Multi Wavelet Research Articles

Deep Bharatanatyam pose recognition: a wavelet multi head progressive attention

Deep Bharatanatyam pose recognition: a wavelet multi head progressive attention

Enhancing Medical Image Classification: A Deep Learning Perspective with Multi Wavelet Transform

Classification of medical images is a very important area of research for both the medical industry and academia. In recent years, automated classification algorithms have become very important in most medical applications, saving time and effort, such as disease detection and diagnostic radiology. Deep learning offers a plethora of advantages when applied to medical image classification, revolutionizing medical diagnosis and patient care. In this study, deep convolutional neural networks (DCNNs) is used to classify medical im-ages and multi-wavelet transform will be applied to extract features. The proposed method aims to improve medical image classification accuracy, thereby assisting healthcare professionals in making more accurate and efficient diagnoses. DCNNs based on the VGG16 model were trained and used in this study. Combining VGG16, a powerful convolutional neural network (CNN), with multiwavelet transform offers several advantages for image processing and analysis tasks, particularly in areas like image classification and feature extraction. To evaluate the performance of the proposed method six publicly available brain tumour MRI datasets are analysed with DCNNs. A fully connected layer is used to categorize the extracted features. According to the results, the deep CNN model combined with the multi-wavelet trans-form achieves an impressive accuracy of 96.43 %. It is evident from this high level of accuracy that the proposed approach is effective in accurately classifying medical images.

Diabetic Retinopathy Classification Using Swin Transformer with Multi Wavelet

Diabetic retinopathy (DR) impacts over a third of individuals diagnosed with diabetes and stands as the leading cause of vision loss in working-age adults worldwide. Therefore, the early detection and treatment of DR can play a crucial role in minimizing vision loss. This research paper proposes a novel technique that combines Wavelet and multi-Wavelet transforms with Swin Transformer to automatically identify the progression level of diabetic retinopathy. A notable innovation of this study lies in the implementation of the multi-Wavelet transform for extracting relevant features. By incorporating the resulting images into the Swin Transformer model, a unique approach is introduced during the feature extraction phase. The researchers conducted experiments using the publicly available Kaggle APTOS 2019 dataset, which comprises 3662 images. The achieved training accuracy in the experiments was an impressive 97.78%, with a test accuracy of 97.54%. The highest accuracy observed during training reached 98.09%. In comparison, when applying the multi-Wavelet approach to multiclass classification, the training and validation accuracies were 91.60% and 82.42%, respectively, with a testing accuracy of 82%. These results indicate that the multi-Wavelet approach outperforms alternative methods in the study. The model demonstrated exceptional performance in binary classification tasks, exhibiting high accuracies on both the training and test sets. However, it is important to note that the model's accuracy decreased when employed in multiclass classification, emphasizing the need for further investigation and refinement to handle more diverse classification scenarios.

Image processing By Using Multi Discrete Laguerre Wavelets Transform (MDLWT)

In this work the new operations matrix was derived for Multi Discrete Laguerre Wavelets Transform (MDLWT) with the dimension and this matrix was derived by the integrals of the functions that obtained from the mother function or the mother wave. This matrix is the coefficients matrix of the developed multi wavelet and this wavelet was applied in image processing using the Matlab program, where a new program was built that applies to many of the images and examples the proposed wave efficiency. In addition, MSE and PSNR were calculated and good results were compared to the previous methods.

A proposed brain tumor detection algorithm using Multi wavelet Transform (MWT)

Magnetic resonance imaging)MRI) is a technological development in the medical field. It is used to give images of the human body with high accuracy and good quality, facilitating the process of identifying and classifying diseases in the human body. One of the diseases that are diagnosed using MRI is a brain tumor, MRI which helps in the early diagnosis of the tumor, and helps the doctor to diagnose and identify the tumor and thus make the fastest medical decision. This paper used to identification and extraction of brain tumors from magnetic resonance images based on Multi Wavelet Transform (MWT) and image processing techniques. This project Multi Wavelet Transform (MWT) used in preprocessing stage to enhance the input image and denoising. Thresholding based is used for segmentation methods, classification of brain tumor is done using Statistical Classification Methods to classify the input MRI of brain into normal or abnormal. Firstly the location of tumor was presented & next its area computed.

