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High performance reconfigurable computing for numerical simulation and deep learning

Lin Gan, Ming Yuan, Jinzhe Yang, Wenlai Zhao, Wayne Luk, Guangwen Yang
<span title="2020-06-11">2020</span> <i title="Springer Science and Business Media LLC"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/oyzrfqv3i5ghbipholgpvqu37y" style="color: black;">CCF Transactions on High Performance Computing</a> </i> &nbsp;
High performance reconfigurable computing for numerical simulation and deep learning 1 3 deploy more computing units into a single chip, and accommodate more computing chips into a single system, the expenses  ...  Since FPGAs have become promising options for current and future high performance computing, this report summarises and analyses recent FPGA-related efforts, including the latest industrial approaches,  ...  Fig. 7 Dataflow to compute stencils based on a customizable window buffer Reconfigurable solutions for deep learning In recent years, deep learning technologies based on convolutional neural network  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/s42514-020-00032-x">doi:10.1007/s42514-020-00032-x</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/mbnb73zazzgohhe4quhuqlryky">fatcat:mbnb73zazzgohhe4quhuqlryky</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200711165057/https://link.springer.com/content/pdf/10.1007/s42514-020-00032-x.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/32/1b/321be3070ff45818e1e4be332bbe9e75ea8b2318.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/s42514-020-00032-x"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> springer.com </button> </a>

Hybrid Electrical/Optical Switch Architectures for Training Distributed Deep Learning in Large-Scale

Thao-Nguyen TRUONG, Ryousei TAKANO
<span title="2021-08-01">2021</span> <i title="Institute of Electronics, Information and Communications Engineers (IEICE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/xosmgvetnbf4zpplikelekmdqe" style="color: black;">IEICE transactions on information and systems</a> </i> &nbsp;
Thao-Nguyen TRUONG †a) , Nonmember and Ryousei TAKANO †b) , Member SUMMARY Data parallelism is the dominant method used to train deep learning (DL) models on High-Performance Computing systems such as  ...  Simulation results on the Simgrid simulator show that our approach speed-up the training time of deep learning applications, especially in a large-scale manner. key words: distributed deep learning, high  ...  Acknowledgments This paper is based on results obtained from a project, JPNP16007, commissioned by the New Energy and Industrial Technology Development Organization (NEDO).  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1587/transinf.2020edp7201">doi:10.1587/transinf.2020edp7201</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/jljuf3xivnc2dimnw3dwl7xvpe">fatcat:jljuf3xivnc2dimnw3dwl7xvpe</a> </span>
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Data-Driven Reachability Analysis for the Reconfiguration of Vehicle Control Systems

Dániel Fényes, Balázs Németh, Péter Gáspár
<span title="">2018</span> <i title="Elsevier BV"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/4oklsgzkjvbihhegxblzc6b7re" style="color: black;">IFAC-PapersOnLine</a> </i> &nbsp;
In the analysis several scenarios with faults in the steering and in-wheel systems are considered using a high-fidelity simulation software.  ...  The paper presents a reconfigurable control strategy for the lateral stability of autonomous vehicles.  ...  A reachability analysis using machine learning approach for space aircraft and robotic vehicles is performed in Allen et al. (2014) .  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/j.ifacol.2018.09.671">doi:10.1016/j.ifacol.2018.09.671</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/gqatpuumv5hexk5ivbjxojtq5i">fatcat:gqatpuumv5hexk5ivbjxojtq5i</a> </span>
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Special Issue on Next Generation Multiple Access—Part I

Yuanwei Liu, Shuowen Zhang, Zhiguo Ding, Robert Schober, Naofal Al-Dhahir, Ekram Hossain, Xuemin Shen
<span title="">2022</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/onirm7ye2bfobnpwuwaopap5yu" style="color: black;">IEEE Journal on Selected Areas in Communications</a> </i> &nbsp;
massive and ubiquitous connectivity for supporting diverse disruptive applications (e.g., virtual reality (VR), augmented reality (AR), and industry 4.0).  ...  , and localization.  ...  The numerical results show that the proposed algorithm can achieve better BER performance with less computational complexity compared to existing algorithms. In [A5] , Han et al.  ... 
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Application of Machine Learning in Electromagnetics: Mini-Review

