检索结果-内蒙古大学图书馆

arXiv 2022年

作者： Chen, Jialin Chen, Ruoxi Gong, Zheng Hu, Hao Yang, Yi Zhang, Xinyan Wang, Chan Kaminer, Ido Chen, Hongsheng Zhang, Baile Lin, Xiao Interdisciplinary Center for Quantum Information State Key Laboratory of Extreme Photonics and Instrumentation ZJUHangzhou Global Scientific and Technological Innovation Center College of Information Science & Electronic Engineering Zhejiang University Hangzhou310027 China International Joint Innovation Center The Electromagnetics Academy Zhejiang University Zhejiang University Haining314400 China Department of Electrical and Computer Engineering Technion-Israel Institute of Technology Haifa32000 Israel School of Electrical and Electronic Engineering Nanyang Technological University Singapore639798 Singapore Department of Physics University of Hong Kong 999077 Hong Kong Key Lab of Advanced Micro/Nano Electronic Devices & Smart Systems of Zhejiang Jinhua Institute of Zhejiang University Zhejiang University Jinhua321099 China Shaoxing Institute of Zhejiang University Zhejiang University Shaoxing312000 China Division of Physics and Applied Physics School of Physical and Mathematical Sciences Nanyang Technological University Singapore637371 Singapore Centre for Disruptive Photonic Technologies Nanyang Technological University Singapore637371 Singapore

When a charged particle penetrates through an optical interface, photon emissions emerge — a phenomenon known as transition radiation. Being paramount to fundamental physics, transition radiation has enabled many applications from high-energy particle identification to novel light sources. A rule of thumb in transition radiation is that the radiation intensity generally decreases with the particle velocity v;as a result, low-energy particles are not favored in practice. Here we find that there exist situations where transition radiation from particles with extremely low velocities (e.g. v/c −3) exhibits comparable intensity as that from high-energy particles (e.g. v/c = 0. 999), where c is light speed in free space. The comparable radiation intensity implies an extremely high photon extraction efficiency from low-energy particles, up to eight orders of magnitude larger than that from high-energy particles. This exotic phenomenon of low-velocity-favored transition radiation originates from the excitation of Ferrell-Berreman modes in epsilon-near-zero materials. Our findings may provide a promising route towards the design of integrated light sources based on low-energy electrons and specialized detectors for beyond-standard-model particles. © 2022, CC BY.

关键词： Light sources

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DeePMD-kit v2: A software package for Deep Potential models

arXiv

引用

arXiv 2023年

作者： Zeng, Jinzhe Zhang, Duo Lu, Denghui Mo, Pinghui Li, Zeyu Chen, Yixiao Rynik, Marián Huang, Li'ang Li, Ziyao Shi, Shaochen Wang, Yingze Ye, Haotian Tuo, Ping Yang, Jiabin Ding, Ye Li, Yifan Tisi, Davide Zeng, Qiyu Bao, Han Xia, Yu Huang, Jiameng Muraoka, Koki Wang, Yibo Chang, Junhan Yuan, Fengbo Bore, Sigbjørn Løland Cai, Chun Lin, Yinnian Wang, Bo Xu, Jiayan Zhu, Jia-Xin Luo, Chenxing Zhang, Yuzhi Goodall, Rhys E.A. Liang, Wenshuo Singh, Anurag Kumar Yao, Sikai Zhang, Jingchao Wentzcovitch, Renata Han, Jiequn Liu, Jie Jia, Weile York, Darrin M. Weinan, E. Car, Roberto Zhang, Linfeng Wang, Han Laboratory for Biomolecular Simulation Research Institute for Quantitative Biomedicine Department of Chemistry and Chemical Biology Rutgers University PiscatawayNJ08854 United States AI for Science Institute Beijing100080 China DP Technology Beijing100080 China Academy for Advanced Interdisciplinary Studies Peking University Beijing100871 China HEDPS CAPT College of Engineering Peking University Beijing100871 China College of Electrical and Information Engineering Hunan University Changsha China Yuanpei College Peking University Beijing100871 China Program in Applied and Computational Mathematics Princeton University PrincetonNJ08540 United States Department of Experimental Physics Comenius University Mlynská Dolina F2 Bratislava842 48 Slovakia Center for Quantum Information Institute for Interdisciplinary Information Sciences Tsinghua University Beijing100084 China Center for Data Science Peking University Beijing100871 China ByteDance Research Zhonghang Plaza No. 43 North 3rd Ring West Road Haidian District Beijing China College of Chemistry and Molecular Engineering Peking University Beijing100871 China Baidu Inc. Beijing China Key Laboratory of Structural Biology of Zhejiang Province School of Life Sciences Westlake University Zhejiang Hangzhou China Westlake AI Therapeutics Lab Westlake Laboratory of Life Sciences and Biomedicine Zhejiang Hangzhou China Department of Chemistry Princeton University PrincetonNJ08544 United States SISSA Scuola Internazionale Superiore di Studi Avanzati Trieste34136 Italy Laboratory of Computational Science and Modeling Institute of Materials École Polytechnique Fédérale de Lausanne Lausanne1015 Switzerland Department of Physics National University of Defense Technology Hunan Changsha410073 China State Key Lab of Processors Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China School of Electronics Engineerin

