Real-time signal extraction (RTSE) concerns the determination of optimal asymmetric filters towards end a time series where ⎯otherwise desirable⎯ symmetric cannot be applied anymore. The attractiveness this particular estimation problem resides in generality its scope. For illustrative purposes we here stress realtime monitoring US-economy as well multi-step ahead forecasting. Traditionally, ad...

The use of Carbon Fiber–Reinforced Polymers (CFRP) has increased in number of industries i.e. aerospace, automobiles, marine, medical and sports due to their light weight and high strength-stiffness. However, their properties are greatly affected under extreme e environmental conditions i.e. high temperatures and moisture uptake. The paper reports an experimental study to determine the response...

Pauses in spontaneous speaking constitute a rich source of data for several disciplines. They have been used to enhance automatic segmentation of speech, classification of patients with acquired communication disorders, the design of psycholinguistic models of speaking, and the analysis of psychological disorders. Unfortunately, however, although pause analysis has been with us for more than 40...

In spring 2007, Prof. Sterling’s Introduction to High Performance Computing has been delivered in a virtual classroom consisting of 5 sites spanning Louisiana, Arkansas, and North Carolina in the USA and the Czech Republic in Europe. By utilizing a multi-point uncompressed audio and High Definition (HD) video capturing, transmission, and presentation system, a high fidelity collaborative enviro...

We are interested in real-time learning problems where the underlying stochastic process, which generates the target concept, changes over time. We want our learner to detect when a change has occurred, thus realizing that the learned concept no longer fits the observed data. Our initial approach to this problem has been to analyze offline approaches to addressing concept shifts and to apply th...

We are interested in real-time learning problems where the underlying stochastic process, which generates the target concept, changes over time. We want our learner to detect when a change has occurred, thus realizing that the learned concept no longer fits the observed data. Our initial approach to this problem has been to analyze offline methods for addressing concept shifts and to apply them...