Time-frequency analysis of spontaneous pupillary oscillation signals using the Hilbert-Huang transform

2016 ◽  
Vol 30 ◽  
pp. 106-116 ◽  
Author(s):  
Fabiola M. Villalobos-Castaldi ◽  
José Ruiz-Pinales ◽  
Nicolás C. Kemper Valverde ◽  
Mercedes Flores
Keyword(s):  
Frequency Analysis ◽  
Time Frequency Analysis ◽  
Hilbert Huang Transform ◽  
Time Frequency

The Applicated Comparation of Three Signal Analytical Methods for Ocean CSEM Signature

2013 ◽  
Vol 798-799 ◽  
pp. 561-564
Author(s):  
Ji Yu Zhou ◽  
Feng Dao Zhou
Keyword(s):  
Frequency Analysis ◽  
Oil And Gas ◽  
Time Frequency Analysis ◽  
Hilbert Huang Transform ◽  
Time Frequency ◽  
Analysis Technique ◽  
Oil And Gas Resources ◽  
Concentration Degree ◽  
Short Time ◽  
Better Than

Sea is rich in oil and gas resources, the marine controlled source electromagnetic method (CSEM) is a kind of method seabed oil gas geophysical technology rising in recent years. Because of the problem of CSEM about the air wave in the shallow water, the research of time-frequnecy analysis technique is used to suppress the air wave in this paper. The basic idea is: because of the CSEM signals speed are different in the air and submarine, so the time which received by the receiving points are also different through these two kinds of ways. Using the time-frequency analysis technique and theoretical calculation, we can determine which part of the signal is spread over the ocean, so as to suppress the air wave effectively. This paper lists several methods of time-frequency analysis, such as Short-time Fourier transform, W-V distribution, Wavelet transform, Hilbert Huang transform. Through the time-frequency graph,we get the conclusion that HHT is better than others in concentration degree,and W-V distribution is better than STFT.Compared with the original signal, the time-frequency graph is the best in using Smooth Puseudo W-V Distribution.I have a detailed analysis about real case in using SPWVD at last.

Overview of Time-Frequency Analysis Techniques in Vibration Signals of Rotating Machinery

2014 ◽  
Vol 684 ◽  
pp. 124-130
Author(s):  
Hong Li ◽  
Qing He ◽  
Zhao Zhang
Keyword(s):  
Frequency Analysis ◽  
Local Characteristic ◽  
Vibration Signals ◽  
Time Frequency Analysis ◽  
Hilbert Huang Transform ◽  
Time Frequency ◽  
Analysis Techniques ◽  
Advantages And Disadvantages ◽  
Analysis Methods ◽  
Short Time

There is very rich fault information in vibration signals of rotating machineries. The real vibration signals are nonlinear, non-stationary and time-varying signals mixed with many other factors. It is very useful for fault diagnosis to extract fault features by using time-frequency analysis techniques. Recent researches of time-frequency analysis methods including Short Time Fourier Transform, Wavelet Transform, Wigner-Ville Distribution, Hilbert-Huang Transform, Local Mean Decomposition, and Local Characteristic-scale Decomposition are introduced. The theories, properties, physical significance and applications, advantages and disadvantages of these methods are analyzed and compared. It is pointed that algorithms improvement and combined applications of time-frequency analysis methods should be researched in the future.

Time-Frequency Analysis for Power System Oscillations

2013 ◽  
Vol 336-338 ◽  
pp. 928-931
Author(s):  
Chia Liang Lu ◽  
Pei Hwa Huang
Keyword(s):  
Power Systems ◽  
Power System ◽  
Frequency Analysis ◽  
Low Frequency ◽  
Normal Operation ◽  
Intrinsic Mode Functions ◽  
Time Frequency Analysis ◽  
Hilbert Huang Transform ◽  
Time Frequency ◽  
Low Frequency Oscillations

Low frequency oscillations due to the lack of damping may occur in power systems under normal operation and will cause system instability. These oscillations are essentially nonlinear power responses which are difficult to extract the inherent characteristics by the time domain method. This paper aims to analyze nonlinear power responses by using the Hilbert-Huang transform (HHT) which is a time-frequency signal processing method which comprises steps of the empirical mode decomposition and the Hilbert transform. Dynamic power system responses, including generator output power and line power are to be processed by the HHT and a set of intrinsic mode functions and the associated Hilbert spectrum are obtained. The generator with most effects on the system will be accordingly found out through the time-frequency analysis and the power system stabilizer will be placed at the generator. Numerical results from a sample power system are demonstrated to show the validity of the time-frequency approach in the study of power system low frequency oscillations.

Time-frequency analysis of non-stationary fusion plasma signals using an improved Hilbert-Huang transform

2014 ◽  
Vol 85 (7) ◽  
pp. 073502 ◽  
Author(s):  
Yangqing Liu ◽  
Yi Tan ◽  
Huiqiao Xie ◽  
Wenhao Wang ◽  
Zhe Gao
Keyword(s):  
Frequency Analysis ◽  
Fusion Plasma ◽  
Time Frequency Analysis ◽  
Hilbert Huang Transform ◽  
Time Frequency

Improved Hilbert-Huang Transform and its Application in Seismic Data Spectrum Decomposition

2012 ◽  
Vol 152-154 ◽  
pp. 920-923
Author(s):  
Ping Ping Bing ◽  
Si Yuan Cao ◽  
Jiao Tong Lu
Keyword(s):  
Wavelet Transform ◽  
Frequency Analysis ◽  
Seismic Data ◽  
Narrow Band ◽  
Numerical Study ◽  
Wavelet Packet ◽  
Time Frequency Analysis ◽  
Hilbert Huang Transform ◽  
Time Frequency ◽  
Spectrum Decomposition

In the conventional seismic data time-frequency analysis, the wavelet transform, wigner ville distribution and so on, cannot meet the high precision time-frequency analysis requirements because of uncertainty principle and cross-term interference. The recently popular Hilbert-Huang transform (HHT) although overcomes these conventional methods’ deficiencies; it still has some unsolved deficiencies due to the theory imperfect. This paper focuses on an improved HHT so as to ameliorate the defect of original HHT. First of all, the wavelet packet transform (WPT) as the preprocessing will be used to the inspected signal, to get some narrow band signals. Then use the empirical mode decomposition (EMD) on the narrow band signals and get the real intrinsic mode function (IMF) by the method of correlation coefficient. From the numerical study and comparison of improved HHT, wavelet transform and HHT, it proves the validity and advantages of this improved method. At last, the improved HHT is applied to marine seismic data by the spectrum decomposition technology, and it well reveals the low frequency shadow phenomenon of the reservoir. The results show that this new method has effectiveness and feasibility in seismic data spectrum decomposition.

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