Identifying and Quantifying Analytes in Real Life Environments with Chemical Noise

Developing smart technology determines the future economy of societies nowadays. Electronic nose is a device that audits the chemicals and transforms it to human odor perception. One of the most challenging steps to transform electronic nose to smart nose is its patter recognition machinery, because electronic nose data are imprecise and noisy. This pattern recognition machinery builds an empirical statistical model using machine learning algorithms over electronic nose data, to transform the these data to human odour perception. This project is one of the first steps in building such algorithms.

Intern: 
Mina Mirshahi
Faculty Supervisor: 
Andrea Lodi
Province: 
Quebec
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