Abstract
Air quality sensing systems, such as e-nose, are one of the complex dynamic systems; due to their sensitivity to electromagnetic interference, humidity, temperature, pressure and airflow. This yield to a Multi-Dependency effect over the output signal. To address the Multi-Dependency effect, we propose a multi-dimensional signal transformation for feature extraction. Our idea is analogous to viewing one huge object from different angles and arriving at different perspectives. Every perspective is partially true, but the final picture can be inferred by combining all perspectives. We evaluated our method extensively on two data sets including a publicly available e-nose dataset generated over a three-year period. Our results show higher performance in term of accuracies, F-measure, and stability when compared to standard methods.