16 May 2011 Automated classification of single airborne particles from two-dimension, angle-resolved optical scattering (TAOS) patterns
Author Affiliations +
Abstract
Two-dimension, angle-resolved optical scattering (TAOS) is an experimental technique by which patterns of LASER light intensity scattered by single (micrometer or sub-micrometer sized) airborne particles are collected. In the past 10 years TAOS instrumentation has evolved from laboratory prototypes to field-deployable equipment; patterns are collected by the thousands during indoor or outdoor sampling in short times. Although comparison between experimental and computed scattering patterns has been carried out extensively, there is no satisfactory way to relate a given pattern to the particle it comes from. This paper reports about the ongoing development and implementation of a method which is aimed at classifying patterns, rather than identifying original particles. A machine learning algorithm includes the extraction of morphological features and their multivariate statistical analysis. A classifier is trained and validated in a supervised mode, by relying on patterns from known materials. Then the tuned classifier is applied to the recognition of patterns of unknown origin.
© (2011) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Giovanni F. Crosta, Yong-Le Pan, Richard K. Chang, "Automated classification of single airborne particles from two-dimension, angle-resolved optical scattering (TAOS) patterns", Proc. SPIE 8029, Sensing Technologies for Global Health, Military Medicine, Disaster Response, and Environmental Monitoring; and Biometric Technology for Human Identification VIII, 80290Y (16 May 2011); doi: 10.1117/12.883607; https://doi.org/10.1117/12.883607
PROCEEDINGS
9 PAGES


SHARE
Back to Top