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Plume Process Detection Research 

Research Objective

I am interested in the filtering and search of data from networks and real time anomaly detection of high level information.  Networks of sensing devices embedded in our world (weather, traffic, environmental, seismic, temperature) produce massive amounts of real-time information and require new search methods.  Rather than traditional database searches based on parameter thresholds or boolean queries, my  thesis work develops a method to detect and track high level physical processes.  This approach develops techniques from information theory, tracking theory, Bayesian statistics, and HMMs (Hidden Markov Models.)  One example of a physical "process" is the release of a chemical or radioactive plume, however the process detection method applies to any well understood process.  Other applications include physical target tracking, computer network security, and social network analysis.

 Plume Tracking In Sensor Networks

Given a network of simple binary sensors capable of detecting a chemical of radioactive substance, how can the network deconstruct large observations sets to identify the number, time, size, and location of re:lease sites in a region?  Imagine a city outfitted with thousands or tens-of-thousand of these low resolution sensors: how can the data be made useful?  This was the main focus of my thesis work at Dartmouth.  The  approach treats the plume sources as "targets" and then makes likelihood assignments between sensor observations.  Groups of observations are partitioned into "tracks" where each track is believed to consist of observations generated by the  same plume source.

  The forward plume simulation environment with three chemical release plumes. The wind field was generated with a 2D Markov model, where the transition probabilities were trained from historical weather statistics.

plume simulation
  Observations at binary sensor nodes (red) for  2 plume sources with unknown locations.

Plume Tracking
Assignment of sensor observations to "tracks."


Tracking Based Plume Detection. Glenn Nofsinger.  Draft for PhD Thesis Proposal at Dartmouth College, April 5, 2006

Airborne Plume Tracking With Sensor Networks.  Glenn Nofsinger and George Cybenko.  In  Proceedings of the SPIE , Defense and Security Symposium - Conference 6231 - Unattended Ground, Sea, and Air Sensor Technologies and Applications VII, Orlando, FL, April 2006

Distributed Chemical Plume Process Detection. Glenn Nofsinger, George Cybenko, In IEEE Military Communications Conference, MILCOM, Altaltic City, NJ, October 2005

Plume Source Detection Using a Process Query System.  Glenn Nofsinger, George Cybenko, In  Proceedings of the SPIE Vol. 5416, Defense and Security Symposium - Chemical and Biological Sensing V,Orlando, FL, 2004