Increased performance in DDM analysis by calculating structure functions through Fourier transform in time

• URL: http://arxiv.org/abs/2012.05695v1
• Date: Wed, 2 Dec 2020 21:12:45 GMT
• Title: Increased performance in DDM analysis by calculating structure functions through Fourier transform in time
• Authors: M. Norouzisadeh, G. Cerchiari and F. Croccolo
• Abstract summary: We present an algorithm to efficiently process a set of images according to the Differential Dynamic Microscopy analysis scheme. The new implementation computes the DDM analysis faster, thanks to an additional Fourier transform in time instead of performing differences of signals. Without GPU hardware acceleration and for the same set of images, we found that the new algorithm is 300 faster than the old one both running only on the CPU.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Differential Dynamic Microscopy (DDM) is the combination of optical microscopy to statistical analysis to obtain information about the dynamical behaviour of a variety of samples spanning from soft matter physics to biology. In DDM, the dynamical evolution of the samples is investigated separately at different length scales and extracted from a set of images recorded at different times. A specific result of interest is the structure function that can be computed via spatial Fourier transforms and differences of signals. In this work, we present an algorithm to efficiently process a set of images according to the DDM analysis scheme. We bench-marked the new approach against the state-of-the-art algorithm reported in previous work. The new implementation computes the DDM analysis faster, thanks to an additional Fourier transform in time instead of performing differences of signals. This allows obtaining very fast analysis also in CPU based machine. In order to test the new code, we performed the DDM analysis over sets of more than 1000 images with and without the help of GPU hardware acceleration. As an example, for images of $512 \times 512$ pixels, the new algorithm is 10 times faster than the previous GPU code. Without GPU hardware acceleration and for the same set of images, we found that the new algorithm is 300 faster than the old one both running only on the CPU.

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