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International Symposium on Power Electronics, Electrical Drives, Automation and Motion

作者： Hussein M. Al-Masri A. Abu-Errub Walaa R. Ayyad Mark Ehsani Sustainable Energy & Vehicle engineering Program Department of Electrical & Computer Engineering Texas A&M University College Station USA

ISBN: (纸本)9781509020683

Photovoltaic (PV) renewable energy resources are of interest for researchers today. An investigation of the PV characteristics is useful to understand its behavior. In this paper, the characteristics of the PV module have been explored and analyzed for different values of temperature at constant insolation (1kW/m~2), and with different values of irradiance at constant temperature (25℃). Moreover, the characteristics of the PV module have been compared to an ideal voltage source in many cases by varying the ripple and average value of the PV voltage, current and power. The maximum power point (MPP) of the PV module is found graphically and analytically. Then, the duty ratio at which the PV module operates at maximum power, effect of changing the inductance value and switching frequency are explored for the PV voltage, current and power. The results show that the PV module behaves like an ideal voltage source at the MPP in case of average value comparison. Furthermore, the two sources have approximately the same ripple current. The effects of increasing the inductance value as well as the switching frequency values are investigated. As the inductance value and frequency increase, this will give very low ripple current, voltage and power, and thereby a better approximation for the MPP.

关键词： PV Modeling MPPT Boost Converter Ripple

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Fast Saliency Detection Using Sparse Random Color Samples and Joint Upsampling

Fast Saliency Detection Using Sparse Random Color Samples an...

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Brazilian Symposium on computer Graphics and Image Processing (SIBGRAPI)

作者： Maiko Min Ian Lie Gustavo Benvenutti Borba Hugo Vieira Neto Humberto Remigio Gamba Grad. Program in Electr. & Comput. Eng. Fed. Univ. of Technol.-Parana Curitiba Brazil Graduate Program in Biomedical Engineering Graduate Program in Electrical and Computer Engineering

ISBN: (纸本)9781509035694;9781509035687

The human visual system employs a mechanism of visual attention, which selects only part of the incoming information for further processing. Through this mechanism, the brain avoids overloading its limited cognitive capacities. In computer vision, this task is usually accomplished through saliency detection, which outputs the regions of an image that are distinctive with respect to its surroundings. This ability is desirable in many technological applications, such as image compression, video quality assessment and content-based image retrieval. In this paper, a saliency detection method based on color distance with sparse random samples and joint upsampling is presented. This approach computes full-resolution saliency maps with short runtime by leveraging both edge-preserving smoothing and joint upsampling capabilities of the Fast Global Smoother. The proposed method is assessed through precision-recall curves, F-measure and average runtime on the MSRA1K dataset. Results show that the method is competitive with state-of-the-art algorithms in both saliency detection accuracy and runtime.

关键词： Image color analysis Runtime Visualization Image edge detection Smoothing methods Computational modeling Noise measurement

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Iran in transition (vol 393, pg 1984, 2019)

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LANCET 2019年第10186期393卷 2124-2124页

作者： Danaei, G. Farzadfar, F. Kelishadi, R. [a]Department of Global Health and Population and Department of Epidemiology Harvard T H Chan School of Public Health Boston MA USA Department of Global Health and Population and Department of Epidemiology Harvard T H Chan School of Public Health Boston MA USA [b]Scientific Association for Public Health in Iran Boston MA USA Scientific Association for Public Health in Iran Boston MA USA [c]Non-Communicable Diseases Research Center Endocrinology and Metabolism Population Sciences Institute Tehran University of Medical Sciences Tehran Iran Non-Communicable Diseases Research Center Endocrinology and Metabolism Population Sciences Institute Tehran University of Medical Sciences Tehran Iran [d]Endocrinology and Metabolism Research Center Tehran University of Medical Sciences Tehran Iran Endocrinology and Metabolism Research Center Tehran University of Medical Sciences Tehran Iran [e]Diabetes Research Center Tehran University of Medical Sciences Tehran Iran Diabetes Research Center Tehran University of Medical Sciences Tehran Iran [f]Endocrinology and Metabolism Clinical Sciences Institute Department of Health Management and Economics Tehran University of Medical Sciences Tehran Iran Endocrinology and Metabolism Clinical Sciences Institute Department of Health Management and Economics Tehran University of Medical Sciences Tehran Iran [g]Department of Global Health and Public Policy Tehran University of Medical Sciences Tehran Iran Department of Global Health and Public Policy Tehran University of Medical Sciences Tehran Iran [h]Department of Epidemiology and Biostatistics Tehran University of Medical Sciences Tehran Iran Department of Epidemiology and Biostatistics Tehran University of Medical Sciences Tehran Iran [i]School of Public Health Tehran University of Medical Sciences Tehran Iran School of Public Health Tehran University of Medical Sciences Tehran Iran [j]Faculty of Medicine Tehran University of Medical Sciences Tehran Iran Faculty of Medicine Te

