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26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 Antwerp, Belgium. 15-20 July 2017 Abstracts

BMC NEUROSCIENCE 2017年第SUPPL 1期18卷 95-176页

作者： [Anonymous] Department of Neuroscience Yale University New Haven CT 06520 USA Department Physiology & Pharmacology SUNY Downstate Brooklyn NY 11203 USA NYU School of Engineering 6 MetroTech Center Brooklyn NY 11201 USA Departament de Matemàtica Aplicada Universitat Politècnica de Catalunya Barcelona 08028 Spain Institut de Neurobiologie de la Méditerrannée (INMED) INSERM UMR901 Aix-Marseille Univ Marseille France Center of Neural Science New York University New York NY USA Aix-Marseille Univ INSERM INS Inst Neurosci Syst Marseille France Laboratoire de Physique Théorique et Modélisation CNRS UMR 8089 Université de Cergy-Pontoise 95300 Cergy-Pontoise Cedex France Department of Mathematics and Computer Science ENSAT Abdelmalek Essaadi’s University Tangier Morocco Laboratory of Natural Computation Department of Information and Electrical Engineering and Applied Mathematics University of Salerno 84084 Fisciano SA Italy Department of Medicine University of Salerno 84083 Lancusi SA Italy Dipartimento di Fisica Università degli Studi Aldo Moro Bari and INFN Sezione Di Bari Italy Data Analysis Department Ghent University Ghent Belgium Coma Science Group University of Liège Liège Belgium Cruces Hospital and Ikerbasque Research Center Bilbao Spain BIOtech Department of Industrial Engineering University of Trento and IRCS-PAT FBK 38010 Trento Italy Department of Data Analysis Ghent University Ghent 9000 Belgium The Wellcome Trust Centre for Neuroimaging University College London London WC1N 3BG UK Department of Electronic Engineering NED University of Engineering and Technology Karachi Pakistan Blue Brain Project École Polytechnique Fédérale de Lausanne Lausanne Switzerland Departement of Mathematics Swansea University Swansea Wales UK Laboratory for Topology and Neuroscience at the Brain Mind Institute École polytechnique fédérale de Lausanne Lausanne Switzerland Institute of Mathematics University of Aberdeen Aberdeen Scotland UK Department of Integrativ

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Microwave Sensing of Sand Production from Petroleum Wells

Microwave Sensing of Sand Production from Petroleum Wells

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IEEE International Instrumentation and Measurement technology Conference

作者： Jaswanth N. Vutukury Kristen M. Donnell Steven Hilgedick Applied Microwave Nondestructive Testing Laboratory (amntl) Department of Electrical and Computer Engineering Missouri University of Science and Technology Rolla MO 65409 Petroleum Engineering Program Department of Geological Sciences and Engineering Missouri University of Science and Technology Rolla MO 65409

ISBN: (纸本)9781479961153

Sand production in petroleum wells is a major concern for the petroleum industry. Sand production results in erosion of pipelines and damage to equipment which may lead to production shutdown and large economic losses. Detection of produced sand is important for optimizing flow rates and measuring the success of sand mitigation techniques. Recently, microwave nondestructive testing was considered as a potential new sensing solution for the detection of sand production. This paper presents preliminary simulations and measurements for such a sensing approach using open-ended waveguides. Different production scenarios are considered for a stratified flow regime. The results indicate that this sensing method can detect the presence of sand. Preliminary measurements are also presented for varying amounts of sand that further support the potential of this technique.

关键词： microwave nondestructive testing sandproduction petroleum oil and gas microwave sensing open-ended waveguide material characterization

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Cough Detection Through Mechanomyographic Signal in Synchronized Respiratory electrical Stimulation Systems

Cough Detection Through Mechanomyographic Signal in Synchron...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society

作者： Taisa D. Costa Guilherme N. Nogueira-Neto Percy Nohama the Graduate Program in Electrical and Computer Engineering Federal University of Technology Parana Brazil Pontifical Catholic University of Parana Parana Brazil Pontifical Catholic University of Parana and Federal University of Technology Parana Brazil

ISBN: (纸本)9781424492695

Synchronization of transcutaneous functional electrical stimulation (TFES) with the spontaneous inspiration and expiration phases is a new approach for respiratory rehabilitation. One of the requirements for proper operation is the identification of cough events to automatically change the stimulation parameters in order to increase muscle strength during the cough. The aim of this work is to assess the viability in detection of cough events with a mechanomyographic (MMG) sensor on the abdominal region, and to evaluate if it can be used simultaneously with the synchronized TFES system. An MMG sensor was placed in contact with skin lined with the last ribs, above the rectus abdominis muscle and the linea alba. Two tests were accomplished which included quiet breathing, speaking and coughing episodes. The developed system efficiently distinguishes quiet breathing and coughing signals, but speaking is still confused with coughing episodes. The MMG sensor suffered detectable amplitude changes mainly during the forced expiration phase of the cough, but it could also detect the compression phase at lower amplitude. Therefore, the MMG system can be used for cough detection in this application.

