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A fast cellular automata algorithm for liquid diffusion phenomenon modeling

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Abstract

Liquid Diffusion Phenomenon (LDP) has been studied by numerous works in order to predict its dynamical behavior. In this article, two main contributions are presented and discussed. The first one, a Cellular Automata based algorithm is proposed in order to predict the LDP dynamical behavior. The proposed algorithm creates a set of images to form a video stream. The predicted images are compared to the original ones taken from the actual LDP scenario. In the second contribution, three benchmark strategies are discussed, which are pixel intensity, area of region of diffusion (ROD), and shape of ROD. The results of comparisons have shown high similarities to original images with an averaged percentage of similarity of 94 %. The experiments contain 600 predicted and 600 original images. The obtained results have been compared to a Cellular Neural Network based model in terms of computation time.

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References

  • Adwan O, Awwad AA, Sleit A, Abu Alhoum A (2011) A novel watermarking scheme based on two dimensional cellular automata. In: Proceedings of the International Conference on Computers and Computing, World Scientific and Engineering Academy and Society (WSEAS). Canary Islands, Spain, pp 88–94

  • Basuki A, Harsono T, Arai K (2009) Modeling and simulation of hot mudflow movement prediction using cellular automata. In: Proceedings of the 3rd IEEE International Conference on Modelling & Simulation. Bali, Indonesia, pp 176–181

  • Boghosian B, Taylor W, Rothman D (1988) A cellular automata simulation of two-phase flow on the CM-2 Connection Machine computer. J Supercomput Sci Appl 2:34–44

    Google Scholar 

  • Bunge HJ (1997) Some remarks on modelling and simulation of physical phenomena. Textures Microstruct 28(3):151–165

    Article  Google Scholar 

  • Dalhoum A, Mahafzah BA, Awwad A, Al-Dhamari I, Ortega A, Alfonseca M (2012) Digital image scrambling method based on two dimensional cellular automata: a test of the lambda value. IEEE Multimedia 19(4):28–36

    Article  Google Scholar 

  • Ganguly N, Sikdar B, Deutsch A, Canright G, Chaudhuri P (2003) A survey on cellular automata. Dresden University of Technology, Technical Report Centre for High Performance Computing, pp 1–30

  • Gray L (2003) A mathematician looks at Wolfram’s new kind of science. Notices Am Math Soc 50(2):200–211

    MATH  Google Scholar 

  • Holzer R, Meer HD (2011) Modeling and application of self-organizing systems. In: Proceedings of the 5th IEEE International Conference on Self-Adaptive and Self-Organizing Systems. Michigan, USA, pp 235–243

  • Hora C, Vesselenyi T, Dzitac S, Dzitac I, Hora H (2009) Wear simulation through cellular automata method. In: Proceedings of the 3rd IEEE International Workshop on Soft Computing Applications. Szeged (Hungary)–Arad (Romania), pp 43–48

  • Huang SW, Way DL, Shih ZC (2003) Physical-based model of ink diffusion in chinese ink paintings. J WSCG 10:520–527

    Google Scholar 

  • Jalalian A (2011) Cellular Automaton for Efficient Impulse Noise Removal and Edge Detection Using Graphic Processor Unit, Vol Master Thesis. Universiti Putra Malaysia, Malaysia

  • Kamaruddin HD, Koros WJ (1997) Some observations about the application of Fick’s first law for membrane separation of multicomponent mixtures. J Membr Sci 135(2):147–159

    Article  Google Scholar 

  • Kapela R, Rybarczyk A, Sniatala P, Rudnicki R (2006) Hardware realization of the MPEG-7 edge histogram descriptor. In: Proceedings of the IEEE International Conference on Mixed Design of Integrated Circuits and System. Gdynia, Poland, pp 675–678

  • Kari J (2005) Theory of cellular automata: a survey. Theor Comput Sci 334(1–3):3–33

    Article  MATH  MathSciNet  Google Scholar 

  • Kyoomarsi F, Dehkordy P, Torkestani J (2007) Using chemical cellular automata in simulation of chemical materials. In: Proceedings of the 5th IEEE International Conference on Software Engineering Research, Management and Applications. Busan, South Korea, pp 769–773

