Wavelet Detail Research Articles

Alcohol use disorder detection using EEG Signal features and flexible analytical wavelet transform

The frequent excessive drinking of alcohol severely affects the neuronal composition and working of the brain and consequently developed Alcohol Use Disorder (AUD). Subjects suffering from AUD are prone to various diseases, psychological and cognitive issues if not identified and treated timely. Electroencephalogram (EEG) signals are used to record the internal structure and activity of the brain. The manual screening of EEG signals for AUD detection is complicated for practitioners because EEG signals are recorded in microvolts (μv) and consists of the inherent internal complexity of the brain. Therefore, an automated computer-aided diagnosis (CAD) is used for assisting the medical practitioner in AUD screening process. The recorded EEG signals of a subject are nonlinear and oscillatory, and CAD methods examine these signals in their frequency sub-bands. In this paper, flexible analytical wavelets transform (FAWT) based machine learning models are proposed for automated alcoholism detection. In the proposed methodology, EEG signals are decomposed into approximate and detailed wavelet coefficients using FAWT. The statistical features such as mean, standard deviation, kurtosis, skewness, and Shannon entropy are extracted from the selected wavelet coefficients. The features are fed to the various machine learning models including Least Square -Support Vector Machine (LS-SVM), Support Vector Machine (SVM) and Naïve Bayes learners for training. The training and testing are performed using 10-fold cross-validation. The performance of models is evaluated using all essential measures such as accuracy, sensitivity, specificity, F-measure, precision, Matthews correlation coefficient (MCC) and ROC. The results suggest that LS-SVM using polynomial kernel performed best with accuracy 99.17%, Sensitivity 99.17%, and Specificity as 99.44% using 10-fold cross-validation technique.

Detecting sleep spindles in EEGs using wavelet fourier analysis and statistical features

Abstract One of the more difficult tasks in sleep stage scoring is the detection of sleep spindles. Developing an effective method to identify these transitions in sleep electroencephalogram (EEG) recordings is an ongoing challenge, as there are typically hundreds of such transitions in each recording. This paper proposes a statistical model and a method based on wavelet Fourier analysis to detect sleep spindles. In this work, spindle detection is achieved in two phases: a training phase and a testing phase. An EEG signal is first divided into segments, using a sliding window technique. The size of the window is 0.5 s, with an overlap of 0.4 s. Then, each EEG segment is decomposed using a discrete wavelet transform into different levels of decompositions. The wavelet detail coefficient at level 3 (D3) is selected from these parameters, and this is passed through a fast Fourier transform to identify the desired frequency bands {α, β, θ, δ, γ}. Ten statistical characteristics are extracted from each band. Nonparametric Kruskal-Wallis one-way analysis of variance is used to select the important features, representing each of the 0.5 s EEG segments. To detect all possible occurrences of sleep spindles in the original EEG signals, four different window sizes of 0.25, 1.0, 1.5 and 2.0 s are also tested. Finally, the extracted features are used as the input to four classifiers to detect the sleep spindles: a least-squares support vector machine (LS-SVM), K-nearest neighbours, a K-means algorithm and a C4.5 decision tree. The obtained results demonstrate that the proposed method yields optimal results with a window size of 0.5 s. The maximum averages of accuracy, sensitivity and specificity are 97.9%, 98.5% and 97.8%, respectively. This method can efficiently detect spindles in EEG signals, and can assist sleep experts in analysing EEG signals.

A Novel Synthesis Method for Nonperiodic Function Generation of an RCCC Mechanism

In this paper, the nonperiodic function synthesis of an RCCC mechanism is presented using a wavelet feature parameter (WFP) method. The output function and the sliding displacement of the RCCC mechanism are described by the wavelet approximate and wavelet details. Based on the relationship of wavelet details of the sliding displacement and its scaling, a normalization method for wavelet details of the sliding displacement is presented. The influence of proportional scaling of the linkage lengths is eliminated. An adaptive database is established. The problem of nonperiodic design requirements of RCCC mechanism function synthesis is solved. To demonstrate the feasibility of this method, two numerical examples are proposed. Based on the nonperiodic design requirements, the RCCC mechanisms are designed and simulated using matlab and catia software. The results show that the method proposed is effective for nonperiodic function generation of the RCCC mechanism with multiple positions.

