Optical Memory and Neural Networks最新文献_第8页

Formation of Narrow Atomic Lines of Rb in the UV Region Using a Magnetic Field 利用磁场形成紫外区铷原子窄线

IF 0.9

Optical Memory and Neural Networks Pub Date : 2024-01-30 DOI: 10.3103/s1060992x23070196

A. Tonoyan, A. Sargsyan, R. Momier, C. Leroy, D. Sarkisyan

{"title":"Formation of Narrow Atomic Lines of Rb in the UV Region Using a Magnetic Field","authors":"A. Tonoyan, A. Sargsyan, R. Momier, C. Leroy, D. Sarkisyan","doi":"10.3103/s1060992x23070196","DOIUrl":"https://doi.org/10.3103/s1060992x23070196","url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Abstract</h3>Magnetically induced (MI) transitions Fg = 1 → Fe = 3 of 87Rb D2 line are among the most promising atomic transitions for applications in laser physics. They reach their maximum intensity in the 0.2–2 kG magnetic field range and are more intense than many conventional atomic transitions. An important feature of MI transitions is their large frequency shift with respect to the unperturbed hyperfine transitions which reaches ~12 GHz in magnetic fields of ~3 kG, while they are formed on the high-frequency wing of the spectrum and do not overlap with other transitions. Some important peculiarities have been demonstrated for the MI 5S1/2 → 5P3/2 transitions (λ = 780 nm). Particularly, it was shown that using a nanocell with thickness L = 100 nm it is possible to realize 1 μm-spatial resolution which is important when determining magnetic fields with strong spatial gradient (of >3G/μm). Earlier, our studies have been performed for 5S1/2 → nP3/2 transition with n = 5, while it is also theoretically shown to be promising for the transitions with n = 6, 7, 8 and 9, corresponding to the transition wavelengths of 420.2, 358.7, 334.9 and 322.8 nm, respectively.","PeriodicalId":721,"journal":{"name":"Optical Memory and Neural Networks","volume":"230 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2024-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139648842","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

Micrometric-Thin Cell Filled with Rb Vapor for High-Resolution Atomic Spectroscopy 用于高分辨率原子光谱的充满铷原子蒸气的微米级薄电池

IF 0.9

Optical Memory and Neural Networks Pub Date : 2024-01-30 DOI: 10.3103/s1060992x23070135

R. Momier, A. Sargsyan, A. Tonoyan, C. Leroy, D. Sarkisyan

引用次数: 0

Representation of the Electromagnetic Field of an Arbitrarily Moving Charged Particle by Electric Field Lines 用电场线表示任意运动的带电粒子的电磁场

IF 0.9

Optical Memory and Neural Networks Pub Date : 2024-01-30 DOI: 10.3103/s1060992x23070032

S. G. Arutunian, M. A. Aginian, E. G. Lazareva, M. Chung

{"title":"Representation of the Electromagnetic Field of an Arbitrarily Moving Charged Particle by Electric Field Lines","authors":"S. G. Arutunian, M. A. Aginian, E. G. Lazareva, M. Chung","doi":"10.3103/s1060992x23070032","DOIUrl":"https://doi.org/10.3103/s1060992x23070032","url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Abstract</h3>The paper discusses the representation of the electromagnetic field of an arbitrarily moving charged particle by means of electric field lines. Expressions for the field line equations are derived on the basis of exact Lienar-Wichert field formulas. Parameterization of field lines by means of light signals (dots) emitted at delayed moments of time allows us to avoid the problem of solving the retardation equation. The resulting nonlinear equations are linearized using the Lorentz transformation applied to the emission rate of these light dots in the particle’s rest frame. These linear equations coincide with the Thomas precession equation, which allows us to state that field lines can be thought of as comprised of light dots that were emitted isotropically in the particle’s rest frame at speed (c). The exact solution of the equations is found in the case when the ratio of the trajectory torsion to the product of the trajectory curvature by the Lorentz factor of the particle is a constant value for the trajectory. The class of such fields in particular includes all flat trajectories. Illustrations of field lines are given for two applications of practical interest – the motion of a charged particle in the field of a plane monochromatic linearly polarized wave and for a helical undulator. In addition, it is shown that the developed mathematical apparatus admits consideration of the superluminal motion of the charge. Exact solutions and illustrations of lines for the superluminal motion of a particle along a circle (superluminal synchrotron radiation) are given.","PeriodicalId":721,"journal":{"name":"Optical Memory and Neural Networks","volume":"46 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2024-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139649445","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

