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arXiv 2022年

作者： Satapathy, Sitakanta Liu, Bin Deshmukh, Prathmesh Molinaro, Paul M. Dirnberger, Florian Khatoniar, Mandeep Koder, Ronald L. Menon, Vinod M. Department of Physics Center for Discovery and Innovation The City College of New York 85 St. Nicholas Terrace New YorkNY10031 United States The PhD Program in Physics The Graduate Center of the City University of New York 365 5th Ave New YorkNY10016 United States Department of Electrical Engineering and Computer Science University of Michigan Ann ArborMI48109 United States

Fluorescent proteins (FPs) have recently emerged as a serious contender for realizing ultra-low threshold room temperature exciton-polariton condensation and lasing. Our contribution investigates the thermalization of FP microcavity exciton-polaritons upon optical pumping under ambient conditions. We realize polariton cooling using a new FP molecule, called mScarlet, coupled strongly to the optical modes in a Fabry-Pérot cavity. Interestingly, at the threshold excitation energy (fluence) of ~9 nJ/pulse (15.6 mJ/cm2), we observe an effective temperature, Teff ~350 ± 35 K close to the lattice temperature indicative of strongly thermalized exciton-polaritons at equilibrium. This efficient thermalization results from the interplay of radiative pumping facilitated by the energetics of the lower polariton branch and the cavity Qfactor. Direct evidence for dramatic switching from an equilibrium state into a metastable state is observed for the organic cavity polariton device at room temperature via deviation from the Maxwell-Boltzmann statistics at k1 = 0 above the threshold. Thermalized polariton gases in organic systems at equilibrium hold substantial promise for designing room temperature polaritonic circuits, switches, and lattices for analog simulation. © 2022, CC BY-NC-SA.

关键词： Polariton

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Impacts of Distributed Generation Integration and Electric Vehicles in Distribution Networks

Impacts of Distributed Generation Integration and Electric V...

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2019 IEEE PES Conference on Innovative Smart Grid Technologies, ISGT Latin America 2019

作者： Da Veiga, Matheus F. Darui, Caroline B. F. Lucca, Tiago G. Abaide, Alzenira Da R. Wust, Lucas L. Da Cruz, Matheus S. Neto, Nelson K. Figueiró, Iuti Castro Federal University of Santa Maria Graduate Program in Electrical Engineering Santa Maria Brazil Integrated Regional University of Alto Uruguay and Missions Departament of Engineering and Computer Science Santo Ângelo Brazil

ISBN: (纸本)9781538695678

The growing increase in energy consumption and the concern for the environment have driven research and use of renewable sources of energy. Within the concept of the use of renewable energy sources, the integration of Distributed Generation (DG) systems have grown. On the other hand, a subject that has gained prominence is the use of Electric Vehicles (EVs), which in response to the eminent shortage of fossil fuels. The integration of DG systems and the EVs charging has led to several discussions about the impacts that the use of these systems will bring to the low voltage distribution networks, influencing the behavior of the network. This work has the objective of analyzing the impacts caused by the integration of DG and VEs into the Distribution System (DS). Simulations were performed using OpenDSS software, which is software that was developed for the analysis of power flow in distribution systems. © 2019 IEEE.

关键词： Distributed power generation

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Network externalities in cybersecurity information sharing ecosystems 1

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15th International Conference on the Economics of Grids, Clouds, Systems, and Services, GECON 2018

作者： Rashid, Zahid Noor, Umara Altmann, Jörn Technology Management Economics and Policy Program College of Engineering Seoul National University Seoul Korea Republic of School of Electrical Engineering and Computer Sciences National University of Sciences and Technology Islamabad Pakistan

ISBN: (数字)9783030133429

ISBN: (纸本)9783030133412

The utilization of cybersecurity information for improving security posture of an organization resulted in the evolution of cybersecurity information sharing ecosystems. In this study, we consider three stakeholders i.e. cybersecurity solution providers, information providers, and end users, who have different values. Their values depend on interrelationship among them and are also based on several value parameters. We identified six value parameters and analyzed their impacts on the values of stakeholders. A simulation model has been developed using system dynamics to analyze the impact of value parameters on the values of stakeholders. The results show that end users are the main source of value in cybersecurity information sharing ecosystems, implying an effect of demand side economies of scale. The cybersecurity solution and information providers are majorly benefiting from a growing number of end users. The value of end users is mainly affected by quality of services, quality of information and the size of trusted communities. © 2019, Springer Nature Switzerland AG.

