The Phase-Locked Loop is used to track an incoming signal and provide accurate carrier phase measurements on GPS receivers. However, the PLL performance is affected by the thermal noise and dynamic stress. In order to resolve the conflict between reducing PLL noise and overcoming the dynamic stress, some compromises must be taken in PLL design. This paper proposes a wavelet packet de-noising te...

Electrocardiogram (ECG) is a non-invasive tool that monitors the electrical activity of the heart. An ECG signal is highly prone to the disturbances such as noise contamination, artifacts and other signals interference. So, an ECG signal has to be de-noised so that the distortions can be eliminated from the original signal for the perfect diagnosing of the condition and performance of the heart...

Recently, kernel Principal Component Analysis is becoming a popular technique for feature extraction. It enables us to extract nonlinear features and therefore performs as a powerful preprocessing step for classification. There is one drawback, however, that extracted feature components are sensitive to outliers contained in data. This is a characteristic common to all PCA-based techniques. In ...

Wavelet Shrinkage de-noi si ng i s appl i ed to El ect rophoret i c Nucl ear Magnet i c Resonance (ENMR) data. Both threshol d rul es for removi ng noi se, namel y sof t and hard, proposed i n Donoho's Vi suShri nk are used simul t aneousl y. Sof t t hreshol di ng i s appl i ed to ne l evel s of wavel et decomposi t i on coe ci ent s and hard threshol di ng to coarse l evel s. Thi s impl ementa...

This paper introduces the Laplace algorithm for de-noising in the cepstrum domain with applications to speech recognition. Our method uses Gaussian mixture priors for clean speech and noise cepstra and assumes that speech and noise mix linearly in the spectrum domain. The Laplace algorithm involves two steps (a) computing the posterior mode of the observed noisy cepstra and (b) Gaussian approxi...

An image is considered as a collection of information stored as intensities and the occurrence of noises. The occurrence of noise present in the image causes degradation in the quality of the image. The basic idea behind image processing is how we estimate the correct pixel values. Image De-noising is one of the fundamental problems which is faced in image processing and computer vision .There ...

The major problem that wireless communication systems undergo is multipath fading caused by scattering of the transmitted signal. However, we can treat multipath propagation as multiple channels between the transmitter and receiver to improve the signal-to-scattering-noise ratio. While using Single Input Multiple Output (SIMO) systems, the diversity receivers extract multiple signal branches or...

In order to preserve the integrity of edge and detail information in the underwater image, a NSCT de-noising method based on Non-local means with modified parameter is proposed. Since NSCT has the feature of translation invariance, it is used to decompose the underwater image in multi-scale and multi-direction. For the noise and detail information are normally distributed in the high frequency ...

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 ...

Recent research has shown that deep neural network is very powerful for object recognition task. However, training the deep neural network with more than two hidden layers is not easy even now because of regularization problem. To overcome such a regularization problem, some techniques like dropout and de-noising were developed. The philosophy behind de-noising is to extract more robust feature...