Discrete Wavelet Transform Algorithm Research Articles

Wavelet-based Filter Bank Approach for Image Compression and Enhancement in Alzheimer\'s Disease Diagnosis Using Medical Imaging

Abstract: This research presents a wavelet-based filter bank approach for image compression and enhancement in the context of Alzheimer's disease diagnosis using medical imaging. The proposed system leverages the PyWavelets and OpenCV libraries in Python to implement Discrete Wavelet Transform (DWT) and Inverse Discrete Wavelet Transform (IDWT) algorithms. The primary objective is to develop an efficient image compression technique while preserving visual quality and enhancing relevant features for improved diagnostic accuracy. The system utilizes filter banks to process wavelet coefficients, enabling selective enhancement of patterns or biomarkers associated with Alzheimer's disease. Results show that the decompressed images closely resemble the originals, with low Mean Squared Error (MSE) and high Peak Signal-to-Noise Ratio (PSNR) values. Visual inspection and quantitative metrics confirm the successful preservation of image quality and structural details. The wavelet coefficients visualization highlights the captured significant features crucial for reconstruction

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

PurposeBearings 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/approachTo 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.FindingsThe 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/valueThis 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.

Estimation of the Resultant Expanded Uncertainty of the Output Quantities of the Measurement Chain Using the Discrete Wavelet Transform Algorithm

This paper discusses the role of the discrete wavelet transform algorithm in processing error signals present in the input quantities of the algorithm. In considering the error model of the measurement chain, the parameters of the error signals in the input quantities of the wavelet transform algorithm are estimated. Subsequently, in accounting for the algorithm’s properties, the parameters of its output values are determined, and the resulting uncertainty values of the output quantities of the measurement chain are estimated. The interval reduction arithmetic method is employed in the calculations for estimating the expanded uncertainty. All findings were validated through measurements conducted using the implemented measurement chain.

Error Model of a Measurement Chain Containing the Discrete Wavelet Transform Algorithm

This paper presents an error model of a measurement chain containing a link that executes a discrete wavelet transform algorithm, which is most often the last stage of measurement signal processing. The goal is to determine the uncertainty budget of the input quantities of the wavelet transform algorithm. The error model takes into account parts of analog, analog-to-digital and digital processing, describing the properties of subsequent fragments of the chain using their transmittance and processing functions. The proposed model enables the description of both the deterministic and non-deterministic properties of signal errors. The proposed model was validated using an example measurement chain created for this purpose.

An investigation of an athlete injury likelihood monitoring system using the random forest algorithm and DWT.

The main goal of sports science is to monitor sports injuries. Nevertheless, the existing sports injury monitoring projects have many expensive instruments and excessively extended monitoring periods, which makes it difficult to expand sports injury monitoring on a large scale. The advancement of machine learning algorithms opens up new avenues for the tracking of sports injuries. A training set of sports injuries was created using the Discrete Wavelet Transform (DWT) and Random Forest algorithms. Next, a basic analytic framework was created based on the lower-body movement of runners, and an athlete's injury likelihood monitoring system was established. First off, the wearable gyroscope device can efficiently plot the motion displacement curve and monitor the three-dimensional mechanics of the athlete's hips, thighs, and calves. Secondly, the system has a higher computational efficiency and an advantage over other classifier-based systems in terms of testing and training times. The suggested system framework identifies athletes' injury propensity, providing preventive recommendations based on displacement curves, and offering a low total cost and high testing accuracy, making it easy to implement and cost-effective. All things considered, the sports injury monitoring device is very accurate and reasonably priced, making it appropriate for widespread use.

