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3rd International Conference on Information Technology, InCIT 2018

作者： Boonyuen, Kitinan Kaewprapha, Phisan Srivihok, Patchanok Department of Electrical and Computer Engineering Thammasat University Rangsit Campus Pathum Thani Thailand PTT Public Company Limited Innovation Institute Wangnoi Ayuttaya13170 Thailand

ISBN: (纸本)9789741960323

The purpose of this paper is to investigate the capability of artificial intelligence by using convolutional neural networks (CNN), to forecast daily rainfall. The input of this model were the satellite images of the areas in Asia. The output of the model was daily rainfall prediction. Klong Yai rain station in Rayong province of Thailand was selected as our case study. We chose Inception-v3 model, which is an advance technique in convolutional neural networks. The model got very high accuracy on the ImageNet database which is the largest database of images. We helped the model to focus by reducing the size of the images and divided them into three different datasets. We used our 3 datasets to train the inception-v3 model by using 2 methods, the first method used transfer learning technique where we used a pre-trained model to train our dataset at the last fully connected layer. The second one was done from scratch where we trained all the layers of inception-v3. The training dataset consisted of satellite images of July, August and September 2017. The testing dataset had satellite images of October 2017. The result of forecasting revealed that the models were able to predict today rainfall, 1 day ahead rainfall, 2 days ahead rainfall and 3 days ahead rainfall successfully. © 2018 Mahasarakham University, Faculty of Informatics.

关键词： Rain

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Spectrum Sharing Between WLANs and Fixed Microwave Links in 6 and 13 GHz Bands: a Case Study

Spectrum Sharing Between WLANs and Fixed Microwave Links in ...

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IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks, DySPAN

作者： Nadia Yoza-Mitsuishi Ruoyu Sun Peter Mathys Technology Cybersecurity and Policy Program University of Colorado Boulder Boulder CO R&D Wireless CableLabs Louisville CO Department of Electrical and Computer Engineering University of Colorado Boulder Boulder CO

ISBN: (纸本)9781728123776

The 6 GHz band (5925-7125 MHz) has recently been proposed by the FCC to be shared with unlicensed devices This band is allocated to fixed, mobile (temporary fixed) and fixed satellite service links. The 13 GHz band (12700-13250 MHz) could also be subject to more flexible use, considering that it is allocated to the same type of incumbents. The purpose of this paper is to analyze coexistence between Wireless Local Area Networks (WLANs) and current terrestrial fixed links in these bands based on a case study approach, which can be replicated to other parts of the country. The coexistence analysis uses information from real links and accounts for the terrain morphology and actual land use to calculate the potential interference. We have conducted interference simulations from low-power indoor (LPI) WLANs to terrestrial fixed links and vice versa, from terrestrial fixed links to LPI WLANs, in the Denver metro area. The simulations have been performed in the upper part of the 6 GHz band, from 6875 to 7125 MHz, designated as the U-NII-8 band by the FCC, and in the 13 GHz band. To validate the simulations, we measured the incumbent signal power detected on the ground in a few locations in this area. We found that terrestrial fixed links operating in these bands can coexist with LPI WLANs without significant interference between each other, which will benefit consumers and encourage technology and innovation.

关键词： Interference FCC Wireless fidelity Databases Antennas Automobiles

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Comparison of Gait Speed Estimation of Multiple Sensor-Based Technologies

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Proceedings of the International Symposium on Human Factors and Ergonomics in Health Care 2019年第1期8卷 135 - 139页

作者： Plinio P. Morita Adson S. Rocha George Shaker Doojin Lee Jing Wei Brandon Fong Anjali Thatte Amir-Hossein Karimi Lin Lin Xu Avery Ma Alex Wong Jennifer Boger School of Public Health and Health Systems University of WaterlooCentre for Global eHealth Innovation Techna Institute University Health NetworkInstitute of Health Policy Management and Evaluation University of TorontoDepartment of Systems Design Engineering University of WaterlooResearch Institute for Aging University of WaterlooWaterloo Artificial Intelligence Institute University of Waterloo School of Public Health and Health Systems University of WaterlooGoiano Federal Institute Research Institute for Aging University of WaterlooDepartment of Electrical and Computer Engineering University of WaterlooDepartment of Mechanical and Mechatronics Engineering University of WaterlooWaterloo Artificial Intelligence Institute University of Waterloo Department of Mechanical and Mechatronics Engineering University of Waterloo Department of Systems Design Engineering University of Waterloo Department of Systems Design Engineering University of WaterlooResearch Institute for Aging University of WaterlooWaterloo Artificial Intelligence Institute University of Waterloo Department of Systems Design Engineering University of WaterlooResearch Institute for Aging University of Waterloo

