Level Discrete Wavelet Transform Research Articles

Evaluation of wavelet decomposition level to enhance numerical analysis of seismic responses

In this study, the discrete wavelet transform (DWT) is used to enhance the efficiency of numerical analysis by decomposing earthquake excitation signals into low- and high-frequency components. The low-frequency components, considered as the main damaging factor in earthquake waves, stored in approximation coefficients have been used to perform time response analysis. In the selection of the ground motions, the special attention is given to the ratio of peak ground acceleration (PGA) to peak ground velocity (PGV). An ensemble of nine earthquake records have been selected and used. For each earthquake, displacement response spectra have been generated for both the original signal and its approximations at different DWT levels. To determine the most accurate decomposition level, the normalized root-mean-squared error (NRMSE) of wavelet energy has been computed in spectral displacement responses. The outcomes reveal a dependency of the decomposition level on the specific characteristics of each earthquake. Subsequently, structural analyses are conducted on base-isolated structures with varying story numbers, to examine the accuracy of decomposed signals. Employing both the original signal and its approximations to the structures, base mat displacements and roof accelerations are computed. The findings of the seismic analyses demonstrate the effectiveness of the proposed decomposition levels.

StackedEnC-AOP: prediction of antioxidant proteins using transform evolutionary and sequential features based multi-scale vector with stacked ensemble learning

BackgroundAntioxidant proteins are involved in several biological processes and can protect DNA and cells from the damage of free radicals. These proteins regulate the body's oxidative stress and perform a significant role in many antioxidant-based drugs. The current invitro-based medications are costly, time-consuming, and unable to efficiently screen and identify the targeted motif of antioxidant proteins.MethodsIn this model, we proposed an accurate prediction method to discriminate antioxidant proteins namely StackedEnC-AOP. The training sequences are formulation encoded via incorporating a discrete wavelet transform (DWT) into the evolutionary matrix to decompose the PSSM-based images via two levels of DWT to form a Pseudo position-specific scoring matrix (PsePSSM-DWT) based embedded vector. Additionally, the Evolutionary difference formula and composite physiochemical properties methods are also employed to collect the structural and sequential descriptors. Then the combined vector of sequential features, evolutionary descriptors, and physiochemical properties is produced to cover the flaws of individual encoding schemes. To reduce the computational cost of the combined features vector, the optimal features are chosen using Minimum redundancy and maximum relevance (mRMR). The optimal feature vector is trained using a stacking-based ensemble meta-model.ResultsOur developed StackedEnC-AOP method reported a prediction accuracy of 98.40% and an AUC of 0.99 via training sequences. To evaluate model validation, the StackedEnC-AOP training model using an independent set achieved an accuracy of 96.92% and an AUC of 0.98.ConclusionOur proposed StackedEnC-AOP strategy performed significantly better than current computational models with a ~ 5% and ~ 3% improved accuracy via training and independent sets, respectively. The efficacy and consistency of our proposed StackedEnC-AOP make it a valuable tool for data scientists and can execute a key role in research academia and drug design.

Development of overcurrent relay based on wavelet transform for fault detection in transmission line

This study proposes a protection relay using a microcontroller to detect and classify faults in transmission lines based on the wavelet transform. An experimental model was constructed from an actual 115 kV transmission system prototype. The current signal was observed based on the fault type, phase, and position. Clark’s transform and the discrete wavelet transform (DWT) were applied to transform signals for analysis. Moreover, the performance of fault detection based on the output signals of Clark’s transform (alpha sequence, beta sequence, and zero sequence current) was compared to the performance of the alternative proposed fault detection method, which is based on the combining factor between alpha and beta sequence current. In addition, the influence of DWT level on fault analysis is also considered and is used to confirm the accuracy of fault detection. Results show that the proposed method is efficient for fault detection and classification. This finding allows the researcher to choose the appropriate analytical method. Moreover, it can also be used as the basis for overcurrent relay algorithm design in the effort to develop more advanced technologies.

