Abstract
Data storage related with writing and retrieving requires high storage capacity, fast transfer rate and less access time. Today any data storage system cannot satisfy all of these conditions, however holographic data storage system (HDSS) can perform faster data transfer rate because it is a page oriented memory system using volume hologram in writing and retrieving data. System can be constructed without mechanically actuating part therefore fast data transfer rate and high storage capacity about 1Tb/cm3 can be realized. In this research, to reduce errors of binary data stored in HDSS, a new method for bit error reduction is suggested. Firstly, find fuzzy rule using test bed system for Element of Holographic Digital Data System and make fuzzy rule table using subtractive clustering algorithm and genetic algorithm and Reduce prior error element and recording digital data. Secondly, Reduce prior error element and recording digital data using the particle filter method. Finally, Recording ratio and reconstruction ratio show good performance and we suggest intelligence control method and filter method. Our format table include intelligence control algorithm and filter method.
Similar content being viewed by others
References
Boskovitz V, Guterman H (2002) An adaptive neuro-fuzzy system for automatic image segmentation and edge detection. Fuzzy Syst IEEE Trans 10(2):247–262
Coufal HJ, Psaltis D, Sincerbox GT (2000) Holographic data storage. Springer, New York
Eugene H (2000) Optics. Addison Wesley, Reading
Goldberg DE (1989) Genetic algorithms in search, optimization and machine learning. Addison-Wesley, Reading
Goodman JW (1996) Introduction to fourier optics. McGraw-Hill, San Francisco
Hadjili ML, Wertz V (2002) Takagi-Sugeno fuzzy modeling incorporating input variables selection. Fuzzy Syst IEEE Trans 10(6):728–742
Jang JSR, Sun CT, Mizutani E (1997) Neuro-fuzzy and soft computing. Prentice Hall, Englewood Cliffs, pp 353–360
Karayiannis NB, Bezdek JC (1997) An integrated approach to fuzzy learning vector quantization and fuzzy c-means clustering. Fuzzy Syst IEEE Trans 5(4):622–628
Liska J, Melsheimer SS (1994) Complete design of fuzzy logic systems using genetic algorithms. In: Proceedings of the 3rd IEEE conference on fuzzy systems, pp 1377–1382
Psaltis D, Levene M, Pu A, Barbastathis G (1995) Holographic storage using shift multiplexing. Opt Lett 20(7):782
Sugeno M, Kang GT (1985) Fuzzy identification of systems and its application to modeling and control. IEEE Trans Syst Man Cybern 15:116–132
Sugeno M, Yasukawa T (1993) A fuzzy-logic-based approach to qualitative modeling. IEEE Trans Fuzzy Syst 1:7–31
Wang LX, Mendel J (1992) Fuzzy basic functions, universal approximation, and orthogonal least square learning. IEEE Trans Neural Netw 3:807–874
Xu CW (1987) Fuzzy model identification and self-learning for dynamic systems. IEEE Trans Syst Man Cybern 17:683–689
Yoshikawa T, Uchikawa Y (1996) Effect of new mechanism of development from artificial DNA and discovery of fuzzy control rules. In: Proceedings of IIZUKA’96, pp 498–501
Zadeh LA (1965) Fuzzy sets. Inf Control 8:338–353
Acknowledgments
This research was supported by the MOCIE (Ministry of Commerce, Industry and Energy) of Korea through the program for the Next Generation Ultra-High Density Storage (00008145).
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Kim, J.H., Kim, Sh., Yang, H. et al. Suggest a format for intelligence control and structure of holographic data storage system. Microsyst Technol 13, 1153–1160 (2007). https://doi.org/10.1007/s00542-007-0390-5
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00542-007-0390-5