Dr. Charles W. Glover Profile

Dr. Charles W. Glover

Research Scientist at Oak Ridge National Lab

SPIE Involvement:

Author

Proceedings Article | 29 May 2014 Paper

Performance bounds on micro-Doppler estimation and adaptive waveform design using OFDM signals

Satyabrata Sen, Jacob Barhen, Charles Glover

Proceedings Volume 9077, 907713 (2014) https://doi.org/10.1117/12.2049404

KEYWORDS: Orthogonal frequency division multiplexing, Signal to noise ratio, Scattering, Statistical analysis, Einsteinium, Analytical research, Target recognition, Statistical modeling, Systems modeling, Sensors

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Proceedings Article | 15 September 2005 Paper

Performance evaluation of time-weighted backvalues least squares vs. NOGA track estimators via sensor data fusion and track fusion for small target detection applications

Neena Imam, Charles Glover, Jacob Barhen

Proceedings Volume 5913, 59130Z (2005) https://doi.org/10.1117/12.617690

KEYWORDS: Sensors, Detection and tracking algorithms, Data fusion, Electroluminescence, Process modeling, Target detection, Algorithm development, Mathematical modeling, Error analysis, Data analysis

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Proceedings Article | 4 October 1999 Paper

Does extra information always help in data fusion?

Steven Waugh, Albert Perrella, Charles Glover

Proceedings Volume 3809, (1999) https://doi.org/10.1117/12.364033

KEYWORDS: Data fusion, Sensors, Monte Carlo methods, Motion models, Data processing, Sensor fusion, Statistical analysis, Defense and security, Signal processing, Data modeling

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Proceedings Article | 4 April 1997 Paper

Accuracy estimation for supervised learning algorithms

Charles Glover, Nageswara Rao, Ed Oblow

Proceedings Volume 3077, (1997) https://doi.org/10.1117/12.271548

KEYWORDS: Machine learning, Error analysis, Analytical research

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Proceedings Article | 19 February 1996 Paper

Image exploitation using multisensor/neural network systems

Reinhold Mann, R. Lee, Y. Xu, Charles Glover, Edward Uberbacher, Martin Beckerman

Proceedings Volume 2645, (1996) https://doi.org/10.1117/12.233058

KEYWORDS: Sensors, Image fusion, Algorithm development, Computing systems, Machine vision, Computer vision technology, Image sensors, Neural networks, Image segmentation, Evolutionary algorithms

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Proceedings Article | 1 January 1990 Paper

Hybrid neural network and rule-based pattern recognition system capable of self-modification

Phil Spelt, Charles Glover, Mike Silliman, Nageswara Rao, Mark Walker

Proceedings Volume 1293, (1990) https://doi.org/10.1117/12.21079

KEYWORDS: Neural networks, Pattern recognition, Rule based systems, Sensors, Data modeling, Feature extraction, Computer architecture, Environmental sensing

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This paper describes a hybrid neural network and rule-based pattern recognition system architecture which is capable of self-modification or learning. The central research issue to be addressed for a multiclassifier hybrid system is whether such a system can perform better than the two classifiers taken by themselves. The hybrid system employs a hierarchical architecture, and it can be interfaced with one or more sensors. Feature extraction routines operating on raw sensor data produce feature vectors which serve as inputs to neural network classifiers at the next level in the hierarchy. These low-level neural networks are trained to provide further discrimination of the sensor data. A set of feature vectors is formed from a concatenation of information from the feature extraction routines and the low-level neural network results. A rule-based classifier system uses this feature set to determine if certain expected environmental states, conditions, or objects are present in the sensors’ current data stream. The rule-based system has been given an a priori set of models of the expected environmental states, conditions, or objects which it is expected to identify. These models are represented as a directed graph of features vectors. The rule-based system forms many candidate directed graphs of various combinations of incoming features vectors, and it uses a suitably chosen metric to measure the similarity between candidate and model directed graphs. The rule-based system must decide if there is a match between one of the candidate graphs and a model graph. If a match is found, then the rule-based system invokes a routine to create and train a new high-level neural network from the appropriate feature vector data to recognize when this model state is present in future sensor data streams. A different high-level neural network is created for each model state identified by the rule-based system. Both the high-level neural networks and the rule-based system receive all subsequent feature vector data streams, and each of these classifiers provides an estimate of whether a certain model states exist in the data. If the high-level neural network finds that a model state is present and the rule-based system does not, then the rule-based system will alter its model of the state - the rule-based system learns from the high-level neural net. If the high-level neural network does not find that a model state is present and the rule-based system does, then the rule-based system will invoke a neural network retraining routine - the high-level neural network learns from the rule-base. A second research issue is whether the self-modification of models can be controlled, such that models are only altered to reflect new information and not erroneous information from noisy sensors.