Fingerprint Image Compression using Biorthogonal and Orthogonal Wavelets at Different Levels of Discrete Wavelet Transform

Fingerprint Image Compression using Biorthogonal and Orthogonal Wavelets at Different Levels of Discrete Wavelet Transform

Ultrasound super resolution imaging for accurate uterus tumor detection and malignancy prediction

The term ‘tumor’ describes an atypical development of cells that forms a mass inside an organ; depending on the organ's invasive nature and propensity for metastasis, the growth may be benign or malignant. Improving patient outcomes requires the early detection of malignant tumors. Despite its lower resolution and noise problems, ultrasound, which is frequently used to diagnose uterine tumors, is safer and more economical than magnetic resonance imaging (MRI) scans and biopsies. Ultrasound pictures can be processed using methods including denoising, enhancement, segmentation, and feature extraction to get around these restrictions and boost their quality. Enhanced ultrasound images can reach even higher accuracy, cementing them as plausible alternatives to MRI. A comparative analysis of copious relevant image de-speckling, image enhancement, segmentation, and feature extraction methods are carried out. Higher resolution and superior quality, strong segmented real-time ultrasound uterus tumour images are produced by using diffusion-based hybrid filters, Super Resolution Convolutional Neural Networks (SRCNN), and U-net segmentation technique. The Grey Level Co-occurrence Matrix (GLCM) and Discrete Wavelet Transform (DWT) are used to extract textural features. With the help of several machine learning approaches, such as Support Vector Machine (SVM), K-Nearest Neighbour (KNN), and Random Forest classifiers (RFC), the extracted characteristics are immediately sent to classifiers to classify uterus tumours from ultrasound images between benign and malignant. For the categorization of uterine tumors, the RFC classifier outperformed the other classifiers. The viability of cancer detection using ultrasound pictures is significantly strengthened by the suggested machine learning methodology. Multiple hospitals provided data on ultrasound pictures of uterine tumors, which were used to develop the model and obtain the prediction findings. A radiologist with 17 years of expertise in diagnostic radiology further assessed this dataset. We could produce high-quality ultrasonic real-time images of uterine tumor datasets with the suggested machine learning model at a 97.8 % accuracy rate utilizing RFC.

Optimizing Visual Sensor Node Image Compression for Resource-Constrained Environments

Abstract: An Approximate hybrid multiplier design is proposed to reduce the data-path width of lifting 2-D discrete wavelet transform (DWT) especially for wireless visual sensor node applications. The proposed multiplier design takes multiplier operand of size less by 3 bit and calculates full-width outputs with marginal loss of accuracy, which is significant. The proposed approximate 2-D DWT structure uses data-path DWT levels and produces reconstructed color images higher than the 70-dB peak-signal-to-noise ratio (PSNR). Compared with the existing fractional wavelet transform (FrWT)-based 2D DWT structures, the proposed structure has a smaller data-path width and free from overhead. Compared with the existing parallel structure

Two-phase flow regime identification using multi-method feature extraction and explainable kernel Fisher discriminant analysis

PurposeIdentifying the flow regime is a prerequisite for accurately modeling two-phase flow. This paper aims to introduce a comprehensive data-driven workflow for flow regime identification.Design/methodology/approachA numerical two-phase flow model was validated against experimental data and was used to generate dynamic pressure signals for three different flow regimes. First, four distinct methods were used for feature extraction: discrete wavelet transform (DWT), empirical mode decomposition, power spectral density and the time series analysis method. Kernel Fisher discriminant analysis (KFDA) was used to simultaneously perform dimensionality reduction and machine learning (ML) classification for each set of features. Finally, the Shapley additive explanations (SHAP) method was applied to make the workflow explainable.FindingsThe results highlighted that the DWT + KFDA method exhibited the highest testing and training accuracy at 95.2% and 88.8%, respectively. Results also include a virtual flow regime map to facilitate the visualization of features in two dimension. Finally, SHAP analysis showed that minimum and maximum values extracted at the fourth and second signal decomposition levels of DWT are the best flow-distinguishing features.Practical implicationsThis workflow can be applied to opaque pipes fitted with pressure sensors to achieve flow assurance and automatic monitoring of two-phase flow occurring in many process industries.Originality/valueThis paper presents a novel flow regime identification method by fusing dynamic pressure measurements with ML techniques. The authors’ novel DWT + KFDA method demonstrates superior performance for flow regime identification with explainability.

