J
J. Derek Tucker
Researcher at Sandia National Laboratories
Publications - 47
Citations - 412
J. Derek Tucker is an academic researcher from Sandia National Laboratories. The author has contributed to research in topics: Computer science & Sonar. The author has an hindex of 9, co-authored 38 publications receiving 322 citations. Previous affiliations of J. Derek Tucker include Florida State University & University of Illinois at Urbana–Champaign.
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Generative models for functional data using phase and amplitude separation
TL;DR: This paper presents an approach that relies on separating the phase and amplitude of functional data, then modeling these components using joint distributions, and imposes joint probability models on principal coefficients of these components while respecting the nonlinear geometry of the phase representation space.
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Variability of Ventricular Repolarization Dispersion Quantified by Time-Warping the Morphology of the T-Waves
TL;DR: The proposed ECG-derived markers can help to quantify the variability in the dispersion of ventricular repolarization, showing a great potential to be used as arrhythmic risk predictors in clinical situations.
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Image-Based Automated Change Detection for Synthetic Aperture Sonar by Multistage Coregistration and Canonical Correlation Analysis
Tesfaye G-Michael,Bradley Marchand,J. Derek Tucker,Timothy M. Marston,Daniel D. Sternlicht,Mahmood R. Azimi-Sadjadi +5 more
TL;DR: Robustness of the coregistration methods and analysis of scene coherence over time is characterized by analysis of repeat pass as well as synthetically modified data sets.
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Elastic Depths for Detecting Shape Anomalies in Functional Data
TL;DR: The elastic depths are proposed, a new family of depth measures called the elastic depths that can be used to greatly improve shape anomaly detection in functional data and are assessed on simulated shape outlier scenarios and against popular shape anomaly detectors.
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Handling missing data in self-exciting point process models
TL;DR: A Bayesian estimation procedure for self-exciting point processes with missing histories is developed that naturally handles the missing data mechanism probabilistically through a specific step and is demonstrated on simulated data and real conflict monitoring data where records over a period of time have been lost.