PARAMETRIZATION OF COMPACTLY SUPPORTED TRIVARIATE ORTHOGONAL WAVELET FILTER

2007 ◽  
Vol 05 (04) ◽  
pp. 627-639 ◽  
Author(s):  
YONGDONG HUANG ◽  
ZHENGXING CHENG
Keyword(s):  
Signal Processing ◽  
Tensor Product ◽  
Parametric Representation ◽  
Wavelet Filter ◽  
Orthogonal Wavelet ◽  
Compactly Supported ◽  
Wavelets Analysis

Multivariate wavelets analysis is a powerful tool for multi-dimensional signal processing, but tensor product wavelets have a number of drawbacks. In this paper, we give an algorithm of parametric representation compactly supported trivariate orthogonal wavelet filter, which simplifies the study of trivariate orthogonal wavelet. Four examples are also given to demonstrate the method.

Spectral-Based Analysis and Synthesis of Audio Signals

2011 ◽  
pp. 56-92 ◽  
Author(s):  
Paulo A.A. Esquef ◽  
Luiz W.P. Biscainho
Keyword(s):  
Signal Processing ◽  
Audio Signal ◽  
Parametric Representation ◽  
Sound Generation ◽  
Audio Signal Processing ◽  
Audio Signals ◽  
Sinusoidal Modeling ◽  
Analysis And Synthesis ◽  
Processing Techniques ◽  
Signal Processing Techniques

This chapter reviews audio signal processing techniques related to sound generation via additive synthesis. Particular focus will be put on sinusoidal modeling. Each processing stage involved in obtaining a sinusoidal representation for audio signals is described. Then, synthesis techniques that allow reconstructing an audio signal based on a given parametric representation are presented. Finally, some audio applications where sinusoidal modeling is employed are briefly discussed.

scholarly journals Hypertension Diagnosis Index for Discrimination of High-Risk Hypertension ECG Signals Using Optimal Orthogonal Wavelet Filter Bank

2019 ◽  
Vol 16 (21) ◽  
pp. 4068 ◽  
Author(s):  
Jaypal Singh Rajput ◽  
Manish Sharma ◽  
U. Rajendra Acharya
Keyword(s):  
Blood Pressure ◽  
High Risk ◽  
Filter Bank ◽  
Early Stage ◽  
Fractional Dimension ◽  
Wavelet Filter ◽  
Orthogonal Wavelet ◽  
Ecg Signals ◽  
Wavelet Filter Bank ◽  
Hypertension Diagnosis

Hypertension (HT) is an extreme increment in blood pressure that can prompt a stroke, kidney disease, and heart attack. HT does not show any symptoms at the early stage, but can lead to various cardiovascular diseases. Hence, it is essential to identify it at the beginning stages. It is tedious to analyze electrocardiogram (ECG) signals visually due to their low amplitude and small bandwidth. Hence, to avoid possible human errors in the diagnosis of HT patients, an automated ECG-based system is developed. This paper proposes the computerized segregation of low-risk hypertension (LRHT) and high-risk hypertension (HRHT) using ECG signals with an optimal orthogonal wavelet filter bank (OWFB) system. The HRHT class is comprised of patients with myocardial infarction, stroke, and syncope ECG signals. The ECG-data are acquired from physionet’s smart health for accessing risk via ECG event (SHAREE) database, which contains recordings of a total 139 subjects. First, ECG signals are segmented into epochs of 5 min. The segmented epochs are then decomposed into six wavelet sub-bands (WSBs) using OWFB. We extract the signal fractional dimension (SFD) and log-energy (LOGE) features from all six WSBs. Using Student’s t-test ranking, we choose the high ranked WSBs of LOGE and SFD features. We develop a novel hypertension diagnosis index (HDI) using two features (SFD and LOGE) to discriminate LRHT and HRHT classes using a single numeric value. The performance of our developed system is found to be encouraging, and we believe that it can be employed in intensive care units to monitor the abrupt rise in blood pressure while screening the ECG signals, provided this is tested with an extensive independent database.

Texture image retrieval based on non-tensor product wavelet filter banks

2009 ◽  
Vol 89 (8) ◽  
pp. 1501-1510 ◽  
Author(s):  
Zhenyu He ◽  
Xinge You ◽  
Yuan Yuan
Keyword(s):  
Tensor Product ◽  
Image Retrieval ◽  
Filter Banks ◽  
Wavelet Filter ◽  
Texture Image

M-CHANNEL MRA AND APPLICATION TO ANISOTROPIC SOBOLEV SPACES

2005 ◽  
Vol 03 (02) ◽  
pp. 153-166
Author(s):  
ELENA CORDERO
Keyword(s):  
Tensor Product ◽  
Sobolev Spaces ◽  
Unit Interval ◽  
Biorthogonal Wavelet ◽  
Bernstein Type ◽  
Wavelet Bases ◽  
Compactly Supported ◽  
Anisotropic Sobolev Spaces ◽  
Dilation Factor ◽  
Bernstein Type Inequalities

In this paper we construct compactly supported biorthogonal wavelet bases of the interval, with dilation factor M. Next, the natural MRA on the cube arising from the tensor product of a multilevel decomposition of the unit interval is developed. New Jackson and Bernstein type inequalities are proved, providing a characterization for anisotropic Sobolev spaces.

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