Bedoukian   RussellIPM   RussellIPM   Piezoelectric Micro-Sprayer


Home
Animal Taxa
Plant Taxa
Semiochemicals
Floral Compounds
Semiochemical Detail
Semiochemicals & Taxa
Synthesis
Control
Invasive spp.
References

Abstract

Guide

Alphascents
Pherobio
InsectScience
E-Econex
Counterpart-Semiochemicals
Print
Email to a Friend
Kindly Donate for The Pherobase

« Previous AbstractIdentification of the structural gene for dipeptidyl aminopeptidase yscV (DAP2) of Saccharomyces cerevisiae    Next AbstractMedfly Population Suppression through Augmentative Release of an Introduced Parasitoid in an Irrigated Multi-Fruit Orchard of Central-Western Argentina »

Chemosphere


Title:Bluetooth gas sensing module combined with smartphones for air quality monitoring
Author(s):Suarez JI; Arroyo P; Lozano J; Herrero JL; Padilla M;
Address:"Industrial Engineering School, University of Extremadura, Badajoz, Spain. Industrial Engineering School, University of Extremadura, Badajoz, Spain. Electronic address: jesuslozano@unex.es"
Journal Title:Chemosphere
Year:2018
Volume:20180426
Issue:
Page Number:618 - 626
DOI: 10.1016/j.chemosphere.2018.04.154
ISSN/ISBN:1879-1298 (Electronic) 0045-6535 (Linking)
Abstract:"This study addresses the development of a miniaturized (60?ª+x?ª+60?ª+mm) Wireless Sensing Module (WSM) for environmental application and air quality detection. The proposed prototype has six sensors: one for humidity, one for ambient temperature (SHT21 from Sensirion), and four for gas detection (MiCS-4514, MiCS-5526 and MiCS-5914 from SGX Sensortech). The core of the system is based on a high performance 8-bit microcontroller, model PIC18F46K80, from Microchip. The obtained data values were transmitted to the Smartphone through a Bluetooth communication module and a home-developed Android app. The discrimination capability of the module is tested with 10 volatile organic compounds (acetone, acetic acid, benzene, ethanol, ethyl acetate, ethylbenzene, formaldehyde, toluene, xylene, and dimethylacetamide) and the effect of humidity and drift of the sensors is also studied. Results show that 88.33% and 92.22% success rates in classification stage are obtained using Multilayer Perceptron with BackPropagation Learning algorithm and Radial-Basis based Neural Networks, respectively"
Keywords:"Acetone Air Pollution/*analysis Algorithms Benzene/analysis Environmental Monitoring/instrumentation/*methods Formaldehyde Gases/*analysis *Neural Networks, Computer Smartphone/*standards Toluene/analysis Xylenes Air quality Bluetooth Gas sensors Machine;"
Notes:"MedlineSuarez, Jose Ignacio Arroyo, Patricia Lozano, Jesus Herrero, Jose Luis Padilla, Manuel eng England 2018/05/02 Chemosphere. 2018 Aug; 205:618-626. doi: 10.1016/j.chemosphere.2018.04.154. Epub 2018 Apr 26"

 
Back to top
 
Citation: El-Sayed AM 2024. The Pherobase: Database of Pheromones and Semiochemicals. <http://www.pherobase.com>.
© 2003-2024 The Pherobase - Extensive Database of Pheromones and Semiochemicals. Ashraf M. El-Sayed.
Page created on 22-11-2024