We present an estimation-based approach to the design of adaptive IIR filters. We also use this approach to design adaptive filters when a feedback signal from the output of the adaptive filter contaminates the reference signal. We use an H, criterion to cast the problem as a nonlinear H, filtering problem, and present an approximate linear H, filtering solution. This linear filtering solution ...

This paper discusses the issues which arise when combining multigrid strategies with adaptive meshing techniques for solving steady-state problems on unstructured meshes. A basic strategy is described, and demonstrated by solving several inviscid and viscous ow cases. Potential ine ciencies in this basic strategy are exposed, and various alternate approaches are discussed, some of which are dem...

The linear systems arising from MFS calculations share certain numerical effects with other systems involving radial basis functions. These effects concern approximation error and stability, which are closely related, and they can already be studied for simple interpolation problems without PDEs. In MFS calculations, they crucially depend on the position and density of the source points and the...

Several sparseness penalties have been suggested for delivery of good predictive performance in automatic variable selection within the framework of regularization. All assume that the true model is sparse. We propose a penalty, a convex combination of the L1- and L∞-norms, that adapts to a variety of situations including sparseness and nonsparseness, grouping and nongrouping. The proposed pena...

This paper focuses on the adaptation mechanisms in adaptive hybrid controllers. Most adaptive hybrid controllers update two filters individually according to the filteredreference least mean squares (FxLMS) algorithm. Because this algorithm was derived for feedforward control, it does not take into account the presence of a feedback loop in the gradient calculation. This paper provides a deriva...

Several sparseness penalties have been suggested for delivery of good predictive performance in automatic variable selection within the framework of regularization. All assume that the true model is sparse. We propose a penalty, a convex combination of the L1and L∞-norms, that adapts to a variety of situations including sparseness and nonsparseness, grouping and nongrouping. The proposed penalt...

The nearly optimal, interoperable utilization of various grid resources play an important role in the world of grids. Though well-designed, evaluated and widely used resource brokers have been developed, these existing solutions still cannot cope with the high uncertainty ruling current grid systems. To ease the simultaneous utilization of different middleware systems, researchers need to revis...

A Solution-Adaptive Upwind Scheme for Ideal Magnetohydrodynamics Kenneth G. Powell,∗ Philip L. Roe,∗ Timur J. Linde,∗ Tamas I. Gombosi,† and Darren L. De Zeeuw† ∗W. M. Keck Foundation CFD Laboratory, Department of Aerospace Engineering, University of Michigan, Ann Arbor, Michigan 48109-2140; and †Space Physics Research Laboratory, Department of Atmospheric, Oceanic and Space Sciences, Universit...

We consider solution-driven adaptive variants of Total Variation, in which the adaptivity is introduced as a fixed point problem. We provide existence theory for such fixed points in the continuous domain. For the applications of image denoising, deblurring and inpainting, we provide experiments which demonstrate that our approach in most cases outperforms state-of-the-art regularization approa...