Improved DWT and IDWT architectures for image compression

In the recent era, a rapid development in the field of image processing has been observed. One of the important applications in image processing is compression. Several wavelet transform based image compression techniques have already been introduced. In this paper, Discrete Wavelet Transform (DWT) and Inverse Discrete Wavelet Transform (IDWT) based improved image compression and decompression techniques have been proposed by incorporating a scaling factor. The DWT and IDWT algorithms are implemented using folded architecture. To reduce the usages of hardware resources, a multiplier is recursively used. Image compression and decompression schemes based on proposed DWT and IDWT architectures are tested using four different image databases. The proposed technique provides better results in terms of bits per pixel, compression ratio, mean square error, peak-signal-to-noise ratio, normalized correlation coefficient and structural similarity index. FPGA based synthesis has been performed using Xilinx Vivado Synthesis tool in terms of slice LUTs, slice registers, clock frequency, delay and power. The synthesis results show that proposed DWT and IDWT architectures are amenable for image compression and decompression applications.

Enhancement of DWT-SVD digital image watermarking against noise attack using time–frequency representation

Information security has been defined as one of the most critical issues in the information era, as it is utilized to protect confidential information during transfers in real-world applications. In the case of image encryption, a variety of information security approaches were used. Domain spatial and domain frequency are two domains in which such techniques can be classified. This study uses a combination of the singular value de-composition (SVD) and discrete wavelet transformation (DWT) to construct an encryption method based on a traditional watermarking system. Compared with other traditional methods, the proposed DWT-SVD approach has excellent robustness, and it has been strengthened for having high degree of the robustness against the additive white Gaussian noise (AWGN) attacks by utilizing a de-noising strategy based upon S-transform approach. Compared with DWT algorithm denoising approach, the results reveal that the S-transform denoising algorithm that has been deployed in the present article has a robust protection towards the Gaussian noise attack for mean squared error (MSE) around 0.005 and peak signal to noise ratio (PSNR) around 24 dB.

Improved Parallel Implementation of 1D Discrete Wavelet Transform Using CPU-GPU

This work describes a data-level parallelization strategy to accelerate the discrete wavelet transform (DWT) which was implemented and compared in two multi-threaded architectures, both with shared memory. The first considered architecture was a multi-core server and the second one was a graphics processing unit (GPU). The main goal of the research is to improve the computation times for popular DWT algorithms for representative modern GPU architectures. Comparisons were based on performance metrics (i.e., execution time, speedup, efficiency, and cost) for five decomposition levels of the DWT Daubechies db6 over random arrays of lengths 103, 104, 105, 106, 107, 108, and 109. The execution times in our proposed GPU strategy were around 1.2×10−5 s, compared to 3501×10−5 s for the sequential implementation. On the other hand, the maximum achievable speedup and efficiency were reached by our proposed multi-core strategy for a number of assigned threads equal to 32.

DWT-BSS: Blind Source Separation applied to EEG signals by extracting wavelet transform’s approximation coefficients

The Electroencephalogram (EEG) signal is widely contaminated by a physiological artifact, such as muscle activity, heart rhythm, and eye movement. The researcher has proposed a number of methods to clean the EEG signal. A group of these methods is called Blind Source Separation (BSS). In this paper, we suggest an approach that combines the BSS methods and the Discrete Wavelet Transform (DWT) algorithm, in order to evaluate the BSS methods after applying them to the approximation coefficients extracted using the DWT. The aim of this work is to identify which BSS algorithms, using which family of wavelet and at which decomposition level, would provide excellent performance. We used the Spearman Correlation Coefficient (SCC) to rate our methods. The technique that performs the best, as evaluated by the SCC between the generated component and the approximation coefficient obtained from the Horizontal EOG results, is AMICA, which obtains a value of 0.81 for levels 2 while using the wavelet symlet at scales 7 and 11. With a value of 0.70 and the use of the wavelet Daubechies at scale 9 and Coiflets at scales 2 and 5 for level 1, AMICA also has the best SCC value calculated between the separated component and the approximation coefficient recovered from the Vertical EOG. While employing the wavelet symlets at scales 5, 7, 8, and 11. for level 2, and level 3 when using the wavelet symlets at scales 1 and 2.