Imperceptible reversible watermarking of radiographic images based on quantum noise masking

Background and objectiveAdvances in information and communication technologies boost the sharing and remote access to medical images. Along with this evolution, needs in terms of data security are also increased. Watermarking can contribute to better protect images by dissimulating into their pixels some security attributes (e.g., digital signature, user identifier). But, to take full advantage of this technology in healthcare, one key problem to address is to ensure that the image distortion induced by the watermarking process does not endanger the image diagnosis value. To overcome this issue, reversible watermarking is one solution. It allows watermark removal with the exact recovery of the image. Unfortunately, reversibility does not mean that imperceptibility constraints are relaxed. Indeed, once the watermark removed, the image is unprotected. It is thus important to ensure the invisibility of reversible watermark in order to ensure a permanent image protection. MethodsWe propose a new fragile reversible watermarking scheme for digital radiographic images, the main originality of which stands in masking a reversible watermark into the image quantum noise (the dominant noise in radiographic images). More clearly, in order to ensure the watermark imperceptibility, our scheme differentiates the image black background, where message embedding is conducted into pixel gray values with the well-known histogram shifting (HS) modulation, from the anatomical object, where HS is applied to wavelet detail coefficients, masking the watermark with the image quantum noise. In order to maintain the watermark embedder and reader synchronized in terms of image partitioning and insertion domain, our scheme makes use of different classification processes that are invariant to message embedding. ResultsWe provide the theoretical performance limits of our scheme into the image quantum noise in terms of image distortion and message size (i.e. capacity). Experiments conducted on more than 800 12 bits radiographic images of different anatomical structures show that our scheme induces a very low image distortion (PSNR∼ 76.5 dB) for a relatively important capacity (capacity∼ 0.02 bits of message per pixel). ConclusionsThe proposed watermarking scheme, while being reversible, preserves the diagnosis value of radiographic images by masking the watermark into the quantum noise. As theoretically and experimentally established our scheme offers a good capacity/image quality compromise that can support different watermarking based security services such as integrity and authenticity control. The watermark can be kept into the image during the interpretation of the image, offering thus a continuous protection. Such a masking strategy can be seen as the first psychovisual model for radiographic images. The reversibility allows the watermark update when necessary.

Experimental investigation on induction motors inter-turns short-circuit and broken rotor bars faults diagnosis through the discrete wavelet transform

This paper deals with the problem of fault detection in induction motors using the discrete wavelet transform (DWT) method. The DWT is a mathematical method used to extract different frequency components from a given signal. It is based on the decomposition of the processed signals into wavelet approximation and detail coefficients. In order to detect inter-turns short-circuit (ITSC) and broken rotor bars (BRBs) faults, the DWT is applied on two different signals: the current envelope and the current Park’s vector modulus. This study is performed using experimental tests carried-out on a 3 kW squirrel cage induction motor. The energy evaluation of known bandwidth details allows defining a fault severity factor (FSF). This FSF is used to show which signals, wavelet type and wavelet order are more sensitive for the fault detection task.

Hyperspectral Features of Oil-Polluted Sea Ice and the Response to the Contamination Area Fraction.

