In this paper we proposed a technique to remove eye blink artifact from electroencephalogram (EEG) using lifting wavelet transform (LWT). The LWT has been successfully used in eye blink artifact suppression form the recorded electroencephalography (EEG) signals using a data-adaptive subband filtering approach. The LWT is applied to decompose EEG signal into a finite set of subbands. The energy ...

De-noising of speech signal polluted by background noise, which is of great practical significance for effective transmission and accurate recognition of sound, is important. Local characteristic-scale decomposition is introduced, and it can divide the speech into lowand high-frequency parts without a loss in useful speech. The algorithm accelerates the speed of convergence, as well as pretreat...

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...

I introduce a unified approach for de-noising and interpolation of seismic data in the frequency-wavenumber (f-k) domain. First an angular search in the f-k domain is carried out to identify a sparse number of dominant dips, not only using low frequencies but over the whole frequency range. Then, an angular mask function is designed based on the identified dominant dips. The mask function is ut...

EEMD Algorithm is usually applied in noise reduction of rolling bearing signal because of its powerful ability in de-noising. But misjudgment in selecting sensitive IMF exists, it results in the incomplete processing of noise reduction. In order to solve this problem, this paper proposes an improved EEMD algorithm. This algorithm adopts Cloud Similarity Measurement in selecting the sensitive in...

There exists a number of cepstral de-noising algorithms which perform quite well when trained and tested under similar acoustic environments, but degrade quickly under mismatched conditions. We present two key results that make these algorithms practical in real noise environments, with the ability to adapt to different acoustic environments over time. First, we show that it is possible to leve...

In image processing, image is corrupted by different type of noises. But generally medical image is corrupted by speckle noise. So image de-noising has become a very essential exercise all through the diagnose. Noises are of two type additive and multiplicative noise. Speckle noise is multiplicative noise, so it’s difficult to remove the multiplicative noise as compared to additive noise. The t...

In allusion to the losses of image detail and texture structure information during image de-noising process, an image de-noising algorithm based on non-related dictionary learning is proposed in this paper. Firstly, this algorithm is adopted to obtain selfadaption redundant dictionary for the noisy image through the dictionary learning algorithm; secondly, HOG features and gray-level statistica...

432 www.scientistlink.com Abstract: A physiological signal monitoring system and alerting system using wireless technology is presented. The two types of physiological signal monitoring are captured from the body through leads and using the radio-frequency transmitting and receiving module the data are interfaced to computer systems. Furthering using a developed user interface module the captur...

Abstract–Planetary gearbox is widely used in many fields due to its robustness and high power-weight ratio, but implementation of fault diagnosis on it is challenging. This paper proposes a new fault diagnosis method for planetary gearbox based on empirical mode decomposition (EMD) and adaptive multi-scale morphological gradient filter (AMMGF). The proposed method has two dominant strengths: it...