We have developed a method focusing on ECG signal de-noising using Independent component analysis (ICA). This approach combines JADE source separation and binary decision tree for identification and subsequent ECG noise removal. In order to to test the efficiency of this method comparison to standard filtering a wavelet- based de-noising method was used. Freely data available at Physionet medic...

This paper investigates an improved noise reduction method and its application on gearbox vibration signal de-noising. A hybrid de-noising algorithm based on local mean decomposition (LMD), sample entropy (SE), and time-frequency peak filtering (TFPF) is proposed. TFPF is a classical filter method in the time-frequency domain. However, there is a contradiction in TFPF, i.e., a good preservation...

With the increasing demands of precise positioning in weak signal environment, high sensitive GNSS receiver research and development has been pushed badly in need. Conventional GNSS signal acquisition techniques are considered inadequate when the incoming signal is too weak. In this paper we have mainly consider wavelet denoising algorithm applying in weak GNSS signal acquisition. Conventional ...

An interactive mathematical methodology for time series prediction that integrates wavelet de-noising and decomposition with an Artificial Neural Network (ANN) method is put forward here. In this methodology, the underlying time series is initially decomposed into trend and noise components by a wavelet de-noising method. Both trend and noise components are then further decomposed by a wavelet ...

De-noising algorithms based on wavelet thresholding replace small wavelet coeecients by zero and keep or shrink the coeecients with absolute value above the threshold. The optimal threshold minimizes the error of the result as compared to the unknown, exact data. To estimate this optimal threshold, we use Generalized Cross Validation. This procedure is fast and does not require an estimation fo...

Patch-based de-noising algorithms and patch manifold smoothing have emerged as efficient de-noising methods. This paper provides a new insight on these methods, such as the Non Local Means [1] or the image graph de-noising [8], by showing its use for filtering a selected pattern. K ̄ eywords: NL-Means, diffusion processes, diffusion geometry, graph filtering, patch manifold.

The amplitude of nuclear magnetic resonance (NMR) longing signal general is very small while the signal to noise ratio (SNR) is also very low, so de-noising NMR signal before T2 spectrum inversion is necessary and important. In this paper, an improved de-noising algorithm based on wavelet transform is put forward. The main idea of this improved algorithm is that NMR signal is divided to several...

Image Preprocessing is a vital step in the field of image processing for biometric pattern recognition. This paper studies and reviews various classical and modern fingerprint image de-noising models. The various model used for de-noising ranges widely from transform matrix using frequency, histogram model denoising, de-noising by introducing Gabor filter and its types to enhance fingerprint im...

Over the past decade wavelet transforms have received a lot of attention from researchers in many diierent areas. Both discrete and continuous wavelet transforms have shown great promises in such diverse elds as image compression, image De-noising, signal processing, computer graphics, and pattern recognition, to name a few. Most of the work has been done on scalar wavelets, i.e., wavelets gene...

In digital image different kinds of noises exist in an image and a variety of noise reduction techniques are available to perform de-noising. Selection of the de-noising algorithm depends on the types of noise. Gaussian noise, speckle noise, salt & pepper noise, shot noise are types of noises that are present in an image. The principle approach of image de-noising is filtering. Available filter...