A factor pricing model based on machine learning algorithm

We adopt Discrete Wavelet Transform (DWT) and Support Vector Regression (SVR) algorithm to predict stock returns and form a time-varying Machine Learning (ML) factor based on the predicted returns for improving the classical asset pricing in the Chinese stock market from 2000 to 2020. The results show that the sorted portfolios formed by the predicted return rates can obtain significant excess returns in the Chinese market. Furthermore, our research shows that incorporating the ML factor into the CH4 and FF5 model can improve the pricing power significantly. It indicates that the ML factor can complement the traditional pricing models. We also find the performance of factor models depend on macroeconomy and market sentiment, that is, the better the macroeconomic and stock market performance, the stronger the pricing power of the factor models in the Chinese market. Additionally, we explore whether ML factor falls under short-term, median-term, and long-term momentum and reversal factors, and our analysis demonstrates that the ML factor is more effective than momentum and reversal factors.

Spectrum sensing in cognitive radio networks using an ensemble of machine learning frameworks and effective feature extraction

From the signal received on a particular frequency band, spectrum sensing (SS) is used in cognitive radio (CR) to assess whether the primary user (PU) is using the spectrum and, consequently, whether the secondary user (SU) can utilize the spectrum. The main issue with SS is determining the presence of the primary signal in a low signal-to-noise ratio (SNR). Compared to conventional technologies, machine learning techniques are more effective and accurate at identifying the qualities of input data. This paper proposes a machine learning (ML) based SS model for CR with effective feature extraction and reduction techniques. The proposed work comprises five phases: noise removal, wavelet transform, feature extraction, dimensionality reduction, and classification. Firstly, noise filtering is done on the received signal to remove the noise present in the input signal using the filters such as moving median filter (MMF), Gaussian filter (GF), and Gabor filter (GBF). After that, the filtered signal is transformed into a wavelet domain using Discrete Wavelet Transform (DWT) algorithm. Then the statistical features such as average absolute value, wavelet energy, variance, standard deviation, and peak value features are extracted from the DWT. Next, the dimensionality reduction (DR) is performed using Linear Discriminant Analysis (LDA). Finally, the classification is performed using the ensemble ML classifiers such as Support Vector Machine (SVM), Naive Bayes (NB), and K-Nearest Neighbour (KNN), which classify whether the PU signal is active or not. Simulations are carried out to analyze the efficiency of the presented models for SS. The results proved that SVM obtains the best performance for SS with higher accuracy and lower SNR.

Real-time ship motion prediction based on adaptive wavelet transform and dynamic neural network

Ship motion attitude is the time series data with highly nonlinear and non-stationary characteristics significantly affected by environmental factors. To accurately predict the ship motion attitude in real-time, a ship motion attitude prediction model based on the Adaptive Discrete Wavelet Transform Algorithm (ADWT) and the space-time Residual Recurrent Neural Network (RRNN) with a time-varying structure is proposed. The ADWT decomposes the original data into components easier to predict, each element in the optimal component combination is predicted using the RRNN, the structure and parameters of which are adjusted in real-time with the sliding of data window. The model performance tests are conducted based on the simulation data of the ship motion of DTMB5415. The results show that the subsequences decomposed by the ADWT are easier to predict. Compared with other prediction models, the prediction accuracy of the ADWT-RRNN is the highest under all working conditions, its prediction accuracy and stability of it do not fluctuate significantly over a long prediction period. Hence, the more severe the sea states, the more pronounced the performance advantage is over other models. Finally, a reliable and efficient tool is eventually provided for real-time and accurate prediction of ship motion.

