Abstract
The Gaussian function (GF) is widely used to explain the behavior or statistical distribution of many natural phenomena as well as industrial processes in different disciplines of engineering and applied science. For example, the GF can be used to model an approximation of the Airy disk in image processing, laser heat source in laser transmission welding [1], practical microscopic applications [2], and fluorescence dispersion in flow cytometric DNA histograms [3]. In applied sciences, the noise that corrupts the signal can be modeled by the Gaussian distribution according to the central limit theorem. Thus, by fitting the GF, the corresponding process/phenomena behavior can be well interpreted. This article introduces a novel fast, accurate, and separable algorithm for estimating the GF parameters to fit observed data points. A simple mathematical trick can be used to calculate the area under the GF in two different ways. Then, by equating these two areas, the GF parameters can be easily obtained from the observed data.
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