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Calculation and Modeling of a Metalens for Detection of Fractional Order Vortices 一种用于检测分数阶涡的超透镜的计算与建模
IF 1
Optical Memory and Neural Networks Pub Date : 2024-12-23 DOI: 10.3103/S1060992X24700656
A. G. Nalimov, V. V. Kotlyar
{"title":"Calculation and Modeling of a Metalens for Detection of Fractional Order Vortices","authors":"A. G. Nalimov,&nbsp;V. V. Kotlyar","doi":"10.3103/S1060992X24700656","DOIUrl":"10.3103/S1060992X24700656","url":null,"abstract":"<p>A metalens for detection an incident field with initially a fractional topological charge in the range from –2 to 0 is considered in this work. The metalens is constructed utilizing a spiral zone plate with a topological charge of –1.5. A change in the topological charge of the focused incident beam is shown by simulation to lead to a displacement of its focal spot from the center on the optical axis and to a change in the intensity maximum value, which results in the change in the intensity on the optical axis by 6.9, the change from –0.6 to –1.5 of the topological charge of the incident beam was considered. The intensity at the focus on the optical axis is also affected by the rotation of the beam with a fractional topological charge. This makes it possible using the metalens to measure the tilt angle of the incident beam in the range from 0° to 110°.</p>","PeriodicalId":721,"journal":{"name":"Optical Memory and Neural Networks","volume":"33 2 supplement","pages":"S376 - S385"},"PeriodicalIF":1.0,"publicationDate":"2024-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142875286","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Optimizing a Spatial Ring Filter for Edge Extraction Using Convolutional Neural Network 基于卷积神经网络的空间环形滤波器边缘提取优化
IF 1
Optical Memory and Neural Networks Pub Date : 2024-12-23 DOI: 10.3103/S1060992X24700632
D. Serafimovich, P. Khorin
{"title":"Optimizing a Spatial Ring Filter for Edge Extraction Using Convolutional Neural Network","authors":"D. Serafimovich,&nbsp;P. Khorin","doi":"10.3103/S1060992X24700632","DOIUrl":"10.3103/S1060992X24700632","url":null,"abstract":"<p>The effectiveness of using convolutional neural networks to optimize the parameters of a spatial-frequency ring filter that provides contrasting edge detection is investigated. To create a data set, arbitrary images in the form of test objects and their Fourier transform are used. It was found that, value regardless of the internal and external radius, the intensity maximum is detected in the test figure corners of a square and a triangle. However, these values affect the uniformity of energy distribution along the contour of the figures. The energy distribution along the contour of the test circle figure occurs in the same way, virtually size regardless of the internal and external annular diaphragm radius. As for the contour width, it increases in direct proportion to the inner radius size. A convolutional neural network with 8 layers was trained. The images were classified into two groups according to the required contrast in order to determine the optimal parameters of the bandpass filter for identifying edges in an arbitrary test image. The criterion for dividing the training set into two classes is the specified contrast threshold value. After 10 epochs of training the convolutional neural network, an accuracy rate of 0.836 was obtained for the “hook” test image.</p>","PeriodicalId":721,"journal":{"name":"Optical Memory and Neural Networks","volume":"33 2 supplement","pages":"S343 - S358"},"PeriodicalIF":1.0,"publicationDate":"2024-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142875184","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Application of Computer Vision Algorithms to Solve the Problem of Smoke Detection in Industrial Production 应用计算机视觉算法解决工业生产中的烟雾检测问题
IF 1
Optical Memory and Neural Networks Pub Date : 2024-12-23 DOI: 10.3103/S1060992X24700553
G. Algashev, A. Kupriyanov