关键词： Ecosystems

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Domain Adversarial Neural Networks for Domain Generalization: When It Works and How to Improve

arXiv

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arXiv 2021年

作者： Sicilia, Anthony Zhao, Xingchen Hwang, Seong Jae Intelligent Systems Program University of Pittsburgh 210 S. Bouquet Street PittsburghPA15260 United States Department of Electrical & Computer Engineering Northeastern University 360 Huntington Ave BostonMA02115 United States Department of Artificial Intelligence Yonsei University Korea Republic of

Theoretically, domain adaptation is a well-researched problem. Further, this theory has been well-used in practice. In particular, we note the bound on target error given by Ben-David et al. (2010) and the well-known domain-aligning algorithm based on this work using Domain Adversarial Neural Networks (DANN) presented by Ganin and Lempitsky (2015). Recently, multiple variants of DANN have been proposed for the related problem of domain generalization, but without much discussion of the original motivating bound. In this paper, we investigate the validity of DANN in domain generalization from this perspective. We investigate conditions under which application of DANN makes sense and further consider DANN as a dynamic process during training. Our investigation suggests that the application of DANN to domain generalization may not be as straightforward as it seems. To address this, we design an algorithmic extension to DANN in the domain generalization case. Our experimentation validates both theory and algorithm. © 2021, CC BY.

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Tools and Strategy for Distance Learning to Respond COVID-19 Pandemic in Indonesia

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Ingenierie des Systemes d'Information 2020年第3期25卷 383-390页

作者： Utomo, Muhammad Nur Yasir Sudaryanto, Memet Saddhono, Kundharu Computer and Network Engineering Study Program Department of Electrical Engineering Politeknik Negeri Ujung Pandang Makassar90245 Indonesia Faculty of Teacher and Training Graduate School of Javanese Language and Literature Universitas Sebelas Maret Surakarta57126 Indonesia

Distance learning is a learning process that allows students and instructors are in different physical locations. During the COVID-19 pandemic, various countries including Indonesia chose the distance learning option to avoid the spread of the pandemic. However, the implementation of distance learning is not without problems. In Indonesia, the problems to implement distance learning are enormous both in terms of equitable distribution of infrastructure and costs for accessing the internet. To overcome these problems, research about best distance learning tools and its implementation strategy is really needed. Therefore, this research aimed to analyze the strategies and tools for implementing distance learning that reliable and cost-effective for the student at the same time. Based on research that has been conducted by analyzing questionnaire surveys of 256 students in Indonesia, this research recommends tools combination strategy consists of Google Classroom tool as a learning app, Whatsapp group as broadcast messaging and Zoom as a video conference media for distance learning. This research also recommended to maximize the e-learning/learning app features for theoretical lessons and consider video conference just in practical lessons so that the distance learning costs become cheaper for students. © 2020 International Information and engineering Technology Association. All rights reserved.

关键词： Students

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Light polarization interaction with skin conditions assessed by polarization sensitive optical coherence tomography

Light polarization interaction with skin conditions assessed...