DeePMD-kit is a powerful open-source software package that facilitates molecular dynamics simulations using machine learning potentials (MLP) known as Deep Potential (DP) models. This package, which was released in 2017, has been widely used in the fields of physics, chemistry, biology, and material science for studying atomistic systems. The current version of DeePMD-kit offers numerous advanced features such as DeepPot-SE, attention-based and hybrid descriptors, the ability to fit tensile properties, type embedding, model deviation, Deep Potential - Range Correction (DPRc), Deep Potential Long Range (DPLR), GPU support for customized operators, model compression, non-von Neumann molecular dynamics (NVNMD), and improved usability, including documentation, compiled binary packages, graphical user interfaces (GUI), and application programming interfaces (API). This article presents an overview of the current major version of the DeePMD-kit package, highlighting its features and technical details. Additionally, the article benchmarks the accuracy and efficiency of different models and discusses ongoing developments. © 2023, CC BY-NC-ND.

关键词： Molecular dynamics

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Anti-windup FOPID-based DPC for SAPF interconnected to a PV system tuned using PSO algorithm

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European Journal of Electrical Engineering 2020年第4-5期22卷 313-324页

作者： Boudechiche, Ghania Sarra, Mustapha Aissa, Oualid Gaubert, Jean-Paul Benlahbib, Boualam Lashab, Abderezak Electronics Department University of Mohamed El-Bachir El-Ibrahimi Bordj Bou Arreridj34000 Algeria Electromechanical Department University of Mohamed El-Bachir El-Ibrahimi Bordj Bou Arreridj34000 Algeria Laboratory of Computer Science and Automatic Control for Systems University of Poitiers Poitiers86073 France Applied Research Unit in Renewable Energies Renewable Energy Development Center CDER Ghardaïa47133 Algeria Department of Energy Technology Aalborg University Aalborg9220 Denmark

This paper deals with a shunt active power filter (SAPF) integrated in a photovoltaic (PV) system, which is interfaced to the grid via a double-stage configuration, for simultaneously improving the power quality in the existence of non-linear loads and injecting the PV harvested power to the power grid. The direct power control (DPC) based on the conventional Proportional-integral (PI) suffers from some shortcomings in the transient state, such as large overshoots and undershoots in the voltage. Long response time is another disadvantage when using such a controller. To overcome this situation, the proposed control method is equipped by an anti windup fractional order proportional-integral differentiator (AW-FOPID) regulator, replacing the standard PI or PID regulators to maintain the DC link voltage at its desired value with small overshoots and undershoots in the voltage, while maintaining a short response time. The AW-FOPID controller, however, has five parameters, which makes it troublesome to tune. Accordingly, to adjust this AW-FOPID parameters, the Particle Swarm Optimization (PSO) algorithm is employed by minimizing the Integral Time Absolute Error (ITAE). Furthermore, an intelligent algorithm for tracking the maximum power point (MPPT) based on fuzzy logic has been applied to eventually resolve the drawback of the rapidly changing weather conditions. The overall control scheme is examined by simulation using MATLAB/Simulink software. The obtained simulation results and comparative study demonstrate the feasibility and performance of this control strategy. © 2020 International Information and Engineering Technology Association. All rights reserved.

