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JCR Impact Factor: 0.699
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Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
Suceava - 720229
ROMANIA

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 643243560
doi: 10.4316/AECE


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LATEST NEWS

2018-Jun-27
Clarivate Analytics published the InCites Journal Citations Report for 2017. The JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.699, and the JCR 5-Year Impact Factor is 0.674.

2017-Jun-14
Thomson Reuters published the Journal Citations Report for 2016. The JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.595, and the JCR 5-Year Impact Factor is 0.661.

2017-Feb-16
With new technologies, such as mobile communications, internet of things, and wide applications of social media, organizations generate a huge volume of data, much faster than several years ago. Big data, characterized by high volume, diversity and velocity, increasingly drives decision making and is changing the landscape of business intelligence, from governments to private organizations, from communities to individuals. Big data analytics that discover insights from evidences has a high demand for computing efficiency, knowledge discovery, problem solving, and event prediction. We dedicate a special section of Issue 4/2017 to Big Data. Prospective authors are asked to make the submissions for this section no later than the 31st of May 2017, placing "BigData - " before the paper title in OpenConf.

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  2/2018 - 5

Design of an High Frequency RFID Multi-Loop Antenna for Applications in Metallic Environments

PETRARIU, A. I. See more information about PETRARIU, A. I. on SCOPUS See more information about PETRARIU, A. I. on IEEExplore See more information about PETRARIU, A. I. on Web of Science, LAVRIC, A. See more information about  LAVRIC, A. on SCOPUS See more information about  LAVRIC, A. on SCOPUS See more information about LAVRIC, A. on Web of Science, COCA, E. See more information about COCA, E. on SCOPUS See more information about COCA, E. on SCOPUS See more information about COCA, E. on Web of Science
 
Click to see author's profile in See more information about the author on SCOPUS SCOPUS, See more information about the author on IEEE Xplore IEEE Xplore, See more information about the author on Web of Science Web of Science

Download PDF pdficon (1,342 KB) | Citation | Downloads: 112 | Views: 190

Author keywords
data engineering, knowledge representation, machine learning, social network services, social computing

References keywords
rfid(17), antenna(10), metal(8), metallic(7), environments(6), design(5), loop(4), frequency(4)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2018-05-31
Volume 18, Issue 2, Year 2018, On page(s): 35 - 40
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2018.02005
SCOPUS ID: 85047855885

Abstract
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Due to the fast growing of automatic processing in the majority of industrial applications, use of RFID technology is almost mandatory. These systems must identify, with high accuracy, objects placed at different angles or objects placed in harsh environments. In this paper, a new design of an HF RFID reader antenna is proposed, which uses a multi-loop configuration. This configuration ensures a uniform magnetic field on its entire surface, even if the antenna is placed on or very close to a metallic plane. The structure of the proposed antenna is mathematically modelled and computer simulated, as a proof of the concept, followed by a validation on an experimental prototype. Results confirm an increase of up to 9 times for the reading distance, compared with a standard HF RFID reader antenna, working in the same environment. The proposed model can be used in order to improve the accuracy of RFID tag identification in real life applications.


References | Cited By  «-- Click to see who has cited this paper

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References Weight

Web of Science® Citations for all references: 410 TCR
SCOPUS® Citations for all references: 1,545 TCR

Web of Science® Average Citations per reference: 18 ACR
SCOPUS® Average Citations per reference: 67 ACR

TCR = Total Citations for References / ACR = Average Citations per Reference

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

Citations for references updated on 2018-08-14 10:24 in 123 seconds.




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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania


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