Wavelet domain nonlinear filtering for evoked potential signal enhancement
Computers and Biomedical Research
Filtering for a class of nonlinear discrete-time stochastic systems with state delays
Journal of Computational and Applied Mathematics
Expert Systems with Applications: An International Journal
Multivariate denoising using wavelets and principal component analysis
Computational Statistics & Data Analysis
New approach to information fusion steady-state Kalman filtering
Automatica (Journal of IFAC)
Singularity detection and processing with wavelets
IEEE Transactions on Information Theory - Part 2
De-noising by soft-thresholding
IEEE Transactions on Information Theory
Wavelet transform domain filters: a spatially selective noise filtration technique
IEEE Transactions on Image Processing
Hi-index | 12.05 |
In this paper, an effective noise reduction method is proposed to remove Gaussian noise embedded in chaotic signals. The proposed method has two major steps: an optimal choice of wavelet decomposition scales and an estimation of the wavelet coefficients; the former is determined by the noise residual ratio based on dual-wavelet, whereas the latter is analyzed combining with the singular spectrum analysis and the spatial correlation theory. The chaotic signals generated by Lorenz model as well as the observed monthly series of sunspots are, respectively, applied for simulation analysis, the experimental results show that the performance of the proposed method is superior to that of other methods.