Discrete nonorthogonal wavelet transforms play an important role in signal processing by offering finer resolution in time and scale than their orthogonal counterparts. The standard inversion procedure for such transforms is a finite expansion in terms of the analyzing wavelet. While this approximation works quite well for many signals, it fails to achieve good accuracy or requires an excessive...

Wavelet transforms and other multi-scale analysis functions have been used for compact signal and image representations in de-noising, compression and feature detection processing problems for about twenty years. Numerous research works have proven that space-frequency and spacescale expansions with this family of analysis functions provided a very efficient framework for signal or image data. ...

BACKGROUND One of the most important limitations of standard pulse oximeters is the inability to detect changes in respiratory rate until oxygenation is affected. This study sought to determine if analysis of the plethysmogram by wavelet transforms would enable the determination of changes in respiratory rate at an earlier stage. METHODS Ten healthy adult volunteers were monitored, breathing ...

This paper presents a computer based manipulating and analyzing a digital images. The proposed method is used wavelet transform, this transformation arrange orthogonal series of both imaginary and real values. In this paper we are proposes a two algorithms one is dual tree complex wavelet transforms (DTCWT), and second one is dual tree complex wavelet transform with orthogonal shift property th...

This work explores the use of a discrete wavelet transform, a feature extractor mechanism for speech recognition. Speech recognition is a fascinating application of digital signal processing offering unparalleled opportunities. The real-world applications deploying speech recognition and its implications can be varied across various fields. Speech recognition can automate many tasks that previo...

In a series of papers, Donoho and Johnstone develop a powerful theory based on wavelets for extracting nonsmooth signals from noisy data. Several nonlinear smoothing algorithms are presented which provide high performance for removing Gaussian noise from a wide range of spatially inhomogeneous signals. However, like other methods based on the linear wavelet transform, these algorithms are very ...

Recently, images and video are being extensively used in numerous areas such as video-conferencing, multimedia documentation, telemedicine, high definition television (HDTV) etc. These diverse applications can benefit from the design of a family of image compression algorithms that address their specific needs. We propose a framework for wavelet-based image compression that leads to a family of...

Pre-processing of speech signals is considered a crucial step in the development of a robust and efficient speech or speaker recognition system. This paper deals with different speech processing techniques and the recognition accuracy with respect to wavelet transforms. It is shown that by applying wavelet transform to the conventional methods the signal recognition accuracy will be increased b...

Classical discrete wavelet packet transforms are sensitive to changes in image orientation and translation. Therefore, it is hardly possible to extract rotation invariant features from images in the transform domain. This paper proposes several algorithms for invariant discrete wavelet decomposition to produce an invariant representation for an image. The procedure can be divided into several s...

It has been observed from image denoising experiments that translation invariant (TI) wavelet transforms often outperform orthogonal wavelet transforms. This paper compares the two transforms from the viewpoint of approximation theory, extending previous results based on Haar wavelets. The advantages of the TI expansion over orthogonal expansion are twofold: the TI expansion produces smaller ap...