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Optimized ultrabroadband absorbing multilayer thin film structure

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Optimized ultrabroadband absorbing multilayer thin film stru...

Frontiers in Optics, FIO 2018

作者： Matyas, Corey You, Chenglong Huang, Yin Dowling, Jonathan P. Veronis, Georgios Hearne Institute for Theoretical Physics Department of Physics and Astronomy Louisiana State University Baton RougeLA70803 United States Department of Optoelectrics Information Science and Engineering School of Physics and Electronics Central South University Changsha Hunan410012 China NYU-ECNU Institute of Physics NYU Shanghai 3663 Zhongshan Road North Shanghai200062 China CAS-Alibaba Quantum Computing Laboratory CAS Center for Excellence in Quantum Information and Quantum Physics University of Science and Technology of China Shanghai201315 China School of Electrical Engineering and Computer Science Louisiana State University Baton RougeLA70803 United States Center for Computation and Technology Louisiana State University Baton RougeLA70803 United States

We design an optimized aperiodic multilayer thin film structure with ultra-broadband absorption over a broad angular range. Using a hybrid optimization algorithm, we achieve an average 97.9% absorption from 400 nm to ... 详细信息

ISBN: (纸本)9781943580460

关键词： Film preparation

Privacy-preserving and efficient aggregation based on blockchain for power grid communications in smart communities

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arXiv 2018年

作者： Guan, Zhitao Si, Guanlin Zhang, Xiaosong Wu, Longfei Guizani, Nadra Du, Xiaojiang Ma, Yinglong School of Control and Computer Engineering North China Electric Power University Beijing102206 China Center for Cyber Security University of Electronic Science and Technology of China Chengdu China Department of mathematics and Computer Science Fayetteville State University FayettevilleNC United States School of Electrical and Computer Engineering Purdue University West LafayetteIN United States Department of Computer and Information Sciences Temple University PhiladelphiaPA United States

Intelligence is one of the most important aspects in the development of our future communities. Ranging from smart home, smart building, to smart city, all these smart infrastructures must be supported by intelligent power supply. Smart grid is proposed to solve all challenges of future electricity supply. In smart grid, in order to realize optimal scheduling, a Smart Meter (SM) is installed at each home to collect the near real-time electricity consumption data, which can be used by the utilities to offer better smart home services. However, the near real-time data may disclose user's privacy. An adversary may track the application usage patterns by analyzing the user's electricity consumption profile. In this paper, we propose a privacy-preserving and efficient data aggregation scheme. We divide users into different groups and each group has a private blockchain to record its members' data. To preserve the inner privacy within a group, we use pseudonym to hide user's identity, and each user may create multiple pseudonyms and associate his/her data with different pseudonyms. In addition, the bloom filter is adopted for fast authentication. The analysis shows that the proposed scheme can meet the security requirements, and achieve a better performance than other popular methods. Copyright © 2018, The Authors. All rights reserved.

关键词： Data structures

Machine discovery of partial differential equations from spatiotemporal data

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arXiv 2019年

作者： Yuan, Ye Li, Junlin Li, Liang Jiang, Frank Tang, Xiuchuan Zhang, Fumin Liu, Sheng Goncalves, Jorge Voss, Henning U. Li, Xiuting Kurths, Jürgen Ding, Han School of Artificial Intelligence and Automation State Key Laboratory of Digital Manufacturing Equipments and Technology Huazhong University of Science and Technology Wuhan430074 China School of Artificial Intelligence and Automation Key Laboratory of Image Processing and Intelligent Control Huazhong University of Science and Technology Wuhan430074 China School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm11428 Sweden School of Mechanical Science and Engineering Huazhong University of Science and Technology Wuhan430074 China School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta30332 United States School of Power and Mechanical Engineering Wuhan University Wuhan430074 China Department of Engineering University of Cambridge CambridgeCB2 1PZ United Kingdom Luxembourg Centre for Systems Biomedicine University of Luxembourg BelvauxL-4367 Luxembourg Citigroup Biomedical Imaging Center Weill Cornell Medical College New York10021 United States Research Domain IV - Transdisciplinary Concepts & Methods Potsdam Institute for Climate Impact Research Potsdam14473 Germany School of Mechanical Science and Engineering State Key Laboratory of Digital Manufacturing Equipments and Technology Huazhong University of Science and Technology Wuhan430074 China

