Biologically Inspired Signal Processing for Chemical Sensing

  • Agustín Gutiérrez
  • Santiago Marco

Part of the Studies in Computational Intelligence book series (SCI, volume 188)

Table of contents

  1. Front Matter
  2. Biological Olfaction

    1. Front Matter
      Pages 1-1
    2. Anders Lansner, Simon Benjaminsson, Christopher Johansson
      Pages 33-43
    3. I. Montoliu, K. C. Persaud, M. Shah, S. Marco
      Pages 53-72
  3. Artificial Olfaction and Gustation

    1. Front Matter
      Pages 73-73
    2. Tim C. Pearce, Manuel A. Sánchez-Montañés, Julian W. Gardner
      Pages 75-91
    3. Eugenio Martinelli, Francesca Dini, Giorgio Pennazza, Maurizio Canosa, Arnaldo D’Amico, Corrado Di Natale
      Pages 109-120
    4. R. Cartas, L. Moreno-Barón, A. Merkoçi, S. Alegret, M. del Valle, J. M. Gutiérrez et al.
      Pages 137-167
  4. Back Matter

About this book


This volume presents a collection of research advances in biologically inspired signal processing for chemical sensing. The olfactory system, and the gustatory system to a minor extent, has been taken in the last decades as a source of inspiration to develop artificial sensing systems. The performance of this biological system outperforms in many aspects that of their artificial counterpart. Thus, the goal of researchers in this field is to understand and capture those features that make the olfactory system especially suited for the processing of chemical information. The recognition of odors by the olfactory system entails a number of signal processing functions such as preprocessing, dimensionality reduction, contrast enhancement, and classification. Using mathematical models to mimic the architecture of the olfactory system, these processing functions can be applied to chemical sensor signals. This book provides some background on the olfactory system including a review on information processing in the insect olfactory system along with a proposed signal processing architecture based on the mammalian cortex. It also provides some bio-inspired approaches to process chemical sensor signals such as an olfactory mucosa to improve odor separation and a model of olfactory receptor neuron convergence to correlate sensor responses to an odor and his organoleptic properties.


Base Computational Intelligence Data Processing Sensor Signal Processing biologically inspired cognition environment information processing model neural network

Editors and affiliations

  • Agustín Gutiérrez
    • 1
  • Santiago Marco
    • 2
  1. 1.Departament d’ElectronicaUniversitat de Barcelona BarcelonaSpain
  2. 2.Departament d’ElectronicaUniversitat de BarcelonaBarcelonaSpain

Bibliographic information

  • DOI
  • Copyright Information Springer Berlin Heidelberg 2009
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Engineering Engineering (R0)
  • Print ISBN 978-3-642-00175-8
  • Online ISBN 978-3-642-00176-5
  • Series Print ISSN 1860-949X
  • Series Online ISSN 1860-9503
  • Buy this book on publisher's site
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