This is the data sets that used to develop, verify, and demonstrate the use of Image-based Probability Estimation of Displacement (iPED). iPED is an image-based algorithm to find the probability density function (PDF) of particle displacements from a sequence of images. iPED does not make any assumptions about the shape of the particle intensity profile or the PDF of the displacements. We compare iPED’s performance with the previous correlation-based method for both Gaussian and non-Gaussian particle intensity profiles undergoing Gaussian or non-Gaussian processes. The datasets present here can be used to reproduce the figures and analysis reported in the paper.

Researchers should cite this work as follows:

• Ahmadzadegan, A.; Ardekani, A.; Vlachos, P. (2020). Estimation of the Probability Density Function of Random Displacements from Images. Purdue University Research Repository. doi:10.4231/34TJ-S109

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1. Diffusion measurement
2. image processing
3. Mechanical Engineering
4. particle image velocimetry
5. Probability density function estimation
6. Random Process