关键词： Controllers

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Author Correction: The high-dimensional space of human diseases built from diagnosis records and mapped to genetic loci

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Nature computational science 2023年第7期3卷 658页

作者： Gengjie Jia Yu Li Xue Zhong Kanix Wang Milton Pividori Rabab Alomairy Aniello Esposito Hatem Ltaief Chikashi Terao Masato Akiyama Koichi Matsuda David E Keyes Hae Kyung Im Takashi Gojobori Yoichiro Kamatani Michiaki Kubo Nancy J Cox James Evans Xin Gao Andrey Rzhetsky Shenzhen Branch Guangdong Laboratory of Lingnan Modern Agriculture Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs Agricultural Genomics Institute at Shenzhen Chinese Academy of Agricultural Sciences Shenzhen China. jiagengjie@***. Computational Bioscience Research Center King Abdullah University of Science and Technology Thuwal Saudi Arabia. Computer Science Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal Saudi Arabia. Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong People's Republic of China. Department of Medicine and Vanderbilt Genetics Institute Vanderbilt University Medical Center Nashville TN US. Department of Medicine Institute of Genomics and Systems Biology Committee on Genomics Genetics and Systems Biology University of Chicago Chicago IL US. Department of Operations Business Analytics and Information Systems University of Cincinnati Cincinnati OH US. Department of Systems Pharmacology and Translational Therapeutics Perelman School of Medicine University of Pennsylvania Philadelphia PA US. Extreme Computing Research Center Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal Saudi Arabia. College of Computer Science and Engineering University of Jeddah Jeddah Saudi Arabia. HPE HPC/AI EMEA Research Laboratory Bristol UK. RIKEN Center for Integrative Medical Sciences Yokohama Japan. Clinical Research Center Shizuoka General Hospital Shizuoka Japan. Department of Applied Genetics The School of Pharmaceutical Sciences University of Shizuoka Shizuoka Japan. Department of Ophthalmology Graduate School of Medical Sciences Kyushu University Fukuoka Japan. Department of Computational Biology and Medical Sciences Graduate School of Frontier Sciences The University of Tokyo Tokyo Japan. Biological and Environmenta

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DPA-2: a large atomic model as a multi-task learner

arXiv

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arXiv 2023年

作者： Zhang, Duo Liu, Xinzijian Zhang, Xiangyu Zhang, Chengqian Cai, Chun Bi, Hangrui Du, Yiming Qin, Xuejian Peng, Anyang Huang, Jiameng Li, Bowen Shan, Yifan Zeng, Jinzhe Zhang, Yuzhi Liu, Siyuan Li, Yifan Chang, Junhan Wang, Xinyan Zhou, Shuo Liu, Jianchuan Luo, Xiaoshan Wang, Zhenyu Jiang, Wanrun Wu, Jing Yang, Yudi Yang, Jiyuan Yang, Manyi Gong, Fu-Qiang Zhang, Linshuang Shi, Mengchao Dai, Fu-Zhi York, Darrin M. Liu, Shi Zhu, Tong Zhong, Zhicheng Lv, Jian Cheng, Jun Jia, Weile Chen, Mohan Ke, Guolin Weinan, E. Zhang, Linfeng Wang, Han AI for Science Institute Beijing100080 China DP Technology Beijing100080 China Academy for Advanced Interdisciplinary Studies Peking University Beijing100871 China State Key Lab of Processors Institute of Computing Technology Chinese Academy of Sciences Beijing100871 China University of Chinese Academy of Sciences Beijing100871 China HEDPS CAPT College of Engineering Peking University Beijing100871 China Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences Ningbo315201 China CAS Key Laboratory of Magnetic Materials and Devices Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology Chinese Academy of Sciences Ningbo315201 China School of Electronics Engineering and Computer Science Peking University Beijing100871 China Shanghai Engineering Research Center of Molecular Therapeutics & New Drug Development School of Chemistry and Molecular Engineering East China Normal University Shanghai200062 China Laboratory for Biomolecular Simulation Research Institute for Quantitative Biomedicine Department of Chemistry and Chemical Biology Rutgers University PiscatawayNJ08854 United States Department of Chemistry Princeton University PrincetonNJ08540 United States College of Chemistry and Molecular Engineering Peking University Beijing100871 China Yuanpei College Peking University Beijing100871 China School of Electrical Engineering and Electronic Information Xihua University Chengdu610039 China State Key Laboratory of Superhard Materials College of Physics Jilin University Changchun130012 China Key Laboratory of Material Simulation Methods & Software of Ministry of Education College of Physics Jilin University Changchun130012 China International Center of Future Science Jilin University Changchun130012 China Key Laboratory for Quantum Materials of Zhejiang Province Department of Physics School of Science Westlake University Zhejiang Hangzhou310030 China Atomistic Simulations Italia

