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Variations in the appearance of a tracked object, such as changes in geometry/photometry, camera viewpoint, illumination , or partial occlusion, pose a major challenge to object tracking. Here, we adopt cognitive psychology principles to design a flexible representation that can adapt to changes in object appearance during tracking. Inspired by the(More)
OBJECTIVE We explored the ability of specifically designed and trained recurrent neural networks (RNNs), combined with wavelet preprocessing, to discriminate between the electroencephalograms (EEGs) of patients with mild Alzheimer's disease (AD) and their age-matched control subjects. METHODS Twomin recordings of resting eyes-closed continuous EEGs (as(More)
Combining multiple observation views has proven beneficial for tracking. In this paper, we cast tracking as a novel multi-task multi-view sparse learning problem and exploit the cues from multiple views including various types of visual features, such as intensity, color, and edge, where each feature observation can be sparsely represented by a linear(More)
— We discuss the role of random basis function approximators in modeling and control. We analyze the published work on random basis function approximators and demonstrate that their favorable error rate of convergence O(1/n) is guaranteed only with very substantial computational resources. We also discuss implications of our analysis for applications of(More)
Predicting the onset of epileptic seizure is an important and di$cult biomedical problem, which has attracted substantial attention of the intelligent computing community over the past two decades. We apply recurrent neural networks (RNN) combined with signal wavelet decomposition to the problem. We input raw EEG and its wavelet-decomposed subbands into RNN(More)
For the first time, different adaptive critic designs (ACDs), a conventional proportional integral derivative (PID) regulator and backpropagation of utility are compared for the same control problem-automatic aircraft landing. The original problem proved to contain little challenge since various conventional and neural network techniques had solved it very(More)
—This paper describes the design of a single learning network that integrates both object location (" where ") and object type (" what "), from images of learned objects in natural complex backgrounds. The in-place learning algorithm is used to develop the internal representation (including synaptic bottom-up and top-down weights of every neuron) in the(More)
We propose a technique for the design and analysis of adaptation algorithms in dynamical systems. The technique applies both to systems with conventional Lyapunov-stable target dynamics and to ones of which the desired dynamics around the target set is nonequilibrium and in general unstable in the Lyapunov sense. Mathematical models of uncertainties are(More)
—Imperfect test outcomes, due to factors such as unreliable sensors, electromagnetic interference, and environmental conditions, manifest themselves as missed detections and false alarms. This paper develops near-optimal algorithms for dynamic multiple fault diagnosis (DMFD) problems in the presence of imperfect test outcomes. The DMFD problem is to(More)