The study presents a general framework for discovering underlying Partial Differential Equations (PDEs) using measured spatiotemporal data. The method, called Sparse Spatiotemporal System Discovery (S3d), decides which physical terms are necessary and which can be removed (because they are physically negligible in the sense that they do not affect the dynamics too much) from a pool of candidate functions. The method is built on the recent development of Sparse Bayesian Learning;which enforces the sparsity in the to-be-identified PDEs, and therefore can balance the model complexity and fitting error with theoretical guarantees. Without leveraging prior knowledge or assumptions in the discovery process, we use an automated approach to discover ten types of PDEs, including the famous Navier-Stokes and sine-Gordon equations, from simulation data alone. Moreover, we demonstrate our data-driven discovery process with the Complex Ginzburg-Landau Equation (CGLE) using data measured from a traveling-wave convection experiment. Our machine discovery approach presents solutions that has the potential to inspire, support and assist physicists for the establishment of physical laws from measured spatiotemporal data, especially in notorious fields that are often too complex to allow a straightforward establishment of physical law, such as biophysics, fluid dynamics, neuroscience or nonlinear optics. Copyright © 2019, The Authors. All rights reserved.

关键词： Nonlinear optics

Optimal multicast subgrouping in mobility-aware 5G systems: Challenges, modeling, and opportunities 12

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Optimal multicast subgrouping in mobility-aware 5G systems: ...

Selected Papers of the 12th International Workshop on Applied Problems in Theory of Probabilities and Mathematical Statistics (Summer Session) in the Framework of the Conference on Information and Telecommunication Technologies and Mathematical Modeling of High-Tech Systems, APTP+MS 2018

作者： Beschastnyi, Vitalii A. Ostrikova, Darya Yu. Zeifman, Alexander I. Gudkova, Irina. A. 6 Miklukho-Maklaya St. Moscow117198 Russia Department of Applied Mathematics Vologda State University 6 Orlova St. Vologda160000 Russia Institute of Informatics Problems Federal Research Center Computer Science and Control of the Russian Academy of Sciences 44-2 Vavilov St. Moscow119333 Russia Faculty of Electrical Engineering and Communication Brno University of Technology 3058/10 Technická Brno61600 Czech Republic

The expected growth in the mobile video (including streaming video, video downloading, conferencing, etc.) now is the key driver for rapid development of 5G wireless network technologies. This paradigm forces wireless networks to manage their resources as effectively as possible. One of the most appropriate solutions for video traffic that may provide the sufficient spectral efficiency is multicasting. Multicast sessions allows a group of users to simultaneously access the same multimedia content with the same QoE parameters. In this paper we consider different methods of multicast subgrouping. An analytical framework in the from of a queuing network is proposed to study the network performance, and a comparative study of network performance metrics under different user movement scenarios is presented. Copyright © 2018 for the individual papers by the papers' authors.

关键词： Multicasting

Large-Scale Neuromorphic Spiking Array Processors: A quest to mimic the brain

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arXiv 2018年

作者： Thakur, Chetan Singh Molin, Jamal Cauwenberghs, Gert Indiveri, Giacomo Kumar, Kundan Qiao, Ning Schemmel, Johannes Wang, Runchun Chicca, Elisabetta Hasler, Jennifer Olson Seoa, Jae sun Yu, Shimeng Cao, Yu van Schaik, André Etienne-Cummings, Ralph Department of Electronic Systems Engineering Indian Institute of Science Bangalore India Department of Electrical and Computer Engineering Johns Hopkins University BaltimoreMD United States Department of Bioengineering and Institute for Neural Computation University of California San Diego La JollaCA United States Institute of Neuroinformatics University of Zurich ETH Zurich Zurich Switzerland Kirchhoff Institute for Physics University of Heidelberg Heidelberg Germany MARCS Institute Western Sydney University KingswoodNSW2751 Australia Cognitive Interaction Technology Center of Excellence Bielefeld University Bielefeld Germany School of Electrical and Computer Engineering Georgia Institute of Technology AtlantaGA United States School of Electrical Computer and Engineering Arizona State University TempeAZ United States