{"title":"Application of Computer Vision Algorithms to Solve the Problem of Smoke Detection in Industrial Production","authors":"G. Algashev,&nbsp;A. Kupriyanov","doi":"10.3103/S1060992X24700553","DOIUrl":"10.3103/S1060992X24700553","url":null,"abstract":"<p>This paper proposes an approach for detecting smoke in industrial production using computer vision. The task of detecting smoke and fire can be framed as a detection problem, making modern convolutional neural network models well-suited for this task. The main issues of detection in industrial production are considered, and solutions to these problems are proposed. In the study, the Faster R-CNN, MobileNet SSD v2, and YOLOv8 models were trained and tested in combination with various image preprocessing algorithms. The best result was achieved by the YOLOv8 model combined with the adaptive histogram equalization algorithm for image preprocessing, showing a precision value of 80.1%. As a result, it was demonstrated that deep convolutional networks are well-suited for the task of detecting smoke and fire. Additionally, the main problems and solutions for preparing data for training deep convolutional models were explored.</p>","PeriodicalId":721,"journal":{"name":"Optical Memory and Neural Networks","volume":"33 2 supplement","pages":"S270 - S276"},"PeriodicalIF":1.0,"publicationDate":"2024-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142875227","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Sharp Focusing of Vector Beams Which Do Not Contain Longitudinal Component of the Electric Field 不含电场纵向分量的矢量光束的尖锐聚焦
IF 1
Optical Memory and Neural Networks Pub Date : 2024-12-23 DOI: 10.3103/S1060992X24700590
S. S. Stafeev, V. V. Kotlyar
{"title":"Sharp Focusing of Vector Beams Which Do Not Contain Longitudinal Component of the Electric Field","authors":"S. S. Stafeev,&nbsp;V. V. Kotlyar","doi":"10.3103/S1060992X24700590","DOIUrl":"10.3103/S1060992X24700590","url":null,"abstract":"<p>In this work, we investigated tight focusing characteristics of beams, which do not contain longitudinal component of intensity. The investigated beams have azimuthal or sector-azimuthal polarization and could contain vortex phase. It was numerically shown that beams with azimuthal and sector azimuthal polarization do not contain longitudinal component of intensity. Moreover, the helical phase added to the beams does not add longitudinal component to the electric field; however, it could be used for manipulation with longitudinal component of spin angular momentum in the tight focus. The possibility of generation of investigated beams was demonstrated using vector waveplates and spatial light modulator.</p>","PeriodicalId":721,"journal":{"name":"Optical Memory and Neural Networks","volume":"33 2 supplement","pages":"S335 - S342"},"PeriodicalIF":1.0,"publicationDate":"2024-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142875186","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Analysis of Hyperspectral Images of River Waters 河流水体的高光谱图像分析
IF 1
Optical Memory and Neural Networks Pub Date : 2024-12-23 DOI: 10.3103/S1060992X24700668
I. Novikov, A. Makarov, A. Pirogov, V. Podlipnov, A. Nikonorov, R. Skidanov, V. Platonov, V. Lobanov, Yu. Pridanova, Yu. Vybornova, O. Kalashnikova, T. Podladchikova
{"title":"Analysis of Hyperspectral Images of River Waters","authors":"I. Novikov,&nbsp;A. Makarov,&nbsp;A. Pirogov,&nbsp;V. Podlipnov,&nbsp;A. Nikonorov,&nbsp;R. Skidanov,&nbsp;V. Platonov,&nbsp;V. Lobanov,&nbsp;Yu. Pridanova,&nbsp;Yu. Vybornova,&nbsp;O. Kalashnikova,&nbsp;T. Podladchikova","doi":"10.3103/S1060992X24700668","DOIUrl":"10.3103/S1060992X24700668","url":null,"abstract":"<p>This article proposes an approach to the analysis of high-resolution hyperspectral images in the applied problem of analyzing the state of river waters. This method allows you to detect blooming or contamination of water by foreign substances. High-resolution hyperspectral images were obtained using a hyperspectrometer mounted on a small unmanned aerial vehicle. The difference between the spectra of river areas with different intensity of algal blooms is demonstrated. Samples of river water were taken, chemical analysis was carried out, which confirmed the different content of magnesium and calcium in all samples, corresponding to the intensity of algal blooms in the water. The effectiveness of using machine learning algorithms and the construction of index images for the classification of water areas with different intensity of algal blooms is shown.