Stationary Wavelet Transform Research Articles

High capacity video hiding based on multi-resolution stationary wavelet transform and hybrid-matrix decomposition techniqueset Transform and hybrid-matrix decomposition techniques

Internet simplified digital data transferring. This data needs to be secured; so securing digital data becomes an important concern. Steganography provides security for data by inserting it into a cover and concealing it. In this paper, a steganography algorithm was introduced. This algorithm used the stationary wavelet transform (SWT) and اhybrid-matrix decomposition techniques, singular value decomposition (SVD) and QR factorization to conceal a video in another video. The algorithm performance was measured with reference to the peak signal to noise ratio (PSNR) and structural similarity index measure (SSIM) for the cover and the secret videos. The algorithm successfully hid a video in another cover video and both are of the same size; the hiding capacity was 100%. The algorithm achieved a SSIM that reached 0.97 and a high PSNR value that reached 68.8 proving that the imperceptibility of the proposed algorithm is very high. The comparative analysis shows that the suggested algorithm achieved higher imperceptibility than the other state-of-the-art algorithms regarding the average PSNR. The enhanced version of the proposed method was more robust against different types of attacks.

Filtering of Corneal Images using Hybrid Wavelet Transform in the Cases of Keratoconus

One of the most prevalent, bilateral, asymmetric, and progressive corneal diseases, keratoconus can have a slight to severe impact on vision. Early on, the condition is frequently misdiagnosed as irregular astigmatism, delaying diagnosis. Although we have cutting-edge diagnostic techniques, the results are insufficient to fully assess the corneal health at different areas, making it challenging to plan additional treatment programmes. Here, image pre-processing techniques using a Hybrid Wavelet Transform of Discrete Wavelet Transform (DWT) and Stationary Wavelet Transform (SWT), followed by soft and/or hard thresholding and Inverse Wavelet Transform, are proposed in order to achieve early and accurate diagnosis and assess the health of the cornea. The qualitative and quantitative metrics are reached by taking into account the several Electronic Corneal Topography picture modes, which would be useful to an ophthalmologist in moving on with therapy. This approach has been proven to have greater promise than the ones currently in use, particularly in relation to corneal diseases like keratoconus. Additionally, this approach aids in more accurate keratoconus stage determination.

ROSEmark: Robust semi-blind ECG watermarking scheme using SWT-DCT framework

Coronary heart disease is a major cause of fatality worldwide. Online access to a patient's ECG record on networked hand-held devices enables its timely assessment by a cardiologist. However, the transmission of the ECG data on an unsecured network leads to a major concern about its authenticity. As a solution to this problem, this paper proposes ROSEmark – a robust semi-blind scheme for watermarking the ECG records. Each ECG record from the MIT-BIH Arrhythmia database is decomposed into equal-sized segments, and a set of key segments is selected randomly for watermarking. Each key segment is transformed using a 3-level stationary wavelet transform (SWT), and the resultant approximation vector is subjected to discrete cosine transform (DCT). An extreme learning machine (ELM) is applied for modulating the low-frequency coefficients, and the watermark is embedded in the modulated coefficients. Further, to ensure the security of the algorithm, the watermark is XORed with a randomly generated bitmask sequence. Finally, ROSEmark's performance is evaluated for fidelity and robustness using a variety of interferences. It is found that ROSEmark yields an average PSNR and PRD values of 50.72 dB and 0.7636, respectively. The experiments demonstrate that ROSEmark outperforms its competitors in terms of the standard evaluation metrics and is also time-efficient, thus making it suitable for real-time applications such as telemedicine.

Hyperspectral images classification based on multiple kernel learning using SWT and KMNF with few training samples

ABSTRACT In this paper, a new techniquebased on Multiple kernel learning (MKL) with just a few training samples is proposed for HSI classification utilising stationary wavelet transform (SWT) and kernel minimal noise fraction (KMNF). 2D-SWT is applied to each spectral band to discriminate spatial information, and feature sets are created by concatenating wavelet bands. The base kernels associated with each feature set are constructed, and the optimum kernel for maximum separability is learned. The experimental results indicate that the suggested approach provides high accuracy with a low number of training samples and outperforms state-of-the-art MKL-based classifiers with no increase in computing complexity.

