A frequency measurement algorithm for non-stationary signals by using wavelet transform

2016 ◽  
Vol 87 (11) ◽  
pp. 11E710 ◽  
Author(s):  
Seong-Heon Seo ◽  
Dong Keun Oh
Keyword(s):  
Wavelet Transform ◽  
Frequency Measurement ◽  
Stationary Signals ◽  
Measurement Algorithm

Analysis of Wavelet Transform for Interharmonic Measurement

2013 ◽  
Vol 732-733 ◽  
pp. 813-816
Author(s):  
Da Hai Zhang ◽  
Jian He ◽  
Zhi Guang Tian ◽  
Biao Li
Keyword(s):  
Wavelet Transform ◽  
Real Time ◽  
Low Frequency ◽  
Frequency Measurement ◽  
Sampling Frequency ◽  
Difficult Problem ◽  
Measurement Results ◽  
Sampling Window ◽  
The Impact ◽  
Real Time Application

Fast and accurate measurement of interharmonic is a difficult problem. Fourier transform is widely used but it is not suitable for real-time application. Wavelet transform has better real-time performance, but its accuracy needs to be improved, so the paper studies the performance of wavelet transform for interharmonic measurement. By software simulation, it evaluates the impact of interharmonic frequency, sampling window and sampling frequency on the accuracy of interharmonic frequency measurement. Results show that wavelet method doesn’t require long sampling window or high sampling frequency, which makes it easy to be applied, and it can provide better measurement accuracy for high frequency interharmonics than low frequency interharmonics.

An Analysis of Deviations of Cylindrical Surfaces with the Use of Wavelet Transform

2013 ◽  
Vol 20 (1) ◽  
pp. 139-150 ◽  
Author(s):  
Krzysztof Stępień ◽  
Włodzimierz Makieła
Keyword(s):  
Wavelet Transform ◽  
Fourier Analysis ◽  
Mechanical Engineering ◽  
Science And Technology ◽  
Measurement Data ◽  
Common Method ◽  
Good Tool ◽  
Signal Measurement ◽  
Stationary Signals ◽  
Analyze Data

Abstract Wavelet transform becomes a more and more common method of processing 3D signals. It is widely used to analyze data in various branches of science and technology (medicine, seismology, engineering, etc.). In the field of mechanical engineering wavelet transform is usually used to investigate surface micro- and nanotopography. Wavelet transform is commonly regarded as a very good tool to analyze non-stationary signals. However, to analyze periodical signals, most researchers prefer to use well-known methods such as Fourier analysis. In this paper authors make an attempt to prove that wavelet transform can be a useful method to analyze 3D signals that are approximately periodical. As an example of such signal, measurement data of cylindrical workpieces are investigated. The calculations were performed in the MATLAB environment using the Wavelet Toolbox.

Evaluation of Gabor Transform Filter Threshold Identified by Initial Highest Inter-Cluster Distance Probability

2011 ◽  
Vol 204-210 ◽  
pp. 1166-1169
Author(s):  
Di Fan ◽  
Chang Zhi Lv ◽  
Mao Yong Cao
Keyword(s):  
Wavelet Transform ◽  
Frequency Analysis ◽  
Experimental Results ◽  
Gabor Transform ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Stationary Signals ◽  
Key Factor ◽  
Cluster Distance ◽  
Better Than

Gabor transform is very suitable for time-frequency analysis and good for filtering non-stationary signals. The threshold of the Gabor transform filter is a key factor for the filter’s effectiveness. A novel threshold based on initial highest inter-cluster distance probability (IH-ICDP) is described in this paper and it can make the filter more efficient. Some experiments have been carried out under several conditions to evaluate the new threshold’s characteristics. The experimental results show that Gabor transform filter with this proposed threshold works better than wavelet transform filter, especially when the signal’s SNR is very low. From the evaluation results, it is possible to consider that the threshold presented is optimal or nearly optimal.

scholarly journals Incursion Model for Nomenclature of EEG Signals via Wavelet Transform