</p>","PeriodicalId":721,"journal":{"name":"Optical Memory and Neural Networks","volume":"33 2 supplement","pages":"S386 - S397"},"PeriodicalIF":1.0,"publicationDate":"2024-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142875285","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Adaptive Compensation of Wavefront Aberrations Using the Method of Moments 基于矩量法的波前像差自适应补偿
IF 1
Optical Memory and Neural Networks Pub Date : 2024-12-23 DOI: 10.3103/S1060992X24700644
S. Volotovskiy, P. Khorin, A. Dzyuba, S. Khonina
{"title":"Adaptive Compensation of Wavefront Aberrations Using the Method of Moments","authors":"S. Volotovskiy,&nbsp;P. Khorin,&nbsp;A. Dzyuba,&nbsp;S. Khonina","doi":"10.3103/S1060992X24700644","DOIUrl":"10.3103/S1060992X24700644","url":null,"abstract":"<p>An adaptive method for wavefront aberrations compensating has been developed based on the use of a spatial light modulator, the phase function of which is matched to a set of Zernike functions. It is proposed to use the second central moment of intensity of the focal image as a functional. A study of the second central moment was carried out for both individual wavefront aberrations and their superposition. It is shown that achieving the reference value of the second moment can serve as a sign of sufficient compensation for aberration.</p>","PeriodicalId":721,"journal":{"name":"Optical Memory and Neural Networks","volume":"33 2 supplement","pages":"S359 - S375"},"PeriodicalIF":1.0,"publicationDate":"2024-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142875183","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Study of the Influence of Turbulent Media on the Propagation of Squared Laguerre-Gaussian Beams 湍流介质对平方拉盖尔-高斯光束传播影响的研究
IF 1
Optical Memory and Neural Networks Pub Date : 2024-12-23 DOI: 10.3103/S1060992X24700528
E. S. Kozlova, A. A. Savelyeva, E. A. Kadomina, V. V. Kotlyar
{"title":"Study of the Influence of Turbulent Media on the Propagation of Squared Laguerre-Gaussian Beams","authors":"E. S. Kozlova,&nbsp;A. A. Savelyeva,&nbsp;E. A. Kadomina,&nbsp;V. V. Kotlyar","doi":"10.3103/S1060992X24700528","DOIUrl":"10.3103/S1060992X24700528","url":null,"abstract":"<p>The paper considers the squared Laguerre-Gaussian beams. Using the Fresnel integral, numerical modeling of the propagation of such beams in turbulent media is performed. The topological charges of the resulting fields are calculated. The behavior of squared Laguerre-Gaussian beams is compared with conventional Laguerre-Gaussian modes. Analysis of the obtained results showed that even for strong turbulence, the average deviation of the topological charge does not exceed 5%.</p>","PeriodicalId":721,"journal":{"name":"Optical Memory and Neural Networks","volume":"33 2 supplement","pages":"S237 - S248"},"PeriodicalIF":1.0,"publicationDate":"2024-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142875221","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Robust Implementation of Coplanarity-Based Method for Camera Pose Estimation 基于共面性的相机姿态估计方法的鲁棒实现
IF 1
Optical Memory and Neural Networks Pub Date : 2024-12-23 DOI: 10.3103/S1060992X24700541
Ye. V. Goshin