A comparative study on estimation of fractal dimension of EMG signal using SWT and FLP

ABSTRACT Most of the biomedical signals gathered from various electronic devices contain a certain amount of noise based on the external atmosphere, the working condition of the devices and the activity of the clinician handling the observation section. Analysing such signals may result in an ambiguous prediction of the patients’ diagnosis. Thus, the eradication of noises in the biosignal waves gets a tremendous attraction in the medical field. Hence, a specific aim of this study is to handle two perfect and precise methods of clearing the defectiveness in the electromyography (EMG) signals which are termed stationary wavelet transform (SWT) and fractional linear prediction (FLP). Once we get the filtered signal from these two techniques, we can calculate the fractal dimension of the denoised EMG signals for obtaining the accurate way for offering medication. In this regard, the present study primarily reveals the comparison between fractal dimensions that are gained through the following specified methods, Higuchi, Katz, rescaled-range analysis, relative dispersion and power spectral density. Ultimately, these dimensions are compared to show a clear-cut result by considering both the filtering mechanisms. For the comparison of statistical data of an undertaken sample, mathematics provides an estimating procedure called the ANOVA test. This paper also deals with some of the statistical measures for comparing the denoising methods and results to attain a better one. As a consequence, we have concluded that the FLP filtering method is more reliable than the SWT technique, based on the fractal dimension forecasted via the five mechanisms.

Basis pursuit sparse decomposition using tunable-Q wavelet transform (BPSD-TQWT) for denoising of electrocardiograms.

The electrocardiogram (ECG) is an essential diagnostic tool to identify cardiac abnormalities. So, the primary issue in an ECG acquisition unit is noise interference. Essentially, the prominent ECG noise sources are power line interference (PLI) and Baseline drift (BD). Therefore, in the study, a new technique called the basis pursuit sparse decomposition (BPSD) using tunable-Q wavelet transform (TQWT) is proposed to remove the PLI and BD present in the ECG recordings. Chiefly, the TQWT method is a wavelet transform with distinct Quality factors (Q) which can adjust the signal to the natural non-stationary behaviour in time and space. Further, the method decomposes the signal into high-Quality factor and low-Quality factor components of wavelet coefficients to eliminate PLI and BD by choosing appropriate redundancy (r) and decomposition levels (J2). The 'r' and 'J' values are chosen based on the trial-and-error method concerning signal-to-noise ratio (SNR). It has been found that the PLI noise has been suppressed significantly with the redundancy of 3 and decomposition levels of 10; more so, the BD has been removed with the redundancy of 4 and decomposition levels of 19. The proposed method BPSD-TQWT was evaluated using the open-source MIT-BIH Arrhythmia database and the real-time ECG recordings collected through a wearable Silver Plated Nylon Woven (Ag-NyW) textile-based ECG monitoring system. The performance was then evaluated using fidelity metrics such as SNR, maximum absolute error (MAX), and normalized cross-correlation coefficient (NCC). The results were compared with IIR filter, stationary wavelet transform (SWT), non-local means (NLM) and local means (LM) methods. Using the proposed method on MIT-BIH Arrhythmia Database, performance evaluation parameters such as SNR, MAX, and NCC were improved by 4.3dB and 6.8dB, 0.37 and 0.78, 0.2 and 0.46 compared to IIR and SWT methods respectively. On the other hand, using the proposed method on the real-time datasets, values of SNR, MAX, and NCC were improved by 0.3dB and 0.6dB, 0.009 and 0.74 and 0.3 and 0.35 compared to IIR and SWT methods respectively. Finally, it can be concluded that the proposed method shows improved performance over IIR, SWT, NLM and LM methods for PLI and BD removal.

