scholarly journals DYNAMIC RESPONSE ESTIMATION OF SDOF ELASTIC SYSTEM BY WAVELET COEFFICIENTS : The relationship between discrete wavelet transform of seismic waves and input energy Part 1

2004 ◽  
Vol 69 (578) ◽  
pp. 37-42 ◽  
Author(s):  
Jun IYAMA
Keyword(s):  
Wavelet Transform ◽  
Dynamic Response ◽  
Discrete Wavelet Transform ◽  
Seismic Waves ◽  
Elastic System ◽  
Discrete Wavelet ◽  
Input Energy ◽  
Wavelet Coefficients ◽  
Energy Part ◽  
The Relationship

scholarly journals The Improvement of Discrete Wavelet Transform

2021 ◽  
Author(s):  
Zhihua Zhang
Keyword(s):  
Wavelet Transform ◽  
Decay Rate ◽  
Discrete Wavelet Transform ◽  
Wavelet Transforms ◽  
Polynomial Interpolation ◽  
Discrete Wavelet ◽  
Two Dimensional ◽  
Wavelet Coefficients ◽  
Periodic Wavelet ◽  
Improved Algorithm

Discrete wavelet transform and discrete periodic wavelet transform have been widely used in image compression and data approximation. Due to discontinuity on the boundary of original data, the decay rate of the obtained wavelet coefficients is slow. In this study, we use the combination of polynomial interpolation and one-dimensional/two-dimensional discrete periodic wavelet transforms to mitigate boundary effects. The decay rate of the obtained wavelet coefficients in our improved algorithm is faster than that of traditional two-dimensional discrete wavelet transform. Moreover, our improved algorithm can be extended naturally to the higher-dimensional case.

scholarly journals Fuzzy Logic based Improvement in Discrete Wavelet Transform Edge Representation in Images.doc

2020 ◽  
Author(s):  
Anand Swaminathan
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Fuzzy Inference ◽  
Directional Derivative ◽  
Rule Base ◽  
Discrete Wavelet ◽  
Two Dimensional ◽  
Wavelet Coefficients ◽  
Inference System ◽  
Rule Structure

We introduce a rule base fuzzy technique on decomposed wavelet coefficients, to improve the wavelet edge representation. Our algorithm mitigates ‘incorrect’ responses, due primarily to the symmetries of directional derivative filters. Since the Discrete Wavelet Transform (DWT) coefficients are revealed from two dimensional symmetric filter banks and undermine some gradient information. These wavelet coefficients are prearranged into ‘if-then’ rule structure of a fuzzy inference system, to improve the wavelet edge representation.

MONITORING AUTOCORRELATED PROCESSES WITH WAVELETS

2010 ◽  
Vol 17 (02) ◽  
pp. 133-156
Author(s):  
CANAN BILEN
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Control Chart ◽  
Control Charts ◽  
Simulated Data ◽  
Discrete Wavelet ◽  
Wavelet Coefficients ◽  
Process Data ◽  
Original Series ◽  
Autocorrelated Processes

This paper develops a wavelet control chart for monitoring autocorrelated processes. The procedure uses the discrete wavelet transform of the original series, and traditional control charts are applied to the stream of wavelet coefficients. Unlike other control charts for monitoring autocorrelated processes found in the literature, the wavelet control chart does not require that a model be specified for the process data. The wavelet-based control chart is simple enough that it can be easily automated. Real and simulated data are used to illustrate the effectiveness of the proposed wavelet control chart.

DISCRETE WAVELET TRANSFORM OF FINITE SIGNALS: DETAILED STUDY OF THE ALGORITHM

2013 ◽  
Vol 12 (01) ◽  
pp. 1450001 ◽  
Author(s):  
PAVEL RAJMIC ◽  
ZDENEK PRUSA
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Detailed Analysis ◽  
Input Signal ◽  
Analysis Of Algorithms ◽  
Wavelet Coefficient ◽  
Discrete Wavelet ◽  
Wavelet Coefficients ◽  
Rigorous Analysis ◽  
Boundary Treatment

The paper presents a detailed analysis of algorithms used for the forward and the inverse discrete wavelet transform (DTWT) of finite-length signals. The paper provides answers to questions such as "how many wavelet coefficients are computed from the signal at a given depth of the decomposition" or conversely, "how many signal samples are needed to compute a single wavelet coefficient at a given depth of the decomposition" or "how many coefficients at a given depth are influenced by the selected type of boundary treatment" or "how many samples of the input signal simultaneously influence two neighboring wavelet coefficients at a given depth of the decomposition". As a byproduct, the rigorous analysis of the algorithms gives details needed for the implementation. The paper is accompanied by several Matlab functions.

scholarly journals Fuzzy Logic based Improvement in Discrete Wavelet Transform Edge Representation in Images.doc

2020 ◽  
Author(s):  
Anand Swaminathan
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Fuzzy Inference ◽  
Directional Derivative ◽  
Rule Base ◽  
Discrete Wavelet ◽  
Two Dimensional ◽  
Wavelet Coefficients ◽  
Inference System ◽  
Rule Structure

We introduce a rule base fuzzy technique on decomposed wavelet coefficients, to improve the wavelet edge representation. Our algorithm mitigates ‘incorrect’ responses, due primarily to the symmetries of directional derivative filters. Since the Discrete Wavelet Transform (DWT) coefficients are revealed from two dimensional symmetric filter banks and undermine some gradient information. These wavelet coefficients are prearranged into ‘if-then’ rule structure of a fuzzy inference system, to improve the wavelet edge representation.

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