scholarly journals Eliminated aliasing effect on wavelet transform based multiresolution analysis

2016 ◽  
Vol 55 (3) ◽  
Author(s):  
Ernesto González-Flores ◽  
José Oscar Campos-Enríquez ◽  
Erick Camacho-Ramírez ◽  
David Ernesto Rivera-Recillas
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Multiresolution Analysis ◽  
Synthetic Data ◽  
Seismic Signal ◽  
Discrete Wavelet ◽  
Frequency Bands ◽  
Analysis Technique ◽  
The Time Domain ◽  
Seismic Signal Processing

Multiresolution analysis, based on the discrete wavelet transform, is here incorporated in seismic signal processing. This analysis technique enables decomposing a seismic signal, in different frequency bands, and thus to analyze the information contained in these frequency bands. Multiresolution analysis allows visualizing in the time domain the information contained in the frequency bands. Wavelets commonly used in the discrete wavelet transform present an overlay between scales, this constitutes an aliasing effect that gives rise to spurious effects. Vaidyanathan wavelet minimizes the overlay between scales. We applied this wavelet to synthetic data and to a 3D seismic cube. Accordingly, spurious effects from aliasing generated by overlay between scales are minimized with the Vaidyanathan wavelet.

scholarly journals Wavelet Transform Based Fault Identification and Reconfiguration for a Reduced Switch Multilevel Inverter Fed Induction Motor Drive

Electronics ◽  
2021 ◽  
Vol 10 (9) ◽  
pp. 1023
Author(s):  
Arigela Satya Veerendra ◽  
Akeel A. Shah ◽  
Mohd Rusllim Mohamed ◽  
Chavali Punya Sekhar ◽  
Puiki Leung
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Multiresolution Analysis ◽  
Short Circuit ◽  
Open Circuit ◽  
Multilevel Inverter ◽  
Fault Identification ◽  
Discrete Wavelet ◽  
The Novel ◽  
Inverter Topology

The multilevel inverter-based drive system is greatly affected by several faults occurring on switching elements. A faulty switch in the inverter can potentially lead to more losses, extensive downtime and reduced reliability. In this paper, a novel fault identification and reconfiguration process is proposed by using discrete wavelet transform and auxiliary switching cells. Here, the discrete wavelet transform exploits a multiresolution analysis with a feature extraction methodology for fault identification and subsequently for reconfiguration. For increasing the reliability, auxiliary switching cells are integrated to replace faulty cells in a proposed reduced-switch 5-level multilevel inverter topology. The novel reconfiguration scheme compensates open circuit and short circuit faults. The complexity of the proposed system is lower relative to existing methods. This proposed technique effectively identifies and classifies faults using the multiresolution analysis. Furthermore, the measured current and voltage values during fault reconfiguration are close to those under healthy conditions. The performance is verified using the MATLAB/Simulink platform and a hardware model.

scholarly journals Multiscale Analysis of GPS Time Series from Non-decimated Wavelet to Investigate the Effects of Ionospheric Scintillation

2015 ◽  
Vol 16 (2) ◽  
pp. 119 ◽  
Author(s):  
Gabriela De Oliveira Nascimento Brassarote ◽  
Eniuce Menezes de Souza ◽  
João Francisco Galera Monico
Keyword(s):  
Time Series ◽  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Artificial Satellite ◽  
Stationary Time Series ◽  
Ionospheric Scintillation ◽  
Discrete Wavelet ◽  
The Time Domain ◽  
Gps Time Series ◽  
Redundant Discrete Wavelet Transform

Due to the numerous application possibilities, the theory of wavelets has been applied in several areas of research. The Discrete Wavelet Transform is the most known version. However, the downsampling required for its calculation makes it sensitive to the origin, what is not ideal for some applications,mainly in time series. On the other hand, the Non-Decimated Discrete Wavelet Transform (or Maximum Overlap Discrete Wavelet Transform, Stationary Wavelet Transform, Shift-invariant Discrete Wavelet Transform, Redundant Discrete Wavelet Transform) is shift invariant, because it considers all the elements of the sample, by eliminating the downsampling and, consequently, represents a time series with the same number of coefficients at each scale. In the present paper, the objective is to present the theorical aspects of the a multiscale/multiresolution analysis of non-stationary time series from non-decimated wavelets in terms of its implementation using the same pyramidal algorithm of the decimated wavelet transform. An application with real time series of the effect of the ionospheric scintillation on artificial satellite signals is investigated. With this analysis some information and hidden patterns which can not be detected in the time domain, may therefore be explained in the space-frequency domain.

