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Parallelization of Image Encryption Algorithm Based On Chaotic Neural Networks

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Artificial Intelligence and Soft Computing (ICAISC 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9692))

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Abstract

In this paper the results of parallelizing an image encryption algorithm based on chaotic neural networks are presented. A data dependence analysis of loops is applied in order to parallelize the algorithm. The parallelism of the algorithm is demonstrated in accordance with the OpenMP standard. As a result of this study, it is stated that the most time-consuming loops of the algorithm are suitable for parallelization. The efficiency measurements of a parallel algorithm working in standard modes of operation are shown.

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Authors and Affiliations

  1. Faculty of Computer Science and Information Technology, West Pomeranian University of Technology, 49 Żołnierska St., 71-210, Szczecin, Poland

    Dariusz Burak

Authors
  1. Dariusz Burak
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Correspondence to Dariusz Burak .

Editor information

Editors and Affiliations

  1. Częstochowa University of Technology, Czestochowa, Poland

    Leszek Rutkowski

  2. Częstochowa University of Technology, Czestochowa, Poland

    Marcin Korytkowski

  3. Częstochowa University of Technology, Czestochowa, Poland

    Rafał Scherer

  4. AGH University of Science and Technology, Krakow, Poland

    Ryszard Tadeusiewicz

  5. University of California, Berkeley, California, USA

    Lotfi A. Zadeh

  6. University of Louisville, Louisville, Kentucky, USA

    Jacek M. Zurada

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Burak, D. (2016). Parallelization of Image Encryption Algorithm Based On Chaotic Neural Networks. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L., Zurada, J. (eds) Artificial Intelligence and Soft Computing. ICAISC 2016. Lecture Notes in Computer Science(), vol 9692. Springer, Cham. https://doi.org/10.1007/978-3-319-39378-0_7

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  • DOI: https://doi.org/10.1007/978-3-319-39378-0_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-39377-3

  • Online ISBN: 978-3-319-39378-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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