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arXiv 2022年

作者： Craig, Michael T. Wohland, Jan Stoop, Laurens P. Kies, Alexander Pickering, Bryn Bloomfield, Hannah C. Browell, Jethro De Felice, Matteo Dent, Chris J. Deroubaix, Adrien Frischmuth, Felix Gonzalez, Paula L.M. Grochowicz, Aleksander Gruber, Katharina Härtel, Philipp Kittel, Martin Kotzur, Leander Labuhn, Inga Lundquist, Julie K. Pflugradt, Noah van der Wiel, Karin Zeyringer, Marianne Brayshaw, David J. School for Environment and Sustainability University of Michigan United States Institute for Environmental Decisions ETH Zurich Switzerland Helmholtz-Zentrum Hereon Germany Information and Computing Science Copernicus Institute of Sustainable Development Utrecht University Netherlands TenneT TSO B.V. Arnhem Netherlands Royal Netherlands Meteorological Institute Netherlands Frankfurt Institute for Advanced Studies Germany Department of Electrical and Computer Engineering Aarhus University Denmark ETH Zürich Switzerland School of Geographical Sciences University of Bristol United Kingdom Department of Meteorology University of Reading United Kingdom University of Glasgow United Kingdom European Commission Joint Research Centre Netherlands School of Mathematics University of Edinburg United Kingdom The Alan Turing Institute United Kingdom Max Planck Institute Germany Fraunhofer Institute for Energy Economics and Energy System Technology Germany Department of Mathematics University of Oslo Norway University of Natural Resources and Life Sciences Austria German Institute for Economic Research Germany Institute of Energy and Climate Research Techno-economic Systems Analysis Germany University of Bremen Institute of Geography Germany Dept. of Atmospheric and Oceanic Science University of Colorado Boulder United States National Renewable Energy Laboratory United States Renewable and Sustainable Energy Institute United States Forschungszentrum Jülich Germany Department of Technology Systems University of Oslo Norway

Energy system models underpin decisions by energy system planners and operators. Energy system modelling faces a transformation: accounting for changing meteorological conditions imposed by climate change. To enable that transformation, a community of practice in energy-climate modelling has started to form that aims to better integrate energy system models with weather and climate models. Here, we evaluate the disconnects between the energy system and climate modelling communities, then lay out a research agenda to bridge those disconnects. In the near-term, we propose interdisciplinary activities for expediting uptake of future climate data in energy system modelling. In the long-term, we propose a transdisciplinary approach to enable development of (1) energy-system-tailored climate datasets for historical and future meteorological conditions and (2) energy system models that can effectively leverage those datasets. This agenda increases the odds of meeting ambitious climate mitigation goals by systematically capturing and mitigating climate risk in energy sector decision making. © 2022, CC BY-NC-ND.

关键词： Decision making

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Towards Optimal Search: An Efficient Search Algorithm for Arabic Text using Secant Method 2

Towards Optimal Search: An Efficient Search Algorithm for Ar...

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2nd Borneo International Conference on Applied Mathematics and engineering, BICAME 2018

作者： Kabir, Muhammad Nomani Alginahi, Yasser Ali, Jahan Benseghir, Hocine University Malaysia Pahang Faculty of Computer Systems Software Engineering Gambang Kuantan26300 Malaysia University of Windsor Dept. of Electrical and Computer Engineering WindsorONN9B 3P4 Canada University Malaysia Faculty of Engineering Technology Pahang Kuantan26300 Malaysia

ISBN: (纸本)9781538677254

Search techniques are an integral part for text authentication and plagiarism checks. In this work, our aim is to develop an efficient search algorithm for Arabic text (words and sentences) in a large database. The database contains the authentic source of information where information is ordered;thus the efficient algorithm can be used for searching the text in the database. The proposed search algorithm uses a quasi-Newton method - Secant method instead of the traditional binary search. The results show that the secant method has much faster convergence property than the binary search and hence the algorithm can find the search text in a shorter time period. © 2018 IEEE.

