The electroencephalograph (EEG) signal is one of the most widely signal used in the bioinformatics field due to its rich information about human tasks. In this work EEG waves classification is achieved using the Discrete Wavelet Transform DWT with Fast Fourier Transform (FFT) by adopting the normalized EEG data. The DWT is used as a classifier of the EEG wave’s frequencies, while FFT is impleme...

Image compression is very important for efficient transmission and storage of images .Images contain large amounts of information that requires much storage space, large transmission bandwidths and long transmission times. Therefore it is advantageous to compress the image by storing only the essential information needed to reconstruct the image. Image compression is a widely addressed research...

Electrocardiogram is used to measure the rate and regularity of heartbeats to detect any irregularity to the heart. An ECG translates the heart electrical activity into wave-line on paper or screen. For the feature extraction and classification task we will be using discrete wavelet transform (DWT) as wavelet transform is a two-dimensional timescale processing method, so it is suitable for the ...

In this paper, a new face recognition system based on Haar wavelet transform (HWT) and Principal Component Analysis (PCA) using Levenberg-Marquardt backpropagation (LMBP) neural network is presented. The image face is preprocessed and detected. The Haar wavelet is used to form the coefficient matrix for the detected face. The image feature vector is obtained by computing PCA for the coefficient...

In recent years, access to multimedia data has become much easier due to rapid growth of the internet. While this is usually considered an improvement of everyday life, it also makes unauthorized copying and distributing of multimedia data much easier, therefore presenting a field of watermarking.Many literatures have reported about Discrete Wavelet Transform (DWT) based watermarking for data s...

Abstract— This paper proposes an image compression scheme using a personalized storage Discrete Wavelet Transform (DWT). In image compression schemes based on DWT, the module that generates these wavelet coefficients is sequentially attached to some encoding bitplanes. As the level of DWT decomposition increases the quantity of bits required to represent the wavelet coefficients is increased. A...

This paper describes the evolution of new wavelet and scaling numbers for optimized transforms that consistently outperform the 9/7 discrete wavelet transform (DWT) for fingerprint compression and reconstruction. Key-Words: compression, reconstruction, wavelets, genetic algorithms, fingerprints

There are generally two algorithms to traverse an image to implement the 2D Discrete Wavelet Transform (DWT), namely Row-Column Wavelet Transform (RCWT) and Line-Based Wavelet Transform (LBWT). In the RCWT algorithm, the 2D DWT is divided into two 1D DWT: horizontal and vertical filtering. The horizontal filtering processes the rows of the original image and stores the wavelet coefficients in a...

Soft thresholding has been a standard wavelet de-noising procedure in many signal and image processing applications. Theoretically, it is also almost optimal in the sense of nearly achieving the minimax mean-squared error. Inspired by this property, this paper proposes the addition of coefficient de-noising before soft thresholding. This extra step serves to reduce noise in the empirical wavele...

This study investigates the usefulness of wavelet transforms in phoneme recognition. Both discrete wavelet transforms (DWT) and sampled continuous wavelet transforms (SCWT) are tested. The wavelet transform is used as a part of the front-end processor which extracts feature vectors for a speakerindependent HMM-based phoneme recognizer. The results are evaluated on a portion of TIMIT corpus cons...