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Identifying relevant machine-learning features for multi-sensing platforms isboth an applicative limitation to recognize environments and a necessity tointerpret the physical relevance of transducers' complementarity in theirinformation processing. Particularly for long acquisitions, feature extractionmust be fully automatized without human intervention and resilient toperturbations without increasing significantly the computational cost of aclassifier. In this study, we investigate on the relative resistance andcurrent modulation of a 24-dimensional conductimetric electronic nose, whichuses the exponential moving average as a floating reference in a low-costinformation descriptor for environment recognition. In particular, weidentified that depending on the structure of a linear classifier, the 'modema'descriptor is optimized for different material sensing elements' contributionsto classify information patterns. The low-pass filtering optimization leads toopposite behaviors between unsupervised and supervised learning: the latter onefavors longer integration of the reference, allowing to recognize fivedifferent classes over 90%, while the first one prefers using the latest eventsas its reference to clusterize patterns by environment nature. Its electronicimplementation shall greatly diminish the computational requirements ofconductimetric electronic noses for on-board environment recognition withouthuman supervision.

A Temporal Filter to Extract Doped Conducting Polymer Information Features from an Electronic Nose