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Novel Techniques in Microscopy, NTM 2021 - Part of Biophotonics Congress: Optics in the Life Sciences 2021

作者： Zhou, Xin Louie, Daniel C. Maloufia, Sina Tchvialeva, Lioudmila Lee, Tim K. Tang, Shuo Department of Electrical and Computer Engineering University of British Columbia VancouverBCV6T 1Z4 Canada School of Biomedical Engineering University of British Columbia VancouverBCV6T 1Z4 Canada Department of Dermatology and Skin Science Photomedicine Institute University of British Columbia Vancouver Coastal Health Research Institute VancouverBCV5Z 4E8 Canada Cancer Control Research Program BC Cancer VancouverBCV5Z 1L3 Canada

Polarization sensitive optical coherence tomography (PS-OCT) is utilized to assess skin characterizations, including layered tissue structures, light depolarization, and tissue birefringence. The multiple contrasts fr... 详细信息

ISBN: (纸本)9781557528209

关键词： Optical tomography

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Bio-inspired Butterfly-shaped Patch Antenna Array for 2.4 GHz WLAN Applications

Bio-inspired Butterfly-shaped Patch Antenna Array for 2.4 GH...

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Antennas and Propagation Society International Symposium

作者： M. A. Oliveira S. M. A. Morais J. I. L. Araujo G. K. F. Serres A. J. R. Serres P. F. Silva P. H. F. Silva A. G. D'Assunção Graduation Program in Electrical Engineering Federal University of Campina Grande Campina Grande Brazil Graduation Program in Computer Engineering and Systems State University of Maranhão São Luís Brazil Graduation Program in Electrical Engineering Federal Institute of Education Science and Technology of Paraíba João Pessoa Brazil Federal University of Rio Grande do Norte Natal Brazil

ISBN: (数字)9781728166704

ISBN: (纸本)9781728166711

The design of a bio-inspired butterfly-shaped patch antenna array, generated by polar transformations, covering the wireless local area network (WLAN) band at 2.4 GHz (2.400-2.483 GHz), with similar and dissimilar elements is presented in this paper. The dissimilar bio-inspired antenna presented a bandwidth of 96MHz covering the IEEE802.11b, g band, maximum gain of 8.64dBi, concentrated half power beamwidth of 52 degrees and front-to-back ratio of 15.21dB.

关键词： Wireless LAN Protocols Shape Patch antennas Meetings Bandwidth Antenna arrays

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Performance limits due to thermal transport in graphene single-photon bolometers

arXiv

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arXiv 2023年

作者： Fried, Caleb Jordan Russell, B. Arnault, Ethan G. Huang, Bevin Lee, Gil-Ho Englund, Dirk Henriksen, Erik A. Fong, Kin Chung Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA02139 United States Department of Earth and Planetary Sciences Harvard University CambridgeMA02138 United States Department of Physics Harvard University CambridgeMA02138 United States Raytheon BBN Quantum Engineering and Computing Group CambridgeMA02138 United States Department of Physics Washington University in St. Louis St. LouisMO United States Intelligence Community Postdoctoral Research Fellowship Program Massachusetts Institute of Technology CambridgeMA02139 United States Department of Physics Pohang University of Science and Technology Pohang790-784 Korea Republic of

In high-sensitivity bolometers and calorimeters, the photon absorption often occurs at a finite distance from the temperature sensor to accommodate antennas or avoid the degradation of superconducting circuitry exposed to radiation. As a result, thermal propagation from the input to the temperature readout can critically affect detector performance. In this report we model the performance of a graphene bolometer, accounting for electronic thermal diffusion and dissipation via electron-phonon coupling at low temperatures in three regimes: clean, supercollision, and resonant scattering. Our results affirm the feasibility of a superconducting readout without Cooper-pair breaking by mid- and near-infrared photons, and provide a recipe for designing graphene absorbers for calorimetric single-photon detectors. We investigate the tradeoff between the input-readout distance and detector efficiency, and predict an intrinsic timing jitter of ∼2.7 ps. Based on our result, we propose a spatial-mode-resolving photon detector to increase communication bandwidth. Copyright © 2023, The Authors. All rights reserved.

