Dr. Kevin L. Priddy Profile

Dr. Kevin L. Priddy

Chief Scientist at Dhara Consulting Group, Inc.

SPIE Involvement:

Author | Editor | Instructor

Area of Expertise:

Wide Area Motion Imaging , Pattern Recognition , Machine Learning , Layered Sensing , Artificial Neural Networks , Systems Engineering

Profile Summary

Dr. Kevin L. Priddy, is Technical Advisor, Sensor Exploitation Applications Branch, Sensors Directorate, Air Force Research Laboratory, Wright-Patterson Air Force Base, Ohio. The directorate specializes in developing science and technology for superior Air Force systems in the areas of intelligence, surveillance, reconnaissance, precision engagement and electronic warfare. He directs the technical efforts of the Sensor Exploitation Applications Branch in performing research and transitioning technology to meet Air Force needs.

AWARDS AND HONORS

2004, Jacobs Sverdrup, Senior Technical Award
2007, Air Force Research Laboratory Senior Leadership Award
2008, Sensors Directorate Senior Leadership Award
2008, Sensors Directorate Director’s Cup Team Award: Project AngelFire
2009, Air Force Research Laboratory, Commander’s Cup Team Award: Project AngelFire
2009, Air Force STEM Award Exploratory or Advanced Technology Development Team: Angel Fire Team
2010, Sensors Directorate, Director’s Cup, Senior Individual
2011, Air Force Research Laboratory, Commander’s Cup, Senior Individual
2011, Air Force Achievement Medal

BOOKS

“Artificial Neural Networks: An Introduction,” Kevin L. Priddy and Paul E. Keller, SPIE-International Society for Optical Engineering, October 2005, ISBN-13:9780819459879

PATENTS

United States Patent 5,850,625; “Sensor Fusion Apparatus and Method,” December 15, 1998
United States Patent 6,889,165; “Application Specific Intelligent Microsensor,” May 3, 2005.
United States Patent 6,995,655 “Method of simultaneously reading multiple radio frequency tags, RF tags, and RF reader,” February 7, 2006.

PROFESSIONAL MEMBERSHIPS AND ASSOCIATIONS
Senior Member IEEE
Member SPIE
Tau Beta Pi
Eta Kappa Nu

PROFESSIONAL CERTIFICATIONS
SPRDE-Science and Technology Manager, Level III
SPRDE-Systems Engineer, Level III

Publications (20)

Proceedings Article | 13 June 2023 Presentation + Paper

Presentation + Paper

Explorations in transfer learning and machine learning architectures utilizing the DSIAC ATR algorithm development data set

Kevin Priddy, Sastry Dhara

Proceedings Volume 12521, 125210K (2023) https://doi.org/10.1117/12.2662873

KEYWORDS: Detection and tracking algorithms, Education and training, Machine learning, Mid-IR, Feature extraction, Automatic target recognition, Image processing, Image segmentation, Image classification, Algorithm development

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Proceedings Article | 20 May 2015 Paper

Paper

Predicting exploitation success on compressed imagery through common image quality metrics: an initial look

Christopher McGuinness, Patrick Hytla, Kevin Priddy

Proceedings Volume 9464, 94640T (2015) https://doi.org/10.1117/12.2181940

KEYWORDS: Image compression, Image quality, Image processing, Distortion, JPEG2000, Detection and tracking algorithms, Computer programming, Data modeling, Video compression, Video

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Proceedings Article | 20 May 2015 Paper

Paper

Beyond H.264: implications of next generation video compression on surveillance imagery

Christopher McGuinness, Eric Balster, Kevin Priddy

Proceedings Volume 9464, 94640V (2015) https://doi.org/10.1117/12.2181939

KEYWORDS: Video compression, Image compression, Computer programming, Video, JPEG2000, Image processing, Video surveillance, Quantization, Transform theory, Algorithm development

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Proceedings Article | 3 May 2012 Paper

Paper

Automated recognition challenges for wide-area motion imagery

Kevin Priddy, Daniel Uppenkamp

Proceedings Volume 8391, 83910G (2012) https://doi.org/10.1117/12.924597

KEYWORDS: Sensors, Data storage, 3D modeling, 3D image processing, Image processing, Pattern recognition, Algorithm development, Detection and tracking algorithms, Image compression, Visualization

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Proceedings Article | 5 May 2011 Paper

Paper

A study of multi-static ultrasonic tomography using propagation and back-propagation method