In light of our aging population, there is an immediate need for non-obtrusive, continuous, and ubiquitous health monitoring technologies that will enable our population to age with a higher quality of life and independence. Research has demonstrated that gait indicators, such as walking speed, can reflect cognitive and physical functioning. However, gradual changes in such indicators usually go undetected until critical problems arise; being able to detect changes in indicators, such as gait deterioration, of older adults while in their home environments would enable clinicians to tailor more effective and personalized interventions by better understanding user behaviour in real-world settings. Real-world data is essential to enabling our healthcare system to act where patients most need help and to optimize the effect of designed eHealth solutions.

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Meta-learning techniques to analyze the raman data for optical diagnosis of oral cancer detection

Meta-learning techniques to analyze the raman data for optic...

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2019 IEEE International Conferences on Ubiquitous Computing and Communications and Data Science and Computational Intelligence and Smart Computing, Networking and Services, IUCC/DSCI/SmartCNS 2019

作者： Sharma, Mukta Sharma, Lokesh Jeng, Ming-Jer Chang, Liann-Be Huang, Shiang-Fu Wu, Shih-Lin Department of Electronic Engineering Chang Gung University Taoyuan333 Taiwan Department of Otolaryngology-Head and Neck Surgery Chang Gung Memorial Hospital Linkou244 Taiwan Department of Public Health Chang Gung University Taoyuan 333 Taiwan AI Innovation Research Center Chang Gung University Taoyuan333 Taiwan Department of Cardiology Chang Gung Memorial Hospital Taoyuan333 Taiwan Department of Electrical Engineering Ming Chi University of Technology New Taipei City24301 Taiwan Department of Computer Science and Information Engineering Chang Gung University Taoyuan333 Taiwan

ISBN: (纸本)9781728152097

Recently, Machine Learning methods have shown great improvement while analyzing the biomedical data. Raman Spectroscopy (RS), a non-invasive technique, and widely used in screening to diagnose the oral cancer. In order to spot cancer in a smarter and faster way, we have employed Meta-Learning (ML) techniques to learn such as Bagging and Boosting on RS data. Further, we employed normal and tumor tissue class classification by Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA) and Adaptive Boosting (AdaBoost) classifiers. The present study aims at examining the RS data with total 110 samples, including 57 tumor and 53 normal ones. To evaluate the performance, we have used the training samples to optimize, and testing samples to generalize the model parameters. The results show that the AdaBoost classifier with Bagging techniques showed the significant changes in accuracy. © 2019 IEEE.

关键词： Adaptive boosting

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Deep-learning-enabled Brain Hemodynamic Mapping Using Resting-state fMRI

arXiv

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arXiv 2022年

作者： Hou, Xirui Guo, Pengfei Wang, Puyang Liu, Peiying Lin, Doris D.M. Fan, Hongli Li, Yang Wei, Zhiliang Lin, Zixuan Jiang, Dengrong Jin, Jin Kelly, Catherine Pillai, Jay J. Huang, Judy Pinho, Marco C. Thomas, Binu P. Welch, Babu G. Park, Denise C. Patel, Vishal M. Hillis, Argye E. Lu, Hanzhang Department of Biomedical Engineering Johns Hopkins University School of Medicine 360 Park Building 600 N. Wolfe Street BaltimoreMD21287 United States The Russell H. Morgan Department of Radiology and Radiological Science Johns Hopkins University School of Medicine BaltimoreMD United States Department of Computer Science Johns Hopkins University BaltimoreMD United States Department of Electrical and Computer Engineering Johns Hopkins University BaltimoreMD United States Department of Diagnostic Radiology and Nuclear Medicine University of Maryland School of Medicine BaltimoreMD United States F.M. Kirby Research Center for Functional Brain Imaging Kennedy Krieger Institute BaltimoreMD United States Department of Biostatistics Johns Hopkins Bloomberg School of Public Health BaltimoreMD United States Department of Neurology Johns Hopkins University School of Medicine BaltimoreMD United States Department of Neurosurgery Johns Hopkins University School of Medicine BaltimoreMD United States Department of Radiology UT Southwestern Medical Center DallasTX United States Department of Neurologic Surgery UT Southwestern Medical Center DallasTX United States Center for Vital Longevity School of Behavioral and Brain Sciences University of Texas at Dallas DallasTX United States

