Subventions et des contributions :

Subvention ou bourse octroyée s'appliquant à plus d'un exercice financier (2017-2018 à 2018-2019).

Drinking water poisoning are not rare even in well developed countries like USA and Canada. Pollutants likex000D
CaCO3, Sodium Chloride, Lead, K12 E.Coli, Fungal could enter our water system in every stage, water plant,x000D
pipe, or the end faucet. Hence it is not enough to monitor water quality only at the source. Historically it isx000D
impossible or prohibitively expensive to install monitoring devices at the end user side. Only big entities likex000D
hospitals, food processing companies, etc. can afford water quality monitoring devices. Now the developmentx000D
of sensor technology makes it possible and economical to install sensors at each individual faucet. However,x000D
sensors at individual faucets are subject to great unknown factors and noises. After all, a kitchen is not ax000D
spotless scientific lab. This causes greater difficulties in classifying whether or not the drinking water underx000D
monitoring is polluted or not. We need a new robust machine learning model and algorithm, which canx000D
accurately trigger alarms such a noisy environment. The advancement of big data technologies and machinex000D
learning algorithms make this otherwise impossible goal highly likely.