Using Markov Models and Statistics to Learn, Extract, Fuse, and Detect Patterns in Raw Data

Many systems are partially stochastic in nature. We have derived data driven approaches for extracting stochastic state machines (Markov models) directly from observed data. This chapter provides an overview of our approach with numerous practical applications. We have used this approach for inferring shipping patterns, exploiting computer system side-channel information, and detecting botnet activities. For contrast, we include a related data-driven statistical inferencing approach that detects and localizes radiation sources.Comment: Accepted by 2017 International Symposium on Sensor Networks, Systems and Securit

Expanding the use of real-time electromagnetic tracking in radiation oncology.

In the past 10 years, techniques to improve radiotherapy delivery, such as intensity-modulated radiation therapy (IMRT), image-guided radiation therapy (IGRT) for both inter- and intrafraction tumor localization, and hypofractionated delivery techniques such as stereotactic body radiation therapy (SBRT), have evolved tremendously. This review article focuses on only one part of that evolution, electromagnetic tracking in radiation therapy. Electromagnetic tracking is still a growing technology in radiation oncology and, as such, the clinical applications are limited, the expense is high, and the reimbursement is insufficient to cover these costs. At the same time, current experience with electromagnetic tracking applied to various clinical tumor sites indicates that the potential benefits of electromagnetic tracking could be significant for patients receiving radiation therapy. Daily use of these tracking systems is minimally invasive and delivers no additional ionizing radiation to the patient, and these systems can provide explicit tumor motion data. Although there are a number of technical and fiscal issues that need to be addressed, electromagnetic tracking systems are expected to play a continued role in improving the precision of radiation delivery

Improvements to MLE Algorithm for Localizing Radiation Sources with a Distributed Detector Network

Maximum Likelihood Estimation (MLE) is a widely used method for the localization of radiation sources using distributed detector networks. While robust, MLE is computationally intensive, requiring an exhaustive search over parameter space. To mitigate the computational load of MLE, many techniques have been presented, including iterative and multi-resolution methods. In this work, we present two ways to improve the MLE localization of radiation sources. First, we present a method to mitigate the pitfalls of a standard multi-resolution algorithm. Our method expands the search region of each layer before performing the MLE search. Doing so allows the multi-resolution algorithm to correct an incorrect selection made in a prior layer. We test our proposed method against single-resolution MLE and standard multi-resolution MLE algorithms, and find that the use of grid expansion incurs a general decrease in localization error and a negligible increase in computation time over the standard multi-resolution algorithm. Second, we present a method to perform the MLE localization without prior knowledge of the background radiation intensity. We estimate the source and background intensities using linear regression (LR) and then use these estimates to initialize the intensity parameter search for MLE. We test this method using single-resolution, multi-resolution, and multi-resolution with grid expansion MLE algorithms and compare performance to MLE algorithms that don\u27t use the LR initialization method. We found that using the LR estimates to initialize the intensity parameter search caused a marginal increase in both localization error and computation time for the tested algorithms. The technique is only beneficial in the case of an unknown background intensity