CAIT project no.: FHWA NJ 2003 010
Fiscal Year: 2000/2001
Rutgers-CAIT Author(s): Patrick J. Szary
External Author(s): Darrin Hanna, Oakland University, Mahmoud Al'Nsour, Oakland UniversityKen Stevenson
Sponsor(s): NJDOT, FHWA-USDOT
Gas recognition technology has seen tremendous progress recently, as a result of the wide spread utilization ranging from applications in the automotive industry to food processing to environmental engineering. The progress ranges from developing new sensors that have a faster reaction time and higher sensitivity to the targeted gas, to developing new signal processing techniques that takes an off-the-shelf sensor array as inputs and outputs an accurate, fast, and highly sensitive, repeatable reading. The latter approach utilizes relatively low cost sensors and an inexpensive, intelligent Very Large Scale Integration Application Specific Integrated Circuit (VLSI ASIC) to provide a high quality gas recognition system. Current instruments used for vehicle emission testing to quantify the amount of unwanted gases, although very sophisticated and can reliably measure as low as few parts per million, are very sensitive to environmental changes such as temperature, humidity, oxygen content, and sometimes any non-gaseous content and particulates. In this project, a type of signal processing technique called reinforcement artificial neural networks with an array of off-the-shelf sensors were used to enhance the output of vehicle emission instrument.