Principal component analysis in the spectral analysis of the dynamic laser speckle patterns

Abstract

Dynamic laser speckle is a phenomenon that interprets an optical patterns formed by illuminating a surface under changes with coherent light. Therefore, the dynamic change of the speckle patterns caused by biological material is known as biospeckle. Usually, these patterns of optical interference evolving in time are analyzed by graphical or numerical methods, and the analysis in frequency domain has also been an option, however involving large computational requirements which demands new approaches to filter the images in time. Principal component analysis (PCA) works with the statistical decorrelation of data and it can be used as a data filtering. In this context, the present work evaluated the PCA technique to filter in time the data from the biospeckle images aiming the reduction of time computer consuming and improving the robustness of the filtering. It was used 64 images of biospeckle in time observed in a maize seed. The images were arranged in a data matrix and statistically uncorrelated by PCA technique, and the reconstructed signals were analyzed using the routine graphical and numerical methods to analyze the biospeckle. Results showed the potential of the PCA tool in filtering the dynamic laser speckle data, with the definition of markers of principal components related to the biological phenomena and with the advantage of fast computational processing.

A Proposed Sign Language Model for Speechless Persons Using Eeg Signals

“…This can be achieved by converting the huge set of variables into a smaller one which contains most of the information in the large set. To implement PCA, the mean values must be computed firstly, so that we can compute the standardization (Z) of the initial values of the dataset to transform all the variables to the same range [16], [27], [28] .…”

Procesamiento de señales no intrusivas para el monitoreo de condición de emisiones y análisis energético en un motor de combustión interna diésel

“…La transformación de la información al dominio de PCA se realiza por descomposición de la matriz de covarianza en valores y vectores propios. Esta es una metodología ampliamente utilizada en diferentes áreas de la ingeniería como el desarrollo de técnicas de filtrado, gracias a que puede ser implementada fácilmente desde el punto de vista algorítmico y computacional (Ribeiro et al, 2014).…”

“…De la misma forma como un vector se puede representar en un espacio vectorial por una familia de vectores ortogonales, es posible representar una señal a partir de un grupo de señales ortogonales (Semenovich y Castellanos, 2007) La transformación de la información al dominio de PCA se realiza por descomposición de la matriz de covarianza en valores y vectores propios. Esta es una metodología ampliamente utilizada en diferentes áreas de la ingeniería como el desarrollo de técnicas de filtrado, gracias a que puede ser implementada fácilmente desde el punto de vista algorítmico y computacional (Ribeiro et al, 2014).…”

Visualization of in vitro deep blood vessels using principal component analysis based laser speckle imaging

“…However, in these invasive methods a pulsed laser or external agents are needed to modify the contrast to improve the visualization of blood vessels. In Refs [9,10] the authors use a noninvasive image processing method based on PCA to analyze the dynamic speckle patterns in maize seed, on apple and the drying process of a painted coin. In these works, PCA is used as a filtering process to study spatially and temporally the dynamic of the speckle patterns.…”