Robust medical image watermarking in frequency domain

Protecting patient information in medical image watermarking poses a significant challenge, especially when traditional methods like the Arnold transform prove inadequate in ensuring security. This paper introduces a novel approach within the Discrete Wavelet Transform (DWT) domain to address this issue effectively. By employing the Advanced Encryption Standard (AES), the security and robustness of the system are greatly enhanced through the encryption of both the medical image and patient data. The encrypted medical image undergoes a 2-level DWT process, allowing the concealment of encrypted patient information while maintaining its invisibility. This proposed scheme surpasses others in experimental evaluations, as evidenced by metrics such as PSNR and NC, solidifying its position as a more secure choice for medical image watermarking. The results validate the scheme’s robustness and imperceptibility.

Support vector machine based discrete wavelet transform for magnetic resonance imaging brain tumor classification

Here, a brain tumor classification method using the support vector machine (SVM) algorithm by utilizing discrete wavelet transform (DWT) transformation and feature extraction of gray-level co-occurrence matrix (GLCM) and local binary pattern (LBP) has been implemented using the magnetic resonance imaging (MRI) image belong to the low-grade glioma (LGG) or high-grade glioma (HGG) group. SVM algorithm used as a classification method has been widely used in research that raises the topic of classification. Through the formation of a hyperplane between 2 data classes, the SVM algorithm can be said to be a reliable method but does not require complicated computations. The DWT transformation is intended to provide clearer feature details from the MRI image, so that when the feature extraction algorithm is applied, it is expected that the extracted features will differ between benign tumor MRI images and malignant tumor MRI images. In 1 level DWT using high-low (HL) sub-band yield the highest specificity, sensitivity, and accuracy than using 3 levels using HL or low-high (LH) sub-band in LGG MRI image.Compared with another research, our proposed method is slightly better in terms of accuracy to classify the brain tumor image with achieved the accuracy of 98.6486%.

Analysis and effectiveness of deeper levels of SVD on performance of hybrid DWT and SVD watermarking

In this paper, an analysis on hybrid Discrete Wavelet Transform (DWT) and Singular Value Decomposition (SVD) for image watermarking is carried out to investigate the effect of a deeper level of the SVD on imperceptibility and robustness to resist common signal processing and geometric attacks. For this purpose, we have designed two hybrid watermarking schemes, the first one with DWT and first level of SVD, whereas, in the second scheme, the same design is employed with a second level of SVD. In this experiment, a comprehensive analysis is performed on the two designed schemes and the effect of robustness and imperceptibility is compared in the first and second levels of SVD in each DWT sub-band. Having analyzed more than 100 medical and non-medical images in standard datasets and real medical samples of patients, the experimental outcomes show a remarkable increase in both imperceptibility and robustness in the second level of SVD, in comparison to the first level. In addition, the achieved result shows that the SVD2 scheme offers the highest imperceptibility in the LL sub-band (more than 60 dB on average PSNR), with satisfactory robustness against noise attacks, but less persistence in some geometric attacks such as cropping. For the HH sub-band, strong robustness against all types of tested of attacks is obtained, though its imperceptibility is slightly lower than the achieved PSNR in the LL sub-band. In HH sub-band, an average growth of 5 dB in PSNR and 2% in NC can be observed from the second level of SVD in comparison to the first level. These results make SVD2 a good candidate for content protection, especially for medical images.

