Fast Approximated POD for a Flat Plate Benchmark with a Time Varying Angle of Attack

An approximate POD algorithm provides an empirical Galerkin approximation with guaranteed a priori lower bound on the required resolution. The snapshot ensemble is partitioned into several sub-ensembles. Cross correlations between these sub-ensembles are approximated in terms of a far smaller correlation matrix. Computational speedup is nearly linear in the number of partitions, up to a saturation that can be estimated a priori. The algorithm is particularly suitable for analyzing long transient trajectories of high dimensional simulations, but can be applied also for spatial partitioning and parallel processing of very high spatial dimension data. The algorithm is demonstrated using transient data from two simulations. First, a two dimensional simulation of the flow over a flat plate, as it transitions from AOA = 30° to a horizontal position and back. Second, a three dimensional simulation of a flat plate with aspect ratio two as it transitions from a horizontal position to AOA = 30°

Computationally efficient nonlinear edge preserving smoothing of n-D medical images via scale-space fingerprint analysis

Nonlinear edge preserving smoothing often is performed prior to medical image segmentation. The goal of the nonlinear smoothing is to improve the accuracy of the segmentation by preserving changes in image intensity at the boundaries of structures of interest, while smoothing random variations due to noise in the interiors of the structures. Methods include median filtering and morphology operations such as gray scale erosion and dilation, as well as spatially varying smoothing driven by local contrast measures. Rather than irreversibly altering the image data prior to segmentation, the approach described here has the potential to unify nonlinear edge preserving smoothing with segmentation based on differential edge detection at multiple scales. The analysis of n-D image data is decomposed into independent 1-D problems that can be solved quickly. Smoothing in various directions along 1-D profiles through the n-D data is driven by a measure of local structure separation, rather than by a local contrast measure. Isolated edges are preserved independent of their contrast, given an adequate contrast to noise ratio

Picture quality evaluation model for color coded images: considering observing points and local feature of image

International audienc

A Novel Sound Localization Experiment for Mobile Audio Augmented Reality Applications

This paper describes a subjective experiment in progress to study human sound localization using mobile audio augmented reality systems. The experiment also serves to validate a new methodology for studying sound localization where the subject is outdoors and freely mobile, experiencing virtual sound objects corresponding to real visual objects. Subjects indicate the perceived location of a static virtual sound source presented on headphones, by walking to a position where the auditory image coincides with a real visual object. This novel response method accounts for multimodal perception and interaction via self-motion, both ignored by traditional sound localization experiments performed indoors with a seated subject, using minimal visual stimuli. Results for six subjects give a mean localization error of approximately thirteen degrees; significantly lower error for discrete binaural rendering than for ambisonic rendering, and insignificant variation to filter lengths of 64, 128 and 200 samples