Multiresolution analysis signal in a three beam path Mach-Zehnder interferometer based on a discrete wavelet transform

2018 ◽  
Author(s):  
Daniel Jauregui Vasquez ◽  
Eduardo Cabal Yepez ◽  
Julian Moises Estudillo Ayala ◽  
Juan Carlos Hernandez Garcia ◽  
Roberto Rojas Laguna ◽  
...  
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Multiresolution Analysis ◽  
Zehnder Interferometer ◽  
Discrete Wavelet ◽  
Beam Path ◽  
Mach Zehnder Interferometer

Non-invasive fetal ECG extraction using discrete wavelet transform recursive inverse adaptive algorithm

2020 ◽  
Vol 28 (5) ◽  
pp. 507-520
Author(s):  
Bahaa Al-Sheikh ◽  
Mohammad Shukri Salman ◽  
Alaa Eleyan ◽  
Shadi Alboon
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Adaptive Filtering ◽  
Synthetic Data ◽  
Recursive Least Squares ◽  
Discrete Wavelet ◽  
Significant Information ◽  
Filtering Algorithms ◽  
Filtering Algorithm ◽  
Fetal Ecg

BACKGROUND: Fetal heart activity adds significant information about the status of the fetus health. Early diagnosis of issues in the heart before delivery allows early intervention and significantly improves the treatment. OBJECTIVE: This paper presents a new adaptive filtering algorithm for fetal electrocardiogram (FECG) extraction from the maternal abdominal signal, known in literature as abdominal electrocardiogram (AECG) signal. Fetal QRS complex waves will be identified and extracted accurately for fetal health care and monitoring purposes. METHODS: We use discrete wavelet transform recursive inverse (DWT-RI) adaptive filtering algorithm for this objective. Thoracic maternal electrocardiogram (MECG) is used as a reference in the proposed algorithm and FECG components are extracted from AECG signal after suppressing the MECG projections. The proposed algorithm is compared to other typical adaptive filtering algorithms, least mean squares (LMS), recursive least squares (RLS), and recursive inverse (RI). RESULTS: Fetal QRS waveforms successful identification and extraction from AECG signal is evaluated objectively and visually and compared to other algorithms. We validated the proposed algorithm using both synthetic data and real clinical data. CONCLUSIONS: The proposed algorithm is capable of extracting fetal QRS waveforms successfully from AECG and outperforms other adaptive filtering algorithms in terms of accuracy and positive predictivity.

Discrete wavelet transform-based multiresolution analysis and spectral enhancement to characterize heavy oil reservoirs in the southern Gulf of Mexico region

2016 ◽  
Vol 4 (4) ◽  
pp. T497-T505 ◽  
Author(s):  
Erik Camacho-Ramírez ◽  
Ernesto Gonzalez-Flores ◽  
José Oscar Campos-Enriquez
Keyword(s):  
Wavelet Transform ◽  
Gulf Of Mexico ◽  
Discrete Wavelet Transform ◽  
Multiresolution Analysis ◽  
Heavy Oil ◽  
Resolution Enhancement ◽  
Discrete Wavelet ◽  
Seismic Resolution ◽  
Spectral Enhancement ◽  
Southern Gulf Of Mexico

In Neogene geologic settings, seismic resolution plays an important role in the static characterization of large stratigraphically complex reservoirs. Attenuation of high frequencies due to absorption and scattering of waves propagating through the subsurface contributes to the loss of seismic resolution, resulting, in particular, to a poor delineation of thin beds. This case history comprised the resolution enhancement of seismic data from one petroleum field located in the southern Gulf of Mexico. We applied discrete wavelet transform-based multiresolution analysis to identify frequency components in the data that required spectral enhancement. The resulting enhanced power spectrum contains a higher frequency content, which made it possible to identify new heavy oil (11°–23° API) reservoirs located in thin sandstones of deltaic sedimentary environments. The methodology used here helped us to cope with the attenuation problem.

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