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In the context of parameter estimation, subspace-based methods like ESPRIT have become common. They require a subspace separation e.g. based on eigenvalue/-vector decomposition. In time-varying environments, this can be done by subspace trackers. One class of these is based on the PAST algorithm. Our non-linear parameter estimation algorithm DaPT builds on-top of the ESPRIT algorithm. Evaluation of the different variants of the PAST algorithm shows which variant of the PAST algorithm is worthwhile in the context of frequency estimation.

The transient simulation of electro-quasistatic fields requires a formulation that takes both dielectric effects as well as nonlinear conductive effects into account. The successive solution of large linear systems of equations with similar or even identical system matrices has to be performed repeatedly if this formulation is discretized by the Finite-Element method and an Implicit-Runge-Kutta method, respectively. The solution processes can be accelerated by using subspace recycling techniques and subspace projection extrapolation techniques. Numerical results for three-dimensional high-voltage applications are presented and the efficiency of these techniques is shown.

The paper explores several statistical pattern recognition techniques to classify utterances according to their emotional content. The authors have recorded a corpus containing emotional speech with over a 1000 utterances from different speakers. They present a new method of extracting prosodic features from speech, based on a smoothing spline approximation of the pitch contour. To make maximal use of the limited amount of training data available, they introduce a novel pattern recognition technique: majority voting of subspace specialists. Using this technique, they obtain classification performance that is close to human performance on the task.