Author_Institution :
Dept. of Electr. & Syst. Eng., Oakland Univ., Rochester, MI, USA
Abstract :
Advanced microsystems that include, sensors, interface-circuits, and pattern-recognition integrated monolithically or in a hybrid module are needed for civilian, military, and space applications. These include: automotive, medical applications, environmental engineering, and manufacturing automation. ASICs with Artificial Neural Networks (ANN) are considered in this paper, with the objective of recognizing air-borne volatile organic compounds, especially alcohols, ethers, esters, halocarbons, NH3, NO2, and other warfare agent simulants. The ASIC inputs are connected to the outputs from array-distributed sensors which measure three-features for identifying each of four chemicals. A Specialized Reinforcement Neural Network (RNN) learning approach is chosen for the chemicals classification problem. Hardware implementation of the RNN is presented for 2 μm CMOS process, MOSIS chip. Design implementation and evaluation are also presented
Keywords :
CMOS integrated circuits; VLSI; application specific integrated circuits; gas sensors; learning (artificial intelligence); neural chips; pattern classification; 2 micron; ANN electronic nose; ASIC; CMOS process; MOSIS chip; NH3; NO2; air-borne volatile compounds; alcohols; array-distributed sensors; artificial neural network; chemicals classification problem; esters; ethers; halocarbons; reinforcement neural network learning approach; volatile organic compounds; warfare agent simulants; Artificial neural networks; Automotive engineering; Biomedical engineering; Biomedical equipment; Chemical sensors; Electronic noses; Manufacturing automation; Medical services; Recurrent neural networks; Sensor arrays;
