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This paper presents a real-time multiresolution approach for respiratory motion modeling. The proposed methodology utilizes a hybrid approach that combines well-known linear identification methods with a novel global-local orthogonal mapping (GLO-MAP) network. Further, adaptation laws are derived using the recent advances in adaptive control to adapt for(More)
Many mobile devices compress images excessively to meet limited bandwidth requirements and adopt the Block-based Discrete Cosine Transform (BDCT) coding transformation. When the images are decoded, it produces inevitably the visually annoying noises including blocking artifacts. We present a Signal Adaptive Weighted Sum (SAWS) technique of block boundary(More)
In this paper, we present new ideas to greatly enhance the quality of uncertainty quantification in the DDDAS framework. We build on ongoing work in large scale transport of geophysical mass of volcanic origin – a danger to both land based installations and airborne vehicles. The principal new idea introduced is the concept of a localized Bayes linear model(More)
Introduction: Mathematical models are approximate representations of physical processes and consequently have uncertainties associated with them. Furthermore, no sensor is perfect. Sensor measurements are generally some linear/nonlinear combination of states and are usually corrupted with quantization errors, superimposed noise, etc. Propagating the states(More)
Knowledge of roadmaps can provide an indication of how information, materials, and people move. Historically, maps have equated to a static look at a network that contains only established and sanctioned routes. Even now, Google map images and hand held Global Positioning System (GPS) units represent a somewhat static look at roadmaps, requiring either(More)
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