scholarly journals Internet traffic characterisation: Third-order statistics & higher-order spectra for precise traffic modelling

2018 ◽  
Vol 134 ◽  
pp. 183-201 ◽  
Author(s):  
A.K. Marnerides ◽  
D.P. Pezaros ◽  
D. Hutchison
Keyword(s):  
Order Statistics ◽  
Internet Traffic ◽  
Higher Order ◽  
Third Order ◽  
Traffic Modelling ◽  
Higher Order Spectra ◽  
Traffic Characterisation

scholarly journals Rolling Bearing Diagnosis Based on the Higher Order Spectra

2020 ◽  
Vol 4 (2) ◽  
Author(s):  
Len Gelman ◽  
Tejas H. Patel ◽  
Brian Murray ◽  
Allan Thomson
Keyword(s):  
Ball Bearing ◽  
Rolling Bearing ◽  
Higher Order ◽  
Third Order ◽  
Spectral Kurtosis ◽  
The Third ◽  
Bearing Defects ◽  
Bearing Defect ◽  
Envelope Signal ◽  
Higher Order Spectra

Bearing defect diagnosis is traditionally done using the demodulation/enveloping technology. Diagnosis is mostly based on the spectrum of the squared envelope signal. In literature, the use of the higher order spectra (HOS) has shown to have a tremendous potential for vibration based diagnostics. In this paper we implemented and experimentally validated the higher order spectra based on the envelope analysis for the diagnosis of ball bearing defects. The implemented technology employs the spectral kurtosis to obtain a frequency band for the demodulation and the third order normalized spectra, i.e. the bicoherence for diagnosis of bearing fault. The high effectiveness of the diagnostics of the implemented technology has been experimentally revealed and compared with that of well-known demodulation/enveloping technology.

scholarly journals A Review of Higher Order Statistics and Spectra in Communication Systems

2013 ◽  
pp. 31-50 ◽  
Author(s):  
M. Sanaullah
Keyword(s):  
Order Statistics ◽  
Statistical Methods ◽  
Communication Systems ◽  
Nonlinear Behavior ◽  
Higher Order ◽  
Second Order ◽  
Spectral Techniques ◽  
Detection Techniques ◽  
Non Gaussian ◽  
Higher Order Spectra

There are many statistical tools to extract information from random signals. They predominantly use first and second order statistics. However, in the presence of nonlinearity in systems, many signals cannot be analyzed adequately by second order statistical methods. For this reason, higher order statistical methods have been developed. These methods are very useful in problems where non-Gaussian, non-minimum phase, phase coupling or nonlinear behavior and robustness to additive noise are important. Detection and classification using higher order statistical and spectral techniques have been proposed for use in communication and pattern recognition. They have the potential to elicit better performance from sensors, sensor networks and channels with applications in coding, filtering and detection techniques. This paper provides an introduction to higher order spectra and reviews a number of these techniques.

scholarly journals Noisy texture analysis based on higher order spectra

2018 ◽  
Vol 7 (3) ◽  
pp. 1622
Author(s):  
Salwa LAGDALI ◽  
Mohammed RZIZA
Keyword(s):  
Noise Immunity ◽  
Likelihood Function ◽  
Cauchy Distribution ◽  
Higher Order ◽  
Phase Information ◽  
Third Order ◽  
Noise Classification ◽  
Preservation Property ◽  
Higher Order Spectra ◽  
Wrapped Cauchy Distribution

Texture is described in several approaches by 1st and 2nd order statistics which cannot preserve phase information carried by the Fourier spectrum. Besides, these statistics are very sensitive to noise. In this paper, we study features derived from higher order spectra, especially the third order spectrum, namely the bispectrum, known to offer a high noise immunity and to recover Fourier phase information. In this paper, we exploit phase preservation property by using bispectrum phase. We propose wrapped Cauchy distribution to model phase. Wrapped Cauchy parameters are estimated by maximizing the log-likelihood function. Experiments show that the wrapped Cauchy distribution fits our phase information well. Hence, their parameters are used to feed our feature vector in order to classify textures corrupted by Gaussian noise. Classification results using the proposed approach show a good noise immunity compared to a statistical model based on Gabor phase.

Evaluation of a new Power Quality index, based in Higher Order Statistics

2017 ◽  
Vol 1 (15) ◽  
pp. 37-42
Author(s):  
J.M. Sierra-Fernández ◽  
J.J. González De La Rosa ◽  
A. Agüera-Pérez ◽  
J.C. Palomares Salas ◽  
O. Florencias-Oliveros
Keyword(s):  
Order Statistics ◽  
Power Quality ◽  
Quality Index ◽  
Higher Order ◽  
Higher Order Statistics
Sign in / Sign up

Export Citation Format

Share Document