The rapid advancements in artificial intelligence (AI) are catalyzing transformative changes in atomic modeling, simulation, and design. AI-driven potential energy models have demonstrated the capability to conduct large-scale, long-duration simulations with the accuracy of ab initio electronic structure methods. However, the model generation process remains a bottleneck for large-scale applications. We propose a shift towards a model-centric ecosystem, wherein a large atomic model (LAM), pre-trained across multiple disciplines, can be efficiently fine-tuned and distilled for various downstream tasks, thereby establishing a new framework for molecular modeling. In this study, we introduce the DPA-2 architecture as a prototype for LAMs. Pre-trained on a diverse array of chemical and materials systems using a multi-task approach, DPA-2 demonstrates superior generalization capabilities across multiple downstream tasks compared to the traditional single-task pre-training and fine-tuning methodologies. Our approach sets the stage for the development and broad application of LAMs in molecular and materials simulation research. Copyright © 2023, The Authors. All rights reserved.

关键词： Multi-task learning

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Broadband enhancement of on-chip single photon extraction via tilted hyperbolic metamaterials

arXiv

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arXiv 2020年

作者： Shen, Lian Lin, Xiao Shalaginov, Mikhail Low, Tony Zhang, Xianmin Zhang, Baile Chen, Hongsheng Interdisciplinary Center for Quantum Information State Key Laboratory of Modern Optical Instrumentation College of Information Science and Electronic Engineering Zhejiang University Hangzhou310027 China Key Lab. of Advanced Micro/Nano Electronic Devices & Smart Systems of Zhejiang Electromagnetics Academy at Zhejiang University Zhejiang University Hangzhou310027 China Division of Physics and Applied Physics School of Physical and Mathematical Sciences Nanyang Technological University Singapore637371 Singapore DepartmentofMaterialsScienceandEngineering MassachusettsInstituteofTechnology 77 MassachusettsAvenue CambridgeMA02139 United States Department of Electrical and Computer Engineering University of Minnesota MinneapolisMN55455 United States School of Physical and Mathematical Sciences Nanyang Technological University Singapore637371 Singapore

A fundamental building block for on-chip quantum photonics is a single-photon source with high repetition rates, which can enable many applications such as high-speed quantum communication and quantum information processing. Ideally, such single photon sources would then require a large on-chip photon extraction decay rate, namely the rate of excited photons coupled into nanofibers or waveguides, over a broad spectral range. However, this goal has remained elusive till date. Here we propose a feasible scheme to enhance the on-chip photon extraction decay rate of quantum emitters, through the tilting of the optical axis of hyperbolic metamaterials with respect to the end-facet of nanofibers. Importantly, the revealed scheme is applicable to arbitrarily orientated quantum emitters over a broad spectral range, e.g., up to ~80 nm for visible light. The underlying physics relies on the emerging unique feature of hyperbolic metamaterials if their optical axis is judiciously tilted. That is, their supported high-k (i.e., wavevector) hyperbolic eigenmodes, which are intrinsically confined inside them if their optical axis is un-tilted, can now become momentum-matched with the guided modes of nanofibers, and more importantly, they can safely couple into nanofibers almost without reflection. Copyright © 2020, The Authors. All rights reserved.

关键词： Nanofibers

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Roadmap on Deep Learning for Microscopy

