Ensemble methods provide a principled framework in which to build high performance classifiers and represent
many types of data. As a result, these methods can be useful for making inferences about biometric and biological
events. We introduce a novel ensemble method for combining multiple representations (or views). The method
is a multiple view generalization of AdaBoost. Similar to AdaBoost, base classifiers are independently built from
each represetation. Unlike AdaBoost, however, all data types share the same sampling distribution computed
from the base classifier having the smallest error rate among input sources. As a result, the most consistent
data type dominates over time, thereby significantly reducing sensitivity to noise. The method is applied to
the problem of facial and gender prediction based on biometric traits. The new method outperforms several
competing techniques including kernel-based data fusion, and is provably better than AdaBoost trained on any
single type of data.
Dynamic, accurate near-real time environmental data is critical to the success of the mine countermeasures operations. Bathymetric data acquired from the AQS-20 mine hunting sensor should be adjusted for local tide variations related to the specific geographic area and time interval. This problem can be overcome by a spatio-temporal estimate of tide corrections provided for the area and time of interest by the Naval Research Laboratory tide prediction code PCTides. For each geographic position of the AQS-20 sonar, a tide height relative to mean sea level is computed by interpolating the tidal information from the K - nearest neighbored stations for the corresponding time. The value is used to correct the measured depth generated by the AQS-20 sonar in that location to mean sea level for fusion with other bathymetric data products. It is argued that this paper provides a useful tool to the MCM decision factors during Mine Warfare operations.
Predicting the degree of burial of mines in soft sediments is one of the main concerns of Naval Mine CounterMeasures (MCM) operations. This is a difficult problem to solve due to uncertainties and variability of the sediment parameters (i.e., density and shear strength) and of the mine state at contact with the seafloor (i.e., vertical and horizontal velocity, angular rotation rate, and pitch angle at the mudline). A stochastic approach is proposed in this paper to better incorporate the dynamic nature of free-falling cylindrical mines in the modeling of impact burial. The orientation, trajectory and velocity of cylindrical mines, after about 4 meters free-fall in the water column, are very strongly influenced by boundary layer effects causing quite chaotic behavior. The model's convolution of the uncertainty through its nonlinearity is addressed by employing Monte Carlo simulations. Finally a risk analysis based on the probability of encountering an undetectable mine is performed.