Discrete Wavelet Transform Method Research Articles

Denoising Ambulatory Electrocardiogram Signal Using Interval Dedependent Thresholds based Stationary Wavelet Transform

Noise contamination in electrocardiogram (ECG) monitoring systems can lead to errors in analysis and diagnosis, resulting in a high false alarm rate (FAR). Various studies have been conducted to reduce or eliminate noise in ECG signals. However, some noise characteristics overlap with the frequency range of ECG signals, which occur randomly and are transient. This results in shape alteration and amplitude reduction in P and R waves. The author proposed a framework for eliminating noise in ECG signals using the stationary wavelet transform method and interval-dependent thresholds (IDT) based on the change point detection method to address these challenges. The proposed framework decomposes the input electrocardiogram (ECG) signal at a specific level using the Stationary Wavelet Transform method, resulting in detail and approximation coefficients. Interval detection focuses on the initial detailed coefficient, d1, chosen due to its significant content of noise coefficients, especially high-frequency noise. Subsequently, threshold values are computed for each interval. Hard and soft thresholding processes are then applied individually to each interval. Finally, reconstruction occurs using the inverse stationary wavelet transform method on the threshold coefficient outcomes. Two measurement matrices, root mean square error (RMSE) and percentage root mean squared difference (PRD), were used to measure the performance of the proposed framework. In addition, the proposed framework was compared to stationary wavelet transform (SWT) and discrete wavelet transform (DWT). The test results showed that the proposed method outperforms DWT and SWT. The proposed framework obtained an average increase in RMSE scores of 18% and 45% compared to the SWT and DWT methods, respectively, and PRD values of 17% and 37% compared to the SWT and DWT methods, respectively. So, using IDT in the stationary wavelet transform method can improve the denoising performance. With the development of this new framework for denoising ECG signals, we hope it can become an alternative method for other researchers to utilize in denoising ECG signals.

EMG Instrumentation Modeling and Feature Processing Based On Discrete Wavelet Transform

Electromyography (EMG) instrumentation is essential in generating electrical signals from skeletal muscles. EMG sensors are helpful in various cases requiring the detection of human muscle contractions, neuromuscular disorders, and rehabilitation. EMG instrumentation is divided into two parts, namely, the analogue part and the digital part. The EMG instrumentation design comprises a digital-to-analog converter (DAC), instrumentation amplifier, filter, and analog-to-digital converter (ADC). Meanwhile, in digital signal processing adopting the Discrete Wavelet Transform (DWT) method, frequency analysis using DWT has proven superior. It is used in various research and has exceptionally detailed coefficient features for classifying neuromuscular disease signals. Therefore, this research aims to design analogue and digital EMG instrumentation and identify features in the form of detailed coefficients. The data used are two Physionet signals from the anterior tibialis body with myopathy and neuropathy disorders. The results obtained for EMG analogue instrumentation provide the expected results until they reach the filter component stage. The resulting signal forms a block in the filter component, different from the initial EMG signal. Meanwhile, the DWT decomposition results are of the Daubechies4 wavelet type with the highest level 17, which has a high detail coefficient at low frequencies, high dilation and the result of a mixture of neuropathy and myopathy EMG signals, or in other words, at low energies, this result is by the DWT theorem. Determining the efficiency of the DWT detailed coefficient feature requires further study with the same signal subject. The DWT features obtained can then be developed for various needs in EMG signal recognition.

Bearing fault diagnosis of induction machines using VMD-DWT and composite multiscale weighted permutation entropy

Purpose Bearings play a critical role in the reliable operation of induction machines, and their failure can lead to significant operational challenges and downtime. Detecting and diagnosing these defects is imperative to ensure the longevity of induction machines and preventing costly downtime. The purpose of this paper is to develop a novel approach for diagnosis of bearing faults in induction machine. Design/methodology/approach To identify the different fault states of the bearing with accurately and efficiently in this paper, the original bearing vibration signal is first decomposed into several intrinsic mode functions (IMFs) using variational mode decomposition (VMD). The IMFs that contain more noise information are selected using the Pearson correlation coefficient. Subsequently, discrete wavelet transform (DWT) is used to filter the noisy IMFs. Second, the composite multiscale weighted permutation entropy (CMWPE) of each component is calculated to form the features vector. Finally, the features vector is reduced using the locality-sensitive discriminant analysis algorithm, to be fed into the support vector machine model for training and classification. Findings The obtained results showed the ability of the VMD_DWT algorithm to reduce the noise of raw vibration signals. It also demonstrated that the proposed method can effectively extract different fault features from vibration signals. Originality/value This study suggested a new VMD_DWT method to reduce the noise of the bearing vibration signal. The proposed approach for bearing fault diagnosis of induction machine based on VMD-DWT and CMWPE is highly effective. Its effectiveness has been verified using experimental data.

