Wavelet Transform Technique Research Articles

Acoustic Emission Signal Analysis for Tool Condition Monitoring in Microendmilling of Aluminium Alloy

In this study, the effect of tool wear is correlated with acoustic emission (AE) signal during microendmilling of aluminium alloy (AA 1100). The AE signals were acquired using Kistler make AE sensor and the signal features are analyzed in time domain (root mean square (RMS)) and frequency domain (dominant frequency and amplitude). The dominant frequency of the AE signal shows increasing trend with increase in the tool wear, where as AERMS show uneven trend. The discrete wavelet transformation technique (DWT) has also been carried out by decomposing the required AE signal in different frequency bands. The AERMS and specific AE energy were computed for the decomposed AE signals. From the specific AE energy, it is observed that shearing occurs during microendmilling and also found to be similar that of macro-regieme endmilling. The result demonstrated that the AE signals are potential indicator for tool condition monitoring in microendmilling.

Wavelet Analysis of High Resolution Aeromagnetic (HRAM) Data over Part of Chad Basin, Nigeria

A High Resolution Aeromagnetic Data over part of Chad basin, Nigeria has been analyzed and interpreted with the objective of identifying the nature of magnetic source geometries responsible for hydrocarbon prospectivity using wavelet transformation technique. The technique was chosen because of the inability of Fourier transform to interpret location of features from the transform coefficients. In this work, wavelet transform coefficients were obtained using Morlet mother wavelet. The Scalogram was computed from the transform coefficients which reveal various magnetic source geometries. In other to identify the nature and location of sources geometries, a scale normalization factor was introduced on the wavelet coefficient which increases the resolution of various magnetic sources in the Scalogram plots. The result obtained from the analysis has revealed various magnetic source geometries which are important in hydrocarbon prospectivity in Chad basin, Nigeria.

Fuzzy Watermarking for Colour Images based on Fusion of Discrete Wavelet Transform (DWT) Technique and Histogram Stretching

This study presents a method of scheming colour image by using fuzzy watermarking through Discrete Wavelet Transform (DWT) and histogram stretching methods. This study aims further at achieving and developing robustness, imperceptibility and available capacity of cover image for the watermarked image, which can withstand against various attacks. Digital watermarking techniques are used to protect the copyrights of multimedia data by embedding secret information in the host media, for example, embedding in images, audios, or videos. Many watermarking techniques have been proposed in the literature to solve the copyright violation problems, but most of these techniques failed to satisfy both imperceptibility and robustness requirements. In this research, adaptive RGB colour image fuzzy watermarking technique is proposed. The host image is converted from RGB to YCbCr colour space to preserve imperceptibility and robustness, then, Cb component is extracted and partitioned into four quadrants. While the Histogram Stretching will be used to convert the Watermark image to 4-bits plan to preserve the capacity for watermarking.

Detection of fluvial sand systems using seismic attributes and continuous wavelet transform spectral decomposition: case study from the Gulf of Thailand

Fluvial sands host excellent oil and gas reservoirs in various fields throughout the world. However, the lateral heterogeneity of reservoir properties within these reservoirs can be significant and determining the distribution of good reservoirs is a challenge. This study attempts to predict sand distribution within fluvial depositional systems by applying the Continuous Wavelet Transformation technique of spectral decomposition along with full spectrum seismic attributes, to a 3D seismic data set in the Pattani Basin, Gulf of Thailand. Full spectrum seismic attributes such as root mean square and coherency help to effectively map fluvial systems down to certain depth below which imaging is difficult in the intervals of interest in this study. However, continuous wavelet transform used in conjunction with other attributes by applying visualization techniques of transparency and RGB can be used at greater depths to extract from 3D seismic data useful information of fluvial depositional elements. This workflow may help to identify different reservoir compartments within the fluvial systems of the Gulf of Thailand.