Cerebrovascular disease is a leading cause of death globally. Prevention and early intervention are known to be the most effective forms of its management. Non-invasive imaging methods hold great promises for early stratification, but at present lack the sensitivity for personalized prognosis. Resting-state functional magnetic resonance imaging (rs-fMRI), a powerful tool previously used for mapping neural activity, is available in most hospitals. Here we show that rs-fMRI can be used to map cerebral hemodynamic function and delineate impairment. By exploiting time variations in breathing pattern during rs-fMRI, deep learning enables reproducible mapping of cerebrovascular reactivity (CVR) and bolus arrive time (BAT) of the human brain using resting-state CO2fluctuations as a natural "contrast media". The deep-learning network was trained with CVR and BAT maps obtained with a reference method of CO2-inhalation MRI, which included data from young and older healthy subjects and patients with Moyamoya disease and brain tumors. We demonstrate the performance of deep-learning cerebrovascular mapping in the detection of vascular abnormalities, evaluation of revascularization effects, and vascular alterations in normal aging. In addition, cerebrovascular maps obtained with the proposed method exhibited excellent reproducibility in both healthy volunteers and stroke patients. Deep-learning resting-state vascular imaging has the potential to become a useful tool in clinical cerebrovascular imaging. Copyright © 2022, The Authors. All rights reserved.

关键词： Mapping

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Institutional Origins of Protective COVID-19 public Health policy Responses: Informational and Authority Redundancies and policy Stringency

Journal of Political Institutions and Political Economy

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Journal of Political Institutions and Political Economy 2020年第4期1卷 585-613页

作者： Shvetsova, Olga Zhirnov, Andrei VanDusky-Allen, Julie Adeel, Abdul Basit Catalano, Michael Catalano, Olivia Giannelli, Frank Muftuoglu, Ezgi Riggs, Tara Sezgin, Mehmet Halit Tahir, Naveed Zhao, Tianyi Department of Political Science Binghamton University P.O. Box 6000 Binghamton 13902 NY United States Q-Step Centre Department of Politics University of Exeter Clayden Streatham Rise Exeter EX4 4PE United Kingdom School of Public Service Boise State University 1910 University Dr. Boise 83725 ID United States 3215 Stack Avenue Endicott 13760 NY United States School of Health Professions Rutgers The State University of New Jersey 675 Hoes Lane West 6th Floor Piscataway 08854 NJ United States Department of Electrical Engineering and Computer Science Syracuse University 111 College Place Syracuse 13210 NY United States

In this essay, we argue that institutional systems that allow redundancies in information channels and in policy-making are more likely to generate a rapid policy response to crises such as the onset of COVID-19 pandemic than more streamlined systems. Since democracies and decentralized polities feature higher informational and authority redundancies, we theorize improved crisis response in democracies, and in more decentralized democracies. To assess our theoretical expectations, we construct an original data set of stringency of policy measures that were adopted in response to COVID-19 by governments at different levels in 64 countries between January and May 2020. We find that democracies and liberal democracies responded to COVID-19 stronger and faster. Federalism and decentralization in addition to democratic institutions played a less uniform, but still a positive role. Beyond their other acknowledged merits, democratic institutions have superior capacity to mount a quick policy response to unqualified threats. © 2020 O. Shvetsova, et al.

关键词： COVID-19 democracy federalism public health policy system redundancies

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Scoping review of the potential health effects of exposure to extremely low-frequency electric and magnetic fields

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Critical Reviews in Biomedical engineering 2019年第4期47卷 323-347页

作者： Habash, Mara Gogna, Priyanka Krewski, Daniel Habash, Riadh Department of Public Health Sciences Queen’s University 62 Fifth Field Company Lane KingstonONK7L3N6 Canada McLaughlin Centre for Population Health Risk Assessment University of Ottawa 600 Peter Morand Crescent OttawaONK1G 5Z3 Canada School of Electrical Engineering and Computer Science University of Ottawa 800 King Edward Avenue OttawaONK1N 6N5 Canada

