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JCR Impact Factor: 0.595
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Issues per year: 4
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Avg review time: 78 days


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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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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-Apr-04
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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.

2017-Jan-30
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2016-Dec-17
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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  2/2011 - 11

TCP Congestion Control for the Networks with Markovian Jump Parameters

ABOLMASOUMI, A. H. See more information about ABOLMASOUMI, A. H. on SCOPUS See more information about ABOLMASOUMI, A. H. on IEEExplore See more information about ABOLMASOUMI, A. H. on Web of Science, MOMENI, H. R. See more information about MOMENI, H. R. on SCOPUS See more information about MOMENI, H. R. on SCOPUS See more information about MOMENI, H. R. 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

Download PDF pdficon (638 KB) | Citation | Downloads: 892 | Views: 2,673

Author keywords
communication system traffic control, delay systems, linear matrix inequalities, Markov processes, random variables

References keywords
control(15), systems(14), markovian(9), delay(9), time(8), jump(6), design(6), robust(5), stability(4), parameters(4)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2011-05-30
Volume 11, Issue 2, Year 2011, On page(s): 67 - 72
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2011.02011
Web of Science Accession Number: 000293840500011
SCOPUS ID: 79958802202

Abstract
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This paper is concerned with the problem of TCP congestion control for the class of communication networks with random parameters. The linear dynamic model of TCP New Reno in congestion avoidance mode is considered which contains round trip delays in both state and input. The randomness of link capacity, round trip time delay and the number of TCP sessions is modeled with a continuous-time finite state Markov process. An Active Queue Management (AQM) technique is then used to adjust the queue level of the congested link to a predefined value. For this purpose, a dynamic output feedback controller with mode dependent parameters is synthesized to stochastically stabilize the TCP/AQM dynamics. The procedure of the control synthesis is implemented by solving a linear matrix inequality (LMI). The results are tested within a simulation example and the effectiveness of the proposed design method is verified.


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SCOPUS® Times Cited: 4
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Cited-By CrossRef

[1] Stochastic hyperbolic random early detection controller based on probability density function estimator and particle swarm optimisation algorithm, Abharian, A.E., Khaloozadeh, H., Amjadifard, R., IET Communications, ISSN 1751-8628, Issue 6, Volume 6, 2012.
Digital Object Identifier: 10.1049/iet-com.2011.0120
[CrossRef]

[2] An LMI approach to design extended predictor feedback controllers for dynamical systems with input delay, Abolmasoumi, Amir Hossein, International Journal of Systems Science, ISSN 0020-7721, Issue 9, Volume 48, 2017.
Digital Object Identifier: 10.1080/00207721.2017.1295330
[CrossRef]

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Copyright ©2001-2017
Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania


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