Wavelet Multi Resolution Analysis based Data Hiding with Scanned Secrete Images

Wavelet Multi Resolution Analysis (MRA) based data hiding with scanned secrete images is proposed for improvement of invisibility of the secrete images. Daubechies (biorthogonal basis was adopted as the wavelet, but it was demonstrated that the key image (or secret image data) information can be restored with the biorthogonal wavelet. Also, the information of what to adopt as the biorthogonal wavelet is hidden. Key image information can also be protected by doing so, that the horizontal biorthogonal wavelet of the image does not have to be the same as the vertical biorthogonal wavelet, and the insertion position of the secret image data can be freely selected. It is also possible to divide the bit string of the secret image data and insert it into an arbitrary high frequency component, that the information hiding capability changes depending on the number of bit strings (information amount) of the secret image data, and the secret image in the public image data. Random scanning is effective for improving the visibility of data, selection of scanning method type, random number initial value It was shown that sharing only among parties is useful for improving confidentiality, resistance to noise, resistance to data compression, and resistance to tampering with data.

Fault Type Classification of 150 kV Transmission Line using Wavelet Multi-Resolution Analysis Method

To identify the fault, wavelet method is used in solving complex protection problems. This study uses a new approach, namely the wavelet multi resolution analysis method with its application where multi resolution analysis works to analyze signals at different frequencies with the same resolution. In this study, the classification of fault types that occur in the 150 kV transmission line quickly and accurately is carried out using the wavelet multi resolution analysis method. This research is included in applied research and was designed using computer simulation software, namely ATP and MATLAB. The data transmission system used is the Maninjau Hydroelectric Power Plant transmission line to Pauh Limo Substation. The modeled transmission system is given 1-phase to ground, 2-phase to ground, 2- phase, 3-phase and lightning faults. To determine the accuracy of this classification, the fault is varied according to the distance and impedance of the disturbance. From the analysis of the simulation results and calculations, based on the wavelet multi resolution analysis method used in fault classifying, the average value of the approximation coefficient used to classify the type of fault is obtained. Based on the results of the study, it can be said that all types of fault analyzed in this study have met the classification requirements using the wavelet multi resolution analysis method

Fault Type Classification of 150 kV Transmission Line using Wavelet Multi-Resolution Analysis Method

To identify the fault, wavelet method is used in solving complex protection problems. This study uses a new approach, namely the wavelet multi resolution analysis method with its application where multi resolution analysis works to analyze signals at different frequencies with the same resolution. In this study, the classification of fault types that occur in the 150 kV transmission line quickly and accurately is carried out using the wavelet multi resolution analysis method. This research is included in applied research and was designed using computer simulation software, namely ATP and MATLAB. The data transmission system used is the Maninjau Hydroelectric Power Plant transmission line to Pauh Limo Substation. The modeled transmission system is given 1-phase to ground, 2-phase to ground, 2- phase, 3-phase and lightning faults. To determine the accuracy of this classification, the fault is varied according to the distance and impedance of the disturbance. From the analysis of the simulation results and calculations, based on the wavelet multi resolution analysis method used in fault classifying, the average value of the approximation coefficient used to classify the type of fault is obtained. Based on the results of the study, it can be said that all types of fault analyzed in this study have met the classification requirements using the wavelet multi resolution analysis method

