scholarly journals Cross-Correlation-Based Method vs. Classical Fft for Spectral Analysis of Impulse Response

2014 ◽  
Vol 8 (4) ◽  
pp. 219-222
Author(s):  
Adam Kotowski
Keyword(s):  
Spectral Analysis ◽  
Impulse Response ◽  
Hilbert Transform ◽  
Spectral Resolution ◽  
Cross Correlation ◽  
Fourier Method ◽  
Time Signal ◽  
Harmonic Signals ◽  
Response Spectral Analysis ◽  
Short Time

Abstract The paper presents comparison of results of impulse response spectral analysis that has been obtained using a method based on cross-correlation with results obtained using classical FFT. The presented non-Fourier method is achieved by correlating the analyzed signal and reference single-harmonic signals and using Hilbert transform to obtain an envelope of cross-correlation. The envelope of crosscorrelation makes it possible to calculate appropriate indicator and make its plot in frequency domain as a spectrum. The spectrum obtained this way has its advantage over the FFT that the spectral resolution does not depend on duration of signal. At the same time, the spectral resolution can be much greater than spectral resolution resultant from FFT. Obtained results show that presented non-Fourier method gives frequency readout more accurate in comparison to FFT when the impulse response is a short-time signal e.g. few dozen of miliseconds lasting.

SEMI-CLASSICAL PROPAGATION AND SPECTRAL ANALYSIS IN THE H- ION INTERACTING WITH A METALLIC SURFACE

2005 ◽  
Vol 04 (02) ◽  
pp. 357-372
Author(s):  
JOHN JAIRO ZULUAGA ◽  
JORGE MAHECHA ◽  
EUGENE CHULKOV
Keyword(s):  
Spectral Analysis ◽  
Local Potential ◽  
Metallic Surface ◽  
Electron Interaction ◽  
Time Signal ◽  
Filter Diagonalization ◽  
Resonant States ◽  
Classical Trajectories ◽  
Short Time ◽  
Harmonic Inversion

A semi-classical propagator method combined with harmonic inversion of short time signals is used to find the resonant states of an electron interacting with a hydrogen atom near a metallic surface. The atom-electron interaction corresponds to one electron in the presence of a neutral compact core, which can be described by a simple local potential proposed by Cohen. On the other hand, the electron-surface interaction is described by a model proposed by Jennings, the so-called Jelly Model, or by a more realistic local potential that takes into account the shell structure of the metal. A semi-classical propagator approach, proposed by Herman and Kluk, is used to calculate an approximation to the autocorrelation function A(t) = <ψt|ψ0> entirely in terms of classical trajectories. A filter-diagonalization method for harmonic inversion of the complex time signal A(t) is applied to extract the resonances. We verified that the spectral analysis of the signal obtained by semi-classical methods gives satisfactory numerical results for the position and width of the lowest lying resonances.

scholarly journals Fault Detection in DC Microgrids Using Short-Time Fourier Transform

Energies ◽  
2021 ◽  
Vol 14 (2) ◽  
pp. 277
Author(s):  
Ivan Grcić ◽  
Hrvoje Pandžić ◽  
Damir Novosel
Keyword(s):  
Fourier Transform ◽  
Fault Detection ◽  
Operating Conditions ◽  
Time Signal ◽  
Short Time Fourier Transform ◽  
Dc Microgrid ◽  
Protection Scheme ◽  
Simulink Model ◽  
Battery Energy ◽  
Short Time

Fault detection in microgrids presents a strong technical challenge due to the dynamic operating conditions. Changing the power generation and load impacts the current magnitude and direction, which has an adverse effect on the microgrid protection scheme. To address this problem, this paper addresses a field-transform-based fault detection method immune to the microgrid conditions. The faults are simulated via a Matlab/Simulink model of the grid-connected photovoltaics-based DC microgrid with battery energy storage. Short-time Fourier transform is applied to the fault time signal to obtain a frequency spectrum. Selected spectrum features are then provided to a number of intelligent classifiers. The classifiers’ scores were evaluated using the F1-score metric. Most classifiers proved to be reliable as their performance score was above 90%.

