Multiple artificial neural networks to determine the optical properties of semi-infinite media

2013 ◽  
Author(s):  
M. Jäger ◽  
F. Foschum ◽  
A. Kienle
Keyword(s):  
Neural Networks ◽  
Optical Properties ◽  
Artificial Neural Networks ◽  
Artificial Neural ◽  
Infinite Media

scholarly journals Application of Artificial Neural Networks in Analysis of Time-Variable Optical Reflectance Spectra in Digital Light Projection Spectroscopy

Coatings ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 37
Author(s):  
Marek Gąsiorowski ◽  
Piotr Szymak ◽  
Leszek Bychto ◽  
Aleksy Patryn
Keyword(s):  
Neural Networks ◽  
Optical Properties ◽  
Artificial Neural Networks ◽  
Mean Squared Error ◽  
Reflectance Spectra ◽  
Training Methods ◽  
Optical Reflectance ◽  
Research Cycle ◽  
Evaluation Module ◽  
Artificial Neural

This article undertakes the subject matter of applying artificial neural networks to analyze optical reflectance spectra of objects exhibiting a change of optical properties in the domain of time. A compact Digital Light Projection NIRscan Nano Evaluation Module spectrometer was used to record spectra. Due to the miniature spectrometer’s size and its simplicity of measurement, it can be used to conduct tests outside of a laboratory. A series of plant-derived objects were used as test subjects with rapidly changing optical properties in the presented research cycle. The application of artificial neural networks made it possible to determine the aging time of plants with a relatively low mean squared error, reaching 0.56 h for the Levenberg–Marquardt backpropagation training method. The results of the other ten training methods for artificial neural networks have been included in the paper.

scholarly journals Artificial neural networks based estimation of optical parameters by diffuse reflectance imaging under in vitro conditions

2017 ◽  
Vol 10 (01) ◽  
pp. 1650027 ◽  
Author(s):  
Mahmut Ozan Gökkan ◽  
Mehmet Engin
Keyword(s):  
Neural Networks ◽  
Optical Properties ◽  
Artificial Neural Networks ◽  
Biological Tissue ◽  
Diffuse Reflectance ◽  
Optical Parameters ◽  
Scattering Coefficients ◽  
Surface Image ◽  
Artificial Neural

Optical parameters (properties) of tissue-mimicking phantoms are determined through noninvasive optical imaging. Objective of this study is to decompose obtained diffuse reflectance into these optical properties such as absorption and scattering coefficients. To do so, transmission spectroscopy is firstly used to measure the coefficients via an experimental setup. Next, the optical properties of each characterized phantom are input for Monte Carlo (MC) simulations to get diffuse reflectance. Also, a surface image for each single phantom with its known optical properties is obliquely captured due to reflectance-based geometrical setup using CMOS camera that is positioned at 5[Formula: see text] angle to the phantoms. For the illumination of light, a laser light source at 633[Formula: see text]nm wavelength is preferred, because optical properties of different components in a biological tissue on that wavelength are nonoverlapped. During in vitro measurements, we prepared 30 different mixture samples adding clinoleic intravenous lipid emulsion (CILE) and evans blue (EB) dye into a distilled water. Finally, all obtained diffuse reflectance values are used to estimate the optical coefficients by artificial neural networks (ANNs) in inverse modeling. For a biological tissue it is found that the simulated and measured values in our results are in good agreement.

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