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IEEE International Conference on electronics and Nanotechnology (ELNANO)

作者： Vladimir Chumakov Alla Taranchuk Victor Stetsiuk Victor Michan Department of Engineering of Embedded Control System Kharkiv National University of Radio Electronics Kharkiv Ukraine Department of Telecommunications and Computer-Integrated Technologies Khmelnitsky National University Khmelnitsky Ukraine

ISBN: (纸本)9781538663844

The technology of bactericidal and sterilizing processing of Koch's Bacillus on the basis of pulsed electromagnetic ultraviolet (UV) radiation has been proposed. This technology is based on the pulse method of bactericidal processing of objects with low-temperature plasma using pulse sterilizer developed by the authors. The mathematical model of generating broadband pulsed radiation by the source of powerful optical radiation, based on the technique of charged particles accelerator, has been developed. The structure of the electromagnetic radiation source of the high-intensity optical range has been presented. The research of the bactericidal effect of pulsed UV radiation on Koch's bacillus has been conducted.

关键词： Discharges (electric) Microorganisms Plasmas Conferences Nanotechnology Electromagnetic radiation Optical pulses

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The 2021 report of the Lancet Countdown on health and climate change: code red for a healthy future

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The Lancet 2021年第10311期398卷 1619-1662页

作者： Romanello, Marina McGushin, Alice Di Napoli, Claudia Drummond, Paul Hughes, Nick Jamart, Louis Kennard, Harry Lampard, Pete Solano Rodriguez, Baltazar Arnell, Nigel Ayeb-Karlsson, Sonja Belesova, Kristine Cai, Wenjia Campbell-Lendrum, Diarmid Capstick, Stuart Chambers, Jonathan Chu, Lingzhi Ciampi, Luisa Dalin, Carole Dasandi, Niheer Dasgupta, Shouro Davies, Michael Dominguez-Salas, Paula Dubrow, Robert Ebi, Kristie L Eckelman, Matthew Ekins, Paul Escobar, Luis E Georgeson, Lucien Grace, Delia Graham, Hilary Gunther, Samuel H Hartinger, Stella He, Kehan Heaviside, Clare Hess, Jeremy Hsu, Shih-Che Jankin, Slava Jimenez, Marcia P Kelman, Ilan Kiesewetter, Gregor Kinney, Patrick L Kjellstrom, Tord Kniveton, Dominic Lee, Jason K W Lemke, Bruno Liu, Yang Liu, Zhao Lott, Melissa Lowe, Rachel Martinez-Urtaza, Jaime Maslin, Mark McAllister, Lucy McMichael, Celia Mi, Zhifu Milner, James Minor, Kelton Mohajeri, Nahid Moradi-Lakeh, Maziar Morrissey, Karyn Munzert, Simon Murray, Kris A Neville, Tara Nilsson, Maria Obradovich, Nick Sewe, Maquins Odhiambo Oreszczyn, Tadj Otto, Matthias Owfi, Fereidoon Pearman, Olivia Pencheon, David Rabbaniha, Mahnaz Robinson, Elizabeth Rocklöv, Joacim Salas, Renee N Semenza, Jan C Sherman, Jodi Shi, Liuhua Springmann, Marco Tabatabaei, Meisam Taylor, Jonathon Trinanes, Joaquin Shumake-Guillemot, Joy Vu, Bryan Wagner, Fabian Wilkinson, Paul Winning, Matthew Yglesias, Marisol Zhang, Shihui Gong, Peng Montgomery, Hugh Costello, Anthony Hamilton, Ian Institute for Global Health University College London London UK United Kingdom Institute for Sustainable Resources University College London London UK United Kingdom UCL Energy Institute University College London London UK United Kingdom Institute for Environmental Design and Engineering University College London London UK United Kingdom Department of Geography University College London London UK United Kingdom The Bartlett School of Sustainable Construction University College London London UK United Kingdom Centre for Human Health and Performance University College London London UK United Kingdom School of Agriculture Policy and Development University of Reading Reading United Kingdom Department of Meteorology University of Reading Reading United Kingdom The Walker Institute University of Reading Reading United Kingdom Department of Health Sciences University of York York United Kingdom Institute for Environment and Human Security United Nations University Bonn Germany Centre on Climate Change and Planetary Health London School of Hygiene & Tropical Medicine London UK United Kingdom Centre for Mathematical Modelling of Infectious Diseases London School of Hygiene & Tropical Medicine London UK United Kingdom Department of Public Health Environments and Society London School of Hygiene & Tropical Medicine London UK United Kingdom Department of Earth System Science Tsinghua University Beijing China Department of Environment Climate Change and Health World Health Organization Geneva Switzerland Institute for Environmental Sciences World Health Organization Geneva Switzerland Centre for Climate Change and Social Transformations School of Psychology Cardiff University Cardiff United Kingdom Yale Center on Climate Change and Health Yale University New Haven CT United States School of Government University of Birmingham Birmingham United Kingdom Economic analysis of Climate Impacts and Policy Centro Euro-Mediterraneo sui Cambiamenti Climatic