Digital Image Watermarking

Digital watermarking has become an essential method for encrypting multimedia files by adding undetectable data. In this work, we present a novel approach to digital watermarking that makes use of the Singular Value Decomposition (SVD) and Discrete Wavelet Transform (DWT) methods. By combining these methods, robustness against different types of attacks can be achieved while preserving excellent perceptual quality. Our approach ensures resilience and invisibility by embedding the watermark in the singular values that are obtained from the decomposition of DWT subbands. The outcomes of the experiments show that the suggested strategy is more efficient and effective than the current techniques when it comes to maintaining image quality and being resistant to common image processing threats.

Medical Image Fusion for Brain Tumor Diagnosis Using Effective Discrete Wavelet Transform Methods

Background: The field of clinical or medical imaging is beginning to experience significant advancements in recent years. Various medical imaging methods such as computed tomography (CT), X-radiation (X-ray), and magnetic resonance imaging (MRI) produce images with distinct resolution differences, goals, and noise levels, making it challenging for medical experts to diagnose diseases. Objective: The limitations of a single medical image modality have increased the necessity for medical image fusion. The proposed solution is to create a fusion method of merging two types of medical images, such as MRI and CT. Therefore, this study aimed to develop a software solution that swiftly identifies the precise region of a brain tumor, speeding up the diagnosis and treatment planning. Methods: The proposed methodology combined clinical images by using discrete wavelet transform (DWT) and inverse discrete wavelet transform (IDWT). This strategy depended on a multi-goal decay of the image information using DWT, and high-frequency sub-bands of the disintegrated images were combined using a weighted averaging method. Meanwhile, the low-frequency sub-bands were straight-forwardly replicated in the resulting image. The combined high-quality image was recreated using the IDWT. This method can handle images with various modalities and resolutions without the need for previous data. Results: The results showed that the outcomes of the proposed method were assessed by different metrics such as accuracy, recall, F1-score, and visual quality. The method showed a high accuracy of 98% over the familiar neural network techniques. Conclusion: The proposed method was found to be computationally effective and produced high-quality medical images to assist professionals. Furthermore, the method can be stretched out to other image modalities and exercised by hybrid techniques of wavelet transform and neural networks and used for different clinical image analysis tasks. Keywords: CT and MRI, Image fusion, brain tumor, wavelet transform methods, medical images, machine learning, CNN

Distinctive approach in brain tumor detection and feature extraction using biologically inspired DWT method and SVM

Brain tumors result from uncontrolled cell growth, potentially leading to fatal consequences if left untreated. While significant efforts have been made with some promising results, the segmentation and classification of brain tumors remain challenging due to their diverse locations, shapes, and sizes. In this study, we employ a combination of Discrete Wavelet Transform (DWT) and Principal Component Analysis (PCA) to enhance performance and streamline the medical image segmentation process. Proposed method using Otsu's segmentation method followed by PCA to identify the most informative features. Leveraging the grey-level co-occurrence matrix, we extract numerous valuable texture features. Subsequently, we apply a Support Vector Machine (SVM) with various kernels for classification. We evaluate the proposed method's performance using metrics such as accuracy, sensitivity, specificity, and the Dice Similarity Index coefficient. The experimental results validate the effectiveness of our approach, with recall rates of 86.9%, precision of 95.2%, F-measure of 90.9%, and overall accuracy. Simulation of the results shows improvements in both quality and accuracy compared to existing techniques. In results section, experimental Dice Similarity Index coefficient of 0.82 indicates a strong overlap between the machine-extracted tumor region and the manually delineated tumor region.

