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Physical Sciences and Mathematics Commons™
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Articles 1 - 4 of 4
Full-Text Articles in Physical Sciences and Mathematics
Glme3_Ado_Do_Files, Joseph Hilbe
Detecting Clustered Chem/Bio Signals In Noisy Sensor Feeds Using Adaptive Fusion, Randy Paffenroth, Scott Lundberg, Chris Calderon
Detecting Clustered Chem/Bio Signals In Noisy Sensor Feeds Using Adaptive Fusion, Randy Paffenroth, Scott Lundberg, Chris Calderon
Randy C. Paffenroth
Chemical and biological monitoring systems are faced with the challenge of detecting weak signals from contam- inants of interest while at the same time maintaining extremely low false alarm rates. We present methods to control the number of false alarms while maintaining power to detect; evaluating these methods on a fixed sensor grid. Contaminants are detected using signals produced from underlying sensor-specific detection algorithms. By learning from past data, an adaptive background model is constructed and used with a multi-hypothesis testing method to control the false alarm rate. Detection methods for chemical/biological releases often depend on specific models for release …
Distributed Pattern Detection In Cyber Networks, Randy Paffenroth, Philip Du Toit, Louis Scharf, Anura Jayasumana, Vidarshana Banadara, Ryan Nong
Distributed Pattern Detection In Cyber Networks, Randy Paffenroth, Philip Du Toit, Louis Scharf, Anura Jayasumana, Vidarshana Banadara, Ryan Nong
Randy C. Paffenroth
In this paper we describe an approach for the detection and classication of weak, distributed patterns in sensor networks. Of course, before one can begin development of a pattern detection algorithm, one must rst dene the term "pattern", which by nature is a broad and inclusive term. One of the key aspects of our work is a denition of pattern that has already proven eective in detecting anomalies in real world data. While designing detection algorithms for all classes of patterns in all types of networks sounds appealing, this approach would almost certainly require heuristic methods and only cursory statements …
R Code: A Non-Iterative Implementation Of Tango's Score Confidence Interval For A Paired Difference Of Proportions, Zhao Yang
Zhao (Tony) Yang, Ph.D.
For matched-pair binary data, a variety of approaches have been proposed for the construction of a confidence interval (CI) for the difference of marginal probabilities between two procedures. The score-based approximate CI has been shown to outperform other asymptotic CIs. Tango’s method provides a score CI by inverting a score test statistic using an iterative procedure. In the developed R code, we propose an efficient non-iterative method with closed-form expression to calculate Tango’s CIs. Examples illustrate the practical application of the new approach.