Hybrid Techniques of Analyzing MRI Images for Early Diagnosis of Brain Tumours Based on Hybrid Features

Brain tumours are considered one of the deadliest tumours in humans and have a low survival rate due to their heterogeneous nature. Several types of benign and malignant brain tumours need to be diagnosed early to administer appropriate treatment. Magnetic resonance (MR) images provide details of the brain’s internal structure, which allow radiologists and doctors to diagnose brain tumours. However, MR images contain complex details that require highly qualified experts and a long time to analyse. Artificial intelligence techniques solve these challenges. This paper presents four proposed systems, each with more than one technology. These techniques vary between machine, deep and hybrid learning. The first system comprises artificial neural network (ANN) and feedforward neural network (FFNN) algorithms based on the hybrid features between local binary pattern (LBP), grey-level co-occurrence matrix (GLCM) and discrete wavelet transform (DWT) algorithms. The second system comprises pre-trained GoogLeNet and ResNet-50 models for dataset classification. The two models achieved superior results in distinguishing between the types of brain tumours. The third system is a hybrid technique between convolutional neural network and support vector machine. This system also achieved superior results in distinguishing brain tumours. The fourth proposed system is a hybrid of the features of GoogLeNet and ResNet-50 with the LBP, GLCM and DWT algorithms (handcrafted features) to obtain representative features and classify them using the ANN and FFNN. This method achieved superior results in distinguishing between brain tumours and performed better than the other methods. With the hybrid features of GoogLeNet and hand-crafted features, FFNN achieved an accuracy of 99.9%, a precision of 99.84%, a sensitivity of 99.95%, a specificity of 99.85% and an AUC of 99.9%.

The Use of the Multi-Scale Discrete Wavelet Transform and Deep Neural Networks on ECGs for the Diagnosis of 8 Cardio-Vascular Diseases

Cardiovascular diseases (CVD) continues to be the leading cause of death worldwide, with over 17 million deaths each year. In 2015, approximately 422 million people suffered from cardiovascular disease (CVD). Reading and analyzing electrocardiograms (ECGs) can be time consuming, and the development of decision support tools based on automated systems can facilitate and speed up the diagnosis of ECGs. In this paper, we propose a 12 leads ECG signals classification using Multi-level Discrete Wavelet Transform and ResNet34 Deep Learning algorithm which classifies 8 types of cardiovascular diseases: Atrial fibrillation (AF), 1st degree atrioventricular block (AV), Left bundle branch block (LBBB), Right bundle branch block (RBBB), Premature ventricular contraction (PVC), Premature atrial contraction (PAC), ST segment depression (STD), and ST segment elevation (STE). The ECGs are preprocessed, and different features are extracted using Multi-level Discrete Wavelet Transform. The model is trained on a database of more than 6000 electrocardiograms which includes 9 types of 12-lead ECGs: a normal type and the 8 abnormal ones which correspond to the diseases mentioned above.

BIOMEDICAL IMAGE AUGMENTATION AND TRANSMISSION USING STEGANOGRAPHY

Objective: The aim of the study was to embed sensitive patient information and image data in a cover image to enable secure transmission of confidential data. Methods: Image steganography is a process used to embed (hide with encryption) data which includes text, images or audio, and video files inside the main image file. This embedding is done by altering the values of some pixels which are chosen by the encryption algorithm. The algorithm used in this case is the Discrete Wavelet Transformation Algorithm using MATLAB. Results: Statistical features such as signal to noise ratio (SNR), Peak SNR, and (mean squared error) were extracted from the medical images to test the loss during steganography and transmission. Conclusion: A technique that enables secure and swift image data transmission using image steganography technique is proposed.

Detector of DC Series Arc Fault in a Large Photovoltaic System using Discrete Wavelet Transform

In this paper, the frequency analysis methods can detect the fire accident of the solar arc we propose Discrete Wavelet Transform (DWT). The DWT can express time-axis information even in the frequency domain. In case of DWT algorithm, it was implemented based on Arduino linked with MATLAM. The DC arc accident has been implemented using standard using Rogowski coil. This coil is used to simulate the current sensor for large-capacity PV system application. The constructing and simulated DC series arc generation circuit was verified under actual arc generation conditions. In addition, the performance of the accident detector manufactured was studied The experiment results and its simulation demonstrate, the proposed method is more efficient than others.

