Empirical mode decomposition (EMD) is one of the most efficient methods used for nonparametric signal denoising. In this study wavelet thresholding principle is used in the decomposition modes resulting from applying EMD to a signal. The principles of hard and soft wavelet thresholding including translation invariant denoising were appropriately modified to develop denoising methods suited for ...

The paper presents a new digital image hiding algorithm based on wavelet packets transform and singular value decomposition. The low-frequency sub-band of wavelet packets transform has strong antijamming capacity and the singular value has very strong stability. The presented algorithm implements bit plane decomposition on the secret image and wavelet packet decomposition on the carrier image. ...

In this study, we develop a new automated pattern recognition system for interpretation of heart sound based on wavelet decomposition of signals and classification using neural network. Inputs to the system are the heart sound signals acquired by a stethoscope in a noiseless environment. We generate features for the objective concise representation of heart sound signals by means of wavelet dec...

In the threshold de-noising method based on wavelet transform, not only the threshold and threshold function, but also the decomposition level is an important factor in practical application. Signals under different noise levels correspond with different optimal decomposition levels. A method to determine the optimal decomposition level based on the white noise verification of wavelet detail co...

In this paper we focus on image segmentation by proposing a new algorithm based on Haar wavelet decomposition and Kmeans algorithm. When Haar wavelet decomposition is applied to an image it gives an idea about high frequency components. If higher levels of decomposition are performed, different texture region information can be captured. The paper deals with the texture segmentation of an image...

Cloud-related shadows represent areas with low illumination conditions that affect remote sensing image quality. In this research, a wavelet-based image sharpening algorithm was developed to enhance shadow areas independently using the defected cloudy image information. The developed algorithm is applied locally by boosting the image high frequency content in the shadow areas using the defected...

A wavelet-based forecasting method for time series is introduced. It is based on a multiple resolution decomposition of the signal, using the redundant “à trous” wavelet transform which has the advantage of being shift-invariant. The result is a decomposition of the signal into a range of frequency scales. The prediction is based on a small number of coefficients on each of these scales. In its...

A plenty of research about thresholding methods in wavelet domain has been proposed by many authors. Multiwavelet domain can be decomposed as scalar wavelets and differently according to the structure of scaling functions. When scaling functions are symmetric-antisymmetric, the antisymmetric part is low pass filter in mathematical formula. But in practice it works as high pass filter because of...

A novel approach for classifying different types of faults occurring in power transmission lines is proposed by considering wavelet transform, singular value decomposition and Fuzzy Reasoning Spiking Neural P Systems (FRSNPS). In this approach, singular value decomposition in wavelet domain is used to extract features of fault current components recorded from power transmission lines; FRSNPS is...