
  17. M. Ringnér, “What is principal component analysis?,” Nat. Biotechnol. 26, 303–304 (2008). [Google Scholar]
  18. C. R. Souza Filho, and A. Dinniss, “Periodic noise suppression techniques applied to remote sensing images,” Bol. IG-USP Sér. Cient. 28, 23–62 (1997). [CrossRef] [Google Scholar]
  19. G. Chen, and S. Qian, “Denoising of hyperspectral imagery using principal component analysis and wavelet shrinkage,” IEEE Trans. Geosci. Remote. Sens. 49, 973–980 (2011). [NASA ADS] [CrossRef] [Google Scholar]
  20. H. Abdi, and L. J. Williams, “Principal component analysis,” Wiley Interdiscip. Rev.: Comput. Statistics 2, 433–459 (2010). [CrossRef] [Google Scholar]
  21. P. Xanthopoulos, P. P. M. Pardalos, and T. B. Trafalis, Robust data mining (Springer, New York, 2013). [CrossRef] [Google Scholar]
  22. J. D. Hadfield, “MCMC methods for multi-response generalized linear mixed models: The MCMCglmm R package,” J. Statistical Softw. 33, 1–22 (2010). [CrossRef] [Google Scholar]
  23. L. Batina, J. Hogenboom, and J. G. J. Van Woudenberg, “Getting more from PCA: First results of using principal component analysis for extensive power analysis,” Lect. Notes Comput. Sci. 7178, 383–397 (2012). [CrossRef] [Google Scholar]
  24. H. Fujii, K. Nohira, Y. Yamamoto, H. Ikawa, and T. Ohura, “Evaluation of blood flow by laser speckle image sensing. Part 1,” Appl. Opt. 26, 5321–5325 (1987). [NASA ADS] [CrossRef] [Google Scholar]
  25. R. Arizaga, N. L. Cap, H. Rabal, and M. Trivi, “Display of the local activity using dynamical speckle patterns,” Opt. Eng. 41, 287–294 (2002). [NASA ADS] [CrossRef] [Google Scholar]
  26. L. P. Specht, S. C. Callai, O. A. Khatchatourian, and R. Kohler, “Noise evaluation using the SPBI (Statistical Pass-By Index) for different pavements,” Rem: Rev. Esc. Minas 62, 439–445 (2009). [CrossRef] [Google Scholar]
  27. R. A. Braga, A. Souza, M. G. G. C. Vieira, E. V. R. Von Pinho, H. J. Rabal, and I. M. Dal Fabro, “Biospeckle laser as a potential test of seed viability,” Ciênc. Agrotec. 25, 645–649 (2001). [Google Scholar]
  28. S. E. Skipetrov, J. Peuser, R. Cerbino, P. Zakharov, B. Weber, and F. Scheffold, “Noise in laser speckle correlation and imaging techniques,” Opt. Express 18, 14519–14534 (2010). [NASA ADS] [CrossRef] [Google Scholar]
  29. H. F. Kaiser, “The application of eletronic computers to factor analysis,” Educ. Psychol. Meas. 20, 111–117 (1960). [Google Scholar]
  30. P. R. Scalassara, C. S. Barin, and C. D. Maciel, “Electrochemical noise minimization using digital signal processing,” Semina: Ciênc. Exact. Tecnol. 25, 135–144 (2004). [CrossRef] [Google Scholar]

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