Hybrid Wavelet Research Articles

Hybrid wavelet transform with artificial neural network for forecasting of shear wave velocity from wireline log data: a case study

Shear wave velocity (Vs) is an important variable for performing geomechanical and geophysical modeling and reservoir studies. Field tests to measure this variable directly are high costs and time consuming. Due to the operational difficulties mentioned above, it is more convenient estimating Vs without direct measurements from conventional log data. In this research, the hybrid of wavelet transform with artificial neural network is utilized to estimate the Vs. To input variables (log gamma, log compressional wave velocity, and log bulk density), preprocessing is done using wavelet transform and then variables are entered to artificial neural network model. The estimation abilities of the hybrid artificial neural network with wavelet transform were substantiated using field data achieved from Marun reservoir, Iran. The results obtained in this study show a positive effect of input parameters’ preprocessing using wavelet transform in the estimation of Vs, and it has led to noticeable increase in the accuracy of model calculations.

A hybrid approach to diagnosis mammogram breast cancer using an optimally pruned hybrid wavelet kernel-based extreme learning machine with dragonfly optimisation

The detection of breast cancer is difficult at the initial stage because the cancerous tumours are rooted in the common breast tissue structures. The objective of this research is to model a breast cancer prediction system with a novel machine learning approach based on wavelets to classify mammogram images as benign, malignant and normal. The prediction of breast cancer for the diagnosis process is made by the proposed algorithm Hybrid Optimally Pruned Wavelet Kernel-based Extreme Learning Machine (HOP-WKELM). Initially, the input is pre-processed for noise reduction using a Kuan filter. Then a quantum evolutionary algorithm (QEA) is applied to segment the cancer part in a mammogram image and extract its features. The extracted features are classified using HWKELM classifier. The HWKELM utilised the dragonfly swarm behaviour-based optimisation (DSBO) approach to optimise the parameters of kernel functions. The proposed strategies achieved a maximum accuracy of 98.8% and maximum precision of 98.1% when compared with AdaBoost systems.

Smooth pulse recovery based on hybrid wavelet threshold denoising and first derivative adaptive smoothing filter

Abstract Based on the pulse-shaping unit in the front end of high-power laser facilities, we propose a new hybrid scheme in a closed-loop control system including wavelet threshold denoising for pretreatment and a first derivative adaptive smoothing filter for smooth pulse recovery, so as to effectively restrain the influence of electrical noise and FM-to-AM modulation in the time–power curve, and enhance the calibration accuracy of the pulse shape in the feedback control system. The related simulation and experiment results show that the proposed scheme can obtain a better shaping effect on the high-contrast temporal shape in comparison with the cumulative average algorithm and orthogonal matching pursuit algorithm combined with a traditional smoothing filter. The implementation of the hybrid scheme mechanism increased the signal-to-noise ratio of the laser pulse from about 11 dB to 30 dB, and the filtered pulse is smooth without modulation, with smoothness of about 98.8%.

A hybrid approach to diagnosis mammogram breast cancer using an optimally pruned hybrid wavelet kernel-based extreme learning machine with dragonfly optimisation

The detection of breast cancer is difficult at the initial stage because the cancerous tumours are rooted in the common breast tissue structures. The objective of this research is to model a breast cancer prediction system with a novel machine learning approach based on wavelets to classify mammogram images as benign, malignant and normal. The prediction of breast cancer for the diagnosis process is made by the proposed algorithm Hybrid Optimally Pruned Wavelet Kernel-based Extreme Learning Machine (HOP-WKELM). Initially, the input is pre-processed for noise reduction using a Kuan filter. Then a quantum evolutionary algorithm (QEA) is applied to segment the cancer part in a mammogram image and extract its features. The extracted features are classified using HWKELM classifier. The HWKELM utilised the dragonfly swarm behaviour-based optimisation (DSBO) approach to optimise the parameters of kernel functions. The proposed strategies achieved a maximum accuracy of 98.8% and maximum precision of 98.1% when compared with AdaBoost systems.

