Electricity price forecasting has become an integral part of power system operation and control. In this paper, a wavelet transform (WT) based neural network (NN) model to forecast price profile in a deregulated electricity market has been presented. The historical price data has been decomposed into wavelet domain constitutive sub series using WT and then combined with the other time domain va...

The requirements for large amounts of medical imaging data that have to be stored eeciently and transmitted at low bit rates, has generated considerable interest in the design of eeective compression algorithms. The main challenge in medical image compression is to achieve high compression ratios while preserving important diagnostic information. The current Joint Photographic Experts' Group (J...

Recently, several wavelet transform based image coding techniques are reported in literature. Though these schemes yield very low bit rates, they suffer from the drawback of computational complexity. There is always a need for simple, fast and easy to implement image coding techniques that yield good subjective quality at low bitrates. To obtain good data compression, the wavelet transform coef...

This study presents a novel approach by using two-dimension Wavelet Transform (2-D WT) to localize the open and short boundary defect candidates on Ball Grid Array (BGA) substrate conduct paths. Once the potential defects are addressed, traditional inspection algorithms can be implemented for further analysis on these defect candidates. Therefore, the defect-detecting scope and inspection effor...

This paper presents a statistical learning algorithm based on Support Vector Machines (SVMs) for the classification of Malayalam Consonant – Vowel (CV) speech unit in noisy environments. We extend SVM for multiclass classification using Decision Directed Acyclic Graph Support Vector Machine (DDAGSVM) algorithm. For classification, acoustical features are extracted using Wavelet Transform (WT) b...

The wavelet transform (WT) has been developed over 20 years and successfully applied in defect detection on plain (unpatterned) fabric. This paper is on the use of the wavelet transform to develop an automated visual inspection method for defect detection on patterned fabric. A method called direct thresholding (DT) based on WT detailed subimages has been developed. The golden image subtraction...

introduction: wavelet transform is one of the useful and suitable tools for time series and signal analysis. nowadays wavelet transform is frequently used in geophysical data processing and interpretation, especially seismic data. however, the use of this method isn’t widespread in gravity and geomagnetic. fedi and quarta (1998), martelet et al. (2001) and de oliveira lyrio (2004) used the wave...

The Electroencephalograph (EEG) signals is one of the most widely used in the bioinformatics field due to its rich information about human tasks. The Electroencephalogram is a neuronal activity that represents the electrical activity of the brain. The uses of EEG signals in the field of Brain computer Interface (BCI) have obtained a lot of interest with diverse applications ranging from medicin...

The objective of this paper is to develop a novel wind speed forecasting technique, which produces more accurate prediction. The Wavelet Transform (WT) along with the Auto Regressive Moving Average (ARMA) is chosen to form a hybrid whose combination is expected to give minimum Mean Absolute Prediction Error (MAPE). A simulation study has been conducted by comparing the forecasting results using...