scholarly journals Time-frequency analysis methods and their application in developmental EEG data

2022 ◽  
pp. 101067
Author(s):  
Santiago Morales ◽  
Maureen E. Bowers
Keyword(s):  
Frequency Analysis ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Analysis Methods ◽  
Eeg Data

Application of Joint-Time Frequency Analysis Methods for Nondestructive Evaluation

1999 ◽  
Author(s):  
Ki-Woo Nam ◽  
Kun-Chan Lee ◽  
Jeong-Hwan Oh
Keyword(s):  
Crack Propagation ◽  
Nondestructive Evaluation ◽  
Frequency Analysis ◽  
Cyclic Fatigue ◽  
Frequency Characteristics ◽  
Time Frequency Analysis ◽  
Ultrasonic Signals ◽  
Time Frequency ◽  
Analysis Methods ◽  
Ae Signals

Abstract Application of signal processing techniques to nondestructive evaluation (NDE) in general and acoustic emission (AE) studies in particular has become a standard tool in determining the frequency characteristics of the signals and relating these characteristics to the integrity of the structure under consideration. Recent studies have shown that the frequency characteristics of ultrasonic signals from evolving damage during cyclic (fatigue) and dynamic loads change with time; in other words, the signals are nonstationary, and that these changes can be related to the nature of the damage taking place during loading. A joint time-frequency analysis such as Short Time Fourier Transform (STFT) and Wigner-Ville distribution (WVD), can in principle be used to determine the time dependent frequency characteristics of nonstationary signals in presence of background noise. In this study these techniques are applied to analyze AE signals from fatigue crack propagation in 5083 aluminum alloys and ultrasonic signals in degraded austenitic 316 stainless steels, to study the evolution of damage in these materials. It is demonstrated that the nonstationary characteristics of both AE and ultrasonic signals could be analyzed effectively by these methods. STFT was found to be more effective in analyzing AE signals, and WVD was more effective for analyzing the attenuation and frequency characteristics of degraded materials through ultrasonics. It is indicated that the time-frequency analysis methods should also be useful in evaluating crack propagation and final fracture process resulting from various damages and defects in structural members.

scholarly journals SEMG Feature Extraction Based on Stockwell Transform Improves Hand Movement Recognition Accuracy

Sensors ◽  
2019 ◽  
Vol 19 (20) ◽  
pp. 4457 ◽  
Author(s):  
She ◽  
Zhu ◽  
Tian ◽  
Wang ◽  
Yokoi ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Frequency Analysis ◽  
Recognition Accuracy ◽  
Hand Movement ◽  
Analysis Method ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Analysis Methods ◽  
Movement Recognition ◽  
S Transform

Feature extraction, as an important method for extracting useful information from surfaceelectromyography (SEMG), can significantly improve pattern recognition accuracy. Time andfrequency analysis methods have been widely used for feature extraction, but these methods analyzeSEMG signals only from the time or frequency domain. Recent studies have shown that featureextraction based on time-frequency analysis methods can extract more useful information fromSEMG signals. This paper proposes a novel time-frequency analysis method based on the Stockwelltransform (S-transform) to improve hand movement recognition accuracy from forearm SEMGsignals. First, the time-frequency analysis method, S-transform, is used for extracting a feature vectorfrom forearm SEMG signals. Second, to reduce the amount of calculations and improve the runningspeed of the classifier, principal component analysis (PCA) is used for dimensionality reduction of thefeature vector. Finally, an artificial neural network (ANN)-based multilayer perceptron (MLP) is usedfor recognizing hand movements. Experimental results show that the proposed feature extractionbased on the S-transform analysis method can improve the class separability and hand movementrecognition accuracy compared with wavelet transform and power spectral density methods.

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.

Damage Detection in RAPTOR Telescope Systems Using Time-Frequency Analysis Methods

2013 ◽  
Vol 588 ◽  
pp. 43-53 ◽  
Author(s):  
Ifigeneia Antoniadou ◽  
Keith Worden ◽  
Graeme Manson ◽  
Nikolaos Dervilis ◽  
S.G. Taylor ◽  
...  
Keyword(s):  
Frequency Analysis ◽  
Gamma Ray ◽  
Energy Operator ◽  
Experimental Tests ◽  
Energy Separation ◽  
Driving Mechanism ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Remote Locations ◽  
Analysis Methods

The RAPTOR telescope systems are astronomical observatories that operate in remote locations in New Mexico searching for astrophysical transients called gamma-ray bursts. Their operating condition should remain at good levels in order to have accurate observations. Currently, the first component of the RAPTOR telescopes to fail is a capstan driving mechanism that operates in a run-to failure mode. The capstans wear relatively frequently because of their manufacturing material and can cause damage to other more expensive components, such as the drive wheels and the telescope optics. Monitoring the condition of these systems seems a reasonable solution since the unpredictable rate at which the capstans experience wear, in combination with the remote locations and high duty cycles of these telescope systems, make it unprofitable to choose a strategy of replacing the capstans at chosen intervals. Experimental tests of the telescope systems reported here recorded vibration signals during clockwise and counterclockwise rotations, similar to a motion known as "homing-sequence". The Empirical Mode Decomposition (EMD) method in combination with the Hilbert Transform (HT) and a new alternative method for the estimation of the instantaneous features of a signal that applies an energy tracking operator, called Teager-Kaiser Energy operator, and an energy separation algorithm to the data being analysed, are the time-frequency analysis methods used for analysis here.

scholarly journals The simulation study of three typical time frequency analysis methods

2017 ◽  
Vol 8 ◽  
pp. 02007
Author(s):  
Yifeng Li ◽  
Lihui Zhang ◽  
Baohui Li ◽  
Yan Xu ◽  
Sanyuan Wu ◽  
...  
Keyword(s):  
Frequency Analysis ◽  
Simulation Study ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Analysis Methods ◽  
Typical Time
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