关键词： Database systems

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Automated Detection and Forecasting of COVID-19 using Deep Learning Techniques: A Review

arXiv

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arXiv 2020年

作者： Shoeibi, Afshin Khodatars, Marjane Jafari, Mahboobeh Ghassemi, Navid Sadeghi, Delaram Moridian, Parisa Khadem, Ali Alizadehsani, Roohallah Hussain, Sadiq Zare, Assef Sani, Zahra Alizadeh Khozeimeh, Fahime Nahavandi, Saeid Rajendra Acharya, U. Gorriz, Juan M. The Data Science and Computational Intelligence Institute University of Granada Spain The Computer Engineering Department Ferdowsi University of Mashhad Mashhad Iran Deakin University VIC3217 Australia The Faculty of Electrical Engineering K. N. Toosi University of Technology Tehran Iran System Administrator at Dibrugarh University Assam786004 India Faculty of Electrical Engineering Gonabad Branch Islamic Azad University Gonabad Iran Rajaie Cardiovascular Medical and Research Center Iran University of Medical Sciences Tehran Iran The School of Mathematics Physics and Computing University of Southern Queensland Springfield Australia Dept. of Psychiatry University of Cambridge United Kingdom

Coronavirus, or COVID-19, is a hazardous disease that has endangered the health of many people around the world by directly affecting the lungs. COVID-19 is a medium-sized, coated virus with a single-stranded RNA, and also has one of the largest RNA genomes and is approximately 120 nm. The X-Ray and computed tomography (CT) imaging modalities are widely used to obtain a fast and accurate medical diagnosis. Identifying COVID-19 from these medical images is extremely challenging as it is time-consuming and prone to human errors. Hence, artificial intelligence (AI) methodologies can be used to obtain consistent high performance. Among the AI methods, deep learning (DL) networks have gained popularity recently compared to conventional machine learning (ML). Unlike ML, all stages of feature extraction, feature selection, and classification are accomplished automatically in DL models. In this paper, a complete survey of studies on the application of DL techniques for COVID-19 diagnostic and segmentation of lungs is discussed, concentrating on works that used X-Ray and CT images. Additionally, a review of papers on the forecasting of coronavirus prevalence in different parts of the world with DL is presented. Lastly, the challenges faced in the detection of COVID-19 using DL techniques and directions for future research are discussed. © 2020, CC BY-NC-ND.

关键词： COVID-19

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Spatial MMF Harmonic Mitigation in Aluminum-Cage Induction Motors

Spatial MMF Harmonic Mitigation in Aluminum-Cage Induction M...

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IEEE Energy Conversion Congress and Exposition

作者： Andrea Cavagnino Silvio Vaschetto Luca Ferraris Zbigniew Gmyrek Emmanuel Agamloh Gerd Bramerdorfer Dipartimento Energia Politecnico di Torino Torino Italy Institute of Mechatronics and Information Systems Lodz University of Technology Lodz Poland Dept. of Electrical and Computer Engineering Baylor University Waco Texas USA Dep.of Electrical Drives and Power Electronics Johannes Kepler University Linz Linz Austria

This study investigates the effects of the spatial harmonics of magneto-motive force that appear in the air-gap of aluminum-cage induction motors, proposing some techniques for their mitigation. With respect to previous researches, special magnetic wedges to be mounted in the stator opening regions of semi-closed slots of existing motors are proposed, together with a special winding layout having a very low magnetomotive force harmonic content. Both 2D- and 3D-FEM numerical simulations have been carried out to quantify the problems and to understand the potential benefits of inserting the stator wedges. At the end, the mechanical feasibility of the proposed wedges is considered together with some attempts at implementation.

关键词： Rotors Harmonic analysis Magnetic flux Windings Induction motors Air gaps Stator windings

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Challenges and Opportunities in Utilizing IoT-Based Stress Maps as a Community Mood Detector

Challenges and Opportunities in Utilizing IoT-Based Stress M...

