A Temporal Filter to Extract Doped Conducting Polymer Information
  Features from an Electronic Nose
Wiem Haj Ammar Aicha Boujnah Antoine Baron Aimen Boubaker Adel Kalboussi Kamal Lmimouni Sebastien Pecqueur
Identifying relevant machine-learning features for multi-sensing platforms is
both an applicative limitation to recognize environments and a necessity to
interpret the physical relevance of transducers' complementarity in their
information processing. Particularly for long acquisitions, feature extraction
must be fully automatized without human intervention and resilient to
perturbations without increasing significantly the computational cost of a
classifier. In this study, we investigate on the relative resistance and
current modulation of a 24-dimensional conductimetric electronic nose, which
uses the exponential moving average as a floating reference in a low-cost
information descriptor for environment recognition. In particular, we
identified that depending on the structure of a linear classifier, the 'modema'
descriptor is optimized for different material sensing elements' contributions
to classify information patterns. The low-pass filtering optimization leads to
opposite behaviors between unsupervised and supervised learning: the latter one
favors longer integration of the reference, allowing to recognize five
different classes over 90%, while the first one prefers using the latest events
as its reference to clusterize patterns by environment nature. Its electronic
implementation shall greatly diminish the computational requirements of
conductimetric electronic noses for on-board environment recognition without
human supervision.