关键词： TFES MMG cough Cough Rectus Abdominis respiration Electric Stimulation abdominal region normal operation

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Multi-class classification of objects in images using principal component analysis and genetic programming

Multi-class classification of objects in images using princi...

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Latin America Congress on Computational Intelligence (LA-CCI)

作者： Manassés Ribeiro Heitor Silvério Lopes Federal Catarinense Institute of Education Science and Technology (IFC) Videira Santa Catarina Brazil Graduate Program in Electrical and Computer Engineering (CPGEI) Federal University of Technology of Paraná (UTFPR) Curitiba Paraná Brazil

This work presents a methodology for using Principal Component Analysis (PCA) and Genetic programming (GP) for the classification of multi-class objects found in digital images. The image classification process is performed by using features extracted from images, through feature extraction algorithms, reduced by PCA and labeled by similarity comparing with other previously classified objects. GP uses two sets of elements: terminals, composed by the features extracted by PCA; and non-terminals, composed by algebraic operations. The fitness function was defined by the product of sensibility and specificity, two performance measures. A penalty term is also used to decrease the number of nodes of the tree, while minimally affecting the quality of solutions. The proposed approach was applied to set of 2739 digital images divided into objects representing airplanes, motorbikes, background from google, faces and watch classes, provided by the Caltech101 image database. The proposed approach was compared with SVM, Naïve Bayes and C4.5. Results suggest that the approach PCA+GP is able to evolve solutions for the problem as a simple classification rule with true positive rate above 70%. Additionally, we observe that PCA+PG obtained results slightly better than SVM and C4.5, besides these methods give a result that is not comprehensible by humans.

关键词： Principal component analysis Image color analysis Feature extraction Histograms Shape Genetic programming Support vector machines

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26th Annual Computational Neuroscience Meeting (CNS*2017) of the Organization for Computational Neuroscience Antwerp, Belgium, July 15-20, 2017

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BMC NEUROSCIENCE 2017年第SUPPL 1期18卷 59-59页

作者： [Anonymous] Indiana University Purdue University Indianapolis Indianapolis IN 46032 USA Stark Neurosciences Research Institute Indiana University School of Medicine Indianapolis IN 46032 USA Department of Mathematics East Carolina University Greenville NC 27858 USA Jülich Supercomputing Centre Forschungszentrum Jülich 52425 Jülich Germany Future Systems Swiss National Supercomputing Centre 8092 Zurich Switzerland User Engagement and Support Swiss National Supercomputing Centre 6900 Lugano Switzerland Institut de Neurosciences des Systèmes Aix Marseille Univ 13005 Marseille France Simulation Lab Neuroscience Forschungszentrum Jülich Jülich Germany Department of Experimental Psychology Ghent University 9000 Ghent Belgium Donders Center for Cognitive Neuroimaging Radboud University 6525HR Nijmegen The Netherlands Department of Electrical Computer and Energy Engineering University of Colorado Boulder CO 80309 USA Department of Neurosurgery Johns Hopkins School of Medicine Baltimore MD 21287 USA Department of Neurology Johns Hopkins School of Medicine Baltimore MD 21287 USA Department of Otolaryngology Johns Hopkins School of Medicine Baltimore MD 21287 USA INSERM U968 Paris France Sorbonne Universités UPMC University Paris 06 UMR_S 968 Institut de la Vision Paris France CNRS UMR_7210 Paris France Department of Computer Architecture and Technology University of Granada (CITIC) Granada Spain Sorbonne Universités UPMC Univ Paris 06 INSERM CNRS Institut de la Vision Paris France Department of Adaptive Machine Systems Osaka University Osaka Japan Department of Computer Science University of Cergy-Pontoise Cergy-Pontoise France Department of Physics and Astronomy College of Charleston Charleston SC 29424 USA School of Physics Faculty of Science University of Sydney Sydney NSW 2006 Australia Center of Excellence for Integrative Brain Function Australian Research Council Sydney Australia Max Planck Institute for Human Cognitive and Brain Sciences Saxony Lei

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Photonic Crystals: Light‐Patterned Crystallographic Direction of a Self‐Organized 3D Soft Photonic Crystal (Adv. Mater. 42/2017)

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Advanced Materials 2017年第42期29卷

作者： Zhi‐Gang Zheng Cong‐Long Yuan Wei Hu Hari Krishna Bisoyi Ming‐Jie Tang Zhen Liu Pei‐Zhi Sun Wei‐Qiang Yang Xiao‐Qian Wang Dong Shen Yannian Li Fangfu Ye Yan‐Qing Lu Guoqiang Li Quan Li Department of Physics East China University of Science and Technology Shanghai 200237 China Liquid Crystal Institute and Chemical Physics Interdisciplinary Program Kent State University Kent OH 44242 USA National Laboratory of Solid State Microstructures Collaborative Innovation Center of Advanced Microstructures and College of Engineering and Applied Sciences Nanjing University Nanjing 210093 China Beijing National Laboratory for Condensed Matter Physics and CAS Key Laboratory of Soft Matter Physics Institute of Physics Chinese Academy of Sciences Beijing 100190 China Visual and Biomedical Optics Lab Department of Ophthalmology and Visual Science and Department of Electrical and Computer Engineering Ohio State University Columbus OH 43212 USA

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Vibration Measurement of Induction Motor Under Dynamic Eccentricity Using Optical Fiber Bragg Grating Sensors

Vibration Measurement of Induction Motor Under Dynamic Eccen...

