Biochemical Transport Modeling, Estimation, and Detection in Realistic Environments
Mathias Ortner
INRIA, Sophia Antipolis, France
Department of Electrical & Systems Engineering, Washington University, St. Louis, MO, USA
Search for more papers by this authorArye Nehorai
Department of Electrical & Systems Engineering, Washington University, St. Louis, MO, USA
Search for more papers by this authorMathias Ortner
INRIA, Sophia Antipolis, France
Department of Electrical & Systems Engineering, Washington University, St. Louis, MO, USA
Search for more papers by this authorArye Nehorai
Department of Electrical & Systems Engineering, Washington University, St. Louis, MO, USA
Search for more papers by this authorSimon Haykin
Department of Electrical Engineering, McMaster University, Hamilton, Ontario, Canada
Search for more papers by this authorK. J. Ray Liu
Department of Electrical & Computer Engineering, University of Maryland, College Park, MD, USA
Search for more papers by this authorSummary
This chapter contains sections titled:
-
Introduction
-
Physical and Statistical Models
-
Transport Modeling Using Monte Carlo Approximation
-
Localizing the Source(s)
-
Sequential Detection
-
Conclusion
-
References
REFERENCES
- M. Ortner, A. Nehorai, and A. Jeremic, “Biochemical transport modeling and bayesian source estimation in realistic environments,” IEEE Trans. Signal Process., vol. 55, pp. 2520–2532, June 2007.
- M. Ortner and A. Nehorai, “A sequential detector for biochemical release in realistic environments,” IEEE Trans. Signal Process., vol. 55, pp. 4173–4182, July 2007.
- “DHS and national academies highlight role of media in terrorism response,” available: http://www.nae.edu/.
- J. Fitch, E. Raber, and D. Imbro, “Technology challenges in responding to biological or chemical attacks in the civilian sector,” Science, vol. 32, pp. 1350–1354, Nov. 2003.
-
H. Banks and C. Castillo-Chavez, “Bioterrorism: Mathematical modeling applications in homeland security,” Philadelphia: Society for Industrial and Applied Mathematics, 2003.
10.1137/1.9780898717518 Google Scholar
- A. Nehorai, B. Porat, and E. Paldi, “Detection and localization of vapor-emitting sources,” IEEE Trans. Signal Process., vol. SP-43, pp. 243–253, Jan. 1995.
- B. Porat and A. Nehorai, “Localizing vapor-emitting sources by moving sensors,” IEEE Trans. Signal Process., vol. SP-44, pp. 1018–1021, Apr. 1996.
- A. Jeremić and A. Nehorai, “Design of chemical sensor arrays for monitoring disposal sites on the ocean floor,” IEEE J. Ocean. Eng., vol. 23, pp. 334–343, 1998.
- A. Jeremić and A. Nehorai, “Landmine detection and localization using chemical sensor array processing,” IEEE Trans. Signal Process., vol. SP48, May 2000.
- T. Zhao and A. Nehorai, “Detecting and estimating biochemical dispersion of a moving source in a semi-infinite medium,” IEEE Trans. Signal Process., 2005, in revision.
- A. Jeremić and A. Nehorai, “Detection and estimation of biochemical sources in arbitrary 2D environments,” in IEEE Int. Conf. Acoust., Speech, Signal Processing, Philadelphia, PA, Mar. 2005.
- A. Gershman and V. Turchin, “Nonwave field processing using sensor array approach,” Signal Process., vol. 44, no. 2, pp. 197–210, June 199.
- A. Pardo, S. Marco, and J. Samitier, “Nonlinear inverse dynamic models of gas sensing systems based on chemical sensor arrays for quantitative measurements,” IEEE Trans. Instrum. Meas., vol. 47, no. 3, pp. 644–651, June.
- H. Ishida, T. Nakamoto, and T. Moriizumi, “Remote sensing and localization of gas/odor source and distribution using mobile sensing system,” in Proceeding of the 2002 45th Midwest Symposium on Circuits and Systems, Vol. 1, Aug. 2002, pp. 52–55.
- J. Matthes, L. Groll, and H. Keller, “Source localization based on pointwise concentration measurements,” Sensors Actuators A: Phys., vol. 115, no. 1, pp. 32–37, Sept. 2004.
- H. Niska, M. Rantamäki, T. Hiltunen, A. Karppinen, J. Kukkonen, J. Ruuskanen, and M. Kolehmainen, “Valuation of an integrated modelling system containing a multi-layer perceptron model and the numerical weather prediction model hirlam for the forecasting of urban airborne pollutant concentrations.” Atmospher. Environ., vol. 39, no. 35, pp. 6524–6536, 2005.
- A. Venestanos, T. Huld, P. Adams, and J. Bartzis, “Source, dispersion and combustion modeling of an accidental release of hydrogen in an urban environment.” J. Hazard. Mater., vol. 105, pp. 1–25, 2003.
- H. Chang and T. Lai, “Importance sampling for generalized likelihood ratio procedures in sequential analysis,” Sequential Analysis, to appear.
- “Gerris Flow Solver,” available: http://gfs.sourceforge.net.