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2019 IEEE International Symposium on Technologies for Homeland Security, HST 2019

作者： Yuksel, Murat Wang, Wei Chaudhry, Shafaq Turgut, Damla Millican, Charles Kapucu, Naim Electrical Computer Engineering UCF 4328 Scorpius St. OrlandoFL32816 United States Graduate Center Baruch College CUNY 365 5th Ave. New YorkNY10016 United States Networking Wireless Systems Lab UCF 4328 Scorpius St. OrlandoFL32816 United States UCF Dept. of Computer Science 4328 Scorpius St. OrlandoFL32816 United States School of Public Administration UCF 4364 Scorpius St. OrlandoFL32816 United States

ISBN: (纸本)9781728150925

Stress has been known to cause physical and mental issues like depression, anxiety, insomnia, lower immunity, stroke, as well as leading to suicidal thoughts or violence towards others. Stress is not just a state of mind, but it is measurable. With the ubiquity of Internet of Things (IoT), and the integration with highly sensitive biosensors, it may be feasible to use these devices for detecting stress in public places. Moreover, correlating such stress data with social media streams can lead to insights into the psychological well-being of the community as a whole. We present a framework of such a community stress map based on social media and explore techniques for gathering data for measuring stress levels as well as detecting abnormal levels. This stress map can then be leveraged by emergency and crisis response teams for public safety and help them be proactive in allocating resources to the stressed areas indicated in the map. © 2019 IEEE.

关键词： Internet of things

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CHAOS Challenge - Combined (CT-MR) healthy abdominal organ segmentation

arXiv

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arXiv 2020年

作者： Kavur, A. Emre Gezer, N. Sinem Barış, Mustafa Conze, Pierre-Henri Groza, Vladimir Pham, Duc Duy Chatterjee, Soumick Ernst, Philipp Özkan, Savaş Baydar, Bora Lachinov, Dmitry Han, Shuo Pauli, Josef Isensee, Fabian Perkonigg, Matthias Sathish, Rachana Rajan, Ronnie Aslan, Sinem Sheet, Debdoot Dovletov, Gurbandurdy Speck, Oliver Nürnberger, Andreas Maier-Hein, Klaus H. Akar, Gözde Bozdagı Ünal, Gözde Dicle, Oguz Selver, M. Alper Graduate School of Natural and Applied Sciences Dokuz Eylul University Izmir Turkey Department of Radiology Faculty Of Medicine Dokuz Eylul University Izmir Turkey Dept. of Ophthalmology and Optometry Medical Uni. of Vienna Austria Intelligent Systems Faculty of Engineering University of Duisburg-Essen Germany Division of Medical Image Computing German Cancer Research Center Heidelberg Germany CIR Lab Dept of Biomedical Imaging and Image-guided Therapy Medical Uni. of Vienna Austria IMT Atlantique LaTIM UMR 1101 Brest France Department of Electrical and Electronics Engineering Middle East Technical University Ankara Turkey Department of Electrical Engineering Indian Institute of Technology Kharagpur India School of Medical Science and Technology Indian Institute of Technology Kharagpur India Ca’ Foscari University of Venice ECLT DAIS Venice Italy Ege University International Computer Institute Izmir Turkey Johns Hopkins University Baltimore United States Data and Knowledge Engineering Group Otto von Guericke University Magdeburg Germany Magnetic Resonance Otto von Guericke University Magdeburg Germany Median Technologies Valbonne France Department of Computer Engineering Istanbul Technical University Istanbul Turkey Department of Electrical and Electronics Engineering Dokuz Eylul University Izmir Turkey