Summary Being the second-largest country in the Middle East, Iran has a long history of civilisation during which several dynasties have been overthrown and established and health-related structures have been reorganised. Iran has had the replacement of traditional practices with modern medical treatments, emergence of multiple pioneer scientists and physicians with great contributions to the advancement of science, environmental and ecological changes in addition to large-scale natural disasters, epidemics of multiple communicable diseases, and the shift towards non-communicable diseases in recent decades. Given the lessons learnt from political instabilities in the past centuries and the approaches undertaken to overcome health challenges at the time, Iran has emerged as it is today. Iran is now a country with a population exceeding 80 million, mainly inhabiting urban regions, and has an increasing burden of non-communicable diseases, including cardiovascular diseases, hypertension, diabetes, malignancies, mental disorders, substance abuse, and road injuries.

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Smart ITS sensor for the transportation planning using the IoT and Bigdata approaches to produce ITS cloud services

Smart ITS sensor for the transportation planning using the I...

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Euro American Conference on Telematics and Information Systems (EATIS)

作者： Luis Felipe Herrera-Quintero Klaus Banse Julián Vega-Alfonso Andrés Venegas-Sanchez Computer Science Program Telecommunication Engineering Program Universidad Piloto de Colombia ITS Colombia Colombia Universidad de los Andes AXIS

ISBN: (纸本)9781509024377

Currently, there exist a lot of challenges in the transportation scope that researcher are trying to resolve and one of them can be focused on transportation planning. The main contribution of this paper was the design and implementation of an ITS smart sensor prototype that incorporates and combine the Internet of Things (IoT) and Bigdata approaches in order to produce ITS cloud services for helping transportation planning for Bus Rapid Transit (BRT) systems. The ITS smart sensor prototype is capable of detecting several Bluetooth signals belonging to several devices (for instance from mobile phones) that people uses into the BRT (for instance, in Bogota city) system. As from that information, the ITS smart sensor prototype can create the O/D (origin/Destiny) Matrix for several BRT routes and this information can be used by the Administrator Authorities (AA) in order to produce a suitable transportation planning for the BRT systems. In addition, that information can be used by AA as from cloud services.

关键词： Decision support systems

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Multistatic Microwave Holography: Initial Results on Anthropomorphic Phantoms

Multistatic Microwave Holography: Initial Results on Anthrop...

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European Conference on Antennas and Propagation

作者： Daniel Flores Tapia Mario Solis Nepote Diego Rodriguez Herrera Lei Fu Yonsheng Gui Valerie Benyon Can-Min Hu Stephen Pistorius Saeed Latif Department of Physics and Astronomy Biomedical Engineering Program Department of Electrical and Computer Engineering University of Manitoba Winnipeg Manitoba Canada Department of Electrical and Computer Engineering University of South Alabama Mobile Alabama USA

ISBN: (纸本)9781509022342

Ultra Wide Band Radar is a novel technology used for breast cancer detection. This imaging modality uses ultra wide band waveforms to form a reflectivity map of the breast region in which tumors can potentially been clearly distinguished. In this study, the authors assess the use of a novel Breast UWBR reconstruction approach, microwave holography, on anthropomorphic phantoms. These phantoms mimic both the shape and dielectric properties of human breast. The images formed using microwave holography are consistent with the location of the synthetic breast structures and show signal to clutter level above 3dB.