{"title":"Robust Implementation of Coplanarity-Based Method for Camera Pose Estimation","authors":"Ye. V. Goshin","doi":"10.3103/S1060992X24700541","DOIUrl":"10.3103/S1060992X24700541","url":null,"abstract":"<p>In this paper, we consider a method for estimating camera motion parameters from images acquired from this camera, which is based on the use of vector coplanarity estimation. It has been previously shown that the proposed approach can be effectively applied to three-dimensional scenes invariant to their depth. However, due to the criterion used, it is difficult to utilize the RANSAC method to ensure the robustness of the developed method. In this paper, an approach based on the minimum covariance determinant estimation method is proposed. The proposed approach allows us to select the most consistent observations and make an estimation based on these observations. An experimental study of the proposed approach on synthetic data has been carried out. It is shown that the proposed algorithm can provide a significant increase in the reliability of motion parameters determination even in conditions of a small number of corresponding points</p>","PeriodicalId":721,"journal":{"name":"Optical Memory and Neural Networks","volume":"33 2 supplement","pages":"S261 - S269"},"PeriodicalIF":1.0,"publicationDate":"2024-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142875188","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Off-Electrode Plasma of High-Voltage Gas Discharge for Micro- and Nanotechnology Problems 高压气体放电的非电极等离子体的微纳米技术问题
IF 1
Optical Memory and Neural Networks Pub Date : 2024-12-23 DOI: 10.3103/S1060992X24700619
V. A. Kolpakov, S. V. Krichevskiy, M. A. Markushin
{"title":"Off-Electrode Plasma of High-Voltage Gas Discharge for Micro- and Nanotechnology Problems","authors":"V. A. Kolpakov,&nbsp;S. V. Krichevskiy,&nbsp;M. A. Markushin","doi":"10.3103/S1060992X24700619","DOIUrl":"10.3103/S1060992X24700619","url":null,"abstract":"<p>The original features of low-temperature off-electrode plasma of a high-voltage gas discharge, the basis of its occurrence and self-sustainment are demonstrated. As part of a new approach to the formation of wide-format (diameter up to 200 mm) directed flows of low-temperature off-electrode plasma and a class of corresponding gas-discharge devices (free from the disadvantages characteristic of modern domestic and foreign analogues), complex electrode systems are considered. They make it possible to generate directed flows of such plasma at a discharge current in hundreds and thousands of milliamps and electrode voltages of 0.3–1 kV. Based on experimental testing of these electrode systems, methods for cleaning the surface, increasing the adhesive strength of thin metal films and spatially selective etching of semiconductor and dielectric materials in off-electrode plasma for micro- and nano-sized structuring of their surface have been proposed.</p>","PeriodicalId":721,"journal":{"name":"Optical Memory and Neural Networks","volume":"33 2 supplement","pages":"S320 - S334"},"PeriodicalIF":1.0,"publicationDate":"2024-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142875253","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Differential Diagnosis of Retinal Edema Based on OCT Image Analysis 基于OCT图像分析的视网膜水肿鉴别诊断
IF 1
Optical Memory and Neural Networks Pub Date : 2024-12-23 DOI: 10.3103/S1060992X24700589
A. Yu. Ionov, N. Yu. Ilyasova, N. S. Demin, E. A. Zamytskiy
{"title":"Differential Diagnosis of Retinal Edema Based on OCT Image Analysis","authors":"A. Yu. Ionov,&nbsp;N. Yu. Ilyasova,&nbsp;N. S. Demin,&nbsp;E. A. Zamytskiy","doi":"10.3103/S1060992X24700589","DOIUrl":"10.3103/S1060992X24700589","url":null,"abstract":"<p>The aim of the work is differential diagnosis of retinal edema, study of deep learning methods and their application to image analysis. The application of convolutional neural networks to the task of semantic segmentation of retinal layers was investigated and its efficiency in selecting two selected layers (pigment epithelium and retina) was proved. A disease classifier based on intelligent analysis of the layers extracted by the neural network was implemented. A proof of its applicability for differential diagnosis of retinal edema was presented. The accuracy of disease prediction amounted to 90%.</p>","PeriodicalId":721,"journal":{"name":"Optical Memory and Neural Networks","volume":"33 2 supplement","pages":"S295 - S304"},"PeriodicalIF":1.0,"publicationDate":"2024-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142875189","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
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