Md. Samiul Islam Sagar, Hassna Ouassal, Asif I. Omi, Anna Wisniewska, Harikrishnan M. Jalajamony, Renny E. Fernandez, Praveen K. Sekhar
<span title="2021-11-11">2021</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/ikdpfme5h5egvnwtvvtjrnntyy" style="color: black;">Electronics</a> </i> &nbsp;
Numerous antenna simulations, synthesis, and pattern recognition of radiations as well as non-linear inverse scattering-based object identifications are now leveraging ML techniques.  ...  In this context, this paper aims to present an overview of machine learning, and its applications in Electromagnetics, including communication, radar, and sensing.  ...  ML and EM In the era of advanced communication characterized by high speed and high bandwidth network topologies, the need for EM systems to offer reconfigurability, compactness, directivity, and energy  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/electronics10222752">doi:10.3390/electronics10222752</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/n6jb47p67fh47oyqhxiwldtevi">fatcat:n6jb47p67fh47oyqhxiwldtevi</a> </span>
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Dynamic Network Slice Reconfiguration by Exploiting Deep Reinforcement Learning

Fengsheng Wei, Gang Feng, Yao Sun, Yatong Wang, Ying-Chang Liang
<span title="">2020</span> <i title="IEEE"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/jtcdcetsmvdmjcshq24lhzakwu" style="color: black;">ICC 2020 - 2020 IEEE International Conference on Communications (ICC)</a> </i> &nbsp;
However, it is commonly believed that the fine-grained resource reconfiguration problem is intractable due to the extremely high computational complexity caused by the numerous variables.  ...  In this paper, we investigate network slice reconfiguration with aim of minimizing long-term resource consumption by exploiting Deep Reinforcement Learning (DRL).  ...  In this paper, we resort to Deep Reinforcement Learning (DRL) to solve NSRP.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/icc40277.2020.9148848">doi:10.1109/icc40277.2020.9148848</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/icc/Wei0SWL20.html">dblp:conf/icc/Wei0SWL20</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/yadheluo65c5lnmet2craozjoy">fatcat:yadheluo65c5lnmet2craozjoy</a> </span>
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Efficient Graphene Reconfigurable Reflectarray Antenna Electromagnetic Response Prediction Using Deep Learning

Li Ping Shi, Qing He Zhang, Shi Hui Zhang, Chao Yi, Guang Xu Liu
<span title="">2021</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/q7qi7j4ckfac7ehf3mjbso4hne" style="color: black;">IEEE Access</a> </i> &nbsp;
(EM) response based on deep learning is proposed.  ...  The experimental results show that CNN method has good performance and accuracy in the EM response prediction of the graphene reconfigurable reflectarray antenna, with an accuracy of over 99%, and can  ...  For solving the problem of data storage and computing time, this paper proposes a deep learning method based on CNN.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/access.2021.3054944">doi:10.1109/access.2021.3054944</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/zryzplnbfvh3vkhmngd4f5hjwe">fatcat:zryzplnbfvh3vkhmngd4f5hjwe</a> </span>
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Reconfigurable Hardware Accelerators: Opportunities, Trends, and Challenges [article]

Chao Wang, Wenqi Lou, Lei Gong, Lihui Jin, Luchao Tan, Yahui Hu, Xi Li, Xuehai Zhou
<span title="2017-12-13">2017</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
satisfy requirements of high performance and low power consumption when specific applications are running.  ...  Second, the reconfigurable architectures of employing FPGA performs prototype systems rapidly and features excellent customizability and reconfigurability.  ...  such as target matching, large numerical calculation, data mining, model simulation and other functions [130] .  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1712.04771v1">arXiv:1712.04771v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/3lxv45qb4zaqpagtn3eghrmroe">fatcat:3lxv45qb4zaqpagtn3eghrmroe</a> </span>
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Series Editorial The Fourth Issue of the Series on Machine Learning in Communications and Networks