Two Classes of Gamma-ray Bursts Distinguished within the First Second of Their Prompt Emission

Studies of Gamma-Ray Burst (GRB) properties, such as duration and spectral hardness, have found evidence for additional classes, beyond the short/hard and long/soft prototypes, using model-dependent methods. In this paper, a model-independent approach was used to analyse the gamma-ray light curves of large samples of GRBs detected by BATSE, Swift/BAT and Fermi/GBM. All the features were extracted from the GRB time profiles in four energy bands using the Stationary Wavelet Transform and Principal Component Analysis. t-distributed Stochastic Neighbourhood Embedding (t-SNE) visualisation of the features revealed two distinct groups of Swift/BAT bursts using the T100 interval with 64 ms resolution data. When the same analysis was applied to 4 ms resolution data, two groups were seen to emerge within the first second (T1) post-trigger. These two groups primarily consisted of short/hard (Group 1) and long/soft (Group 2) bursts, and were 95% consistent with the groups identified using the T100 64 ms resolution data. Kilonova candidates, arising from compact object mergers, were found to belong to Group 1, while those events with associated supernovae fell into Group 2. Differences in cumulative counts between the two groups in the first second, and in the minimum variability timescale, identifiable only with the 4 ms resolution data, may account for this result. Short GRBs have particular significance for multi-messenger science as a distinctive EM signature of a binary merger, which may be discovered by its gravitational wave emissions. Incorporating the T1 interval into classification algorithms may support the rapid classification of GRBs, allowing for an improved prioritisation of targets for follow-up observations.

Quantitative Evaluation of Supported Catalysts Key Properties from Electron Tomography Studies: Assessing Accuracy Using Material-Realistic 3D-Models

Electron Tomography (ET) reconstructions can be analysed, via segmentation techniques, to obtain quantitative, 3D-information about individual nanoparticles in supported catalysts. This includes values of parameters out of reach for any other technique, like their volume and surface, which are required to determine the dispersion of the supported particle system or the specific surface area of the support; two figures that play a major role in the performance of this type of catalysts.However, both the experimental conditions during the acquisition of the tilt series and the limited fidelity of the reconstruction and segmentation algorithms, restrict the quality of the ET results and introduce an undefined amount of error both in the qualitative features of the reconstructions and in all the quantitative parameters measured from them.Here, a method based on the use of well-defined 3D geometrical models (phantoms), with morphological features closely resembling those observed in experimental images of an Au/CeO2 catalyst, has been devised to provide a precise estimation of the accuracy of the reconstructions. Using this approach, the influence of noise and the number of projections on the errors of reconstructions obtained using a Total Variation Minimization in 3D (TVM-3D) algorithm have been determined. Likewise, the benefits of using smart denoising techniques based on Undecimated Wavelet Transforms (UWT) have been also evaluated.The results clearly reveal a large impact of usual noise levels on both the quality of the reconstructions and nanometrological measurement errors. Quantitative clues about the key role of UWT to largely compensate them are also provided.

Wavelet Transform Data-Driven Machine Learning-Based Real-Time Fault Detection for Naval DC Pulsating Loads

All-electric warships require high power-dense distribution system to power advanced weapon loads. Medium voltage dc (MVDC) power distribution is well suited to fulfill this requirement if certain risks are addressed. A particularly emerging problem is that the advanced pulsating loads draw large currents in extremely short periods of time and behave similarly to the shunt fault. The nature of the load and the operating cycle determines the unique structure of the pulse in time and frequency domains. If the load operating cycle consists of a finite number of transitions, then the corresponding frequency content of the current profile can be used to identify these transients. The wavelet transform is used to extract this useful frequency domain information from the sampled current data. A proposed computationally light data-driven machine learning based fault detection and load monitoring solution extracts the frequency domain features of the observed transient and compares that to a database of stored features to identify the observed transient, then to further identify faults that may create an abnormal disturbance in the load current profile such as arcing and shunt faults. It can be further applied to any load profile with prerequisite of a finite number of repetitive transients during normal condition. This paper focuses on the fault detection only and not for fault isolation while it can achieve isolation capability once the fault was diagnosed. In final real-time implementation, the recursive Haar stationary wavelet transform (SWT) fed computationally light machine learning is employed to validate the proposed scheme in a single-core Texas Instrument (TI) Digital Signal Processer (DSP) TMS320F28335.

