Single aerosol recognition based on deep learning of multidimensional polarization signals

Aerosol particle classification are of great importance in many cases. But currently there is almost no universal method to deal with this task. In this study, we develop a one-dimensional convolution neural network taking multi-angle polarization time series signal of single suspended particle as input to identify its category. We train the network and reach quite high accuracy on a large dataset which contains signals of multi-kinds particles such as carbon black, PSL, dust and water-soluble salts. This method gives us a new way of looking deeper into single suspended particles in the air and the knowledge learned in this task may be able to be transferred to deal with tasks of recognizing more kinds or more complex aerosol particles such as bioaerosol or even airborne pathogen.