Machine-learning-enhanced spatial spectroscopy for environmental sensing

Martin P. J. Lavery; Mingjian Cheng; Zhaozhong Chen

doi:10.1117/12.2584261

5 March 2021 Machine-learning-enhanced spatial spectroscopy for environmental sensing

Martin P. J. Lavery, Mingjian Cheng, Zhaozhong Chen

Proceedings Volume 11701, Complex Light and Optical Forces XV; 1170109 (2021) https://doi.org/10.1117/12.2584261
Event: SPIE OPTO, 2021, Online Only

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.

Conference Presentation

Citation Download Citation

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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