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Bio-Aerosol Detection Using Mobile Microscopy and Machine Learning (Case No. 2019-722)

Summary: UCLA researchers in the Department of Electrical and Computer Engineering have developed an air analysis instrument and accompanying virtual aerosol detection method that combines imaging and deep learning to sense and classify airborne particles without external labeling or post processing steps. Background: Air quality management, particularly...

Published: 11/7/2023 | Inventor(s): Aydogan Ozcan

Keywords(s): aerosol, Agricultural & plant biology research, air quality measurement, bioaerosol, Bioterrorism detection, Deep Learning, Deep learning-based sensing, Digital Holography, Holography, Indoor air quality monitoring, Industrial applications: food processing, fermentation, label-free sensing, pollen detection, real-time sensing/monitoring/tracking, smart sensing

Category(s): Software & Algorithms > Artificial Intelligence & Machine Learning, Life Science Research Tools > Microscopy And Imaging, Chemical > Chemical Sensors