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Dynamic Contaminant Identification in Water

  • Craig C. Douglas
  • J. Clay Harris
  • Mohamed Iskandarani
  • Chris R. Johnson
  • Robert J. Lodder
  • Steven G. Parker
  • Martin J. Cole
  • Richard Ewing
  • Yalchin Efendiev
  • Raytcho Lazarov
  • Guan Qin
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3993)

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.

Keywords

Markov Chain Monte Carlo Hyperspectral Imaging Spectral Element Spectral Element Method Kalman Filter Approach 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Craig C. Douglas
    • 1
    • 2
  • J. Clay Harris
    • 3
  • Mohamed Iskandarani
    • 4
  • Chris R. Johnson
    • 5
  • Robert J. Lodder
    • 3
  • Steven G. Parker
    • 5
  • Martin J. Cole
    • 5
  • Richard Ewing
    • 6
  • Yalchin Efendiev
    • 6
  • Raytcho Lazarov
    • 6
  • Guan Qin
    • 6
  1. 1.Department of Computer ScienceUniversity of KentuckyLexingtonUSA
  2. 2.Department of Computer ScienceYale UniversityNew HavenUSA
  3. 3.Department of ChemistryUniversity of KentuckyLexingtonUSA
  4. 4.Rosenstiel School of Marine and Atmospheric ScienceUniversity of MiamiMiamiUSA
  5. 5.Scientific Computing and Imaging InstituteUniversity of UtahSalt Lake CityUSA
  6. 6.Institute for Scientific ComputationTexas A&M UniversityCollege StationUSA

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