Researchers have studied oil spills in open waters using remote sensors, but few have focused on extracting reflectance features of oil pollution on sea ice. An experiment was conducted on natural sea ice in Bohai Bay, China, to obtain the spectral reflectance of oil-contaminated sea ice. The spectral absorption index (SAI), spectral peak height (SPH), and wavelet detail coefficient (DWT d5) were calculated using stepwise multiple linear regression. The reflectances of some false targets were measured and analysed. The simulated false targets were sediment, iron ore fines, coal dust, and the melt pool. The measured reflectances were resampled using five common sensors (GF-2, Landsat8-OLI, Sentinel3-OLCI, MODIS, and AVIRIS). Some significant spectral features could discriminate between oil-polluted and clean sea ice. The indices correlated well with the oil area fractions. All of the adjusted R2 values exceeded 0.9. The SPH model1, based on spectral features at 507–670 and 1627–1746 nm, displayed the best fitting. The resampled data indicated that these multi-spectral and hyper-spectral sensors could be used to detect crude oil on the sea ice if the effect of noise and spatial resolution are neglected. The spectral features and their identified changes may provide reference on sensor design and band selection.

A wavelet-based method for power-line interference removal in ECG signals

Abstract Introduction The analysis of electrocardiogram (ECG) signals allows the experts to diagnosis several cardiac disorders. However, the accuracy of such diagnostic depends on the signals quality. In this paper it is proposed a simple method for power-line interference (PLI) removal based on the wavelet decomposition, without the use of thresholding techniques. Methods This method consists in identifying the ECG and noise frequency range for further zeroing wavelet detail coefficients in the subbands with no ECG coefficients in the frequency content. Afterward, the enhanced ECG signal is obtained by the inverse discrete wavelet transform (IDWT). In order to choose the wavelet function, several experiments were performed with synthetic signals with worse Signal-to-Noise Ratio (SNR). Results Considering the relative error metrics and runtime, the best wavelet function for denoising was Symlet 8. Twenty synthetic ECG signals with different features and eight real ECG signals, obtained in the Physionet Challenge 2011, were used in the experiments. Results show the advantage of the proposed method against thresholding and notch filter techniques, considering classical metrics of assessment. The proposed method performed better for 75% of the synthetic signals and for 100% of the real signals considering most of the evaluation measures, when compared with a thresholding technique. In comparison with the notch filter, the proposed method is better for all signals. Conclusion The proposed method can be used for PLI removal in ECG signals with superior performance than thresholding and notch filter techniques. Also, it can be applied for high frequencies denoising even without a priori frequencies knowledge.

Numerical study of vortex-induced vibrations of a flexible cylinder in an oscillatory flow

The vortex-induced vibrations of a flexible cylinder oscillating harmonically in still water have been numerically simulated using a CFD method based on the strip theory. The algorithm PIMPLE in OpenFOAM is adopted to compute the flow field while the small-displacement Bernoulli–Euler bending beam theory is used to model the cylinder. Two ends of the flexible cylinder are forced to oscillate harmonically. The simulation results have been compared with experimental results and further analyzed. Features such as the hysteresis phenomenon and the build-up–lock-in–die-out cycle are observed in the cross-flow vibration. The in-line vibrations consist of three components, the low-frequency oscillation, the first-natural-frequency vibration during the cylinder reversal, and the second-natural-frequency vibration due to vortex shedding. The butterfly-shaped trajectory has been observed. Detailed wavelet analyses of the vibrations have been given at the end.

Entropy with Local Binary Patterns for Efficient Iris Liveness Detection

Iris anti-spoofing is one of the most important topics, in which the development is increasing rapidly. This paper introduces an efficient system for detecting iris attacks. The system avoids the segmentation and the normalization stages employed traditionally in fake detection systems. Wavelet packets (WPs) are used to decompose the original image into wavelet approximation and detail channels. Entropy values are extracted from the wavelet channels, and also from the local binary pattern (LBP) images of the channels. These features are used for discriminating between real and fake iris images. Support vector machines are used for the classification purpose. The aim is to contribute for improved classification accuracy with less computational complexity and reduced processing time. Entropy of the WP channels gives 99.9237% classification accuracy, and the entropy of the LBP images yields 99.781%, using ATVS-FIr-DB. Fusion of these features yields 100% classification accuracy. Entropy of the wavelet channels is sufficient to obtain 100% accuracy using CASIA-Iris-Syn database, without fusion. All images in both databases are used, without the need to discard images with unsuccessful segmentation. Segmented images from both databases are used for comparison. Results show that more discriminative features can be obtained using the proposed algorithm. System complexity and processing time are reduced noticeably, and the system is robust to different types of fakes.