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Preface

Advances in Intelligent Systems and Computing

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Advances in Intelligent systems and Computing 2019年 873卷 v-vi页

作者： Ören, Tuncer De Rango, Floriano School of Electrical Engineering and Computer Science University of Ottawa OttawaON Canada Department of Informatics Electronics and System Engineering University of Calabria Modeling Rende Italy

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Swarm robotics in wireless distributed protocol design for coordinating robots involved in cooperative tasks

arXiv

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arXiv 2018年

作者： De Rango, Floriano Palmieri, Nunzia Yang, Xin-She Marano, Salvatore Dept. Computer Engineering Modeling Electronics and System Science University of Calabria Italy Department of Design Engineering and Mathematics School of Science and Technology Middlesex University London United Kingdom

The mine detection in an unexplored area is an optimization problem where multiple mines, randomly distributed throughout an area, need to be discovered and disarmed in a minimum amount of time. We propose a strategy to explore an unknown area, using a stigmergy approach based on ants behavior, and a novel swarm based protocol to recruit and coordinate robots for disarming the mines cooperatively. Simulation tests are presented to show the effectiveness of our proposed Ant-based Task Robot Coordination (ATRC) with only the exploration task and with both exploration and recruiting strategies. Multiple minimization objectives have been considered: the robots' recruiting time and the overall area exploration time. We discuss, through simulation, different cases under different network and field conditions, performed by the robots. The results have shown that the proposed decentralized approaches enable the swarm of robots to perform cooperative tasks intelligently without any central control.90C30, 68T40 Copyright © 2018, The Authors. All rights reserved.

关键词： Swarm intelligence

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Bubble Passage: Underwater Superaerophobicity/Superaerophilicity and Unidirectional Bubble Passage Based on the Femtosecond Laser‐Structured Stainless Steel Mesh (Adv. Mater. Interfaces 14/2020)

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Advanced Materials Interfaces 2020年第14期7卷

作者： Jinglan Huo Qing Yang Jiale Yong Peixun Fan Yong feng Lu Xun Hou Feng Chen State Key Laboratory for Manufacturing System Engineering and Shaanxi Key Laboratory of Photonics Technology for Information School of Electronics & Information Engineering Xi'an Jiaotong University Xi'an 710049 P. R. China School of Mechanical Engineering Xi'an Jiaotong University Xi'an 710049 P. R. China Department of Electrical and Computer Engineering University of Nebraska‐Lincoln Lincoln NE 68588 USA

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Author Correction: Wireless multilateral devices for optogenetic studies of individual and social behaviors

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Nature neuroscience 2022年第10期25卷 1394页

作者： Yiyuan Yang Mingzheng Wu Abraham Vázquez-Guardado Amy J Wegener Jose G Grajales-Reyes Yujun Deng Taoyi Wang Raudel Avila Justin A Moreno Samuel Minkowicz Vasin Dumrongprechachan Jungyup Lee Shuangyang Zhang Alex A Legaria Yuhang Ma Sunita Mehta Daniel Franklin Layne Hartman Wubin Bai Mengdi Han Hangbo Zhao Wei Lu Yongjoon Yu Xing Sheng Anthony Banks Xinge Yu Zoe R Donaldson Robert W Gereau Cameron H Good Zhaoqian Xie Yonggang Huang Yevgenia Kozorovitskiy John A Rogers Department of Mechanical Engineering Northwestern University Evanston IL USA. Department of Neurobiology Northwestern University Evanston IL USA. Center for Bio-Integrated Electronics Northwestern University Evanston IL USA. US Army Research Laboratory Aberdeen Proving Ground MD USA. US Army Medical Research Institute of Chemical Defense Aberdeen Proving Ground MD USA. Washington University Pain Center and Department of Anesthesiology Washington University St. Louis MO USA. State Key Laboratory of Mechanical System and Vibration Shanghai Jiao Tong University Shanghai China. Department of Civil and Environmental Engineering Northwestern University Evanston IL USA. Department of Materials Science and Engineering Northwestern University Evanston IL USA. Department of Physics Tsinghua University Beijing China. SURVICE Engineering Belcamp MD USA. Chemistry of Life Processes Institutes Northwestern University Evanston IL USA. Neurolux Inc Evanston IL USA. School of Civil Engineering Southwest JiaoTong University Chengdu China. School of Chemical Engineering and Technology Tianjin University Tianjin People's Republic of China. CSIR-Central Scientific Instruments Organization Ministry of Science & Technology Sector 30-C Chandigarh India. Department of Biomedical Engineering Northwestern University Evanston IL USA. Department of Electronic Engineering Tsinghua University Beijing China. Simpson Querrey Institute & Feinberg Medical School Northwestern University Evanston IL USA. Department of Biomedical Engineering City University of Hong Kong Kowloong Tong Hong Kong. Psychology and Neuroscience Molecular Cellular and Developmental Biology University of Colorado Boulder Boulder CO USA. State Key Laboratory of Structural Analysis for Industrial Equipment Department of Engineering Mechanics Dalian University of Technology Dalian P.R. China. zxie@***. Department of Mechanical Engineering Northwestern University Evanston IL USA. y-huang@northwest