A new methodology for anomaly detection of attacks in IEC 61850-based substation system

Smart substation is a crucial Cyber-Physical system and is prone to cyber-attack. In this paper, we propose a novel anomaly detection mechanism tailored for detecting the IEC 61850-based network traffic. Three types of traffic features are taken into account for comprehensively characterizing the network traffic during a time window. To eliminate the subjectivity of manually selecting the traffic features, we exploit Discrete Wavelet Transform (DWT) algorithm to secondarily extract the deep features. An improved Locally Linear Embedding (LLE) algorithm is proposed to reduce the dimension of deep feature vectors with more effective dimensionality reduction ability. By doing so, the LSTM (Long Short Term Memory)-based Autoencoder network that can learn to reconstruct the normal traffic time-series behavior, and thereafter uses the reconstruction error to detect the anomalies. We assess the performance of our proposed mechanism with the comprehensive experiments on the real smart substation. The results indicate that the proposed mechanism can be performed in a fast way with satisfactory detection performance.

Transient Wavelet Energy-Based Protection Scheme for Inverter-Dominated Microgrid

When faults occur in the microgrids, high frequency transients will be superimposed on the system currents and voltages. The magnitude of those transients will attenuate as it encounters the discontinuity points in the network such as busbars, or any other impedance discontinuity points. This phenomenon can also be quantified by wavelet energy, which provides a useful tool to detect faults and locate the faulted feeder in the microgrid. In this paper, a novel protection scheme based on the transient wavelet energy of the superimposed current extracted by the Maximal Overlap Discrete Wavelet Transform (MODWT) algorithm is developed to detect faults and locate the faulted feeder in microgrids. Compared with existing protection schemes, the proposed protection scheme has the advantage of being largely immune to the changes in system fault level, fault types and positions, microgrid operating status and the control strategies deployed on the inverters, while presenting much lower requirement on the sampling frequency (10 kHz) compared with travelling wave-based methods. Unlike the conventional differential protection, the proposed scheme does not require synchronized measurement or high bandwidth communication channels, and thus, it can be considered as an economical and promising solution for microgrids.

Deep Neural Network Approach Based Segmentation, Detection and Classification of Brain Tumor

The segmentation, detection and extraction of malignant tumor regions from magnetic resonance (MR) images are challenging tasks in medical image analysis. Approaches based on machine and deep learning have been introduced, which performed better than traditional image processing methods. However, many approaches still show limited ability due to the complex dataset and image modalities. This study evaluated the deep learning approach's performance and traditional image processing algorithms for medical imaging segmentation, detection, and classification. The proposed system comprises multiple stages. The Median filters are used in the pre-processing step, and morphological operation and Otsu thresholding are used to segment MR images. Discrete Wavelet Transform (DWT) algorithm is considered in the extraction features, and their classification is executed by a convolutional neural network (CNN) and support vector machine (SVM) algorithms. The Mat lab has been used for simulation and experimental findings to evaluate the suggested method's performance on the brain's complex and highly 2D structures. The results show that the methodology is reliable and efficient, with 93.5% accuracy.

A Machine Learning Approach for the Detection of QRS Complexes in Electrocardiogram (ECG) Using Discrete Wavelet Transform (DWT) Algorithm.

This study describes a modified approach for the detection of cardiac abnormalities and QRS complexes using machine learning and support vector machine (SVM) classifiers. The suggested technique overtakes prevailing approaches in terms of both sensitivity and specificity, with 0.45 percent detection error rate for cardiac irregularities. Moreover, the vector machine classifiers validated the proposed method's superiority by accurately categorising four ECG beat types: normal, LBBBs, RBBBs, and Paced beat. The technique had 96.67 percent accuracy in MLP-BP and 98.39 percent accuracy in support of vector machine classifiers. The results imply that the SVM classifier can play an important role in the analysis of cardiac abnormalities. Furthermore, the SVM classifier also categorises ECG beats using DWT characteristics collected from ECG signals.