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Technology of Automatic Determination of Indications for 2RT-Laser Treatment of AMD from SD-OCT Images Based on Artificial Intelligence Methods 基于人工智能方法的SD-OCT 2rt激光治疗AMD适应症自动确定技术
IF 1
Optical Memory and Neural Networks Pub Date : 2024-12-23 DOI: 10.3103/S1060992X24700565
A. Yu. Ionov, N. Yu. Ilyasova, N. S. Demin, E. A. Zamytskiy, E. Yu. Zubkova
{"title":"Technology of Automatic Determination of Indications for 2RT-Laser Treatment of AMD from SD-OCT Images Based on Artificial Intelligence Methods","authors":"A. Yu. Ionov,&nbsp;N. Yu. Ilyasova,&nbsp;N. S. Demin,&nbsp;E. A. Zamytskiy,&nbsp;E. Yu. Zubkova","doi":"10.3103/S1060992X24700565","DOIUrl":"10.3103/S1060992X24700565","url":null,"abstract":"<p>The aim of this work is to develop and study the technology of automatic determination of indications for 2RT-laser treatment of AMD by SD-OCT images based on artificial intelligence methods. This is necessary to improve the accuracy and efficiency of AMD diagnosis, as well as to provide faster and more accurate treatment assignment to each patient. The U-Net architecture was chosen as the neural network architecture to extract the area of interest in the retinal OCT image. The VGG16 architecture was used as the neural network architecture for classification. These architectures are well established. As a result of training, the model showed a fairly high accuracy of 90% for segmentation and 98% for classification. Automatic localization and classification based on SD-OST images will allow the most accurate determination of indications for 2RT laser treatment. This will significantly reduce the burden on physicians and make diagnostics more accessible.</p>","PeriodicalId":721,"journal":{"name":"Optical Memory and Neural Networks","volume":"33 2 supplement","pages":"S277 - S284"},"PeriodicalIF":1.0,"publicationDate":"2024-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142875169","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
Common Topological Charge of a Superposition of Several Identical Off-Axis Vortex Beams with an Arbitrary Circularly Symmetric Transverse Shape 具有任意圆对称横向形状的几个相同离轴涡旋光束叠加的公共拓扑电荷
IF 1
Optical Memory and Neural Networks Pub Date : 2024-12-23 DOI: 10.3103/S1060992X24700577
A. A. Kovalev, V. V. Kotlyar, A. G. Nalimov
{"title":"Common Topological Charge of a Superposition of Several Identical Off-Axis Vortex Beams with an Arbitrary Circularly Symmetric Transverse Shape","authors":"A. A. Kovalev,&nbsp;V. V. Kotlyar,&nbsp;A. G. Nalimov","doi":"10.3103/S1060992X24700577","DOIUrl":"10.3103/S1060992X24700577","url":null,"abstract":"<p>We investigate the common topological charge of a superposition of parallel identical vortex beams with an arbitrary transverse shape, either Laguerre–Gaussian beams or Bessel–Gaussian beams or some other vortex beams with rotationally symmetric intensity distribution. It is known that if all the beams in the superposition have the same phase then the common topological charge of the whole superposition equals the topological charge of each constituent beam <i>n</i>. We show that if the beams are located on a circle and their phases increase linearly along this circle so that the phase delay between the neighbor beams on the circle is 2π<i>p</i>/<i>N</i> with <i>N</i> being the number of beams and <i>p</i> being an integer number, then the common topological charge of the superposition is equal to <i>n</i> + <i>p</i>.</p>","PeriodicalId":721,"journal":{"name":"Optical Memory and Neural Networks","volume":"33 2 supplement","pages":"S285 - S294"},"PeriodicalIF":1.0,"publicationDate":"2024-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142875269","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
Topological Properties of Reflection Zeros of Optical Differentiators Based on Layered Metal-Dielectric-Metal Structures 基于层状金属-介电-金属结构的光学微分器反射零的拓扑性质
IF 1
Optical Memory and Neural Networks Pub Date : 2024-12-23 DOI: 10.3103/S1060992X24700607
A. I. Kashapov, E. A. Bezus, D. A. Bykov, A. A. Mingazov, L. L. Doskolovich
