Wavelet Signal Research Articles

Denoising of Contrast-Enhanced Ultrasound Cine Sequences Based on a Multiplicative Model

Speckle noise is an inherent characteristic of dynamic contrast-enhanced ultrasound (DCEUS) movies and ultrasound images in general. Speckle noise considerably reduces the quality of these images and limits their clinical use. Currently, temporal compounding and maximum intensity persistence (MIP) are among the most widely accepted processing methods enabling the visualization of vasculature using DCEUS. A different approach has been used in this study, in order to improve the noise removal, while enabling the investigation of CEUS dynamics. A multiplicative model for the formation of DCEUS speckled images is adopted and the log-transformed cines are processed. A preprocessing step was performed, locally removing low value outliers. Due to the fast-changing spatial distribution of microbubbles inside the vasculature, the noise in consecutive DCEUS frames is independent, facilitating its removal by temporal denoising. Noise reduction is efficiently achieved by wavelet denoising, in which the signal's wavelet coefficients are thresholded and small-value noise-related coefficients are discarded. The main advantage of using wavelet denoising in the present context is its ability to estimate ultrasound contrast agents' (UCA) concentration over time adaptively, without assuming a model or predefining the signal's degree of smoothness. The performance of wavelet denoising was compared against MIP, temporal compounding, and Log-normal model fitting. Phantom experiments showed improved SNR, using wavelet denoising over a wide range of UCA concentrations (MicroMarker, 0.001-1%). In the in vivo tests, improved noise removal was achieved, reflected by a significantly lower coefficient of variation in homogeneous vascular regions (p < 0.01).

Multi-class fault diagnosis of induction motor using Hilbert and Wavelet Transform

The information extraction capability of two widely used signal processing tools, Hilbert Transform (HT) and Wavelet Transform (WT), is investigated to develop a multi-class fault diagnosis scheme for induction motor using radial vibration signals. The vibration signals are associated with unique predominant frequency components and instantaneous amplitudes depending on the motor condition. Using good systematic and analytical approach this fault frequencies can be identified. However, some faults either electrical or mechanical in nature are associated with same or similar vibration frequencies leading to erroneous conclusions. Genetic Algorithm (GA) is proposed and used successfully to find the most relevant fault frequencies in radial (vertical) frame vibration signal which can be used to diagnose the induction motor faults very effectively even in the presence of noise. The information obtained by Continuous Wavelet Transform (CWT) was found to be highly redundant compared to HT and thus by selecting the most relevant features using GA, the fault classification accuracy has considerably improved especially for CWT. Almost similar fault frequencies were found using CWT+GA and HT+GA for radial vibration signal.

Feature extraction of bearing vibration signals using second generation wavelet and spline-based local mean decomposition

In order to extract the fault feature frequency of weak bearing signals, we put forward a local mean decomposition (LMD) method combining with the second generation wavelet transform. After performing the second generation wavelet denoising, the spline-based LMD is used to decompose the high-frequency detail signals of the second generation wavelet signals into a number of production functions (PFs). Power spectrum analysis is applied to the PFs to detect bearing fault information and identify the fault patterns. Application in inner and outer race fault diagnosis of rolling bearing shows that the method can extract the vibration features of rolling bearing fault. This method is suitable for extracting the fault characteristics of the weak fault signals in strong noise.

A saliency detection model using aggregation degree of color and texture

Visual saliency detection aims to extract the region that attracts greater attention of human vision system than other parts of an image, which is widely exploited but remains a challenge in the computer vision field. Relative to the background, the salient regions are more aggregative in color or texture distribution. In this paper, we introduce the conception of aggregation degree, and propose a novel bottom-up saliency detection framework using the aggregation degree of color and texture. Firstly, the color saliency areas are extracted from each channel in the Lab and HSV color space, and the significance of each saliency area is evaluated by the aggregation degree and their divergence. Then the texture saliency map is extracted with the local phase of hypercomplex, which is constructed by the detail signal of stationary wavelet transform (SWT) and its Hilbert transform. Finally, we design an effective mechanism for credit feature maps selection depending on aggregation degree, and the final saliency map is reconstructed by fusing credit feature maps. Extensive experiments with ASD database and the performance comparison with seven state-of-the-art algorithms demonstrate that our algorithm achieves better performance.

