Short Time Frequency Analysis of Theta Activity for the Diagnosis of Bruxism on EEG Sleep Record

2019 ◽  
pp. 63-83
Author(s):  
Md Belal Bin Heyat ◽  
Dakun Lai ◽  
Faijan Akhtar ◽  
Mohd Ammar Bin Hayat ◽  
Shajan Azad
Keyword(s):  
Frequency Analysis ◽  
Theta Activity ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Short Time

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.

Time-frequency analysis of heart rate variability using short-time Fourier analysis

2000 ◽  
Vol 21 (2) ◽  
pp. 229-240 ◽  
Author(s):  
Sigrid Elsenbruch ◽  
Zhishun Wang ◽  
William C Orr ◽  
J D Z Chen
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Fourier Analysis ◽  
Frequency Analysis ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Short Time

Stationarity test of vibration signals with surrogate data and time–frequency analysis

2016 ◽  
Vol 20 (8) ◽  
pp. 1143-1154
Author(s):  
Zuo-Cai Wang ◽  
Feng Wu ◽  
Wei-Xin Ren
Keyword(s):  
Frequency Analysis ◽  
Surrogate Data ◽  
Transform Method ◽  
Vibration Signals ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Analysis Methods ◽  
Stationarity Test ◽  
Modulated Signals ◽  
Short Time

The stationarity test of vibration signals is critical for the extraction of the signal features. In this article, the surrogate data with various time–frequency analysis methods are proposed for stationary test of vibration signals. The surrogate data are first generated from the Fourier spectrum of the original signal with keeping the magnitude of the spectrum unchanged and replacing its phase by a random sequence. The local and global spectra of the original signal and the surrogate data are then estimated by four time–frequency analysis methods, which are short-time Fourier transform, multitaper spectrograms, wavelet transform, and S-transform methods. The index of nonstationarity is then defined based on the distances between the local and global spectra. Three kinds of synthetic signals, which are stationary signals, frequency-modulated signals, and amplitude-modulated signals, are tested to compare the efficiency of the four time–frequency analysis methods as mentioned. The results show that with a certain observation scale value, the index of nonstationarity based on the short-time Fourier transform or wavelet transform method may fail to test the stationarity of the signal. The parametric studies and sensitivity analysis of the observation scale and noise-level effect are also extensively conducted. The results show that the index of nonstationarity calculated using the multitaper spectrograms’ method is more suitable for stationarity test of frequency-modulated signals, while the index of nonstationarity calculated using the S-transform method is more suitable for stationarity test of amplitude-modulated signals. The results also show that the noise has a significant effect on the stationarity test results. Finally, the stationarity of a real vibration signal measured from a cable is tested, and the results show that the proposed index of nonstationarity can effectively test the stationarity of real vibration signals.

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.

scholarly journals Coherence and time-frequency analysis of impulse voltage and current measurements

2017 ◽  
Vol 26 (2) ◽  
pp. 118
Author(s):  
Jelena Dikun ◽  
Emel Onal
Keyword(s):  
Frequency Analysis ◽  
High Voltage ◽  
Electromagnetic Interference ◽  
Digital Processing ◽  
Noise Sources ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Interference Level ◽  
Impulse Voltage ◽  
Short Time

The aim of this paper is to point out the advantages of the use of the time-frequency analysis in the digital processing of waveforms recorded in high voltage impulse tests. Impulse voltage tests are essential to inspect and test insulation integrity of high voltage apparatus. On the other hand, generated impulse currents are used for different test applications such as investigation of high current effects, electromagnetic interference (EMI) testing, etc. Obtained voltage and current waveforms usually have some sort of interferences originated from the different sources. These interferences have to be removed from the original impulse data in order to evaluate the waveform characteristics precisely. When the interference level is high enough, it might not be possible to distinguish signal parameters from the recorded data. Conventional filtering methods cannot be useful for some interference like white noise. In that case, time-frequency filtering methods might be necessary. In this study, the wavelet analysis, which is a powerful time-frequency signal processing tool, is used to recognize the noise of impulse current and voltage data. Thus, the noise sources can be determined by short time Fourier Transform, and a coherence approach is used to determine the bandwidth of noises.

Time-Frequency Analysis Methods for Power Quality Disturbances Feature Representation

2010 ◽  
Vol 439-440 ◽  
pp. 298-303
Author(s):  
Lin Lin ◽  
Jia Jin Qi ◽  
Nan Tian Huang ◽  
Shi Guang Luo
Keyword(s):  
Power Quality ◽  
Frequency Analysis ◽  
Time Instant ◽  
Feature Representation ◽  
Quality Analysis ◽  
Status Report ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
S Transform ◽  
Short Time

Power quality (PQ) analysis is the foundation of power system automation. The premise of power quality analysis is feature representation of power quality events. Time-frequency analysis (TFA) is very suitable for nonstationary signals analysis. The TFA of a PQ signal is to determine the energy distribution along the frequency axis at each time instant. This paper provides a status report of feature representation for PQ events by TFA methods, including short time Fourier transform (STFT), wavelet transform (WT) and S-transform (ST), overview the basic TFA theories for PQ analysis and compare the effectiveness of different TFA methodology. The expectation is that further research and applications of these TFA algorithms will flourish for PQ feature representation in the near future. The analysis direction and emphasis of studying are also put forward.

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