Watermarking using DCT and DWT on Pneumonia images

Watermarking is a branch of the data hiding technique. Watermarking is a technique used to insert a copyright label on an image, so that the copyright of the image can be protected. Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT) are techniques that can be used to watermark. In this study, the Discrete Cosine Transform and Discrete Wavelet Transform methods will be used to watermark images to 5 different host images. In the tests carried out, watermarking techniques will be compared using DCT, DWT, DCT-DWT combination and DWT-DCT combination. The results obtained in this study were the highest PSNR value obtained at 41.931, the highest SSIM obtained 0.99515, the highest entropy was also obtained at 7.4186, The best UACI value is 0.0071158 and the best NCPR value is obtained at 93.9068% then, for the best CC value is obtained at 0.99953. As well as the NCC value, the value obtained is the same all in each test, namely with a value of 1.

A Hybrid Stacked CNN Model with Weighted Average Ensembling for Effective Face Recognition

The discipline of computer vision has given a lot of attention to facial recognition. Automated face recognition is extensively employed in various practical scenarios, including systems for streamlining immigration checkpoints, intelligent monitoring of visual data, and authentication of personal identity. Depending on the situation, it may be divided into the two separate duties of facial verification and face identification. This study proposed a hybrid stacked CNN model for face recognition system. The models have mastered the art of making their own inferences. The models are then combined to predict a class value using a cutting-edge method called weighted average ensembling. A more accurate estimate should be produced by the new assembly process. Pre-trained CNN models are used in our proposed method: AlexNet, Resnet50V2, and VGG-19, Yolov5, VGG-16 and ResNet50. When applied to Tufts dataset images, our suggested model successfully achieved 98.05% accuracy. We have also used the Discrete Wavelet Transform (DWT) method for denoising, SegNet for Image segmentation for better performance of the model proposed.

Robust Digital Watermarking on Vital Archives using Hybrid SVD and DWT Methods

The development of Internet technology affects the dissemination of data, especially in vital government archives. This research uses a hybrid singular value decomposition (SVD) and discrete wavelet transform (DWT) method, which aims to protect the copyright of vital archives. The stages of the insertion and extraction process are carried out to test the effect of the alpha value on the quality (imperceptibility) and robustness of the inserted image by measuring the Peak Signal-to-Noise Ratio (PSNR), verifying similarity by measuring the Normalized Cross-Correlation (NC) and Structural Similarity Index (SSIM). The results of research with ten vital archives and a watermark protection logo in JPEG format with a size of 512x512 pixels obtained a maximum PSNR with a value of α = 0.01 of 41.0567 dB, NC of 0.98904, and SSIM of 0.98023 in the Cibereum Land Certificate. So, it can be proven that this method produces vital archive watermarks that can be extracted and are robust to JPEG compression attacks of 75%, median filtering 3x3, Gaussian noise 0.01, speckle noise 0.01, and salt and pepper noise 0.01 but not resistant to rotation 80 and cropping attacks 2%.

A Weighted-Average-Ensembling-Based Hybrid CNN Model for Improved Covid-19 Detection

Background: People are at a much higher risk now because of the new coronavirus that causes COVID-19 disease. The virus is quickly spreading evetywhere in the world. Therefore, it is crucial that we create rapid diagnostic tools to identify infected people and stop the virus from spreading. The development of ML would allow us for early diagnosis of COVID-19.Methods: Since all of the prevalent CNN models are conceptually similar, we advocated for their combined usage. They have learned to infer things on their own. The models are then integrated using a cutting-edge technique called weighted average ensembling to forecast a class value. The new method of assembly should result in a more precise estimate. Our strategy utilises three pre-trained CNN models: DenseNet201, Resnet50V2, and VGG-19. We have also used the Discrete Wavelet Transform (DWT) method for denoising, U-Net for Image segmentation, SMOTE for data balancing, Keras Tuner for hyperparameteroptimisation for better accuracy in result. Findings: when applied to X-ray pictures, our suggested model successfully achieved 99.17\% of accuracy while detecting COVID-19.Novelty and applications: We have modified CNN architecture for batch normalization to avoid covariat shift and used Weighted Average Ensembling to pick best of the results. It is hoped that this study, together with the GUI interface, would allow clinicians to use computer-aided analysis to quickly and accurately identify infected individuals. We anticipate this to be of great use to the medical community.