Intelligent drowsy eye detection using image mining

Cell phones, MP4 players, and GPS all distract drivers, forcing them to divide their attention to what is happening outside and what is happening inside the vehicle. Preventing drivers from falling asleep at the wheel and drifting off on the road is an increasingly hard task but a solution can be achieved with today's technology: by capturing driver's eye gaze and thus the level of their exhaustion, the potential for safer roads is real. Today, image mining systems can automatically extract important information from an image dataset which can then be used to classify images and image relationship that been applied in different areas. The basic challenge in image mining research area is to determine how image level pixel representation in an original image can be used and constructed to then identify meaningful information or form relationships. In this research Intelligent Drowsy Eye Detection, using an Image Mining (IDEDIM) system is proposed. The proposed architecture would use different feature extraction techniques and three data mining classification techniques to aid with accurate information collection. Two thousand left and right eye images were used to test the developed system by Discrete Wavelet Transform (DWT), Statistical features, and Local binary pattern (LBP) feature extraction techniques. The extracted features were used as input for Decisions Tree C5.0, K Nearest Neighbor (KNN), and the Support Vector Machine (SVM) Classifier. After several experimental sets, the C5.0 and KNN classifier were performing better than SVM classifier. Based on the results, we recommend the inclusion of LBP and DWT in conjunction with C5.0 with KNN as a classification technique. To validate the achieved results, Receiver Operating Characteristic (ROC) curve is used to compare among the proposed classifiers. The proposed system can be integrated with the existing subsystem into real time Drowsy Detection System to achieve excellent accuracy and performance.

A hybrid wavelet transform based short-term wind speed forecasting approach.

It is important to improve the accuracy of wind speed forecasting for wind parks management and wind power utilization. In this paper, a novel hybrid approach known as WTT-TNN is proposed for wind speed forecasting. In the first step of the approach, a wavelet transform technique (WTT) is used to decompose wind speed into an approximate scale and several detailed scales. In the second step, a two-hidden-layer neural network (TNN) is used to predict both approximated scale and detailed scales, respectively. In order to find the optimal network architecture, the partial autocorrelation function is adopted to determine the number of neurons in the input layer, and an experimental simulation is made to determine the number of neurons within each hidden layer in the modeling process of TNN. Afterwards, the final prediction value can be obtained by the sum of these prediction results. In this study, a WTT is employed to extract these different patterns of the wind speed and make it easier for forecasting. To evaluate the performance of the proposed approach, it is applied to forecast Hexi Corridor of China's wind speed. Simulation results in four different cases show that the proposed method increases wind speed forecasting accuracy.

Dynamic Parameter Estimation of Inter-area Oscillations in a Power System by a Combination of Kalman-Filtering and Wavelet Transformation Techniques

Within this paper two techniques for online estimation of frequency and damping ratio of inter-area oscillations in a power system are compared. The techniques are the Kalman filtering technique and the wavelet transformation in combination with the random decrement technique. First, the two methods are compared regarding the accuracy of frequency estimation and the accuracy of damping estimation by use of a test system with known parameters. Second the capability of detecting changes in the frequency or in the damping ratio is investigated. Subsequently a combined approach consisting of the Kalman filtering technique as well as the wavelet transformation is developed to overcome the drawbacks of both methods. The usability of the developed method is finally shown on basis of measurement data of the European power system

Compression of ECG Signals Based on DWT and Exploiting the Correlation between ECG Signal Samples

