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ThinkMind // SENSORCOMM 2020, The Fourteenth International Conference on Sensor Technologies and Applications // View article sensorcomm_2020_1_50_10030


Rapid Detection of Toxic Emissions Using DNN Based Sensing

Authors:
Ibrahim Bhavnagarwala
Adam Bhavnagarwala

Keywords: DNN; IoT; Cigarette; Toxic; Detection; Sensors

Abstract:
Smoking remains one of the top 3 causes of illness in the US; it is one of top 5 causes of fire hazards in a home and is the single most preventable cause of illness and premature death in the US. The use of Deep Neural Networks (DNN) is demonstrated to detect cigarette smoke much sooner and with much higher accuracy than conventional smoke/carbon monoxide detectors used today. The hardware demonstration and prototype engages machine learning to not only discriminate cigarettes from other sources of smoke and carbon monoxide such as burning coal, wood or food – typically not possible with conventional smoke detectors, but also to accurately detect cigarette smoke produced in a room from a single cigarette when concentrations of component gases of cigarette smoke are extremely low. Our prototype also demonstrates the opportunity to classify and discriminate different levels of toxicity and flammability for spaces used by different people.

Pages: 28 to 33

Copyright: Copyright (c) IARIA, 2020

Publication date: November 21, 2020

Published in: conference

ISSN: 2308-4405

ISBN: 978-1-61208-819-8

Location: Valencia, Spain

Dates: from November 21, 2020 to November 25, 2020

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