scholarly journals Frequency response measurement of electro-optic phase modulators using time-frequency analysis method

2021 ◽  
Author(s):  
Hongzhi Yang ◽  
Yuan Gao ◽  
Lei Wang ◽  
sijia wang ◽  
Peng Qin ◽  
...  
Keyword(s):  
Frequency Response ◽  
Frequency Analysis ◽  
Analysis Method ◽  
Response Measurement ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Phase Modulators ◽  
Frequency Response Measurement ◽  
Electro Optic

Large-Dynamic Frequency Response Measurement for Broadband Electro-Optic Phase Modulators

2019 ◽  
Vol 31 (4) ◽  
pp. 291-294 ◽  
Author(s):  
Yuqing Heng ◽  
Min Xue ◽  
Wei Chen ◽  
Shunli Han ◽  
Jiaqing Liu ◽  
...  
Keyword(s):  
Frequency Response ◽  
Response Measurement ◽  
Phase Modulators ◽  
Frequency Response Measurement ◽  
Electro Optic ◽  
Dynamic 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.

Sign in / Sign up

Export Citation Format

Share Document