Effectiveness of Wavelet and Fourier Transform Methods for Denoising ECG Apnea Signals

An Electrocardiogram (ECG) signal results from recording the heart's electrical activity. Currently, diseases originating from heart abnormalities still dominate the world. Therefore, doctors need to find an ECG signal that is free from noise, this cleaning process is called denoising. The denoising methods commonly used are the wavelet transform method, and the Fourier transform method. Testing was conducted on ECG signal data with abnormalities in Apnea with RR intervals filled with noise, making the QRS complex difficult to identify. As a result, the Discrete Wavelet Transform (DWT) method produces the best form of reconstruction, resembling the initial ECG signal, while the Fast Fourier transform (FFT) method produces the best signal-to-noise ratio (SNR) for the denoising process. So, we get a superior FFT method in the ECG signal denoising process.

Comparative analysis of ANN performance of four feature extraction methods used in the detection of epileptic seizures

Epilepsy, a prevalent neurological disorder characterized by disrupted brain activity, affects over 70 million individuals worldwide, as reported by the World Health Organization (WHO). The development of computer-aided diagnosis systems has become vital in assessing epilepsy severity promptly and initiating timely treatment. These systems enable the detection of epileptic seizures by analyzing the electrical activity in the EEG recordings of the patients. In addition, it helps doctors to choose suitable treatment by quickly determining the type, duration, and characteristics of seizures and increases the patient's quality of life. The proposed computer-aided diagnosis system in this study comprises three modules: preprocessing, feature extraction, and classification. The initial module employs a low-pass Chebyshev II filter to eliminate noise artifacts from signal recordings. The second module involves deriving feature vectors using Bispectrum Analysis, Empirical Mode Decomposition, Discrete Wavelet Transform, and Wavelet Packet Analysis. The third module employs the Artificial Neural Networks method for epileptic seizure detection. This study not only enables the comparison of feature extraction efficacy among Bispectrum Analysis, Empirical Mode Decomposition, Discrete Wavelet Transform, and Wavelet Packet Analysis techniques, but it also reveals that Bispectrum Analysis and Empirical Mode Decomposition yield the highest accuracy rate. The method achieves 100% accuracy in detecting epileptic seizures. Additionally, sensitivity analysis has been conducted to enhance the success of Discrete Wavelet Transform and Wavelet Packet Analysis methods and to identify significant features.

Testing the Short, Medium, and Long-Term Effects of Shocks on Ecological Balance.

Ecological balance is an indicator of environmental degradation, which is computed as the difference between biocapacity and ecological footprint. In this study, we examine whether the effect of shocks on the ecological balance is permanent or temporary in the EU-15 countries, considering the period from 1961 to 2018. The application of unit root tests, with and without a Fourier function, indicates that the effects of shocks are temporary in only four countries. To reveal the persistence of shocks at different frequencies, that is, the short-, medium-, and long-run, we decomposed the ecological balance series by using the Discrete Wavelet Transform method. The unit root test results show that the effects of shocks are temporary in all countries in the short-run. However, the results also show that the shocks are temporary in nine countries in the medium-run and ten countries in the long-run. Thus, the implemented policies to balance ecology are not effective in the short-run but are effective in only six countries in the medium-term and in only five countries in the long-term. These findings demonstrate the importance of considering different frequencies when testing the effectiveness of empirically implemented policies. Regarding policy implications, this study suggests focusing on medium- and long-term environmental policies rather than short-term ones.