  • Lee S, Lee H-Y, Lee I-F, Tseng C-Y (2004) Ink diffusion in water. Eur J Phys 25(2):331–336

    Article  MathSciNet  Google Scholar 

  • Mainzer K, Chua L (2012) Life and brain in the universe of cellular automata. The universe as automaton from simplicity and symmetry to complexity, vol 1., Springer briefs in complexitySpringer, Berlin, pp 87–103

    Google Scholar 

  • Nakano T, Kasegawa J, Morishita S (1998) Coastal oil pollution prediction by a tanker using cellular automata. In: Proceedings of the IEEE Oceanic Engineering Society (3), Nice, France, pp 1324–1328

  • Nakornphanom KN, Lursinsap C, Asavanant J, Lin FC (2004) Prediction and animation of dynamical behavior of color diffusion in water using 2-D tightly coupled neural cellular network. In: Proceedings of the IEEE International Conference on Systems, Man and Cybernetics. The Hague, Netherlands, pp 5947–5951

  • Park DK, Jeon YS, Won CS (2000) Efficient use of local edge histogram descriptor. In: Proceedings of the ACM workshops on Multimedia. ACM Press, California, pp 51–54

  • Patel DK, More SA (2013) Edge detection technique by fuzzy logic and cellular learning automata using fuzzy image processing. In: Proceedings of the IEEE International Conference on Computer Communication and Informatics. Coimbatore, India, pp 1–6

  • Patel DK, More SA (2012) An enhanced approach for edge image enhancement using fuzzy set theory and cellular learning automata (CLA). World J Sci Technol 2(4):158–162

    Google Scholar 

  • Piazza E, Cuccoli F (2001) Cellular automata simulation of clouds in satellite images. In: Proceedings of the IEEE International Geoscience and Remote Sensing Symposium. Sydney, NSW, Australia, pp 1722–1724

  • Qian M, Guo Z (2004) Cellular automata simulation of microstructural evolution during dynamic recrystallization of an HY-100 steel. Mater Sci Eng 365:180–185

    Article  Google Scholar 

  • Richa H, Ruan X (2003) Differential equation and cellular automata model. In: Proceedings of the IEEE International Conference on Robotics, Intelligent Systems and Signal Processing, vol 2. Hunan, China, pp 1047–1051

  • Schiff JL (2007) Cellular automata: a discrete view of the world. Wiley-Interscience, USA

    Book  Google Scholar 

  • Schliebs S, Fiasché M, Kasabov N (2012) Constructing robust liquid state machines to process highly variable data streams. In: Artificial Neural Networks and Machine Learning–ICANN 2012, vol 7552, Lecture Notes in Computer Science. Springer, Berlin Heidelberg, pp 604–611

  • Smith WF, Hashemi J (2009) Foundations of materials science and engineering, 5th edn. McGraw-Hill Science/Engineering/Math, USA

    Google Scholar 

  • Suratanee A, Nakornphanom K, Plaimas K, Lursinsap C (2008) Partitioning for high performance of predicting dynamical behavior of color diffusion in water using 2-D tightly coupled neural cellular network. In: Bock HG, Kostina E, Phu HX, Rannacher R (eds) Modeling, Simulation and Optimization of Complex Processes, Proceedings of the Third International Conference on High Performance Scientific Computing, March 6–10, 2006, Hanoi, Vietnam. Springer, Heidelberg, pp 565–574

  • Topa P (2011) Network systems modelled by complex cellular automata paradigm. In: Salcido (ed) Cellular automata-simplicity behind complexity. InTech, Europe, China, pp 259–274

  • Wang H, Nie G, Fu K (2008) Cellular automata simulation of the growth of bone tissue. In: Proceedings of the 4th IEEE International Conference on Natural Computation. Jinan, China, pp 421–424

  • Wolfram S (2002) A new kind of science. Wolfram Media Inc, USA

    MATH  Google Scholar 

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Correspondence to Abbas M. Al-Ghaili.

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Al-Ghaili, A.M., Samsudin, K., Saripan, M.I. et al. A fast cellular automata algorithm for liquid diffusion phenomenon modeling. Evolving Systems 6, 229–241 (2015). https://doi.org/10.1007/s12530-013-9094-5

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  • DOI: https://doi.org/10.1007/s12530-013-9094-5

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