{"title":"Topological Properties of Reflection Zeros of Optical Differentiators Based on Layered Metal-Dielectric-Metal Structures","authors":"A. I. Kashapov,&nbsp;E. A. Bezus,&nbsp;D. A. Bykov,&nbsp;A. A. Mingazov,&nbsp;L. L. Doskolovich","doi":"10.3103/S1060992X24700607","DOIUrl":"10.3103/S1060992X24700607","url":null,"abstract":"<p>We investigate the topological properties of reflection zeros of three-layer structures consisting of a dielectric layer sandwiched between two metal layers, which can be used as optical differentiators. We show that the reflection zeros possess non-zero topological charges, which makes them topologically protected. With a small perturbation of the parameters of the structure (e.g., a change in one of the layer thicknesses), the reflection zero does not disappear, but shifts in the parameter space, i.e., appears at different wavelength and angle of incidence. We demonstrate that with a further parameter change, two zeros with opposite topological charges (+1 and –1) approach each other, merge, and then disappear. We believe that the obtained results give useful insight regarding the operation of layered metal-dielectric-metal structures possessing reflection zeros.</p>","PeriodicalId":721,"journal":{"name":"Optical Memory and Neural Networks","volume":"33 2 supplement","pages":"S313 - S319"},"PeriodicalIF":1.0,"publicationDate":"2024-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142875226","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
Influence of Atmospheric Turbulence on the Topological Charge of the Superposition of Optical Vortices 大气湍流对光学涡旋叠加拓扑电荷的影响
IF 1
Optical Memory and Neural Networks Pub Date : 2024-12-23 DOI: 10.3103/S1060992X2470053X
D. O. Shilov, E. S. Kozlova, E. A. Kadomina
{"title":"Influence of Atmospheric Turbulence on the Topological Charge of the Superposition of Optical Vortices","authors":"D. O. Shilov,&nbsp;E. S. Kozlova,&nbsp;E. A. Kadomina","doi":"10.3103/S1060992X2470053X","DOIUrl":"10.3103/S1060992X2470053X","url":null,"abstract":"<p>The paper considers beams in the form of geometric progression of optical vortices. Numerical modelling of the propagation of such optical fields in turbulent media is simulated using the Fresnel integral. The topological charges of the initial and resulting fields have been calculated. As expected, the analysis of the obtained results showed that superpositions with a smaller number of beams are more resistant to distortions by strongly turbulent media. However, in the case of a superposition in the form of a geometric progression with a parameter, the stability of beam propagation is affected not only by the medium parameters, but also by the parameters of the superposition.</p>","PeriodicalId":721,"journal":{"name":"Optical Memory and Neural Networks","volume":"33 2 supplement","pages":"S249 - S260"},"PeriodicalIF":1.0,"publicationDate":"2024-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142875190","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
Diffractive Optical Elements for Multi-Channel Atmospheric Communication Systems in the Visible and Near-IR Ranges 可见光和近红外多通道大气通信系统的衍射光学元件
IF 1
Optical Memory and Neural Networks Pub Date : 2024-12-23 DOI: 10.3103/S1060992X24700504
N. A. Ivliev
{"title":"Diffractive Optical Elements for Multi-Channel Atmospheric Communication Systems in the Visible and Near-IR Ranges","authors":"N. A. Ivliev","doi":"10.3103/S1060992X24700504","DOIUrl":"10.3103/S1060992X24700504","url":null,"abstract":"<p>Currently, wireless laser communication technologies demonstrate high throughput, but the implemented communication systems are not widely used. This feature is due to the low reliability of the communication channels being formed. Recent technological developments have shown successful results in the field of sealing and increasing the noise immunity of communication channels. Therefore, this paper presents a review of modern achievements in the field of multichannel atmospheric optical communication in the visible and near–infrared ranges. The advantages of using diffraction optical elements (DOE) in such systems, which form vortex beams of laser radiation with the required amplitude-phase structure for multiplexing tasks and increasing the noise immunity of information channels, are shown.</p>","PeriodicalId":721,"journal":{"name":"Optical Memory and Neural Networks","volume":"33 2 supplement","pages":"S217 - S225"},"PeriodicalIF":1.0,"publicationDate":"2024-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142875187","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
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
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
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
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