2024/01/01 by Wiem Haj Ammar, Ammar, Wiem Haj, Aicha Boujnah +11 · 1 citation
Agricultural and Biological Sciences · Chemical Engineering · Engineering · #Advanced Chemical Sensor Technologies #Analytical Chemistry and Sensors #FOS: Computer and information sciences #FOS: Physical sciences #Insect Pheromone Research and Control #Machine Learning (cs.LG) #Materials Science (cond-mat.mtrl-sci)
paper · pdf · doi:10.48550/arxiv.2401.00684
openalex publication_date 2024/01/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.