Optical Memory and Neural Networks最新文献_第7页

Methods of Anomalous Data Detection in Datasets 数据集中异常数据的检测方法

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Optical Memory and Neural Networks Pub Date : 2026-02-27 DOI: 10.3103/S1060992X25603045

Akbar Soliev, Akmal Akhatov, Akbar Rashidov

引用次数: 0

Modeling the Behavior of a Flock of Birds to Generate Synthetic Data in Unreal Engine 5 在虚幻引擎5中建模一群鸟的行为以生成合成数据

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Optical Memory and Neural Networks Pub Date : 2026-02-27 DOI: 10.3103/S1060992X25700274

D. A. Sharin, K. E. Vorobyev, T. D. Kazarkin, L. A. Taskina

{"title":"Modeling the Behavior of a Flock of Birds to Generate Synthetic Data in Unreal Engine 5","authors":"D. A. Sharin, K. E. Vorobyev, T. D. Kazarkin, L. A. Taskina","doi":"10.3103/S1060992X25700274","DOIUrl":"10.3103/S1060992X25700274","url":null,"abstract":"The paper proposes a system for modeling the collective behavior of flocks of birds to generate synthetic data in Unreal Engine 5. The system provides flexible adjustment of flock dynamics parameters, including species, speed and flight radius, as well as simulation of complex interactions in a virtual environment. Automated data markup is performed in YOLO (You Only Look Once) formats and with the preservation of spatial metadata of objects. A comparative analysis of the influence of the proportion of synthetic data in training samples on the quality of detection of computer vision models using the example of YOLOv11 is carried out. The results showed that the inclusion of up to 60 to 80 percent of synthetically generated data in the training dataset allows achieving an optimal balance between the accuracy of the model and the cost of collecting real data. The experiments showed that combining real and synthetic data significantly improves detection quality. For example, with 20% real and 80% synthetic data the YOLO v11 model achieved mAP@0.5 = 0.7157, Precision = 0.8157, and Recall = 0.6396, which substantially exceeds the results obtained when training only on real images. The developed system opens up prospects for application in the tasks of airspace monitoring, environmental research and virtual reality.","PeriodicalId":721,"journal":{"name":"Optical Memory and Neural Networks","volume":"34 3","pages":"S457 - S464"},"PeriodicalIF":0.8,"publicationDate":"2026-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147342091","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

Determination of Optical Vortex Superposition Characteristics Using Artificial Intelligence Technologies 利用人工智能技术测定光涡旋叠加特性

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Optical Memory and Neural Networks Pub Date : 2026-02-27 DOI: 10.3103/S1060992X25602878

D. O. Shilov, E. S. Kozlova

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Metasurfaces and Prospects for the Development of Information Optics 信息光学的元表面及其发展前景

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Optical Memory and Neural Networks Pub Date : 2026-02-27 DOI: 10.3103/S1060992X25602180

N. L. Kazanskiy, S. N. Khonina

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Measurement of Orbital Stokes Parameters of Structured Beams in Critical Planes of a First-Order Optical System 一阶光学系统关键平面结构光束轨道斯托克斯参数的测量

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Optical Memory and Neural Networks Pub Date : 2026-02-27 DOI: 10.3103/S1060992X25602325

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

{"title":"Measurement of Orbital Stokes Parameters of Structured Beams in Critical Planes of a First-Order Optical System","authors":"S. I. Khalilov, M. V. Bretsko, S. I. Yakubov, D. V. Maksimov, Ya. E. Akimova","doi":"10.3103/S1060992X25602325","DOIUrl":"10.3103/S1060992X25602325","url":null,"abstract":"In this work, we theoretically and experimentally demonstrate a method for measuring the orbital Stokes parameters of structured Laguerre–Gaussian beams in the critical planes formed in a first-order optical system consisting of a cylindrical and a spherical lens. The positions of the critical planes are determined by the condition of equality of the beam radii in the x- and y-directions at the Rayleigh length ({{z}_{0}} = 2{{f}_{{CL}}}). These planes are characterized by specific values of the Gouy phase difference ({{Gamma }_{{xy}}}): in the first critical plane it equals (Gamma _{{xy}}^{{left( 1 right)}} = pi {text{/}}2), while in the second it takes discrete values (Gamma _{{xy}}^{{left( 2 right)}} = 2pi n,,,n = 0,1,2, ldots ~). By employing the reciprocity effect between the transverse intensity moment ({{W}_{{xy}}}) and the orbital angular momentum ({{ell }_{z}}), related through ({{ell }_{z}}) = 4Wxy, we carried out the measurement of the third orbital Stokes parameter. Owing to the self-healing effect in this system, it was shown that a structured Laguerre–Gaussian beam is reconstructed in the second critical plane, while its astigmatic analogue is formed in the first. For an astigmatic structured Laguerre–Gaussian beam, the situation is reversed: reconstruction occurs in the first critical plane, and its astigmatic analogue appears in the second. This behavior ensures the applicability of the method to both structured Laguerre–Gaussian beams and their astigmatic modifications. The experimental results demonstrate good agreement with numerical modeling with a measurement error not exceeding four percent. It is also shown that structured Laguerre–Gaussian beams can be mapped onto the orbital Poincaré sphere, which opens up prospects for their practical use.","PeriodicalId":721,"journal":{"name":"Optical Memory and Neural Networks","volume":"34 3","pages":"S392 - S401"},"PeriodicalIF":0.8,"publicationDate":"2026-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147342090","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