This paper presents a hybrid technique for the compression of ECG signals based on DWT and exploiting the correlation between signal samples. It incorporates Discrete Wavelet Transform (DWT), Differential Pulse Code Modulation (DPCM), and run-length coding techniques for the compression of different parts of the signal; where lossless compression is adopted in clinically relevant parts and lossy compression is used in those parts that are not clinically relevant. The proposed compression algorithm begins by segmenting the ECG signal into its main components (P-waves, QRS-complexes, T-waves, U-waves and the isoelectric waves). The resulting waves are grouped into Region of Interest (RoI) and Non Region of Interest (NonRoI) parts. Consequently, lossless and lossy compression schemes are applied to the RoI and NonRoI parts respectively. Ideally we would like to compress the signal losslessly, but in many applications this is not an option. Thus, given a fixed bit budget, it makes sense to spend more bits to represent those parts of the signal that belong to a specific RoI and, thus, reconstruct them with higher fidelity, while allowing other parts to suffer larger distortion. For this purpose, the correlation between the successive samples of the RoI part is utilized by adopting DPCM approach. However the NonRoI part is compressed using DWT, thresholding and coding techniques. The wavelet transformation is used for concentrating the signal energy into a small number of transform coefficients. Compression is then achieved by selecting a subset of the most relevant coefficients which afterwards are efficiently coded. Illustrative examples are given to demonstrate thresholding based on energy packing efficiency strategy, coding of DWT coefficients and data packetizing. The performance of the proposed algorithm is tested in terms of the compression ratio and the PRD distortion metrics for the compression of 10 seconds of data extracted from records 100 and 117 of MIT-BIH database. The obtained results revealed that the proposed technique possesses higher compression ratios and lower PRD compared to the other wavelet transformation techniques. The principal advantages of the proposed approach are: 1) the deployment of different compression schemes to compress different ECG parts to reduce the correlation between consecutive signal samples; and 2) getting high compression ratios with acceptable reconstruction signal quality compared to the recently published results.

Lossy Image Compression Using Discrete Wavelet Transform and Thresholding Techniques

Results of lossy image compression using wavelet transforms and several thresholding techniques are presented here. The analyzed image was divided into little sub-images and each one was decomposed in a vector following a Hilbert fractal curve. The wavelet transform was applied to each vector and some of the high frequency components were suppressed based on some threshold criteria. Different levels of wavelet decomposition and wavelet mother functions were assessed. The Huffman coding algorithm was then applied in order to reduce image weight. Simulation results have revealed that high compression ratios were obtained with the mean and the standard deviation thresholding algorithms at different levels of wavelet decomposition.

A Novel Islanding Detection Method for Inverter-Based Distributed Generation Systems

In the area of renewable energy technologies, islanding of distributed generation system needs to be prevented due to safety reasons and to ensure quality of power supplied to the customers. Several islanding detection methods based on passive and active schemes have been proposed in the literature. Passive methods have a large non detection zone (NDZ), While active schemes degrade power quality. This paper proposed a new passive islanding detection method which combines the Wavelet-transform and neural network techniques. This method can reduce the NDZ to zero without any perturbation that deteriorates the power quality. The simulation results show that the proposed islanding detection method is effective and robust in all kind of conditions.

Short-term wind speed forecasting based on a hybrid model

Wind power is currently one of the types of renewable energy with a large generation capacity. However, operation of wind power generation is very challenging because of the intermittent and stochastic nature of the wind speed. Wind speed forecasting is a very important part of wind parks management and the integration of wind power into electricity grids. As an artificial intelligence algorithm, radial basis function neural network (RBFNN) has been successfully applied into solving forecasting problems. In this paper, a novel approach named WTT–SAM–RBFNN for short-term wind speed forecasting is proposed by applying wavelet transform technique (WTT) into hybrid model which hybrids the seasonal adjustment method (SAM) and the RBFNN. Real data sets of wind speed in Northwest China are used to evaluate the forecasting accuracy of the proposed approach. To avoid the randomness caused by the RBFNN model or the RBFNN part of the hybrid model, all simulations in this study are repeated 30 times to get the average. Numerical results show that the WTT–SAM–RBFNN outperforms the persistence method (PM), multilayer perceptron neural network (MLP), RBFNN, hybrid SAM and RBFNN (SAM–RBFNN), and hybrid WTT and RBFNN (WTT–RBFNN). It is concluded that the proposed approach is an effective way to improve the prediction accuracy.