B-YIA2-03 A GOVERNING EQUATION TO EXPLAIN THE NUMBER OF WAVELETS AND ROTORS OBSERVED IN HUMAN VENTRICULAR FIBRILLATION

VF is commonly characterised by two mechanisms, multi wavelet reentry and rotors, but no governing equation exists to explain and predict their population dynamics. We hypothesized a single equation derived from an M/G/∞ renewal process could explain rotor and wavelet numbers in VF. Phase singularity (PS) and wavefront (WF) tracking was used to identify wavelets and rotors in epicardial recordings of induced VF during cardiac surgery (n=13 patients). Autocorrelation and distributions of PS and WF lifetimes and inter-formation times were assessed to verify an underlying renewal process. Distributions were fitted using maximum likelihood to calculate formation (λf) and destruction (λd) rates, and combined in an M/G/∞ process to develop a potential governing equation of VF dynamics. PS and WF inter-event-time distributions were consistent with the Weibull in all 210 epochs (PS: mean Χ2 P=0.23(95%CI,0.18,0.28); WF: mean Χ2 P=0.19(95%CI,0.13,0.27)), with zero autocorrelation at non-zero lags, indicative of an underlying renewal process over all stages (perfusion, ischemia and reflow). The M/G/∞ equation accurately predicted average PS and WF number (R>0.90) and population distribution (Χ2 P>0.05) in all epochs. Differences in λf (term: 0.015/ms (95%CI,0.010,0.020), non-term 0.023/ms (95%CI,0.019,0.027)), and average PS number (term: 1.68 (95%CI, 1.36, 2.00), non-term: (2.00 (95%CI, 1.86, 2.15)) was observed in spontaneous VF termination. M/G/∞ renewal process provides a governing equation to explain the number of wavelets and rotors in VF, which could be used in mechanistic studies to guide development of new therapies.

A Comparative analysis of DPFC with UPQC performance using various control techniques to Improve Power Quality in Smart Grid

Electricity generation from the Wind and sun based photovoltaic (PV) frameworks are profoundly relies on climate conditions. Their irregular nature prompts vacillations in their yield. In this manner, the requirement for quick pay of vitality transmission and circulation frameworks is progressively significant. This paper explores examination of intensity quality issues of utility associated with Smart Grid utilizing Unified Power Quality Conditioner (UPQC) and Distributed Power Flow Controller (DPFC) device. Improvement of PQ in SG from disrupting impacts happened power structure controller utilized DPFC. From start, proposed structure power stream is surveyed and Multi Wavelet Transform (MWT) is used to incorporate extraction system of power parameters for PQ agitating impacts. In event that the yield signal has any aggravations, at that point yield signal is constrained by proposed controller based DPFC. Proposed calculation is utilized to hold supported DC-link voltage of DPFC by controlling yield voltage to enhance PQ. Modeling of solar PV-Wind Hybrid Micro-grid system increases power quality of system. In this paper, a new method was proposed with an Elephant Herding Optimization(EHO) based on DPFC control scheme, which is compared such as ANFIS with FA based UPQC, ANFIS with FA based DPFC , CFA with PSO based UPQC,CFA with PSO based DPFC and modified EHO with RNN technique based UPQC . The main objective of proposed method is to detect and control PQ problems in SG. In order to achieve the objective, simulation is carried out in MATLAB SIMULINK model.

Data Hiding Method with Principal Component Analysis and Image Coordinate Conversion

Data hiding method with Principal Component Analysis (PCA) and image coordinate conversion as a preprocessing of wavelet Multi Resolution Analysis (MRA) is proposed. The method introduced in this paper, based on the characteristics of the original multispectral image, allows recovering the secret data. Through experiments, it is found that the proposed method is superior to the conventional data hiding method without any preprocessing. The method introduced in this paper allows only I who knows the characteristics of the original multispectral image to recover the secret data, i.e., when the information of the original image needs to be protected. Moreover, in the introduced method, the information of the secret data is protected by the existence of the eigenvector and the oblique coordinate transformation, that is, the secret data is restored if at least the information of the true original image is not known. The principal component transformation coefficient differs for each original image and is composed of the eigenvectors of the original image.

Method for Determination of Support Length of Daubechies Basis Function for Wavelet MRA based Moving Characteristic Estimation

Method for determination of support length of Daubechies basis function for wavelet Multi Resolution Analysis: MRA based moving characteristic estimation is proposed. The method based on root mean square difference between original and reconstructed image from just a low frequency component from MRA. Also, one of applications of the method for detection and tracking of moving targets, typhoon and boundary between warm and cold current observed from satellite remote sensing images is shown. It is found that the proposed method allows to detect and to track for moving targets of a typhoon and a boundary in the time series of GOES images.

Static Polarizabilities at the Basis Set Limit: A Benchmark of 124 Species.