—Segmentation of abdominal organs has been a comprehensive, yet unresolved, research field for many years. In the last decade, intensive developments in deep learning (DL) have introduced new state-of-the-art segmentation systems. Despite outperforming the overall accuracy of existing systems, the effects of DL model properties and parameters on the performance is hard to interpret. This makes comparative analysis a necessary tool to achieve explainable studies and systems. Moreover, the performance of DL for emerging learning approaches such as cross-modality and multi-modal tasks have been rarely discussed. In order to expand the knowledge in these topics, CHAOS – Combined (CT-MR) Healthy Abdominal Organ Segmentation challenge has been organized in the IEEE International Symposium on Biomedical Imaging (ISBI), 2019, in Venice, Italy. Despite a large number of the previous abdomen related challenges, the majority of which are focused on tumor/lesion detection and/or classification with a single modality, CHAOS providesboth abdominal CT and MR data from healthy subjects. Five different and complementary tasks have been designed to analyze the capabilities of the current approaches from multiple perspectives. The results are investigated thoroughly, compared with manual annotations and interactive methods. The outcomes are reported in detail to reflect the latest advancements in the field. CHAOS challenge and data will be available online to provide a continuous benchmark resource for segmentation. Copyright © 2020, The Authors. All rights reserved.

关键词： Medical imaging

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3D deep learning enables fast imaging of spines through scattering media by temporal focusing microscopy

arXiv

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arXiv 2019年

作者： Wei, Zhun Boivin, Josiah R. Xue, Yi Chen, Xudong So, Peter T.C. Nedivi, Elly Wadduwage, Dushan N. Center for Advanced Imaging Faculty of Arts and Sciences Harvard University CambridgeMA02138 United States Picower Institute for Learning and Memory Massachusetts Institute of Technology CambridgeMA02139 United States Dept. of Electrical Engineering and Computer Sciences University of California BerkeleyCA94720 United States Department of Electrical and Computer Engineering National University of Singapore 4 Engineering Drive 3 Singapore117583 Singapore Dept. of Biological Engineering Laser Biomedical Research Center Dept. of Mechanical Engineering Massachusetts Institute of Technology 77 Massachusetts Ave. CambridgeMA02139 United States Department of Brain and Cognitive Sciences Massachusetts Institute of Technology CambridgeMA02139 United States Department of Biology Massachusetts Institute of Technology CambridgeMA02139 United States

Today the gold standard for in vivo imaging through scattering tissue is the point-scanning two-photon microscope (PSTPM). Especially in neuroscience, PSTPM is widely used for deep-tissue imaging in the brain. However, due to sequential scanning, PSTPM is slow. Temporal focusing microscopy (TFM), on the other hand, focuses femtosecond pulsed laser light temporally, while keeping wide-field illumination, and is consequently much faster. However, due to the use of a camera detector, TFM suffers from the scattering of emission photons. As a result, TFM produces images of poor spatial resolution and signal-to-noise ratio (SNR), burying fluorescent signals from small structures such as dendritic spines. In this work, we present a data-driven deep learning approach to improve resolution and SNR of TFM images. Using a 3D convolutional neural network (CNN) we build a map from TFM to PSTPM modalities, to enable fast TFM imaging while maintaining high-resolution through scattering media. We demonstrate this approach for in vivo imaging of dendritic spines on pyramidal neurons in the mouse visual cortex. We show that our trained network rapidly outputs high-resolution images that recover biologically relevant features previously buried in the scattered fluorescence in the TFM images. In vivo imaging that combines TFM and the proposed 3D convolution neural network is one to two orders of magnitude faster than PSTPM but retains the high resolution and SNR necessary to analyze small fluorescent structures. The proposed 3D convolution deep network could also be potentially beneficial for improving the performance of many speed-demanding deep-tissue imaging applications such as in vivo voltage imaging. Copyright © 2019, The Authors. All rights reserved.

关键词： Signal to noise ratio

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Performance analysis of fisher-snedecor F composite fading channels

Performance analysis of fisher-snedecor F composite fading c...