Sub-band selection approach to artifact suppression from electroencephalography signal using hybrid wavelet transform

This article presents a hybrid wavelet-based algorithm to suppress the ocular artifacts from electroencephalography (EEG) signals. The hybrid wavelet transform (HWT) method is designed by the combination of discrete wavelet decomposition and wavelet packet transform. The artifact suppression is performed by the selection of sub-bands obtained by HWT. Fractional Gaussian noise (fGn) is used as the reference signal to select the sub-bands containing the artifacts. The multichannel EEG signal is decomposed HWT into a finite set of sub-bands. The energies of the sub-bands are compared to that of the fGn to the desired sub-band signals. The EEG signal is reconstructed by the selected sub-bands consisting of EEG. The experiments are conducted for both simulated and real EEG signals to study the performance of the proposed algorithm. The results are compared with recently developed algorithms of artifact suppression. It is found that the proposed method performs better than the methods compared in terms of performance metrics and computational cost.

Hybrid Wavelet Stacking Ensemble Model for Insulators Contamination Forecasting

Contaminated insulators can have higher surface conductivity, which can result in irreversible failures in the electrical power system. In this paper, the ultrasound equipment is used to assist in the prediction of failure identification in porcelain insulators of the 13.8 kV, 60 Hz pin profile. To perform the laboratory analysis, insulators from a problematic branch are removed after an inspection of the electrical system and are evaluated in the laboratory under controlled conditions. To perform the time series predictions, the stacking ensemble learning model is applied with the wavelet transform for signal filtering and noise reduction. For a complete analysis of the model, variations in its configuration were evaluated. The results of root mean square error (RMSE), mean absolute percentage error (MAPE), mean absolute error (MAE), and coefficient of determination (R2) are presented. To validate the result, a benchmarking is presented with well-established models, such as an adaptive neuro-fuzzy inference system (ANFIS) and long-term short-term memory (LSTM).

An augmented wavelet reconstructor for atmospheric tomography

Atmospheric tomography, i.e., the reconstruction of the turbulence profile in the atmosphere, is a challenging task for adaptive optics (AO) systems for the next generation of extremely large telescopes. Within the AO community, the solver of first choice is the so-called Matrix Vector Multiplication (MVM) method, which directly applies the (regularized) generalized inverse of the system operator to the data. For small telescopes this approach is feasible, however, for larger systems such as the European Extremely Large Telescope (ELT), the atmospheric tomography problem is considerably more complex, and the computational efficiency becomes an issue. Iterative methods such as the Finite Element Wavelet Hybrid Algorithm (FEWHA) are a promising alternative. FEWHA is a wavelet-based reconstructor that uses the well-known iterative preconditioned conjugate gradient (PCG) method as a solver. The number of floating point operations and the memory usage are decreased significantly by using a matrix-free representation of the forward operator. A crucial indicator for the real-time performance are the number of PCG iterations. In this paper, we propose an augmented version of FEWHA, where the number of iterations is decreased by 50% using an augmented Krylov subspace method. We demonstrate that a parallel implementation of augmented FEWHA allows the fulfilment of the real-time requirements of the ELT.

Covid-19 detection in chest X-ray through random forest classifier using a hybridization of deep CNN and DWT optimized features

Chest X-ray image contains sufficient information that finds wide-spread applications in diverse disease diagnosis and decision making to assist the medical experts. This paper has proposed an intelligent approach to detect Covid-19 from the chest X-ray image using the hybridization of deep convolutional neural network (CNN) and discrete wavelet transform (DWT) features. At first, the X-ray image is enhanced and segmented through preprocessing tasks, and then deep CNN and DWT features are extracted. The optimum features are extracted from these hybridized features through minimum redundancy and maximum relevance (mRMR) along with recursive feature elimination (RFE). Finally, the random forest-based bagging approach is used for doing the detection task. An extensive experiment is performed, and the results confirm that our approach gives satisfactory performance compare to the existing methods with an overall accuracy of more than 98.5%.