Neuromorphic engineering (NE) encompasses a diverse range of approaches to information processing that are inspired by neurobiological systems, and this feature distinguishes neuromorphic systems from conventional computing systems. The brain has evolved over billions of years to solve difficult engineering problems by using efficient, parallel, low-power computation. The goal of NE is to design systems capable of brain-like computation. Numerous large-scale neuromorphic projects have emerged recently. This interdisciplinary field was listed among the top 10 technology breakthroughs of 2014 by the MIT Technology Review and among the top 10 emerging technologies of 2015 by the World Economic Forum. NE has two-way goals: one, a scientific goal to understand the computational properties of biological neural systems by using models implemented in integrated circuits (ICs);second, an engineering goal to exploit the known properties of biological systems to design and implement efficient devices for engineering applications. Building hardware neural emulators can be extremely useful for simulating large-scale neural models to explain how intelligent behavior arises in the brain. The principle advantages of neuromorphic emulators are that they are highly energy efficient, parallel and distributed, and require a small silicon area. Thus, compared to conventional CPUs, these neuromorphic emulators are beneficial in many engineering applications such as for the porting of deep learning algorithms for various recognitions tasks. In this review article, we describe some of the most significant neuromorphic spiking emulators, compare the different architectures and approaches used by them, illustrate their advantages and drawbacks, and highlight the capabilities that each can deliver to neural modelers. This article focuses on the discussion of large-scale emulators and is a continuation of a previous review of various neural and synapse circuits [1]. We also explore application

关键词： Large scale systems

Ultra-thin, high-efficiency mid-infrared transmissive huygens meta-optics

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Ultra-thin, high-efficiency mid-infrared transmissive huygen...

Frontiers in Optics, FiO 2017

作者： Zheng, Hanyu Ding, Jun Zhang, Li An, Sensong Lin, Hongtao Zheng, Bowen Du, Qingyang Yin, Gufan Michon, Jerome Zhang, Yifei Fang, Zhuoran Deng, Longjiang Gu, Tian Zhang, Hualiang Hu, Juejun Department of Materials Science and Engineering Massachusetts Institute of Technology CambridgeMA United States National Engineering Research Center of Electromagnetic Radiation Control Materials University of Electronic Science and Technology of China Chengdu Sichuan China Department of Electrical and Computer Engineering University of Massachusetts Lowell LowellMA United States

Mid-IR transmissive meta-optics are experimentally demonstrated, capitalizing on material innovations and a novel two-component Huygens metasurface design. We present, for the first time, diffraction limited focusing ... 详细信息

ISBN: (纸本)9781943580330

关键词： Diffraction efficiency Electric fields Extinction ratios Optical components Optical properties Phase shift

Skin Effect Suppressed NiFe/Cu Electroplated Multilayer Wiring for High Data-Rate and Low Delay-Time I/O Interface Board.

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Skin Effect Suppressed NiFe/Cu Electroplated Multilayer Wiri...

International Conference on Magnetics (INTERMAG)

作者： M. Yamaguchi T. Yanai H. Nakayama R. Sai H. Fujiwara Y. Kitai M. Sato U. Sangawa New Industry Creation Hachery Center Tohoku University Sendai Japan Division of Electrical Engineering and Computer Science Nagasaki University Nagasaki Japan Department of Electronics and Control Engineering National Institute of Technology Nagano College Nagano Japan Electronic Materials Business Division Automotive & Industrial Systems Company Panasonic Corporation Kadoma Japan

This paper proposes a new application of skin effect suppression technology [1]-[4] for long wiring on high-speed & low-delay I/O board (typical wiring length; 200 to 1000 mm). This proposal will overcome the difficulty to further reduce the transmission losses on the I/O board with >50 Gbps data rate, which is currently performed by lowering dielectric substrate losses and surface smoothing of Cu conductor. A major challenge in this paper is to demonstrate the skin effect suppression by electroplated magnetic/conductor multilayer, instead of sputter-deposited thin film in literature [2]-[4], in order to meet coming cost-effective, thick (>5 μm), large area, and high throughput mass productivity requirements. High frequency (>10 GHz) estimation of complex permeability and measurements of low resistance devices are also investigated.

