Faculty Profile - Christopher F Barnes
Digital Signal Processing
Office: Cent 5191
Christopher F. Barnes is an associate professor in the School of Electrical and Computer Engineering (ECE) at the Georgia Institute of Technology.
Dr. Barnes received his Ph.D. degree from Brigham Young University in 1989, after which he joined the Georgia Tech Research Institute (GTRI) as research faculty. He transferred to the School of ECE as an associate professor in 2002, and retains GTRI adjunct status as a principal research engineer.
Dr. Barnes has 27 years of experience in basic and applied research and has published approximately 140 papers and holds one patent.
He is recognized internationally for his expertise in synthetic aperture radar (SAR) analysis and SAR image formation processing. Dr. Barnes has twenty years of experience teaching SAR at the professional education and graduate student level. He has invented radar imaging image formation processing methods capable of three-dimensional coherently fused SAR imaging in remote sensing applications. This advanced radar signal processing method has applications in sonar, medical imaging and seismology. He has provided subject matter expertise in many research and development oversight activities of ground based and ship based radar signal processing and radar software engineering programs. Dr. Barnes is an expert in hardware/software architectures, software engineering, middleware, and object oriented/behavior decompositions for both functional and non-functional attributes of software intensive radar systems. He is also an expert in object-oriented software engineering tools, UML, C++, C and scripted languages, and software processes metrics.
Dr. Barnes' areas of basic research include image and video driven data mining and data compression. He has developed image driven data mining methods and systems that have proven capability in remotely sensed image processing for hurricane damage assessments. Dr. Barnes is supervising research in developing video driven data mining for video tracking. These video and image driven data mining systems are also being explored as possible foundations for new Internet search capabilities that correlate Internet images and videos based on image/video content similarity/dissimilarity. He is also applying data driven data mining systems to tasks such as computer assisted diagnosis of mammography and in various bioinformatic applications.
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Last revised on May 14, 2014.