Continuous wavelet transform for time-varying motion extraction

2008 ◽  
Author(s):  
A. Briassouli ◽  
D. Matsiki ◽  
I. Kompatsiaris
Keyword(s):  
Wavelet Transform ◽  
Continuous Wavelet Transform ◽  
Time Varying ◽  
Continuous Wavelet ◽  
Motion Extraction

Continuous wavelet transform for time-varying motion extraction

2010 ◽  
Vol 4 (4) ◽  
pp. 271 ◽  
Author(s):  
A. Briassouli ◽  
D. Matsiki ◽  
I. Kompatsiaris
Keyword(s):  
Wavelet Transform ◽  
Continuous Wavelet Transform ◽  
Time Varying ◽  
Continuous Wavelet ◽  
Motion Extraction

scholarly journals Analysis of time-varying signals using continuous wavelet and synchrosqueezed transforms

2018 ◽  
Vol 376 (2126) ◽  
pp. 20170254 ◽  
Author(s):  
Jean Baptiste Tary ◽  
Roberto Henry Herrera ◽  
Mirko van der Baan
Keyword(s):  
Wavelet Transform ◽  
Continuous Wavelet Transform ◽  
Seismic Signal ◽  
American Football ◽  
Frequency Resolution ◽  
Time Varying ◽  
Continuous Wavelet ◽  
Time Frequency ◽  
Frequency Information ◽  
Synchrosqueezing Transform

The continuous wavelet transform (CWT) has played a key role in the analysis of time-frequency information in many different fields of science and engineering. It builds on the classical short-time Fourier transform but allows for variable time-frequency resolution. Yet, interpretation of the resulting spectral decomposition is often hindered by smearing and leakage of individual frequency components. Computation of instantaneous frequencies, combined by frequency reassignment, may then be applied by highly localized techniques, such as the synchrosqueezing transform and ConceFT, in order to reduce these effects. In this paper, we present the synchrosqueezing transform together with the CWT and illustrate their relative performances using four signals from different fields, namely the LIGO signal showing gravitational waves, a ‘FanQuake’ signal displaying observed vibrations during an American football game, a seismic recording of the M w 8.2 Chiapas earthquake, Mexico, of 8 September 2017, followed by the Irma hurricane, and a volcano-seismic signal recorded at the Popocatépetl volcano showing a tremor followed by harmonic resonances. These examples illustrate how high-localization techniques improve analysis of the time-frequency information of time-varying signals. This article is part of the theme issue ‘Redundancy rules: the continuous wavelet transform comes of age’.

scholarly journals Tacholess Rotational Speed Estimation via The Integration of Continuous Wavelet Transform and Time-Dependent Autoregression

2020 ◽  
Author(s):  
Ran Zhang ◽  
Xingxing Liu ◽  
Yongjun Zheng ◽  
Haotun Lv ◽  
Baosheng Li ◽  
...  
Keyword(s):  
Wavelet Transform ◽  
Continuous Wavelet Transform ◽  
Estimation Method ◽  
Rotational Frequency ◽  
Time Dependent ◽  
Speed Estimation ◽  
Time Varying ◽  
Continuous Wavelet ◽  
Vibration Sensors ◽  
Preprocessing Technique

Abstract Speed estimation is crucial to monitor the conditions of rotational machinery. Most speed measurements are carried out by installing encoders or tachometers inside the machines. In many cases, such method could be cumbersome or even inaccessible. This paper proposes a vibration-based speed estimation method. The vibration sensors are often cheaper and easier to install than angle encoders. In the proposed method, the continuous wavelet transform (CWT) is used as a preprocessing technique to extract the signal of importance. Then, the time-varying autoregressive (TAR) model is applied to analyze the rotational frequency. Additionally, the paper presents a fast algorithm for implementation. The proposed method is validated by both synthetic and empirical data.

scholarly journals Spectrogram analysis of selected tremor signals using short-time Fourier transform and continuous wavelet transform

1999 ◽  
Vol 42 (3) ◽  
Author(s):  
T. Bartosch ◽  
D. Seidl
Keyword(s):  
Fourier Transform ◽  
Wavelet Transform ◽  
Continuous Wavelet Transform ◽  
Time Varying ◽  
Continuous Wavelet ◽  
Short Time Fourier Transform ◽  
Array Data ◽  
Definition Of ◽  
Short Time

Among a variety of spectrogram methods Short-Time Fourier Transform (STFT) and Continuous Wavelet Transform (CWT) were selected to analyse transients in non-stationary tremor signals. Depending on the properties of the tremor signal a more suitable representation of the signal is gained by CWT. Three selected broadband tremor signals from the volcanos Mt. Stromboli, Mt. Semeru and Mt. Pinatubo were analyzed using both methods. The CWT can also be used to extend the definition of coherency into a time-varying coherency spectrogram. An example is given using array data from the volcano Mt. Stromboli.

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