Monte Carlo Probability To Determine Attenuation Coefficient In Identifying The Selectivity Properties Of Near-Infrared-Based Materials

Abstract

Accurate attenuation coefficient measurements are able to display the nature of selectivity in an image. The methods used to measure the attenuation coefficient are Monte Carlo probability and linear regression models. This attenuation coefficient will appear if an image is taken in transmit mode. Selectivity properties are characterized by contrast ratios between different materials. In this transmission mode, if a near-infrared beam with a different wavelength is passed, it will produce different intensity values depending on the type of wavelength. According to Beer-Lambert's law, the intensity value will experience attenuation depending on the exponential function of the attenuation coefficient and thickness. By using Monte Carlo probability, we get the thickness value of a material per point. This thickness value is then displayed in image form. This thickness function is then used to display an image. The near-infrared wavelengths used are 780 nm and 808 nm. The results show that Monte Carlo probability shows more selectivity than the linear regression method. The wavelength of 808 nm can show more selectivity properties than 780 nm.

Keywords

• attenuation coefficient, imaging, monte carlo, tranmittance, selectivity.