Chengdong Dong, Yuanwei Jin, Matthew Ferrara, Kevin Priddy

Proceedings Volume 8051, 805106 (2011) https://doi.org/10.1117/12.883336

KEYWORDS: Tomography, Transducers, Reconstruction algorithms, Receivers, Ultrasonics, Wave propagation, Ultrasonography, Evolutionary algorithms, Imaging systems, Signal processing

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Proceedings Article | 13 May 2010 Paper

Paper

Open-source-based architecture for layered sensing applications

Daniel Uppenkamp, Todd Rovito, Kevin Priddy

Proceedings Volume 7694, 76940R (2010) https://doi.org/10.1117/12.852433

KEYWORDS: Sensors, Databases, Image sensors, Software development, Computer architecture, Standards development, Human-machine interfaces, Data modeling, Image resolution, Defense and security

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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

Read Description +

Artificial Neural Networks: An Introduction

Proceedings Article | 19 May 2005 Paper

Paper

Detection system performance metrics with scene content dependencies

Timothy Ross, William Pierson, Edmund Zelnio, Kevin Priddy

Proceedings Volume 5808, (2005) https://doi.org/10.1117/12.609758

KEYWORDS: Probability theory, Target detection, Palladium, Sensors, Single crystal X-ray diffraction, Stars, Standards development, Monte Carlo methods, Detector development, Tissues

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Proceedings Article | 28 March 2005 Paper

Paper

Self-localization of wireless sensor networks using self-organizing maps

Emre Ertin, Kevin Priddy

Proceedings Volume 5803, (2005) https://doi.org/10.1117/12.606812

KEYWORDS: Sensors, Sensor networks, Data modeling, Machine learning, Detection and tracking algorithms, Environmental monitoring, Surveillance, Global Positioning System, Acoustics, Radio propagation

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Proceedings Article | 12 April 2004 Paper

Paper

A comparative analysis of machine classifiers

Kevin Priddy

Proceedings Volume 5421, (2004) https://doi.org/10.1117/12.548345

KEYWORDS: Classification systems, Machine learning, Fuzzy logic

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Proceedings Article | 11 March 2002 Paper

Paper

Computational intelligence: Is it real or smoke and mirrors?

Kevin Priddy

Proceedings Volume 4739, (2002) https://doi.org/10.1117/12.458720

KEYWORDS: Artificial intelligence, Brain, Fuzzy logic, Neural networks, Mirrors, Computing systems, Evolutionary algorithms, Complex systems, Data processing, Neurons

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Proceedings Article | 11 March 2002 Paper

Paper

Reinforcement learning and design of nonparametric sequential decision networks

Emre Ertin, Kevin Priddy

Proceedings Volume 4739, (2002) https://doi.org/10.1117/12.458718

KEYWORDS: Sensors, Computer programming, Neural networks, Automatic target recognition, Binary data, Statistical analysis, Error analysis, Feature selection, Statistical modeling, Computing systems

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Proceedings Article | 21 March 2001 Paper

Paper

Dynamic optimization for optimal control of water distribution systems

Emre Ertin, Anthony Dean, Matthew Moore, Kevin Priddy

Proceedings Volume 4390, (2001) https://doi.org/10.1117/12.421163

KEYWORDS: Computer programming, Monte Carlo methods, Control systems, Device simulation, Optimization (mathematics), Systems modeling, Dynamical systems, Energy efficiency, Control systems design, Scientific programming

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Proceedings Article | 30 March 2000 Paper

Paper

Grand challenges

Kevin Priddy

Proceedings Volume 4055, (2000) https://doi.org/10.1117/12.380606

KEYWORDS: Computing systems, Evolutionary algorithms, Detection and tracking algorithms, Fuzzy logic, Neural networks, Fuzzy systems, Genetic algorithms, Artificial neural networks, Algorithm development, Embedded systems

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Proceedings Article | 4 April 1997 Paper

Paper

Application of highly parallel computing hardware to pattern recognition problems

Kevin Priddy, Karl Mathia, Timothy Robinson, Robert Pap

Proceedings Volume 3077, (1997) https://doi.org/10.1117/12.271485

KEYWORDS: Pattern recognition, Neural networks, Parallel computing, Parallel processing, Computer architecture

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Proceedings Article | 6 April 1995 Paper

Paper

Real-time stable adaptive control implementation using a neural network processor

Timothy Robinson, Mohammad Bodruzzaman, Kevin Priddy, Karl Mathia

Proceedings Volume 2492, (1995) https://doi.org/10.1117/12.205122

KEYWORDS: Neural networks, Control systems, Adaptive control, Adaptive optics, Computer simulations, Fluctuations and noise, Complex systems, Chaos, Differential equations, Artificial neural networks

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Proceedings Article | 2 March 1994 Paper

