Optical Memory and Neural Networks最新文献

{"title":"Diffractive Optics in Laser Processing: Digital Approaches to Design and Application","authors":"S. P. Murzin","doi":"10.3103/S1060992X25602647","DOIUrl":"10.3103/S1060992X25602647","url":null,"abstract":"<p>Effective control of the spatial distribution of laser beam energy plays a key role in material processing technologies. The integration of diffractive optical elements (DOE) with digital tools opens new prospects for the control of laser processes, enabling precise energy distribution, adaptive adjustment of processing parameters, and improved predictability of outcomes. This paper explores digital approaches to the design and application of diffractive optics in laser processing. It describes methods for tuning laser beam parameters, the use of digital twins, and machine learning algorithms to enhance processing accuracy. The paper also thoroughly examines the potential applications of digital technologies for optimizing micro- and nano-processing, laser welding, and improving the quality and stability of materials during laser modification. The future development of these technologies, including integration with adaptive systems and optimization algorithms, is presented, opening new horizons for precision manufacturing processes.</p>","PeriodicalId":721,"journal":{"name":"Optical Memory and Neural Networks","volume":"34 2","pages":"S357 - S368"},"PeriodicalIF":0.8,"publicationDate":"2026-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146043411","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}

{"title":"Research of the Influence of Wave Aberrations by Distortioning the Formation of Light Curves","authors":"L. B. Dubman,&nbsp;P. A. Khorin","doi":"10.3103/S1060992X25602246","DOIUrl":"10.3103/S1060992X25602246","url":null,"abstract":"<p>The influence of vortex phase singularity and wave aberrations on the distortion of the pattern of formation of light curves is investigated. The Gillis transform was used to form the parametric type of the curve, and the diffraction optical elements were calculated using the Whittaker integral. It was found that the influence of vortex phase singularity mainly affects the change in the peripheral part of the amplitude of the formed curve and leading to the formation of zones with zero intensity in the central part. It is also shown that aberrations in optical systems distort the wavefront and degrading the image quality of formed light curves. Different types of aberrations introduce characteristic changes in the intensity distributions of light curves, which can be further used for detection and recognition of aberrations.</p>","PeriodicalId":721,"journal":{"name":"Optical Memory and Neural Networks","volume":"34 2","pages":"S278 - S291"},"PeriodicalIF":0.8,"publicationDate":"2026-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146043416","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}

{"title":"Hyperspectral Imaging for Soil Type Classification","authors":"A. R. Makarov,&nbsp;A. A. Muzyka,&nbsp;K. E. Savelev,&nbsp;A. A. Rastorguev,&nbsp;V. V. Podlipnov","doi":"10.3103/S1060992X25700262","DOIUrl":"10.3103/S1060992X25700262","url":null,"abstract":"<p>This paper explores the application of soil type classification algorithms (red clay, loamy soil, chernozem) in agricultural fields based on hyperspectral imaging data (400–1000 nm) acquired from an unmanned aerial vehicle (UAV). As part of the dataset preparation, the data were processed using a set of algorithms, from classical approaches to deep models. The ensemble NM3D-CNN achieved the best performance—0.874 weighted F1. Results of other approaches—PCA segmentation: 0.864, 1D-CNN: 0.842, SSFTT: 0.829, SVM: 0.764. These results indicate that Vis-NIR UAV-borne HIS enables reliable soil mapping, while spectral-spatial deep ensembles provide the strongest gains.</p>","PeriodicalId":721,"journal":{"name":"Optical Memory and Neural Networks","volume":"34 2","pages":"S312 - S318"},"PeriodicalIF":0.8,"publicationDate":"2026-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146043417","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}

{"title":"Synthetic Data Generation for Tasks of Recognizing Actions and Interaction Objects of an Agricultural Drone Operator","authors":"T. D. Kazarkin,&nbsp;L. A. Abakumov,&nbsp;K. S. Gerasimova,&nbsp;R. M. Khabibullin,&nbsp;L. A. Taskina","doi":"10.3103/S1060992X25602489","DOIUrl":"10.3103/S1060992X25602489","url":null,"abstract":"<p>This paper presents a prototype application for generating synthetic data used to train neural network models for recognizing actions and interaction objects of an agricultural drone operator. This application has been implemented with the ability to customize various generation parameters. The obtained generation results are saved as images and text files in YOLO format.</p>","PeriodicalId":721,"journal":{"name":"Optical Memory and Neural Networks","volume":"34 2","pages":"S319 - S326"},"PeriodicalIF":0.8,"publicationDate":"2026-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146043477","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}

