Discrete Wavelet Transforms in the Large Time-Frequency Analysis Toolbox for MATLAB/GNU Octave

2016 ◽  
Vol 42 (4) ◽  
pp. 1-23 ◽  
Author(s):  
ZdenĚK Průša ◽  
Peter L. Søndergaard ◽  
Pavel Rajmic
Keyword(s):  
Frequency Analysis ◽  
Wavelet Transforms ◽  
Large Time ◽  
Discrete Wavelet ◽  
Discrete Wavelet Transforms ◽  
Time Frequency Analysis ◽  
Time Frequency

The Large Time-Frequency Analysis Toolbox 2.0

2014 ◽  
pp. 419-442 ◽  
Author(s):  
Zdeněk Průša ◽  
Peter L. Søndergaard ◽  
Nicki Holighaus ◽  
Christoph Wiesmeyr ◽  
Peter Balazs
Keyword(s):  
Frequency Analysis ◽  
Large Time ◽  
Time Frequency Analysis ◽  
Time Frequency

Research on Goniometer Fault Detection of a Certain Missile Based on Wavelet Analysis

2012 ◽  
Vol 490-495 ◽  
pp. 1600-1604
Author(s):  
Zhu Lin Wang ◽  
Jiang Kun Mao ◽  
Zi Bin Zhang ◽  
Xi Wei Guo
Keyword(s):  
Wavelet Transform ◽  
Wavelet Analysis ◽  
Discrete Wavelet Transform ◽  
Frequency Analysis ◽  
Discrete Wavelet ◽  
Analysis Method ◽  
Frequency Method ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Fault Characteristic

Aiming at the problem of existing time-frequency analysis methods was not effective to goniometer keeping fault of a certain missile, combined time -frequency analysis method of CWT and DWT for the fault was put forward based on the fault characteristic. The process of the method proposed was given and the time-frequency method of continuous and discrete wavelet transform was analysed. The signal when goniometer keeping fault occurred was analysed by the method that was put forward. The simulation showed that the method which was effective to the fault detecting could accurately detect the time and location of goniometer fault occurred.

CONTAGION AMONG SELECT GLOBAL EQUITY MARKETS: A TIME-FREQUENCY ANALYSIS

2019 ◽  
Vol 19 (04) ◽  
pp. 1950023
Author(s):  
AVISHEK BHANDARI ◽  
BANDI KAMAIAH
Keyword(s):  
Frequency Analysis ◽  
Financial Crises ◽  
East Asian ◽  
Equity Markets ◽  
Discrete Wavelet ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Short Run ◽  
Global Equity Markets ◽  
Wavelet Methods

This paper investigates the phenomenon of contagion among some selected global equity markets using novel methods from wavelet-based time-frequency analysis. It surveys some seminal literature on contagion and examines, using both continuous and discrete wavelet methods, the effects of major financial crises on Indian markets. Strong evidence of co-movements in the short run, which indicates contagion, between Indian and some East Asian markets is observed, signifying diversification risks for Indian investors during periods of financial turbulence.

Time-frequency analysis of electroencephalogram series. II. Gabor and wavelet transforms

1996 ◽  
Vol 54 (6) ◽  
pp. 6661-6672 ◽  
Author(s):  
S. Blanco ◽  
C. E. D'Attellis ◽  
S. I. Isaacson ◽  
O. A. Rosso ◽  
R. O. Sirne
Keyword(s):  
Frequency Analysis ◽  
Wavelet Transforms ◽  
Time Frequency Analysis ◽  
Time Frequency

Application of bi-Gaussian S-transform in high-resolution seismic time-frequency analysis

2017 ◽  
Vol 5 (1) ◽  
pp. SC1-SC7 ◽  
Author(s):  
Zixiang Cheng ◽  
Wei Chen ◽  
Yangkang Chen ◽  
Ying Liu ◽  
Wei Liu ◽  
...  
Keyword(s):  
High Resolution ◽  
Frequency Analysis ◽  
Time Resolution ◽  
Wavelet Transforms ◽  
Superior Performance ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Gaussian Window ◽  
Hydrocarbon Reservoir ◽  
S Transform

The S-transform is one of the most widely used methods of time-frequency analysis. It combines the respective advantages of the short-time Fourier transform and wavelet transforms with scale-dependent resolution using Gaussian windows, scaled inversely with frequency. One of the problems with the traditional symmetric Gaussian window is the degradation of time resolution in the time-frequency spectrum due to the long front taper. We have studied the performance of an improved S-transform with an asymmetric bi-Gaussian window. The asymmetric bi-Gaussian window can obtain an increased time resolution in the front direction. The increased time resolution can make event picking high resolution, which will facilitate an improved time-frequency characterization for oil and gas trap prediction. We have applied the slightly modified bi-Gaussian S-transform to a synthetic trace, a 2D seismic section, and a 3D seismic cube to indicate the superior performance of the bi-Gaussian S-transform in analyzing nonstationary signal components, hydrocarbon reservoir predictions, and paleochannels delineations with an obviously higher resolution.

scholarly journals From Time–Frequency to Vertex–Frequency and Back

Mathematics ◽  
2021 ◽  
Vol 9 (12) ◽  
pp. 1407
Author(s):  
Ljubiša Stanković ◽  
Jonatan Lerga ◽  
Danilo Mandic ◽  
Miloš Brajović ◽  
Cédric Richard ◽  
...  
Keyword(s):  
Fourier Transform ◽  
Frequency Analysis ◽  
Large Time ◽  
Signal Reconstruction ◽  
Spectral Domain ◽  
Computationally Efficient ◽  
Spectral Window ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Short Time

The paper presents an analysis and overview of vertex–frequency analysis, an emerging area in graph signal processing. A strong formal link of this area to classical time–frequency analysis is provided. Vertex–frequency localization-based approaches to analyzing signals on the graph emerged as a response to challenges of analysis of big data on irregular domains. Graph signals are either localized in the vertex domain before the spectral analysis is performed or are localized in the spectral domain prior to the inverse graph Fourier transform is applied. The latter approach is the spectral form of the vertex–frequency analysis, and it will be considered in this paper since the spectral domain for signal localization is well ordered and thus simpler for application to the graph signals. The localized graph Fourier transform is defined based on its counterpart, the short-time Fourier transform, in classical signal analysis. We consider various spectral window forms based on which these transforms can tackle the localized signal behavior. Conditions for the signal reconstruction, known as the overlap-and-add (OLA) and weighted overlap-and-add (WOLA) methods, are also considered. Since the graphs can be very large, the realizations of vertex–frequency representations using polynomial form localization have a particular significance. These forms use only very localized vertex domains, and do not require either the graph Fourier transform or the inverse graph Fourier transform, are computationally efficient. These kinds of implementations are then applied to classical time–frequency analysis since their simplicity can be very attractive for the implementation in the case of large time-domain signals. Spectral varying forms of the localization functions are presented as well. These spectral varying forms are related to the wavelet transform. For completeness, the inversion and signal reconstruction are discussed as well. The presented theory is illustrated and demonstrated on numerical examples.

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