Discrete wavelet transform based branched deep hybrid network for environmental noise classification

SummaryWith ever growing urbanization, the environmental noise is becoming hazardous. Vehicular traffic, locomotives, heavy machinery in industry, and construction processes are the major sources of noise pollution. It has adverse effects on the health of humans as well as that of the wild life. World Health Organization (WHO) puts noise pollution as the second major cause of illness due to environmental reasons. The effects of noise pollution on the quality of life are usually ignored. Due to this reason it is common, even in the first world countries, to have the WHO's peak noise standards violated in residential areas. Therefore, there is a need to have a real time, portable and easy to replicate, mechanism to monitor the noise sources. In this work, we propose a novel architecture of a deep neural network to classify a 10‐class environmental noise data called URBANSOUND8K. This network is comprised of three components, (1) one dimensional two level Discrete Wavelet Transform (DWT) component, (2) branched component for feature extraction through auto‐encoders, and (3) LSTM and fully‐connected layers based classification component. With all components combined, we call this network DWTNet. By embedding the DWT component as a part of network, we eliminate the need of prior data conversion into spectral and/or spectro‐temporal domains. The efficiency of DWTNet is comparable to the state of the art networks with significantly lower number of trainable parameters. We analyze the contribution of classification accuracy. We further study some of the classification results individually and show that some of the mis‐classifications are actually multi‐class classifications with distributed decision confidence.

Short Circuit Failure Detection in Induction Motor Using Wavelet Transform and Fuzzy C-Means

Induction motors need to be monitored regularly because it involves the company's productivity. The induction motor monitoring method in this study uses a motor current variable which is transformed using the Discrete Wavelet Transform. Discrete Wavelet Transform (DWT) is used in this study because the results are satisfactory for detecting a short circuit in the stator winding of an induction motor. Of the many types and levels of discrete wavelet transforms, the haar wavelet transform at the third level is used in this study. Furthermore, the results of the discrete wavelet transform are processed using the Fuzzy C-means method. Fuzzy C-Mean (FCM) is the grouping approach that each part has a member degree of cluster according to the fuzzy logic algorithm. Motor modeling is shown in this article as normal condition, final fault current, and initial fault current. For this analysis, a combination of wavelet transform and Fuzzy C-means is used to classify motor currents into three motor states. The motor current is processed by Haar DWT level 3 to generate a high frequency signal. Then the high frequency signal is processed to get the energy signal. The energy signal is then fed to Fuzzy C-means to identify its condition. The results show that fuzzy C-means produces an error of 0% for the normal case, 33.3% for the initial error case and 0% for the final error case.

Approximate Lifting 2-D DWT Hardware Design for Image Encoder of Wireless Visual Sensors

In this paper, a novel approximate hybrid multiplier design is proposed to reduce the data-path width of lifting two-dimensional (2-D) discrete wavelet transform (DWT) especially for wireless visual sensor node applications. The proposed multiplier design takes multiplier operand of size less by 3-bit and calculates full-width outputs with marginal loss of accuracy which is significant. A parallel approximate lifting 2-D DWT structure is derived using the proposed multiplier design. The proposed approximate 2-D DWT structure uses data-path width 9 and 11 for first and second DWT levels and produces reconstructed colour images higher than 70dB peak-signal-to-noise ratio (PSNR). Compared with the existing fractional wavelet transform (FrWT)-based 2-D DWT structures, the proposed structure has a smaller data-path width and free from overhead. The proposed structure has equal or marginally higher on-chip memory requirement than the existing FrWT based 2-D DWT structures, but involves a substantially less area-delay product (ADP) and energy per image (EPI). The ADP and EPI reduction is O(10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ×). Compared with the existing parallel structure based on hybrid approximate radix-4 and radix-8 encoded Booth multiplier, the proposed approximate structure involves nearly 23% less ADP and 22% less EPI and calculates output images with nearly (4 ~ 7)dB higher PSNR.

