Application of principal component analysis in phase-shifting photoelasticity

Principal component analysis phase shifting (PCA) is a useful tool for fringe pattern demodulation in phase shifting interferometry. The PCA has no restrictions on background intensity or fringe modulation, and it is a self-calibrating phase sampling algorithm (PSA). Moreover, the technique is well suited for analyzing arbitrary sets of phase-shifted interferograms due to its low computational cost. In this work, we have adapted the standard phase shifting algorithm based on the PCA to the particular case of photoelastic fringe patterns. Compared with conventional PSAs used in photoelasticity, the PCA method does not need calibrated phase steps and, given that it can deal with an arbitrary number of images, it presents good noise rejection properties, even for complicated cases such as low order isochromatic photoelastic patterns. © 2016 Optical Society of America.

Sobre la admisión de la separabilidad o de la autonomía de la cláusula arbitral respecto del contrato principal: un test de la práctica panameña.

La autonomía del convenio arbitral respecto del contrato en el que se inserta es un principio que no es privativo de un determinado sistema jurídico, sino que se ha ex-tendido universalmente, figurando en la generalidad de las legislaciones de arbitra-je, y constituyendo una de las manifestaciones más expresivas de la denominada lex mercatoria. Cuestión distinta es la determinación de su contenido en determinados supuestos, sobre todo vinculados a los contratos celebrados con indicios de corrup-ción, que ha dado lugar a un amplio debate. Los razonamientos vertidos hasta este momento en dicho debate encuentran reflejo directo en el asunto Ministerio de la Presidencia (Panamá) / Selex Es S.P.A iniciado el 31 julio 2015 ante la jurisdicción pa-nameña. Nuestra pretensión es utilizar los hechos para verificar que el tema objeto de consideración no es un mero ejercicio retórico sino un excelente test para comprobar cuál es el estado del principio de separabilidad.

Characterization of spatial–temporal patterns in dynamic speckle sequences using principal component analysis

Abstract. Speckle is being used as a characterization tool for the analysis of the dynamics of slow-varying phenomena occurring in biological and industrial samples at the surface or near-surface regions. The retrieved data take the form of a sequence of speckle images. These images contain information about the inner dynamics of the biological or physical process taking place in the sample. Principal component analysis (PCA) is able to split the original data set into a collection of classes. These classes are related to processes showing different dynamics. In addition, statistical descriptors of speckle images are used to retrieve information on the characteristics of the sample. These statistical descriptors can be calculated in almost real time and provide a fast monitoring of the sample. On the other hand, PCA requires a longer computation time, but the results contain more information related to spatial–temporal patterns associated to the process under analysis. This contribution merges both descriptions and uses PCA as a preprocessing tool to obtain a collection of filtered images, where statistical descriptors are evaluated on each of them. The method applies to slow-varying biological and industrial processes.