Benchmarking molecular properties with Gaussian-type orbital (GTO) basis sets can be challenging, because one has to assume that the computed property is at the complete basis set (CBS) limit, without a robust measure of the error. Multiwavelet (MW) bases can be systematically improved with a controllable error, which eliminates the need for such assumptions. In this work, we have used MWs within Kohn–Sham density functional theory to compute static polarizabilities for a set of 92 closed-shell and 32 open-shell species. The results are compared to recent benchmark calculations employing the GTO-type aug-pc4 basis set. We observe discrepancies between GTO and MW results for several species, with open-shell systems showing the largest deviations. Based on linear response calculations, we show that these discrepancies originate from artifacts caused by the field strength and that several polarizabilies from a previous study were contaminated by higher order responses (hyperpolarizabilities). Based on our MW benchmark results, we can affirm that aug-pc4 is able to provide results close to the CBS limit, as long as finite difference effects can be controlled. However, we suggest that a better approach is to use MWs, which are able to yield precise finite difference polarizabilities even with small field strengths.

An Efficient Perspective of Neuro Fuzzy on Fog Net work for Latency Driven View Point

In the present milieu, changes in guidelines and the opening of intensity markets have showed as enormous measure of intensity move across transmission lines with visit changes in stacking conditions dependent on advertise cost. Since ordinary separation transfers may consider power swing as a shortcoming, stumbling in view of such breaking down would prompt genuine ramifications for power framework strength. A recurrence area approach for advanced handing-off of transmission line flaws relieving the unfriendly impacts of intensity swing on customary separation handing-off is introduced. A wavelet-neuro-fluffy consolidated methodology for deficiency area is likewise exhibited. It is not the same as customary calculations that depend on deterministic calculations on a well-characterized model for transmission line security. The wavelet change catches the dynamic qualities of flaw signals utilizing wavelet multi goals examination coefficients. The fluffy surmising framework and the versatile neuro fluffy derivation frameworkare both used to extricate significant highlights from wavelet MRA coefficients and in this manner to arrive at resolutions with respect to blame location.The results contained here approve the prevalence of the methodology over the for deficiency area.

Wavelet based performance analysis of AC transmission systems with unified power flow controller under power quality issues

The developments in power quality are fast and difficult to predict. The majority of power quality issues experienced by industrial customers can be attributed to momentary interruptions, voltage sags or swells, transients, harmonic distortion, electrical noise, and flickering lights, among others. A new device may be invented tomorrow solving power quality problems. The FACTS devices could provide fast control of active and reactive power through a transmission line. The unified power-flow controller (UPFC) is a member of the FACTS family with very attractive features. This device can independently control many parameters, so it is the combination of the properties of a static synchronous compensator (STATCOM) and static synchronous series compensator (SSSC).The performance of AC Transmission system with Unified power flow controller under various power quality problems analysis described. The proposed system is formulated and research work is done by wavelet multi resolution analysis using Bior1.5 mother wavelet with MATLAB/SIMULINK software. It is observed that the effectiveness of AC power transmission through Unified power flow controller under power quality problems of sag, swell, transient, temporary faults and capacitive switching.

Wavelet Based Power Quality Assessment of Wind Energy Source Integrated 5-Bus System under Sudden Load Conditions In Presence of FACTS Devices

The structure, operation, and planning of electric power networks will undergo considerable and rapid changes due to increased global energy consumption. The requirement of renewable energy sources due to the increase of electrical demand. This paper presents wind energy integration with electrical grid under impact on sudden load conditions. To fill the demand and supply gap requires reactive power. To compensate reactive power more prominent devices are needed. The Static Synchronous Series Compensator (SSSC), Static Synchronous Compensator (STATCOM) and Unified power flow controller (UPFC) are the members of the Flexible AC Transmission system family having very attractive features of series and shunt reactive power compensating devices. The proposed research work is carried by wavelet multi resolution analysis using Bior1.5 mother wavelet under Power Quality issue of sudden load 150MW is introduced at bus-3, which can be compensated by a wind energy source and FACTS devices. The analysis of 5-bus network model of sudden load impact is tested and observed that the effectiveness of the system with Wind Energy source integration in presence of SSSC, STATCOM &UPFC.

