Ion collision crosssection measurements in quadrupole ion traps using a time–frequency analysis method

The Analyst ◽  
2014 ◽  
Vol 139 (23) ◽  
pp. 6144-6153 ◽  
Author(s):  
Muyi He ◽  
Dan Guo ◽  
Yu Chen ◽  
Xingchuang Xiong ◽  
Xiang Fang ◽  
...  
Keyword(s):  
Frequency Analysis ◽  
Ion Traps ◽  
Analysis Method ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Ion Collision ◽  
Quadrupole Ion Traps

In this study, a method for measuring ion collision crosssections (CCSs) was proposed through time–frequency analysis of ion trajectories in quadrupole ion traps.

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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