IJARCCE - Computer and Communication Engineering

Lower Atmospheric Wind Profiler (LAWP) is used to provide continuous high resolution wind measurements in the lower atmosphere. Signal processing of this atmospheric radar is necessary to estimate the wind profiler moments and signal to noise ratio (SNR). The signals, which are processed in the present work has been obtained from the LAWP at National Atmospheric Research Laboratory (NARL), Gadanki, India. SNR computation is an important parameter in signal processing. This paper discusses improvement of SNR using wavelets for LAWP signals. In this paper wavelet denoising is used to improve SNR. Denoising is performed by selecting a DB10 wavelet. SNR for this data is computed for original signal and de-noised signal. Results show that there is 7dB improvement in SNR after denoising using DB10 wavelet for LAWP signals.

The New Methodology of Diagnosis for Transformer Inter-Turn Short Circuit Based on Wavelet Analysis

Based on the large number of experimental data analysis and processing, a new inter-turn short circuit transformer diagnostic methods is proposed. In this paper, take the grounding current of transformer core as the signal source. Using wavelet multi-resolution technology the signal wavelet multiscale decomposition, we can get high frequency components of the signal. Achieve short-circuit fault diagnosis between transformer winding turns by comparing the number of the high-frequency component contained in different signals in the decomposition of the same scale.

Speech Steganography In Wavelet Domain Using Continuous Genetic Algorithm

In this paper, we present a new adaptive steganography method using Lifting Wavelet Transform (LWT). In this method, we first calculate the LWT of the sample of host and secret speech signal. Then wavelet coefficients of secret speech signal will be fitted effectively and efficiently in host signal wavelet coefficients using continuous genetic algorithm. We used indirect replacement technique in 5 bits host using a proposed formula. Due to the quantization error, there are some differences between the secret signal before steganography and the extracted signal after steganography. However, these differences have an appropriate Gaussian noise model. We compress these differences using Huffman lossless compression method. The compression rate of such differences approach to the entropy, which is derived from Shannon's first theorem. Huffman lossless compression method, cause to small noise. We these compressed differences sent along the stego signal. The experimental results show that the proposed model has a statistical transparency higher than Least Significant Bit (LSB), Frequency Masking (FM) and Efficient Wavelet Masking (EWM) algorithms in time domain and frequency domain.

Continuous wavelet application for the assessment of neural potential interaction during time discrimination task.

The aim of the paper is to assess the interaction of the neural potentials during a time discrimination psychoacoustic task. Ten subjects participated in the experiment and were asked about the equality of two acoustic pulses: one reference of 500ms and one trial that varied from 420ms to 620ms. During the experimental procedure, Electroencephalogram (EEG) and Event Related Potential (ERP) signals were recorded. The analysis combines results from Continuous Wavelet signal processing and subjects responses which were analyzed based on psychoacoustic theory. The Wavelet Coherence metric index is employed to assess the interaction of neural potentials. The results indicate the points at which the duration of the trial pulse is equal to 560ms and 460ms as the minimum and maximum of the Wavelet Coherence metric index, respectively. This observation is valid in most electrodes, for all basic EEG rhythms, revealing in parallel the differentiation of the gamma rhythm, in relation to the others. These maximum and minimum values are correlated to the Just Noticeable Difference (JND) in pulses duration, calculated by the psychoacoustic analysis.

Asymmetric Wavelet Signal of Gravitational Waves

On the example of a complex of 10 pulsars shows a method to identify the statistical models and the analysis of the model amplitude of gravitational waves, depending on the rotation periods of pulsars. Hilbert applied invariants. Shows the levels of adequacy asymmetric wavelet signals detected by statistical data. Proved insufficient deterministic models for the Laplace-Mandelbrot law. For 10 pulsars obtained statistical model pulsating universe. The model contains two modified 's law and two asymmetric wavelets oscillatory perturbation of gravitational waves in overcoming the energy crisis. Interpretation of the resulting statistical model is given based on the relativistic theory of gravitation. To do this, four components of the statistical model are divided into two cones of causality.

An Audio Hiding Algorithm Based on Spline Interpolation and Wavelet Transform

Based on the characteristics of sampling points to get approximate numerical interpolation point, first to the whole speech signal wavelet decomposition, the extract is not sensitive to the human ear hearing wavelet coefficients as the location of the embedded watermark information, the interpolation factor by using spline interpolation to replace known the low-frequency wavelet coefficients to embed watermark. The simulation results show that the algorithm not only has imperceptibility, such as MP3 compression, low-pass filtering, white noise, sampling, quantitative attack has good robustness. It achieves a better balance in robustness and imperceptibility.

