In this paper a signal denoising scheme based a multiresolution approach referred to as Empirical mode decomposition (EMD) [1] is presented. The denoising method is a fully data driven approach. Noisy signal is decomposed adaptively into intrinsic oscillatory components called Intrinsic mode functions (IMFs) using a decomposition algorithm algorithm called sifting process. The basic principle o...

The surface Electromyography (sEMG) signal is affected by different sources of noises: current technology is considerably robust to the interferences of the power line or cable motion artifacts, but still there are many limitations in denoising the baseline. In this paper we introduce a new technique, named Baseline Adaptive Denoising Algorithm (BADA), for denoising the sEMG signal by wavelet t...

We present a general wavelet-based denoising scheme for functional neuroimages and compare it to Gaussian smoothing, the standard method in functional neuroimaging. We adapted WaveLab thresholding routines to 2D data, and tested their effect on the signal-to-noise ratio of noisy images. In a simulated time series test, we also investigated the shapes of detected activations after denoising. key...

With progress in radar systems, a number of methods have been developed for signal processing and detection in radars. A number of modern radar signal processing methods use time-frequency transforms, especially the wavelet transform (WT) which is a well-known linear transform. The interference canceling is one of the most important applications of the wavelet transform. In Ad-hoc detection met...

In order to overcome the discontinuance of the hard thresholding function and the defect of seriously slashing singularity in the soft thresholding function, improve the denoising effect and detect the transformer partial discharge signal more accurately, in this paper an improved wavelet threshold denoising method is put forward through analyzing the interference noise of transformer partial d...

This papers gives an overview of wavelet transforms application to signal denoising problem. Wavelets perform well for this application and have been used before with great success. To further improve the transform efficiency adaptivity is introduced to traditional wavelet transforms. The lifting framework as a method to construct wavelets facilitates the effort to develop efficient adaptation ...