-
K. Radics, J. Bartholy, and R. Pongrácz, “Modeling studies of wind field on urban environment,” Atmospher. Chem. Phys. Discuss., vol. 2, 2002.
10.5194/acpd-2-1979-2002 Google Scholar
- K. P. U. D. Baldocchi, T. Meyers, and K. Wilson, “Correction of eddy-covariance measurements incorporating both advective effects and density fluxes source,” Boundary-Layer Meteorol., vol. 97, no. 3, pp. 487–511, 2000.
- J. Anderson, Fundamentals of Aerodynamics, New York: Mc Graw-Hill, 1984.
- C. Costantini, B. Pachiarotti, and F. Sartoretto, “Numerical approximation for functionals of reflecting diffusion processes,” SAM J. Appl. Math., vol. 58, no. 1, pp. 73–102, 1998.
- D. Talay, “Simulations of stochastic differential systems,” in Probabilistic Methods in Applied Physics, Series Lecture Notes in Physics 451, Springer Verlag, 1995.
- D. Lépingle, “Euler scheme for reflected stochastic differential equations,” Math. Comput. Simul., vol. 38, pp. 119–126, 1995.
- M. Bossy, E. Gobet, and D. Talay, “Symmetrized Euler scheme for an efficient approximation of reflected diffusions,” J. Appl. Prob., vol. 41, no. 3, pp. 877–889, 2004.
- E. Gobet, “Efficient schemes for the weak approximation of killed diffusions,” Stochastic Process. Applicat., vol. 7, pp. 167–197, 2000.
-
J. Ruanaidh and W. Fitzgerald, Numerical Bayesian Methods Applied to Signal Processing, Statistics and Computing, New York: Springer, 1996.
10.1007/978-1-4612-0717-7 Google Scholar
- T. Lai, “Sequential analysis: Some classical problems and new challenges,” Statist. Sinica, vol. 11, pp. 303–408, 2001.
- A. Tartakovsky, “Asymptotic properties of CUSUM and Shiryaev's procedures for detecting a change in a nonhomogeneous Gaussian process,” Math. Methods Statist., vol. 4, no. 4, 1995.
-
A. G. Tartakovsky, “Asymptotic optimality of certain multihypothesis sequential tests: Non-iid case,” Statist. Inf. Stochast. Process., vol. 1, pp. 265–295, 1998.
10.1023/A:1009952514505 Google Scholar
- A. Tartakovsky, S. Kligys, and A. Petroc, “Adaptative sequential algorithms for detecting targets in a heavy IR clutter,” in SPIE Proceedings: Signal and Data Processing of Small Targets, Vol. 3809, Denver, CO, 1999.
- B. Blažek, H. Kim, B. Rozovskii, and A. Tartakovsky, “A novel approach to detection of ‘denial-of-service’ attacks via adaptive sequential and batch-sequential change-point detection methods,” in IEEE Workshop on Information Assurance and Security United States Military Academy West Point, June 2001.
- M. Girshik and H. Rubin, “A bayes approach to a quality control model,” Ann. Math. Statist., vol. 23, pp. 114–125, 1952.
-
E. S. Page, “A test for a change in a parameter occurring at an unkown point,” Biometrika, 1955.
10.1093/biomet/42.3-4.523 Google Scholar
- S. Roberts, “Control chart tests based on geometric moving averages,” Technometrics, vol. 1, pp. 239–250, 1959.
-
A. Shiryaev, “On optimum methods in quickest detection problems,” Theory Prob. Its Applicat., vol. 8, pp. 22–46, 1963.
10.1137/1108002 Google Scholar
- M. Pollack, “Optimal detection of a change in distribution,” Ann. Statist., vol. 13, pp. 206–227, 1986.
- G. Lorden, “Procedures for reacting to a change in distribution,” Ann. Math. Statist., vol. 42, pp. 1897–1908, 1971.
- S. M. Kay, Fundamentals of statistical signal processing, Vol. II: Detection Theory, Upper Saddle River, NJ: Prentice Hall, 1998.
- A. S. Monin and A. M. Yaglom, Statistical Fluid Mechanics, Cambridge MA: MIT Press, 1975.
-
G. Winkler, Image Analysis, Random Fields and Markov Chain Monte Carlo Methods: A Mathematical Introduction, Springer-Verlag, 2003.
10.1007/978-3-642-55760-6 Google Scholar
-
C. Robert and G. Casella, Monte Carlo Statistical Methods, New York: Springer-Verlag, 1999.
10.1007/978-1-4757-3071-5 Google Scholar
- H. Pikkarainen, “State estimation approach to nonstationary inverse problems: Discretization error and filtering problem,” Inverse Problems, vol. 22, pp. 365–379, 2006.
- S. Roberts, “Control chart tests based on geometric moving averages,” Technometrics, vol. 1, pp. 239–250, 1959.
- A. Willsky and H. Jones, “A generalized likelihood ratio approach to the detection and estimation of jumps in linear systems,” IEEE Trans. on Automat. Control, pp. 108–112, Feb. 1976.
- M. Basseville and A. Benveniste, “Design and comparatice strudy of some sequential jump detection algorithms for digital signals,” IEEE Trans. Acoust. Speech Signal Process., vol. ASSP-31, pp. 521–535, June 1983.