A Detailed Spatiotemporal Wavelet Study to Improve the P‐Phase Picking Performance for the 2007–2010 Shallow Earthquake Swarms near Matata, New Zealand

A Detailed Spatiotemporal Wavelet Study to Improve the P‐Phase Picking Performance for the 2007–2010 Shallow Earthquake Swarms near Matata, New Zealand

An Efficient Algorithm for Multimodal Medical Image Fusion based On Feature Selection and PCA Using DTCWT

Background: In the two past decades, medical image fusion has become an essential part of modern medicine due to the availability of numerous imaging modalities (MRI, CT, SPECT. etc). This paper presents a new medical image fusion algorithm based on DTCWT and uses different fusion rules in order to obtain a new image which contains more information than any of the input images. Methods: In order to improve the visual quality of the fused image, we propose a new image fusion algorithm based on Dual Tree Complex Wavelet Transform (DTCWT). Using different fusion rules in a single algorithm leads to a perfect reconstruction of the output (fused image).This combination will create a new method which exploits the advantages of each method separately. DTCWT present good directionality since it considers the edge information in six directions and provides approximate shift invariant. The goal of Principal Component Analysis (PCA) is to extract the most significant features (wavelet coefficients in our case) in order to improve the spatial resolution. The proposed algorithm fuses the detailed wavelet coefficients of input images using features selection rule. Results: We have conducted several experiments over different sets of multimodal medical images such as CT/MRI, MRA/T1-MRI; however, only results of two sets have been presented (due to pages-limit). The proposed fusion algorithm is compared to recent fusion methods presented in the literature (eight methods) in terms of visual quality and quantitatively using well known fusion performance metrics (five metrics). Results showed that the proposed algorithm outperforms the existing ones in terms of visual and quantitative evaluations. Conclusion: This paper focuses on image fusion of medical images obtained from different modalities. We have proposed a novel algorithm based on DTCWT in order to merge multimodal medical images. Experiments have been performed over two different sets of multimodal medical images. The results show that the proposed method significantly outperforms other techniques reported in the literature.

Enhancing seismic P phase arrival picking based on wavelet denoising and kurtosis picker

P phase arrival picking of weak signals is still challenging in seismology. A wavelet denoising is proposed to enhance seismic P phase arrival picking, and the kurtosis picker is applied on the wavelet-denoised signal to identify P phase arrival. It has been called the WD-K picker. The WD-K picker, which is different from those traditional wavelet-based pickers on the basis of a single wavelet component or certain main wavelet components, takes full advantage of the reconstruction of main detail wavelet components and the approximate wavelet component. The proposed WD-K picker considers more wavelet components and presents a better P phase arrival feature. The WD-K picker has been evaluated on 500 micro-seismic signals recorded in the Chinese Yongshaba mine. The comparison between the WD-K pickings and manual pickings shows the good picking accuracy of the WD-K picker. Furthermore, the WD-K picking performance has been compared with the main detail wavelet component combining-based kurtosis (WDC-K) picker, the single wavelet component-based kurtosis (SW-K) picker, and certain main wavelet component-based maximum kurtosis (MMW-K) picker. The comparison has demonstrated that the WD-K picker has better picking accuracy than the other three-wavelet and kurtosis-based pickers, thus showing the enhanced ability of wavelet denoising.

High-bandwidth and flexible tracking control for precision motion with application to a piezo nanopositioner.