arXiv

引用

arXiv 2023年

作者： Volpe, Giovanni Wählby, Carolina Tian, Lei Hecht, Michael Yakimovich, Artur Monakhova, Kristina Waller, Laura Sbalzarini, Ivo F. Metzler, Christopher A. Xie, Mingyang Zhang, Kevin Lenton, Isaac C.D. Rubinsztein-Dunlop, Halina Brunner, Daniel Bai, Bijie Ozcan, Aydogan Midtvedt, Daniel Wang, Hao Sladoje, Nataša Lindblad, Joakim Smith, Jason T. Ochoa, Marien Barroso, Margarida Intes, Xavier Qiu, Tong Yu, Li-Yu You, Sixian Liu, Yongtao Ziatdinov, Maxim A. Kalinin, Sergei V. Sheridan, Arlo Manor, Uri Nehme, Elias Goldenberg, Ofri Shechtman, Yoav Moberg, Henrik K. Langhammer, Christoph Špačková, Barbora Helgadottir, Saga Midtvedt, Benjamin Argun, Aykut Thalheim, Tobias Cichos, Frank Bo, Stefano Hubatsch, Lars Pineda, Jesus Manzo, Carlo Bachimanchi, Harshith Selander, Erik Homs-Corbera, Antoni Fränzl, Martin de Haan, Kevin Rivenson, Yair Korczak, Zofia Adiels, Caroline Beck Mijalkov, Mite Veréb, Dániel Chang, Yu-Wei Pereira, Joana B. Matuszewski, Damian Kylberg, Gustaf Sintorn, Ida-Maria Caicedo, Juan C. Cimini, Beth A. Bell, Muyinatu Lediju A. Saraiva, Bruno M. Jacquemet, Guillaume Henriques, Ricardo Ouyang, Wei Le, Trang Gómez-De-Mariscal, Estibaliz Sage, Daniel Muñoz-Barrutia, Arrate Lindqvist, Ebba Josefson Bergman, Johanna Department of Physics University of Gothenburg Gothenburg41296 Sweden Dept. IT and SciLifeLab Uppsala University Sweden Department of Electrical and Computer Engineering Boston University BostonMA02215 United States Department of Biomedical Engineering Boston University BostonMA02215 United States Görlitz Germany Department of Renal Medicine Division of Medicine University College London Royal Free Hospital Campus London United Kingdom Artificial Intelligence for Life Sciences CIC Dorset United Kingdom Roche Pharma International Informatics Roche Diagnostics GmbH Mannheim Germany Department of Electrical Engineering and Computer Sciences UC Berkeley United States Technische Universität Dresden Faculty of Computer Science Dresden Germany Max Planck Institute of Molecular Cell Biology and Genetics Center for Systems Biology Dresden Dresden Germany University of Maryland College Park United States Klosterneuburg3400 Austria School of Mathematics and Physics The University of Queensland Brisbane4072 Australia Australian Research Council Centre of Excellence for Engineered Quantum Systems The University of Queensland Brisbane4072 Australia Département d’Optique P. M. Duffieux Institut FEMTO-ST Université Franche-Comté CNRS UMR 6174 Besançon France Department of Electrical and Computer Engineering University of California Los Angeles Los AngelesCA United States Department of Information Technology Uppsala University Sweden Department of Biomedical Engineering Rensselaer Polytechnic Institute TroyNY12180 United States Elephas 1 Erdman Pl. Madison53705 United States University of Wisconsin–Madison Department of Medical Physics MadisonWI53705 United States Department of Molecular and Cellular Physiology Albany Medical College AlbanyNY12208 United States Research Laboratory of Electronics Electrical Engineering and Computer Science Massachusetts Institute of Technology BostonMA United States Center for Nanophase Materials Sciences Oak R

Through digital imaging, microscopy has evolved from primarily being a means for visual observation of life at the micro- and nano-scale, to a quantitative tool with ever-increasing resolution and throughput. Artificial intelligence, deep neural networks, and machine learning are all niche terms describing computational methods that have gained a pivotal role in microscopy-based research over the past decade. This Roadmap is written collectively by prominent researchers and encompasses selected aspects of how machine learning is applied to microscopy image data, with the aim of gaining scientific knowledge by improved image quality, automated detection, segmentation, classification and tracking of objects, and efficient merging of information from multiple imaging modalities. We aim to give the reader an overview of the key developments and an understanding of possibilities and limitations of machine learning for microscopy. It will be of interest to a wide cross-disciplinary audience in the physical sciences and life sciences. Copyright © 2023, The Authors. All rights reserved.

关键词： Image enhancement

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Navigating the challenges in creating complex data systems: a development philosophy

