• DocumentCode
    2847306
  • Title

    LG-Graph Based Detection of NRF Spectrum Signatures: Initial Results and Comparison

  • Author

    Pantelopoulos, Alexandros ; Alamaniotis, Miltiadis ; Jevremovic, Tatjana ; Park, Sang M. ; Chung, Soon M. ; Bourbakis, Nikolaos

  • Author_Institution
    Dept. of Comput. Sci. & Eng., Wright State Univ., Dayton, OH, USA
  • fYear
    2009
  • fDate
    2-4 Nov. 2009
  • Firstpage
    683
  • Lastpage
    686
  • Abstract
    In this paper we present an enhanced version of our NRF spectra classifier based on local-global graphs (LG-graphs) and provide a comparison with other possible alternative detection schemes. Experimental results verify our claims that the proposed nuclear resonance fluorescence (NRF) signature detection methodology is favorable over other widely employed conventional feature extraction and signal representation methods. The LG-graph methodology is based on the representation of the signal´s peaks as triangle-like shapes and then on extracting significant geometrical features from these triangles to derive a concise representation of the peaks that correspond to specific NRF signatures of interest. These features are used to enable the matching of the materials of interest in a new unknown NRF spectrum.
  • Keywords
    feature extraction; graph theory; nuclear resonances; signal detection; signal representation; spectral analysis; LG-graph methodology; NRF spectra classifier; NRF spectrum signatures; feature extraction; local-global graphs; nuclear resonance fluorescence; signal representation methods; signature detection methodology; spectra analysis; Artificial intelligence; Computer science; Feature extraction; Fluorescence; Libraries; Pattern matching; Resonance; Shape; Signal representations; USA Councils; discrete wavelet transform; nrf signature detection; pattern matching;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Tools with Artificial Intelligence, 2009. ICTAI '09. 21st International Conference on
  • Conference_Location
    Newark, NJ
  • ISSN
    1082-3409
  • Print_ISBN
    978-1-4244-5619-2
  • Electronic_ISBN
    1082-3409
  • Type

    conf

  • DOI
    10.1109/ICTAI.2009.105
  • Filename
    5365156