This study compares the proposed hybrid method (wavelet robust M-estimation) to traditional ordinary least square) when there are de-noising or outlier problems for estimating multiple linear regression models using statistical criterion root mean square error (RMSE). According simulated and real data, is better than classical (Wavelet Ordinary Least Square) more accurate. The of less Wavelet S...

Introduction: An efficient method of tomographic imaging in nuclear medicine is positron emission tomography (PET). Compared to SPECT, PET has the advantages of higher levels of sensitivity, spatial resolution and more accurate quantification. However, high noise levels in the image limit its diagnostic utility. Noise removal in nuclear medicine is traditionally based on Fourier decomposition o...

De-noising algorithms based on wavelet thresholding replace small wavelet coefficients by zero and keep or shrink the coefficients 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 does not require an estimation for the no...

Recently the non local (NL) means algorithm was introduced in order to suppress additive Gaussian noise in images. Although its very good performances for Gaussian noise, the NL-means method has two disadvantages. Its computational complexity is very large and the impulse rejection is poor. In this paper, we propose a simplified version of the NL-means algorithm with a much smaller computationa...

The main purpose of this paper was to introduce an efficient algorithm for fault identification in fruits images. First, input image was de-noised using the combination of Block Matching and 3D filtering (BM3D) and Principle Component Analysis (PCA) model. Afterward, in order to reduce the size of images and increase the execution speed, refined Discrete Cosine Transform (DCT) algorithm was uti...

The use of electroencephalograms (EEGs) to diagnose and analyses Alzheimer's disease (AD) has received much attention in recent years. The sample entropy (SE) has been widely applied to the diagnosis of AD. In our study, nine EEGs from 21 scalp electrodes in 3 AD patients and 9 EEGs from 3 age-matched controls are recorded. The calculations show that the kurtoses of the AD patients' EEG are pos...

In this paper we propose a new approach for speech enhancement. The method used to remove the noise components is a combination of two methods: Wavelet de-noising and spectral subtraction. The idea is to apply the spectral subtraction to wavelet approximations and details coefficients. A new parameter for spectral subtraction in unvoiced speech frames is introduced and the existing power factor...

Derivation of new features of observed variables has two important goals: reduction of dimensionality and de-noising. A desired property of the derived new features is their meaningful interpretation. The SCoTLASS method (Jolliffe, Trendafilov and Uddin, 2003) offers such possibility. We explore the properties of the SCoTLASS method applied to the yeast genes data investigated in (Bartkowiak et...

accuracy of inertial navigation system (ins) is limited by inertial sensors imperfections. before using inertial sensors signals in the data fusion algorithm, noise removal method should be performed, in which, wavelet decomposition method is used. in this method the raw data is decomposed into high and low frequency data sets. in this study, wavelet multi-level resolution analysis (wmra) techn...

In this study, a new algorithm is proposed—Asynchronous Averaging and Filtering (AAF) for ECG signal de-noising. R-peaks are detected with another proposed algorithm—Minimum Slot and Maximum Point selecting method (MSMP). AAF algorithm reduces random noise (major component of EMG noise) from ECG signal and provides comparatively good results for baseline wander noise cancellation. Signal to noi...