Results 31 to 40 of about 286,009 (201)

Lyapunov Modes and Time-Correlation Functions for Two-Dimensional Systems [PDF]

open access: yes, 2005
The relation between the Lyapunov modes (delocalized Lyapunov vectors) and the momentum autocorrelation function is discussed in two-dimensional hard-disk systems. We show numerical evidence that the smallest time-oscillating period of the Lyapunov modes
Morriss, Gary P., Taniguchi, Tooru
core   +3 more sources

Revisiting the Autocorrelation of Long Memory Time Series Models

open access: yesMathematics, 2023
In this article we first revisit some earlier work on fractionally differenced white noise and correct some issues with previously published formulae. We then look at vector processes and derive formula for the Autocorrelation function, which is extended
Shelton Peiris, Richard Hunt
doaj   +1 more source

Anisotropy studies with multiscale autocorrelation function

open access: yes, 2010
We present a novel method, based on a multiscale approach, for detecting anisotropy signatures in the arrival direction distribution of the highest energy cosmic rays. This method is catalog independent, i.e.
Abbasi   +38 more
core   +3 more sources

Bimodal distribution of the autocorrelation function in gamma-ray bursts [PDF]

open access: yes, 2004
Autocorrelation functions (ACFs) are studied for a sample of 16 long gamma-ray bursts (GRBs) with known redshift z, that were observed by the BATSE and Konus experiments. When corrected for cosmic time dilation, the ACF shows a bimodal distribution.
Andersen   +28 more
core   +1 more source

Roughness Analysis of Sea Surface From Visible Images by Texture

open access: yesIEEE Access, 2020
This paper presents a roughness analysis of sea surface from visible images by feature measurements of texture for the first time. The algorithms presented in this paper include six texture feature measurements of sea surface use gray level co-occurrence
Hailang Pan   +5 more
doaj   +1 more source

Sequences With Identical Autocorrelation Functions

open access: yesIEEE Transactions on Information Theory
Aperiodic autocorrelation is an important indicator of performance of sequences used in communications, remote sensing, and scientific instrumentation. Knowing a sequence's autocorrelation function, which reports the autocorrelation at every possible translation, is equivalent to knowing the magnitude of the sequence's Fourier transform.
Daniel J. Katz   +2 more
openaire   +3 more sources

Statistical and spectral analysis of wind power: Fractional oscillation dynamics

open access: yesPhysics of Complex Systems, 2021
Time-dependent changes of the wind speed, as for example in Hera Campus (East Timor), are analysed by the statistical and the autocorrelation function in time domain and by the frequency spectrum (frequency domain) using the Fast Fourier Transform (FFT).
Abelito Filipe Belo, Koichi Shimakawa
doaj   +1 more source

Single-tone frequency estimation based on reformed covariance for half-length autocorrelation

open access: yesMetrology and Measurement Systems, 2020
This paper presents a new simple and accurate frequency estimator of a sinusoidal signal based on the signal autocorrelation function (ACF). Such an estimator was termed as the reformed covariance for half-length autocorrelation (RC-HLA).
Sergiusz Sienkowski, Mariusz Krajewski
doaj   +1 more source

Measure of Autocorrelation Times of Local Hybrid Monte Carlo Algorithm for Lattice QCD

open access: yes, 1993
We report on a study of the autocorrelation times of the local version of the Hybrid Monte Carlo (LHMC) algorithm for pure gauge $SU(3)$. We compare LHMC to standard multi-hit Metropolis and to the global version of the same HMC.
Akemi   +7 more
core   +2 more sources

Fast Autocorrelated Context Models for Data Compression [PDF]

open access: yes, 2013
A method is presented to automatically generate context models of data by calculating the data's autocorrelation function. The largest values of the autocorrelation function occur at the offsets or lags in the bitstream which tend to be the most highly ...
Scoville, John
core  

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