This paper proposes time-frequency analysis of EEG spectrum and wavelet analysis in EEG de-noising. In this paper, the basic idea is to use the characteristics of multi-scale multi-resolution, using four different thresholds to wipe off interference and noise after decomposition of the EEG signals. By analyzing the results, understanding the effects of four different methods, it comes to a conc...

An approach based on the singular spectrum decomposition (SSD)-singular value (SVD) and frequency weight energy operator (FWEO) was proposed for early fault diagnosis of rolling bearings. Since interference heavy noise in stage bearing fault, SSD could eliminate abundant noise, meanwhile adaptively decompose nonlinear, non-stationary signals into multiple mono-components which had distinct phys...

The electromagnetic acoustic transducer (EMAT) is a powerful and useful non-destructive testing technology for structural health monitoring. However, EMAT has an issue of low efficiency in conversion its signal easily affected by noise, which make it difficult to accurately identify evaluate defects. Thereby, de-noising preprocessing essential the evaluation In this paper, we proposed improved ...

We present a novel signal de-noising algorithm for recovery of signals corrupted by a high levels of noise and applicable in the situations of low sampling rates. This method uses a modi cation of kernel partial least squares regression (KPLS) de ned in reproducing kernel Hilbert space [1]. We treat signal de-noising as a regression problem, in which de-noising consists of estimation of functio...

There are various de-noising algorithms and optimization methods for different signal and noise characteristics. However, the signals used in real application may have deviations from the model. For example: signal and/or noise may not be stationary or a proper model for them may not be available. MCG (magnetocardiography) is an example signal, where conventional de-noising methods are not givi...

The purpose of the ct-image de-noising is an important research topic both in image processing and biomedical engineering. Independent component analysis (ica) is a statistical technique where the goal is to represent a set of random variables as a linear transformation of statistically independent component variables. The curve let transform as a multiscale transform has directional parameters...

We construct an estimation and de-noising procedure for an input signal perturbed by a continuous-time Gaussian noise, using the local and occupation times of Gaussian processes. The method relies on the almost-sure minimization of a Stein Unbiased Risk Estimator (SURE) obtained through integration by parts on Gaussian space, and applied to shrinkage estimators which are constructed by soft and...

Soft thresholding has been a standard wavelet de-noising procedure in many signal and image processing applications. Theoretically, it is also almost optimal in the sense of nearly achieving the minimax mean-squared error. Inspired by this property, this paper proposes the addition of coefficient de-noising before soft thresholding. This extra step serves to reduce noise in the empirical wavele...

A new de-noising and compression method for ECG signals has been developed based on the wavelet transform. It has been designed for mobile telecardiology scenarios, where reliability as well as spectral efficiency are essential. The signal is segmented into beats and a beat template is subtracted to them. Beat templates as well as residual signals are coded with a wavelet expansion. De-noising ...