Coiflet Wavelet Research Articles

A Four-Grade Astrocytoma Classification System with Optimal Feature Selection Using Ant Colony Optimization Algorithm

An efficient Four-Grade Astrocytoma brain tumor classification is proposed in this research work. The work highlights the optimal feature selection using a hybrid combination of Ant Colony Optimization (ACO) algorithm and machine learning techniques, leading to better classification accuracy. A diverse set of features including spatial and frequency domain features will provide better discrimination of patterns. ACO provides a robust and efficient mechanism for exploring the vast space of possible feature subsets. A total of 135 features are extracted including Tamura features, Gabor Wavelet Features, Coiflet Wavelet Coefficients, Speeded Up Robust Features, and GLCM features. Ant Colony Optimization is used to find out the optimum set of features. The selected feature set is used for training a Decision Tree classifier, where the Astrocytoma MRI images are categorized into four grades of astrocyoma namely, pilocytic astrocytoma, diffuse astrocytoma, anaplastic astrocytoma and glioblastoma multiforne. Classification is also performed using KNN classifier, SVM classifier and Ensemble Bagged Tree Classifier. Performance metrics like accuracy, sensitivity, specificity, precision and FScore are utilized to evaluate the proposed system’s performance. It is found out that the decision tree classifier outperforms the other classifiers. Thus this integrated approach of ACO with machine learning classifiers results in improved Astrocytoma classification performance aiding clinicians in making accurate treatment decisions.

Mixed convection flow in a square lid-driven cavity subject to inclined magnetic field with highly accurate wavelet-homotopy solutions

Numerical studies of flow in square enclosures attain great interest due to their wide applications in engineering, such as in the meteorology, navigation, mechanology and mining, etc. In this paper, the solutions are obtained to the square lid driven cavity describing classical mixed convection in the presence of an inclined magnetic field, chemical radiation and heat absorption/generation. The problem is mathematically reduced into Navier-Stokes equations. The Coiflet wavelet homotopy approach, a newly suggested computational technique, is then used to solve these nonlinear partial differential equations with their non-homogeneous boundary conditions. The numerical solutions for streamlines, isotherms, iso-concentrations contours, Nusselt number are carefully determined. The solutions so obtained are analyzed through various plots to demonstrate the effects of several physical parameters such as Reynolds numbers (Re), Richardson number (Ri), Grashof number (Gr), Hartman number (Ha), thermal radiation parameter (NR), chemical reaction coefficient (K), heat generation/absorption coefficient (Qh), the angle of inclination of magnetic field (γ), slipping parameter (λ) and Schmidt number (Sc) and their physical insights are also reported. Rigid comparison of with previous studies shows excellent computational effectiveness of newly proposed method. Additionally, according to the results, our recently proposed approach has superior characteristics and strong nonlinear processing capabilities, which render it more appropriate than the Coiflet wavelet analysis method and the traditional homotopy analysis method for tackling complex nonlinear problems.

Facial Wrinkle Detection with Multiscale Spatial Feature Fusion Based on Image Enhancement and ASFF-SEUnet

Wrinkles, crucial for age estimation and skin quality assessment, present challenges due to their uneven distribution, varying scale, and sensitivity to factors like lighting. To overcome these challenges, this study presents facial wrinkle detection with multiscale spatial feature fusion based on image enhancement and an adaptively spatial feature fusion squeeze-and-excitation Unet network (ASFF-SEUnet) model. Firstly, in order to improve wrinkle features and address the issue of uneven illumination in wrinkle images, an innovative image enhancement algorithm named Coiflet wavelet transform Donoho threshold and improved Retinex (CT-DIR) is proposed. Secondly, the ASFF-SEUnet model is designed to enhance the accuracy of full-face wrinkle detection across all age groups under the influence of lighting factors. It replaces the encoder part of the Unet network with EfficientNet, enabling the simultaneous adjustment of depth, width, and resolution for improved wrinkle feature extraction. The squeeze-and-excitation (SE) attention mechanism is introduced to grasp the correlation and importance among features, thereby enhancing the extraction of local wrinkle details. Finally, the adaptively spatial feature fusion (ASFF) module is incorporated to adaptively fuse multiscale features, capturing facial wrinkle information comprehensively. Experimentally, the method excels in detecting facial wrinkles amid complex backgrounds, robustly supporting facial skin quality diagnosis and age assessment.