A cross correlation phase offset method based on Hilbert transform

2014 ◽  
Author(s):  
Su Dan ◽  
Luo Jianyuan ◽  
Shuang Tao ◽  
Tu Yaqing ◽  
Li Changqiang ◽  
...  
Keyword(s):  
Hilbert Transform ◽  
Cross Correlation ◽  
Phase Offset

Application of spectral analysis for determine the phase shift of signals with equal amplitudes using full-wave transformation when measuring the characteristics of weapons

2021 ◽  
pp. 12-23
Author(s):  
S. Tyshko ◽  
O. Lavrut ◽  
V. Smolar ◽  
O. Zabula ◽  
Yu. Chernichenko
Keyword(s):  
Spectral Analysis ◽  
Phase Shift ◽  
Information Transmission ◽  
Measurement Problem ◽  
Wave Transformation ◽  
Measuring Systems ◽  
Transmission Channels ◽  
Harmonic Signals ◽  
Full Wave ◽  
The Relationship

The article defines the list of technical characteristics of armaments and military equipment (ARM), the value of which is measured using phase methods. An analysis of known methods that have found wide application in measuring technology, which is designed to determine the technical characteristics associated with the measurement of phase shift during the development, manufacture and operation of weapons. Based on this analysis, it was determined that the measuring systems are designed to determine the phase shift of two harmonic signals in their composition have two channels of information transmission. This architecture of the implementation of measuring systems leads to the fact that a significant impact on the accuracy of the proposed measurement problem, makes a component of the error due to the phase symmetry of the signal transmission channels, as well as internal and external noise. As an alternative approach to solving the measurement problem of determining the phase shift of two harmonic signals, which will significantly reduce the error component due to phase asymmetry of information transmission channels, it is proposed to use the signal obtained by summing harmonic signals after full-wave transformation followed by spectral analysis. In order to implement the above approach, a measurement problem was set to determine the phase shift of two harmonic signals, using spectral analysis of the signal obtained by summing the harmonic signals after their full-wave transformation. A list of assumptions required for the synthesis of analytical relations that establish the relationship between the spectra of phases and amplitudes (power) of the signal obtained by summing harmonic signals after their full-wave transformation and phase shift of two harmonic signals. Analytical relationships are proposed that establish the relationship between the above characteristics. It is shown that the values of the spectrum of phases and amplitudes, which are calculated using the proposed expressions, differ from the values obtained in the calculations using the Fourier series coefficients, not more than 0.1%.

scholarly journals Long-Range Behaviour and Correlation in DFA and DCCA Analysis of Cryptocurrencies

2019 ◽  
Vol 7 (3) ◽  
pp. 51 ◽  
Author(s):  
Natália Costa ◽  
César Silva ◽  
Paulo Ferreira
Keyword(s):  
Time Scales ◽  
Cross Correlation ◽  
Data Availability ◽  
Fluctuation Analysis ◽  
Financial Assets ◽  
Sliding Windows ◽  
Cross Correlation Analysis ◽  
Short Time ◽  
Detrended Fluctuation ◽  
Great Development

In recent years, increasing attention has been devoted to cryptocurrencies, owing to their great development and valorization. In this study, we propose to analyse four of the major cryptocurrencies, based on their market capitalization and data availability: Bitcoin, Ethereum, Ripple, and Litecoin. We apply detrended fluctuation analysis (the regular one and with a sliding windows approach) and detrended cross-correlation analysis and the respective correlation coefficient. We find that Bitcoin and Ripple seem to behave as efficient financial assets, while Ethereum and Litecoin present some evidence of persistence. When correlating Bitcoin with the other cryptocurrencies under analysis, we find that for short time scales, all the cryptocurrencies have statistically significant correlations with Bitcoin, although Ripple has the highest correlations. For higher time scales, Ripple is the only cryptocurrency with significant correlation.

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