An E-nose haar wavelet preprocessing circuit for spiking neural network classification

A simulation model for polymer film chemical sensors is developed based on a 1 dimensional diffusion equation. Using this model, electronic nose smell prints produced by the 32 sensor array of a Cyranose 320 are simulated to test pattern classification. A Haar wavelet Alter reduces noise and captures information about the diffusion rate of the analyte in each sensor. Inputs are encoded into a binary Hamming pattern and fed into a binary spiking neural network for pattern classification. The preprocessing circuit for the spiking neural network, including the wavelet Alter, is designed using standard cells for an 180 nm process. Real and simulated results from the spiking neural network classification algorithm are favorably compared to Bayes, canonical, and PCA-PNN classifiers.