ORTHOGONAL WAVELET TRANSFORM OF SIGNAL BASED ON COMPLEX B-SPLINE BASES

2012 ◽  
Vol 10 (06) ◽  
pp. 1250054 ◽  
Author(s):  
XIU-GE ZHU ◽  
BAO-BIN LI ◽  
DENG-FENG LI
Keyword(s):  
Wavelet Transform ◽  
Haar Wavelet ◽  
Orthogonal Wavelet ◽  
Explicit Formulas ◽  
Daubechies Wavelets ◽  
B Spline ◽  
Continuous Signal ◽  
Accurate Computation ◽  
Base Functions ◽  
Orthogonal Wavelet Transform

In this paper, an orthogonal wavelet transform of signal based on complex B-spline bases is given. The new wavelet transform realizes accurate computation of coefficients of complex B-spline base functions. It integrates good properties of orthogonality, symmetry and continuity, and offers better approximations to continuous signal than do the Haar wavelet and Daubechies wavelets. All algorithms of the new orthogonal wavelet transform are based on explicit formulas and easy to be implemented.

scholarly journals SNR Improvement for Evoked Potential Estimation using Bi – Orthogonal Wavelet Transform

IJARCCE ◽  
2015 ◽  
Vol 4 (8) ◽  
pp. 408-413
Author(s):  
Shailesh M L ◽  
Dr. Anand Jatti ◽  
Madhushree K S ◽  
Siddesh M B
Keyword(s):  
Wavelet Transform ◽  
Evoked Potential ◽  
Orthogonal Wavelet ◽  
Snr Improvement ◽  
Evoked Potential Estimation ◽  
Orthogonal Wavelet Transform

Orthogonal Wavelet Transform Blind Equalization Algorithm Based on the Optimization of Artificial Fish Swarm Algorithm

2012 ◽  
Vol 198-199 ◽  
pp. 1399-1402
Author(s):  
Wei Huang ◽  
Ye Cai Guo
Keyword(s):  
Wavelet Transform ◽  
Convergence Rate ◽  
Blind Equalization ◽  
Weight Vector ◽  
Orthogonal Wavelet ◽  
Artificial Fish Swarm Algorithm ◽  
Equalization Algorithm ◽  
Artificial Fish Swarm ◽  
Swarm Algorithm ◽  
Orthogonal Wavelet Transform

According to disadvantages of big steady-state error, low convergence rate, and local convergence of traditional Constant Modulus blind equalization Algorithm (CMA), an orthogonal Wavelet Transform blind equalization Algorithm based on the optimization of Artificial Fish Swarm Algorithm(AFSA-WT-CMA) is proposed. In this proposed algorithm, the weight vector of the blind equalizer is regarded as artificial fish, the equalizer weight vector can be optimized via making full use of global search and information sharing mechanism of artificial fish school algorithm, the de-correlation ability of normalizing orthogonal wavelet transform. The computer simulations in underwater acoustic channels indicate that the proposed algorithm outperforms CMA and WT-CMA in convergence rate and mean square error.

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