<script type="text/javascript">
<!--
document.write('<div id="oa_widget"></div>');
document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=undefined&type=result"></script>');
-->
</script>

COPY SCRIPT

For further information contact us at helpdesk@openaire.eu

Wavelet based flickering flame detector using differential PIR sensors

Name: Wavelet based flickering flame detector using differential PIR sensors
Keywords: Decision algorithms, Markov Models, Sensor signals, Flame Detection, Pyro-electric Infrared (pir) Sensor, Fire detection, Sensors, Markov processes, Markov models, Human being

descriptionPublicationkeyboard_double_arrow_right Article , Other literature type 01 Oct 2012 Turkey English Publisher:Elsevier BVJournal:Fire Safety Journal, volume 53, pages 13-18 (issn: 0379-7112,

Authors: Erden, F.; Toreyin, B. U.; Soyer, E. B.; Inac, I.; Gunay, O.; Kose, K.; Cetin, A. E.;

doi: 10.1016/j.firesaf.2012.06.006

handle: 11693/21317 , 11693/13274

Wavelet based flickering flame detector using differential PIR sensors

- Summary
- Subjects
- Metrics

Abstract

A Pyro-electric Infrared (PIR) sensor based flame detection system is proposed using a Markovian decision algorithm. A differential PIR sensor is only sensitive to sudden temperature variations within its viewing range and it produces a time-varying signal. The wavelet transform of the PIR sensor signal is used for feature extraction from sensor signal and wavelet parameters are fed to a set of Markov models corresponding to the flame flicker process of an uncontrolled fire, ordinary activity of human beings and other objects. The final decision is reached based on the model yielding the highest probability among others. Comparative results show that the system can be used for fire detection in large rooms.

Country

Turkey

Related Organizations

Keywords

Decision algorithms, Markov Models, Sensor signals, Flame Detection, Pyro-electric Infrared (pir) Sensor, Fire detection, Sensors, Markov processes, Markov models, Human being, Wavelet Transform, Wavelet analysis, Markov model, Temperature variation, Flame flicker, Wavelet transforms, Markovian, Flame detection, Feature extraction, Wavelet transform, Wavelet parameters, Flickering flames, Final decision, Pyro-electric Infrared (PIR) sensor, Time varying signal

Impact byBIP!

	citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	36
	popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.	Top 10%
	influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	Top 10%
	impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.	Top 10%