Evaluation Of Digital Wavelet Filter On Low Voltage Arcing Detection Equipment

Arcing is one of the causes of fire disasters that occur quite a lot in the world. The danger of arcing causes enormous losses. The fault current caused by arcing usually occurs in a very short duration, so that safety equipment such as Miniature Circuit Breaker (MCB) and Fuse cannot detect the disturbance. If arcing takes place continuously, there will be heat that can damage the equipment and cause a fire. In this study, an evaluation of the use of mother wavelets in the Discrete Wavelet Transform (DWT) method on low voltage detection equipment will be carried out. DWT is a mathematical function that is the most successful in the field of signal processing. Signal processing using the DWT method will be analyzed and compared its performance with mother wavelets Daubechies-1, Daubechies-4, Coiflet-4, and Symlet-4. Then applied to low voltage arcing detection equipment. From the results of testing and analysis obtained the mother wavelet Daubechies-4 produces a high level of accuracy and sensitivity. Mother wavelet Coiflet-4 and Symlet-4 produce the lowest level of accuracy at 400W and 700W load. Meanwhile, on the mother wavelet Daubechies-1, the normal signal amplitude value is close to the arcing signal amplitude value, so it will be difficult to determine the threshold value. In this study, the results obtained are arcing detection equipment using the mother wavelet Daubechies-4 method can detect arcing disturbances very well and produces a high level of accuracy and sensitivity compared to the mother wavelet Daubechies-1, Coiflet-4 , and Symlet-4.

The Wavelet Multi-Scale Analysis of Exchange Rate Exposure: An Application to Malaysian Consumer Products and Services Sector

Objective – Past efforts in assessing foreign exchange rate exposure assume homogeneity in the level of exposure across time horizons which seems to be impractical due to the dynamic nature of comovement between firm value and exchange rate. Methodology – Given this, the study aimed to investigate the multi-scale relationships between changes in exchange rates and firm values of 56 multinational corporations in the consumer products and services sector from January 2000 to December 2020. Findings – The novelty of the study lies upon the application of Maximal Overlap Discrete Wavelet Transformation (MODWT) method which allows decomposition of a single time series domain into different time domains. The fragmentation into multiple time domains allows the measurement of scale-dependent foreign exchange exposure. As a result, the study discovered a non-monotonic trend for wavelet scale j of currency exposure across time scales. In particular, there was a gradual increase in the magnitude of beta exchange exposure and the proportion of exposed firms from low to high time scales. This finding suggested that firm value is more sensitive to changes in the exchange rate within a widened time domain. Novelty – The study demonstrated on how the wavelet technique can be used to measure foreign exchange risk and aided firm managers and market participants in managing foreign exchange risk for a specific time interval. Type of Paper: Empirical JEL Classification: E42, F31, F39 Keywords: Currency exposure; multi-horizon exchange rate; Malaysian Sectors; Wavelet Analysis; MODWT. Reference to this paper should be made as follows: Wahab, H.A; Nasir, N.A.M. (2023). The Wavelet Multi-scale Analysis of Exchange Rate Exposure: An Application to Malaysian Consumer Products and Services Sector, J. Fin. Bank. Review, 8(1), 58 – 72. https://doi.org/10.35609/jfbr.2023.8.1(3)

Leaf chlorophyll content retrieval for AVIRIS-NG imagery using different feature selection and wavelet analysis

The Leaf Chlorophyll Content (LCC) is a crucial indicator of plant vitality. It plays a crucial role in photosynthetic processes and regulates metabolic activities in plants. Thus, it is an important task for the scientific community to estimate its precise quantity. In this study, we used AVIRIS-NG imagery, a wavelet analysis, and a number of feature selection approaches to estimate the chlorophyll content of several agricultural species. Eight different Discrete Wavelet Transform (DWT) methods, including Daubechies (db), Biorthogonal (bior), Reverse biorthogonal (rbio), etc., were employed to generate denoised vegetation spectra, in which bior produced the best approximation. Recursive Feature Elimination (RFE), Regularized Random Forest (RRF), Least Absolute Shrinkage and Selection Operator (LASSO), and Partial Least Square (PLS) were used to select features from the approximated signals, and the top three bands were chosen to be used in the creation of new indices for LCC retrieval. In order to estimate LCC, linear regression models were developed using these indicators. The best results were obtained by the PLS-based LCC retrieval model, with a correlation of r = 0.948, a Root Mean Square Error (RMSE) of 5.464, and a bias of 3.305. The LASSO model yielded the worst results. Hence, for any hyperspectral images such as of AVIRIS-NG, the chlorophyll content may be reliably estimated using a PLS model combined with wavelet analysis.

