Electronic-nose for detecting environmental pollutants: signal processing and analog front-end design

  • Authors:

  • Hyuntae Kim;Bharatan Konnanath;Prasanna Sattigeri;Joseph Wang;Ashok Mulchandani;Nosang Myung;Marc A. Deshusses;Andreas Spanias;Bertan Bakkaloglu

  • Affiliations:

  • Arizona State University, Tempe, USA 85287;Arizona State University, Tempe, USA 85287;Arizona State University, Tempe, USA 85287;University of California-San Diego, San Diego, USA 92093;University of California-Riverside, Riverside, USA 92501;University of California-Riverside, Riverside, USA 92501;Duke University, Durham, USA 27708;Arizona State University, Tempe, USA 85287;Arizona State University, Tempe, USA 85287

  • Venue:
  • Analog Integrated Circuits and Signal Processing
  • Year:
  • 2012

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Abstract

Environmental monitoring relies on compact, portable sensor systems capable of detecting pollutants in real-time. An integrated chemical sensor array system is developed for detection and identification of environmental pollutants in diesel and gasoline exhaust fumes. The system consists of a low noise floor analog front-end (AFE) followed by a signal processing stage. In this paper, we present techniques to detect, digitize, denoise and classify a certain set of analytes. The proposed AFE reads out the output of eight conductometric sensors and eight amperometric electrochemical sensors and achieves 91 dB SNR at 23.4 mW quiescent power consumption for all channels. We demonstrate signal denoising using a discrete wavelet transform based technique. Appropriate features are extracted from sensor data, and pattern classification methods are used to identify the analytes. Several existing pattern classification algorithms are used for analyte detection and the comparative results are presented.