Mechanical Seal Condition Monitoring Technology Research

Since the measurement of mechanical seals film thickness is difficult, the condition monitoring method based on empirical mode decomposition (EMD) is proposed. EMD is a time-frequency analysis method. It is adaptive and does not need predefined decomposition basis function. The AE signal is decomposed into a series of Intrinsic mode function (IMF) with independent characteristics of different time scales and different frequency band. It is realized that the mechanical seal contact status is accurately identified through extracting the feature of correlation coefficient which is calculated between original AE signal and multi-scale Laplace wavelet.

Quantification of signal and noise in two-way transmitted wavefields to improve well ties in Cooper Basin, Australia

Permian coal beds at 2400–2900 m depth in Cooper Basin, Australia have normal-incident reflection coefficient values as large as [Formula: see text]. If internal multiples are included in synthetic seismograms, excellent correlations exist between the synthetic seismogram and seismic, even when more than 50 coal beds are present. However, neither the synthetic seismogram nor the seismic tie the well-log lithologic boundaries because the incident wavefield that strikes a lithologic boundary and returns to the surface contains a signal wavelet followed by high-amplitude noise, which are interbed multiple reflections. Because the spectra of the signal and noise coda at a given two-way time normally do not overlap, time-varying Gaussian filters applied to the near-offset stack enhance the signal and suppress the noise coda. After filtering, the apparent time delay of reflections introduced by the coal beds is removed with variable time shifts (time compression), based on the estimated time-varying signal wavelets. The two-step process of low-pass filtering and compression yields seismic events that successfully tie the lithologic boundaries in the borehole, although with limited resolution. Our preliminary tests on a seismic line indicate that the horizon event associated with the base of a 500-m-thick coal sequence is more coherently imaged with our processing than with conventional processing.

The Wavelet De-Noising of Vibration Signals for Aircraft Rolling Bearings

Aiming at air rolling bearing vibration signals low SNR and nonstationary characteristics, taking wavelet theory and principles of the wavelet noise reduction for air vibration signals of rolling bearings to conduct wavelet noise reduction processing.By means of the simulation signal wavelet noise reduction processing and fast Fourier transform, the contrast analysis of the vibration signals after wavelet noise reduction and FFT transform and the original signal directly to the result of the fast Fourier transform, and thus prove the validity of the vibration signal wavelet noise reduction. Through the actual vibration signals of bearing conductnoise reduction processing, the result is a further indication of the superiority of wavelet noise reduction in eliminate noise interference.

Simultaneous determination of propanil and monalide by modified glassy carbon electrode with nickel oxide nanoparticles, using partial least squares modified by orthogonal signal correction and wavelet packet transform

In this study a Glassy carbon electrode (GCE) modified with nickel oxide nanoparticles was developed. This electrode has a good sensitivity and electrocatalytic activity toward the oxidation of propanil and monalide over a wide concentration range. The propanil and monalide oxidation mechanism on the electrode surface was investigated, and the overall number of electrons involved in the electrocatalytic oxidation was found to be 3. The detection limit of this electrode for determination of propanil and monalide was 0.05 and 0.21μM and its relative standard deviation for 10 independent measurements was 3 and 3.7%, respectively. Finally the proposed electrode exhibited several advantageous features, including simple preparation, fast response time, and good repeatability. The electrode was successfully applied for simultaneous determination of propanil and monalide based on partial least squares (PLS) regression with orthogonal signal correction (OSC) and wavelet packet transform (WPT) as pre-processing tools with overlapping peaks. In this case, by optimization, the kind of wavelet function, the decomposition level, the number of OSC components and number of PLS factors for the OSC-WPT-PLS method were selected as Daubechies 3, 1, 1 and 4, respectively. The relative standard errors (R.S.E) of prediction obtained for two components using OSC-WPT-PLS, WPT-PLS and PLS were compared. Experimental results demonstrated that the OSC-WPT-PLS method produced the best results among the three methods.

Simulating the signature produced by a single airgun under real gas conditions

Models that simulate the signature of single airguns form the basis for modeling the signals of airgun arrays. Most of the existing models assume that the air inside the produced bubble is ideal gas, which may lead to errors because of the high operating pressure of the airguns. In this study, we propose a model that precisely simulates the signals of single airguns by applying the Van der Waals equation based on the Ziolkowski algorithm. We also consider a thermodynamically open quasistatic system, the heat transition between water and gas, the throttling effect of the port and the bubble rise, and the effect of the sea surface. Modeling experiments show that (1) the energy of the source increases and the signal-to-noise ratio of the signature wavelet decreases with increasing seawater temperature, (2) the reflection coefficient of the sea surface under the actual state and depth of the source affects the notch caused by the surface reflection, (3) the computed signature with the proposed model is very close to the actual data, and (4) the proposed model accurately simulates the signature of single airguns.

