The features of dynamic, noise and instability, make the network traffic eruptive and unstable, and this obstructs the network traffic prediction. In order to figure out its characteristics and developing tendency accurately, the paper proposes a wavelet-transform-based prediction algorithm: Firstly, with the multi-resolution analysis of wavelet transform, the network traffic, which is difficul...

Nowadays Epileptic disorder is a most challenge aspects in brain activation. Electroencephalograph (EEG) is one of the popular procedures to understand the human brain condition. The activation of brain will be changed due to the symptoms of neurological disorder. We have been proposed a procedure to find epilepsy disorder, using discrete wavelet transform and neural network classifier. The EEG...

Compressive Sensing(CS) theory can reconstruct the original images from the less measurements with using the priors of the image sparse representation. The CS theory is applied into the video super-resolution(SR) reconstruction, and a new algorithm based on wavelet transform is proposed in this paper. Firstly, wavelet transform is used to decompose the low resolution image so as to get the low ...

In this study, an image denoising algorithm is presented, which takes into account wavelet thresholding and bilateral filtering in transform domain. The proposed algorithm gives an extension of the bilateral filter i.e., multiresolution bilateral filter, in which bilateral filtering is applied to the approximation sub bands and normal shrink is used for thresholding the wavelet coefficients of ...

This article presents a hybrid wavelet-based algorithm to suppress the ocular artifacts from electroencephalography (EEG) signals. The wavelet transform (HWT) method is designed by combination of discrete decomposition and packet transform. artifact suppression performed selection sub-bands obtained HWT. Fractional Gaussian noise (fGn) used as reference signal select containing artifacts. multi...

Feature extraction is a collection of the necessary detailed information from given source, which holds for further analysis. The quality fused image depends on many parameters, particularly its directional selectivity and shift-invariance. On other hand, traditional wavelet-based transforms produce ringing distortions artifacts due to poor directionality shift invariance. Dual-Tree Complex Wav...

Introduction: Temporal lobe epilepsy (TLE) is the most common and drug resistant epilepsy in adults. Due to behavioral, morphologic and electrographic similarities, pilocarpine model of epilepsy best resembles TLE. This study was aimed at determination of the changes in electroencephalogram (EEG) sub-bands amplitude during focal seizures in the pilocarpine model of epilepsy. Analysis of thes...

Wavelet thresholding is a signal estimation technique that exploits the capabilities of wavelet transform for signal denoising. But the choice of thresholding function has restricted there wide spread use in image denoising application. In this paper we proposed a computationally more efficient thresholding scheme by incorporating the neighbouring wavelet coefficients, with different threshold ...

Abstract—A multi-scale texture segmentation algorithm for SAR images based on the discrete wavelet transform is presented. Responses from different sub-bands are used to form a feature vector for each pixel position that is the input to the classification scheme. To further improve the classification results, the tree-structured wavelet packet transform is used to automatically identify a suita...

In this paper, a new efficient feature extraction method based on the adaptive threshold of wavelet package coefficients is presented. This paper especially deals with the assessment of autonomic nervous system using the background variation of the signal Heart Rate Variability HRV extracted from the wavelet package coefficients. The application of a wavelet package transform allows us to obtai...