This paper is a contribution to the analysis of the statistical correlation of the wavelet packet coefficients resulting from the decomposition of a random process, stationary in the wide-sense, whose power spectral density is bounded with support in [−π, π]. Consider two quadrature mirror filters (QMF) that depend on a parameter r, such that these filters tend almost everywhere to the Shannon ...

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...

In the field of structural health monitoring (SHM), with mature development artificial intelligence, deep learning-based damage identification techniques have attracted wide attention. this paper, convolutional neural network (CNN) is used to extract feature simple supported steel beams. Firstly, transient dynamic analysis beam carried out by finite element software, and acceleration response s...

Breast cancer is a commonly diagnosed disease in women. Early detection, personalized treatment approach, and better understanding are necessary for patients to survive. In this work, deep learning network traditional convolution were both employed with the Digital Database Screening Mammography (DDSM) dataset. images subjected background removal followed by Wiener filtering contrast limited hi...

Wavelet packet decompositions based on tree structured 2-channel filter banks with conjugate quadrature filters (CQF) have recently found many applications in the area of audio coding. Their time-frequency tiling is dual to that of timevarying modulated lapped transforms (MLT). In this paper we present a new orthonormal wavelet packet basis, which is constructed by frequency-varying MLT. These ...

Feature extraction from audio data is a major concern in computer assisted music applications and content based audio retrieval. For general non-stationary signals, wavelet packet decomposition is used with entropy functions for best basis search. Musical instruments have well defined frequency ranges. Thus when audio data containing a solo instrument is concerned, wavelet packet decomposition ...

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...

Wavelet packets have been the focus of active research for twenty years, both in theory and applications. In this work, the notion of orthogonal nonseparable five-variantl wavelet packets is introduced. A new approach for de-signing them is presented by iteration method. We proved that the fivevariant wavelet packets are of the orthogonality trait. We give three orthogonality formulas regarding...

In this article a texture feature extraction scheme based on M-band wavelet packet frames is investigated. The features so extracted are used for segmentation of satellite images which usually have complex and overlapping boundaries. The underlying principle is based on the fact that different image regions exhibit different textures. Since most signifcant information of a texture often lies in...

This paper introduces a new implementation of wavelet packet decomposition which is combined with SPIHT (Set Partitioning in Hierarchical Trees) compression scheme. We provide the analysis of the problems arising from the application of zerotree quantisation based algorithms (such as SPIHT) to wavelet packet transform coefficients. We established the generalized parent–child relationships for w...