Geoffrey Y. Li, Walid Saad, Ayfer Ozgur, Peter Kairouz, Zhijin Qin, Jakob Hoydis, Zhu Han, Deniz Gunduz, Jaafar Elmirghani
<span title="">2022</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/onirm7ye2bfobnpwuwaopap5yu" style="color: black;">IEEE Journal on Selected Areas in Communications</a> </i> &nbsp;
HE third call for papers of the Series on Machine Learning in Communications and Networks has continued to receive a great number of high-quality papers covering various aspects of intelligent communications  ...  The proposed architecture uses a novel design providing a shortcut for the input. Weight punning is used to trade-off the computational complexity and accuracy.  ...  To optimize the system performance, the evolutionary game and deep-learning-based auction are leveraged to dynamically select data owners and FL workers. VII.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/jsac.2021.3126188">doi:10.1109/jsac.2021.3126188</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/6aohhlq55fco5gnndq6cusjbbi">fatcat:6aohhlq55fco5gnndq6cusjbbi</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20211217164424/https://ieeexplore.ieee.org/ielx7/49/9653871/09653893.pdf?tp=&amp;arnumber=9653893&amp;isnumber=9653871&amp;ref=" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/ce/2d/ce2de9a968cd6d08f15747270ba17537c251caeb.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/jsac.2021.3126188"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> ieee.com </button> </a>

Reconfigurable Metasurface Hologram of Dynamic Distance via Deep Learning

Yijun Zou, Rongrong Zhu, Lian Shen, Bin Zheng
<span title="2022-05-20">2022</span> <i title="Frontiers Media SA"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/uunhnq4jsbhmpkcnw2uws7hiie" style="color: black;">Frontiers in Materials</a> </i> &nbsp;
Simulation results of time–distance division for three-dimensional imaging are provided to demonstrate the reliability and high efficiency of the proposed algorithm.  ...  Herein, a computer-generated hologram algorithm with a dynamic imaging distance and a reconfigurable metasurface are proposed, which is referred to as a generator and physical diffractive network.  ...  S. performed the numerical and simulation verifications. Y. Z., B. Z., R. Z. and L. S. analyzed the data. Y. Z. and L. S. wrote the paper.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3389/fmats.2022.907672">doi:10.3389/fmats.2022.907672</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/ogbpb5s72favljnztc4iqop27u">fatcat:ogbpb5s72favljnztc4iqop27u</a> </span>
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Guest Editorial: Intelligent Surfaces for 5G and Beyond

Chau Yuen, Chongwen Huang, Ian F. Akyildiz, Marco Di Renzo, Merouane Debbah
<span title="">2021</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/kg4jy3hqtrbk7opmhgzlagufnu" style="color: black;">IEEE wireless communications</a> </i> &nbsp;
A deep-reinforcement-learning-based computation offloading scheme is proposed to minimize the total offloading latency of mobile devices.  ...  His main research interests are focused on holographic MIMO surface/reconfigurable intelligent surface, B5G/6G wireless communication, mmWave/THz communications, and deep learning technologies for wireless  ...  ., and Ph.D. degrees in electrical and computer engineering from the University of Erlangen Nürnberg, Germany, in 1978 , 1981 , and 1984, respectively.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/mwc.2021.9690146">doi:10.1109/mwc.2021.9690146</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/rhwkvfl25nbuph3pdor7qplagq">fatcat:rhwkvfl25nbuph3pdor7qplagq</a> </span>
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A Study of Reconfigurable Accelerators for Cloud Computing

Noor Mohammedali, Michael Opoku Agyeman
<span title="">2018</span> <i title="ACM Press"> Proceedings of the 2nd International Symposium on Computer Science and Intelligent Control - ISCSIC &#39;18 </i> &nbsp;
Field Programmable Gate Arrays (FPGAs) with Cloud ecosystem offer high performance in efficiency and energy, making them active resources, easy to program and reconfigure.  ...  This paper looks at FPGAs as reconfigurable accelerators for the cloud computing presents the main hardware accelerators that have been presented in various widely used cloud computing applications such  ...  In order to achieve a high performance, they used Widx because it contain strolling numerous hash buckets concurrently and input hashing keys depend on their use.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1145/3284557.3284563">doi:10.1145/3284557.3284563</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/chmip5oxmjcdnmjde2g52fxg4i">fatcat:chmip5oxmjcdnmjde2g52fxg4i</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20201104164237/http://nectar.northampton.ac.uk/13418/1/Mohammedali_etal_ACM_2018_A_Study_of_Reconfigurable_Accelerators_for_Cloud_Computing.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/88/81/88818bb305cdc2b0a9a39d39b6839a3d11551e21.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1145/3284557.3284563"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> acm.org </button> </a>

