Abstract

An integrated sensor array system is developed for detection and identification of environmental pollutants in diesel and gasoline exhaust fumes. The system includes a low noise floor analog front-end followed by a signal processing stage. Classification methods are used since the pollutants are often encountered as complex mixtures. In this paper, we present techniques to detect, digitize and classify analytes. This is done by extracting appropriate features from sensor data and using pattern recognition methods to identify the analytes. The detection analog front-end achieves 54dB SNR. The low-noise digitization technique is presented along with the feature extraction and classification algorithms. Comparative results are given for a series of pattern classifiers.

Original languageEnglish (US)
Title of host publicationDSP 2009:16th International Conference on Digital Signal Processing, Proceedings
DOIs
StatePublished - Nov 20 2009
EventDSP 2009:16th International Conference on Digital Signal Processing - Santorini, Greece
Duration: Jul 5 2009Jul 7 2009

Publication series

NameDSP 2009: 16th International Conference on Digital Signal Processing, Proceedings

Other

OtherDSP 2009:16th International Conference on Digital Signal Processing
CountryGreece
CitySantorini
Period7/5/097/7/09

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ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Signal Processing

Cite this

Konnanath, B., Kim, H., Spanias, A., Bakkaloglu, B., Wang, J., Mulchandani, A., & Myung, N. (2009). A real-time monitoring system for diesel and gasoline exhaust exposure. In DSP 2009:16th International Conference on Digital Signal Processing, Proceedings [5201081] (DSP 2009: 16th International Conference on Digital Signal Processing, Proceedings). https://doi.org/10.1109/ICDSP.2009.5201081