Multiwavelet Transform Research Articles

PERFORMANCE IMPROVEMENT OF MULTIPLE-INPUT/MULTIPLE-OUTPUT (MIMO) ORTHOGONAL FREQUENCY-DIVISION MULTIPLEXING (OFDM) SYSTEMS USING MIMO DISCRETE MULTIWAVELET TRANSFORM CODED OFDM

In the present work, we proposed to modify and improve multiple-input/multiple-output (MIMO) fast Fourier transform (FFT) coded orthogonal frequency-division multiplexing (COFDM) by MIMO discrete multiwavelet transform (DMWT) COFDM to reduce the interference level and increase the spectral efficiency and complexity of the system. Combining space division multiplexing with multiwavelet and error correction coding and the diversity of the orthogonal frequency division coding to improve the performance of wireless systems requires mastery of all techniques. This DMWT-COFDM system is new and has many applications in telecommunication systems. In this paper, we present the MIMO-FFT-COFDM and MIMO-DMWT-COFDM system block diagrams. Compared with the MIMO-FFT-COFDM system, the bit error rate of the system can be reduced by more than 10 dB.

A Novel Feature Extraction Method Based on Legendre Multi-Wavelet Transform and Auto-Encoder for Steel Surface Defect Classification

A Novel Feature Extraction Method Based on Legendre Multi-Wavelet Transform and Auto-Encoder for Steel Surface Defect Classification

Legendre Multiwavelet Transform and Its Application in Bearing Fault Detection

Bearing failures often result from compound faults, where the characteristics of these compound faults span across multiple domains. To tackle the challenge of extracting features from compound faults, this paper proposes a novel fault detection method based on the Legendre multiwavelet transform (LMWT) combined with envelope spectrum analysis. Additionally, to address the issue of identifying suitable wavelet decomposition coefficients, this paper introduces the concept of relative energy ratio. This ratio assists in identifying the most sensitive wavelet coefficients associated with fault frequency bands. To assess the performance of the proposed method, the results obtained from the LMWT method are compared with those derived from the empirical wavelet transform (EWT) method using different datasets. Experimental findings demonstrate that the proposed method exhibits more effective frequency spectrum segmentation and superior detection performance across various experimental conditions.

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.

Gearbox Compound Fault Diagnosis in Edge-IoT Based on Legendre Multiwavelet Transform and Convolutional Neural Network

The application of edge computing combined with the Internet of Things (edge-IoT) has been rapidly developed. It is of great significance to develop a lightweight network for gearbox compound fault diagnosis in the edge-IoT context. The goal of this paper is to devise a novel and high-accuracy lightweight neural network based on Legendre multiwavelet transform and multi-channel convolutional neural network (LMWT-MCNN) to fast recognize various compound fault categories of gearbox. The contributions of this paper mainly lie in three aspects: The feature images are designed based on the LMWT frequency domain and they are easily implemented in the MCNN model to effectively avoid noise interference. The proposed lightweight model only consists of three convolutional layers and three pooling layers to further extract the most valuable fault features without any artificial feature extraction. In a fully connected layer, the specific fault type of rotating machinery is identified by the multi-label method. This paper provides a promising technique for rotating machinery fault diagnosis in real applications based on edge-IoT, which can largely reduce labor costs. Finally, the PHM 2009 gearbox and Paderborn University bearing compound fault datasets are used to verify the effectiveness and robustness of the proposed method. The experimental results demonstrate that the proposed lightweight network is able to reliably identify the compound fault categories with the highest accuracy under the strong noise environment compared with the existing methods.

