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dc.contributor.authorSit, Hilarie
dc.contributor.authorEarls, Christopher J
dc.descriptionCopyright (c) 2019 Hilarie Sit, Developed by Hilarie Sit, Cornell University All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: * Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. * Neither the name of the copyright holders nor the name of Cornell University may be used to endorse or promote products derived from this software without specific prior written permission. Private, research, and institutional usage is without charge. Distribution of modified versions of this soure code is admissible UNDER THE CONDITION THAT THIS SOURCE CODE REMAINS UNDER COPYRIGHT OF THE ORIGINAL DEVELOPERS, BOTH SOURCE AND OBJECT CODE ARE MADE FREELY AVAILABLE WITHOUT CHARGE, AND CLEAR NOTICE IS GIVEN OF THE MODIFICATIONS. Distribution of this code as part of a commercial system is permissible ONLY BY DIRECT ARRANGEMENT WITH THE DEVELOPERS. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.en_US
dc.description.abstractAbstract: We apply a multilayer perceptron machine learning (ML) regression approach to infer electromagnetic (EM) duct heights within the marine atmospheric boundary layer (MABL) using sparsely sampled EM propagation data obtained within a bistatic context. EM propagation data is simulated using PETOOL, a MATLAB-based software developed by Ozgun et al. 2011 for solving the split-step parabolic equation approximation of Helmholtz wave equation. Three cases in the data folder correspond to different sparse sampling techniques detailed in our paper. Artificial neural networks are implemented utilizing Tensorflow, and its hyperparameters are selected with grid search. Results for model selection and evaluation can be found in their respective folders. The resulting speed of our ML predictions of EM duct heights, using sparse data measurements within MABL, indicates the suitability of the proposed method for real-time applications.en_US
dc.description.sponsorshipThe authors gratefully acknowledge ONR Division 331 and Dr. Steve Russell for the financial support of this work through grant N00014-19-1-2095.en_US
dc.relation.isreferencedbySit, H., Earls, C.J. (2019) Characterizing Evaporation Ducts Within the Marine Atmospheric Boundary Layer Using Artificial Neural Networks, Radio Science, American Geophysical Union.
dc.subjectEM propagationen_US
dc.subjectevaporation ductsen_US
dc.subjectneural networken_US
dc.subjectmodel selectionen_US
dc.titleCode and data from: Characterizing Evaporation Ducts Within the Marine Atmospheric Boundary Layer Using Artificial Neural Networksen_US

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