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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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ABC Algorithm based Fuzzy Modeling of Optical Glucose Detection, SARACOGLU, O. G., BAGIS, A., KONAR, M., TABARU, T. E.
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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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  4/2015 - 8

Output Choice of a Chaotic Jerk Circuit Used as Transmitter in Data Secure Communications

DATCU, O. See more information about DATCU, O. on SCOPUS See more information about DATCU, O. on IEEExplore See more information about DATCU, O. on Web of Science, STANCIU, M. See more information about  STANCIU, M. on SCOPUS See more information about  STANCIU, M. on SCOPUS See more information about STANCIU, M. on Web of Science, TAULEIGNE, R. See more information about  TAULEIGNE, R. on SCOPUS See more information about  TAULEIGNE, R. on SCOPUS See more information about TAULEIGNE, R. on Web of Science, BURILEANU, C. See more information about  BURILEANU, C. on SCOPUS See more information about  BURILEANU, C. on SCOPUS See more information about BURILEANU, C. on Web of Science, BARBOT, J.-P. See more information about BARBOT, J.-P. on SCOPUS See more information about BARBOT, J.-P. on SCOPUS See more information about BARBOT, J.-P. on Web of Science
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Author keywords
chaotic communication, nonlinear dynamical systems, observers, signals analysis, sliding mode control

References keywords
chaotic(11), chaos(11), systems(9), cryptography(5), circuits(5), system(4), review(4), physical(4), control(4), barbot(4)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2015-11-30
Volume 15, Issue 4, Year 2015, On page(s): 63 - 68
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2015.04008
Web of Science Accession Number: 000368499800008
SCOPUS ID: 84949960782

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Usually, when analyzing a data series, dynamical systems theory is used to reconstruct the state space of the original system. This work aims to determine which of a chaotic system's states is best suited as output when transmitting secret messages. This is the first step prior to designing an actual communication scheme. As an example, the three states of Sprott's jerk circuit are analyzed in terms of the local observability they ensure for the original dynamics when transmitted as a scalar data series. Results show that its first two states enable accurate estimation of the transmitter's dynamics at the receiving end. However, its third state generates, in some regions of the state space, a non-invertible transformation between the original state space and the one the receiver sees. This is due to the exponential nonlinearities present in this state's derivatives. Given that these nonlinearities remain inaccessible to the receiver, they are neglected in order to allow the partial reconstruction of the dynamics of the transmitter. But, since these nonlinearities are essential for the chaotic behavior, this makes the third state unusable for cryptographic purposes. This analysis may be applied to any bipolar junction transistor or diode based chaotic circuit.

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

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References Weight

Web of Science® Citations for all references: 8,889 TCR
SCOPUS® Citations for all references: 9,508 TCR

Web of Science® Average Citations per reference: 261 ACR
SCOPUS® Average Citations per reference: 280 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 2017-06-16 23:59 in 160 seconds.

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