Previous studies suggest that extremely low-frequency (ELF) electric and magnetic fields (EMFs) may impact human health. However, epidemiologic studies have provided inconsistent results on the association between exposure to ELF EMFs and various health outcomes. This scoping review reports on primary investigations that were published during the ten-year period of 2007-2017 on the association between ELF EMFs and cancer, cardiovascular disease (CVD), reproductive health effects, and neurodegenerative diseases. We identified a total of 361 articles from two bibliographic databases (PubMed and EMBASE). Of these, 39 articles (19 cancer studies, two CVD studies, nine reproductive health studies, and ten neurodegenerative disease studies [with one repeated for two outcomes]) met inclusion criteria. Articles identified in this study focus on three different types of exposure: occupational (22 studies), residential (15 studies), and electric blanket (two studies). This review suggests that ELF EMFs may be associated with neurodegenerative diseases, specifically Alzheimer’s disease;however, limited evidence was found to suggest that ELF EMFs are associated with several types of cancer, CVD, and reproductive outcomes. Additional epidemiological studies in large study populations with improved exposure assessments are needed to clarify current inconclusive relationships. © 2019 by Begell House, Inc.

关键词： Neurodegenerative diseases

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RRNet: Relational reasoning network with parallel multi-scale attention for salient object detection in optical remote sensing images

arXiv

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arXiv 2021年

作者： Cong, Runmin Zhang, Yumo Fang, Leyuan Li, Jun Zhang, Chunjie Zhao, Yao Kwong, Sam Institute of Information Science Beijing Jiaotong University Beijing100044 China Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing100044 China Department of Computer Science City University of Hong Kong Hong Kong College of Electrical and Information Engineering Hunan University Changsha410082 China Peng Cheng Laboratory Shenzhen518000 China Guangdong Provincial Key Laboratory of Urbanization and Geosimulation School of Geography and Planning Sun Yat-sen University Guangzhou510275 China Key Laboratory of Information Perception and Systems for Public Security of MIIT Nanjing University of Science and Technology Nanjing210094 China Department of Computer Science City University of Hong Kong Hong Kong City University of Hong Kong Shenzhen Research Institute Shenzhen51800 China

Salient object detection (SOD) for optical remote sensing images (RSIs) aims at locating and extracting visually distinctive objects/regions from the optical RSIs. Despite some saliency models were proposed to solve the intrinsic problem of optical RSIs (such as complex background and scale-variant objects), the accuracy and completeness are still unsatisfactory. To this end, we propose a relational reasoning network with parallel multi-scale attention for SOD in optical RSIs in this paper. The relational reasoning module that integrates the spatial and the channel dimensions is designed to infer the semantic relationship by utilizing high-level encoder features, thereby promoting the generation of more complete detection results. The parallel multi-scale attention module is proposed to effectively restore the detail information and address the scale variation of salient objects by using the low-level features refined by multi-scale attention. Extensive experiments on two datasets demonstrate that our proposed RRNet outperforms the existing state-of-the-art SOD competitors both qualitatively and quantitatively. https://***/proj_***. © 2021, CC BY-NC-SA.

关键词： Object detection

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Assessing Trustworthy AI in Times of COVID-19: Deep Learning for Predicting a Multiregional Score Conveying the Degree of Lung Compromise in COVID-19 Patients

IEEE Transactions on Technology and Society

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IEEE Transactions on Technology and Society 2022年第4期3卷 272-289页

