Versatile Gas Monitoring System on the Heterogeneous Zynq SoC Platform

Abstract

This paper presents a gas identification system which is part of an ongoing research project aiming to develop a low power reconfigurable self-calibrated multi-sensing platform for gas applications where real time parameters such as temperatures and gas concentrations are monitored. The gas identification system is based on decision tree classifier; training is performed in MATLAB environment. It is first carried out using the steady states extracted from the raw data obtained from the sensors, and then using transformed data by applying principal component analysis. The data used for training is collected from a 16-Array SnO2 gas sensor. The sensor array is exposed to three types of gases (CO, C2H6O and H2) at ten different concentrations (20, 40, 60, 80, 100, 120, 140, 160, 180 and 200ppm). The resulting models are implemented in C and synthesized using Vivado High Level Synthesis tool for rapid prototyping on the heterogeneous Zynq platform.