Transient Impact Signal’s Detection Based on Wavelet Transformation

2012 ◽  
pp. 309-313
Author(s):  
Xu Cao ◽  
Hua-xun Zhang
Keyword(s):  
Wavelet Transformation

Evaluvation of Multiresolution Watermarking Algorithm

2017 ◽  
Vol 7 (8) ◽  
pp. 85
Author(s):  
S. Thabasu Kannan ◽  
S. Azhagu Senthil
Keyword(s):  
Digital Watermarking ◽  
Wavelet Transformation ◽  
Signal To Noise Ratio ◽  
Rapid Development ◽  
Threshold Value ◽  
Discrete Wavelet ◽  
Digital Production ◽  
Common Framework ◽  
Sampling Operation ◽  
Watermarking Scheme

Now-a-days watermarking plays a pivotal role in most of the industries for providing security to their own as well as hired or leased data. This paper its main aim is to study the multiresolution watermarking algorithms and also choosing the effective and efficient one for improving the resistance in data compression. Computational savings from such a multiresolution watermarking framework is obvious. The multiresolutional property makes our watermarking scheme robust to image/video down sampling operation by a power of two in either space or time. There is no common framework for multiresolutional digital watermarking of both images and video. A multiresolution watermarking based on the wavelet transformation is selected in each frequency band of the Discrete Wavelet Transform (DWT) domain and therefore it can resist the destruction of image processing.   The rapid development of Internet introduces a new set of challenging problems regarding security. One of the most significant problems is to prevent unauthorized copying of digital production from distribution. Digital watermarking has provided a powerful way to claim intellectual protection. We proposed an idea for enhancing the robustness of extracted watermarks. Watermark can be treated as a transmitted signal, while the destruction from attackers is regarded as a noisy distortion in channel.  For the implementation, we have used minimum nine coordinate positions. The watermarking algorithms to be taken for this study are Corvi algorithm and Wang algorithm. In all graph, we have plotted X axis as peak signal to noise ratio (PSNR) and y axis as Correlation with original watermark. The threshold value ά is set to 5. The result is smaller than the threshold value then it is feasible, otherwise it is not.

Evaluation of lifting and Using Impact Echo Technique and Wavelet Transformation method at Urban Railroad

2019 ◽  
Vol 7 (4) ◽  
pp. 533-544
Author(s):  
ChanYong Choi ◽  
JinWook Lee ◽  
HoJin Cho
Keyword(s):  
Wavelet Transformation ◽  
Transformation Method ◽  
Impact Echo

A Study on the Crack Detection Using the Wavelet Transformation of Mode Shape for Hull Girder

2002 ◽  
Vol 39 (2) ◽  
pp. 19-27 ◽  
Author(s):  
Dae-Sung Lee ◽  
Dae-Seung Cho
Keyword(s):  
Mode Shape ◽  
Wavelet Transformation ◽  
Crack Detection ◽  
Hull Girder

scholarly journals Applying Ateb–Gabor Filters to Biometric Imaging Problems

Symmetry ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 717
Author(s):  
Mariia Nazarkevych ◽  
Natalia Kryvinska ◽  
Yaroslav Voznyi
Keyword(s):  
Wavelet Transformation ◽  
Symmetric Functions ◽  
Gabor Filter ◽  
Signal To Noise Ratio ◽  
The Other ◽  
Mean Square ◽  
Image Transformation ◽  
Gabor Filtering ◽  
Signal To Noise ◽  
Filtration Method

This article presents a new method of image filtering based on a new kind of image processing transformation, particularly the wavelet-Ateb–Gabor transformation, that is a wider basis for Gabor functions. Ateb functions are symmetric functions. The developed type of filtering makes it possible to perform image transformation and to obtain better biometric image recognition results than traditional filters allow. These results are possible due to the construction of various forms and sizes of the curves of the developed functions. Further, the wavelet transformation of Gabor filtering is investigated, and the time spent by the system on the operation is substantiated. The filtration is based on the images taken from NIST Special Database 302, that is publicly available. The reliability of the proposed method of wavelet-Ateb–Gabor filtering is proved by calculating and comparing the values of peak signal-to-noise ratio (PSNR) and mean square error (MSE) between two biometric images, one of which is filtered by the developed filtration method, and the other by the Gabor filter. The time characteristics of this filtering process are studied as well.

scholarly journals Damage Identification of RC Structures Using Wavelet Transformation

2016 ◽  
Vol 144 ◽  
pp. 336-342 ◽  
Author(s):  
S.S. Patel ◽  
A.P. Chourasia ◽  
S.K. Panigrahi ◽  
J. Parashar ◽  
N. Parvez ◽  
...  
Keyword(s):  
Wavelet Transformation ◽  
Damage Identification ◽  
Rc Structures

Inter-turn fault stability enrichment and diagnostic analysis of power system network using wavelet transformation-based sample data control and fuzzy logic controller

2021 ◽  
pp. 014233122110070
Author(s):  
Arunesh Kumar Singh ◽  
Abhinav Saxena ◽  
Nathuni Roy ◽  
Umakanta Choudhury
Keyword(s):  
Fuzzy Logic ◽  
Power System ◽  
Wavelet Transformation ◽  
Fuzzy Logic Controller ◽  
Power Transformer ◽  
Harmonic Distortion ◽  
Inrush Current ◽  
Data Control ◽  
Sample Data ◽  
Z Transformation

In this paper, performance analysis of power system network is carried out by injecting the inter-turn fault at the power transformer. The injection of inter-turn fault generates the inrush current in the network. The power system network consists of transformer, current transformer, potential transformer, circuit breaker, isolator, resistance, inductance, loads, and generating source. The fault detection and termination related to inrush current has some drawbacks and limitations such as slow convergence rate, less stability and more distortion with the existing methods. These drawbacks motivate the researchers to overcome the drawbacks with new proposed methods using wavelet transformation with sample data control and fuzzy logic controller. The wavelet transformation is used to diagnose the fault type but contribute lesser for fault termination; due to that, sample data of different signals are collected at different frequencies. Further, the analysis of collected sample data is assessed by using Z-transformation and fuzzy logic controller for fault termination. The stability, total harmonic distortion and convergence rate of collected sample data among all three methods (wavelet transformation, Z-transformation and fuzzy logic controller) are compared for fault termination by using linear regression analysis. The complete performance of fault diagnosis along with fault termination has been analyzed on Simulink. It is observed that after fault injection at power transformer, fault recovers faster under fuzzy logic controller in comparison with Z-transformation followed by wavelet transformation due to higher stability, less total harmonic distortion and faster convergence.

Sign in / Sign up

Export Citation Format

Share Document