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
Link To Document :
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