scholarly journals Compressive estimation of a stochastic process with unknown autocorrelation function

2017 ◽  
Author(s):  
Mahdi Barzegar Khalilsarai ◽  
Saeid Haghighatshoar ◽  
Giuseppe Caire ◽  
Gerhard Wunder
Keyword(s):  
Stochastic Process ◽  
Autocorrelation Function

Analysis and Probabilistic Modeling of the Stationary Ice Loads Stochastic Process With Lognormal Distribution

2014 ◽  
Author(s):  
Petr Zvyagin ◽  
Kirill Sazonov
Keyword(s):  
Stochastic Process ◽  
Stochastic Processes ◽  
Normal Distribution ◽  
Autocorrelation Function ◽  
Strain Gauge ◽  
Lognormal Distribution ◽  
Probabilistic Modeling ◽  
Stationary Stochastic Process ◽  
Strain Gauge Transducer ◽  
Ice Loads

Until recent times researchers who investigated ice loads stochastic processes usually stated the fact of normal distribution for them. In the paper the model of a stationary stochastic process with a lognormal distribution for ice loads is offered. This model relates to the strain gauge transducer ice loads measurements as well as to some examples considered in different papers that were published earlier. For this model dependencies of the autocorrelation function were found that allows to simulate the ice loads process relatively easily. The procedure of such a simulation is described in details and the example of the analysis and simulation ice loads measurements is provided.

Simulation of Normal Differentiable Stochastic Process of Ice Loads

2017 ◽  
Author(s):  
Petr Zvyagin
Keyword(s):  
Stochastic Process ◽  
Autocorrelation Function ◽  
Critical Level ◽  
Process Approach ◽  
The Other ◽  
Normal Process ◽  
Expected Number ◽  
Stationary Model ◽  
Tank Experiment ◽  
Ice Loads

Ice loads time series should be treated in the other way than separate independent ice loads observations. The stochastic process approach can provide information about such important characteristic as mean length of signal’s outcome beyond some critical level and expected number of such outcomes. The paper considers global ice loads registered in an ice tank experiment with a cylindrical indenter of 100 mm width. The autocorrelation function is fitted in a manner that the observed load process is differentiable. The study conducted in the paper demonstrates that characteristics, such as the number outcomes beyond some critical level and the time spent off this level, are governed in the same way as parameters of a stationary differentiable normal process. Normal stationary model of ice loads process allows its simulation, if autocorrelation function is given. In the paper, such simulation is performed.

Simulation of Stochastic Processes by Spectral Representation

1991 ◽  
Vol 44 (4) ◽  
pp. 191-204 ◽  
Author(s):  
Masanobu Shinozuka ◽  
George Deodatis
Keyword(s):  
Stochastic Process ◽  
Stochastic Processes ◽  
Autocorrelation Function ◽  
Structural Engineering ◽  
Spectral Representation ◽  
Target Function ◽  
Mean Value ◽  
Random Loading ◽  
Cosine Series ◽  
Spectral Representation Method

The subject of this paper is the simulation of one-dimensional, uni-variate, stationary, Gaussian stochastic processes using the spectral representation method. Following this methodology, sample functions of the stochastic process can be generated with great computational efficiency using a cosine series formula. These sample functions accurately reflect the prescribed probabilistic characteristics of the stochastic process when the number N of the terms in the cosine series is large. The ensemble-averaged power spectral density or autocorrelation function approaches the corresponding target function as the sample size increases. In addition, the generated sample functions possess ergodic characteristics in the sense that the temporally-averaged mean value and the autocorrelation function are identical with the corresponding targets, when the averaging takes place over the fundamental period of the cosine series. The most important property of the simulated stochastic process is that it is asymptotically Gaussian as N → ∞. Another attractive feature of the method is that the cosine series formula can be numerically computed efficiently using the Fast Fourier Transform technique. The main area of application of this method is the Monte Carlo solution of stochastic problems in engineering mechanics and structural engineering. Specifically, the method has been applied to problems involving random loading (random vibration theory) and random material and geometric properties (response variability due to system stochasticity).

