An embedded probabilistic neural network with on-chip learning capability

Wang, Jen-Huo; Tang, Kea-Tiong; Chen, Hsin

doi:10.1109/biocas.2013.6679632

Cited by 5 publications

(2 citation statements)

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“…VOC detection could be done through an array of gas sensors conformed as an electronic nose [2] where a data acquisition module converts sensor signals to a standard output to be analyzed and classified. To facilitate VOC detection in situ, an embedded low-power device is required for portable solutions, as well as reliable classification in real time [3].…”

Section: Introductionmentioning

confidence: 99%

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Classification via an Embedded Approach

Rubio

Avila

Melendez

et al. 2017

Designs

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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 (SnO 2 ) 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.

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Section: Introductionmentioning

confidence: 99%

“…Four semi-supervised learning methods are discussed in [2]. In [3], a specific ensemble strategy is developed. A dynamic pattern recognition method is proposed in [4].…”

Section: Introductionmentioning

confidence: 99%