Discriminating Gas Emission Patterns in Low-Cost Sensor Setups

This work presents a two-step heuristic that employs extremely low-cost sensors for gaseous emission event discrimination. These events are triggered by particular patterns of sensor responses possibly occurring when a certain gas is emitted; patterns are then used to produce human-understandable inference rules describing the kind of emission measured. The technique, challenged by the high cross-sensitivity of the employed sensors, is based on two steps: first, sensor response patterns are extracted (unsupervisedly) from measurement signals by means of a recently proposed computational intelligence technique; second, a `credibility index' is applied (supervisedly) to each pattern via fuzzy membership functions. The outcome is a set of IF THEN statements weighted by fuzzy constraints. Experiments show that such inferences allow for accurate gaseous emission event discrimination.