Wavelet analysis on developable surface base on area preserving projection

2015 ◽  
Vol 13 (01) ◽  
pp. 1550007 ◽  
Author(s):  
Bao Qin Wang ◽  
Gang Wang ◽  
Xiao Hui Zhou ◽  
Yu Su
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Scaling Function ◽  
Discrete Wavelet ◽  
Developable Surface ◽  
Simple Method ◽  
Multi Resolution Analysis ◽  
Resolution Analysis ◽  
Area Preserving ◽  
The Scaling Function

In this paper, a simple method is given in order to construct an area preserving mapping from a developable surface M to a plane. Based on the area preserving projection, we give some important formulas on M, and define a multi-resolution analysis on L2(M). We provide the conditions to further discuss the continuous wavelet transform and discrete wavelet transform on developable surface. At the same time, we derived two-scale equations that the scaling function and wavelet function on developable surface satisfied, we also define and discuss the orthogonality, and several important theorems are given. Finally, we construct the numerical examples. The focus of this paper is the area preserving mapping that from developable surface M to a plane, and the discrete wavelet transform on developable surface.

A New Approach for Classification of Fault in Transmission Line with Combination of Wavelet Multi Resolution Analysis and Neural Networks

2017 ◽  
Vol 8 (1) ◽  
pp. 505
Author(s):  
Y Srinivasa Rao ◽  
G. Ravi Kumar ◽  
G. Kesava Rao
Keyword(s):  
Neural Networks ◽  
Wavelet Transform ◽  
Transmission Line ◽  
Discrete Wavelet Transform ◽  
Discrete Wavelet ◽  
New Approach ◽  
The Neural Network ◽  
Multi Resolution Analysis ◽  
Resolution Analysis

An appropriate fault detection and classification of power system transmission line using discrete wavelet transform and artificial neural networks is performed in this paper. The analysis is carried out by applying discrete wavelet transform for obtained fault phase currents. The work represented in this paper are mainly concentrated on classification of fault and this classification is done based on the obtained energy values after applying discrete wavelet transform by taking this values as an input for the neural network. The proposed system and analysis is carried out in Matlab Simulink.

A New Approach for Classification of Fault in Transmission Line with Combination of Wavelet Multi Resolution Analysis and Neural Networks

2017 ◽  
Vol 8 (1) ◽  
pp. 505
Author(s):  
Y Srinivasa Rao ◽  
G. Ravi Kumar ◽  
G. Kesava Rao
Keyword(s):  
Neural Networks ◽  
Wavelet Transform ◽  
Transmission Line ◽  
Discrete Wavelet Transform ◽  
Discrete Wavelet ◽  
New Approach ◽  
The Neural Network ◽  
Multi Resolution Analysis ◽  
Resolution Analysis

An appropriate fault detection and classification of power system transmission line using discrete wavelet transform and artificial neural networks is performed in this paper. The analysis is carried out by applying discrete wavelet transform for obtained fault phase currents. The work represented in this paper are mainly concentrated on classification of fault and this classification is done based on the obtained energy values after applying discrete wavelet transform by taking this values as an input for the neural network. The proposed system and analysis is carried out in Matlab Simulink.

An Introduction to Wavelet-Based Image Processing and Its Applications

2018 ◽  
pp. 110-128 ◽  
Author(s):  
Mahesh Kumar H. Kolekar ◽  
G. Lloyds Raja ◽  
Somnath Sengupta
Keyword(s):  
Wavelet Transform ◽  
Low Frequency ◽  
Discrete Wavelet ◽  
Jpeg 2000 ◽  
Parent Child Relationship ◽  
Child Relationship ◽  
Multi Resolution Analysis ◽  
Low Pass ◽  
Multiple Resolutions ◽  
Resolution Analysis

This chapter gives a brief introduction of wavelets and multi-resolution analysis. Wavelets overcome the limitations of Discrete Cosine Transform and hence found its application in JPEG 2000. In wavelet transform, the scaling functions provide approximations or low-pass filtering of the signal and the wavelet functions add the details at multiple resolutions or perform high-pass filtering of the signal. Applying Discrete Wavelet Transform to an image decomposes it into LL, LH, HL, and HH subbands. The low frequency LL band carries most of the significant information in the image. Wavlet transform allows us to analyse the local properties of a signal or image by shifting and scaling operations. The inherent properties of wavelets makes it useful in image denoising, edge detection, image compression, compressed sensing and illumination normalization. The wavelet coefficients at various levels of decomposition follows a parent-child relationship.

An Introduction to Wavelet-Based Image Processing and its Applications

2014 ◽  
pp. 38-53
Author(s):  
Mahesh Kumar H. Kolekar ◽  
G. Lloyds Raja ◽  
Somnath Sengupta
Keyword(s):  
Wavelet Transform ◽  
Low Frequency ◽  
Discrete Wavelet ◽  
Jpeg 2000 ◽  
Parent Child Relationship ◽  
Child Relationship ◽  
Multi Resolution Analysis ◽  
Low Pass ◽  
Multiple Resolutions ◽  
Resolution Analysis

This chapter gives a brief introduction of wavelets and multi-resolution analysis. Wavelets overcome the limitations of Discrete Cosine Transform and hence found its application in JPEG 2000. In wavelet transform, the scaling functions provide approximations or low-pass filtering of the signal and the wavelet functions add the details at multiple resolutions or perform high-pass filtering of the signal. Applying Discrete Wavelet Transform to an image decomposes it into LL, LH, HL, and HH subbands. The low frequency LL band carries most of the significant information in the image. Wavlet transform allows us to analyse the local properties of a signal or image by shifting and scaling operations. The inherent properties of wavelets makes it useful in image denoising, edge detection, image compression, compressed sensing and illumination normalization. The wavelet coefficients at various levels of decomposition follows a parent-child relationship.

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