Forward integrals and SDE with fractal noise

2019 ◽  
pp. 279-302
Author(s):  
Martina Zähle ◽  
Erik Schneider
Keyword(s):  
Fractal Noise

Resolution Enhancement of Composite Spectra with Fractal Noise in Derivative Spectrometry

2000 ◽  
Vol 54 (5) ◽  
pp. 721-730 ◽  
Author(s):  
S. S. Kharintsev ◽  
D. I. Kamalova ◽  
M. Kh. Salakhov
Keyword(s):  
A Priori ◽  
Resolution Enhancement ◽  
Numerical Differentiation ◽  
A Priori Information ◽  
Experimental Ir ◽  
Composite Spectra ◽  
Self Similar ◽  
Priori Information ◽  
Fractal Noise ◽  
Derivative Spectrometry

The problem of improving the resolution of composite spectra with statistically self-similar (fractal) noise is considered within the framework of derivative spectrometry. An algorithm of the numerical differentiation of an arbitrary (including fractional) order of spectra is produced by the statistical regularization method taking into account a priori information on statistical properties of the fractal noise. Fractal noise is analyzed in terms of the statistical Hurst method. The efficiency and expedience of this algorithm are exemplified by treating simulated and experimental IR spectra.

The Impact Mechanism of Fractal Noise on PN Code Detection System

2014 ◽  
Vol 13 (02) ◽  
pp. 1450017 ◽  
Author(s):  
Gang Xiong ◽  
Shu-Ning Zhang ◽  
Hui-Chang Zhao
Keyword(s):  
White Noise ◽  
Detection System ◽  
Random Phase ◽  
Average Power ◽  
Fractal Model ◽  
Sea Clutter ◽  
Impact Mechanism ◽  
Multi Scale ◽  
Fractal Noise ◽  
The Impact

Taking the pseudo-random phase modulated CW radar for example, this paper studies the impact mechanism of a class of non-stationary fractal noise on PN code detection system, especially signal mixing and matching filter. The cross correlation function, power spectrum function and average power of pseudo-random signal and fractal noise are deduced, compared with the impact of white noise on the pseudo code detection system. We analyze the impact mechanism of three kinds of sea clutter model, namely fractal Brownian model (FBM), the multifractal (MF) model and the non-stationary random fractal model (e.g., infinitely divisible cascades, IDC), on the pseudo-random code detection system, and demonstrate the reason why the multi-scale filtering method in wavelet domain and the MF methods fail to eliminate the effect of sea clutter. Based on the natural sea clutter data, we simulate and analyze the influence of white noise and fractal noise comparatively on detection system, which indicates that the effect of fractal noise cannot be inhibited effectively by the traditional correlation detection and MF analysis, and finally we put forward possible solutions.

Spatial-scale contribution to the detection of mirror symmetry in fractal noise

1999 ◽  
Vol 16 (9) ◽  
pp. 2112 ◽  
Author(s):  
Stéphane J. M. Rainville ◽  
Frederick A. A. Kingdom
Keyword(s):  
Spatial Scale ◽  
Mirror Symmetry ◽  
Fractal Noise

Fractal stochastic modeling of aeromagnetic data

Geophysics ◽  
1991 ◽  
Vol 56 (11) ◽  
pp. 1706-1715 ◽  
Author(s):  
Mark E. Gregotski ◽  
Olivia Jensen ◽  
Jafar Arkani‐Hamed
Keyword(s):  
Power Spectra ◽  
Innovation Process ◽  
Original Data ◽  
Dimensional System ◽  
Deterministic System ◽  
System Function ◽  
Aeromagnetic Data ◽  
Near Surface ◽  
Traditional Assumption ◽  
Fractal Noise

We present a convolutional linear data model for the processing of aeromagnetic data. The model assumes that the data derive from the superposition of a deterministic system function and a stochastic innovation process. The two‐dimensional system function is described by a four‐pass autoregressive (AR) filtering procedure and is radially symmetric. The innovation process represents the distribution of near‐surface magnetic sources and is modeled as a spectrally self‐scaling (i.e., fractal) noise. The appropriate fractal noise is determined by examining aeromagnetic power spectra from various areas of the Canadian Shield. The AR coefficients of the system are determined using an iterative deconvolution procedure. For computational convenience, we make the traditional assumption of a spectrally white innovation, but modify the data prior to its deconvolution by prewhitening the assumed fractal innovation. The recovered system function is then removed from the original data in order to produce the fractal stochastic surface. This deconvolution technique is applied to two aeromagnetic maps from northeastern Ontario, Canada and is shown to be effective in delineating lithologies and enhancing magnetic field anomalies. We propose a particular statistical description of near‐surface magnetic sources for modeling aeromagnetic data in “shield‐type” geologic environments.

scholarly journals Do happy faces really modulate liking for Jackson Pollock art and statistical fractal noise images?

Psihologija ◽  
2017 ◽  
Vol 50 (3) ◽  
pp. 219-237
Author(s):  
Katrin Mundloch ◽  
Marie Winterberg ◽  
Wanja Hemmerich ◽  
Philipp Holzwig ◽  
Anna Rupanova ◽  
...  
Keyword(s):  
Visual Stimuli ◽  
Image Features ◽  
Affective Priming ◽  
Abstract Art ◽  
Jackson Pollock ◽  
Visual Material ◽  
Low Level ◽  
Prime Condition ◽  
Fractal Noise

Flexas et al. (2013) demonstrated that happy faces increase preference for abstract art if seen in short succession. We could not replicate their findings. In our first experiment, we tested whether valence, saliency or arousal of facial primes can modulate liking of Jackson Pollock art crops. In the second experiment, the emphasis was on testing another type of abstract visual stimuli which possess similar low-level image features: statistical fractal noise images. Pollock crops were rated significantly higher when primed with happy faces in contrast to neutral faces, but not differently to the no-prime condition. Findings of our study suggest that affective priming with happy faces may be stimulus-specific and may have inadvertent effects on other abstract visual material.

Threshold devices: Fractal noise and neural talk

1994 ◽  
Vol 50 (4) ◽  
pp. 2513-2522 ◽  
Author(s):  
Peter Jung
Keyword(s):  
Fractal Noise

Fractal Noise in Breathing

2007 ◽  
pp. 161-173 ◽  
Author(s):  
Bernard Hoop ◽  
Melvin D. Burton ◽  
Homayoun Kazemi
Keyword(s):  
Fractal Noise
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