scholarly journals Wavelet analysis of molecular dynamics: Efficient extraction of time-frequency information in ultrafast optical processes

2013 ◽  
Vol 139 (22) ◽  
pp. 224103 ◽  
Author(s):  
Javier Prior ◽  
Enrique Castro ◽  
Alex W. Chin ◽  
Javier Almeida ◽  
Susana F. Huelga ◽  
...  
Keyword(s):  
Molecular Dynamics ◽  
Wavelet Analysis ◽  
Time Frequency ◽  
Frequency Information ◽  
Ultrafast Optical ◽  
Efficient Extraction

Identifying Reservoir Fluids by Wavelet Transform of Well Logs

2006 ◽  
Vol 9 (05) ◽  
pp. 574-581 ◽  
Author(s):  
Wenzheng Yue ◽  
Guo Tao ◽  
Zhengwu Liu
Keyword(s):  
Wavelet Transform ◽  
Wavelet Analysis ◽  
Data Interpretation ◽  
Well Logging ◽  
Oil Field ◽  
New Method ◽  
Well Logs ◽  
Time Frequency ◽  
Frequency Information ◽  
Reservoir Fluid

Summary The wavelet-transform (WT) method has been applied to logs to extract reservoir-fluid information. In addition to the time (depth)/frequency analysis generally performed by the wavelet method, we also have performed energy spectral analysis for time/frequency-domain signals by the WT method. We have further developed a new method to identify reservoir fluid by setting up a correlation between the energy spectra and reservoir fluid. We have processed 42 models from an oil field in China using this method and have subsequently applied these rules to interpret reservoir layers. It is found that identifications by use of this method are in very good agreement with the results of well tests. Introduction An important log-analysis application is determining reservoir-fluid properties. It is common practice to calculate the water and oil saturations of reservoir formations by use of electrical logs. With the development of well-logging technology, a number of methods have been developed for reservoir-fluid typing with well logs (Hou 2002; Geng et al. 1983; Dahlberg and Ference 1984). A recent report has also described reservoir-fluid typing by the T2 differential spectrum from nuclear-magnetic-resonance (NMR) logs (Coates et al. 2001). However, because of the interference from vugs, fractures, clay content, and mud-filtrate invasion, the reservoir-fluid information contained in well logs is often concealed. The reliability of these log interpretations is thus limited in many cases. Therefore, it is desirable to find a more reliable and consistent way of reservoir-fluid typing with well logs. In this paper, we present a new method using the WT for fluid typing with well logs. The WT technique was developed with the localization idea from Gabor's short-time Fourier analysis and has been expanded further. Wavelets provide the ability to perform local analysis (i.e., analyze a small portion of a larger signal) (Daubechies 1992).This localized analysis represents the next logical step: a windowing technique with variable-sized regions. Wavelet analysis allows the use of long time intervals, where more-precise low-frequency information is wanted, and shorter intervals, where high-frequency information is needed. Wavelet analysis is capable of revealing aspects of data that other signal-analysis techniques miss: aspects such as trends, breakdown points, discontinuities in higher derivatives, and self-similarity. In well-logging-data processing, wavelet analysis has been used to identify formation boundaries, estimate reservoir parameters, and increase vertical resolution (Lu and Horne 2000; Panda et al. 1996; Jiao et al. 1999; Barchiesi and Gharbi 1999). For data interpretation, however, the identification of hydrocarbon-bearing zones by wavelet analysis is still under investigation. In this study, we have developed a technique of wavelet-energy-spectrum analysis (WESA) to identify reservoir-fluid types. We have applied this technique to field-data interpretation and have achieved very good results.

Wavelet analysis based on time–frequency information discriminate chronic ankle instability

2010 ◽  
Vol 25 (3) ◽  
pp. 256-264 ◽  
Author(s):  
A. Martínez-Ramírez ◽  
P. Lecumberri ◽  
M. Gómez ◽  
M. Izquierdo
Keyword(s):  
Wavelet Analysis ◽  
Ankle Instability ◽  
Chronic Ankle Instability ◽  
Time Frequency ◽  
Frequency Information

LFM Signal Detection Method Based on Fractional Fourier Transform

2014 ◽  
Vol 989-994 ◽  
pp. 4001-4004 ◽  
Author(s):  
Yan Jun Wu ◽  
Gang Fu ◽  
Yu Ming Zhu
Keyword(s):  
Fourier Transform ◽  
Signal Detection ◽  
Frequency Analysis ◽  
Experimental Analysis ◽  
Fractional Fourier Transform ◽  
Detection Method ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Frequency Information ◽  
Strong Impulse

As a generalization of Fourier transform, the fractional Fourier Transform (FRFT) contains simultaneity the time-frequency information of the signal, and it is considered a new tool for time-frequency analysis. This paper discusses some steps of FRFT in signal detection based on the decomposition of FRFT. With the help of the property that a LFM signal can produce a strong impulse in the FRFT domain, the signal can be detected conveniently. Experimental analysis shows that the proposed method is effective in detecting LFM signals.

Fusion-Net: Time-Frequency Information Fusion Y-Network for Speech Enhancement

2021 ◽  
Author(s):  
Santhan Kumar Reddy Nareddula ◽  
Subrahmanyam Gorthi ◽  
Rama Krishna Sai S. Gorthi
Keyword(s):  
Speech Enhancement ◽  
Information Fusion ◽  
Time Frequency ◽  
Frequency Information

scholarly journals Adapted Wavelet Transform for Twisted Blade Diagnosis in Multi Stage Rotor

2019 ◽  
Vol 255 ◽  
pp. 02011
Author(s):  
Ahmed M. Abdelrhman ◽  
M. Salman Leong ◽  
Y.H. Ali ◽  
Iftikhar Ahmad ◽  
Christina G. Georgantopoulou ◽  
...  
Keyword(s):  
Wavelet Transform ◽  
Wavelet Analysis ◽  
Rotor System ◽  
Fault Detection And Diagnosis ◽  
Frequency Resolution ◽  
Time Frequency ◽  
Multi Stage ◽  
The Common ◽  
Detection And Diagnosis ◽  
Twisted Blade

This paper studies the diagnosis of twisted blade in a multi stages rotor system using adapted wavelet transform and casing vibration. The common detection method (FFT) is effective only if sever blade faults occurred while the minor faults usually remain undetected. Wavelet analysis as alternative technique is still unable to fulfill the fault detection and diagnosis accurately due to its inadequate time-frequency resolution. In this paper, wavelet is adapted and its time-frequency is improved. Experimental study was undertaken to simulate multi stages rotor system. Results showed that the adapted wavelet analysis is effective in twisted blade diagnosis compared to the conventional one.

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