KEYWORDS: Control systems, Laser induced breakdown spectroscopy, Principal component analysis, Digital signal processing, Binary data, Sensors, Control systems design, Performance modeling, Distance measurement, Statistical analysis
Portable LIBS sensor communication bandwidth limitations favor local material classification for low power consumption. Partial Least Squares - Discriminant Analysis (PLS-DA) and Principle Component Analysis (PCA) have been implementation via general purpose computers and are accepted for some Department of Defense applications. Prior publications address the creation of a low mass, low power, robust hardware spectra classifier for a limited set of predetermined materials in an atmospheric matrix. The incorporation of a PCA or a PLS-DA classifier into a predictorcorrector implementation on a TI6701 has been developed. The performance modeling of the control system with an emphasis on further optimization needs addressing. This paper characterizes, from a control system standpoint, the predictor-corrector architecture applied to LIBS data collection. In addition, the application of this as a material classifier is presented. Updates in the model implemented on a low power multi-core DSP will be presented as well. Performance comparisons to alternative control system structures will be considered.
KEYWORDS: Digital signal processing, Principal component analysis, Signal processing, Laser induced breakdown spectroscopy, Distance measurement, Spectroscopy, Binary data, Sensors, Sensor technology, Data analysis
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.
Turbulence mitigation techniques require input data representing a wide variety of turbulent atmospheric
and weather conditions in order to produce robust results and wider ranges of applicability. In the past, this
has implied the need for numerous data collection equipment items to account for multiple frequency bands
and various system configurations. However, recent advancements in turbulence simulation techniques
have resulted in viable options to real-time data collection with various levels of available simulation
accuracy. This treatment will detail the development and implementation of an extension to the second
order statistical turbulence simulation model presented by Repasi1 and others. The Repasi model is
extended to include the effects of various wavelengths, optical configurations, and short exposure imaging
on angle of arrival fluctuation statistics. The result of the development is an atmospheric turbulence
simulation technique that is physics-based but less computationally intensive than phase-based or deflector
screen approaches. In these cases, the statistical approach detailed in this paper provides the user with an
opportunity to obtain a better trade-off between accuracy and simulation run-time. The mathematical
development and reasoning behind the changes to the previous statistical model will be presented, and
sample imagery produced by the extended technique will be included. The result is a model that captures
the major turbulence effects required for algorithm development for large classes of mitigation techniques.
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