关键词： UWBR Multistatic Microwave

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DEA and Fuzzy Simple Additive Weighting for Benchmarking Qualitative Data

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Journal of Physics: Conference Series 2019年第1期1361卷

作者： Dahlan Abdullah H Hartono Erianto Ongko Cut Ita Erliana Defi Irwansyah Akbar Iskandar Ayu Esteka Sari Abdussakir Yuniningsih Nurintan Asyiah Siregar Muhammad Syahrul Kahar Raditya Faisal Waliulu Ainna Amalia Nuning Kurniasih Muhammad Arifin Syafiul Muzid Department of Informatics Universitas Malikussaleh Aceh Indonesia Department of Computer Science STMIK IBBI Medan Indonesia Department of Informatics Akademi Teknologi Industri Immanuel Medan Indonesia Department of Industrial Engineering Universitas Malikussaleh Aceh Indonesia Department of Informatics STMIK AKBA Makassar Indonesia Department of Management STIE Sakti Alam Kerinci Jambi Indonesia Department of Mathematics Education State Islamic University of Malang MalangIndonesia Department of Economic and Business Universitas Pembangunan Nasional 'Veteran' Surabaya Indonesia Department of Management STIE Labuhanbatu Sumatera Utara Indonesia Mathematics Education Department Universitas Muhammadiyah Sorong Sorong Indonesia Informatics Engineering Department Universitas Muhammadiyah Sorong Sorong Indonesia Islamic Institut of Miftahul Ula Kertosono Nganjuk East Java Indonesia Faculty of Communication Sciences Library and Information Science Program Universitas Padjadjaran Bandung Indonesia Department of Information System Faculty of Engineering Universitas Muria Kudus Indonesia Information System Department Universitas Muria Kudus Indonesia

Data Envelopment Analysis (DEA) is one method that is often used in the benchmarking process. The benchmarking process with DEA is often faced with problems where there is a Decision Making Unit (DMU) both Input and Output which is not in the form of qualitative data but in the form of qualitative data. Qualitative data requires an assessment from the appraiser so that it can become quantitative data. This study will use the Fuzzy Simple Additive Weighting method to convert qualitative data into quantitative. The results of this study will produce a model that integrates DEA with Fuzzy Simple Additive Weighting.

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First Constraints on the Epoch of Reionization Using the non-Gaussianity of the Kinematic Sunyaev-Zel’dovich Effect from the South Pole Telescope and Herschel-SPIRE Observations