An efficient copy move forgery detection using adaptive watershed segmentation with AGSO and hybrid feature extraction

Copy-move forgery detection (CMFD) is the process of determining the presence of copied areas in an image. CMFD approaches are mainly classified into two groups: keypoint-based and block-based techniques. In this paper, a new CMFD approach is proposed on the basis of both block and keypoint based approaches. Initially, the forged image is partitioned into non overlapped segments utilizing adaptive watershed segmentation, wherein adaptive H-minima transform is used for extracting the markers. Also, an Adaptive Galactic Swarm Optimization (AGSO) algorithm is used to select optimal gap parameter while selecting the markers for reducing the undesired regional minima, which can increase the segmentation performance. After that, the features from every segment are extracted as segment features (SF) using Hybrid Wavelet Hadamard Transform (HWHT). Then, feature matching is performed using adaptive thresholding. The false matches or outliers can be removed with the help of Random Sample Consensus (RANSAC) algorithm. Finally, the Forgery Region Extraction Algorithm (FREA) is utilized for detecting the copied portion from the host image. Experimental results indicate that the proposed scheme find out image forgery region with Precision = 92.45%; Recall = 93.67% and F1 = 92.75% on MICC-F600 dataset and Precision = 94.52%; Recall = 95.32% and F1 = 93.56% on Bench mark dataset at pixel level. Also, it outperforms the existing approaches when the image undergone certain geometrical transformation and image degradation.

Performance enhancement of image segmentation analysis for multi‐grade tumour classification in MRI image

Medical applications have a massive footprint in human's day-to-day life. Among that, MRI has a significant role, as it incorporates a significant impact on a brain tumour. Segmenting the tumour from MRI is substantial, but it is a time-consuming process. Both the normal and abnormal tissues found in the brain look similar, which increases the difficulty of the tumour detection process. The digital image needs to be processed to obtain an exact tumour detection result. The tumour detection process comprises five different stages, such as pre-processing, segmentation, feature extraction, feature selection, and classification. In this proposed work, hybrid wavelet Hadamard transform and grey-level co-occurrence matrix are included for feature extraction. Feature selection utilises sequential forward selection, which is an easy greedy search algorithm. This algorithm chooses only the predominant features for classification. The classification uses a hybrid support vector machine and adaptive emperor penguin optimisation. The experimental analysis shows the efficiency of the proposed work in terms of accuracy, specificity, and sensitivity values by computing the true positive, false positive, true negative, and false negative.

Low bit-rate compression of underwater image based on human visual system

Image plays an irreplaceable role compared with the text and sound in the underwater data collection and transmission researches. However, it suffers from the limited bandwidth of the underwater acoustic communication which cannot afford the large image data. Compressing the image data before transmission is an inevitable process in the underwater image communication. As usual, the natural image compression methods are directly applied to the underwater scene. As we all know, underwater image has different degradation from the natural one due to the optical transmission property. Low illumination in underwater will cause more seriously blurring and color fading than that in the air. It is a great challenge to decrease the bit-rate of the underwater image while preserving the compressed image quality as much as possible. In this paper, the Human Visual System (HVS) is taken into account during the compressing and the evaluating stages for the underwater image communication. We present a new methodology for underwater image compression. Firstly, by taking the human visual system into account, the chrominance perception operator is proposed in this paper to neglect the imperceptible chrominance shift which is widely exited in the underwater imaging to improve the image compression rate. Secondly, depth of field(DOF) of underwater image is usually shallow and most of the usable image has targets in it. An ROI extraction algorithm based on Boolean map detection is then used for the underwater image compression so as to reduce the bitrate of the compressed image. Furthermore, the underwater image is grainy and low contrast, that means the degradation happens in some regions of the image would not be perceived. Just notice difference(JND) sensing algorithm based on the spatial and frequency domain masking feature of HVS is also considered in the image processing. By combining the three aspects above, hybrid wavelet and asymmetric coding are used together to promote the underwater image compression, so that the image can have better quality and less redundancy. Experiments show that the proposed method can make full use of the inherent characteristics of underwater images, and maximize the visual redundancy of underwater images without reducing the visual perception quality of reconstructed images.