Performance Evaluation of SWT and ICA Techniques for Automatic Separation between Oscillations and Epileptic Spikes by SWT and ICA Techniques

In this work, we studied two approaches for separating the signal mixtures observed by FO electrodes, which are filtered techniques, independent component analysis (ICA) and transformed into wavelets. Precisely, we will study two methods to solve the problem of overlap between the spikes and the oscillation. To do this, we must first study the technique of analysis in independent components and implement it for the separation between these two biomarkers of epilepsy. Next, we will evaluate the performance of the ICA method and the Stationary Wavelet Transform (SWT) filtering method.

Integrating Companding and Deep Learning on Bandwidth-Limited Image Transmission

<p>The image companding is a simple image compression technique which is very easy to be implemented in the bandwidth-limited environment. This paper presents a simple way for improving the quality of decompressed image in the image companding task. The proposed method consists of two networks, namely Sub-band Network (SubNet) and Pixel Network (PixNet), for performing an image reconstruction. The SubNet module exploits the effectiveness of Stationary Wavelet Transform (SWT) and Convolutional Neural Network (CNN) in order to recover the lost information in the wavelet sub-bands basis. Whilst, the PixNet part applies CNN with identity mapping to improve the quality of initial reconstructed image obtained from the SubNet module. As reported in this paper, the proposed method outperforms the former existing schemes in the image companding task. It has also been proven that the proposed method is able to improve the quality of reconstructed image with some simple steps.</p> <p> </p>

Extraction of Timber’s Features using GLCM, Color Moment and Isotropic Undecimated Wavelet Transform (IUWT)

Feature extraction can be used to know the characteristic of timber’s texture. Characteristic of timber’s texture can be used to help researcher to identify DNA of timbers which can be detected illegal logging in Indonesia. In this reseach the proposed method is combination GLCM, Color Moment, and Isotropic Undecimated Wavelet Transform (IUWT). The result of the proposed method has agood accuracy.

Image Enhancement based on the Histogram Equalization and Multiresolution Discrete Stationary Wavelet Transforms

In recent years, due to the tremendous development that took place on the Internet and its applications in the aspects of human life, the demands of using digital images have also increased dramatically, and that opens up horizons for scientific research in the field of improving the quality of the digital images by removing the noise that caused by to processes that are applied within network transmissions like performing storage, retrieval, and encryption to preserve privacy. All these effects are yielded to reduce image quality and loss of visual information. To surmount this problem, image enhancement methods are used to eliminate the noise while preserving supreme exact details and essential characteristics as much as possible in the digital image. The wavelet image enhancement technique played a critical role in this field by attempts to reduce noise in the image while retaining the vital features of the image due to the capability to separate the image into sub-bands (sub-images) and influence the frequencies of each sub-band separately, where acquiring the original image content is essential to obtaining reliable performance.
 Different enhancement techniques have been realized by many researchers so far. Each technique has its own privileges and shortcomings. In this work, a proposed procedure is presented and effectuated to the image modified by Additive White Gaussian Noise (AWGN). The proposed enhancement operation was achieved using the combination of Histogram Equalization with a two-dimensional stationary discrete wavelet transform (2D-SWT) as a multi-resolution analysis technique in image processing at three levels of decomposition to obtain revised results of the method of noise removing. To distinguish and eliminate noise from affected pixel points in the wavelet domain the 2D-SWT is used based on the hard and soft threshold systems on both high and low frequencies to decrease noise from the noisy image. Then, the multi-level 2D inverse wavelet transform (2D-IWT) is applied to eliminate noise and complete the synthesis of the image by the proposed image enhancement techniques.
 In the end, the performance of the proposed methods has been evaluated by using the Peak Signal to Noise Ratio (PSNR). Experimental measurements determine that the results of the proposed techniques enhanced the PSNR by about (16.16%) with respect to the results of the related works, and the structure of the image quality has also been improved in terms of edges retaining and greater noise elimination.