Compressed sensing for ECG signal compression using DWT based sensing matrices

ABSTRACT In this article, we have investigated the 1-D discrete wavelet transform (DWT)-based measurement matrices for electrocardiogram (ECG) compression. Moreover, the current work examines the suitability of the diverse DWT matrices, namely Symlets, Battle, Coiflets, Vaidyanathan, and Beylkin wavelet families, for ECG compression. Furthermore, this article shows the comparative performance study of the proposed DWT matrices with conventional deterministic and random measurement matrices. Overall, the Battle1 wavelet-based measurement matrices demonstrate the enhanced performance against the db3, coif5, and sym6 based measurement matrices in terms of Percentage Root-Mean Squared Difference (PRD), Root Mean Square Error (RMSE), and Signal-to-Noise Ratio (SNR). Finally, it was seen that the proposed Battle1 matrix demonstrates the improved performance against the conventional measurement matrices such as the Karhunen–Loeve transform (KLT), Discrete Cosine Transform (DCT) matrix, and random Hadamard measurement matrix. Thus, the result shows the adequacy of DWT measurement matrices for the compression of ECG.

Accurate Coiflet wavelet solution of extended [formula omitted]-dimensional Kadomtsev–Petviashvili equation using the novel wavelet-homotopy analysis approach

We established a novel wavelet-homotopy analysis technique to give accurate solutions to nonlinear wave problems with unsteady state, which is the originality of the paper. The extended (2+1)-dimensional Kadomtsev–Petviashvili equation is taken as the research object owing to its complexity and importance. The convergence of the proposed technique is proved. The modulation instability is employed to determine the range of the normalized wave numbers of the transformed traveling wave equation. The validity and reliability of our results are checked via a rigid comparison with analytical solutions. Results and the comparison with the numerical solutions reveal that the proposed technique maintains capabilities of the homotopy analysis method for strong nonlinear processing and the wavelet technique for excellent local detail representation. It is expected that the proposed method can be used as an alternative tool to analyze more complex wave problems such as rogue wave or shock wave problems.

A Coiflet Wavelet Homotopy Technique for Nonlinear PDEs: Application to the Extreme Bending of Orthotropic Plate with Forced Boundary Constraints

A Coiflet Wavelet Homotopy Technique for Nonlinear PDEs: Application to the Extreme Bending of Orthotropic Plate with Forced Boundary Constraints

TV-NARX and Coiflets WPT based time-frequency Granger causality with application to corticomuscular coupling in hand-grasping.

The study of the synchronous characteristics and functional connections between the functional cortex and muscles of hand-grasping movements is important in basic research, clinical disease diagnosis and rehabilitation evaluation. The electroencephalogram (EEG) and electromyographic signal (EMG) signals of 15 healthy participants were used to analyze the corticomuscular coupling under grasping movements by holding three different objects, namely, card, ball, and cup by using the time-frequency Granger causality method based on time-varying nonlinear autoregressive with exogenous input (TV-NARX) model and Coiflets wavelet packet transform. The results show that there is a bidirectional coupling between cortex and muscles under grasping movement, and it is mainly reflected in the beta and gamma frequency bands, in which there is a statistically significant difference (p < 0.05) among the different grasping actions during the movement execution period in the beta frequency band, and a statistically significant difference (p < 0.1) among the different grasping actions during the movement preparation period in the gamma frequency band. The results show that the proposed method can effectively characterize the EEG-EMG synchronization features and functional connections in different frequency bands during the movement preparation and execution phases in the time-frequency domain, and reveal the neural control mechanism of sensorimotor system to control the hand-grasping function achievement by regulating the intensity of neuronal synchronization oscillations.

Noise Reduction of Lung Sounds based on Singular Spectrum Analysis combined with Discrete Cosine Transform

Lung sound signals are sensitive to environmental noise. Much research has been conducted on the enhancement of pulmonary sound. This study investigated the normal lung sounds as bronchovesicular (BV) and vesicular (V) signals and proposed a novel denoising method called SSA-DCT. Using Singular Spectrum Analysis (SSA), the noise-related components were separated from respiratory information components. An algorithm was also proposed to determine the information and noise time interval, and by applying Discrete Cosine Transform (DCT), the signal energy was attenuated in the noise intervals. Moreover, the concept of safety component, which is secure against noise, was introduced. Then, an algorithm for automatic identification of BV and V signals using the safety component was presented. The error of this algorithm at SNR of 10 dB was 5%. Lung sounds were recorded from 12 healthy subjects using four channels over the posterior chest wall. The signals were recorded in an acoustic laboratory and then contaminated with additive white Gaussian noise with different levels of SNR. The respiratory signals were also recorded in a relatively quiet environment with real ambient noise and denoised by the proposed method. The proposed method was compared with Coiflet wavelet decomposition with hard SureShrink thresholding. The denoising performance of both methods was evaluated using qualitative and quantitative approaches. The SSA-DCT method (e.g., at an SNR level of 10 dB) with average segmental SNR improvements of 2.52 and 3.44 dB for the BV and V signals, respectively, is significantly superior to the wavelet analysis with average segmental SNR improvements of 0.89 and 1.53 dB for the BV and V signals, respectively.