arXiv

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arXiv 2022年

作者： Dittmer, Sören Roberts, Michael Gilbey, Julian Biguri, Ander Preller, Jacobus Rudd, James H.F. Aston, John A.D. Schönlieb, Carola-Bibiane Selby, Ian Breger, Anna Thorpe, Matthew Weir-McCall, Jonathan R. Gkrania-Klotsas, Effrossyni Korhonen, Anna Jefferson, Emily Langs, Georg Yang, Guang Prosch, Helmut Stanczuk, Jan Tang, Jing Babar, Judith Sánchez, Lorena Escudero Teare, Philip Patel, Mishal Wassin, Marcel Holzer, Markus Walton, Nicholas Lió, Pietro Shadbahr, Tolou Sala, Evis Department of Applied Mathematics and Theoretical Physics University of Cambridge Cambridge United Kingdom Addenbrooke’s Hospital Cambridge University Hospitals NHS Trust Cambridge United Kingdom Department of Medicine University of Cambridge Cambridge United Kingdom Department of Pure Mathematics and Mathematical Statistics University of Cambridge Cambridge United Kingdom ZeTeM University of Bremen Bremen Germany Department of Radiology University of Cambridge Cambridge United Kingdom Faculty of Mathematics University of Vienna Austria Department of Mathematics University of Manchester Manchester United Kingdom Royal Papworth Hospital Cambridge Royal Papworth Hospital NHS Foundation Trust Cambridge United Kingdom Language Technology Laboratory University of Cambridge Cambridge United Kingdom Population Health and Genomics School of Medicine University of Dundee Dundee United Kingdom Department of Biomedical Imaging and Image-guided Therapy Computational Imaging Research Lab Medical University of Vienna Vienna Austria National Heart and Lung Institute Imperial College London London United Kingdom Research Program in Systems Oncology Faculty of Medicine University of Helsinki Helsinki Finland Data Science & Artificial Intelligence AstraZeneca Cambridge United Kingdom Clinical Pharmacology & Safety Sciences AstraZeneca Cambridge United Kingdom Contextflow GmbH Vienna Austria Institute of Astronomy University of Cambridge Cambridge United Kingdom Department of Computer Science and Technology University of Cambridge Cambridge United Kingdom

In this perspective, we argue that despite the democratization of powerful tools for data science and machine learning over the last decade, developing the code for a trustworthy and effective data science system (DSS) is getting harder. Perverse incentives and a lack of widespread software engineering (SE) skills are among many root causes we identify that naturally give rise to the current systemic crisis in reproducibility of DSSs. We analyze why SE and building large complex systems is, in general, hard. Based on these insights, we identify how SE addresses those difficulties and how we can apply and generalize SE methods to construct DSSs that are fit for purpose. We advocate two key development philosophies, namely that one should incrementally grow – not biphasically plan and build – DSSs, and one should always employ two types of feedback loops during development: one which tests the code’s correctness and another that evaluates the code’s efficacy. Copyright © 2022, The Authors. All rights reserved.

关键词： Software engineering

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Smart radio environments

arXiv

引用

arXiv 2021年

作者： Gradoni, Gabriele Renzo, Marco Di Díaz-Rubio, Ana Tretyakov, Sergei A. Caloz, Christophe Gradoni, Gabriele Peng, Zhen Alu, Andrea Lerosey, Geoffroy Fink, Mathias Galdi, Vincenzo Cui, Tie Jun Frazier, Benjamin W. Anlage, Steven M. Salucci, Marco Massa, Andrea Cheng, Qiang Wang, Jinghe Jin, Shi Dardari, Davide Decarli, Nicoló Yurduseven, Okan Matthaiou, Michail Kenney, Mitchell Gordon, George Georgiou, Orestis Nguyen, Cam Ly Martini, Enrica Maci, Stefano Wakatsuchi, Hiroki Phang, Sendy School of Mathematical Sciences Department of Electrical and Electronic Engineering University of Nottingham Nottingham United Kingdom Laboratoire des Signaux et Systémes CNRS CentraleSupélec Université Paris-Saclay Gif-sur-Yvette Paris France Department of of Electronics and Nanoengineering Aalto University Espoo Finland META Research Group KU Leuven Belgium School of Mathematical Sciences Department of Electrical and Electronic Engineering University of Nottingham University Park NG72RD United Kingdom Department of Electrical and Computer Engineering University of Illinois at Urbana-Champaign IL United States Advanced Science Research Center City University of New York United States Greenerwave 6 rue Jean Calvin Paris75005 France Institut Langevin 1 rue Jussieu Paris75005 France Fields & Waves Lab Department of Engineering University of Sannio Benevento Italy State Key Laboratory of Millimeter Waves Southeast University Nanjing China Applied Physics Laboratory Johns Hopkins University LaurelMD20723 United States Department of Electrical and Computer Engineering Department of Physics Quantum Materials Center University of Maryland College ParkMD20742 United States Trento Italy Chengdu China Beijing China Institute of Electromagnetic Space State Key Laboratory of Millimeter Waves Southeast University Nanjing210096 China National Mobile Communications Research Laboratory Southeast University Nanjing China University of Bologna Bologna Italy Bologna Italy Centre for Wireless Innovation Institute of Electronics Communications and Information Technology Queen’s University Belfast Belfast United Kingdom Department of Electrical and Electronic Engineering University of Nottingham Nottingham United Kingdom Department of Electrical and Computer Engineering University of Cyprus Nicosia Cyprus Wireless System Laboratory Corporate Research & Development Center Toshiba Corporation Kawasaki Japan Department of Information Engineering an

