The paper discusses the results of the first stage of research and development an innovative computer vision system for the automatic asbestos content control in stones veins at an asbestos processing factory. The discussed system is based on the applying of a semantic segmentation artificial neural networks, in particular U-Net based network architectures for solving both: the boundaries of stones segmentation and veins inside them. At the current stage, the following tasks were solved. 1. The discussed system prototype is developed. The system is allowing to takes images of the asbestos stones on the conveyor belt in the near-infrared range (NIR), avoiding the outer lighting influence, and processing the obtaining images. 2. The training, validation and test datasets were collected. 3. Substantiated the choice of the U-Net based neural network. 4. Proposed to estimate the resulted specific asbestos concentration as the average relation of all the veins square to all stones square on the image. 5. The resulted deviation between obtained and laboratory given results of the asbestos concentration is about 0.058 in the slope of graduation curve. The farther improvement recommendations for the developed system are given.
The paper is devoted to the problem of short time delays measurement in radar systems. The algorithm which was proposed by authors in paper I [1] it compared with the generally used method based on fast Fourier transform. In numerical simulation estimation of random error are carried out depend on value of delay and value of signal to noise ratio.
1784Mikhail Ronkin et al.
The electroretinography (ERG) is a diagnostic test that measures the electrical activity of the retina in response to a light stimulus. The current ERG signal analysis uses four components, namely amplitude, and the latency of a-wave and b-wave. Nowadays, the international electrophysiology community established the standard for electroretinography in 2008. However, in terms of signal analysis, there were no major changes. ERG analysis is still based on a four-component evaluation. The article describes the ERG database, including the classification of signals via the advanced analysis of electroretinograms based on wavelet scalogram processing. To implement an extended analysis of the ERG, the parameters extracted from the wavelet scalogram of the signal were obtained using digital image processing and machine learning methods. Specifically, the study focused on the preprocessing of wavelet scalogram as images, and the extraction of connected components and thier evaluation. As a machine learning method, a decision tree was selected as one that incorporated feature selection. The study results show that the proposed algorithm more accurately implements the classification of adult electroretinogram signals by 19%, and pediatric signals by 20%, in comparison with the classical features of ERG. The promising use of ERG is presented using differential diagnostics, which may also be used in preclinical toxicology and experimental modeling. The problem of developing methods for electrophysiological signals analysis in ophthalmology is associated with the complex morphological structures of electrophysiological signal components.
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