Awareness of the level of river flow and its fluctuations at different times is one of the significant factor to achieve sustainable development for water resource issues. Therefore, the present study two hybrid models, Wavelet- Adaptive Neural Fuzzy Interference System (WANFIS) and Wavelet- Artificial Neural Network (WANN) are used for flow prediction of Gamasyab River (Nahavand, Hamedan, Iran...

De-noising is a substantial issue in hydrologic time series analysis, but it is a difficult task due to the defect of methods. In this paper an energy-based wavelet de-noising method was proposed. It is to remove noise by comparing energy distribution of series with the background energy distribution, which is established from Monte-Carlo test. Differing from wavelet threshold de-noising (WTD) ...

An adaptive wavelet method for the chemical master equation is constructed. The method is based on the representation of the solution in a sparse Haar wavelet basis, the time integration by Rothe’s method, and an iterative procedure which in each time-step selects those degrees of freedom which are essential for propagating the solution. The accuracy and efficiency of the approach is discussed,...

In this paper, an application to the approximation by wavelets has been obtained by using matrix-Cesàro (Λ · C1) method of Jacobi polynomials. The rapid rate of convergence of matrix-Cesàro method of Jacobi polynomials are estimated. The result of Theorem (6.1) of this research paper is applicable for avoiding the Gibbs phenomenon in intermediate levels of wavelet approximations. There are majo...

The shift-variant property of the discrete wavelet transform (DWT) makes the motion estimation and compensation ine$cient in the wavelet domain. In order to overcome the shift-variant property of the DWT, a low-band-shift (LBS) method has been developed. Using the LBS method in the wavelet domain, two motion estimation and compensation schemes are developed and evaluated. One scheme is the moti...

this paper presents a new multi-sensor data fusion method based on the combination of wavelettransform (wt) and extended kalman filter (ekf). input data are first filtered by a wavelettransform via daubechies wavelet “db4” functions and the filtered data are then fused based onvariance weights in terms of minimum mean square error. the fused data are finally treated byextended kalman filter for...

A wavelet is a basis function used to construct a wavelet transform. The first known wavelet – Haar wavelet was proposed in 1909 by Alfred Haar. Wavelet theory has been applied to various problems including signal processing in communications, image compression-extraction, solution of the linear and nonlinear integral equations etc. Haar wavelet based discretization technique is adopted for sol...

This study proposes and justifies a Bayesian approach to modeling wavelet coefficients and finding statistically significant features in wavelet power spectra. The approach utilizes ideas elaborated in scale-space smoothing methods and wavelet data analysis. We treat each scale of the discrete wavelet decomposition as a sequence of independent random variables and then apply Bayes’ rule for con...