This Roadmap takes the reader on a journey through the research in electromagnetic wave propagation control via reconfigurable intelligent surfaces. Meta-surface modelling and design methods are reviewed along with physical realisation techniques. Several wireless applications are discussed, including beam-forming, focusing, imaging, localisation, and sensing, some rooted in novel architectures for future mobile communications networks towards 6G. © 2021, CC BY.

关键词： Electromagnetic wave propagation

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Correction to: The ELFIN Mission

引用

Space Science Reviews 2020年第6期216卷 1-2页

作者： Angelopoulos, V. Tsai, E. Bingley, L. Shaffer, C. Turner, D. L. Runov, A. Li, W. Liu, J. Artemyev, A. V. Zhang, X.-J. Strangeway, R. J. Wirz, R. E. Shprits, Y. Y. Sergeev, V. A. Caron, R. P. Chung, M. Cruce, P. Greer, W. Grimes, E. Hector, K. Lawson, M. J. Leneman, D. Masongsong, E. V. Russell, C. L. Wilkins, C. Hinkley, D. Blake, J. B. Adair, N. Allen, M. Anderson, M. Arreola-Zamora, M. Artinger, J. Asher, J. Branchevsky, D. Capitelli, M. R. Castro, R. Chao, G. Chung, N. Cliffe, M. Colton, K. Costello, C. Depe, D. Domae, B. W. Eldin, S. Fitzgibbon, L. Flemming, A. Fox, I. Frederick, D. M. Gilbert, A. Gildemeister, A. Gonzalez, A. Hesford, B. Jha, S. Kang, N. King, J. Krieger, R. Lian, K. Mao, J. McKinney, E. Miller, J. P. Norris, A. Nuesca, M. Palla, A. Park, E. S. Y. Pedersen, C. E. Qu, Z. Rozario, R. Rye, E. Seaton, R. Subramanian, A. Sundin, S. R. Tan, A. Turner, W. Villegas, A. J. Wasden, M. Wing, G. Wong, C. Xie, E. Yamamoto, S. Yap, R. Zarifian, A. Zhang, G. Y. Earth Planetary and Space Sciences Department University of California Los Angeles USA Institute of Geophysics and Planetary Physics University of California Los Angeles USA Tyvak Nano-Satellite Systems Inc. Irvine USA Johns Hopkins University Applied Physics Laboratory Laurel USA Department of Astronomy and Center for Space Physics Boston University Boston USA Mechanical and Aerospace Engineering Department Henry Samueli School of Engineering University of California Los Angeles USA GFZ German Research Centre for Geosciences Potsdam Germany Saint Petersburg State University St. Petersburg Russia Northrop Grumman Aerospace Systems Redondo Beach USA Raytheon Space and Airborne Systems El Segundo USA The Aerospace Corporation El Segundo USA Millenium Space Systems El Segundo USA Aptiv Agoura Hills USA Physics and Astronomy Department University of California Los Angeles USA The Boeing Company Long Beach USA SF Motors Santa Clara USA SpaceX Hawthorne USA Planet Labs Inc. San Francisco USA Computer Science Department Henry Samueli School of Engineering University of California Los Angeles USA Electrical and Computer Engineering Department Henry Samueli School of Engineering University of California Los Angeles USA Jet Propulsion Laboratory Pasadena USA Mercedes-Benz Research and Development North America Long Beach USA Epic Systems Corporation Verona USA California State Polytechnic University Pomona USA Economics Department University of California Los Angeles USA Experior Laboratories Oxnard USA Mathematics Department University of California Los Angeles USA Qualcomm San Diego USA

Correction to: Space Sci. Rev. (2020) 216: 103 Affiliation 2 in this article is the wrong affiliation that instead should read: “Institute of Geophysics and Planetary Physics, University of California, Los Angeles, C...

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