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

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 dete...

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Bibliographic Details
Main Authors: Pantelopoulos, A., Alamaniotis, M., Jevremovic, T., Park, S.M., Chung, S.M., Bourbakis, N.
Format: Conference Proceeding
Language:English
Subjects:
Artificial intelligence
Computer science
discrete wavelet transform
Feature extraction
Fluorescence
Libraries
nrf signature detection
Pattern matching
Resonance
Shape
Signal representations
USA Councils
Online Access:Request full text
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Description
Summary: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.
ISSN:1082-3409
2375-0197
DOI:10.1109/ICTAI.2009.105