Wavelet and Neural Structure: A New Tool for Diagnostic of Power System Disturbances

The Fourier transform can be used for analysis of nonstationary signals, but the Fourier spectrum does not provide any time-domain information about the signal. When the time localization of the spectral components is needed, a wavelet transform giving the time-frequency representation of the signal must be used. In this paper, using wavelet analysis and neural systems as a new tool for the analysis of power system disturbances, disturbances are automatically detected, compacted, and classified. An example showing the potential of these techniques for diagnosis of actual power system disturbances is presented

Time-Frequency Analysis of Femoral and Carotid Arterial Doppler Signals

AbstractIn this study, the short time Fourier transform, continuous wavelet transform (CWT) and S-transform have been used for spectral analysis of the carotid and femoral arteries Doppler signal. Each of these methods can represent the temporal evolution of Doppler spectra know as the sonograms. Time-frequency analysis by S-transform presents a linear resolution that surpasses the problem of Fourier Transform by a slipping window (STFT) of fixed length and also corrects phase concept in the wavelet transform for the analysis of non-stationary signals. This transform provides a very suitable space for extracting features and the localization of discriminating information in time and frequency in Doppler ultrasonic signals. The sonograms have been then used to compare the methods in terms of their frequency resolution and effects in determining the stenosis of carotid and femoral arteries

Wavelet-Fourier analysis of electric signal disturbances

The Fourier transform usually has been used in the past for analysis of stationary and periodic signals. Its interest is the knowledge of spectral components existing in a waveform; it doesn't matter the moment where they happen. However, when the time localization of the spectral components is needed, the Wavelet Transform (WT) can be used to obtain the optimal time frequency representation of the signal. In this paper, the joint wavelet-Fourier transform has been proposed for detecting, analyzing and compacting electrical disturbances. Finally, results of experiments have been included

Feature extraction of human sleep EEG signals using Wavelet Transform and Fourier Transform

Electroencephalogram (EEG) is a complex signal resulting from postsynaptic potentials of cortical pyramidal cells and an important brain state indicator with specific state dependent features. Modern brain research is intimately linked to the feasibility to record the EEG and to its quantitative analysis. EEG spectral analysis is an important method to investigate the hidden properties and hence the brain activities. Spectral analysis of sleep EEG signal provides acute insight into the features of different stages of sleep which can be utilized to differentiate between normal and pathological conditions. This paper describes the process of extracting features of human sleep EEG signals through the use of multi resolution Discrete Wavelet Transform and Fast Fourier Transform. Discrete Wavelet Transform offers representations of the signals in the time-frequency plane giving information regarding the time localization of the spectral components at different stages of sleep in human beings and Fast Fourier Transform provides the spectral information. This paper also discusses the clinical correlation associated with sleep EEG signals in brief

Matched wavelet construction and its application to target detection

This dissertation develops a new wavelet design technique that produces a wavelet that matches a desired signal in the least squares sense. The Wavelet Transform has become very popular in signal and image processing over the last 6 years because it is a linear transform with an infinite number of possible basis functions that provides localization in both time (space) and frequency (spatial frequency). The Wavelet Transform is very similar to the matched filter problem, where the wavelet acts as a zero mean matched filter. In pattern recognition applications where the output of the Wavelet Transform is to be maximized, it is necessary to use wavelets that are specifically matched to the signal of interest. Most current wavelet design techniques, however, do not design the wavelet directly, but rather, build a composite wavelet from a library of previously designed wavelets, modify the bases in an existing multiresolution analysis or design a multiresolution analysis that is generated by a scaling function which has a specific corresponding wavelet. In this dissertation, an algorithm for finding both symmetric and asymmetric matched wavelets is developed. It will be shown that under certain conditions, the matched wavelets generate an orthonormal basis of the Hilbert space containing all finite energy signals. The matched orthonormal wavelets give rise to a pair of Quadrature Mirror Filters (QMF) that can be used in the fast Discrete Wavelet Transform. It will also be shown that as the conditions are relaxed, the algorithm produces dyadic wavelets which when used in the Wavelet Transform provides significant redundancy in the transform domain. Finally, this dissertation develops a shift, scale and rotation invariant technique for detecting an object in an image using the Wavelet Radon Transform (WRT) and matched wavelets. The detection algorithm consists of two levels. The first level detects the location, rotation and scale of the object, while the second level detects the fine details in the object. Each step of the wavelet matching algorithm and the object detection algorithm is demonstrated with specific examples

