High-Speed Airborne Particle Monitoring Using Artificial Neural Networks

An instrument to detect particle shape and size from spatial light scattering profiles has High-speed Airborne Particle Monitoring Using Artificial Neural Networks 981 previously been described [6]. The system constrains individual particles to traverse a laser beam. Thus, spatial distributions of the light scattered by individual particles may be recorded as two dimensional grey-scale images. Due to their highly distributed nature, Artificial Neural Networks (ANNs) offer the possibility of high-speed nonlinear pattern classification. Their use in particulate classification has already been investigated. The work by Kohlus [7] used contour data extracted from microscopic images of particles, and so was not real-time. While using laser scattering data to allow real-time analysis, Bevan [2] used only three photomultipliers, from which very little shape information can be collected. This paper demonstrates the plausibility of particle classification based on shape recognition using an ANN. While capable of similar recognition rates, the neural networks are shown to offer a number of advantages over template matching.