Experimental Data Filtration Algorithm

Experimental data reduction is an important topic because the resulting information is used to calibrate the theoretical models and to verify the accuracy of their results. The paper presents some ideas used to extract a subset of points from the initial set of points which defines an experimentally acquired curve. The objective is to get a subset with significantly fewer points as the initial data set and which accurately defines a smooth curve that preserves the shape of the initial curve. Being a general study we used only data filtering criteria based geometric features that at a later stage may be related to upper level conditions specific to the phenomenon under investigation. Five algorithms were conceived and implemented in an original software consisting of more than 1800 computer code lines which has a flexible structure that allows us to easily update it using new algorithms. The software instrument was used to process the data of several case studies. Conclusions are drawn regarding the values of the parameters used in the algorithms to decide if a series of points may be considered either noise, or a relevant part of the curve. Being a general analysis, the result is a computer based trial-and-error method that efficiently solves this kind of problems.