For precision motion, high-bandwidth and flexible tracking are the two important issues for significant performance improvement. Iterative learning control (ILC) is an effective feedforward control method only for systems that operate strictly repetitively. Although projection ILC can track varying references, the performance is still limited by the fixed-bandwidth Q-filter, especially for triangular waves tracking commonly used in a piezo nanopositioner. In this paper, a wavelet transform-based linear time-varying (LTV) Q-filter design for projection ILC is proposed to compensate high-frequency errors and improve the ability to tracking varying references simultaneously. The LVT Q-filter is designed based on the modulus maximum of wavelet detail coefficients calculated by wavelet transform to determine the high-frequency locations of each iteration with the advantages of avoiding cross-terms and segmenting manually. The proposed approach was verified on a piezo nanopositioner. Experimental results indicate that the proposed approach can locate the high-frequency regions accurately and achieve the best performance under varying references compared with traditional frequency-domain and projection ILC with a fixed-bandwidth Q-filter, which validates that through implementing the LTV filter on projection ILC, high-bandwidth and flexible tracking can be achieved simultaneously by the proposed approach.

Application of well logs integration and wavelet transform to improve fracture zones detection in metamorphic rocks

The traditional approach to fracture zones detection is based on a visual inspection of core samples, or the application of special well logging program represented by imaging well logging. However, not all wells are fully cored, as the core description of fractures in laboratory is time consuming and expensive. Additionally, the interpretation of special well logging program in detecting fracture zones also needs the geologists' experience. This study provides a complete set of methodology for fracture zones detection and fracture numbers calculation in which wavelet analysis is utilized. For the sensitive well logs in fracture zones, we have made a contrast between well logs and imaging logging that shows up density, caliper, resistivity and acoustic logs are more or less suitable. To meet little changes with the same trends in one curve which indicates the present of fracture zones, we proposed a method that integrates the above four well logs into one, named fractured integrated index. Decomposing the integrated curve with discrete wavelet analysis shows that the detailed wavelet coefficients (cD1) energy are highly suitable for detection of fracture zones. In addition to the above four well logs, microsphere focusing logging is also used to enhance the accuracy of the detecting analysis. The method is applied to Chinese Continental Scientific Drilling Main Hole located about 17 km southwest of Donghai in the southern segment of the Sulu UHP terrane, and the results are promising in accordance with the imaging logging and cores information.

Wavelet feature parameters representations of open planar curves

• The use of Haar wavelets to describe open planar curves is presented. • The geometric significance of a wavelet approximation and a wavelet detail for open curves in the complex plane are given. • The relationship of the wavelet feature parameters between the open curve and its transformations is discovered. • The application of this method to non-periodic dimensional synthesis of the linkage mechanism is presented. The use of Haar wavelets to describe open planar curves is presented in this paper. The geometric significance of a wavelet approximation and a wavelet detail for open curves in the complex plane are given. By analysing the internal relationship of the wavelet details, a normalised process is established for extracting the wavelet feature parameters of open curves. The application of this method to non-periodic design requirements of the linkage mechanism dimensional synthesis problem is presented. Two examples are proposed to demonstrate the feasibility of this method.

A wavelet-based Gaussian method for energy dispersive X-ray fluorescence spectrum.

This paper presents a wavelet-based Gaussian method (WGM) for the peak intensity estimation of energy dispersive X-ray fluorescence (EDXRF). The relationship between the parameters of Gaussian curve and the wavelet coefficients of Gaussian peak point is firstly established based on the Mexican hat wavelet. It is found that the Gaussian parameters can be accurately calculated by any two wavelet coefficients at the peak point which has to be known. This fact leads to a local Gaussian estimation method for spectral peaks, which estimates the Gaussian parameters based on the detail wavelet coefficients of Gaussian peak point. The proposed method is tested via simulated and measured spectra from an energy X-ray spectrometer, and compared with some existing methods. The results prove that the proposed method can directly estimate the peak intensity of EDXRF free from the background information, and also effectively distinguish overlap peaks in EDXRF spectrum.