Recognition of phonetic Arabic figures via wavelet based Mel Frequency Cepstrum using HMMs

This work describes the recognition of phonetic Arabic figures. Speech recognition technology has made steady progress in its 50years history and has succeeded in creating several substantial applications. The goal of speech recognition research is to produce a machine which will recognize accurately the normal human speech from any speaker.To improve the performance of recognition system, an effective and robust technique is proposed to extract speech feature. The input speech signal is decomposed into various frequency channels based on time-frequency multi-resolution property of wavelet analysis. For capturing the characteristics of the signal, the Mel-Frequency Cepstrum Coefficients “MFCCs” of the wavelet channels are calculated. Hidden Markov Models “HMMs” were used for the recognition stage. Different forms of wavelet functions were used to evaluate the best wavelet signal to extract the best features of the signals. It is found that the wavelet signal “db8” gives the highest values of recognition accuracy rate. A recognition rate of 98% was obtained using the proposed feature extraction technique.A comparison between different features of speech is given. The features based on the Cepstrum give accuracy of 94% for speech recognition while the features based on the short time energy in time domain give accuracy of 92%. The features based on formant frequencies give accuracy of 95.5%. It is clear that the features based on MFCCs with accuracy of 98% give the best accuracy rate. So the features depend on MFCCs with HMMs may be recommended for recognition of the spoken Arabic digits.

Flow regime identification in a novel circulating-turbulent fluidized bed

Abstract Whether flow dynamics in a novel circulating-turbulent fluidized bed (C-TFB) belongs to a new flow regime or just a transition state is controversial. In order to resolve this confusion, transient fluctuation signal processing and wavelet analysis have been used to identify the flow regimes in five specially designed flow systems, which have been proven to be effective tools to extract detailed information of flow features in different flow regimes. Furthermore, the fuzzy system identification method, which was used to process wavelet energy data of various scales obtained by wavelet analysis, was employed to make a quantitative distinction among the flow regimes. The results showed that the flow regime in the C-TFB is a novel flow regime, which is different from traditional flow regimes such as bubbling, turbulent, fast fluidization (FF) and dense suspension up-flow (DSU) regimes. The new regime has some unique characteristics including intensive gas–solids interaction, high gas–solids contact efficiency, two peaks of probability density distribution (PDD) curves in the center of the fluidized bed, broader spectrum of solids holdup for PDD curves, a decreasing extent of gas–solids separation extent and unique radial detail wavelet energy percentage profiles. Quantitative analysis by the fuzzy system identification method further confirmed that the flow behavior in the C-TFB can be described as a novel flow regime.

Study on ECG Signal Wavelet Denoising Algorithm Based on the MSP430 Platform

A novel ECG filtering algorithm was researched, which was suitable for the MSP430 platform. Several ECG signal wavelet denoising algorithms were simulated on matlab to compare their filtering effect. The mexican-hat wavelet denoising algorithm can get a better effect on filtering of ECG signal (No. 203 data from the MIT-BIH ECG database). The time complexity of the algorithm is O(n2), and the SNR can also be up to 66%.

Wavelet De-Noising Method Based on a New Kind of Threshold Function

Based on wavelet threshold de-noising method which put forward by Donoho, we analyze and compare the advantages and disadvantages of hard threshold, soft threshold and some improved threshold methods. Based on polynomial interpolation method, a new threshold function is proposed, which is continuous in the whole threshold area, with little constant deviation between the estimated wavelet coefficients and original signal wavelet coefficient, and high-order derivative and easy processing. The simulation results show that this method can give better optical effect, SNR gains and MSE performance.

Wavelet De-Noising Method Based on a New Kind of Threshold Function

Based on wavelet threshold de-noising method which put forward by Donoho, we analyze and compare the advantages and disadvantages of hard threshold, soft threshold and some improved threshold methods. Based on polynomial interpolation method, a new threshold function is proposed, which is continuous in the whole threshold area, with little constant deviation between the estimated wavelet coefficients and original signal wavelet coefficient, and high-order derivative and easy processing. The simulation results show that this method can give better optical effect, SNR gains and MSE performance.