Machine learning-based artificial nose on a low-cost IoT-hardware


M. Dziubany, M. Garling, A. Schmeink, G. Burger, G. Dartmann, S. Naumann, K. Gollmer,


        In order to make Internet of things applications easily available and cost-effective, we aim at using low-cost hardware for typical measurement tasks, and in return putting more effort into the signal processing and data analysis. By the example of beverage recognition with a low-cost temperature-modulated gas sensor, we demonstrate the benefits of processing techniques in big data such as feature selection and dimensionality reduction. Specifically, we determine a subset of temperatures that yields good support vector machine classification results and thereby shortens the measurement process.

BibTEX Reference Entry 

	author = {Matthias Dziubany and Marcel Garling and Anke Schmeink and Guido Burger and Guido Dartmann and Stefan Naumann and Klaus-Uwe Gollmer},
	title = "Machine learning-based artificial nose on a low-cost IoT-hardware",
	pages = "239-257",
	publisher = "Elsevier",
	series = "Machine Learning for the Internet of Things",
	editor = "Guido Dartmann;Houbing Song;Anke Schmeink",
	ISBN = "9780128166376",
	edition = "1st Edition",
	month = Oct,
	year = 2019,
	hsb = RWTH-2020-04848,


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