Wavelet multiresolution analysis of high-frequency Asian FX rates, Summer 1997

2005 ◽  
Vol 14 (2) ◽  
pp. 211-246 ◽  
Author(s):  
Jeyanthi Karuppiah ◽  
Cornelis A. Los
Keyword(s):  
Multiresolution Analysis ◽  
High Frequency ◽  
Wavelet Multiresolution Analysis

Driver's Mental Stress Visualization by Discrete Wavelet Multiresolution Analysis.

2000 ◽  
Vol 20 (79) ◽  
pp. 356-364
Author(s):  
Taro SEKINE ◽  
Masahiro TAKEI ◽  
Michiharu OKANO ◽  
Hiroyasu NAGAE ◽  
Yoshifuru SAITO ◽  
...  
Keyword(s):  
Multiresolution Analysis ◽  
Mental Stress ◽  
Discrete Wavelet ◽  
Wavelet Multiresolution Analysis

Monitoring HVDC Systems Using Wavelet Multiresolution Analysis

2001 ◽  
Vol 21 (11) ◽  
pp. 54-55 ◽  
Author(s):  
A. M. Gouda ◽  
E. F. El-Saadany ◽  
M. M. A. Salama ◽  
V. K. Sood ◽  
A. Y. Chikhani
Keyword(s):  
Multiresolution Analysis ◽  
Wavelet Multiresolution Analysis

Nonlinear Aerodynamics Reduced-Order Model Based on Multi-Input Volterra Series

2013 ◽  
Vol 748 ◽  
pp. 421-426 ◽  
Author(s):  
Yi Bin Dou ◽  
Min Xu
Keyword(s):  
Multiresolution Analysis ◽  
Volterra Series ◽  
Coupling Effect ◽  
Harmonic Oscillation ◽  
Reduced Order Model ◽  
Least Square ◽  
Recursive Least Square ◽  
Order Model ◽  
Reduced Order ◽  
Wavelet Multiresolution Analysis

This paper gives a low-order approximations of multi-input Volterra series as a nonlinear reduced-order model (ROM) based on wavelet multiresolution analysis. The band-limited pseudorandom multilevel sequence (PRMS) is used as the identification signal and the QR decomposition recursive least square (QRD-RLS) method is utilized as identification method. At last, the ROM is applied to model the nonlinear aerodynamic moment of an airfoil undergoing simultaneous forced pitch and plunge harmonic oscillation. The results show that including the second-order Volterra cross kernels in ROM can capture the coupling effect which significantly improves accuracy in predicting nonlinear aerodynamics under simultaneous motion. And the wavelet multiresolution analysis efficiently reduces the number of identification coefficients for Volterra kernels.

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