Total views : 140

Realization of Aggregate Applications using Dynamic Behaviour of Cellular Automata

Affiliations

  • Department of Computer Science, University of Karachi, Karachi–75270, Pakistan

Abstract


Cellular automata have proved many of its capabilities and have bestowed a lot in many fields. With the emergence of CA, fabric pattern production has increased in less amount of time. For weaving, cellular automata start with some pattern, then continues with a sequence of steps to produce a new pattern or to change the colour of the cell in a lattice, by using particular transition rules. The main purpose of using cellular automata algorithm as it provides the superlative edge maps and the outstanding quality with one pixel wide edge, with edges having no breaks. We assessed cellular automata capabilities and CA a lot contributions in pattern generation, transformation, and data processing. This research paper reflects the integration of cellular automata across different disciplines. In this paper, we assess the computationally enriched CA rules facilitating spot detection in the study of medical images for cancer diagnosis. The dynamic behaviour of CA increases the scope of transformation and makes it practical for morphing. CA provides prediction of protein structural class and processes dynamic simulation of protein. Flexibility of CA facilitates parallel processing using VLSI, colour graph modelling the linear rules, and its exercise in fabric weaving.

Keywords

Cellular Automata, Dynamic Behaviour, Medical Images, Morphing, Parallel Processing, VLSI.

Full Text:

 |  (PDF views: 133)

References


  • Wongthanavasu S, Tangvoraphonkchai V. Cellular automatabased algorithm and its application in medical image processing. IEEE ICIP; 2007. p. 41-4.
  • Athanassopoulos S, et.al. Cellular automata: Simulations using matlab. The 6th International Conference on Digital Society; 2012. p. 63-8.
  • Ding Y, Shao S. Intelligent computation in the computerized flat knitting systems. CSCC; 2012. p. 4881-5.
  • Suyi L, Qian W, Heng Z. Fabric weave design based on cellular automata theory. International Symposium on Intelligent Ubiquitous Computing and Education; 2009. p. 145-7.
  • Xiao X, Ling WZ. Using cellular automata images to predict protein structural classes. IEEE International Conference on Bioinformatics and Biomedical Engineering; 2007. p. 346-9.
  • Bernaschi M, Castiglione F, Ferranti A, Gavrila C, Tinti M, Cesareni G. ProtNet: A tool for stochastic simulations of protein interaction networks dynamics. BMC Bioinformatics. 2007:1-11.
  • Maji P. Cellular automata: Theory and application in artificial intelligence. Fundamental Informaticae ACM. 2008:1-2.
  • Khan AR. One 2-D cellular automata and its VLSI applications. International Journal of Electrical and Computer Sciences. 2010:124-9.
  • Semwal SK, Chandrashekhar K. Cellular Automata for 3-D Morphing of Volume Data. Western States Cobra Group; 2005.
  • Nayak BK, et.al. Color graphs: An Efficient Model for two-dimensional cellular automata linear rules. Orissa Mathematical Society Conference; 2008. p. 1-14.
  • Stauffer A, Sipper M. The DSCA and its application to growing structures. Artificial Life. 2004:463-77.
  • Raouf Khan A, Yahya AA. Orthogonal transformation of 2-D cellular automata and its application in cryptography. International Conference on Information Technology and Natural Sciences; 2003.
  • Kumar U, et. al. Cellular automata calibration model to capture urban growth. Boletin Geologico y Minero. 2004; 285-99.

Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.