Modelling convective transport of hybrid nanofluid in a lid driven square cavity with consideration of Brownian diffusion and thermophoresis

Previously, the homogenous models were predominant in hybrid nanofluid's studies. However, there is a serious deficiency in these models, that is, the heat transfer coefficients obtained by these models are inconsistent with the experimental results. To overcome this issue, we develop a novel hybrid nanofluid model based on the major slip mechanisms of nanofluids caused by Brownian diffusion and thermophoresis. The problem of flow and heat transfer of a hybrid nanofluid in a lid driven square cavity filled with porous medium is studied as an example to show the correctness and validity of this new model. Numerical results are given by means of the novel Coiflet wavelet homotopy analysis method in the terms of the Brownian motion and thermophoresis. The physical behaviors of hybrid nanofluids flow are discussed in terms of various quantities such as the stream-function, the isotherms, and the Nusselt and Sherwood numbers. The average Nusselt number evaluated with the help of the quadratic multiple regression analysis justifies that the thermophoretic effect is more dominant than the Brownian motion on Nusselt numbers at both walls.

INVESTIGATION OF DISCRETE WAVELET TRANSFORM DOMAIN OPTIMAL PARAMETRIC APPROACH FOR DENOISING OF PHONOCARDIOGRAM SIGNAL

Phonocardiogram (PCG) signals are contaminated with various noise signals, which hinders the accurate diagnostic interpretation of the signal. Discrete wavelet transform (DWT) is a well-known technique used to remove noise from PCG signals and improve signal quality. The performance of DWT-based denoising depends upon several parameters involved in the process, such as mother wavelets used for decomposition, the number of decomposition levels (DLs), thresholding technique used and the threshold estimation rule followed. In this work, an investigative study is carried out to select the optimal parameter values which give the best denoising performance. The metrics such as mean-square error (MSE), normalized-mean-square error (NMSE), root-mean-square error (RMSE), percentage root-mean-square difference (PRD) and signal-to-noise ratio (SNR) are used to evaluate the performance of the denoising in this study. The results obtained show that the fifth-order Coiflet wavelet is best suited for denoising PCG signals when applied with the soft thresholding (ST) function and rigrsure threshold selection rule. Also, the optimum number of DLs resulting in better performance is level 6. SNR value obtained from the studies shows the efficacy of the parameters selected for denoising. The denoised PCG signals provide accurate information to determine various kinds of heart valve-related disorders (HVDs).

Short‐term natural gas consumption prediction based on wavelet transform and bidirectional long short‐term memory optimized by Bayesian network

AbstractIn the energy structure, natural gas is an important clean energy, and its consumption forecast is of great significance for energy policy formulation, pipeline network planning design, and peak shaving capacity determination. In the field of forecasting, wavelet transform (WT) is widely used to process time series, especially natural gas consumption. However, in the application of the WT, the wavelet's orders and levels are randomly fixed. To solve this problem, this study proposes a hybrid model, which is based on bidirectional long short‐term memory (BiLSTM). In addition, Bayesian networks are used to optimize the hyperparameters of BiLSTM. And the different wavelets' orders and levels are utilized to deal with the daily natural gas consumption (DNLGSCN). When each level of Coiflets wavelets' four and five orders are used to process the DNLGSCN, the fifth level of Coiflets wavelets' four and five orders have the highest prediction accuracy, respectively, which is 0.3318 and 0.1773, taking U1 as an example. Using Symflets wavelet to decompose the DNLGSCN also gives the same result. Compared with the decomposition method (e.g., ensemble empirical mode decomposition), the prediction performance of the above four wavelet decomposition methods is better. The main conclusions are that decomposing the DNLGSCN with the fifth level of Coiflets wavelet's five orders has the best prediction performance.