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SBMO/IEEE MTT-S International Microwave and Optoelectronics Conference

作者： Kleiton de Morais Sousa Uilian Jose Dreyer Cicero Martelli Jean Carlos Cardozo da Silva Federal University of Technology - Parana Graduate Program in Electrical and Computer Engineering

ISBN: (纸本)9781467394932

The vibration in Three-Phase Induction Motors (TIM) is due to mechanical and electrical forces existing during the motor operation. Vibration problems in induction motors can be extremely frustrating and may lead to greatly reduced reliability increasing the maintenance cost. The vibration was measured using fiber Bragg gratings (FBG) installed inside the motor between two stator teeth. The vibration frequencies in TIM are predicted using a model of the forces present in the air gap considering a static and dynamic rotor eccentricity. Two FBGs were positioned between two stator teeth, separated by 120° from each other. The analysis of the vibration spectrum at 60 Hz motor power supply presents the frequencies 60 Hz, 120 Hz, 180 Hz and 240 Hz. This combination of the mechanical and electrical frequencies continue to appear even when the motor electrical supply frequency is reduced by half. The measurement present in this paper can be used to determine some TIM parameters, like the effect of the motor power supply on its vibration signal and also be used like a potential maintenance tool for predictive purposes with high efficiency to continuously monitor the TIM.

关键词： Induction motors Fiber Bragg gratings Motor eccentricity Vibration measurement

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Back Cover: H2S Sensors: Fumarate-Based fcu-MOF Thin Film Grown on a Capacitive Interdigitated Electrode (Angew. Chem. Int. Ed. 51/2016)

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Angewandte Chemie International Edition 2016年第51期55卷

作者： Dr. Omar Yassine Dr. Osama Shekhah Ayalew H. Assen Dr. Youssef Belmabkhout Prof. Khaled N. Salama Prof. Mohamed Eddaoudi Sensors Lab Electrical Engineering Program Division of Computer Electrical and Mathematical Sciences and Engineering King Abdullah University of Science and Technology Thuwal 23955-6900 Saudi Arabia Functional Materials Design Discovery and Development research group (FMD3) Advanced Membranes & Porous Materials Center Division of Physical Sciences and Engineering King Abdullah University of Science and Technology Thuwal 23955-6900 Saudi Arabia

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H2S Sensors: Fumarate‐Based fcu‐MOF Thin Film Grown on a Capacitive Interdigitated Electrode

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Angewandte Chemie 2016年第51期128卷

作者： Omar Yassine Osama Shekhah Ayalew H. Assen Youssef Belmabkhout Khaled N. Salama Mohamed Eddaoudi Sensors Lab Electrical Engineering Program Division of Computer Electrical and Mathematical Sciences and Engineering King Abdullah University of Science and Technology Thuwal 23955-6900 Saudi Arabia Functional Materials Design Discovery and Development research group (FMD3) Advanced Membranes & Porous Materials Center Division of Physical Sciences and Engineering King Abdullah University of Science and Technology Thuwal 23955-6900 Saudi Arabia

Herein we report the fabrication of an advanced sensor for the detection of hydrogen sulfide (H 2 S) at room temperature, using thin films of rare‐earth metal (RE)‐based metal–organic framework (MOF) with underlying fcu topology. This unique MOF‐based sensor is made via the in situ growth of fumarate‐based fcu ‐MOF (fum‐ fcu ‐MOF) thin film on a capacitive interdigitated electrode. The sensor showed a remarkable detection sensitivity for H 2 S at concentrations down to 100 ppb, with the lower detection limit around 5 ppb. The fum‐ fcu ‐MOF sensor exhibits a highly desirable detection selectivity towards H 2 S vs. CH 4 , NO 2 , H 2 , and C 7 H 8 as well as an outstanding H 2 S sensing stability as compared to other reported MOFs.

关键词： H2S Interdigital-Elektroden Metall-organische Gerüstverbindungen Seltenerdmetalle Sensoren

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Rücktitelbild: H2S Sensors: Fumarate‐Based fcu‐MOF Thin Film Grown on a Capacitive Interdigitated Electrode (Angew. Chem. 51/2016)

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Angewandte Chemie 2016年第51期128卷

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