Wavelet transform has been extensively used in machine fault diagnosis and prognosis owing to its strength to deal with non-stationary signals. The existing Wavelet transform based schemes for fault diagnosis employ wavelet decomposition of the entire vibration frequency which not only involve huge computational overhead in extracting the features but also increases the dimensionality of the fe...

The standard methods for decomposition and analysis of evoked potentials are bandpass filtering, identification of peak amplitudes and latencies, and principal component analysis (PCA). We discuss the limitations of these and other approaches and introduce wavelet packet analysis. Then we propose the "single-channel wavelet packet model," a new approach in which a unique decomposition is achiev...

quantification of arterial elasticity and its dependency to age is considered in this paper. we use radiofrequency (rf) signals fromcarotid artery ultasonography to evaluate this dependency. blood pressure, blood flow, and tethering to surrounding tissue are the maincauses of the motion of the carotid wall. tracking carotid artery wall motion from a series of ultrasound b‑mode images is challen...

Wavelet analysis as a recently data filtering method (or multi-scale decomposition) is particularly useful for describing signals with sharp spiky, discontinuous or fractal structure in financial markets. This study investigates the optimal several wavelet thresholding criteria or techniques to support the multi-signal decomposition methods of a daily Korean won / U.S. dollar currency market as...

—ASIC hardware implementations of the discrete wavelet transform are required to cope with the intensive real-time computations of the transform. In this paper, we describe a parallel implementation of the wavelet transform using one type of reconfigurable ASICs; Filed Programmable Gate Arrays (FPGAs). The implementation is based on reformulating the transform using the distributed arithmetic a...

posed both spatially (S) and spectrally (F). The spatial decomposition is carried out using a quad-tree split. Since the spatial partitions do not overlap, this segmentation allows the four children nodes to be further analyzed independently. The frequency decomposition is performed using a four channel quadrature mirror filter (QMF) bank. This produces a library of wavelet packet bases which c...

A non-uniform speech segmentation method based on wavelet packet transform is used for the localisation of phoneme boundaries. Eleven subbands are chosen by applying the mean best basis algorithm. Perceptual scale is used for decomposition of speech via Meyer wavelet in the wavelet packet structure. A real valued vector representing the digital speech signal is decomposed into phone-like units ...

In this paper, we present a new scheme to classify diierent textures. We propose a scheme to use wavelet decomposition with Markov random eld modeling to classify textures which we called mul-tiresolution MRF(MRMRF). The parameters of each Markov random eld models combined with wavelet energy signatures are used as features in texture classiication. The classiier here we use is nearest linear c...

Lossless compression techniques are essential in archival and communication of large amounts of homogeneous data in radiological image databases. This paper exploits dependencies that exist between the pixel intensities in three dimensions to improve compression for a set of similar medical images. These 3-D dependencies are systematically presented as histograms, plots of wavelet decomposition...

In this paper we present a novel approach to shape similarity estimation. The target application is content-based indexing and retrieval over large image databases. The technique is based on wavelet decomposition and uses polygon approximation over several scales. This technique uses simple features (aspect ratio, angles, distances from the centroid, distance ratios and relative positions) extr...