• DocumentCode
    1837883
  • Title

    Designing efficient CNN algorithms for the Bionic Eyeglass by combining manual and automatic techniques

  • Author

    Pazienza, G.E. ; Karacs, K. ; Horvafh, E.A. ; Mate, G.

  • Author_Institution
    Cellular Sensory & Wave Comput. Lab., MTA - SZTAKI, Budapest, Hungary
  • fYear
    2010
  • fDate
    3-5 Feb. 2010
  • Firstpage
    1
  • Lastpage
    5
  • Abstract
    Programs for the CNN-UM can be designed either manually or automatically. These two approaches have complementary advantages and disadvantages, and hence neither of them can be considered as the better choice. In this paper, we find empirical evidence that these two techniques can be combined in order to obtain more effective and efficient algorithms.
  • Keywords
    cellular neural nets; CNN algorithms; CNN-UM design; automatic technique; bionic eyeglass; cellular neural networks; manual technique; universal machine; Algorithm design and analysis; Automatic control; Cellular networks; Cellular neural networks; Computer networks; Humans; Information technology; Laboratories; Manuals; Vehicle detection;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Cellular Nanoscale Networks and Their Applications (CNNA), 2010 12th International Workshop on
  • Conference_Location
    Berkeley, CA
  • Print_ISBN
    978-1-4244-6679-5
  • Type

    conf

  • DOI
    10.1109/CNNA.2010.5430297
  • Filename
    5430297
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=1837883