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IEEE Middle East and North Africa Communications Conference (MENACOMM)

作者： Taimour Aldalgamouni Mehmet Cagri Ilter Osamah S. Badarneh Halim Yanikomeroglu Dept. of Electrical Engineering Higher Colleges of Technology Dubai UAE Dept. of Systems and Computer Engineering Carleton University Ottawa Canada Electrical Engineering Dept. University of Tabuk Tabuk Saudi Arabia

ISBN: (纸本)9781538612552

In this paper, we consider the Fisher-Snedecor F composite fading channel model and derive exact closed-form expressions for the symbol error rate (SER) of M-ary pulse amplitude modulation (M-PAM) and M-ary quadrature amplitude modulation (M-QAM). We also derive asymptotic expressions for the SER of M-PAM and M-QAM to study the behavior of SER at high values of signal-to-noise ratio. Moreover, we derive an exact closed-form expression for the average capacity. The derived expressions are evaluated for different values of the fading parameters to show the effects of shadowing and small-scale fading on the performance of SER and capacity. Simulation results are also provided to show the accuracy of the derived expressions.

关键词： Shadow mapping Quadrature amplitude modulation Signal to noise ratio Closed-form solutions Device-to-device communication Rayleigh channels

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An international interdisciplinary study abroad program on smart cities

An international interdisciplinary study abroad program on s...

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2018 Smart Cities Symposium Prague, SCSP 2018

作者： Cheu, Ruey Long Rosales, Natalia Villanueva Larios, Victor M. Arrellano, Ricio MacIel Mondragon, Oscar Cabrera, Sergio Ferregut, Carlos Arvizu, Luisa Horak, Tomas Svitek, Miroslav Pribyl, Ondrej Dept. of Civil Engineering University of Texas at El Paso United States Dept. of Computer Science University of Texas at El Paso United States Smart City Innovation Center University of Guadalajara Mexico Dept. of Industrial Manufacturing and Systems Eng. University of Texas at El Paso United States Dept. of Electrical and Computer Engineering University of Texas at El Paso United States College of Engineering University of Texas at El Paso United States Faculty of Transportation Sciences Czech Technical University Czech Republic

ISBN: (纸本)9781538650172

Preparing the next generation of engineers, scientists and decision makers requires the development of new teaching strategies that follow smart cities philosophy: collaborative, inclusive and focused on specific needs of the region. The University of Texas at El Paso (UTEP), USA and University of Guadalajara (UdeG), Mexico, addressed the challenge of creating a new international course on smart cities study abroad program that leverages the long-term relationship of UTEP with Czech Technical University (CTU), Czech Republic, and industry partners in El Paso and Guadalajara, especially IBM Mexico. This program is highly interdisciplinary, involving four departments in the College of engineering at UTEP and the Department of Information systems, Centro Universitario de Ciencias Economico Administrativas at UdeG. Input from city officials in Guadalajara and El Paso along with industry partners was key in designing this novel program. Thirty-two students from UTEP and 32 from UdeG have successfully completed this program which will launch again on its third edition this year. The program included problem-based learning strategies as well as cultural, industry-led activities, culminating in a Smart Cities Hackathon competition. This articles reports on the planning, development and results of this study abroad program, challenges and lessons learned. © 2018 IEEE.

关键词： Smart city

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Metrics Reloaded: Recommendations for image analysis validation