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Automatic crack classification by exploiting statistical event descriptors for Deep Learning

arXiv

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arXiv 2019年

作者： Siracusano, Giulio Garescì, Francesca Finocchio, Giovanni Tomasello, Riccardo Lamonaca, Francesco Scuro, Carmelo Carpentieri, Mario Chiappini, Massimo La Corte, Aurelio Department of Electric Electronic and Computer Engineering University of Catania Viale Andrea Doria 6 CataniaI-95125 Italy Department of Engineering University of Messina C. di Dio S. Agata MessinaI-98166 Italy Department of Mathematical and Computer Sciences Physical Sciences and Earth Sciences University of Messina C. di Dio S. Agata MessinaI- 98166 Italy Via di Vigna Murata 605 RomaI-00143 Italy Department of Electrical and Information Engineering Politecnico di Bari via E. Orabona 4 BariI-70125 Italy Department of Informatics Modeling Electronics and System Engineering University of Calabria Via P. Bucci CS RendeI-87036 Italy Department of Physics University of Calabria Via P. Bucci CS RendeI-87036 Italy

In modern building infrastructures, the chance to devise adaptive and unsupervised data-driven health monitoring systems is gaining in popularity due to the large availability of big data from low-cost sensors with communication capabilities and advanced modeling tools such as Deep Learning. The main purpose of this paper is to combine deep neural networks with Bidirectional Long Short Term Memory and advanced statistical analysis involving Instantaneous Frequency and Spectral Kurtosis to develop an accurate classification tool for tensile, shear and mixed modes originated from acoustic emission events (cracks). We investigated on effective event descriptors to capture the unique characteristics from the different types of modes. Tests on experimental results confirm that this method achieves promising classification among different crack events and can impact on the design of future on structural health monitoring (SHM) technologies. This approach is effective to classify incipient damages with 92% of accuracy, which is advantageous to plan maintenance. Copyright © 2019, The Authors. All rights reserved.

关键词： Structural health monitoring

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Application of quaternion neural network to EMG-based estimation of forearm motion

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2017 International Conference on Artificial Intelligence, ICAI 2017 at 2017 World Congress in computer Science, computer engineering and Applied Computing, CSCE 2017

作者： Firdaus, H. Ogawa, T. Mechanical and Electronic Systems Engineering Course Takushoku University Tokyo Japan Department of Electronics and Computer System Takushoku University Tokyo Japan

ISBN: (纸本)9781601324603

This paper presents the Application of Quaternion Neural Network (QNN) to Electromyography (EMG) based Estimation of Forearm Motion. Motion of human body can be modeled as a set of rotations in three-dimensional space by various joints. The aim of this research is to show the efficiency of QNN in estimation from EMG. We are trying to learn, estimate and simulate combined motion using QNN as well as comparing the estimation efficiency with the feedforward neural network (FNN). We are measuring EMG signal of target muscles while performing basic forearm motion. The efficiency of the estimation then determines by simulation with combine motion of forearm. It is expected in better performance of estimation in QNN compare to FNN. CSREA Press ©.

关键词： Efficiency

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Palm-dorsa vein image enhancement algorithm using webcam for biometric system

引用

AIP Conference Proceedings 2019年第1期2129卷

作者： Suhaimi Yusoff Abdul Rahman Ramli Izanoordina Ahmad Zulhilmi Mustafa 1Electronics Technology Department Universiti Kuala Lumpur British Malaysian Institute Malaysia 2Department of Computer and Communication System Engineering Universiti Putra Malaysia Malaysia

In this article, an algorithm for palm-dorsa vein image enhancement is proposed. Instead of using expensive high-quality vein image capture device, a low-cost webcam was used to capture the vein image. The webcam has been modified to make it possible to capture the vein image of palm-dorsa. In this research, the algorithm to enhance the low-quality vein image, remove the noise and finally detect the palm-dorsa vein pattern has been developed. First, the vein image is applied with image normalization technique to obtain standard image. Then, the Gaussian filter is used to remove the noise in the vein image. Next, Difference of Gaussian and threshold techniques are used to segment the vein image. Median filter is used to remove and smooth the noise introduced from the previous image segmentation process. Finally, thinning technique is used to get single line palm-dorsa vein pattern. Even though the vein images from webcam are noisy and very low contrast, results show that the proposed algorithm still can get good vein image enhancement.