Experimental Analysis of Streamer Currents Under Lightning Impulse Voltage Using Fast Fourier and Discrete Wavelet Transform Methods

This paper presents an experimental comparative study of streamer currents propagating in mineral oil, natural esters (olive oil and rapeseed oil) and synthetic ester (tetra-ester) under both positive and negative lightning impulse voltages. Because of the similarity in the shapes of streamer currents in investigated liquids, it is difficult to differentiate between the current signals corresponding to each liquid. For that purpose, two techniques are used for the analysis of these signals. The first one is the Fast Fourier Transform (FFT) technique, which is based on the frequency spectrum analysis. The second one is the Discrete Wavelet Transform (DWT) method, which has demonstrated its ability for the analysis of the non-stationary signals. The obtained experimental results show spectral signature specific to each liquid, thus making it possible to differentiate the various streamer currents. Overall, the two techniques show good performance. However, the accuracy and effectiveness of the DWT method is better than those of the FFT technique.

A Study on Segmentation and Feature Extraction of Retinal Images using Fuzzy C-Means Clustering

One of the major problem occur due to diabetes is retinopathy. It should be diagnosed earlier for effective treatment. In the initial stage of diabetes, patient may suffer in the earlier stage of diabetic retinopathy which gives the symptoms of starting of vision deteriorate in retina. This symptoms result to the eye deficiency in any one of the type of retinopathy. But the survey of medical refers that many people get affects by non-proliferative retinopathy and proliferative retinopathy. There are many ways to diagnose this eye deficiency. This process uses the digital image processing for filtering the noise in the image and it can be seen at different angle by using digitization method. Each image can be compressed by using Discrete Wavelet Transform method. The blood vessels are segmented using FUZZY C-Means.

Predicting muscle fatigue during dynamic contractions using wavelet analysis of surface electromyography signal

Muscle fatigue is defined as a reduction in the capability of muscle to exert force or power. Although surface electromyography (sEMG) signals during exercise have been used to assess muscle fatigue, analyzing the sEMG signal during dynamic contractions is difficult because of the many signal distorting factors such as electrode movements, and variations in muscle tissue conductivity. Besides the non-deterministic and non-stationary nature of sEMG in dynamic contractions, no fatigue indicator is available to predict the ability of a muscle to apply force based on the sEMG signal properties.In this study, we designed and manufactured a novel wearable sensor system with both sEMG electrodes and motion tracking sensors to monitor the dynamic muscle movements of human subjects. We detected the state of muscle fatigue using a new wavelet analysis method to predict the maximum isometric force the subject can apply during dynamic contraction.Our method of signal processing consists of four main steps. 1- Segmenting sEMG signals using motion tracking signals. 2- Determine the most suitable mother wavelet for discrete wavelet transformation (DWT) based on cross-correlation between wavelets and signals. 3- Deoinsing the sEMG using the DWT method. 4- Calculation of normalized energy in different decomposition levels to predict maximal voluntary isometric contraction force as an indicator of muscle fatigue.The monitoring system was tested on healthy adults doing biceps curl exercises, and the results of the wavelet decomposition method were compared to well-known muscle fatigue indices in the literature.

Efficient key frame extraction and hybrid wavelet convolutional manta ray foraging for sports video classification

ABSTRACT Sports video classification (SVC) is now considered a challenging topic, therefore, developing an automatic sports scene classification technique has received tremendous interest. This research develops an efficient key frame extraction method and hybrid Wavelet Convolution Neural Network (WCNN) framework with optimization scheme to classify sports videos. Initially, input videos are converted into number of frames, and keyframes are extracted using Enhanced threshold with Discrete Wavelet Transform (ETDWT) method. Then, Cross Guided Bilateral Filter (CGBF) method eliminates the noise from the keyframe. After that, segmentation process is performed by the Fuzzy Equilibrium Optimizer (FEO) algorithm, and then motions are detected using the Farneback optical flow (OF) method. Finally, classification process is performed using Hybrid Wavelet Convolutional Manta Ray Foraging Optimization (HWCMRFO) algorithm to categorize different sports videos. The overall work is implemented using Python language. Simulation results proved that the proposed work achieved the highest accuracy (93.17%) compared to existing approaches.