Comparison of wavelet and empirical mode decomposition hybrid models in drought prediction

Drought is a major area of interest within the field of water resources management, agriculture, energy resources and community health. Recently researchers have examined not only the mathematical expression of drought indices but also statistical predictions. Accordingly, high accuracy results were obtained using stand-alone machine learning techniques such as artificial neural networks (ANN) and support vector machine (SVM). However, lately, hybrid models have been introduced, which are created by integrating different time series decomposition techniques into standalone models, since the accuracy of stand-alone models used in the drought prediction being low particularly for mid-term and long-term drought predictions. In this study, self-calibrated Palmer Drought Severity Index (sc-PDSI) values were predicted by using three different standalone models and six hybrid models which are performed by two different decomposition techniques, such as Empirical mode decomposition (EMD) and Wavelet decomposition (WD). The main purpose of this study is to evaluate the effect of using EMD and WD for decomposing time series into their sub-bands on drought prediction. sc-PDSI time series were used to achieve 1, 3 and 6-month lead time predictions for Adana and Antalya cities located in the southern part of Turkey. Model performance indicators such as mean square error (MSE), Nash-Sutcliffe efficiency coefficient (NSE) and determination coefficient (R2) were employed to compare the proposed models. The results revealed that the accuracy of the stand-alone models, particularly in mid-term sc-PDSI predictions, was unsatisfactory. However, the prediction accuracy has been increased significantly with the introduction of EMD and WD techniques. Considering the Adana region, the hybrid wavelet models outperformed the models hybridized by EMD for not only 1-month lead time (NSEWD-ANFIS = 0.981 and NSEEMD-M5 = 0.890), but also for 3-month (NSEWD-SVM = 0.878 and NSEEMD-ANFIS = 0.811) and 6-month (NSEWD-ANFIS = 0.857 and NSEEMD-ANFIS = 0.783) lead times. According to the obtained results for Antalya region, similar findings were also observed among hybrid models. Thus, it is concluded that the predictions made using WD have higher accuracy than EMD, and the correct wavelet type selection has a significant effect on the results.

Grid Search Based SVM Approach for Sea Level Rise

Sea level rise is one of the most damaging impacts of climate change. Rising sea levels leads to loss of coastal wetlands, coastal flooding, degradation of coastal ecosystem, sinking of islands and general loss of quality of life. Countries which are located in low-lying areas as well as small islands are concerned that their land areas would be decreased due to inundation and coastal erosion. In this paper, a hybrid wavelet and grid search based support vector machine was used for statistical downscaling of sea level using climatic variables. The results of the hybrid model were compared with the observed sea level data and the results indicate that the hybrid wavelet and grid search based support vector machine can be used for future sea level projection. Thus the effect of sea level rise on low lying island can be studied with the model.

Sea level prediction using climatic variables: a comparative study of SVM and hybrid wavelet SVM approaches

Climate change is expected to adversely affect the coastal ecosystem in many ways. One of the major consequences of climate change in coastal areas is sea level rise. In order to manage this problem efficiently, it is essential to obtain reasonably accurate estimates of future sea level. This study focuses essentially on the identification of climatic variables influencing sea level and sea level prediction. Correlation analysis and wavelet coherence diagrams were used for identifying the influencing variables, and support vector machine (SVM) and hybrid wavelet support vector machine (WSVM) techniques were used for sea level prediction. Sea surface temperature, sea surface salinity, and mean sea level pressure were observed to be the major local climatic variables influencing sea level. Halosteric effect is found to have a major impact on the sea level. The variables identified were subsequently used as predictors in both SVM and WSVM. WSVM employs discrete wavelet transform to decompose the variables before being input to the SVM model. The performance of both the models was compared using statistical measures such as root mean square error (RMSE), correlation coefficient (r), coefficient of determination (r2), average squared error, Nash–Sutcliffe efficiency, and percentage bias along with graphical indicators such as Taylor diagrams and regression error characteristic curves. Results indicate that the WSVM model predicted sea level with an RMSE of 0.029 m during the training and 0.040 m during the testing phases. The corresponding values for SVM are 0.043 m and 0.069 m, respectively. Also, the other statistical measures and graphical indicators suggest that WSVM technique outperforms the SVM approach in the prediction of sea level.