arXiv

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arXiv 2024年

作者： Raghunathan, S. Ade, P.A.R. Anderson, A.J. Ansarinejad, B. Archipley, M. Austermann, J.E. Balkenhol, L. Beall, J.A. Benabed, K. Bender, A.N. Benson, B.A. Bianchini, F. Bleem, L.E. Bock, J. Bouchet, F.R. Bryant, L. Camphuis, E. Carlstrom, J.E. Cecil, T.W. Chang, C.L. Chaubal, P. Chiang, H.C. Chichura, P.M. Chou, T.-L. Citron, R. Coerver, A. Crawford, T.M. Crites, A.T. Cukierman, A. Daley, C. Dibert, K.R. Dobbs, M.A. Doussot, A. Dutcher, D. Everett, W. Feng, C. Ferguson, K.R. Fichman, K. Foster, A. Galli, S. Gallicchio, J. Gambrel, A.E. Gardner, R.W. Ge, F. George, E.M. Goeckner-Wald, N. Gualtieri, R. Guidi, F. Guns, S. Gupta, N. de Haan, T. Halverson, N.W. Hivon, E. Holder, G.P. Holzapfel, W.L. Hood, J.C. Hrubes, J.D. Hryciuk, A. Huang, N. Hubmayr, J. Irwin, K.D. Kéruzoré, F. Khalife, A.R. Knox, L. Korman, M. Kornoelje, K. Kuo, C.-L. Lee, A.T. Levy, K. Li, D. Lowitz, A.E. Lu, C. Maniyar, A. Martsen, E.S. McMahon, J.J. Menanteau, F. Millea, M. Montgomery, J. Moran, C. Corbett Nakato, Y. Natoli, T. Nibarger, J.P. Noble, G.I. Novosad, V. Omori, Y. Padin, S. Pan, Z. Paschos, P. Patil, S. Phadke, K.A. Prabhu, K. Pryke, C. Quan, W. Rahimi, M. Rahlin, A. Reichardt, C.L. Rouble, M. Ruhl, J.E. Saliwanchik, B.R. Schaffer, K.K. Schiappucci, E. Sievers, C. Smecher, G. Sobrin, J.A. Stark, A.A. Stephen, J. Suzuki, A. Tandoi, C. Thompson, K.L. Thorne, B. Trendafilova, C. Tucker, C. Umilta, C. Veach, T. Vieira, J.D. Viero, M.P. Wan, Y. Wang, G. Whitehorn, N. Wu, W.L.K. Yefremenko, V. Young, M.R. Zebrowski, J.A. Zemcov, M. Center for AstroPhysical Surveys National Center for Supercomputing Applications UrbanaIL61801 United States School of Physics and Astronomy Cardiff University CardiffCF24 3YB United Kingdom Fermi National Accelerator Laboratory MS209 P.O. Box 500 BataviaIL60510 United States Kavli Institute for Cosmological Physics University of Chicago 5640 South Ellis Avenue ChicagoIL60637 United States Department of Astronomy and Astrophysics University of Chicago 5640 South Ellis Avenue ChicagoIL60637 United States School of Physics University of Melbourne ParkvilleVIC3010 Australia Department of Astronomy University of Illinois Urbana-Champaign 1002 West Green Street UrbanaIL61801 United States NIST Quantum Devices Group 325 Broadway Mailcode 817.03 BoulderCO80305 United States Department of Physics University of Colorado BoulderCO80309 United States Institut d’Astrophysique de Paris UMR 7095 CNRS & Sorbonne Université 98 bis boulevard Arago Paris75014 France High-Energy Physics Division Argonne National Laboratory 9700 South Cass Avenue. LemontIL60439 United States Kavli Institute for Particle Astrophysics and Cosmology Stanford University 452 Lomita Mall StanfordCA94305 United States Department of Physics Stanford University 382 Via Pueblo Mall StanfordCA94305 United States SLAC National Accelerator Laboratory 2575 Sand Hill Road Menlo ParkCA94025 United States California Institute of Technology PasadenaCA91125 United States Jet Propulsion Laboratory California Institute of Technology PasadenaCA91109 United States Enrico Fermi Institute University of Chicago 5640 South Ellis Avenue ChicagoIL60637 United States Department of Physics University of Chicago 5640 South Ellis Avenue ChicagoIL60637 United States Department of Physics McGill Space Institute McGill University 3600 Rue University MontrealQCH3A 2T8 Canada School of Mathematics Statistics & Computer Science University of KwaZulu-Natal Durban South Africa Unive

We report results from an analysis aimed at detecting the trispectrum of the kinematic Sunyaev-Zel’dovich (kSZ) effect by combining data from the South Pole Telescope (SPT) and Herschel-SPIRE experiments over a 100 deg2 field. The SPT observations combine data from the previous and current surveys, namely SPTpol and SPT-3G, to achieve depths of 4.5, 3, and 16 µK-arcmin in bands centered at 95, 150, and 220 GHz. For SPIRE, we include data from the 600 and 857 GHz bands. We reconstruct the velocity-induced large-scale correlation of the small-scale kSZ signal with a quadratic estimator that uses two cosmic microwave background (CMB) temperature maps, constructed by optimally combining data from all the frequency bands. We reject the null hypothesis of a zero trispectrum at 10.3σ level. However, the measured trispectrum contains contributions from both the kSZ and other undesired components, such as CMB lensing and astrophysical foregrounds, with kSZ being sub-dominant. We use the Agora simulations to estimate the expected signal from CMB lensing and astrophysical foregrounds. After accounting for the contributions from CMB lensing and foreground signals, we do not detect an excess kSZ-only trispectrum and use this non-detection to set constraints on reionization. By applying a prior based on observations of the Gunn-Peterson trough, we obtain an upper limit on the duration of reionization of (formula presented). We find these constraints are fairly robust to foregrounds assumptions. This trispectrum measurement is independent of, but consistent with, Planck’s optical depth measurement. This result is the first constraint on the epoch of reionization using the non-Gaussian nature of the kSZ signal. © 2024, CC BY.