Wavelet matrix operations and quantum transforms

The currently studied version of the quantum wavelet transform implements the Mallat pyramid algorithm, calculating wavelet and scaling coefficients at lower resolutions from higher ones, via quantum computations. However, the pyramid algorithm cannot replace wavelet transform algorithms, which obtain wavelet coefficients directly from signals. The barrier to implementing quantum versions of wavelet transforms has been the fact that the mapping from sampled signals to wavelet coefficients is not canonically represented with matrices. To solve this problem, we introduce new inner products and norms into the sequence space l2(Z), based on wavelet sampling theory. We then show that wavelet transform algorithms using L2(R) inner product operations can be implemented in infinite matrix forms, directly mapping discrete function samples to wavelet coefficients. These infinite matrix operators are then converted into finite forms for computational implementation. Thus, via singular value decompositions of these finite matrices, our work allows implementation of the standard wavelet transform with a quantum circuit. Finally, we validate these wavelet matrix algorithms on MRAs involving spline and Coiflet wavelets, illustrating some of our theorems.

Highly accurate Coiflet wavelet-homotopy solution of Jeffery–Hamel problem at extreme parameters

The magnetized Jeffery–Hamel flow due to a point sink or source in convergent and divergent channels is studied. Such viscous flow plays an important role in understanding rivers and canals, human anatomy, and the connection between capillaries and arteries. The simplified governing equation ruled by the Reynolds number, the Hartmann number and the divergent–convergent angle with appropriate boundary conditions is solved by the newly proposed Coiflet wavelet-homotopy analysis method (CWHAM). A highly accurate solution is obtained, whose accuracy is rigidly checked. Our proposed method combines the advantages of the homotopy analysis method for strong nonlinearity and the Coiflet wavelet method for excellent local expression capability so that it holds higher computational efficiency, larger applicable range of physical parameters, and better nonlinear processing capability. Different from the previously published research on the CWHAM, we focus on establishing the solution process of nonlinear flow problems with extreme physical parameters, which is not considered before.

Fault Diagnosis in Bevel Gearbox Using Coiflet Wavelet and Fault Classification Based on ANN Including DNN

Fault Diagnosis in Bevel Gearbox Using Coiflet Wavelet and Fault Classification Based on ANN Including DNN

Highly accurate wavelet-homotopy solutions for mixed convection hybrid nanofluid flow in an inclined square lid-driven cavity

The classic problem of mixed convection flow within an inclined square lid-driven cavity is extended to hybrid nanofluid case. The governed partial differential equations with non-homogeneous boundary conditions are studied by means of our proposed Coiflet wavelet-homotopy method. Highly accurate solutions with very clear vortex structures are obtained. The effectiveness and efficiency of these solutions are strictly checked through a series of comparisons with published ones in literature. Results show that our proposed approach holds both strong nonlinear processing capability and excellent local display characteristics so that it can be used to attack complicated nonlinear problems more efficiently and accurately than the traditional homotopy analysis method and the Coiflet wavelet analysis approach. Besides, physical properties of hybrid nanofluid behaviors are examined, it is noticed that, similar to nanofluids, the hybrid nanofluid is superior to traditional heat transfer fluids for heat transfer enhancement but it could be easier and cheaper.

PLANAR SCINTIGRAPHY IMAGE DE-NOISING USING COIFLET WAVELET

The planar scintigraphic image usually has poor resolution and contains noise. This noise can be removed using the coiflet wavelet method so that the image quality gets better. This coiflet wavelet method is a noise reduction method based on frequency analysis. The planar scintigraphy image is the reconstructed image of the gamma radiation count data (phantom with the Cs-137 source in it). The original image is 15×15 pixel. Before the de-noising process, the image went through an interpolation process, which is to increase the pixel size of the image. The original image enlarged to 70×70, 480×480, and 1200×1200 pixel. After de-noising with coiflet wavelet, the image quality is measured based on MSE and PSNR parameters. The resulting images are quite good, with MSE values are close to zero and PSNR values of more than 60 dB. The smaller the MSE and the bigger the PSNR, is getting the better the image quality. In this study, the results show that the 1200×1200 pixel image has the best quality. It means that the image enlargement process has a good effect on the de-noising process, especially if the original image has a low resolution.