关键词： Graphene

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Deep inference of latent dynamics with spatio-temporal super-resolution using selective backpropagation through time

arXiv

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arXiv 2021年

作者： Zhu, Feng Sedler, Andrew R. Grier, Harrison A. Ahad, Nauman Davenport, Mark A. Kaufman, Matthew T. Giovannucci, Andrea Pandarinath, Chethan Neuroscience Graduate Program Emory University Center for Machine Learning Georgia Tech Computational Neuroscience Graduate Program The University of Chicago School of Electrical and Computer Engineering Georgia Tech Dept. of Organismal Biology and Anatomy The University of Chicago Neuroscience Institute The University of Chicago Joint Dept. of Biomedical Engineering UNC Chapel Hill NC State University UNC Chapel Hill Neuroscience Center Coulter Dept. of Biomedical Engineering Emory University Georgia Tech Dept. of Neurosurgery Emory University

Modern neural interfaces allow access to the activity of up to a million neurons within brain circuits. However, bandwidth limits often create a trade-off between greater spatial sampling (more channels or pixels) and the temporal frequency of sampling. Here we demonstrate that it is possible to obtain spatio-temporal super-resolution in neuronal time series by exploiting relationships among neurons, embedded in latent low-dimensional population dynamics. Our novel neural network training strategy, selective backpropagation through time (SBTT), enables learning of deep generative models of latent dynamics from data in which the set of observed variables changes at each time step. The resulting models are able to infer activity for missing samples by combining observations with learned latent dynamics. We test SBTT applied to sequential autoencoders and demonstrate more efficient and higher-fidelity characterization of neural population dynamics in electrophysiological and calcium imaging data. In electrophysiology, SBTT enables accurate inference of neuronal population dynamics with lower interface bandwidths, providing an avenue to significant power savings for implanted neuroelectronic interfaces. In applications to two-photon calcium imaging, SBTT accurately uncovers high-frequency temporal structure underlying neural population activity, substantially outperforming the current state-of-the-art. Finally, we demonstrate that performance could be further improved by using limited, high-bandwidth sampling to pretrain dynamics models, and then using SBTT to adapt these models for sparsely-sampled data. © 2021, CC BY-NC-SA.

关键词： Neurons

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Decision Making using Internet of Things and Machine Learning: A bibliometric approach to tracking main research themes

Decision Making using Internet of Things and Machine Learnin...

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2020 International Conference on Data Analytics for Business and Industry: Way Towards a Sustainable Economy, ICDABI 2020

作者： Gamboa-Rosales, N.K. Castorena-Robles, A. Casas-Valadez, M.A. Cobo, M.J. Castaneda-Miranda, R. Lopez-Robles, J.R. CONACYT-Autonomous University of Zacatecas Academic Unit of Electrical Engineering Zacatecas Mexico Monterrey Institute of Technology and Higher Education Biotechnology Engineering Queretaro Mexico Autonomous University of Zacatecas Academic Unit of Accounting and Administration Zacatecas Mexico University of Cadiz Department of Computer Science and Engineering Cadiz Spain Autonomous University of Zacatecas Post. Program of Eng. and Applied Tech. National Lab. CONACYT-SEDEAM Zacatecas Mexico

ISBN: (纸本)9781728196756

The internet has become an integral part of our life, we connect daily for informational, social, entertainment and even work and economic purposes. Therefore, it is completely normal to connect to the internet;But it is not only people, also these everyday objects or things in our environment who connect to the network to take advantage of its benefits. This next stage in the evolution of the internet, in which connectivity extends to the objects which surround us, is known as the Internet of Things. Sensors, Machines, Devices, Wearables are part of the Internet of Things and these can be identified to be recognized and interact without the need for a human being. With the Internet of Things, the connection extends to any type of object. All these interconnected devices and people produce an immense amount of data, when analyzed, they become a tool that allows organizations to make wise decisions in real time by having information on various market situations and ensuring a better understanding of behavior and consumer preferences. One of the disciplines of Artificial Intelligence responsible for analyzing this large amount of data is Machine Learning, whose sole purpose is to improve the Decision-Making process. To understand the relationship between Internet of Things, application of Machine Learning and the Decision Making process, a bibliometric analysis is carried out from 2013 to 2020 to know its evolution, trends, research areas, authors and other aspects using Scopus. © 2020 IEEE.

关键词： Decision making

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