关键词： Kinematics

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EFFICIENT SUPERPIXEL BASED SEGMENTATION FOR FOOD IMAGE ANALYSIS

EFFICIENT SUPERPIXEL BASED SEGMENTATION FOR FOOD IMAGE ANALY...

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IEEE International Conference on Image Processing

作者： Yu Wang Chang Liu Fengqing Zhu Carol J. Boushey Edward J. Delp School of Electrical and Computer Engineering Purdue University Cancer Epidemiology Program University of Hawaii Cancer Center

ISBN: (纸本)9781467399623

In this paper, we propose a segmentation method based on normalized cut and superpixels. The method relies on color and texture cues for fast computation and efficient use of memory. The method is used for food image segmentation as part of a mobile food record system we have developed for dietary assessment and management. The accurate estimate of nutrients relies on correctly labelled food items and sufficiently well-segmented regions. Our method achieves competitive results using the Berkeley Segmentation Dataset and outperforms some of the most popular techniques in a food image dataset.

关键词： Image segmentation Superpixel Graph model Nutrient analysis image segmentation Graphical models Food NUTRIENT ANALYSIS image analysis RECORDING SYSTEMS

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Signatures of Electronic Nematicity In (111) Laalo3/Srtio3 Interfaces

arXiv

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arXiv 2017年

作者： Davis, S. Huang, Z. Han, K. Ariando Venkatesan, T. Chandrasekhar, V. Graduate Program in Applied Physics Department of Physics and Astronomy Northwestern University 2145 Sheridan Road EvanstonIL60208 United States NUSNNI-Nanocore National University of Singapore 117411 Singapore Department of Physics National University of Singapore 117551 Singapore NUS Graduate School for Integrative Sciences & Engineering National University of Singapore 117456 Singapore Department of Electrical and Computer Engineering National University of Singapore 117576 Singapore Department of Material Science and Engineering National University of Singapore 117575 Singapore

Symmetry breaking is a fundamental concept in condensed matter physics whose presence often heralds new phases of matter. For instance, the breaking of time reversal symmetry is traditionally linked to magnetic phases in a material, while the breaking of gauge symmetry can lead to superfluidity/superconductivity. Nematic phases are phases in which rotational symmetry is broken while maintaining translational symmetry, and are traditionally associated with liquid crystals. Electronic nematic states where the orthogonal in-plane crystal directions have different electronic properties have garnered a great deal of attention after their discovery in Sr3Ru2O7,1 multiple iron based superconductors,2,3 and in the superconducting state of CuBiSe.4,5 Here we demonstrate the existence of an electronic nematic phase in the two-dimensional carrier gas that forms at the (111) LaAlO3 (LAO)/SrTiO3 (STO) interface that onsets at low temperatures, and is tunable by an electric field. Copyright © 2017, The Authors. All rights reserved.

关键词： Electronic properties

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The Brain Tumor Segmentation (BraTS) Challenge: Local Synthesis of Healthy Brain Tissue via Inpainting

