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5 March 2021 Machine-learning-enhanced spatial spectroscopy for environmental sensing
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Abstract
In this presentation we will outline our recent results utilising support vector network machine learning approaches to determine independently variations in Reynolds number and Fried Parameter over an atmospheric channel by analysing optical degradations in optical beams carrying Orbital Angular Momentum (OAM). Through numerical modelling of cascaded optical perturbations a comprehensive training set of OAM mode spatial spectra was produced over a simulated 1.5km's free-space optical channel in an urban environment. Our results indicate this machine learning approach will determine independently the Reynolds number and Fried Parameter with over 90.4% accuracy. These results indicate potential new methods for determination of variation in material properties that could be used for the detection of environmental contamination and weather monitoring.
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© (2021) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Martin P. J. Lavery, Mingjian Cheng, and Zhaozhong Chen "Machine-learning-enhanced spatial spectroscopy for environmental sensing", Proc. SPIE 11701, Complex Light and Optical Forces XV, 1170109 (5 March 2021); https://doi.org/10.1117/12.2584261
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