Integrated Fractional white Noise as an Alternative to Multifractional Brownian Motion

2007 ◽  
Vol 44 (02) ◽  
pp. 393-408 ◽  
Author(s):  
Allan Sly
Keyword(s):  
Stochastic Process ◽  
Brownian Motion ◽  
White Noise ◽  
Gaussian Process ◽  
Discrete Data ◽  
Hölder Exponent ◽  
Scaling Properties ◽  
Multifractional Brownian Motion ◽  
Local Properties

Multifractional Brownian motion is a Gaussian process which has changing scaling properties generated by varying the local Hölder exponent. We show that multifractional Brownian motion is very sensitive to changes in the selected Hölder exponent and has extreme changes in magnitude. We suggest an alternative stochastic process, called integrated fractional white noise, which retains the important local properties but avoids the undesirable oscillations in magnitude. We also show how the Hölder exponent can be estimated locally from discrete data in this model.

Log-log scale roughness spectroscopy of surfaces

1993 ◽  
Vol 51 ◽  
pp. 224-225
Author(s):  
P. Fraundorf ◽  
B. Armbruster
Keyword(s):  
Power Spectrum ◽  
Autocorrelation Function ◽  
Power Spectra ◽  
Scanning Force Microscopy ◽  
Scanning Tunneling ◽  
Tunneling Microscopy ◽  
Force Microscopy ◽  
Scanning Force ◽  
Lateral Structure ◽  
Scale Roughness

Optical interferometry, confocal light microscopy, stereopair scanning electron microscopy, scanning tunneling microscopy, and scanning force microscopy, can produce topographic images of surfaces on size scales reaching from centimeters to Angstroms. Second moment (height variance) statistics of surface topography can be very helpful in quantifying “visually suggested” differences from one surface to the next. The two most common methods for displaying this information are the Fourier power spectrum and its direct space transform, the autocorrelation function or interferogram. Unfortunately, for a surface exhibiting lateral structure over several orders of magnitude in size, both the power spectrum and the autocorrelation function will find most of the information they contain pressed into the plot’s origin. This suggests that we plot power in units of LOG(frequency)≡-LOG(period), but rather than add this logarithmic constraint as another element of abstraction to the analysis of power spectra, we further recommend a shift in paradigm.

PENGUCAPAN MAKHRAJ DARI UNIT BUNYI TERKECIL HURUF HIJAIYAH BERDASARKAN FREKUENSI DASAR DAN FREKUENSI FORMANT UNTUK MEDIA PEMBELAJARAN MEMBACA ALQURAN

ALQALAM ◽  
2015 ◽  
Vol 32 (2) ◽  
pp. 284
Author(s):  
Muhammad Subali ◽  
Miftah Andriansyah ◽  
Christanto Sinambela
Keyword(s):  
College Student ◽  
Fundamental Frequency ◽  
Autocorrelation Function ◽  
Adult Male ◽  
Formant Frequency ◽  
Formant Frequencies ◽  
Male Speaker ◽  
Similarities And Differences

This article aims to look at the similarities and differences in the fundamental frequency and formant frequencies using the autocorrelation function and LPCfunction in GUI MATLAB 2012b on sound hijaiyah letters for adult male speaker beginner and expert based on makhraj pronunciation and both of speaker will be analysis on matching distance of the sound use DTW method on cepstrum. Subject for speech beginner makhraj pronunciation are taken from college student of Universitas Gunadarma and SITC aged 22 years old Data of the speech beginner makhraj pronunciation is recorded using MATLAB algorithm on GUI Subject for speech expert makhraj pronunciation are taken from previous research. They are 20-30 years old from the time of taking data. The sound will be extracted to get the value of the fundamental frequency and formant frequency. After getting both frequencies, it will be obtained analysis of the similarities and differences in the fundamental frequency and formant frequencies of speech beginner and expert and it will shows matching distance of both speech. The result is all of speech beginner and expert based on makhraj pronunciation have different values of fundamental frequency and formant frequency. Then the results of the analysis matching distance using method DTW showed that obtained in the range of 28.9746 to 136.4 between speech beginner and expert based on makhraj pronunciation. Keywords: fundamental frequency, formant frequency, hijaiyah letters, makhraj

Sign in / Sign up

Export Citation Format

Share Document