Search results for "wavelet"
showing 10 items of 329 documents
Trend Analysis Using Discrete Wavelet Transform (DWT) for Non-stationary NDVI Time Series in Tunisia
2021
In this paper, the trends in non-stationary Normalized Difference Vegetation Index (NDVI) Time Series (TS) over different areas in Tunisia are analyzed by applying wavelet transform and statistical tests. In the first step, the Discrete Wavelet Transform (DWT) was applied on three different time series in order to detect changes. Therefore, the different parameters of DWT were tested. In fact, the level of decomposition was calculated. The Maximum Energy to Shannon Entropy Ratio Criterion (MEER) was then investigated to choose the more suitable mother wavelet. Finally, the Mann-Kendall test (MK) was calculated for the last approximation of components to identify the variation in trend. In f…
Improved color interpolation using discrete wavelet transform
2005
New approaches to Color Interpolation based on Discrete Wavelet Transform are described. The Bayer data are split into the three colour components; for each component the Wavelet Coefficient Interpolation (WCI) algorithm is applied and results are combined to obtain the final colour interpolated image. A further anti-aliasing algorithm can be applied in order to reduce false colours. A first approach consists of interpolating wavelet coefficients starting from a spatial analysis of the input image. It was considered an interpolation step based on threshold levels associated to the spatial correlation of the input image pixel. A second approach consists of interpolating wavelet coefficients …
An improved MSD-based method for PD pattern recognition
2007
In this paper a new method for multi source partial discharge (PD) pattern recognition by discrete wavelet transform, is presented. The proposed method is based on the application of multi-resolution signal decomposition (MSD) technique applied to a 3D PD pattern image. The multi-resolution technique has shown that some detail images (horizontal, vertical and diagonal) at different decomposition levels have frequencial characteristics typical of a single discharge phenomenon. By applying this method to a double-source PD pattern it is observed that one source prevails on the other. Taking advantage from the linearity of the biorthogonal wavelet functions by using a removal and reconstructio…
A General Frame-by-Frame Wavelet Transform Algorithm for a Three-Dimensional Analysis with Reduced Memory Usage
2007
The 3D-DWT is a mathematical tool of increasing importance. However, the huge memory requirement of the algorithms that compute it is one of the main drawbacks in practical implementations. In this paper, we introduce a frame-by-frame algorithm to calculate the 3D-DWT with low memory usage. This algorithm is general, in the sense that it can be employed with any wavelet transform and, contrary to other proposals, it gets the same results as the regular wavelet transform. In addition, there is no need to divide the input video sequence into group of frames, and it can be applied in a continuous manner, so that coding efficiency is increased and no blocking artifacts appear.
172 REAL TIME EDGES DETECTION USING WAVELET TRANSFORM
2000
One of the way to extract edges uses the fast wavelet transform algorithm. This technique allows the detection of multiscale edges and is used to detect all the details, which are in a picture by modifying the scale. The real time application for edge detection involves the implementation of the algorithm on an integrated circuit like a FPGA and the development of an appropriated board. This article deals about the implementation of a wavelet transform algorithm onto a FPGA and development of an electronic board to detect multiscale edges.
Effect of parametric variation of center frequency and bandwidth of morlet wavelet transform on time-frequency analysis of event-related potentials
2017
Time-frequency (TF) analysis of event-related potentials (ERPs) using Complex Morlet Wavelet Transform has been widely applied in cognitive neuroscience research. It has been widely suggested that the center frequency (fc) and bandwidth (σ) should be considered in defining the mother wavelet. However, the issue how parametric variation of fc and σ of Morlet wavelet transform exerts influence on ERPs time-frequency results has not been extensively discussed in previous research. The current study, through adopting the method of Complex Morlet Continuous Wavelet Transform (CMCWT), aims to investigate whether time-frequency results vary with different parametric settings of fc and σ. Besides, …
EMG artifacts removal during electrical stimulation, a CWT based technique
2014
International audience; A technique of artifacts removal based on the continuous wavelet transform is presented. It uses common mother wavelets to find the temporal localization of stimulation artifacts on electromyogram (EMG) signal during an electrically evoked contraction of a muscle. This method can be used with standard stimulation pulse waveforms like monophasics or biphasics ones. It uses a histogram representation to find the best threshold to apply on the CWT domain. The algotithm is presented with Haar wavelet and then it is used with common wavelet famillies such as Daubechies or Symlets.
Application of principal component analysis and wavelet transform to fatigue crack detection in waveguides
2010
Ultrasonic Guided Waves (UGWs) are a useful tool in structural health monitoring (SHM) applications that can benefit from built-in transduction, moderately large inspection ranges and high sensitivity to small flaws. This paper describes a SHM method based on UGWs, discrete wavelet transform (DWT), and principal component analysis (PCA) able to detect and quantify the onset and propagation of fatigue cracks in structural waveguides. The method combines the advantages of guided wave signals processed through the DWT with the outcomes of selecting defect-sensitive features to perform a multivariate diagnosis of damage. This diagnosis is based on the PCA. The framework presented in this paper …
The Wavelet Scalogram in the Study of Time Series
2014
Wavelet theory has been proved to be a useful tool in the study of time series. Specifically, the scalogram allows the detection of the most representative scales (or frequencies) of a signal. In this work, we present the scalogram as a tool for studying some aspects of a given signal. Firstly, we introduce a parameter called scale index, interpreted as a measure of the degree of the signal’s non-periodicity. In this way, it can complement the maximal Lyapunov exponent method for determining chaos transitions of a given dynamical system. Secondly, we introduce a method for comparing different scalograms. This can be applied for determining if two time series follow similar patterns.
Diagnosis of Incipient Bearing Faults using Convolutional Neural Networks
2019
The majority of faults occurring in rotating electrical machinery is attributed to bearings. To reduce downtime, it is desired to apply various diagnostic methods so that bearing degradation can be detected in good time prior to a complete failure. The work presented in this paper utilizes a data-driven machine learning approach based on convolutional neural networks (CNNs) in order to diagnose different types of bearing faults. A one-dimensional CNN is trained on vibration signals and compared to a two-dimensional CNN trained in time-frequency domain using continuous wavelet transform (CWT). The proposed method is demonstrated on data collected from run-to-failure tests.The results show th…