作者： Allahabadi, Himanshi Amann, Julia Balot, Isabelle Beretta, Andrea Binkley, Charles Bozenhard, Jonas Bruneault, Frederick Brusseau, James Candemir, Sema Cappellini, Luca Alessandro Chakraborty, Subrata Cherciu, Nicoleta Cociancig, Christina Coffee, Megan Ek, Irene Espinosa-Leal, Leonardo Farina, Davide Fieux-Castagnet, Genevieve Frauenfelder, Thomas Gallucci, Alessio Giuliani, Guya Golda, Adam Van Halem, Irmhild Hildt, Elisabeth Holm, Sune Kararigas, Georgios Krier, Sebastien A. Kuhne, Ulrich Lizzi, Francesca Madai, Vince I. Markus, Aniek F. Masis, Serg Mathez, Emilie Wiinblad Mureddu, Francesco Neri, Emanuele Osika, Walter Ozols, Matiss Panigutti, Cecilia Parent, Brendan Pratesi, Francesca Moreno-Sanchez, Pedro A. Sartor, Giovanni Savardi, Mattia Signoroni, Alberto Sormunen, Hanna-Maria Spezzatti, Andy Srivastava, Adarsh Stephansen, Annette F. Theng, Lau Bee Tithi, Jesmin Jahan Tuominen, Jarno Umbrello, Steven Vaccher, Filippo Vetter, Dennis Westerlund, Magnus Wurth, Renee Zicari, Roberto V. Ey Netherlands Enterprise Intelligence Department Amsterdam1083 HP Netherlands Eth Zurich Health Ethics and Policy Lab Department of Health Sciences and Technology Zürich8092 Switzerland Center for Diplomatic and Strategic Studies Postgraduate Studies in Diplomacy and International Relations Paris75015 France Pisa56124 Italy Hackensack Meridian Health Bioethics Center EdisonNJ08820 United States University of Oxford Faculty of Philosophy OxfordOX2 6GG United Kingdom Collège André- Laurendeau Philosophie Department MontrealQCH8N 2J4 Canada Université du Québec À Montréal École des Médias MontrealQCH2L 2C4 Canada Pace University Philosophy Department New YorkNY10038 United States The Ohio State University Wexner Medical Center Department of Radiology ColumbusOH43210 United States Humanitas Research Hospital Department of Radiology Milan20089 Italy Humanitas University Department of Biomedical Sciences Milan20089 Italy University of New England Faculty of Science Agriculture Business and Law ArmidaleNSW2351 Australia University of Technology Sydney Faculty of Engineering and Information Technology SydneyNSW2007 Australia Scuola Superiore Sant'Anna European Centre of Excellence on the Regulation of Robotics and Ai Pisa56127 Italy University of Bremen Group of Computer Architecture Bremen28359 Germany New York University Grossman School of Medicine Division of Infectious Diseases and Immunology Department of Medicine New YorkNY10016 United States Digital Institute Ai Research Section Stockholm16731 Sweden Arcada University of Applied Sciences Department of Business Management and Analytics Helsinki00550 Finland University of Brescia Radiological Sciences and Public Health Department of Medical and Surgical Specialties Brescia25121 Italy Sncf Reseau Sa Ethique Groupe La Plaine93418 France Institute of Diagnostic and Interventional Radiology University Hospital Zurich Zürich8091 Switzerland Eindhoven University of Tech

This article's main contributions are twofold: 1) to demonstrate how to apply the general European Union's High-Level Expert Group's (EU HLEG) guidelines for trustworthy AI in practice for the domain of healthcare and 2) to investigate the research question of what does 'trustworthy AI' mean at the time of the COVID-19 pandemic. To this end, we present the results of a post-hoc self-assessment to evaluate the trustworthiness of an AI system for predicting a multiregional score conveying the degree of lung compromise in COVID-19 patients, developed and verified by an interdisciplinary team with members from academia, public hospitals, and industry in time of pandemic. The AI system aims to help radiologists to estimate and communicate the severity of damage in a patient's lung from Chest X-rays. It has been experimentally deployed in the radiology department of the ASST Spedali Civili clinic in Brescia, Italy, since December 2020 during pandemic time. The methodology we have applied for our post-hoc assessment, called Z-Inspection®, uses sociotechnical scenarios to identify ethical, technical, and domain-specific issues in the use of the AI system in the context of the pandemic. © 2020 IEEE.

关键词： COVID-19

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Ultra-Low Power Subthreshold Quasi Floating Gate CMOS Logic Family for Energy Harvesting 28

Ultra-Low Power Subthreshold Quasi Floating Gate CMOS Logic ...

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28th IEEE International Symposium on Power and Timing Modeling, Optimization and Simulation, PATMOS 2018

作者： Garde, M. Pilar Lopez-Martin, Antonio Orradre, Daniel Ramírez-Angulo, Jaime Institute of Smart Cities Public University of Navarra Pamplona Spain Klipsch School of Electrical and Computer Engineering New Mexico State University Las CrucesNM United States

ISBN: (纸本)9781538663653

Subthreshold digital circuits are suitable for application niches where high performance is not required, but extremely low power consumption is a must. One of the most relevant applications of this kind nowadays is energy harvesting, where self-powered devices experience severe energy constraints. In this paper, a subthreshold CMOS logic family is proposed based on the Quasi-Floating Gate transistor, aimed to the design of different subcircuits required in energy harvesting. Measurement results of a test chip prototype confirm the feasibility of this approach. © 2018 IEEE.

关键词： Energy harvesting

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