Accuracy and Data Compression Trade-Offs for Power Quality Disturbance Representation with DWT and PCA techniques

This paper analyses and compares different digital signal processing techniques to capture with fidelity the power lines disturbances. The benefits and shortcomings of both Discrete Wavelet Transform (DWT) and Principal Component Analysis (PCA) techniques are assessed. Their implementation, application and test-cases with recorded power signals are described in the paper. We define the data compression ratio (or efficiency) and the signal reconstruction accuracy after compression as the main criteria for comparing these techniques. In this paper the trade-offs of accuracy in the disturbances representation and compression efficiency were exploited. After systematic comparisons, our experimental results have shown shortcomings of the DWT and PCA techniques not yet addressed in previous works.

Imaging-Based Rating for Corrosion States of Weathering Steel Using Wavelet Transform and PSO-SVM Techniques

AbstractWeathering steel with a natural corrosion-resistant feature has been widely applied to the structural components of steel bridges. However, severe surface corrosion damage has been frequent...

Denoising of an Image Using Discrete Stationary Wavelet Transform and Various Thresholding Techniques

Image denoising has remained a fundamental problem in the field of image processing. With Wavelet transforms, various algorithms for denoising in wavelet domain were introduced. Wavelets gave a superior performance in image denoising due to its properties such as multi-resolution. The problem of estimating an image that is corrupted by Additive White Gaussian Noise has been of interest for practical and theoretical reasons. Non-linear methods especially those based on wavelets have become popular due to its advantages over linear methods. Here I applied non-linear thresholding techniques in wavelet domain such as hard and soft thresholding, wavelet shrinkages such as Visu-shrink (non-adaptive) and SURE, Bayes and Normal Shrink (adaptive), using Discrete Stationary Wavelet Transform (DSWT) for different wavelets, at different levels, to denoise an image and determine the best one out of them. Performance of denoising algorithm is measured using quantitative performance measures such as Signal-to-Noise Ratio (SNR) and Mean Square Error (MSE) for various thresholding techniques.

Securing Image Steganogarphy Based on Visual Cryptography And Integer Wavelet Transform

The increased use of internet communication has given rise to the field of image steganography and made it necessary to secure digital content. Current image steganography techniques lack novelty and are based on using traditional cryptography solutions for secret image that is embedded in cover image. This research paper proposed and implemented a new method to secure embedded secret image using visual cryptography. A two level integer wavelet transformation technique was applied on the cover image to obtain the coefficients which were used later during the secret image embedding process. The experimental results indicated that high invisibility was achieved for secret image and the ability to embed more than one secret image within the cover image. Also, the use of visual cryptography eliminated the need for permutation process which would otherwise be required for secret image.

LDPC and SHA based iris recognition for image authentication

We introduce a novel way to authenticate an image using Low Density Parity Check (LDPC) and Secure Hash Algorithm (SHA) based iris recognition method with reversible watermarking scheme, which is based on Integer Wavelet Transform (IWT) and threshold embedding technique. The parity checks and parity matrix of LDPC encoding and cancellable biometrics i.e., hash string of unique iris code from SHA-512 are embedded into an image for authentication purpose using reversible watermarking scheme based on IWT and threshold embedding technique. Simply by reversing the embedding process, the original image, parity checks, parity matrix and SHA-512 hash are extracted back from watermarked-image. For authentication, the new hash string produced by employing SHA-512 on error corrected iris code from live person is compared with hash string extracted from watermarked-image. The LDPC code reduces the hamming distance for genuine comparisons by a larger amount than for the impostor comparisons. This results in better separation between genuine and impostor users which improves the authentication performance. Security of this scheme is very high due to the security complexity of SHA-512, which is 2256 under birthday attack. Experimental results show that this approach can assure more accurate authentication with a low false rejection or false acceptance rate and outperforms the prior arts in terms of PSNR.

