Ellipsoidal Reflectors for Biological Media Light Scattering Photometry

Bezuglyi, Mykhailo

doi:10.1007/978-3-031-44347-3_4

Studies in Systems, Decision and Control

2023

DOI: 10.1007/978-3-031-44347-3_4

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Ellipsoidal Reflectors for Biological Media Light Scattering Photometry

Mykhailo Bezuglyi

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2024

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Cited by 2 publications

References 46 publications

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Fundamentals of Determination of the Biological Tissue Refractive Index by Ellipsoidal Reflector Method

Bezugla,

Romodan,

Komada

et al. 2024

Photonics

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This paper presents the theoretical fundamentals, prerequisites for creation, and peculiarities of modeling a new method for determining the refractive index of biological tissues. The method uses a mirror ellipsoid of revolution as an optical element to ensure total internal reflection phenomena. This paper thoroughly analyzes the differences in the refractive index of healthy and pathological tissues on a biometric diagnostic basis. The analysis is used to model the measurement setup’s parameters. This paper also considers various methods of determining the refractive index of biological tissues based on different principles of physical optics, such as interferometry, refractometry, ellipsometry, and goniophotometry. It systematizes typical optical elements of total internal reflection that can be used in goniophotometry. It justifies the selection of the element base for the goniometric installation based on the ellipsoidal reflector method. A simulation of the installation operation was carried out for various parameters of the ellipsoidal reflector, ensuring the measurement of the biological tissue refractive index from 1.33 to 1.7. This paper also proposes a constructive solution for manufacturing an ellipsoidal reflector of the required configuration.

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Fundamentals of Determination of the Biological Tissue Refractive Index by Ellipsoidal Reflector Method

Bezugla,

Romodan,

Komada

et al. 2024

Photonics

View full text Add to dashboard Cite

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Application of Resnet-152 Neural Networks to Analyze Images From Uav for Fire Detection

Stelmakh,

Mandrovska,

Galagan

2024

IAPGOS

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Timely detection of fires in the natural environment (including fires on agricultural land) is an urgent task, as their uncontrolled development can cause significant damage. Today, the main approaches to fire detection are human visual analysis of real-time video stream from unmanned aerial vehicles or satellite image analysis. The first approach does not allow automating the fire detection process and contains a human factor, and the second approach does not allow detect the fire in real time. The article is devoted to the issue of the relevance of using neural networks to recognize and detect seat of the fire based on the analysis of images obtained in real time from the cameras of small unmanned aerial vehicles. This ensures the automation of fire detection, increases the efficiency of this process, and provides a rapid response to fires occurrence, which reduces their destructive consequences. In this paper, we propose to use the convolutional neural network ResNet-152. In order to test the performance of the trained neural network model, we specifically used a limited test dataset with characteristics that differ significantly from the training and validation dataset. Thus, the trained neural network was placed in deliberately difficult working conditions. At the same time, we achieved a Precision of 84.6%, Accuracy of 91% and Recall of 97.8%.

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Ellipsoidal Reflectors for Biological Media Light Scattering Photometry

Cited by 2 publications

References 46 publications

Fundamentals of Determination of the Biological Tissue Refractive Index by Ellipsoidal Reflector Method

Fundamentals of Determination of the Biological Tissue Refractive Index by Ellipsoidal Reflector Method

Application of Resnet-152 Neural Networks to Analyze Images From Uav for Fire Detection

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