2011 ◽  
pp. 29-33
Author(s):  
P.V.Rama Raju ◽  
V.Malleswara Rao ◽  
N.Anogjna Aurora
Keyword(s):  
Wavelet Transform ◽  
Brain Disorders ◽  
Eeg Signals ◽  
First Line ◽  
Multiple Electrodes ◽  
Accurate Picture ◽  
Short Period ◽  
Stationary Signals ◽  
Scientific Decision ◽  
Eeg Signal Processing

EEG refers to the recording of the brain’s spontaneous electrical activity over a short period of time, usually 20–40 minutes, as recorded from multiple electrodes placed on the scalp. In advance EEG signals used to be a first-line method for the diagnosis of tumors, stroke and other focal brain disorders. The structure generating the signal is not simply linear, but also involves nonlinear contributions [7, 8, 9].These non-stationary signals are may contain indicators of current disease, or even warnings about impending diseases. This work aims at providing new insights on the Electroencephalography (EEG) fragmentation problem using wavelets [2, 5]. The present work describes a computer model to provide a more accurate picture of the EEG signal processing via Wavelet Transform [16, 17, 18, 19]. The Matlab techniques have been uses which provide a system oriented scientific decision making modal [16, 17]. Within this practice the applied signal has been compared in a sequential order with dissimilar cases in attendance in the database. Special EEG signals have been considered from Physio bank [1] and Vijaya Medical Centre, Visakhapatnam, India. Analyze the signal under consideration and renowned the holder 100% truthfully.

scholarly journals Detection and Separation of Eeg Artifacts Using Wavelet Transform

2018 ◽  
Vol 7 (3) ◽  
pp. 149
Author(s):  
R. Suresh Kumar ◽  
P. Manimegalai
Keyword(s):  
Signal Processing ◽  
Wavelet Transform ◽  
Output Signal ◽  
Computation Time ◽  
Basic Structure ◽  
Wide Sense ◽  
Linear Estimation ◽  
Stationary Signals ◽  
Medical Signal ◽  
Medical Signal Processing

Bio-medical signal processing is one of the most important techniques of multichannel sensor network and it has a substantial concentration in medical application. However, the real-time and recorded signals in multisensory instruments contains different and huge amount of noise, and great work has been completed in developing most favorable structures for estimating the signal source from the noisy signal in multichannel observations. Methods have been developed to obtain the optimal linear estimation of the output signal through the Wide-Sense-Stationary (WSS) process with the help of time-invariant filters. In this process, the input signal and the noise signal are assumed to achieve the linear output signal. During the process, the non-stationary signals arise in the bio-medical signal processing in addition to it there is no effective structure to deal with them. Wavelets transform has been proved to be the efficient tool for handling the non-stationary signals, but wavelet provide any possible way to approach multichannel signal processing. Based on the basic structure of linear estimation of non-stationary multichannel data and statistical models of spatial signal coherence acquire through the wavelet transform in multichannel estimation. The above methods can be used for Electroencephalography (EEG) signal denoising through the original signal and then implement the noise reduction technique to evaluate their performance such as SNR, MSE and computation time.

scholarly journals On the Application of Wavelet Transform in Jet Aeroacoustics

Fluids ◽  
2021 ◽  
Vol 6 (8) ◽  
pp. 299
Author(s):  
Roberto Camussi ◽  
Stefano Meloni
Keyword(s):  
Wavelet Transform ◽  
Near Field ◽  
Hydrodynamic Pressure ◽  
Turbulent Jet ◽  
Experimental Database ◽  
Future Developments ◽  
Pressure Time ◽  
Propeller Noise ◽  
Stationary Signals ◽  
Conditional Statistics

Wavelet transform has become a common tool for processing non-stationary signals in many different fields. The present paper reports a review of some applications of wavelet in aeroacoustics with a special emphasis on the analysis of experimental data taken in compressible jets. The focus is on three classes of wavelet-based signal processing procedures: (i) conditional statistics; (ii) acoustic and hydrodynamic pressure separation; (iii) stochastic modeling. The three approaches are applied to an experimental database consisting of pressure time series measured in the near field of a turbulent jet. Future developments and possible generalization to other applications, e.g., airframe or propeller noise, are also discussed.

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