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Designs 2017, 1(1), 7; doi:10.3390/designs1010007

Classification via an Embedded Approach

1
Sección de Estudios de Posgrado e Investigación, ESIME Azcapotzalco, Instituto Politécnico Nacional, Mexico D.F. 02250, Mexico
2
Tecnológico de Estudios Superiores de Ecatepec, Ecatepec, Estado de Mexico 55210, Mexico
3
Sección de Estudios de Posgrado e Investigación, ESIME Zacatenco, Instituto Politécnico Nacional, Mexico D.F. 07738, Mexico
4
Centro de Investigación en Computación, Instituto Politécnico Nacional, Mexico D.F. 07738, Mexico
*
Author to whom correspondence should be addressed.
Received: 5 August 2017 / Revised: 6 September 2017 / Accepted: 7 September 2017 / Published: 15 September 2017
(This article belongs to the Special Issue New Trends in Intelligent Control and Filter Design)
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Abstract

This paper presents the results of an automated volatile organic compound (VOC) classification process implemented by embedding a machine learning algorithm into an Arduino Uno board. An electronic nose prototype is constructed to detect VOCs from three different fruits. The electronic nose is constructed using an array of five tin dioxide (SnO2) gas sensors, an Arduino Uno board used as a data acquisition section, as well as an intelligent classification module by embedding an approach function which receives data signals from the electronic nose. For the intelligent classification module, a training algorithm is also implemented to create the base of a portable, automated, fast-response, and economical electronic nose device. This solution proposes a portable system to identify and classify VOCs without using a personal computer (PC). Results show an acceptable precision for the embedded approach in comparison with the performance of a toolbox used in a PC. This constitutes an embedded solution able to recognize VOCs in a reliable way to create application products for a wide variety of industries, which are able to classify data acquired by an electronic nose, as VOCs. With this proposed and implemented algorithm, a precision of 99% for classification was achieved into the embedded solution. View Full-Text
Keywords: Arduino; artificial intelligence; electronic nose; embedded systems; approach; VOC classification Arduino; artificial intelligence; electronic nose; embedded systems; approach; VOC classification
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Rubio, J.J.; Avila, F.J.; Meléndez, A.; Stein, J.M.; Meda, J.A.; Aguilar, C. Classification via an Embedded Approach. Designs 2017, 1, 7.

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