A model-constrained adaptive sampling methodology is proposed for reduction of large-scale systems with high-dimensional parametric input spaces. Our model reduction method uses a ...
This paper introduces a novel method for real-time estimation of slowly varying parameters in nonlinear dynamical systems. The core concept is built upon the principles of symboli...
Several methods have been developed for face detection with certain success, however these tend to fail under difficult conditions such as partial occlusions and changes in orienta...
The key task in probabilistic reasoning is to appropriately update one’s beliefs as one obtains new information in the form of evidence. In many application settings, however, th...
Due to the complex noise structure of functional magnetic resonance imaging (fMRI) data, methods that rely on information within a single subject often results in unsatisfactory fu...