Paper

Design of a MIMD neural network processor

Richard Saeks, Kevin Priddy, Robert Pap, S. Stowell

Proceedings Volume 2243, (1994) https://doi.org/10.1117/12.169980

KEYWORDS: Neurons, Neural networks, Parallel processing, Data processing, Network architectures, Clocks, Parallel computing, Array processing, Computer architecture, Fermium

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Proceedings Article | 16 September 1992 Paper

Paper

NeuralGraphics: a general-purpose environment for neural network simulation

Gregory Tarr, Kevin Priddy, Steven Rogers

Proceedings Volume 1709, (1992) https://doi.org/10.1117/12.139981

KEYWORDS: Neural networks, Content addressable memory, Artificial neural networks, Pollution control, Visualization, Seaborgium, Binary data, Computer simulations, Imaging systems, Computer engineering

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Proceedings Article | 16 September 1992 Paper

Paper

Neural networks for segmentation, tracking, and identification

Steven Rogers, Dennis Ruck, Kevin Priddy, Gregory Tarr

Proceedings Volume 1709, (1992) https://doi.org/10.1117/12.139964

KEYWORDS: Neural networks, Image segmentation, Data processing, Artificial neural networks, Feature extraction, Facial recognition systems, Mouth, Image processing, Visual system, Object recognition

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SPIE Journal Paper | 1 February 1992

Optical image segmentation using neural-based wavelet filtering techniques

Christopher Veronin, Kevin Priddy, Steven Rogers, Kevin Ayer, Matthew Kabrisky, Byron Welsh

OE, Vol. 31, Issue 02, (February 1992) https://doi.org/10.1117/12.10.1117/12.56067

KEYWORDS: Image segmentation, Wavelets, Image filtering, Optical filters, Forward looking infrared, Spatial filters, Neural networks, Gaussian filters, Scene classification, Image processing

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Showing 5 of 20 publications

Proceedings Volume Editor (17)

SPIE Conference Volume | 30 June 2014

Ground/Air Multisensor Interoperability, Integration, and Networking for Persistent ISR V

SPIE Conference Volume | 20 June 2013

Ground/Air Multisensor Interoperability, Integration, and Networking for Persistent ISR IV

SPIE Conference Volume | 7 May 2012

Ground/Air Multisensor Interoperability, Integration, and Networking for Persistent ISR III

SPIE Conference Volume | 6 May 2011

Ground/Air Multisensor Interoperability, Integration, and Networking for Persistent ISR II

SPIE Conference Volume | 22 April 2010

Ground/Air Multi-Sensor Interoperability, Integration, and Networking for Persistent ISR

Showing 5 of 17 publications

Conference Committee Involvement (18)

Ground/Air Multisensor Interoperability, Integration, and Networking for Persistent ISR VI

20 April 2015 | Baltimore, MD, United States

Ground/Air Multisensor Interoperability, Integration, and Networking for Persistent ISR V

5 May 2014 | Baltimore, MD, United States

Ground/Air Multisensor Interoperability, Integration, and Networking for Persistent ISR IV

29 April 2013 | Baltimore, Maryland, United States

Ground/Air Multisensor Interoperability, Integration, and Networking for Persistent ISR III

23 April 2012 | Baltimore, Maryland, United States

Ground/Air Multisensor Interoperability, Integration, and Networking for Persistent ISR II

26 April 2011 | Orlando, Florida, United States

Evolutionary and Bio-Inspired Computation: Theory and Applications IV

7 April 2010 | Orlando, Florida, United States

Ground/Air Multi-Sensor Interoperability, Integration, and Networking for Persistent ISR

6 April 2010 | Orlando, Florida, United States

Evolutionary and Bio-Inspired Computation: Theory and Applications III

14 April 2009 | Orlando, Florida, United States

Intelligent Computing: Theory and Applications VI

17 March 2008 | Orlando, Florida, United States

Intelligent Computing: Theory and Applications V

9 April 2007 | Orlando, Florida, United States

Intelligent Computing: Theory and Applications IV

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

Intelligent Computing: Theory and Applications III

28 March 2005 | Orlando, Florida, United States

Intelligent Computing: Theory and Applications II

12 April 2004 | Orlando, Florida, United States

Intelligent Computing: Theory and Applications

21 April 2003 | Orlando, Florida, United States

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

Showing 5 of 18 Conference Committees

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.

Expand Description +

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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SC791: A Practical Course in Neural Networks

This course provides attendees with a basic working knowledge of artificial neural network design. The course concentrates on various types of neural networks and where they can be used to solve common classification problems. Many practical and useful examples are included throughout the course.

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