Traveling Wave Energy Analysis of Faults on Power Distribution Systems

This paper explores the most important factors that define the Traveling Wave (TW) propagation on distribution systems. The factors considered in this work are: the distance to the fault location, the fault type, and the crossing of system elements (such as regulators, capacitor banks, laterals, and extra loads within the protection zones). This work uses a realistic, yet simplified, distribution system composed of two protection zones, in which, several combinations of the previously mentioned factors are considered. The simulated fault measurements undergo a signal processing stage in which, first, they are decomposed into independent modes using the Karrenbauer transform. Second, a time–frequency representation is obtained using the Stationary Wavelet Transform (SWT), dividing the signal into several frequency bands. Finally, the Parseval’s Energy (PE) theorem is applied to calculate the signal energy in each frequency band. A qualitative analysis is performed based on the previously calculated energies to outline which are the factors that most affect the TW energy during propagation. The results show that distance, the presence of regulators, either in the propagation path or upstream, and the type of fault are the main factors that affect TW propagation across the system, and therefore they should be considered for TW-based protection schemes for distribution systems.

A new signal processing-based islanding detection method using pyramidal algorithm with undecimated wavelet transform for distributed generators of hydrogen energy

Machine learning-based fault detection methods are frequently combined with wavelet transform (WT) to detect an unintentional islanding condition. In contrast to this condition, these methods have long detection and computation time. Thus, selecting a useful signal processing-based approach is required for reliable islanding detection, especially in real-time applications. This paper presents a new modified signal processing-based islanding detection method (IDM) for real-time applications of hydrogen energy-based distributed generators. In the study, a new IDM using a modified pyramidal algorithm approach with an undecimated wavelet transform (UWT) is presented. The proposed method is performed with different grid conditions with the presence of electric noise in real-time. Experimental results show that oscillations in the acquired signal can be reduced by the UWT, and noise sensitivity is lower than other WT-based methods. The non-detection zone is zero and the maximum detection and computational time is also 75 ms at a close power match.

Magnetoresistance sensor-based rotor fault detection in induction motor using non-decimated wavelet and streaming data

In this paper, the giant magnetoresistance broken rotor (GBR) method is used to diagnose the induction motor (IM) rotor bar fault at an early stage from outward magnetic flux developed by IM.The outward magnetic field signal has anti-clockwise radiation due to broken rotor bar current.In this paper, the outward magnetic signal is acquired using a giant magnetoresistance (GMR) sensor. In the GBR method, IM rotor fault is analysed with a non-decimated wavelet transform (NDWT)-based outward magnetic signal. Experimental result shows the difference in statistical features and energy levels of sub-bands of NDWT for healthy and faulty IM. Least square-support vector machine(LS-SVM)-based classification results are verified by confusion matrix based on 150 outward magnetic signals from a healthy and damaged rotor (broken rotor). The proposed method identifies IM rotor faults with 95% sensitivity, 90% specificity and 92.5% classification accuracy. Furthermore, run-time IM condition monitoring is performed through the ThinkSpeak internet of things (IoT) platform for collecting outer magnetic signal data. ThinkSpeak streaming data of outward magnetic field help detect rotor fault at the initial stage and understand the growth of rotor fault in the motor. The proposed GBR method overcomes sensitivity, translation-invariance limitations of existing IM rotor fault diagnosis methods.