关键词： Magnetics Conferences

Dynamically weighted ensemble-based prediction system for adaptively modeling driver reaction time

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arXiv 2018年

作者： Chuang, Chun-Hsiang Cao, Zehong Chen, Po-Tsang Huang, Chih-Sheng Pal, Nikhil R. Lin, Chin-Teng Department of Computer Science and Engineering National Taiwan Ocean University Taiwan Faculty of Engineering and Information Technology University of Technology Sydney Australia Discipline of ICT School of Technology Environments and Design University of Tasmania TAS Australia Brain Research Center National Chiao Tung University Taiwan Institute of Electrical Control Engineering Indian Statistical Institute India Faculty of Engineering and Information Technology University of Technology Sydney NSW Australia

Motor vehicle crashes are the leading cause of fatalities worldwide. Most of these accidents are caused by human mistakes and behavioral lapses, especially when the driver is drowsy, fatigued, or inattentive. Clearly, predicting a driver's cognitive state, or more specifically, modeling a driver's reaction time (RT) in response to the appearance of a potential hazard warrants urgent research. In the last two decades, the electric field that is generated by the activities in the brain, monitored by an electroencephalogram (EEG), has been proven to be a robust physiological indicator of human behavior. However, mapping the human brain can be extremely challenging, especially owing to the variability in human beings over time, both within and among individuals. Factors such as fatigue, inattention and stress can induce homeostatic changes in the brain, which affect the observed relationship between brain dynamics and behavioral performance, and thus make the existing systems for predicting RT difficult to generalize. To solve this problem, an ensemblebased weighted prediction system is presented herein. This system comprises a set of prediction sub-models that are individually trained using groups of data with similar EEG-RT relationships. To obtain a final prediction, the prediction outcomes of the submodels are then multiplied by weights that are derived from the EEG alpha coherences of 10 channels plus theta band powers of 30 channels, whose changes were found to be indicators of variations in the EEG-RT relationship. The results thus obtained reveal that the proposed system with a time-varying adaptive weighting mechanism significantly outperforms the conventional system in modeling a driver's RT. The adaptive design of the proposed system demonstrates its feasibility in coping with the variability in the brain-behavior relationship. In this contribution surprisingly simple EEG-based adaptive methods are used in combination with an ensemble scheme to significantly

关键词： Electroencephalography

Estimates, trends, and drivers of the global burden of type 2 diabetes attributable to PM_2·5 air pollution, 1990–2019: an analysis of data from the Global Burden of Disease Study 2019

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The Lancet Planetary Health 2022年第7期6卷 e586-e600页