English

In recent times, most of the Facial Recognition framework has shown better outcomes in a constrained environment. Due to its high usage, the FR has gained increased attention in the research field. However, it has faced various issues in the real-time application, such as facial disguise designing in facial features, temporal variations, and low-quality images. Therefore, there is a requirement for significant techniques for detecting and identifying the human face. This paper has implemented an effective FR technique. The adequate dataset relevant to the FR process is initially gathered from the standard dataset. Then, the images are pre-processed with the aid of the median filtering process to obtain the pre-processed images. Then, it is given to the three level-Discrete Wavelet Transform (DWT), which is used to attain the spectral features. Then, the features from the spectral features are optimally chosen by using the Improved Horse Herd Optimization algorithm termed (IHHO) derived from Horse Herd Optimization (HHO). Further, it is subjected to the Adaptive Cascaded framework termed ACLSDN, designed using Long Short Term Memory (LSTM) and Deep Neural Network (DNN) model. It is carried out by averaging the score attained from both the LSTM and DNN models for attaining the classified outcomes in maximized accuracy. Here, the same IHHO algorithm is used for optimizing the parameters in the LSTM and DNN frameworks. Finally, the effectiveness of the designed FR model is validated using various metrics and shows maximized accuracy value.

A Deep Learning Scheme for Detecting Atrial Fibrillation Based on Fusion of Raw and Discrete Wavelet Transformed ECG Features.

Atrial fibrillation is the most common sustained cardiac arrhythmia and the electrocardiogram (ECG) is a powerful non-invasive tool for its clinical diagnosis. Automatic AF detection remains a very challenging task due to the high inter-patient variability of ECGs. In this paper, an automatic AF detection scheme is proposed based on a deep learning network that utilizes both raw ECG signal and its discrete wavelet transform (DWT) version. In order to utilize the time-frequency characteristics of the ECG signal, first level DWT is applied and both high and low frequency components are then utilized in the 1D CNN network in parallel. If only the transformed data are utilized in the network, original variations in the data may not be explored, which also contains useful information to identify the abnormalities. A multi-phase training scheme is proposed which facilitates parallel optimization for efficient gradient propagation. In the proposed network, features are directly extracted from raw ECG and DWT coefficients, followed by 2 fully connected layers to process features furthermore and to detect arrhythmia in the recordings. Classification performance of the proposed method is tested on PhysioNet-2017 dataset and it offers superior performance in detecting AF from normal, alternating and noisy cases in comparison to some state-of-the-art methods.

TWO LEVEL IMAGE CRYPTOSYSTEM USING HYBRID-CHAOTIC KEY AND PRNG KEY FOR MULTI-MODALITY DIGITAL IMAGES WITH DISCRETE WAVELET TRANSFORM

In this information age, the need of secure picture transmission is critical. Medical data is hacked in the form of photographs, which costs more in terms of insurance claims. It is vital to safeguard photographs so that unauthorised individuals cannot view them. Chaos-based picture crypto systems are the most well-known of the several types of image crypto systems because their keys are invulnerable to assault or hacking. The chaotic features play a significant role in the development of keys for secure transmission and reception. The discrete wavelet transform (DWT) is a more basic transform that compresses the input image by combining approximation and detailed coefficients. The incorporation of the key in the approximate coefficients of the input image provides an efficient encryption when compared to other image crypto schemes. A two level image encryption is adopted in this work, where one level is achieved using the hybrid-chaotic sequences and the next level is utilizing the pseudo random noise sequence (PRNG) as the keys. A smaller value of variance value is achieved which shows the higher uniformity of pixel values in encrypted images of our proposed two level image cryptography. Also the better performance metrics in terms of entropy, NPCR, and UACI are achieved compared to a single level image encryption. The developed image crypto system is designed and simulated using MATLAB software package. KEYWORDS: two level chaos image cryptosystems, DWT, encryption, hybrid-chaotic maps, and PRNG key generation.

Machine learning based COVID -19 disease recognition using CT images of SIRM database