Response and first passage probability of linear elastic SDOF systems subjected to nonstationary stochastic excitation modelled through S-transform

The S-transform is a special type of Fourier transform with the time localization and frequency dependent window. It is viewed as a hybrid of continuous wavelet transform and short-time Fourier transform. In the present study, we propose an S-transform-based random vibration theory to evaluate the response of the linear elastic single-degree-of-freedom (SDOF) system subjected to nonstationary stochastic excitation. We show that within the established framework, the time–frequency power spectral density (TFPSD) function of the structural response could be represented as the multiplication of the square of the mechanical admittance function and the TFPSD function of the nonstationary stochastic excitation. This simple relation facilitates the computation and statistical characterization of the structural responses subjected to nonstationary stochastic excitation that is modelled based on the S-transform, which implicitly includes the time-varying amplitude and frequency characteristics. We illustrate the use of the TFPSD function to estimate the statistics of the responses and first passage probability of SDOF systems subjected to stochastic earthquake or wind excitations, where the stochastic excitations are represented using the TFPSD functions obtained from the S-transform.

A comparative analysis of artificial neural networks and wavelet hybrid approaches to long-term toxic heavy metal prediction

The occurrence of toxic metals in the aquatic environment is as caused by a variety of contaminations which makes difficulty in the concentration prediction. In this study, conventional methods of back-propagation neural network (BPNN) and nonlinear autoregressive network with exogenous inputs (NARX) were applied as benchmark models. Explanatory variables of Fe, pH, electrical conductivity, water temperature, river flow, nitrate nitrogen, and dissolved oxygen were used as different input combinations to forecast the long-term concentrations of As, Pb, and Zn. The wavelet transformation was applied to decompose the time series data, and then was integrated with conventional methods (as WNN and WNARX). The modelling performances of the hybrid models of WNN and WNARX were compared with the conventional models. All the given models were trained, validated, and tested by an 18-year data set and demonstrated based on the simulation results of a 2-year data set. Results revealed that the given models showed general good performances for the long-term prediction of the toxic metals of As, Pb, and Zn. The wavelet transform could enhance the long-term concentration predictions. However, it is not necessarily useful for each metal prediction. Therefore, different models with different inputs should be used for different metals predictions to achieve the best predictions.

Haar Hybrid Transform Based Melanoma Identification Using Ensemble of Machine Learning Algorithms

Traditional methods of disease diagnosis can be time-intensive, error prone and invasive to the subject. These methods are also prejudiced by the doctor’s subjectivity. These issues can be resolved by using automated diagnosis methods. There is a considerable dearth of medical experts today, especially in the rural areas. The use of computing technology may help to assist in the diagnostic process. This paper proposes the utilization of computers to detect melanoma skin cancer. Melanoma skin cancer can be fatal, especially in its later stages. However, it shows a high recovery rate when it is detected in its early stages. Considering the lack of medical professionals, early diagnosis of melanoma may be tried using machine learning algorithms. This paper explores hybrid wavelet transform based melanoma identification using ensemble of machine learning algorithms. The hybrid wavelet transform is produced using Discrete Cosine Transform and Haar Wavelet Transform as its components. The sizes of both components are varied from 4x4 to 128x128 to obtain the hybrid wavelet transorm. Experimentation performed on the transformed dermoscopy skin images with machine learning algorithms and their ensembles gives rise to a total of 196 variations. Overall, if the average of the metrics accuracy, sensitivity and specificity is considered, the SVM algorithm using the hybrid transform of Haar 8x8 and DCT 64x64 gives the best performance, followed by the SVM algorithm using hybrid transform of Haar 128x128 and DCT 4x4.

