A generalized S transform and applications to seismic time-frequency analysis

2018 ◽  
Author(s):  
Naihao Liu ◽  
Jinghuai Gao ◽  
Bo Zhang ◽  
Xiudi Jiang ◽  
Zhen Li
Keyword(s):  
Frequency Analysis ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
S Transform ◽  
Generalized S Transform

scholarly journals The Synchrosqueezing Algorithm Based on Generalized S-transform for High-Precision Time-Frequency Analysis

2017 ◽  
Vol 7 (8) ◽  
pp. 769 ◽  
Author(s):  
Hui Chen ◽  
Lingqi Lu ◽  
Dan Xu ◽  
Jiaxing Kang ◽  
Xuping Chen
Keyword(s):  
High Precision ◽  
Frequency Analysis ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
S Transform ◽  
Generalized S Transform ◽  
Precision Time

Parameter Optimization of Generalized S Transform Based on Improved Genetic Algorithm

2019 ◽  
Vol 13 (4) ◽  
pp. 433-441
Author(s):  
Yun Lin ◽  
Xiaowan Yu ◽  
Chunguang Ma
Keyword(s):  
Genetic Algorithm ◽  
Fourier Transform ◽  
Frequency Analysis ◽  
Frequency Modulation ◽  
Improved Genetic Algorithm ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Analysis Methods ◽  
S Transform ◽  
Generalized S Transform

Background: For the traditional Fourier Transform (FT), it cannot effectively detect the frequency of non-stationary signals with time. Analyzing the local characteristics of time-varying signal by using FT is hard to achieve. Therefore, many time-frequency analysis methods which can meet different needs have been proposed on the basis of the traditional Fourier transform, like the Short Time Fourier Transform (STFT), the widely used Continuous Wavelet Transform (CWT), Wigner-Ville Distribution (WVD) and so on. However, the best time and frequency resolution cannot be achieved at the same time due to the uncertainty criterion. Methods: From the point of view of optimizing time-frequency performance, a new Generalized S Transform (GST) window function optimization method is proposed by combining time frequency aggregation with an improved genetic algorithm in this paper. Results: In the noiseless condition, the Linear Frequency Modulation (LFM), Nonlinear Frequency Modulation (NLFM) and binary Frequency Shift Keying (2FSK) signals are simulated. The simulation results prove that the method can improve the time-frequency concentration and decreasing Rényi entropy effectively. Compared with other four traditional time-frequency analysis methods, the improved GST has more advantages. Conclusion: In this paper, a new method of optimizing the window function in GST based on improved GA is proposed in this paper. Experiments on LFM, NLFM and 2FSK signals show that the proposed method not only has the advantages of high resolution, but also determines the optimal parameters via the time frequency focusing criterion and the Rényi entropy. Compared with the other four kinds of time-frequency analysis methods, the optimized GST based on improved GA in this paper has the best time-frequency focusing.

Self-Adaptive Generalized S-Transform and Its Application in Seismic Time–Frequency Analysis

2019 ◽  
Vol 57 (10) ◽  
pp. 7849-7859 ◽  
Author(s):  
Naihao Liu ◽  
Jinghuai Gao ◽  
Bo Zhang ◽  
Qian Wang ◽  
Xiudi Jiang
Keyword(s):  
Frequency Analysis ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
S Transform ◽  
Generalized S Transform ◽  
Self Adaptive

Seismic Time-frequency Analysis Using Bi-Gaussian S Transform

2016 ◽  
Author(s):  
Z. Cheng ◽  
Y. Chen ◽  
Y. Liu ◽  
W. Liu ◽  
G. Zhang ◽  
...  
Keyword(s):  
Frequency Analysis ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
S Transform

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.

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