Performance study of the multiwavelet discontinuous Galerkin approach for solving the Green‐Naghdi equations

SummaryThis paper presents a multiresolution discontinuous Galerkin (DG) scheme for the adaptive solution of Boussinesq‐type equations. The model combines multiwavelet (MW)–based grid adaptation with a DG solver based on the system of fully nonlinear and weakly dispersive Green‐Naghdi (GN) equations. The key feature of the adaptation procedure is to conduct a multiresolution analysis using MWs on a hierarchy of nested grids to improve the efficiency of the reference DG scheme on a uniform grid by computing on a locally refined adapted grid. This way, the local resolution level will be determined by manipulating MW coefficients controlled by a single user‐defined threshold value. The proposed adaptive multiwavelet DG solver for GN equations is assessed using several benchmark problems related to wave propagation and transformation in nearshore areas. The numerical results demonstrate that the proposed scheme retains the accuracy of the reference scheme, while significantly reducing the computational cost.

CERTAIN INVESTIGATIONS ON REVERSIBLE IMAGE WATERMARKING

Objectives: To protect secret information in a digital medium without compromising robustness. There are many approaches introduced regarding the importance of utilizing medical, military and natural images for image watermarking procedure. Reversible watermarking is appreciated that the original media can be recovered without any loss of information after extracting stego-data. With an aim to provide higher security and robustness, certain investigations have been done on reversible image watermarking. Methods/Statistics: This research work mainly addresses on privacy, reversibility, payload and robustness related goals. Considering these challenges, three parts of image watermarking techniques, namely proposed Bi-orthogonal Wavelet Transform and Importance Measure Model (BWT and IMM), introduced Dynamic Prediction Error Integrated Reversible Image Watermarking (DPEIRIW) and proposed Multi Wavelet Transform based and Oppositional Particle Swarm Optimization (MWT - OPSO) are introduced to improvise the data hiding performance. Findings: The methodologies introduced in this research work not only surpass other approaches with considerations to a variety of incursions, but still maintains a satisfactory image quality. From the experimental results, it is clearly understood that this suggested approach MWT and OPSO attains the average PSNR of 59.84 dB and average NC of 1, which is minimal when comparedwith the all the other existing strategies. Applications/Improvements: As the ongoing trend is now towards multi-core computing and the requirements for the real time video communication is very much in demand, the ownership right information also required to be preserved. It could be a great future direction to deploy this design approach under multi-core platforms. Keywords: Correlation Coefficient, Entropy, Importance Measure Model, OPSO Biorthogonal DWT, Prediction Error, Reversible Watermarking, NC, PSNR

Application of wavelet entropy and grey relational analysis in fault diagnosis of hydraulic system of engineering machinery

The working environment of engineering machinery is complex. The signal of the equipment in operation is characterized by non-stationary and complex variability. In this paper, the wavelet transform is used to extract the non-stationary fault signals of the hydraulic system of the equipment. At the same time, a fault classification method based on the multi wavelet entropy feature extraction is proposed with the information entropy. The fault features are extracted quickly and accurately. The grey correlation analysis method is used to identify the fault, and the remote fault is set up. Diagnosis platform. The results show that the method can improve the support and accuracy of the fault diagnosis, and provides a feasible new method for the fault diagnosis of the engineering machinery.

High performance angiogram sequence compression using 2D bi-orthogonal multi wavelet and hybrid speck-deflate algorithm

Fast communication of medical information is a big challenge for today’s researchers. Either bandwidth or information needs to be sacrificed in order to communicate the entire information within a short time with minimal bandwidth. Compression of medical information before transmission helps us to communicate the entire information within short time with minimal cost. This paper concentrates on reducing the transmission bandwidth by compressing the entire medical information using a Hybrid algorithm which uses 2D Bi-orthogonal multi-wavelet transform and SPECK – Deflate encoder. The algorithm is studied with the standard test dataset GRUSELAMBIX. The real time data was analyzed to ensure the perseverance of the diagnostic information. 2D Bi-orthogonal multi-wavelet transform was applied on each frame on the DICOM file and the co-efficients are encoded using SPECK encoder followed by the deflate compression algorithm. To preserve the original diagnostic information, motion estimation and compensation was not applied on the medical videos. The qualitative performance of this algorithm is evaluated using Compression Ratio, PSNR, MSE, UIQIand SSIM. The Mean opinion Score from experts and non-experts was obtained on the real time data to evaluate the quantitative performance. The proposed algorithm revealed a better compression ratio than the traditional compression algorithm.