Application of extended time-frequency domain average in ultrasonic detecting

Ultrasonic signal detection is essential for the ultrasonic-based applications such as ultrasonic flow measurements and nondestructive testing. The paper proposes three extended time-frequency domain average (ETFDA) techniques, which are based on the smoothed pseudo-Wigner-Ville distribution, continuous wavelet transform and Hilbert-Huang transform. These techniques combine beneficial time-frequency localization characteristics of the time-frequency analysis and abilities of the time domain averaging (TDA) to suppress noise interference. They are thus well adapted for detection of the ultrasonic signals even when they are strongly smeared by the noise or distorted in the medium. A number of tests conducted on simulated and actual ultrasonic signals have demonstrated that ETFDA provides a solid performance

Signal De-noising method based on particle swarm algorithm and Wavelet transform

Wavelet analiza je novi alat za analizu odnosa vrijeme-frekvencija, razvijen na temelju Fourierove analize s dobrim svojstvom lokaliziranja vremena i frekvencije i mogućnosti donošenja višestrukih rješenja. Koristi se u cijelom nizu primjena u području obrade signala. U ovom se radu analizira primjena wavelet transforma u filtriranju signala korištenjem poboljšane optimalizacije roja čestica i predlaže inteligentna metoda uklanjanja šuma iz signala zasnovana na wavelet analizi. Metoda koristi Center Based Particle Swarm Algorithm (CBPSO) za izbor optimalnog praga za svaki pod-pojas u različitim mjerilima, inteligentno razaznavajući vrstu šuma iz samog signala, što ne zahtijeva nikakvo prethodno poznavanje šuma. Poboljšani algoritam roja čestica koristi se da potakne optimalni izbor različitih mjerila praga wavelet domena, što je dovelo do uklanjanja šuma iz signala kod različitih tipova pozadinskog šuma, i povećane brzine wavelet transforma i wavelet konstrukcije te ima veću fleksibilnost. Eksperimentalni rezultati su pokazali da se CBPSO algoritmom može postići bolji učinak uklanjanja šuma.Wavelet analysis is a new time-frequency analysis tool developed on the basis of Fourier analysis with good time-frequency localization property and multi-resolution characteristics, which is in a wide range of applications in the field of signal processing. This paper studies the application of wavelet transform in signal filtering, by using an improved particle swarm optimization, proposes an intelligent signal de-noising method based on wavelet analysis. The method uses a Center Based Particle Swarm Algorithm (CBPSO) to select the optimal threshold for each sub-band in different scales, learning the type of noise from the signal itself intelligently, which does not require any prior knowledge of the noise. The improved particle swarm algorithm is used to enhance the optimal choice of the different scales of the wavelet domain threshold, which realized the signal De-noising under different types of noise background, and improved the speed of wavelet transform and wavelet construction, and has greater flexibility. The experimental results showed that CBPSO algorithm can get better De-noising effect

Assessment of Power Quality Events by Hilbert Transform Based Neural Network

Now a day‟s power quality (PQ) and power supply related problems have become important problem both for the end user and the utility company. The PQ issues and related phenomena are getting more dominant due to the use of power electronics devices, non-linear loads, industrial grade rectifiers and inverters, etc. This nonlinear equipment‟s not only introduce distortions in the amplitude but also in frequency, phase of the power signal, thereby degrades quality of power. In order to improve power quality, continuous monitoring of the signal is required. For continuous monitoring of the signal, the detection and classification of the power signal in power systems are important. In this work a new Time-frequency analysis method, has been introduced to detect and analyze for the non-stationary and nonlinear power system disturbance signals, known as HilbertHuang transform (HHT). Hilbert-Huang transform is able to find out, the starting time, ending time, instantaneous frequency-time, and instantaneous amplitude- time of the disturbance signal can be obtained precisely. Hilbert Huang transforms decomposition algorithm can be used for accurate detection & localization of point of disturbance of PQ events like voltage sag, swell, sag with harmonic, swell with harmonic, interruption, etc. Similarly the same power quality event was passed through a wavelet technique. Both results are obtained from decomposition of PQ events and pass through a back propagation neural network for proper classification of different types of PQ events. In this work, detection of PQ disturbances by HHT is compared with an advance wavelet transform technique. The localization and detection of PQ events have been thoroughly investigated for each of the power signal disturbances using HHT and wavelet transform. Finally, comparative classification accuracy has been estimated for both types of the decomposition technique for different types of PQ events

Quantification of LV Volumes with 4D Real-Time Echocardiography

This paper presents a new 4D (3D+Time) expansion of echocardiographic volumes on complex exponential wavelet-like basis functions called Brushlets. Brushlet functions offer good localization in time and frequency and decompose a signal into distinct patterns of oriented textures, invariant to intensity and contrast range. Automatic left ventricle (LV) endocardial border detection is carried out in the transform domain where speckle noise is attenuated while cardiac structure location is preserved. Quantitative validation and clinical applications of this new spatio-temporal analysis tool are reported with results on phantoms and clinical data sets to quantify LV volumes and ejection fraction