Characterizing Evaporation Ducts Within the Marine Atmospheric Boundary Layer Using Artificial Neural Networks

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. This paper explains the rationale behind the selection of the ML network architecture, along with other model hyperparameters, in an effort to demystify the process of arriving at a useful ML model. 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.Comment: 13 pages, 7 figure

Gaussian Process Regression for Estimating EM Ducting Within the Marine Atmospheric Boundary Layer

We show that Gaussian process regression (GPR) can be used to infer the electromagnetic (EM) duct height within the marine atmospheric boundary layer (MABL) from sparsely sampled propagation factors within the context of bistatic radars. We use GPR to calculate the posterior predictive distribution on the labels (i.e. duct height) from both noise-free and noise-contaminated array of propagation factors. For duct height inference from noise-contaminated propagation factors, we compare a naive approach, utilizing one random sample from the input distribution (i.e. disregarding the input noise), with an inverse-variance weighted approach, utilizing a few random samples to estimate the true predictive distribution. The resulting posterior predictive distributions from these two approaches are compared to a "ground truth" distribution, which is approximated using a large number of Monte-Carlo samples. The ability of GPR to yield accurate and fast duct height predictions using a few training examples indicates the suitability of the proposed method for real-time applications.Comment: 15 pages, 6 figure

Computational Study of Hydrogel Ring Device for Ocular Drug Delivery

Researchers have developed many different kinds of ocular drug delivery devices. However, most address anterior eye disorders—very few are designed specifically for the treatment of posterior eye diseases. A recently-developed hydrogel ring device is capable of delivering therapeutic quantities of the drug Ofloxacin to treat ocular infections at the back of the eye—a region typically difficult to access via systemic (e.g. ingestion of pills) and topical (e.g. eye drops) methods. Despite promising preliminary in vivo test results, much remains unknown about the precise drug transport pathway from the hydrogel ring to the posterior segment of the eye, as well as how design parameters may be altered to increase drug delivery efficiency. The aim of this work is to fully characterize the drug release and transport characteristics from the hydrogel, to ocular tissues (anterior and posterior), as well as provide a quantitative method for the optimization of various hydrogel ring design parameters. To achieve the abovementioned goals, we built a computational model using COMSOL Multiphysics to simulate the release of Ofloxacin from the hydrogel ring and to obtain the resulting drug distribution in ocular tissues at various time points. Using the model, we monitored the transient Ofloxacin concentration profile over the entire eye, for a treatment period of ten hours. Our results showed that while Ofloxacin diffuses to the anterior region much more quickly than to posterior tissues, Ofloxacin concentrations do successfully accumulate to therapeutic levels in the posterior tissues during the simulated ten-hour treatment period. This finding supports the therapeutic potential of the hydrogel ring for the treatment of posterior eye diseases. We also performed optimization analyses to determine the ideal set of hydrogel ring design parameters for the treatment of infections caused by three bacterial species commonly associated with ocular disorders: Escherichia coli, Staphylococcus aureus, and Streptococcus pneumoniae. Preliminary findings suggest that the combination of an initial mass of 3 mg/m3 of Ofloxacin in the hydrogel and an Ofloxacin diffusivity of 3.11X10−9 m2/s in the hydrogel provide the best possible therapeutic outcome (from the range of values tested) for the treatment of E. coli and S. aureus infections. To our best knowledge, there is no existing computational model that simulates drug transport through the entire human eye from an ocular drug delivery device. We believe that our computational model will be highly useful for quantitative device characterization of the hydrogel ring, as well as in the optimization of the hydrogel ring design for the treatment of posterior eye disorders. This work may also serve as a model and reference for future computational work on ocular pharmacokinetics and/or ocular drug delivery devices

Machine Learning Approaches for Characterizing Electromagnetic Ducting Within the Marine Atmospheric Boundary Layer

99 pagesThis dissertation explores machine learning approaches for estimating the refractivity within the marine atmospheric boundary layer (MABL) under various electromagnetic ducting conditions. We use simulated radar propagation data that is representative of data that can be sparsely measured in practice. In conjunction with the sparse data collection scheme, a trained artificial neural network can be used to effectively characterize evaporation duct height (EDH) from the data, in real-time. We further show that Gaussian process regression (GPR) can accomplish this task, and produce uncertainty quantification on the EDH predictions, also in real-time. Finally, we show that a two-step deep learning model can classify and characterize different types of ducting conditions

Code and data from: Gaussian Process Regression for Estimating EM Ducting Within the Marine Atmospheric Boundary Layer

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.We show that Gaussian process regression (GPR) can be used to infer the electromagnetic (EM) duct height within the marine atmospheric boundary layer (MABL) from sparsely sampled propagation factors within the context of bistaticradars. These propagation factors are simulated using PETOOL, developed by Ozgun et al. 2011, and the datasets for the three cases that correspond to the different sparse sampling techniques can be found in the data folder. We use GPR to calculate the posterior predictive distribution on the labels (i.e. duct height) from both noise-free and noise-contaminated array of propagation factors. For duct height inference from noise-contaminated propagation factors, we compare a naive approach, utilizing one random sample from the input distribution (i.e. disregarding the input noise), with an inverse-variance weighted approach, utilizing a few random samples to estimate the true predictive distribution. The resulting posterior predictive distributions from these two approaches are compared to a "ground truth" distribution, which is approximated using a large number of Monte-Carlo samples. We use Python 3.6.4 and scikit-learn 0.20.2. The ability of GPR to yield accurate duct height predictions using few training examples, along with its inference speed, indicates the suitability of the proposed method for real-time applications. This is the dataset and code that supports this work.The authors gratefully acknowledge ONR Division 331 and Dr. Steve Russell for the financial support of this work through grant N00014-19-1-2095

Code and data from: Characterizing Evaporation Ducts Within the Marine Atmospheric Boundary Layer Using Artificial Neural Networks

Copyright (c) 2019 Hilarie Sit, [email protected] 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.Abstract: 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.The authors gratefully acknowledge ONR Division 331 and Dr. Steve Russell for the financial support of this work through grant N00014-19-1-2095