Highly Accurate and Reliable Wireless Network Slicing in 5th Generation Networks: A Hybrid Deep Learning Approach [article]

Sulaiman Khan, Suleman Khan, Yasir Ali, Muhammad Khalid, Zahid Ullah, Shahid Mumtaz
<span title="2021-10-07">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
The CNN performs resource allocation, network reconfiguration, and slice selection while the LSTM is used for statistical information (load balancing, error rate etc.) regarding network slices.  ...  In this paper, we propose a hybrid deep learning model that consists of a convolution neural network (CNN) and long short term memory (LSTM).  ...  Figure 4 depicts the simulation results of hybrid deep learning model executed for the first 20 hours.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2111.09416v1">arXiv:2111.09416v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/hqbal2g6lze73pq6u5wnzujcuq">fatcat:hqbal2g6lze73pq6u5wnzujcuq</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20211126143437/https://arxiv.org/ftp/arxiv/papers/2111/2111.09416.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/d5/b6/d5b6ca09b36055df234a75f6a9a9f95449f00f6a.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2111.09416v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Transfer Learning as an Enabler of the Intelligent Digital Twin [article]

Benjamin Maschler, Dominik Braun, Nasser Jazdi, Michael Weyrich
<span title="2020-12-03">2020</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Looking at common challenges in developing and deploying industrial machinery with deep learning functionalities, embracing this concept would offer several advantages: Using an intelligent Digital Twin  ...  , learning algorithms can be designed, configured and tested in the design phase before the physical system exists and real data can be collected.  ...  Fig. 4 .Fig. 5 . 45 Design of deep learning (DL) algorithms for the intelligent digital twin during design phase (see Scenario 1) Training of deep learning (DL) algorithms for the intelligent digital twin  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2012.01913v1">arXiv:2012.01913v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/he3xdaa445cfxg2ybjnicb663y">fatcat:he3xdaa445cfxg2ybjnicb663y</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20201206041007/https://arxiv.org/ftp/arxiv/papers/2012/2012.01913.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/10/a7/10a7ad2fb1b31bd9d63a590620e9d14d7a31f4e8.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2012.01913v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

PINE: Photonic Integrated Networked Energy efficient datacenters (ENLITENED Program)

Madeleine Glick, Nathan Abrams, Qixiang Cheng, Min Yee Teh, Yu-Han Hung, Oscar Jimenez, Songtao Liu, Yoshitomo Okawachi, Michal Lipson, Alexander Gaeta, Keren Bergman, Leif Johansson (+7 others)
<span title="2020-11-02">2020</span> <i title="The Optical Society"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/5lrjf4kbnbddvpsq6nezos4eqe" style="color: black;">Journal of Optical Communications and Networking</a> </i> &nbsp;
The PINE program leverages the unique features of photonic technologies to enable alternative megadatacenters and high-performance computing (HPC) system architectures that deliver more substantial energy  ...  gains through deep resource disaggregation.  ...  INTRODUCTION The recent explosive growth in data analytics applications that rely on machine learning techniques is leading to a convergence between datacenters and high-performance computing (HPC) systems  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1364/jocn.402788">doi:10.1364/jocn.402788</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/evcrlkdyjfeunoy6fhh7aagjdy">fatcat:evcrlkdyjfeunoy6fhh7aagjdy</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210717092511/https://escholarship.org/content/qt01608830/qt01608830.pdf?t=qom965" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/d9/dd/d9ddf1f512094921af8409c06e94af15c617e0f8.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1364/jocn.402788"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> Publisher / doi.org </button> </a>
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