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Real-time particle pollution sensing using machine learning
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AI
DOI：10.1364/OE.26.027237.png)
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Abstract
En 中文
Particle pollution is a global health challenge that is linked to around three million premature deaths per year. There is therefore great interest in the development of sensors capable of precisely quantifying both the number and type of particles. Here, we demonstrate an approach that leverages machine learning in order to identify particulates directly from their scattering patterns. We show the capability for producing a 2D sample map of spherical particles present on a coverslip, and also demonstrate real-time identification of a range of particles including those from diesel combustion. Published by The Optical Society under the terms of the Creative Commons Attribution 4.0 License.
Keywords:
PHASE RETRIEVAL
NEURAL-NETWORKS
HOLOGRAPHIC CHARACTERIZATION
PARTICULATE MATTER
CLASSIFICATION
RECOGNITION