作者： Burkart, Katrin Causey, Kate Cohen, Aaron J Wozniak, Sarah S Salvi, Devashri Digvijay Abbafati, Cristiana Adekanmbi, Victor Adsuar, Jose C Ahmadi, Keivan Alahdab, Fares Al-Aly, Ziyad Alipour, Vahid Alvis-Guzman, Nelson Amegah, Adeladza Kofi Andrei, Catalina Liliana Andrei, Tudorel Ansari, Fereshteh Arabloo, Jalal Aremu, Olatunde Aripov, Timur Babaee, Ebrahim Banach, MacIej Barnett, Anthony Bärnighausen, Till Winfried Bedi, Neeraj Behzadifar, Masoud Béjot, Yannick Bennett, Derrick A Bensenor, Isabela M Bernstein, Robert S Bhattacharyya, Krittika Bijani, Ali Biondi, Antonio Bohlouli, Somayeh Breitner, Susanne Brenner, Hermann Butt, Zahid A Cámera, Luis Alberto Cantu-Brito, Carlos Carvalho, Felix Cerin, Ester Chattu, Vijay Kumar Chauhan, Bal Govind Choi, Jee-Young Jasmine Chu, Dinh-Toi Dai, Xiaochen Dandona, Lalit Dandona, Rakhi Daryani, Ahmad Davletov, Kairat De Courten, Barbora Demeke, Feleke Mekonnen Denova-Gutiérrez, Edgar Dharmaratne, Samath Dhamminda Dhimal, Meghnath Diaz, Daniel Djalalinia, Shirin Duncan, Bruce B El Sayed Zaki, Maysaa Eskandarieh, Sharareh Fareed, Mohammad Farzadfar, Farshad Fattahi, Nazir Fazlzadeh, Mehdi Fernandes, Eduarda Filip, Irina Fischer, Florian Foigt, Nataliya A Freitas, Marisa Ghashghaee, Ahmad Gill, Paramjit Singh Ginawi, Ibrahim Abdelmageed Gopalani, Sameer Vali Guo, Yuming Gupta, Rajat Das Habtewold, Tesfa Dejenie Hamadeh, Randah R Hamidi, Samer Hankey, Graeme J Hasanpoor, Edris Hassen, Hamid Yimam Hay, Simon I Heibati, Behzad Hole, Michael K Hossain, Naznin Househ, Mowafa Irvani, Seyed Sina Naghibi Jaafari, Jalil Jakovljevic, Mihajlo Jha, Ravi Prakash Jonas, Jost B Jozwiak, Jacek Jerzy Kasaeian, Amir Kaydi, Neda Khader, Yousef Saleh Khafaie, Morteza Abdullatif Khan, Ejaz Ahmad Khan, Junaid Khan, Md Nuruzzaman Khatab, Khaled Khater, Amir M Kim, Yun Jin Kimokoti, Ruth W Kisa, Adnan Kivimäki, Mika Knibbs, Luke D Kosen, Soewarta Koul, Parvaiz A Koyanagi, Ai Defo, Barthelemy Kuate Kugbey, Nuworza Lauriola, Paolo Lee, Paul H Leili, Mostafa Lewycka, Sonia Li, Shanshan Lim, Lee-Ling Li Institute for Health Metrics and Evaluation University of Washington Seattle WA United States Department of Health Metrics Sciences School of Medicine University of Washington Seattle WA United States Division of Plastic and Reconstructive Surgery University of Washington Seattle WA United States Research Insights and Innovation Seattle WA United States Health Effects Institute Boston MA United States School of Public Health Boston University Boston MA United States Department of Juridical and Economic Studies La Sapienza University Rome Italy Department of Population Medicine Cardiff University Cardiff United Kingdom Sport Science Department University of Extremadura Badajoz Spain School of Public Health Imperial College London London United Kingdom Department of Primary Care and Public Health Imperial College London London United Kingdom Mayo Evidence-based Practice Center Mayo Clinic Foundation for Medical Education and Research Rochester MN United States John T. Milliken Department of Internal Medicine Washington University in St. Louis St. Louis MO United States Clinical Epidemiology Center US Department of Veterans Affairs (VA) St Louis MO United States Health Management and Economics Research Center Iran University of Medical Sciences Tehran Iran Department of Health Economics Iran University of Medical Sciences Tehran Iran Preventive Medicine and Public Health Research Center Iran University of Medical Sciences Tehran Iran Pars Advanced and Minimally Invasive Medical Manners Research Center Iran University of Medical Sciences Tehran Iran Department of Community and Family Medicine Iran University of Medical Sciences Tehran Iran Research Group in Hospital Management and Health Policies Universidad de la Costa (University of the Coast) Barranquilla Colombia Research Group in Health Economics University of Cartagena Cartagena Colombia Department of Biomedical Science University of Cape Coast Cape Coast Ghana Cardiology Department Ca

Background: Experimental and epidemiological studies indicate an association between exposure to particulate matter (PM) air pollution and increased risk of type 2 diabetes. In view of the high and increasing prevalence of diabetes, we aimed to quantify the burden of type 2 diabetes attributable to PM_2·5 originating from ambient and household air pollution. Methods: We systematically compiled all relevant cohort and case-control studies assessing the effect of exposure to household and ambient fine particulate matter (PM_2·5) air pollution on type 2 diabetes incidence and mortality. We derived an exposure–response curve from the extracted relative risk estimates using the MR-BRT (meta-regression—Bayesian, regularised, trimmed) tool. The estimated curve was linked to ambient and household PM_2·5 exposures from the Global Burden of Diseases, Injuries, and Risk Factors Study 2019, and estimates of the attributable burden (population attributable fractions and rates per 100 000 population of deaths and disability-adjusted life-years) for 204 countries from 1990 to 2019 were calculated. We also assessed the role of changes in exposure, population size, age, and type 2 diabetes incidence in the observed trend in PM_2·5-attributable type 2 diabetes burden. All estimates are presented with 95% uncertainty intervals. Findings: In 2019, approximately a fifth of the global burden of type 2 diabetes was attributable to PM_2·5 exposure, with an estimated 3·78 (95% uncertainty interval 2·68–4·83) deaths per 100 000 population and 167 (117–223) disability-adjusted life-years (DALYs) per 100 000 population. Approximately 13·4% (9·49–17·5) of deaths and 13·6% (9·73–17·9) of DALYs due to type 2 diabetes were contributed by ambient PM2·5, and 6·50% (4·22–9·53) of deaths and 5·92% (3·81–8·64) of DALYs by household air pollution. High burdens, in terms of numbers as well as rates, were estimated in Asia, sub-Saharan Africa, and South Am

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