Information Added U-Net with Sharp Block for Nucleus Segmentation of Histopathology Images 用于组织病理学图像细胞核分割的带有锐块的信息添加 U-Net

IF 1

Optical Memory and Neural Networks Pub Date : 2023-12-22 DOI: 10.3103/S1060992X23040070

Anusua Basu, Mainak Deb, Arunita Das, Krishna Gopal Dhal

{"title":"Information Added U-Net with Sharp Block for Nucleus Segmentation of Histopathology Images","authors":"Anusua Basu, Mainak Deb, Arunita Das, Krishna Gopal Dhal","doi":"10.3103/S1060992X23040070","DOIUrl":"10.3103/S1060992X23040070","url":null,"abstract":"Segmenting nuclei from histopathology images is a crucial step in the early identification and diagnosis of several diseases. Due to the complexity of histopathology images, accurate nucleus segmentation is not a simple operation. However, convolutional neural networks (CNNs) have recently been revealed to be a viable option. The well-known CNN model, namely the U-Net, demonstrated its image segmentation effectiveness in medical field. However, U-Net has several drawbacks, such as information loss after transmission through particular steps. Another significant one is the likelihood of feature mismatches in the encoder and decoder sub-networks in skip connection, which can lead to the fusing of semantically unrelated information and, as a consequence, fuzzy feature maps throughout the learning process. In order to solve these issues, an improved U-Net architecture called Information Added U-Net with Sharp Block (IASB-U-Net) has been proposed for nuclei segmentation from histopathology images. Information is added to the encoder-decoder path in the proposed model after each layer, and sharpening spatial filters are utilized in place of skip connections. The experimental study over a merged dataset demonstrates that the proposed IASB-U-Net produces competitive results when compared to established CNN models such as U-Net, Dense U-Net, SCPP Net, and LiverNet.","PeriodicalId":721,"journal":{"name":"Optical Memory and Neural Networks","volume":"32 4","pages":"318 - 330"},"PeriodicalIF":1.0,"publicationDate":"2023-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139029215","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

Enhancement of Knowledge Distillation via Non-Linear Feature Alignment 通过非线性特征对齐加强知识提炼

IF 1

Optical Memory and Neural Networks Pub Date : 2023-12-22 DOI: 10.3103/S1060992X23040136

Jiangxiao Zhang, Feng Gao, Lina Huo, Hongliang Wang, Ying Dang

引用次数: 0

Video Codec Using Machine Learning Based on Parametric Orthogonal Filters 基于参数正交滤波器的机器学习视频编解码器

IF 1

Optical Memory and Neural Networks Pub Date : 2023-12-22 DOI: 10.3103/S1060992X23040021

M. V. Gashnikov

引用次数: 0

Application of the Luminescent Carbon Nanoparticles for Optical Diagnostics of Structure-Inhomogeneous Objects at the Micro- and Nanoscales 发光碳纳米粒子在微米和纳米尺度结构非均质物体光学诊断中的应用

IF 1

Optical Memory and Neural Networks Pub Date : 2023-12-22 DOI: 10.3103/S1060992X23040069

O. Angelsky, A. Bekshaev, C. Zenkova, D. Ivanskyi, P. Maksymyak, V. Kryvetsky, Zhebo Chen

引用次数: 0

Application of the Luminescent Carbon Nanoparticles for Optical Diagnostics of Structure-Inhomogeneous Objects at the Micro- and Nanoscales 发光碳纳米粒子在微米和纳米尺度结构非均质物体光学诊断中的应用

IF 0.9

Optical Memory and Neural Networks Pub Date : 2023-12-22 DOI: 10.3103/s1060992x23040069

