• DocumentCode
    1867266
  • Title

    Statistical signatures for self-adaptive sensing

  • Author

    Price, E.I. ; Reece, S. ; Probert-Smith, P.

  • Author_Institution
    Dept. of Eng. Sci., Oxford Univ., UK
  • fYear
    2001
  • fDate
    2001
  • Firstpage
    141
  • Lastpage
    146
  • Abstract
    Designing signal processing software is difficult. it is difficult because the world is unpredictable and it is impossible to guarantee software reliability in unforeseen circumstances. Further, it is difficult to anticipate sensor behaviour, as ambient conditions-for example, lighting or weather, can affect the data they output in a multitude of ways. Autonomous, on-line, self-adaptive image processing software is required, that can be adjusted when novel sensing environments are encountered. The appropriate choice of sensor and signal processing tools is a matter of context and the contextual consensus that is available within the framework of a multiple sensor system.
  • Keywords
    adaptive systems; image processing; online operation; sensor fusion; statistical analysis; ambient conditions; autonomous online self-adaptive image processing software; contextual consensus; multiple sensor system; self-adaptive sensing; signal processing software design; signal processing tools; statistical signatures; Adaptive signal processing; Design engineering; Process design; Reliability engineering; Robot sensing systems; Sensor phenomena and characterization; Sensor systems; Signal processing; Signal processing algorithms; Software design;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Multisensor Fusion and Integration for Intelligent Systems, 2001. MFI 2001. International Conference on
  • Print_ISBN
    3-00-008260-3
  • Type

    conf

  • DOI
    10.1109/MFI.2001.1013522
  • Filename
    1013522