Vol. 1, No. 1, December 24, 2007
10.3837/tiis.2007.01.002,
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Abstract
RFID tag is detected by an RFID antenna and information is read from the tag detected, by an RFID reader. RFID tag detection by an RFID reader is very important at the deployment stage. Tag detection is influenced by factors such as tag direction on a target object, speed of a conveyer moving the object, and the contents of an object. The water content of the object absorbs radio waves at high frequencies, typically approximately 900 MHz, resulting in unstable tag signal power. Currently, finding the best conditions for factors influencing the tag detection requires very time consuming work at deployment. Thus, a quick and simple RFID tag detection scheme is needed to improve the current time consuming trial-and-error experimental method. This paper proposes a back-propagation learning-based RFID tag detection prediction scheme, which is intelligent and has the advantages of ease of use and time/cost savings. The results of simulation with the proposed scheme demonstrate a high prediction accuracy for tag detection on a water content, which is comparable with the current method in terms of time/cost savings.
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Cite this article
[IEEE Style]
M. Jo, C. Lim, E. W. Zimmers, "RFID tag detection on a water object using a backpropagation learning machine," KSII Transactions on Internet and Information Systems, vol. 1, no. 1, pp. 19-32, 2007. DOI: 10.3837/tiis.2007.01.002.
[ACM Style]
Minho Jo, Chang-Gyoon Lim, and Emory W. Zimmers. 2007. RFID tag detection on a water object using a backpropagation learning machine. KSII Transactions on Internet and Information Systems, 1, 1, (2007), 19-32. DOI: 10.3837/tiis.2007.01.002.
[BibTeX Style]
@article{tiis:19779, title="RFID tag detection on a water object using a backpropagation learning machine", author="Minho Jo and Chang-Gyoon Lim and Emory W. Zimmers and ", journal="KSII Transactions on Internet and Information Systems", DOI={10.3837/tiis.2007.01.002}, volume={1}, number={1}, year="2007", month={December}, pages={19-32}}