Wavelet Analysis of Geophysical Well-log Data of Bombay Offshore Basin, India

Geophysical well-log (bore-hole) data facilitate understanding of the physical properties of the subsurface formations as a function of depth measured in a well. In the present study, the wavelet transformation technique was applied to the well-log data of three wells in the Bombay High oil field, India, in order to identify depths to the tops of oil and/or gas formation zones (pay zones). Continuous wavelet transformation (CWT) was performed on gamma-ray, resistivity, neutron porosity and velocity log data sets in order to determine the space-localization of the oil and/or gas formation zones. The choice of a mother wavelet is important and largely depends on the data under investigation. We have applied a variety of wavelets to the different log data sets to not only identify the depths to the tops of formation zones, but also to determine the optimum wavelet that best characterizes the pay zones. On examination of scalogram plots of each log corresponding to each wavelet for their better resolution in identifying the formation boundaries, we have found that the scalograms corresponding to the Gaus1 wavelet appeared to give the best resolution in identifying the depths of pay zones in all the well-log data sets of all three wells. To further validate the above observation, a histogram analysis of CWT coefficients is made. This showed that, of all the wavelets considered for the present study, Gaus1 wavelet is the most appropriate and optimum for determining the space-localization of pay zones in all the well-log data sets considered in the present study. The depths of pay zones estimated from scalogram plots of logs agree well with those provided by the Oil and Natural Gas Corporation Ltd., India.

Using discrete wavelet transforms to analyze trends in streamflow and precipitation in Quebec and Ontario (1954–2008)

Summary This paper aims to detect trends in mean flow and total precipitation data over southern parts of Quebec and Ontario, Canada. The main purpose of the trend assessment is to find out what time scales are affecting the trends observed in the datasets used. In this study, a new trend detection method for hydrological studies is explored, which involves the use of wavelet transforms (WTs) in order to separate the rapidly and slowly changing events contained in a time series. More specifically, this study co-utilizes the Discrete Wavelet Transform (DWT) technique and the Mann–Kendall (MK) trend tests to analyze and detect trends in monthly, seasonally-based, and annual data from eight flow stations and seven meteorological stations in southern Ontario and Quebec during 1954–2008. The combination of the DWT and MK test in analyzing trends has not been extensively explored to date, especially in detecting trends in Canadian flow and precipitation time series. The mother wavelet type and the extension border used in the wavelet transform, as well as the number of decomposition levels, were determined based on two criteria. The first criterion is the mean relative error of the wavelet approximation series and the original time series. In addition, a new criterion is proposed and explored in this study, which is based on the relative error of the MK Z -values of the approximation component and the original time series. Sequential Mann–Kendall analysis on the different wavelet detail components (with their approximation component added) that result from the time series decomposition was also used and found to be helpful because it depicts how harmonious each of the detail components (plus approximation) is with respect to the original data. This study found that most of the trends are positive and started during the mid-1960s to early 1970s. The results from the wavelet analysis and Mann–Kendall tests on the different data types (using the 5% significance level) reveal that in general, intra- and inter-annual events (up to 4 years) are more influential in affecting the observed trends.

Detection of low level gaseous releases and dose evaluation from continuous gamma dose measurements using a wavelet transformation technique

Measurement of environmental dose in the vicinity of a nuclear power plant site (Tarapur, India) is carried out continuously for the years 2007–2010 and attempts have been made to quantify the additional contributions from nuclear power plants over natural background by segregating the background fluctuations from the events due to plume passage using a non-decimated wavelet approach. A conservative estimate obtained using wavelet based analysis has shown a maximum annual dose of 38μSv in a year at 1.6km and 4.8μSv at 10km from the installation. The detected events within a year are in good agreement with the month wise wind-rose profile indicating reliability of the algorithm for proper detection of an event from the continuous dose rate measurements. The results were validated with the dispersion model dose predictions using the source term from routine monitoring data and meteorological parameters.

2+ level densities in 40Ca extracted from high energy-resolution (p,p′) experiments

Level densities of 2+ states in 40Ca have been extracted from the high energy-resolution (p,p′) experimental data by means of a fluctuation analysis. Two different methods of background determination have been considered, namely, Quasi-free calculations and Discrete Wavelet Transform (DWT) technique. The results are then compared to theoretical models based on phenomenological and microscopic approaches.