Effect of audiovisual stimulation on adult memory performance based electroencephalography wavelet analysis

Human memory stores various information and events that can be retrieved when needed. Many factors can influence memory performance which either provide positive or negative feedback. This research investigates the effect of audiovisual stimulation on adult memory based on electroencephalography (EEG) analysis. Sixty college students are participating in this experimental study. They must memorize visual assessment at two different levels in Mozart’s Sonata music and white noise stimulation. During memorizing duration, the EEG machine records brain electrical activity based on 10–20 electrode placement. The collected raw brain signals are processed using the wavelet-based method. The stationary wavelet transform (SWT) is used for artifact elimination, whereas discrete wavelet transform (DWT) is applied to obtain alpha, beta, theta, and gamma rhythms. The time–frequency domain features are collected from the EEG signals to discover the influence of audiovisual stimulation. The findings showed a different increasing and decreasing trend of mean, standard deviation, and peak-to-peak EEG signal amplitude before and after audiovisual stimulation exposure. The theta and alpha rhythms showed the most influence with the highest relative power. Suppression of relative gamma and beta power is vital for improving visual information processing and attention level. Memorizing in audio stimulation has suppressed the relative alpha, beta, theta, and gamma power, leading to better visual memorizing ability. The white noise stimulation provides more influence on adult visual memory.

De-noising of EEG signals with shift-based cycle spinning on wave atoms

EEG signals offer qualitative insights during brain disorder analysis and help in the brief assessment of brain diseases. However, EEG signal acquisition is susceptible to noise of various kinds and renders the analysis phase difficult and ill-posed. Hence, an appropriate technique is necessary to reduce the impairment effect caused by the noise on analysis. Wavelet thresholding is one of the most widely used techniques to recover the EEG signals from the noise. However, all the variants of wavelet thresholding algorithms suffer from pseudo-Gibbs phenomenon leading to ringing effect. Wave atom is a novel multiscale–multidirectional transformation technique. However, this wavelet thresholding produces pseudo-Gibbs phenomena, which are visual distortions and oscillations in the area of signal processing. The widely appreciated wave atom transformation too fails from artifacts around the sharp edges. The proposed shift-based cycle spinning technique of wave atom transformation model estimates the thresholding parameter, in an unbiased manner, from the data and de-noises the signal in a translation-invariant manner. De-noising studies on the OpenNeuro EEG dataset indicate the usefulness of the suggested technique. The results are analysed based on the performance measurements SNR and MSE that establish an advantage of shift-based cycle spinning model in getting better results.

Automated classification of power quality disturbances in a SOFC&PV-based distributed generator using a hybrid machine learning method with high noise immunity

In this study, a new hybrid machine learning (ML) method is developed to classify the power quality disturbances (PQDs) for a hydrogen energy-based distributed generator (DG) system. The proposed hybrid ML method uses a new approach for the feature extraction by using a pyramidal algorithm with an un-decimated wavelet transform (UWT). The pyramidal UWT method is used and investigated with the Stochastic Gradient Boosting Trees (SGBT) classifier to classify PQD signals for a Solid Oxide Fuel Cell & Photovoltaic (SOFC&PV)-based DG. The overfitting problem of SGBT in noisy signals is eliminated with the features extracted by pyramidal UWT. Mathematical, simulative and real data results confirm that the developed UWT-SGBT method can classify PQDs with high accuracy of up to 99.59%. The proposed method is also tested under noisy conditions, and the pyramidal UWT-SGBT method outperformed other ML with wavelet transform (WT)-based methods in the literature in terms of noise immunity.

Performance and quality assurance of medical image using hybrid thresholding wavelet transform with Wiener filter

ABSTRACT Medical images like X-ray, computed tomography, ultrasound, and magnetic resonance imaging (MRI) are produced using different techniques; during this process, noise is added that decreases the image quality and image analysis. Image denoising is an important task in image processing; use of wavelet transform improves the quality of an image and reduces noise level. We propose in this research, a denoising approach basing on discrete wavelet transform (DWT) using Hybrid Thresholding (bayesShrink) with Wiener filter technique for enhancing the quality image. This technique improved a better balance between smoothness and accuracy than the traditional wavelet DWT and are less redundant than stationary wavelet transform (SWT). In addition, the Structural Similarity Index Measure (SSIM) and Peak Signal-to-Noise Ratio (PSNR) were used to analyse the denoised images quality.