{"title":"Reverse Flow during Propagation of Half a Plane Wave","authors":"V. V. Kotlyar,&nbsp;A. A. Kovalev,&nbsp;A. G. Nalimov,&nbsp;A. M. Telegin","doi":"10.3103/S1060992X25600909","DOIUrl":"10.3103/S1060992X25600909","url":null,"abstract":"<p>We explore the diffraction of a plane wave by an opaque rectangular screen and show that at any on-axis distance from the obstacle plane, a multitude of diffraction fringes occur in a screen-parallel plane, characterized by a (negative) canonical backflow. Remarkably, the localized fringe recurrence interval is found to decrease with larger distance from the screen edge, with the first fringe of the canonical energy backflow departing from the edge with increasing distance from the optical axis. The energy backflow is shown to occur in the diffraction pattern areas characterized by subwavelength values of phase and amplitude modulation, i.e. where the local wave-vector expressed through the phase gradient is larger than the incident wave wave-vector.</p>","PeriodicalId":721,"journal":{"name":"Optical Memory and Neural Networks","volume":"34 4","pages":"476 - 484"},"PeriodicalIF":0.8,"publicationDate":"2025-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145808676","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}

{"title":"Deep Attention-Multistage GAN with Sparse Dense Fusion R-CNN for High-Resolution and Object Detection in Surveillance System","authors":"Anu Yadav,&nbsp; Ela Kumar","doi":"10.3103/S1060992X2560003X","DOIUrl":"10.3103/S1060992X2560003X","url":null,"abstract":"<p>Video surveillance utilization has developed significantly in sectors such as traffic monitoring, private institution protection, and landmark protection. Identifying an object in a captured surveillance image is difficult because of the poor excellence of the images. The quality of Low-Resolution (LR) images can be enhanced using an image Super-Resolution (SR) reconstruction method. Sophisticated Deep learning methods have been utilized in order to attain state-of-the-art performance in SR. Nevertheless, these techniques are typically prone to losing essential information and perform poorly on complex computations. To overcome these challenges, this research develop a deep Attention-Multistage Generative Adversarial Network (DA-MGAN) for image super-resolution and integrate SDR (Sparse Dense fusion R-CNN) with Non-Maximum Suppression (NMS) to enhance object detection accuracy in surveillance images. DA-MGAN is used to generate high-resolution surveillance images by utilizing attention mechanisms for improved feature extraction and integrating a multistage GAN that progressively enhances the image quality at each stage. After image resolution, Sparse Dense Fusion R-CNN (SDR) is used for object detection in super-resolved images to improve feature extraction through Sparse Dense Fusion. The R-CNN leverages these enhanced features to accurately detect and segment objects at the pixel level. Subsequently, Non-Maximum Suppression (NMS) was applied to improve localization by eliminating overlapping bounding boxes and minimizing false positives. This integrated method boosts overall detection precision and reliability in real time surveillance scenarios. The proposed model achieves a Super Resolution Error Rate (SRER) of 0.19%, a Bit Error Rate (BER) of 0.125%, a Packet Error Rate (PER) of 0.0990%, and a Deep End-to-End Image Metric (DEEIM) of 0.04963%, showcasing its superior performance. In contrast with existing methodologies, these results highlight the effectiveness of the suggested approach in reducing error rates and enhancing image quality metrics. As a result, these methods are ideally suited for real-time applications, particularly in high-resolution scenarios and object detection within surveillance systems.</p>","PeriodicalId":721,"journal":{"name":"Optical Memory and Neural Networks","volume":"34 4","pages":"566 - 580"},"PeriodicalIF":0.8,"publicationDate":"2025-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145808680","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}