arXiv

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arXiv 2022年

作者： Maier-Hein, Lena Reinke, Annika Godau, Patrick Tizabi, Minu D. Buettner, Florian Christodoulou, Evangelia Glocker, Ben Isensee, Fabian Kleesiek, Jens Kozubek, Michal Reyes, Mauricio Riegler, Michael A. Wiesenfarth, Manuel Emre Kavur, A. Sudre, Carole H. Baumgartner, Michael Eisenmann, Matthias Heckmann-Nötzel, Doreen Rädsch, Tim Acion, Laura Antonelli, Michela Arbel, Tal Bakas, Spyridon Benis, Arriel Blaschko, Matthew B. Jorge Cardoso, M. Cheplygina, Veronika Cimini, Beth A. Collins, Gary S. Farahani, Keyvan Ferrer, Luciana Galdran, Adrian Ginneken, Bram Van Haase, Robert Hashimoto, Daniel A. Hoffman, Michael M. Huisman, Merel Jannin, Pierre Kahn, Charles E. Kainmueller, Dagmar Kainz, Bernhard Karargyris, Alexandros Karthikesalingam, Alan Kenngott, Hannes Kofler, Florian Kopp-Schneider, Annette Kreshuk, Anna Kurc, Tahsin Landman, Bennett A. Litjens, Geert Madani, Amin Maier-Hein, Klaus Martel, Anne L. Mattson, Peter Meijering, Erik Menze, Bjoern Moons, Karel G.M. Müller, Henning Nichyporuk, Brennan Nickel, Felix Petersen, Jens Rajpoot, Nasir Rieke, Nicola Saez-Rodriguez, Julio Sánchez, Clara I. Shetty, Shravya Smeden, Maarten Van Summers, Ronald M. Taha, Abdel A. Tiulpin, Aleksei Tsaftaris, Sotirios A. Calster, Ben Van Varoquaux, Gaël Jäger, Paul F. Heidelberg Division of Intelligent Medical Systems and HI Helmholtz Imaging Germany Faculty of Mathematics and Computer Science and Medical Faculty Heidelberg University Heidelberg Germany NCT Heidelberg a partnership between DKFZ University Medical Center Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Heidelberg Division of Intelligent Medical Systems Germany partner site Frankfurt/Mainz a partnership between DKFZ and UCT Frankfurt-Marburg Germany Heidelberg Goethe University Frankfurt Germany Department of Medicine Goethe University Frankfurt Germany Department of Informatics Frankfurt Cancer Insititute Germany Department of Computing Imperial College London London United Kingdom Heidelberg Division of Medical Image Computing and HI Applied Computer Vision Lab Germany Institute for AI in Medicine University Medicine Essen Essen Germany Centre for Biomedical Image Analysis Faculty of Informatics Masaryk University Brno Czech Republic ARTORG Center for Biomedical Engineering Research University of Bern Bern Switzerland Department of Radiation Oncology University Hospital Bern University of Bern Bern Switzerland Simula Metropolitan Center for Digital Engineering Oslo Norway UiT The Arctic University of Norway Romsø Norway Heidelberg Division of Biostatistics Germany Heidelberg Division of Intelligent Medical Systems Division of Medical Image Computing HI Applied Computer Vision Lab Germany MRC Unit for Lifelong Health and Ageing UCL Centre for Medical Image Computing Department of Computer Science University College London London United Kingdom School of Biomedical Engineering and Imaging Science King’s College London London United Kingdom Heidelberg Division of Medical Image Computing Germany Instituto de Cálculo CONICET – Universidad de Buenos Aires Buenos Aires Argentina Centre for Medical Image Computing University College London London United Kingdom McGill University Montréal

Increasing evidence shows that flaws in machine learning (ML) algorithm validation are an underestimated global problem. Particularly in automatic biomedical image analysis, chosen performance metrics often do not reflect the domain interest, thus failing to adequately measure scientific progress and hindering translation of ML techniques into practice. To overcome this, we created Metrics Reloaded, a comprehensive framework guiding researchers in the problem-aware selection of metrics. It was developed by a large international consortium in a multi-stage Delphi process and is based on the novel concept of a problem fingerprint – a structured representation of the given problem that captures all aspects that are relevant for metric selection, from the domain interest to the properties of the target structure(s), data set and algorithm output. Based on the problem fingerprint, users are guided through the process of choosing and applying appropriate validation metrics while being made aware of potential pitfalls. Metrics Reloaded targets image analysis problems that can be interpreted as a classification task at image, object or pixel level, namely image-level classification, object detection, semantic segmentation, and instance segmentation tasks. To improve the user experience, we implemented the framework in the Metrics Reloaded online tool. Following the convergence of ML methodology across application domains, Metrics Reloaded fosters the convergence of validation methodology. Its applicability is demonstrated for various biomedical use cases. © 2022, CC BY.

关键词： Semantic Segmentation

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