The purpose of this study is to investigate the P300 event-related potentials (ERP) from background Electroencephalograph (EEG) of working memory performance by visual stimuli task. The proposed analysis for each channel at the prefrontal cortex is to compare the energy distribution via wavelet scalogram with the change of time and frequency of ERP signals. Wavelet energy distribution gives a l...

Our aim in this study was to assess postural control adaptation quantitatively in unsteady elderly patients at risk of falls in open spaces and given balance training with a virtual-reality system reproducing environmental stimulation. Using a balance rehabilitation unit based on a virtual-reality system that changes sensory information (visual, vestibular, and somatosensory), we treated 26 eld...

The importance of health indicators in the recent years has created challenges in resource allocation. Balanced and fair distribution of health resources is one of the main principles in achieving equity. The goal of this cross-sectional descriptive study, conducted in 2010, was to classify health structural indicators in the Fars province using the scalogram technique. Health structural indica...

The assumption of direction invariance, i.e., isotropy, is often made in the practical analysis of spatial point processes due to simpler interpretation and ease of analysis. However, this assumption is many times hard to find in real applications. Many homogeneous point processes are indeed anisotropic. This paper concerns the analysis and detection of spatial anisotropies in terms of detectio...

This work proposes a methodology to automate the recognition of Partial Discharges (PD) sources in Electrical Distribution Networks using Deep Neural Network (DNN) model called Convolutional Autoencoder (CAE), which is able automatically extract features from data classify different sources. The database used train constructed with real defects commonly found MV switchgear service, and it also ...

The purpose of this paper is a spectrum analysis signals various nature, construction the signal scalogram using Morlet wavelet, modification to obtain more informative graphic representation signal. Spectral constructed by means Fourier transform. A graphical result wavelet transform has been developed with help Mathematica system. For this, used as two-dimensional original scale introduced on...

Viral hashtags spread across a large population of Internet users very quickly. Previous studies use features mostly in an aggregate sense to predict the popularity of hashtags, for example, the total number of hyperlinks in early tweets adopting a tag. Since each tweet is time stamped, many aggregate features can be decomposed into fine-grained time series such as a series of numbers of hyperl...

Cardiovascular diseases (CVDs) are the most leading causes of death every year in world. The threat CVDs can be decreased and controlled with early diagnoses. Therefore, interpreting heart sounds is considered as one common ways to diagnose cardiovascular system. Heart sound signal known phonocardiogram (PCG) provides useful information about condition, which used diagnostic, helps physicians d...

In this paper we propose to apply wavelet theory in forecasting economic time series. The method consists in decomposing the series into its long-term trend and its seasonal component according to the shape of the scalogram of the discrete wavelet transform of the series. Each component is then extended to provide a forecast of the total series. The method is applied to the data set of monthly ...