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A scalable human-in-the-loop decision support system has been built around an active learning algorithm operating on aircraft engine time series data. The system integrates hierarchical clustering and active learning algorithms backed by a big data analytics ecosystem with a browser-based user interface. This combination enables multiple expert users to efficiently train a model to classify aircraft...
A closed loop approach for surveillance was developed leveraging the Spectral Multiscale Coverage (SMC) algorithm for sensor management coupled with the Cardinalized Probability Hypothesis Density (CPHD) multitarget tracker. Additionally, the CPHD was formulated such that it is able to ingest features, if available. Simulations with fixed and mobile sensors (the latter, tasked by the SMC) providing...
We present a new Video Fire Detection (VFD) system for surveillance applications in fire and security industries. The system consists of three modules: pixel-level processing to identify potential fire blobs, blob-based spatial-temporal feature extraction, and a Support Vector Machine (SVM) classifier. The proposed novel spatial-temporal features include a spatial-temporal structural feature and a...
The cardinalized probability hypothesis density (CPHD) filter is a recursive Bayesian algorithm for estimating multiple target states with a varying target number in clutter. In particular the Gaussian mixture variant (GMCPHD), which provides closed-form prediction and update equations for the filter in the case of linear Gaussian systems, is a candidate for real time multi-target tracking. The following...
Sonar tracking using measurements from multistatic sensors has shown promise: there are benefits in terms of robustness, complementarity (covariance-ellipse intersection) and of course simply due to the increased probability of detection that naturally accrues from a well-designed data fusion system. It is not always clear what the placement of the sources and receivers that gives the best fused measurement...
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