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JCR Impact Factor: 0.595
JCR 5-Year IF: 0.661
Issues per year: 4
Current issue: May 2017
Next issue: Aug 2017
Avg review time: 78 days


Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
Suceava - 720229

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


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Broken Bar Fault Detection in IM Operating Under No-Load Condition, RELJIC, D., JERKAN, D., MARCETIC, D., OROS, D.
Issue 4/2016



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.

We have the confirmation Advances in Electrical and Computer Engineering will be included in the EBSCO database.

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.

We have the confirmation Advances in Electrical and Computer Engineering will be included in the Gale database.

IoT is a new emerging technology domain which will be used to connect all objects through the Internet for remote sensing and control. IoT uses a combination of WSN (Wireless Sensor Network), M2M (Machine to Machine), robotics, wireless networking, Internet technologies, and Smart Devices. We dedicate a special section of Issue 2/2017 to IoT. Prospective authors are asked to make the submissions for this section no later than the 31st of March 2017, placing "IoT - " before the paper title in OpenConf.

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  3/2012 - 11

Object Extraction from Architecture Scenes through 3D Local Scanned Data Analysis

NING, X. See more information about NING, X. on SCOPUS See more information about NING, X. on IEEExplore See more information about NING, X. on Web of Science, WANG, Y. See more information about WANG, Y. on SCOPUS See more information about WANG, Y. on SCOPUS See more information about WANG, Y. on Web of Science
Click to see author's profile on 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

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Author keywords
terrestrial laser scanner, point cloud segmentation, similarity measurement, nearest neighboring graph

References keywords
segmentation(8), point(7), image(7), range(5), pattern(5), clouds(5), zhang(4), vision(4), transform(4), robust(4)
No common words between the references section and the paper title.

About this article
Date of Publication: 2012-08-31
Volume 12, Issue 3, Year 2012, On page(s): 73 - 78
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2012.03011
Web of Science Accession Number: 000308290500011
SCOPUS ID: 84865851513

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Terrestrial laser scanning becomes a standard way for acquiring 3D data of complex outdoor objects. The processing of huge number of points and recognition of different objects inside become a new challenge, especially in the case where objects are included. In this paper, a new approach is proposed to classify objects through an analysis on shape information of the point cloud data. The scanned scene is constructed using k Nearest Neighboring (k-NN), and then similarity measurement between points is defined to cluster points with similar primitive shapes. Moreover, we introduce a combined geometrical criterion to refine the over-segmented results. To achieve more detail information, a residual based segmentation is adopted to refine the segmentation of architectural objects into more parts with different shape properties. Experimental results demonstrate that this approach can be used as a robust way to extract different objects in the scenes.

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

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[11] T. Rabbani and F. Van Den Heuvel. "Efficient hough transform for automatic detection of cylinders in point clouds," In ISPRS WG III/3, III/4, V/3 workshop. 2005, pp.60-65.

[12] Kourosh Khoshelham, "Extending generalized hough transform to detect 3d objects in laser range data," Transform, 2007, XXXV, pp. 206-210.

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[17] Jie Chen and Baoquan Chen. "Architectural modeling from sparsely scanned range data," Int. J. Comput. Vision, 2008, 78(2-3), pp.223-236.
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[18] Aleksey Golovinskiy and Thomas Funkhouser, "Min-cut based segmentation of point clouds," In IEEE Workshop on Search in 3D and Video (S3DV) at ICCV, 2009.
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[22] Xiaojuan Ning, Xiaopeng Zhang, Yinghui Wang, Tree segmentation from scanned scene data. In PMA, December 2009.
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References Weight

Web of Science® Citations for all references: 11,999 TCR
SCOPUS® Citations for all references: 17,185 TCR

Web of Science® Average Citations per reference: 522 ACR
SCOPUS® Average Citations per reference: 747 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 2017-07-18 17:44 in 127 seconds.

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

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