The COVID-19 pandemic, probably one of the most widespread pandemics humanity has encountered in the twenty first century, caused death to almost 1.75 M people worldwide, impacting almost 80 M lives with direct contact. In order to contain the spread of coronavirus, it is necessary to develop a reliant and quick method to identify those who are affected and isolate them until full recovery is made. The imagery knowledge has been shown to be useful for quick COVID-19 diagnosis. Though the scans of computational tomography (CT) demonstrate a range of viral infection signals, considering the vast number of images, certain visual characteristics are challenging to distinguish and can take a long time to be identified by radiologists. In this study for detection of the COVID-19, a dataset is formed by taking 3764 images. The feature extraction process is applied to the dataset to increase the classification performance. Techniques like Grey Level Co-occurrence Matrix (GLCM) and Discrete Wavelet Transform (DWT) are used for feature extraction. Then various machine learning algorithms applied such as Support Vector Machines (SVM), Linear Discriminant Analysis (LDA), Multi- Level Perceptron, Naive Bayes, K-Nearest Neighbours and Random Forests are used for classification of COVID-19 disease detection. Sensitivity, Specificity, Accuracy, Precision, and F-score are the metrics used to measure the performance of different machine learning models. Among these machine learning models SVM with GLCM as feature extraction technique using 10-fold cross validation gives the best classification result with 99.70% accuracy, 99.80% sensitivity and 97.03% F-score. We also ran these tests on different data sets and found that the results are similar across those too, as discussed later in the results section.

A partial image encryption scheme based on DWT and texture segmentation

nowadays, image encryption is an interesting research area due to the rapid growth of communication technologies and the increasing demands for safeguarding the privacy of the transmitted images as unencrypted images are vulnerable to interception. Partial encryption is used to overcome the challenge of encryption the large size of digital images, due to the large computational overhead and long processing time needed. In this research, one level Discrete Wavelet Transform is applied to a colored image to divide the image to four sub-bands; then a texture segmentation using a Gabor filter and K-means clustering is applied to lowest frequency band. To achieve that, the texture segmentation is used as a new method for scrambling the image based on the features of another image. Finally, the scrambled segments of the image are encrypted using interchanged AES and RC4 Algorithms. Each one of the image pixels is then XORed using one of the two algorithms and the key of the other algorithm. The proposed work is evaluated using several security and performance analysis tests. The results show that the proposed work is resistant agents many kinds of attack and promising in real-time image transmission and encryption.

Digital watermarking image using three-level discrete wavelet transform under attacking noise

The authentication, identification, and copyright protection can be obtained by constructing the digital image watermarking technique. Watermark robustness and imperceptibility account for the capability of the hidden watermark to survive the manipulation. The proposed paper is a robust algorithm for digital image watermarking with 3-level discrete wavelet transform (DWT) with some attacks method. The 3-level DWT method was used constants α=0.01 and 0.03 as a function of how depth the watermark inserts to the host image in the insertion and extraction process. The algorithm was evaluated using 8 bits per pixel (bpp) grayscale, 1024x1024 pixels for the host image, and 256x256 pixels for the watermark image. The method is also implemented some experimental with attacks such as gaussian, salt and pepper, blurring, and compression. The algorithm is relatively acceptable of good quality, achieves low-value mean squared error (MSE), high peak signals to noise ratio (PSNR), and structural similarity index metric (SSIM) value approach to 1. It is found that the highest image quality measurements by using α=0.03 with the attacking method of salt and pepper yield MSE=0.01, PSNR=45.6 dB and SSIM=0.95, respectively.

Provable Chaotically Authenticated Encrypted Biomedical Image Using OFDM Transmission

In this research, a unique multiband random chaotic key generator based provable authenticated encrypted technique for biomedical picture for the healthcare biomedical system, which can be used in 5G communication system is presented. In addition, the encryption method employed in this research is based on Multiband Random Chaotic Key Generator, and the proposed provable authenticated methodology is based on symmetric authenticated encryption data (MBRCKG). In the proposed proven Orthogonal Frequency Division Multiplexing (OFDM) communication system, the Authenticated Chaotic Encrypted Biomedical Image (ACE-BI) is utilized. This study uses discrete wavelet transformation (DWT) and discrete cosine transformation (DCT) to mask patient data and hospital watermarks in biological images. With various statistical and OFDM settings, channel analysis and statistical analysis have been examined for their effects on the collected hospital logo and patient data. The simulation studies demonstrate how resistant to communication signal processing the proposed ACE-BI method is. Additionally, the proposed algorithm is able to reduce encryption time to one quarter because the partial encryption based in one level DWT scheme.