Bayesian source detection and parameter estimation of a plume model based on sensor network measurements

We consider a network of sensors that measure the intensities of a complex plume composed of multiple absorption–diffusion source components. We address the problem of estimating the plume parameters, including the spatial and temporal source origins and the parameters of the diffusion model for each source, based on a sequence of sensor measurements. The approach not only leads to multiple-source detection, but also the characterization and prediction of the combined plume in space and time. The parameter estimation is formulated as a Bayesian inference problem, and the solution is obtained using a Markov chain Monte Carlo algorithm. The approach is applied to a simulation study, which shows that an accurate parameter estimation is achievable. Copyright © 2010 John Wiley & Sons, Ltd.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/78051/1/859_ftp.pd

Greedy Methods in Plume Detection, Localization and Tracking

Greedy method, as an efficient computing tool, can be applied to various combinatorial or nonlinear optimization problems where finding the global optimum is difficult, if not computationally infeasible. A greedy algorithm has the nature of making the locally optimal choice at each stage and then solving the subproblems that arise later. It iteratively make

Proactive strategies in personal dose monitoring, prevention and mitigation

At certain threshold, nuclear radiation (like x-rays and gamma-rays) may adversely impact the health of living tissues. The exposure to these radiations in nuclear facilities is measured by devices called dosimeters. The devices are generally worn on the torso and are monitored by health physics division to report the radiation dose received by the personnel. However, this approach is not proactive--since the dosimeters reflect the dose that has already been absorbed in the body of the wearer. This work presents a scheme to proactively avoid large dose acquisition at radiation-prone facilities. The work was divided into three major segments: (i) identify and characterize radioactive source(s), (ii) determine the impact of localized source(s), and (iii) estimate the integrated doses in traversing/evacuating the facility. The scope of this work does not extend to the development of proactive dosimeter. However, the approaches developed in these segments will be integrated into a dose monitoring system that would prevent or mitigate large dose acquisition. This work also has applications in nuclear facilities, hospitals, homeland security, and border protection --Abstract, page iv