introduction: one of the major problems in the development of nuclear medicine images is the presence of noise. the noise level in nuclear medicine images is usually reduced by the analysis of imaging data in a fourier transform environment. the main drawback of this environment belongs to low signal to noise ratio in high frequencies because removing noise frequencies may remove data and times...

A novel approach to chaotic time series prediction is proposed. It is based on the use of the Discrete Wavelet Transform for obtaining a proper decomposition of the original sequence and standard multilayer neural networks for performing the prediction of the individual components. Simulation results for the case of chaotic signals obtained by integrating the Lorenz equations are presented, and...

A new type of Wavelet Neural Network (WNN) has been proposed to enhance the function approximation and generalization performance generating an optimal size network. In the proposed WNN, the nonlinear activation function is a linear combination of wavelets that can be updated during the network training process. As a result the approximate error is significantly decreased. The RBF network based...

Image encoding decoding concept is studied by using the techniques of wavelet transformation, artificial neural network(ANN)and threshold value. The aforesaid techniques are useful in encoding and decoding. This process gives more ensured, a three leveled security to the images in three different terms. First is wavelet transformation. Second is encoding decoding keys along with weights of ANN ...

Since past few years, researchers have been concentrating on the classification of Electromyography Signal. This method is very useful in diagnosing the neuro-muscular disorders, which consists of wide spread diseases affecting peripheral nervous system. Progressive muscle weakness is the major form of these disorders. Out of various proposed methods, scholars are commonly focusing on Neural Ne...

In this paper, a classification method for respiratory sounds (RSs) in patients with asthma and in healthy subjects is presented. Wavelet transform is applied to a window containing 256 samples. Elements of the feature vectors are obtained from the wavelet coefficients. The best feature elements are selected by using dynamic programming. Grow and Learn (GAL) neural network is used for the class...

Electricity is of great vital and indispensable to national economies. A new short-term load forecasting for micro grid is proposed in this paper. After comparing and analyzing all load characteristic in the time domain and frequency domain, we apply wavelet transform to decompose the load signal. After that, the training set and text set are selected in consideration of the effects generated b...

in exploration seismology quality factor is widely used as a seismic attribute to identify anomalies related to attenuation, especially those caused by hydrocarbon. previous studies have indicated that seismic energy loss known as attenuation is greater for high frequency components of seismic data compared to the low frequency components. here the continuous wavelet transform is used to study ...