Atomistic methods have successfully modeled different aspects of shock wave propagation in materials over the past several decades, but they suffer from limitations which restrict total runtime and system size. Multiscale been able to increase length time scales that can be employing such schemes simulate evolution through engineering-scale domains is an active area research. In this work, we d...

The perceptual span and eye–hand span of pianists were examined while they played and read single-line melodies (with four beats per measure). The perceptual span was measured by the use of a moving-window technique with four window conditions: two beats, four beats, six beats, or no window. It was found that pianists need to see no more than the whole measure that they are fixating in order to...

Laser-Doppler Anemometry (LDA) is used to measure the velocity of gases and liquids with observations irregularly spaced in time. Equidistant resampling turns out to be better than slotting techniques. After resampling, two ways of spectral estimation are compared. The first estimate is a windowed periodogram and the second is the spectrum of a time series model. That is an estimated autoregres...

In the frequency estimation of sinusoidal signals observed in impulsive noise environments, techniques based on Gaussian noise assumption are unsuccessful. One possible way to 6nd better estimates is to model the noise as an alpha-stable process and to use the fractional lower order statistics (FLOS) of the data to estimate the signal parameters. In this work, we propose a FLOS-based statistica...

This paper describes three visually interactive tools for the analysis, modeling, and generation of long-range dependent (LRD) network traffic. The synTraff toolkit uses a three-step modeling approach based on F-ARIMA processes to generate monofractal traffic; the WsynTraff toolkit implements the Wavelet-domain Independent Gaussian (WIG) model from the literature for representing multifractal t...

We propose a new regression-based filter for multivariate time series that separates signals from noise and outliers in real time. The new method merges the advantageous properties of two existent filtering procedures for online-monitoring time series. Our multivariate and robust procedure yields signal estimations at the right end point of a moving time window whose width is adapted to the cur...

Given a spatial filtering algorithm that has allowed us to identify task-relevant EEG sources, we present a simple approach for monitoring the activity of these sources while remaining relatively robust to changes in other (task-irrelevant) brain activity. The idea is to keep spatial patterns fixed rather than spatial filters, when transferring from training to test sessions or from one time wi...