18 June 2012 LIBS data analysis using a predictor-corrector based digital signal processor algorithm
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Abstract
There are many accepted sensor technologies for generating spectra for material classification. Once the spectra are generated, communication bandwidth limitations favor local material classification with its attendant reduction in data transfer rates and power consumption. Transferring sensor technologies such as Cavity Ring-Down Spectroscopy (CRDS) and Laser Induced Breakdown Spectroscopy (LIBS) require effective material classifiers. A result of recent efforts has been emphasis on Partial Least Squares - Discriminant Analysis (PLS-DA) and Principle Component Analysis (PCA). Implementation of these via general purpose computers is difficult in small portable sensor configurations. This paper addresses the creation of a low mass, low power, robust hardware spectra classifier for a limited set of predetermined materials in an atmospheric matrix. Crucial to this is the incorporation of PCA or PLS-DA classifiers into a predictor-corrector style implementation. The system configuration guarantees rapid convergence. Software running on multi-core Digital Signal Processor (DSPs) simulates a stream-lined plasma physics model estimator, reducing Analog-to-Digital (ADC) power requirements. This paper presents the results of a predictorcorrector model implemented on a low power multi-core DSP to perform substance classification. This configuration emphasizes the hardware system and software design via a predictor corrector model that simultaneously decreases the sample rate while performing the classification.
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Alex Sanders, Steven T. Griffin, Aaron Robinson, "LIBS data analysis using a predictor-corrector based digital signal processor algorithm", Proc. SPIE 8359, Sensors, and Command, Control, Communications, and Intelligence (C3I) Technologies for Homeland Security and Homeland Defense XI, 83590T (18 June 2012); doi: 10.1117/12.919518; https://doi.org/10.1117/12.919518
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