Quality evaluation of metal surfaces treatment by wavelet analysis

Purpose. Analyze the effectiveness of using wavelet analysis to assess the quality of metal surfaces. Investigate the possibility of using wavelet analysis in ultrasonic flaw detection. Determine the optimal wavelet families and their criteria for assessing the quality of metal surface processing.&#x0D; Research methods. Orthogonal wavelets are considered: Daubechies wavelet, Simlet wavelet and Coiflet wavelet, which provide the possibility of performing a discrete wavelet transform procedure. The criteria influencing the effectiveness of ultrasonic signal filtering by methods using wavelet analysis are considered. Ultrasonic signals were filtered using wavelet functions.&#x0D; Results. It has been determined that for successful signal filtering, the selected wavelet method must provide a discrete wavelet transformation and have a similarity in the wavelet function shape in the local features of the ultrasonic signals flaw detector. During the work, a rigid threshold for limiting the detail coefficients of wavelet analysis was chosen, as it is the best for filtering tasks. The filtering efficiency is confirmed by the relatively high signal to noise ratio, as well as by the fact that the shape of the pulse extracted from the defect remained almost unchanged.&#x0D; Scientific novelty. When using the Daubechies and Coiflet wavelets as basic functions, as a result of wavelet filtering, it was possible to increase the signal to noise ratio by 20 dB and confidently isolate the useful signal against the background noise, which indicates the prospects of using this kind of transformations in filtering problems.&#x0D; Practical value. The obtained solutions can be used for implementation in signal filtering algorithms in digital processing units of automated non-destructive ultrasonic control systems.

Coiflet wavelet-homotopy solution of free convection in a closed cavity subjected to an inclined external magnetic field

The Coiflet wavelet solutions are given to the classic cavity flow problem describing natural convection in cavities under the effect of an inclined external magnetic field. The governing partial differential equations are solved by a brand-new and very efficient computational method, namely, the Coiflet wavelet-homotopy method. This approach not only inherits the capability of homotopy analysis to deal with strongly nonlinear problems, but also maintains the orthogonality and high-precision local expression property of the wavelet method. The Coiflet wavelet solutions are obtained via a very efficient homotopy iteration. Rigid comparison with previous studies for a wide range of various physical parameters shows excellent computational efficiency and accuracy of the proposed method.

Surface EMG signal segmentation based on HMM modelling: Application on Parkinson’s disease

The study of burst electromyographic (EMG) activity periods during muscles contraction and relaxation is an important and challenging problem. It can find several applications like movement patterns analysis, human locomotion analysis and neuromuscular pathologies diagnosis such as Parkinson disease. This paper proposes a new frame work for detecting the onset (start) / offset (end) of burst EMG activity by segmenting the EMG signal in regions of muscle activity (AC) and non activity (NAC) using Discrete Wavelet Transform (DWT) for feature extraction and the Hidden Markov Models (HMM) for regions classification in AC and NAC classes. The objective of this work is to design an efficient segmentation system of EMG signals recorded from Parkinsonian group and control group (healthy). The results evaluated on ECOTECH project database using principally the Accuracy (Acc) and the error rate (Re) criterion show highest performance by using HMM models of 2 states associated with GMM of 3 Gaussians, combined with LWE (Log Wavelet decomposition based Energy) descriptor based on Coiflet wavelet mother with decomposition level of 4. A comparative study with state of the art methods shows the efficiency of our approach that reduces the mean error rate by a factor close to 2 for healthy subjects and close to 1.3 for Parkinsonian subjects.

Compression Performance Analysis of Electrogastrogram (Egg) Using Different Wavelet Transforms For Telemedicine

Electrogastrogram (EGG) is the non-invasive graphical representation of stomach’s electrical activity for diagnosing stomach Disorders. EGG signal compression has an important role in Tele-diagnosis, Tele-prognosis and survival analysis of all stomach dysrhythmias, when the patient is geographically isolated. There are plenty of signal compression techniques available and proposed over years. Due to some drawbacks like high cost, signal loss and poor compression ratio leads the signal into inefficient at receiver’s end. The compression of digital EGG in telemedicine holds three major advantages like efficient &amp; economic usage of storage data, reduction of the data transmission rate and good transmission bandwidth conversation. In this study EGG signals are tested with different wavelet transforms such as Biorthogonal, coiflet, Daubechies, Haar, reverse biorthogonal and symlet wavelet transforms using MATLAB software, in order to find best performance wavelet for telemedicine. The performance is mathematically analyzed using the values of Percent Root Mean Square Difference (PRD), Compression ratio (CR) and recovery ratio. The result of better compression performance in signal compression could definitely be a great asset in telemedicine field for transferring more quantities of Biological signals.