Dr. Paul E. Keller Profile

Dr. Paul E. Keller

Senior Research Scientist at Pacific Northwest National Lab

SPIE Involvement:

Author | Instructor

Area of Expertise:

image science , neural networks , pattern recognition , optics , machine learning

Senior Research Scientist with 20+ years experience in three overlapping areas: optics, imaging science, and data analysis as applied to projects spanning non-proliferation, security, defense, medicine, environmental sensing, telecommunications, and energy distribution for a wide range of government and commercial clients.

Specialties
• Optics: System Modeling, Optical Signal Processing, Physical Optics, Photonics, Holography, Imaging Systems
• Imaging Science: Image Formation, Image Reconstruction, Image Processing, Machine Vision, Multimodality Fusion, Image & Video Analytics (millimeter wave [active and passive], THz, IR, visible, UV, X-ray, Gamma Ray, MRI, Ultrasound)
• Data Analysis: Machine Learning, Neural Networks, Information Physics, Signal Processing, Pattern Recognition, Statistics

Proceedings Article | 17 October 2012 Paper

Investigating biofilm structure using x-ray microtomography and gratings-based phase contrast

Erin Miller, Xianghui Xiao, Micah Miller, Paul Keller, Timothy White, Matthew Marshall

Proceedings Volume 8506, 85061H (2012) https://doi.org/10.1117/12.930105

KEYWORDS: Absorption, Phase contrast, X-rays, X-ray imaging, Osmium, Visibility, Spatial resolution, Image resolution, Interferometers, Visualization

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Proceedings Article | 1 May 2009 Paper

Simulation and theory of speckle noise for an annular aperture frequency-modulation differential-absorption LIDAR (FM-DIAL) system

Paul Keller, Michael Batdorf, Jana Strasburg, Warren Harper

Proceedings Volume 7324, 732404 (2009) https://doi.org/10.1117/12.817698

KEYWORDS: Signal to noise ratio, Speckle, Monte Carlo methods, Telescopes, Retroreflectors, Numerical analysis, Solids, LIDAR, Turbulence, Speckle pattern

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Proceedings Article | 30 April 2009 Paper

Neural-network-based state of health diagnostics for an automated radioxenon sampler/analyzer

Paul Keller, Lars Kangas, James Hayes, Brian Schrom, Reynold Suarez, Charles Hubbard, Tom Heimbigner, Justin McIntyre

Proceedings Volume 7347, 73470Y (2009) https://doi.org/10.1117/12.817613

KEYWORDS: Sensors, Principal component analysis, Diagnostics, Data modeling, Systems modeling, Neurons, Xenon, Analytical research, Data centers, Control systems

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Proceedings Article | 30 April 2009 Paper

Dual-surface dielectric depth detector for holographic millimeter-wave security scanners

Douglas McMakin, Paul Keller, David Sheen, Thomas Hall

Proceedings Volume 7309, 73090G (2009) https://doi.org/10.1117/12.817882

KEYWORDS: Dielectrics, 3D image processing, Sensors, 3D image reconstruction, Image segmentation, 3D acquisition, Holography, Scanners, Detection and tracking algorithms, Image restoration

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Proceedings Article | 28 February 2006 Paper

Infrared photonic bandgap materials and structures

S. Sundaram, P. Keller, B. Riley, J. Martinez, B. Johnson, P. Allen, L. Saraf, N. Anheier, F. Liau

Proceedings Volume 6128, 612807 (2006) https://doi.org/10.1117/12.658895

KEYWORDS: Photonic crystals, Waveguides, Infrared radiation, Dielectrics, Chalcogenide glass, Crystals, Chalcogenides, Infrared materials, Silver, Infrared photography

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SPIE Press Book | 30 August 2005

Artificial Neural Networks: An Introduction

Kevin Priddy, Paul Keller

KEYWORDS: Neural networks, Data modeling, Artificial neural networks, Error analysis, Image segmentation, Pattern recognition, Sensors, Evolutionary algorithms, Statistical analysis

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Artificial Neural Networks: An Introduction

Proceedings Article | 30 March 2000 Paper

Privacy algorithm for airport passenger screening portal

Paul Keller, Douglas McMakin, David Sheen, A. McKinnon, Jay Summet

Proceedings Volume 4055, (2000) https://doi.org/10.1117/12.380602

KEYWORDS: Image segmentation, Speckle, Imaging systems, Detection and tracking algorithms, Holography, Sensors, Explosives, Image processing algorithms and systems, Neural networks, Ku band

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Proceedings Article | 22 March 1999 Paper

Pulse-coupled neural networks for medical image analysis

Paul Keller, A. McKinnon

Proceedings Volume 3722, (1999) https://doi.org/10.1117/12.342900

KEYWORDS: Image segmentation, Image processing, Lung, Kidney, Medical imaging, Magnetic resonance imaging, Neural networks, Brain, Abdomen, Tissues

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Proceedings Article | 22 March 1999 Paper

Physiologically inspired pattern recognition for electronic noses

Paul Keller

Proceedings Volume 3722, (1999) https://doi.org/10.1117/12.342868

KEYWORDS: Sensors, Nose, Molecules, Prototyping, Pattern recognition, Receptors, Chemical analysis, Principal component analysis, Molybdenum, Environmental monitoring

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Proceedings Article | 6 April 1995 Paper

Neural network based data analysis for chemical sensor arrays

Sherif Hashem, Paul Keller, Richard Kouzes, Lars Kangas

Proceedings Volume 2492, (1995) https://doi.org/10.1117/12.205155

KEYWORDS: Sensors, Chemical analysis, Neural networks, Data analysis, Analytical research, Chemical fiber sensors, Prototyping, Environmental monitoring, Artificial neural networks, Temperature sensors

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Showing 5 of 10 publications

SPIE Conference Volume | 11 March 2002

Applications and Science of Computational Intelligence V

SPIE Conference Volume | 21 March 2001

Applications and Science of Computational Intelligence IV

SPIE Conference Volume | 30 March 2000

Applications and Science of Computational Intelligence III

SPIE Conference Volume | 22 March 1999

Applications and Science of Computational Intelligence II

Conference Committee Involvement (4)

Applications and Science of Computational Intelligence V

2 April 2002 | Orlando, FL, United States

Applications and Science of Computational Intelligence IV

17 April 2001 | Orlando, FL, United States

Applications and Science of Computational Intelligence III

24 April 2000 | Orlando, FL, United States

Applications and Science of Computational Intelligence II

5 April 1999 | Orlando, FL, United States

Course Instructor

SC166: Fundamentals of Artificial Neural Networks

The course starts with the history of research into biological neural networks used for unsolved problems in information processing. The technology of physiologically motivated information processing to solve engineering problems had advanced. Neural networks for finding patterns in data have progressed out of the laboratory and into products. This course provides the background to understand and apply this technology for recognizing patterns in data.

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SC360: Advanced Neural Networks

Neural networks have been around for over forty years. This course presents many examples of artificial neural networks and provides the attendee with a thorough understanding of the most popular neural networks such as back propagation trained feed-forward neural networks, self-organizing feature maps, adaptive resonance theory (ART), generalized linear and hybrid neural networks. The attendee is given the theoretical background needed to understand why one network or combination of networks works on a given problem but may not be a good choice for others. The instructor introduces the latest algorithms and their applications to many engineering problems.

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