scholarly journals SOLUTIONS OF INVERSE PROBLEM OF GRAVIMETRY ON THE SPHERE USING NATIVE WAVELET TRANSFORM

2014 ◽  
Author(s):  
Natalia Matveeva
Keyword(s):  
Inverse Problem ◽  
Wavelet Transform

scholarly journals The technique of recovery of causative sources in case of strong interference of gravity fields using the continuous wavelet transform

2021 ◽  
pp. 84-89
Author(s):  
E. Utemov ◽  
◽  
D. Nurgaliev ◽  
Keyword(s):  
Inverse Problem ◽  
Wavelet Transform ◽  
Basis Functions ◽  
Wavelet Basis ◽  
Continuous Wavelet ◽  
Direct And Inverse Problems ◽  
Strong Interference ◽  
Gravity Fields ◽  
Close Relationship ◽  
Gravimetric Data

The technique of processing gravimetric data is offered in this study. Offered technique based on wavelet transform with so-called «native» wavelet basis functions. Distinctive feature of the technique is a close relationship with both direct and inverse problems of gravimetry. It was shown that the peculiarity allows to quite simply and quickly location of causative sources even under of strong interference of gravity fields. Keywords: gravimetry; wavelet transform; anomaly; inverse problem.

AN INVERSE PROBLEM IN THE DYNAMICAL REATOR THEORY

1982 ◽  
Vol 2 (1) ◽  
pp. 9-16 ◽  
Author(s):  
Dexing Feng ◽  
Guangtian Zhu
Keyword(s):  
Inverse Problem

Classification of Acceleration Waveforms during Walking by Wavelet Transform

1997 ◽  
Vol 36 (04/05) ◽  
pp. 356-359 ◽  
Author(s):  
M. Sekine ◽  
M. Ogawa ◽  
T. Togawa ◽  
Y. Fukui ◽  
T. Tamura
Keyword(s):  
Wavelet Transform ◽  
Wavelet Analysis ◽  
Center Of Gravity ◽  
Dynamic Responses ◽  
Sequential Data ◽  
Acceleration Signal ◽  
Continuous Dynamic

Abstract:In this study we have attempted to classify the acceleration signal, while walking both at horizontal level, and upstairs and downstairs, using wavelet analysis. The acceleration signal close to the body’s center of gravity was measured while the subjects walked in a corridor and up and down a stairway. The data for four steps were analyzed and the Daubecies 3 wavelet transform was applied to the sequential data. The variables to be discriminated were the waveforms related to levels -4 and -5. The sum of the square values at each step was compared at levels -4 and -5. Downstairs walking could be discriminated from other types of walking, showing the largest value for level -5. Walking at horizontal level was compared with upstairs walking for level -4. It was possible to discriminate the continuous dynamic responses to walking by the wavelet transform.

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