Optical Memory and Neural Networks最新文献_第9页

Self-Healing of the Polarization Structure in Vector Beams Subjected to Sector Perturbation 扇形扰动下矢量光束偏振结构的自愈

IF 0.8

Optical Memory and Neural Networks Pub Date : 2026-01-26 DOI: 10.3103/S1060992X25700249

S. I. Yakubov, M. V. Bretsko, S. I. Khalilov, Ya. E. Akimova, D. V. Maksimov

引用次数: 0

Characterization of Resonance Multimode Planar Structures Based on Transfer Matrix Method 基于传递矩阵法的共振多模平面结构表征

IF 0.8

Optical Memory and Neural Networks Pub Date : 2026-01-26 DOI: 10.3103/S1060992X25602015

D. V. Nesterenko, V. A. Soifer

引用次数: 0

Classical, Polarization, and Hybrid Orbital Angular Momentum of Vector Fields with Polarization Singularities 具有偏振奇点的矢量场的经典、极化和混合轨道角动量

IF 0.8

Optical Memory and Neural Networks Pub Date : 2026-01-26 DOI: 10.3103/S1060992X2560209X

V. V. Kotlyar, A. A. Kovalev, A. M. Telegin, S. S. Stafeev

{"title":"Classical, Polarization, and Hybrid Orbital Angular Momentum of Vector Fields with Polarization Singularities","authors":"V. V. Kotlyar, A. A. Kovalev, A. M. Telegin, S. S. Stafeev","doi":"10.3103/S1060992X2560209X","DOIUrl":"10.3103/S1060992X2560209X","url":null,"abstract":"<p>Besides scalar optical vortices that have a topological charge (TC), helical wave front, and carry an orbital angular momentum (OAM) that can be transferred to particles and rotate them along circular trajectories, polarization optical vortices are also known, whose polarization state in the beam section changes with the azimuthal angle. Such vortices are polarization singularities that are described by indices, similar to the TC. However, polarization OAM for polarization vortices still has not been considered, although laser beams with inhomogeneous polarization can perform spiral mass transport in polarization-sensitive media. In this work, we introduce and consider two analogues of the OAM for vector fields. One polarization OAM analogue is the polarization orientation azimuthally changing velocity, or polarization OAM (pOAM), whereas the hybrid OAM analogue is the polarization ellipticity azimuthally changing velocity, or hybrid OAM (hOAM). For instance, we demonstrate that the normalized pOAM is equal to the order of a cylindrical vector beam and also equals the order of Poincaré beam. In optical material processing, the considered OAM analogues describe how light fields with different polarization affect polarization-sensitive media. In optical data transmission, these OAM analogues allow distinguishing different light fields that are indistinguishable by their conventional OAM.</p>","PeriodicalId":721,"journal":{"name":"Optical Memory and Neural Networks","volume":"34 2","pages":"S292 - S300"},"PeriodicalIF":0.8,"publicationDate":"2026-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146043476","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

Polarization Singularities in Non-Coaxial Interference of Vector Beams 矢量光束非同轴干涉中的偏振奇点

IF 0.8

Optical Memory and Neural Networks Pub Date : 2026-01-26 DOI: 10.3103/S1060992X25602283

M. V. Bretsko, S. I. Yakubov, D. V. Maksimov, S. I. Khalilov, Ya. E. Akimova, A. O. Titova

引用次数: 0

Bessel Beams in Crystals with Optical Activity 具有光学活性晶体中的贝塞尔光束

IF 0.8

Optical Memory and Neural Networks Pub Date : 2026-01-26 DOI: 10.3103/S1060992X25602258

Y. Egorov, V. Mostovskoy, E. Umerov, A. Rubass

引用次数: 0

Using Computer Vision Methods for AlSiC Products Quality Control 计算机视觉方法在AlSiC产品质量控制中的应用

IF 0.8

Optical Memory and Neural Networks Pub Date : 2026-01-26 DOI: 10.3103/S1060992X25700250

V. E. Dementyev, A. G. Tashlinskii, I. V. Chufarov, A. P. Tereshenok, S. N. Potapov

{"title":"Using Computer Vision Methods for AlSiC Products Quality Control","authors":"V. E. Dementyev, A. G. Tashlinskii, I. V. Chufarov, A. P. Tereshenok, S. N. Potapov","doi":"10.3103/S1060992X25700250","DOIUrl":"10.3103/S1060992X25700250","url":null,"abstract":"<p>This paper explores the application of computer vision for quality control of products, focusing on the challenge of surface defect detection with a limited dataset. Substrate made of AlSiC composite material is a good example. To address the small sample size, data augmentation and transfer learning techniques have been employed, pre-training a model on a public crack dataset. The core of approach is utilization of the YOLOv8-OBB object detector, chosen for its support of oriented bounding boxes, which are crucial for accurately capturing elongated defects like cracks. Furthermore, to enhance detection reliability, a method that combines results from multiple images of the same object captured from different angles has been proposed. This multi-view analysis allows for a reduction in the detection confidence threshold, increasing the true positive rate. Therefore, offered technique in article is a combination of YOLOv8-Obb, Augmentation, Transfer Learning and Multi-View Analysis. The proposed system was tested on a dedicated dataset of AlSiC products, achieving a defect detection rate of over 80% with a false alarm probability of approximately 1%. The results demonstrate the feasibility of using modern neural network-based detectors for automated visual inspection in specialized industrial applications.</p>","PeriodicalId":721,"journal":{"name":"Optical Memory and Neural Networks","volume":"34 2","pages":"S301 - S311"},"PeriodicalIF":0.8,"publicationDate":"2026-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146043475","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

Methods of Spectral Matching for Remote Sensing Data 遥感数据的光谱匹配方法

IF 0.8

Optical Memory and Neural Networks Pub Date : 2026-01-26 DOI: 10.3103/S1060992X25602398

A. Borisov

引用次数: 0

Complex Spatial Structures in the Optically Driven VCSELs 光驱动vcsel中的复杂空间结构

IF 0.8

Optical Memory and Neural Networks Pub Date : 2026-01-26 DOI: 10.3103/S1060992X25601927

E. A. Yarunova, D. S. Riashchikov, A. A. Krents, N. E. Molevich

引用次数: 0

Axial and Off-Axis Focal Diffraction Orders Formation Using of Phase Quantized Non-Paraxial Optical Elements 利用相位量化的非近轴光学元件形成轴向和离轴焦衍射阶数