A Hybrid Multiwavelet Transform with Grey Wolf Optimization Used for an Efficient Classification of Documents

In machine learning, feature selection is crucial to increase performance and shorten the model's learning time. It seeks to discover the pertinent predictors from high-dimensional feature space. However, a tremendous increase in the feature dimension space poses a severe obstacle to feature selection techniques. Study process to address this difficulty, the authors suggest a hybrid feature selection method consisting of the Multiwavelet transform and Gray Wolf optimization. The proposed approach minimizes the overall downsides while cherry picking the benefits of both directions. This notable wavelet transform development employs both wavelet and vector scaling functions. Additionally, multiwavelets have orthogonality, symmetry, compact support, and significant vanishing moments. One of the most advanced areas of study of artificial intelligence is optimization algorithms. Grey Wolf Optimization (GWO) here produced artificial techniques that yielded good performance results and were more responsive to current needs. Keywords — About four key words or phrases in order of importance, separated by commas, used to compile the subject index for the last issue for the year.

Multiwavelet based-approach to detect shared congestion in computer networks

Internet paths sharing the same congested link can be identified using several shared congestion detection techniques. The new detection technique which is proposed in this paper depends on the previous novel technique (delay correlation with wavelet denoising (DCW) with new denoising method called Discrete Multiwavelet Transform (DMWT) as signal denoising to separate between queuing delay caused by network congestion and delay caused by various other delay variations. The new detection technique provides faster convergence (3 to 5 seconds less than previous novel technique) while using fewer probe packets approximately half numbers than the previous novel technique, so it will reduce the overload on the network caused by probe packets. Thus, new detection technique will improve the overall performance of computer network.

Image Encryption Algorithm Based on Arnold Transform and Chaos Theory in the Multi-wavelet Domain

Image encryption is essential for ensuring data transmission security over open public networks. Using Multi-Wavelet Transform, Arnold transform, and two chaotic systems, a novel color image encryption technology is designed in this paper. In the proposed algorithm, the primary color components of the input image undergo a multi-wave transform before the Arnold Transform confounds the sub-bands of each color component. Each color component is then divided into blocks shuffled in a predetermined order. Finally, the encrypted image is generated using secret keys derived from Nahrain’s and WAM’s chaotic systems. Notably, the initial conditions of the chaotic maps are generated using image data to increase the algorithm’s sensitivity to the input image. Security analyses conducted to validate the practicability of the new algorithm reveal that it possesses excellent encryption efficiency, high key sensitivity, and the ability to withstand a wide variety of attacks.

A new approach for DNA sequence analysis using multiwavelet transform (MWT)

Finding the nucleotide sequence within a DNA (deoxyribonucleic acid) molecule is the process of DNA sequencing. The DNA of every organism is made up of a specific pattern of nucleotides. Scientists can compare the DNA of different organisms by sequence to understand how organisms are connected. Three different forms of DNA sequencing - fragment analysis, Next Generation Sequencing (NGS), and Sanger sequencing - enable many new applications that increase our knowledge of genomes. It significantly impacts how well we understand how the genome works. Finding the hidden structural information of DNA is an important task. This paper introduces Multi-Wavelet Transformation (MWT) for analysis of the sequence of the DNA symbols. The symbolic DNA sequence has four indicator sequences. The MWT assigned symbols to numbers based on DNA structure. It can change each channel’s weighting to optimize energy distribution over MWT output. Analysis of synthetic and actual DNA sequences allowed the researchers to evaluate how well the proposed MWT works. The results show that the MWT has a higher energy dispersion than the regular WT and the bionic wavelet transform (BWT). Future DNA sequence analyses should be able to discover hidden structural features using this MWT approach.

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.

Computation of Wavelet and Multiwavelet Transforms Using Fast Fourier Transform

A novel fast and efficient algorithm was proposed that uses the Fast Fourier Transform (FFT) as a tool to compute the Discrete Wavelet Transform (DWT) and Discrete Multiwavelet Transform. The Haar Wavelet Transform and the GHM system are shown to be a special case of the proposed algorithm, where the discrete linear convolution will adapt to achieve the desired approximation and detail coefficients. Assuming that no intermediate coefficients are canceled and no approximations are made, the algorithm will give the exact solution. Hence the proposed algorithm provides an efficient complexity verses accuracy tradeoff. The main advantages of the proposed algorithm is that high band and the low band coefficients can be exploited for several classes of signals resulting in very low computation.