Convolutional neural networks for wavelet domain super resolution

We present a single image super resolution technique in which we estimate wavelet detail coefficients of a desired high resolution (HR) image using a convolutional neural network (CNN) on the given low resolution (LR) image. Detail coefficients are necessarily sparse for natural images, unlike pixel intensities, and are thus better suited to be CNN output. This allows us to train a CNN with far fewer samples and lesser training time and yet achieve better reconstruction quality with lesser run time compared to a recent state-of-the-art technique that directly estimates the HR pixels using a CNN.

Detection of Ractopamine and Clenbuterol Hydrochloride Residues in Pork Using Surface Enhanced Raman Spectroscopy

Surface enhanced Raman spectroscopy (SERS) coupled with chemometric methods, such as adaptive iteratively reweighted penalized least squares (AIR-PLS), wavelet transform, and least squares support vector machine (LSSVM), was investigated to realize the rapid detection and identifi cation of ractopamine (RAC) and clenbuterol hydrochloride (CL) residues in pork. First-level wavelet detail signal intensities at 1168 cm–1 were used to establish a standard curve of the RAC residues in pork, and the linear regression equation and the correlation coefficient were y = –4. 3683x – 11.059 and –0.9726. Second-level wavelet detail signal intensities at 1258 cm–1 were used to establish a standard curve of the CL residues in pork, and the linear regression equation and the correlation coeffi cient were y=33.595x + 36.538 and 0.9842. The second-level wavelet detail signals of the SERS spectra were selected as the inputs of the LSSVM classifi cation model for the identifi cation of the RAC and CL residues in pork, with a total accuracy rate reaching 100%. The experimental results demonstrate that the proposed method based on SERS is a good detection scheme for the rapid detection and identifi cation of RAC and CL residues in pork.

Short-Term Load-Forecasting Method Based on Wavelet Decomposition With Second-Order Gray Neural Network Model Combined With ADF Test

Improving the accuracy of power system load forecasting is important for economic dispatch. However, a load sequence is highly nonstationary and hence makes accurate forecasting difficult. In this paper, a method based on wavelet decomposition (WD) and a second-order gray neural network combined with an augmented Dickey–Fuller (ADF) test is proposed to improve the accuracy of load forecasting. First, the load sequence is decomposed by WD to reduce the nonstationary load sequence. Then, the ADF test is adopted as the test method for the stationary load sequence of each decomposed component after WD in which the test results determine the best WD level. Finally, because forecasting the wavelet details characterized by high frequencies is difficult owing to its fluctuation, a second-order gray forecasting model is used to forecast each component after WD. Furthermore, to obtain the optimum parameters of the second-order gray forecasting model, the neural network mapping approach is used to build the second-order gray neural network forecasting model. The simulation result of a real load sequence verifies that the method proposed in this paper can effectively improve the load-forecasting accuracy.

Wavelet-Based Local Contrast Enhancement for Satellite, Aerial and Close Range Images

The methods used for image contrast enhancement in the wavelet domain have been previously documented. The essence of these methods lies in the manipulation of the image during the reconstruction process, by changing the relationship between the components that require transformation. This paper proposes a new variant based on using undecimated wavelet transform and adapting the Gaussian function for scaling the coefficients of detail wavelet components, so that the role of low coefficients in the reconstructed image is greater. The enhanced image is then created by combining the new components. Applying the Haar wavelet minimises the effects of the relationship disturbance between components, and creates a small buffer around the edge. The proposed method was tested using six images at different scales, collected with handheld photo cameras, and aerial and satellite optical sensors. The results of the tests indicate that the method can achieve comparable, or even better enhancement effects for weak edges, than the well-known unsharp masking and Retinex methods. The proposed method can be applied in order to improve the visual interpretation of remote sensing images taken by various sensors at different scales.