Sabra Neal

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Tracking the movement of vehicles in urban environments using fixed position sensors, mobile sensors, and crowd-sourced data is a challenging but important problem in applications such as law enforcement and defense. A dynamic data driven application system (DDDAS) is described to track a vehicle's movements by repeatedly identifying the vehicle under(More)
With the growing use of mobile devices, power aware algorithms have become essential. Data distribution management (DDM) is an approach to disseminate information that was proposed in the High Level Architecture (HLA) for modeling and simulation. This paper explores the power consumption of mobile devices used by pedestrians in an urban environment(More)
In this paper, we present a two-stage process for developing feature extractors (FEs) for facial recognition. In this process, a genetic algorithm is used to evolve a number of local binary patterns (LBP) based FEs with each FE consisting of a number of (possibly) overlapping patches from which features are extracted from an image. These FEs are then(More)
This paper presents a novel approach to feature extraction for face recognition. This approach extends a previously developed method that incorporated the feature extraction techniques of GEFE ML (Genetic and Evolutionary Feature Extraction – Machine Learning) and Darwinian Feature Extraction). The feature extractors evolved by GEFE ML are superior to(More)
Dynamic Data-Driven Application Systems (DDDAS) implemented on mobile devices must conserve energy to maximize battery life. For example, applications for online traffic prediction require use of real-time data streams that drive distributed simulations. These systems involve embedding computations in mobile computing platforms that establish the state of(More)
In [1,2], a Genetic and Evolutionary Biometric Security (GEBS) application was presented for preventing biometric replay attacks. This technique used Genetic and Evolutionary Feature Extraction - Machine Learning (GEFE<sub>ML</sub>) to create disposable feature extractors (FEs). These disposable FEs had higher recognition accuracy than a traditional feature(More)
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