O. Angelsky, A. Bekshaev, C. Zenkova, D. Ivanskyi, P. Maksymyak, V. Kryvetsky, Zhebo Chen

引用次数: 0

Data Augmentation and Fine Tuning of Convolutional Neural Network during Training for Person Re-Identification in Video Surveillance Systems 用于视频监控系统中人员再识别的卷积神经网络在训练过程中的数据增强和微调

IF 1

Optical Memory and Neural Networks Pub Date : 2023-12-22 DOI: 10.3103/S1060992X23040124

S. Ye, R. Bohush, H. Chen, S. Ihnatsyeva, S. V. Ablameyko

{"title":"Data Augmentation and Fine Tuning of Convolutional Neural Network during Training for Person Re-Identification in Video Surveillance Systems","authors":"S. Ye, R. Bohush, H. Chen, S. Ihnatsyeva, S. V. Ablameyko","doi":"10.3103/S1060992X23040124","DOIUrl":"10.3103/S1060992X23040124","url":null,"abstract":"A new image set, augmentation method and fine in-learning adjustment of convolutional neural networks (CNN) are proposed to increase the accuracy of CNN-based person re-identification. Unlike other known sets, we have used many video frames from external and internal surveillance systems shot at all seasons of the year to make up our PolReID1077 set of person images. The PolReID1077-forming samples are subjected to the cyclic shift, chroma subsampling, and replacement of a fragment by a reduced copy of another sample to get a wider range of images. The learning set generating technique is used to train a CNN. The training is carried out in two stages. The first stage is pre-training using the augmented data. At the second stage the original images are used to carry out fine-tuning of CNN weight coefficients to reduce in-learning losses and increase re-identification efficiency. The approach doesn’t allow the CNN to remember learning sets and decreases the chances of overfitting. Different augmentation methods, data sets and learning techniques are used in the experiments.","PeriodicalId":721,"journal":{"name":"Optical Memory and Neural Networks","volume":"32 4","pages":"233 - 246"},"PeriodicalIF":1.0,"publicationDate":"2023-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139015710","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

Review on Pest Detection and Classification in Agricultural Environments Using Image-Based Deep Learning Models and Its Challenges 基于图像的深度学习模型在农业环境中的害虫检测和分类及其挑战综述

IF 1

Optical Memory and Neural Networks Pub Date : 2023-12-22 DOI: 10.3103/S1060992X23040112

P. Venkatasaichandrakanth, M. Iyapparaja

{"title":"Review on Pest Detection and Classification in Agricultural Environments Using Image-Based Deep Learning Models and Its Challenges","authors":"P. Venkatasaichandrakanth, M. Iyapparaja","doi":"10.3103/S1060992X23040112","DOIUrl":"10.3103/S1060992X23040112","url":null,"abstract":"Agronomic pests cause agriculture to incur financial losses because they diminish production, which lowers revenue. Pest control, essential to lowering these losses, involves identifying and eliminating this risk. Since it enables management to take place, identification is the fundamental component of control. Utilizing the pest’s traits, visual identification is done. These characteristics differ between animals and are intrinsic. Since identification is so difficult, specialists in the field handle most of the work, which concentrates the information. Researchers have developed various techniques for predicting crop diseases using images of infected leaves. While progress has been made in identifying plant diseases using different models and methods, new advancements and discussions still offer room for improvement. Technology can significantly improve global crop production, and large datasets can be used to train models and approaches that uncover new and improved methods for detecting plant diseases and addressing low-yield issues. The effectiveness of machine learning and deep learning for identifying and categorizing pests has been confirmed by prior research. This paper thoroughly examines and critically evaluates the many strategies and methodologies used to classify and detect pests or insects using deep learning. The paper examines the benefits and drawbacks of various methodologies and considers potential problems with insect detection via image processing. The paper concludes by providing an analysis and outlook on the future direction of pest detection and classification using deep learning on plants like peanuts.","PeriodicalId":721,"journal":{"name":"Optical Memory and Neural Networks","volume":"32 4","pages":"295 - 309"},"PeriodicalIF":1.0,"publicationDate":"2023-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139013597","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