arXiv

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arXiv 2023年

作者： Kofler, Florian Steinbauer, Felix Meissen, Felix Graf, Robert Ehrlich, Stefan K. Reinke, Annika Oswald, Eva Waldmannstetter, Diana Hoelzl, Florian Horvath, Izabela Turgut, Oezguen Shit, Suprosanna Bukas, Christina Yang, Kaiyuan Paetzold, Johannes C. de da Rosa, Ezequiel Mekki, Isra Vinayahalingam, Shankeeth Kassem, Hasan Zhang, Juexin Chen, Ke Weng, Ying Durrer, Alicia Cattin, Philippe C. Wolleb, Julia Sadique, M.S. Rahman, M.M. Farzana, W. Temtam, A. Iftekharuddin, K.M. Adewole, Maruf Anwar, Syed Muhammad Baid, Ujjwal Janas, Anastasia Kazerooni, Anahita Fathi LaBella, Dominic Bran Li, Hongwei Moawad, Ahmed W. Conte, Gian-Marco Farahani, Keyvan Eddy, James Sheller, Micah Pati, Sarthak Karagyris, Alexandros Aristizabal, Alejandro Bergquist, Timothy Chung, Verena Shinohara, Russell Takeshi Dako, Farouk Wiggins, Walter Reitman, Zachary Wang, Chunhao Liu, Xinyang Jiang, Zhifan Johanson, Elaine Meier, Zeke Familiar, Ariana Davatzikos, Christos Freymann, John Kirby, Justin Bilello, Michel Fathallah-Shaykh, Hassan M. Wiest, Roland Kirschke, Jan Colen, Rivka R. Kotrotsou, Aikaterini Lamontagne, Pamela Marcus, Daniel Milchenko, Mikhail Nazeri, Arash Weber, Marc-André Mahajan, Abhishek Mohan, Suyash Mongan, John Hess, Christopher Cha, Soonmee Villanueva-Meyer, Javier Colak, Errol Crivellaro, Priscila Jakab, Andras Fatade, Abiodun Omidiji, Olubukola Akinola Lagos, Rachel Olatunji, O.O. Khanna, Goldey Kirkpatrick, John Alonso-Basanta, Michelle Rashid, Arif Bornhorst, Miriam Nabavizadeh, Ali Lepore, Natasha Palmer, Joshua Porras, Antonio Albrecht, Jake Anazodo, Udunna Aboian, Mariam Calabrese, Evan Rudie, Jeffrey David Linguraru, Marius George Iglesias, Juan Eugenio Van Leemput, Koen Bakas, Spyridon Wiestler, Benedikt Ezhov, Ivan Piraud, Marie Menze, Bjoern H. Helmholtz AI Germany Technical University of Munich Germany Helmholtz Munich Germany Klinikum Rechts der Isar Munich Germany SETLabs Research GmbH Healthcare Technologies Munich Germany Heidelberg Division of Intelligent Medical Systems Germany Heidelberg HI Helmholtz Imaging Germany Institute of Clinical Neuroimmunology Faculty of Medicine Ludwig Maximilian University Munich Germany University of Zurich Switzerland Helmholtz Research Center Munich Germany Imperial College London United Kingdom Icometrix Leuven Belgium Department of Oral and Maxillofacial Surgery Radboudumc Netherlands Department of Artificial Intelligence Radboud University Netherlands MLCommons University of Nottingham Ningbo China Ningbo315100 China Center for medical Image Analysis and Navigation Department of Biomedical Engineering University of Basel Allschwil Switzerland Old Dominion University NorfolkVA23529 United States Lab Crestview Radiology Lagos Nigeria Children's National Hospital WashingtonDC United States George Washington University WashingtonDC United States University of Pennsylvania PhiladelphiaPA United States Department of Radiology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Yale University New HavenCT United States Children's Hospital of Philadelphia University of Pennsylvania PhiladelphiaPA United States Duke University Medical Center Department of Radiation Oncology United States Athinoula A Martinos Center for Biomedical Imaging Massachusetts General Hospital BostonMA United States Department of Neuroradiology Technical University of Munich Munich Germany Mercy Catholic Medical Center DarbyPA United States Department of Radiology Mayo Clinic RochesterMN United States Cancer Imaging Program National Cancer Institute National Institutes of Health BethesdaMD20814 U

A myriad of algorithms for the automatic analysis of brain MR images is available to support clinicians in their decision-making. For brain tumor patients, the image acquisition time series typically starts with an already pathological scan. This poses problems, as many algorithms are designed to analyze healthy brains and provide no guarantee for images featuring lesions. Examples include but are not limited to algorithms for brain anatomy parcellation, tissue segmentation, and brain extraction. To solve this dilemma, we introduce the BraTS inpainting challenge. Here, the participants explore inpainting techniques to synthesize healthy brain scans from lesioned ones. The following manuscript contains the task formulation, dataset, and submission procedure. Later, it will be updated to summarize the findings of the challenge. The challenge is organized as part of the ASNR-BraTS MICCAI challenge. Copyright © 2023, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

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