Saverio De Vito

TL;DR: It is shown how a multivariate calibration can be achieved with the use of two weeks long on-field data recording and neural regression systems for CO, NO2 and total NOx pollutants concentration estimation with the same set up.

TL;DR: An extensive review of the low-cost particulate matter sensors currently available on the market, their electronic characteristics, and their applications in published literature and from specific tests shows that most of the reviewed LCPMS can accurately monitor PM changes in the environment and exhibit good performances with accuracy that, in some conditions, can reach R2 values up to 0.99.

TL;DR: Results show that quantifying relevant factors probability distribution changes allows to explain and predict performances of in field data driven calibration models, and highlights the role of concept drift in explaining field performances.

TL;DR: A modified version of an Artificial Immune System algorithm is introduced that can achieve a classification model that is substantially immune to the drift of the sensors and is independent from the occurrence frequency of the classes of the problem.

TL;DR: This work presents a hybrid multisensor system equipped with sensor fusion architectures for continuous gas concentration estimation, in a gas mixture scenario, and investigates the use of ad-hoc sensor fusion algorithms based on neural networks and support vector machines to enhance array performances.