Improving OBC Data Quality of the Geophone Components in Shallow-Water Persian Gulf Through Advanced Time-Frequency Analysis

2012 ◽  
Author(s):  
Zhao Zhang ◽  
Yuefeng Sun ◽  
Karl Berteussen
Keyword(s):  
Data Quality ◽  
Shallow Water ◽  
Frequency Analysis ◽  
Persian Gulf ◽  
Time Frequency Analysis ◽  
Time Frequency

Robust recognition and characterization of man‐made objects in shallow water using time‐frequency analysis

2008 ◽  
Vol 123 (5) ◽  
pp. 3945-3945
Author(s):  
Shaun D. Anderson ◽  
Karim G. Sabra ◽  
Manell E. Zakharia ◽  
Mario Zampolli ◽  
Henrik Schmidt ◽  
...  
Keyword(s):  
Shallow Water ◽  
Frequency Analysis ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Robust Recognition

Time‐frequency analysis of mode coupling in shallow‐water sound propagation

2006 ◽  
Vol 120 (5) ◽  
pp. 3221-3221
Author(s):  
Mohsen Badiey ◽  
Valery Grigorev ◽  
Boris Katsnelson ◽  
James Lynch
Keyword(s):  
Shallow Water ◽  
Frequency Analysis ◽  
Mode Coupling ◽  
Sound Propagation ◽  
Time Frequency Analysis ◽  
Time Frequency

scholarly journals Application research on the time–frequency analysis method in the quality detection of ultrasonic wire bonding

2021 ◽  
Vol 17 (5) ◽  
pp. 155014772110183
Author(s):  
Wuwei Feng ◽  
Xin Chen ◽  
Cuizhu Wang ◽  
Yuzhou Shi
Keyword(s):  
Empirical Mode Decomposition ◽  
Frequency Analysis ◽  
Recognition Accuracy ◽  
Wire Bonding ◽  
Analysis Method ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Mode Decomposition ◽  
Ultrasonic Wire Bonding

Imperfection in a bonding point can affect the quality of an entire integrated circuit. Therefore, a time–frequency analysis method was proposed to detect and identify fault bonds. First, the bonding voltage and current signals were acquired from the ultrasonic generator. Second, with Wigner–Ville distribution and empirical mode decomposition methods, the features of bonding electrical signals were extracted. Then, the principal component analysis method was further used for feature selection. Finally, an artificial neural network was built to recognize and detect the quality of ultrasonic wire bonding. The results showed that the average recognition accuracy of Wigner–Ville distribution and empirical mode decomposition was 78% and 93%, respectively. The recognition accuracy of empirical mode decomposition is obviously higher than that of the Wigner–Ville distribution method. In general, using the time–frequency analysis method to classify and identify the fault bonds improved the quality of the wire-bonding products.

scholarly journals Time-frequency analysis of ship wave patterns in shallow water: modelling and experiments

2018 ◽  
Vol 158 ◽  
pp. 123-131 ◽  
Author(s):  
Ravindra Pethiyagoda ◽  
Timothy J. Moroney ◽  
Gregor J. Macfarlane ◽  
Jonathan R. Binns ◽  
Scott W. McCue
Keyword(s):  
Shallow Water ◽  
Frequency Analysis ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Ship Wave ◽  
Wave Patterns

Time-Frequency Analysis of Partial Discharge Phenomena in SF6 gas using Wavelet Transform

1997 ◽  
Vol 117 (3) ◽  
pp. 338-345 ◽  
Author(s):  
Masatake Kawada ◽  
Masakazu Wada ◽  
Zen-Ichiro Kawasaki ◽  
Kenji Matsu-ura ◽  
Makoto Kawasaki
Keyword(s):  
Wavelet Transform ◽  
Frequency Analysis ◽  
Partial Discharge ◽  
Time Frequency Analysis ◽  
Time Frequency
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