scholarly journals APPLICATION OF TIME-FREQUENCY ANALYSIS METHOD FOR BIAS CORRECTION OF DAILY PRECIPITATION OUTPUT FROM CLIMATE MODELS WITH COMPARIING VARIOUS BIAS CORRECTION METHODS

2017 ◽  
Vol 73 (5) ◽  
pp. I_407-I_414
Author(s):  
Yoshihiko ISERI ◽  
Shinjiro KANAE
Keyword(s):  
Frequency Analysis ◽  
Bias Correction ◽  
Climate Models ◽  
Daily Precipitation ◽  
Analysis Method ◽  
Time Frequency Analysis ◽  
Time Frequency

A Nonlinear Time-Frequency Analysis Method

2004 ◽  
Vol 52 (6) ◽  
pp. 1585-1595 ◽  
Author(s):  
M. Karimi-Ghartemani ◽  
A.K. Ziarani
Keyword(s):  
Frequency Analysis ◽  
Analysis Method ◽  
Time Frequency Analysis ◽  
Time Frequency

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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