Showing 5 of 6 publications

Conference Committee Involvement (25)

Signal Processing, Sensor/Information Fusion, and Target Recognition XXIX

27 April 2020 | Online Only, California, United States

Signal Processing, Sensor/Information Fusion, and Target Recognition XXVIII

15 April 2019 | Baltimore, MD, United States

Signal Processing, Sensor/Information Fusion, and Target Recognition XXVII

16 April 2018 | Orlando, FL, United States

Signal Processing, Sensor/Information Fusion, and Target Recognition XXVI

10 April 2017 | Anaheim, CA, United States

Signal Processing, Sensor/Information Fusion, and Target Recognition XXV

18 April 2016 | Baltimore, MD, United States

Signal Processing, Sensor/Information Fusion, and Target Recognition XXIV

20 April 2015 | Baltimore, MD, United States

Signal Processing, Sensor/Information Fusion, and Target Recognition XXIII

5 May 2014 | Baltimore, MD, United States

Signal Processing, Sensor Fusion, and Target Recognition XXII

29 April 2013 | Baltimore, Maryland, United States

Signal Processing, Sensor Fusion, and Target Recognition XXI

23 April 2012 | Baltimore, Maryland, United States

Signal Processing, Sensor Fusion, and Target Recognition XX

25 April 2011 | Orlando, Florida, United States

Signal Processing, Sensor Fusion, and Target Recognition XIX

5 April 2010 | Orlando, Florida, United States

Signal and Data Processing of Small Targets 2009

4 August 2009 | San Diego, California, United States

Signal Processing, Sensor Fusion, and Target Recognition XVIII

13 April 2009 | Orlando, Florida, United States

Signal and Data Processing of Small Targets 2008

18 March 2008 | Orlando, Florida, United States

Signal Processing, Sensor Fusion, and Target Recognition XVII

17 March 2008 | Orlando, Florida, United States

Intelligent Computing: Theory and Applications V

9 April 2007 | Orlando, Florida, United States

Signal Processing, Sensor Fusion, and Target Recognition XVI

9 April 2007 | Orlando, Florida, United States

Intelligent Computing: Theory and Applications IV

17 April 2006 | Orlando (Kissimmee), Florida, United States

Signal Processing, Sensor Fusion, and Target Recognition XV

17 April 2006 | Orlando (Kissimmee), Florida, United States

Intelligent Computing: Theory and Applications III

28 March 2005 | Orlando, Florida, United States

Signal Processing, Sensor Fusion, and Target Recognition XIV

28 March 2005 | Orlando, Florida, United States

Intelligent Computing: Theory and Applications II

12 April 2004 | Orlando, Florida, United States

Signal Processing, Sensor Fusion, and Target Recognition XIII

12 April 2004 | Orlando, Florida, United States

Signal Processing, Sensor Fusion, and Target Recognition XII

21 April 2003 | Orlando, Florida, United States

Intelligent Computing: Theory and Applications

21 April 2003 | Orlando, Florida, United States

Showing 5 of 25 Conference Committees

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