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Small P values may not yield robust findings:an example using REST-meta-PD

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Science Bulletin 2021年第21期66卷 2148-2152,M0003页

作者： Xi-Ze Jia Na Zhao Hao-Ming Dong Jia-Wei Sun Marek Barton Roxana Burciu Nicolas Carrière Antonio Cerasa Bo-Yu Chen Jun Chen Stephen Coombes Luc Defebvre Christine Delmaire Kathy Dujardin Fabrizio Esposito Guo-Guang Fan Federica Di Nardo Yi-Xuan Feng Brett W.Fling Saurabh Garg Moran Gilat Martin Gorges Shu-Leong Ho Fay BHorak Xiao Hu Xiao-Fei Hu Biao Huang Pei-Yu Huang Ze-Juan Jia Christina Jones Jan Kassubek Lenka Krajcovicova Ajay Kurani Jing Li Qing Li Ai-Ping Liu Bo Liu Hu Liu Wei-Guo Liu Renaud Lopes Yu-Ting Lou Wei Luo Tara Madhyastha Ni-Ni Mao Grainne McAlonan Martin J.McKeown Shirley Pang Andrea Quattrone Irena Rektorova Alessia Sarica Hui-Fang Shang James M.Shine Priyank Shukla Tomas Slavicek Xiao-Peng Song Gioacchino Tedeschi Alessandro Tessitore David Vaillancourt Jian Wang Jue Wang Z.Jane Wang Lu-Qing Wei Xia Wu Xiao-Jun Xu Lei Yan Jing Yang Wan-Qun Yang Nai-Lin Yao De-Long Zhang Jiu-Quan Zhang Min-Ming Zhang Yan-Ling Zhang Cai-Hong Zhou Chao-Gan Yan Xi-Nian Zuo Mark Hallett Tao Wu Yu-Feng Zang Center for Cognition and Brain Disorders the Affiliated HospitalHangzhou Normal UniversityHangzhou 310015China Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments Hangzhou 311121China National Basic Science Data Center Beijing 100190China State Key Laboratory of Cognitive Neuroscience and Learning&McGovern Institute for Brain Research Beijing Normal UniversityBeijing 100875China School of Information and Electronics Technology Jiamusi UniversityJiamusi 154007China Neuroscience Program Central European Institute of TechnologyCEITECMasaryk UniversityBrno 62500Czech Republic Department of Applied Physiology and Kinesiology University of FloridaGainesville FL 32611USA Univ.Lille InsermCHU LilleU1172-LilNCog-Lille Neuroscience&CognitionLille F-59000France The Institute for Biomedical Research and Innovation National Research CouncilMangone CS 87050Italy Department of Radiology The First Affiliated Hospital of China Medical UniversityShenyang 110001China Department of Radiology Guangdong Provincial Hospital of Chinese MedicineGuangzhou 510120China Department of Medicine Surgery and DentistryScuola Medica SalernitanaUniversity of SalernoFiscianoSA 132-84084Italy Department of Advanced Medical and Surgery Sciences University of Campania“Luigi Vanvitelli”Caserta 81100Italy Eye Center of the 2nd Affiliated Hospital Medical College of Zhejiang UniversityHangzhou 310020China Zhejiang Provincial Key Lab of Ophthalmology Hangzhou 310020China Department of Health and Exercise Science Colorado State UniversityFort Collins CO 80523USA Pacific Parkinson’s Research Centre University of British ColumbiaVancouver BC V6E 2M6Canada Department of Medicine(Neurology)University of British Columbia Vancouver BC V6T 1B7Canada Brain and Mind Center The University of SydneySydney NSW 2006Australia Department of Neurology Ulm UniversityUlm 89081Germany Division of Neurology Department of MedicineQueen Mary HospitalUniversity of Hong KongHong Kong 999077China Department of

Thousands of resting state functional magnetic resonance imaging(RS-f MRI)articles have been published on brain *** precise localization of abnormal brain activity,a voxel-level comparison is *** of the large number of voxels in the brain,multiple comparison correction(MCC)must be performed to reduce false positive rates,and a smaller P value(usually including either liberal or stringent MCC)is widely recommended[1].

关键词：帕金森病 precise correction

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