Dynamic Contaminant Identification in Water
Craig C. Douglas1,2, J. Clay Harris3, Mohamed Iskandarani4, Chris R. Johnson5, Robert J. Lodder3, Steven G. Parker5, Martin J. Cole5, Richard Ewing6, Yalchin Efendiev6, Raytcho Lazarov6, and Guan Qin6
1 University of Kentucky, Department of Computer Science, 773 Anderson Hall, Lexington, KY 40506-0046, USA
Yale University, Department of Computer Science, P.O. Box 208285 New Haven,
CT 06520-8285, USA
University of Miami, Rosenstiel School of Marine and Atmospheric Science, 4600
Rickenbacker Causeway, Miami, FL 33149-1098, USA
Texas A&M University, Institute for Scientific Computation, 612 Blocker,
3404 TAMU, College Station, TX 77843-3404, USA
Abstract. We describe how we plan to convert a traditional data collection sensor and ocean model into a DDDAS enabled system for identifying contaminants and then reacting with different models, simulations, and sensing strategies in a symbiotic manner. The sensor is just as useful in water as it would be on Mars for material identification. A successful terrestrial application of the sensor will lead to many new applications of the device and possible technology transfer to the private sector.
LNCS 3993, pp. 393-400.