{"title":"Unsupervised Background Estimation Using a Neural Integrator","authors":"Shiva Kamkar,&nbsp;Hamid Abrishami Moghaddam,&nbsp;Wolfram Erlhagen","doi":"10.3103/S1060992X25600156","DOIUrl":"10.3103/S1060992X25600156","url":null,"abstract":"<p>Background estimation is an important part of many computer vision applications. However, it is a challenging task due to illumination changes, camouflage, occlusion, dynamic background, rain or snow fall, and shadows. In this paper, we propose a method to predict the background of videos recorded by fixed cameras. The proposed algorithm is unsupervised and online. It takes inspiration from the processing mechanisms of neural integrator circuits in recurrently connected networks. The neural activities of three distinct integrators, each responsible for processing a color channel in <i>L</i> * <i>a</i> * <i>b</i> color space, are updated according to the recent changes of the scene covering both spatial and temporal aspects. The maxima of the evolving activity distributions in color space are used to predict the background color value of each pixel. Evaluation results demonstrate that the proposed method outperforms several recent competitors on the Scene Background Initialization (SBI) and LASIESTA datasets, based on mean squared error (MSE) metrics.</p>","PeriodicalId":721,"journal":{"name":"Optical Memory and Neural Networks","volume":"34 4","pages":"581 - 591"},"PeriodicalIF":0.8,"publicationDate":"2025-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145808715","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}

{"title":"Intelligent Recommendation of Ideological and Political Course Content Based on the BPNN Algorithm Improved by Attention Mechanism","authors":"Yang Yuan,&nbsp; Lixia Li","doi":"10.3103/S1060992X25601484","DOIUrl":"10.3103/S1060992X25601484","url":null,"abstract":"<p>This paper improved the back-propagation neural network (BPNN) algorithm for recommending ideological and political courses by a squeeze-and-excitation network (SEnet) and a multi-head attention mechanism. Simulation experiments were conducted to compare the improved algorithm with two other recommendation algorithms, followed by ablation experiments. Moreover, the effectiveness of the recommendation algorithm was tested in actual teaching of ideological and political courses. The results demonstrated that the improved BPNN algorithm outperformed others and the SEnet and the multi-head attention mechanism significantly enhanced the accuracy of recommendations. The algorithm effectively improved students’ performance in ideological and political courses and was satisfied by the majority of students.</p>","PeriodicalId":721,"journal":{"name":"Optical Memory and Neural Networks","volume":"34 4","pages":"485 - 491"},"PeriodicalIF":0.8,"publicationDate":"2025-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145808675","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}

{"title":"Silicon Microring Resonator-Based All-Optical Reversible 2 : 1 Multiplexer: Numerical Analysis","authors":"Sankarapuram Siva Saravana Kumar,&nbsp;Jakusani Shirisha,&nbsp;Sultan Mahaboob Basha,&nbsp;Kalimuddin Mondal,&nbsp;Vankadari Nagaraju,&nbsp;Alagar Raja,&nbsp;Manjur Hossain","doi":"10.3103/S1060992X25700237","DOIUrl":"10.3103/S1060992X25700237","url":null,"abstract":"<p>Future computing systems will likely require reversible logic gates since, in the best of circumstances; they are known to provide zero power dissipation. Reversible logic gates also have the advantage of minimizing quantum costs and unused outputs. In this work, reversible 2 : 1 multiplexer has been implemented using all-optical microring resonator. Reversible multiplexer is simulated in MATLAB at 260 Gbps. Some performance indicating factors such as “extinction ratio”, “contrast ratio”, “relative eye opening”, etc are analyzed and developed. The chosen optimal parameters can be validated practically.</p>","PeriodicalId":721,"journal":{"name":"Optical Memory and Neural Networks","volume":"34 4","pages":"492 - 501"},"PeriodicalIF":0.8,"publicationDate":"2025-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145808712","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}

{"title":"Comparison of Training Results of a Convolutional Neural Network with Computed Weights and Random Weight Initialization","authors":"P. Sh. Geidarov","doi":"10.3103/S1060992X2560034X","DOIUrl":"10.3103/S1060992X2560034X","url":null,"abstract":"<p>This paper presents a description of an improved algorithm for computing the weights of a convolutional neural network, as well as the results of comparative experiments on training a convolutional neural network on a handwritten digit dataset MNIST using both precomputed and randomly initialized weights. The experimental results demonstrate the advantages of preliminary analytical computation of neural network weight values compared to random weight initialization. The weights of the convolutional neural network were computed using only 10 digit images, randomly selected from the dataset MNIST. Experiments showed that the time spent on the analytical computation of weight values was negligible. The testing results on the test dataset with the computed but yet untrained neural network showed an accuracy of more than 50% in correctly recognizing the images from the test dataset MNIST. The results of numerous training experiments on the same convolutional neural network, using both computed and random weights, showed that training with precomputed weights yields better results and requires less training time.</p>","PeriodicalId":721,"journal":{"name":"Optical Memory and Neural Networks","volume":"34 4","pages":"502 - 511"},"PeriodicalIF":0.8,"publicationDate":"2025-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145808677","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}