Image Denoising Using Multiwavelet Transform with Different Filters and Rules

<p class="0abstract">Image denoising is a technique for removing unwanted signals called the noise, which coupling with the original signal when transmitting them; to remove the noise from the original signal, many denoising methods are used. In this paper, the Multiwavelet Transform (MWT) is used to denoise the corrupted image by Choosing the HH coefficient for processing based on two different filters Tri-State Median filter and Switching Median filter. With each filter, various rules are used, such as Normal Shrink, Sure Shrink, Visu Shrink, and Bivariate Shrink. The proposed algorithm is applied Salt& pepper noise with different levels for grayscale test images. The quality of the denoised image is evaluated by using Peak Signal to Noise Ratio (PSNR). Depend on the value of PSNR that explained in the result section; we conclude that the (Tri-State Median filter) is better than (Switching Median filter) in denoising image quality, according to the results of applying rules the result of the Shrinking rule for each filter shows that the best result using first the Bivariate Shrink.</p>

Removal of Artifacts from Electroenchaphalography Signal using Multiwavelet Transform

The signal from the brain can be recorded using Electroenchaphalography (EEG). The proposed work summarizes a unique method which is used for the removal of mixed artifacts presented in the electroencephalography signal during the acquisition process. Artifacts comprises of various bio-potential unit such as electrooculogram, electrocardiogram, and electromyogram. These artifacts are referred as a noise sources which is responsible for the complexity of the EEG signal. The artifacts obtained from the EEG signal leads towards improper diagnosis of pathological conditions. The EEG signal which is obtained from the brain is the multi-dimensional signal with the various statistical properties. Time consumption of the EEG signal is not reproducible due to the biological properties of the signal. The information of the EEG signal consists of the data of the neuron levels which is collected for every millisecond with the temporal resolution scale. In account of special cases, EEG signal contains noise and artifacts where information is collected using the extraction of signals. To obtain the information of the artifacts the proposed technique is used to maintain higher accuracy in the extraction process. The proposed technique consists of multiwavelet transform to remove the artifacts from the input EEG signal. In the proposed multiwavelet transform, the signal which consists of noisy features can be decomposed using GHM and thresholding technique. This experimental analysis shows the removal of artifacts from the EEG signals. The pathological conditions are removed which leads to the increase in the accuracy of the system. Also, this research findings shows that the proposed multiwavelet transform based approach outperforms significantly with respect to conventional approaches. The reconstructed EEG signal has the lesser reliability range which is measured in-terms of signal to noise ratio and power spectral density.

Image processing of periapical radiograph on granuloma detection by analysis method based on Android

Objectives: The study assesses periapical radiograph image with various android based analysis method to detect granuloma.
 Materials and Methods: The study uses survey descriptive cross sectional by using questionnaire. The questionnaire is distributed to 70 random respondents. The methods of the android application used are BLOB (Binary Large Object), DCT and LDA (Discrete Cosine Transform and Linier Discriminant Analysis), DWT and PCA (Discrete Wavelet Transform & Principal Component Analysis), and multiwavelet transformation. The questionnaire assessment included accuracy, effectiveness, attractiveness, innovativeness of the android application.
 Results: Android application with BLOB has effectivity and accuracy of 62,5%, attractiveness and innovativeness of 75%. Android application with DCT and LDA has effectivity and accuracy of 50 %, attractiveness of 70% and innovativeness of 80%. Android application with DWT and PCA has effectivity of 50%, accuracy of 60%, attractiveness of 66,66% and innovativeness of 80%. Android application with multiwavelet transformation has effectivity and accuracy of 50%, attractiveness of 55% and innovativeness of 73%.
 Conclusion: Based on assessment, the four methods used to detect granuloma are effective and applicative with android-based application. Android-based Application can detect granuloma with approximately more than 70% successful rate. These methods ease the practitioner to interpret the granuloma image.