Evolution of PAPR Reduction Techniques: A Wavelet Based OFDM Approach

Orthogonal frequency division multiplexing (OFDM) is considered among the most suitable multiplexing technique for realizing high speed wireless communication in the present era. Substantially immune to frequency selective fading, high spectral efficiency and superior data rate made OFDM a preferred and popular modulation technique in 3rd generation and 4th generation mobile communication systems. The high value of peak to average power ratio (PAPR) and inter carrier interference (ICI) are the most severe bottleneck for OFDM applications to deliver high performance in communication system. The present work portraits most of conventional and current PAPR reduction techniques through comparative study assisted by simulative analysis which foretells future research potential in wavelet based OFDM to generate more efficient system. PAPR optimization is achieved through hybridization of wavelet with clipping and companding techniques. Different modulation techniques have been analyzed (through Matlab simulation) over proposed hybrid model to observe the PAPR reduction in OFDM system.

Data driven estimation of novel COVID-19 transmission risks through hybrid soft-computing techniques.

Coronavirus genomic infection-2019 (COVID-19) has been announced as a serious health emergency arising international awareness due to its spread to 201 countries at present. In the month of April of the year 2020, it has certainly taken the pandemic outbreak of approximately 11,16,643 infections confirmed leading to around 59,170 deaths have been recorded world-over. This article studies multiple countries-based pandemic spread for the development of the COVID-19 originated in the China. This paper focuses on forecasting via real-time responses data to inherit an idea about the increase and maximum number of virus-infected cases for the various regions. In addition, it will help to understand the panic that surrounds this nCoV-19 for some intensely affecting states possessing different important demographic characteristics that would be affecting the disease characteristics. This study aims at developing soft-computing hybrid models for calculating the transmissibility of this genome viral. The analysis aids the study of the outbreak of this virus towards the other parts of the continent and the world. A hybrid of wavelet decomposed data into approximations and details then trained & tested through neuronal-fuzzification approach. Wavelet-based forecasting model predicts for shorter time span such as five to ten days advanced number of confirmed, death and recovered cases of China, India and USA. While data-based prediction through interpolation applied through moving average predicts for longer time spans such as 50–60 days ahead with lesser accuracy as compared to that of wavelet-based hybrids. Based on the simulations, the significance level (alpha) ranges from 0.10 to 0.67, MASE varying from 0.06 to 5.76, sMAPE ranges from 0.15 to 1.97, MAE varies from 22.59 to 6024.76, RMSE shows a variation from 3.18 to 8360.29 & R2 varying through 0.0018 to 0.7149. MASE and sMAPE are relatively lesser applied and novel measures that aimed to achieve increase in accuracy. They eliminated skewness and made the model outlier-free. Estimates of the awaited outburst for regions in this study are India, China and the USA that will help in the improvement of apportionment of healthcare facilities as it can act as an early-warning system for government policy-makers. Thus, data-driven analysis will provide deep insights into the study of transmission of this viral genome estimation towards immensely affected countries. Also, the study with the help of transmission concern aims to eradicate the panic and stigma that has spread like wildfire and has become a significant part of this pandemic in these times.

New Hybrid Wavelet and CNN-Based Indirect Tire-Pressure Monitoring System for Autonomous Vehicles

Since the tire pressure has a significant influence on driving safety, even self-driving vehicles need to be aware of their current tire pressures. Two major types of methods for estimating tire pressures exist: direct and indirect methods. In spite of recent advancements in direct Tire Pressure Monitoring Systems (TPMSs), indirect pressure monitoring systems still play a significant role due to their low costs. Indirect systems rely on the processing of signals from wheel speed sensors. In most cases, a transformation is applied to generate a frequency spectrum from which the tire pressure-dependent eigenfrequency can be extracted. The most accurate methods apply the Fourier transform, but these require the highest computational power. After the spectrum of signals from the wheel speed sensor is created, the eigenfrequency must be extracted. Several methods are available to extract significant frequency components. One of the easiest methods is peak searching, however, it is susceptible to noise. On the other hand, more accurate methods that are less sensitive to noise require more computational power. If a transform that consumes less computational power can be applied, then the freed resources can be used by a better eigenfrequency identification method. In this paper, a Hybrid Wavelet-Fourier Transform and Convolutional Neural Network-based method is presented, which exhibits a promising level of noise tolerance.