Optical Memory and Neural Networks最新文献_第5页

Photovoltaic Tweezers Based on Optical Holography: Application to 2D Trapping of DNA Molecules on a Lithium Niobate Crystal 基于光学全息技术的光电镊子：应用于 DNA 分子在铌酸锂晶体上的二维捕获

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Optical Memory and Neural Networks Pub Date : 2024-01-30 DOI: 10.3103/s1060992x23070214

Lusine Tsarukyan, Anahit Badalyan, Lusine Aloyan, Yeva Dalyan, Rafael Drampyan

{"title":"Photovoltaic Tweezers Based on Optical Holography: Application to 2D Trapping of DNA Molecules on a Lithium Niobate Crystal","authors":"Lusine Tsarukyan, Anahit Badalyan, Lusine Aloyan, Yeva Dalyan, Rafael Drampyan","doi":"10.3103/s1060992x23070214","DOIUrl":"https://doi.org/10.3103/s1060992x23070214","url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Abstract</h3><p>The nonuniform 2D photovoltaic fields generated near the surface of a photorefractive Fe-doped lithium niobate (LN:Fe) crystal by a nondiffracting optical Bessel beam with concentric ring structures and 532 nm wavelength are used for the trapping of DNA molecules in NaCl buffer on the crystal surface. The simultaneous observation of the long-living Bessel-like refractive lattice recorded in the LN:Fe crystal and the trapped DNA molecules on the crystal surface was performed by an optical phase microscope operating in the transmission mode. With this approach, the DNA molecules are registered as refractive index nonuniformities on the Bessel lattice refractive index pattern. Observations show that DNA molecules are immobilized and trapped at the borderlines of the concentric rings of the refractive lattice recorded by the Bessel beam. The formation of neutral molecular clusters of DNA by Na<sup>+</sup> counterions with a nearly globular shape and cluster average size of ~4 μm is revealed. A physical model is developed for the analysis of the electric forces map and explanation of the experimental results. The photovoltaic strategy of trapping and manipulation of micro- and nanoparticles on the crystal surface is promising for the elaboration of the lab-on-a-chip devices operating in an autonomous regime with applications in photonics, micro/nano-electronics and biotechnology.</p>","PeriodicalId":721,"journal":{"name":"Optical Memory and Neural Networks","volume":"110 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2024-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140885994","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

Stability of an Optical Neural Network Trained by the Maximum-Likelihood Algorithm 用最大似然算法训练的光学神经网络的稳定性

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Optical Memory and Neural Networks Pub Date : 2024-01-30 DOI: 10.3103/s1060992x2307010x

B. V. Kryzhanovsky, V. I. Egorov

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Asymmetric Version of the Second Demkov–Kunike Level-Crossing Model 第二个德姆科夫-库尼克水平跨越模型的非对称版本

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Optical Memory and Neural Networks Pub Date : 2024-01-30 DOI: 10.3103/s1060992x23070093

A. M. Ishkhanyan, T. A. Shahverdyan, A. M. Ghazaryan

引用次数: 0

Phase-Slip Centers as Cooling Engines 作为冷却引擎的相位滑动中心

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Optical Memory and Neural Networks Pub Date : 2024-01-30 DOI: 10.3103/s1060992x23070147

Iris Mowgood, Serafim Teknowijoyo, Sara Chahid, Armen Gulian

{"title":"Phase-Slip Centers as Cooling Engines","authors":"Iris Mowgood, Serafim Teknowijoyo, Sara Chahid, Armen Gulian","doi":"10.3103/s1060992x23070147","DOIUrl":"https://doi.org/10.3103/s1060992x23070147","url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Abstract</h3><p>Based on time-dependent Ginzburg-Landau system of equations, Éliashberg’s kinetic equations and finite element modeling, we analyze phonon emission by the phase-slip centers in superconducting filaments. Our results show that in the dissipative regime with these centers, thin superconducting filaments can be effective in originating not only positive but also negative thermal fluxes, i.e., they both generate and absorb phonons. In a stationary oscillatory regime, at a given moment of time, this generation and absorption of phonons reveals itself as positive and negative spectrum of phonons at different spectral ranges. Moreover, at a given spectral range, the emission reverses its sign during the period of oscillation. This fact is associated with the reciprocation of the energy emission and absorption at different spectral intervals during the oscillation period of the phase-slip center. The integral value of energy over the whole spectral range is time-dependent, being positive for some part of the period and negative for the rest of it. Its time integral over the period reveals a positive value, which corresponds to the total energy released in this dissipative state of superconducting filament. In a simple case, when the filament is embedded in a thermal heat bath (substrates typically play that role), this energy dissipates, elevating locally the temperature of filament’s environment. However, in a more sophisticated design, the positive and negative fluxes may become separated. This can be achieved by using the thermal diode effect (the Kapitza boundaries can play the role of such diodes). Such a separation may yield to the net cooling of some part of the filament environment, while the other part will serve as a heat sink. Thus, with an appropriate design of their thermal surroundings, the phase-slip centers can serve as effective solid-state cooling engines. They may be effective for reducing further the cryostat cold finger temperature; for example, from 1 K to sub-K temperatures.</p>","PeriodicalId":721,"journal":{"name":"Optical Memory and Neural Networks","volume":"79 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2024-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139648843","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

LaF3–Er3+ Crystal as Materials for MIR-Lasing Operating 作为近红外激光操作材料的 LaF3-Er3+ 晶体

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Optical Memory and Neural Networks Pub Date : 2024-01-30 DOI: 10.3103/s1060992x23070068

G. G. Demirkhanyan, R. B. Kostanyan

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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

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

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

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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

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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><p>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 <span>(c)</span>. 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.</p>","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

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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":"<p>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.</p>","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}

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

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

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

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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

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

M. V. Gashnikov

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