Hybrid Design of Diffractive Optical Elements Using the Supporting Quadric Method 基于二次支撑法的衍射光学元件混合设计

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Optical Memory and Neural Networks Pub Date : 2026-02-27 DOI: 10.3103/S1060992X25602404

A. Mingazov, D. Soshnikov, L. Doskolovich, D. Bykov, E. Bezus, N. Kazanskiy

引用次数: 0

Human Action Recognition from Motion Capture Data Using Machine Learning Techniques 使用机器学习技术从动作捕捉数据中识别人类动作

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Optical Memory and Neural Networks Pub Date : 2026-02-27 DOI: 10.3103/S1060992X25602593

V. Konovalov, A. Borisov, E. Myasnikov

{"title":"Human Action Recognition from Motion Capture Data Using Machine Learning Techniques","authors":"V. Konovalov, A. Borisov, E. Myasnikov","doi":"10.3103/S1060992X25602593","DOIUrl":"10.3103/S1060992X25602593","url":null,"abstract":"Human action analysis is an actively developing area of research. A successful solution to the problem of action analysis can contribute to a number of tasks in various areas of human activity, such as diagnostics in medicine, maintaining public safety, developing human-machine interfaces, training, etc. In this paper, we solve the problem of recognizing human actions based on motion capture data obtained using special means of recording human movements. This allows us to analyze motion based only on information about the position of the controlled parts of a human body on which the sensors are attached. To recognize actions, we use a classical pattern recognition scheme consisting of feature extraction and classification stages. At first, we preprocess initial motion data and generate feature descriptions in a reduced space. Then we use classical machine learning methods to classify extracted features with the subsequent aggregation using simple voting. In our experiments, we use the Berkeley Multimodal Human Action Database as the initial data and achieve a recognition accuracy of 91.6%, which is more than 10% better than the baseline technique.","PeriodicalId":721,"journal":{"name":"Optical Memory and Neural Networks","volume":"34 3","pages":"S402 - S409"},"PeriodicalIF":0.8,"publicationDate":"2026-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147342538","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}

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Dynamics of the OAM in Stable Generalized Structured Laguerre-Gaussian and Rotated Polygon Beams in the Astigmatic Optical System 散像光学系统中稳定广义结构拉盖尔-高斯光束和旋转多边形光束的OAM动力学

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Optical Memory and Neural Networks Pub Date : 2026-02-27 DOI: 10.3103/S1060992X25602313

A. V. Volyar, M. V. Bretsko, S. I. Khalilov, D. V. Maksimov

引用次数: 0

Impact of TiS3 Integration on Porous Silicon Photodetector Performance TiS3集成对多孔硅光电探测器性能的影响

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Optical Memory and Neural Networks Pub Date : 2026-02-27 DOI: 10.3103/S1060992X25602805

A. Rymzhina, Prachi Sharma, V. Platonov, V. Podlipnov, V. Pavelyev, Nishant Tripathi

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Numerical Modeling of Singular Beam Evolution Diffracted on Surface Periodic Inhomogeneities 表面周期性非均匀性衍射奇异光束演化的数值模拟

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Optical Memory and Neural Networks Pub Date : 2026-02-27 DOI: 10.3103/S1060992X25601903

B. V. Sokolenko, N. V. Shostka, D. A. Poletaev, V. V. Skakun, M. D. Babin, A. O. Titova, Yu. A. Egorov

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