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.

Applying the Artificial Neural Networks with Multiwavelet Transform on Phoneme recognition

There are several advantages of Phoneme recognition. identification. It is easier to use speech for data entrance spoken communication for data ingress than other tools. It allows writing user-friendly data entrance exploiter-friendly data ingress programs. There are several difficulties in speech voice communication recognition. One of these difficulties is noise. Variability in speech is another problem. Even the speech of same speaker varies. The ability of artificial neural networks to generalize and optimize more quickly than some conventional algorithms algorithmic rule has been observed in different areas of research inquiry such as speech and pattern convention recognition, financial forecasting prognostication, image data compression and noise reduction simplification in signal processing. Neural networks take advantage of the redundancy incorporated in their distributed processing structures the proposed system depends on Artificial Neural Networks Network as decision making qualification algorithm to find the best match peer for the tested phonemes. Phoneme. The data used in this project are Arabic phonemes language phoneme stored as 8-bit mono infectious mononucleosis 8000Hz PCM WAVE Sound Auditory sensation file. The results showed that the accuracy of the proposed system is 98% recognizes the phonemes efficiently.

Hybrid ANFIS-FA based control strategy for UPQC -Power Quality enhancement in Smart Grid

In the paper, a hybrid technique is proposed for analysing and controlling PQ issues in SG. Initially, the normal behaviour of SG system is analysed. After that, some disturbance is created in the SG system and determined the abnormal behaviours. The first phase of proposed methodology is utilisation of wavelet transformation algorithms for processing the inputs. Here, multi-wavelet transform (MWT) is utilised to extract the features of inputs for classification. The hybrid technique is a combination of hybrid ANFIS and FA, which is utilised to maintain the DC-link voltage of the UPQC. The hybrid ANFIS means, FA is used to select the optimal dataset for training the ANFIS. The main objective of the proposed technique is detecting and controlling the PQ issues in SG. The proposed method is implemented in MATLAB/Simulink platform. In order to analyse the effectiveness of the proposed method, various types of issues are analysed and tested with the existing methods such as, UPQC with ANFIS, UPQC with artificial neural network (ANN) technique and UPQC with gravitational search algorithm (GSA).

MultiWavelet Transform based Glaucoma Classification using Random Forest

MultiWavelet Transform based Glaucoma Classification using Random Forest

Multi-wavelet level comparison on compressive sensing for MRI image reconstruction

In this study, we proposed compressive sampling for MRI reconstruction based on sparse representation using multi-wavelet transformation. Comparing the performance of wavelet decomposition level, which are Level 1, Level 2, Level 3, and Level 4. We used gaussian random process to generate measurement matrix. The algorithm used to reconstruct the image is . The experimental results showed that the use of wavelet multi-level can generate higher compression ratio but requires a longer processing time. MRI reconstruction results based on the parameters of the peak signal to noise ratio (PSNR) and structural similarity index measure (SSIM) show that the higher the level of decomposition in wavelets, the value of both decreases.

Empirical mean curve decomposition with multiwavelet transformation for eye movements recognition using electrooculogram signals.

Many research works are in progress in classification of the eye movements using the electrooculography signals and employing them to control the human-computer interface systems. This article introduces a new model for recognizing various eye movements using electrooculography signals with the help of empirical mean curve decomposition and multiwavelet transformation. Furthermore, this article also adopts a principal component analysis algorithm to reduce the dimension of electrooculography signals. Accordingly, the dimensionally reduced decomposed signal is provided to the neural network classifier for classifying the electrooculography signals, along with this, the weight of the neural network is fine-tuned with the assistance of the Levenberg-Marquardt algorithm. Finally, the proposed method is compared with the existing methods and it is observed that the proposed methodology gives the better performance in correspondence with accuracy, sensitivity, specificity, precision, false positive rate, false negative rate, negative predictive value, false discovery rate, F1 score, and Mathews correlation coefficient.