Wavelet Transform Method Research Articles

An Efficient DWT and EWT Feature Extraction Methods for Classification of Real Data PQ Disturbances

Determination and investigation of incidents affecting Power Quality (PQ) is very important for consumers. In this study, estimation of PQ events is obtained to determine the disturbances of PQ by using Empirical Wavelet Transform (EWT) and Discrete Wavelet Transform (DWT) methods and with this estimated parameters. PQ disturbances were examined with Support Vector Machine (SVM), Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) classification methods. Voltage signals (sag, swell, interruption, transient and normal) used in the classification of PQ disturbances were recorded from grid with the aid of a microcontroller based on device designed with a sampling frequency of 6.4 kHz. Classification consequences using Machine Learning Methods show that DWT outperforms over EWT for feature extraction processing and the classification accuracy is tabled. Classification by ANN and ANFIS through the use of conjecture parameters in PQ disturbances based on DWT Method has been recommended.

Image-based Analysis of Emotional Facial Expressions in Full Face Transplants.

In this study, it is aimed to determine the degree of the development in emotional expression of full face transplant patients from photographs. Hence, a rehabilitation process can be planned according to the determination of degrees as a later work. As envisaged, in full face transplant cases, the determination of expressions can be confused or cannot be achieved as the healthy control group. In order to perform image-based analysis, a control group consist of 9 healthy males and 2 full-face transplant patients participated in the study. Appearance-based Gabor Wavelet Transform (GWT) and Local Binary Pattern (LBP) methods are adopted for recognizing neutral and 6 emotional expressions which consist of angry, scared, happy, hate, confused and sad. Feature extraction was carried out by using both methods and combination of these methods serially. In the performed expressions, the extracted features of the most distinct zones in the facial area where the eye and mouth region, have been used to classify the emotions. Also, the combination of these region features has been used to improve classifier performance. Control subjects and transplant patients' ability to perform emotional expressions have been determined with K-nearest neighbor (KNN) classifier with region-specific and method-specific decision stages. The results have been compared with healthy group. It has been observed that transplant patients don't reflect some emotional expressions. Also, there were confusions among expressions.

Authentication and Tamper Detection in Tele-medicine using Zero Watermarking

In the process of medical image sharing, it is always required that while sending the medical images, the images must be secured so that it does not get tampered or attacked and the integrity of the image and watermark are maintained. This paper presents a technique for authentication of medical image using zero watermarking method. The image is applied with Discrete Wavelet Transform (DWT) method which divides the image into four bands. After applying the DWT, blocking is done and each block is treated with SVD to extract unique feature and a master share is obtained by performing logical operation between extracted unique feature and the watermark. The strategy gives better experimental results and can be utilized in the field of telemedicine in order to protect the shared images from any unlawful use.

Computing Spectral Characteristics from Short Signals and Nonstationary Processes

We propose a method of estimating spectral properties of a system from short signals and processes with varying characteristics, which is based on the wavelet-transform modulus-maxima method. It is shown that this approach allows significant reduction of the computing error as compared with the classical spectral analysis.

Hybrid techniques to improve face recognition based on features extraction methods and Haar discrete wavelet transformation

This paper uses hybrid techniques to improve the rate of recognition for a face from identified data set of faces. These techniques are summarized by applying firstly the Haar discrete wavelet transformation method in order to enhance and compress the images of the data set and store the results for each process in a separate data set. Secondly, applying a hybrid method from two popular face recognition methods called Principal Component Analysis and Singular Value Decomposition for extracting feature from the images. This work applied by using a dataset contains 400 images for 40 different persons called Olivetti Research Laboratory (ORL). In calculating the distances between image vectors, Manhattan measurement is used and its show a very good results in recognition rate. From this work, it can be concluded that recognition rate increments with the decrementing in the number of dataset images and increasing the threshold value. The expended time in execution decreases in a very obvious way when using the compressed dataset rather than the enhanced dataset which its images has four times the size of the images in the compressed dataset.

Classification of Cervical Disc Herniation Disease using Muscle Fatigue based surface EMG signals by Artificial Neural Networks

This study presents the classification of cervical disc herniation patient and healthy persons by using muscle fatigue information. Cervical disc herniation patients suffer from neck pain and muscle fatigue in the neck increases these aches. Neck pain is the most common pain type encountered after back pain. The discomforts that occur in the neck region affect the daily quality of life, so the number of researches done in this area is increasing. In this study surface Electromyography (EMG) signals were used to examine muscle fatigue. EMG signals were obtained from Trapezius and Sternocleidomastoid (SCM) muscles in the cervical region of 10 control subject and 10 cervical disc herniation patients. Surface EMG was preferred because it is a noninvasive method. In the first step of this study, EMG signals were filtered and adapted for analysis. In the second step, muscle fatigue was determined using Median and Mean frequency values obtained by Fourier Transform and Welch methods. Feature extraction was the third step which was performed by Short Time Fourier Transform (STFT), Discrete Wavelet Transform (DWT) and Autoregressive method (AR). Finally, Artificial Neural Network (ANN) was used for classification. Training and test data were created by using feature vectors to classify patients with ANN. According to the results, the superior feature extraction method was investigated on patient classification using muscle fatigue information. The best results were obtained by AR method with %99 classification accuracy. Also, the best results were obtained by DWT with %100 classification accuracy for SCM muscle. This study has contributed that AR and DWT are a suitable feature extraction methods for surface EMG signals by providing high accuracy classification with artificial intelligence methods for cervical disc herniation disease. Besides, it is shown that muscle fatigue distinguishes cervical disc herniation patients from healthy people.

An Improved Downlink MC-CDMA System for Efficient Image Transmission

Image compression is an essential stage of the data archiving and transmitting process, as it reduces the number of bits and the time required to complete the transmission. In this article, a study of image transmission over the Multi-Carrier Code Division Multiple Access (MC-CDMA) downlink system is presented. The solution proposed relies on source coding combined with channel coding. The Discrete Wavelet Transform (DWT) method is used in conjunction with the SPIHT coder to compress the image, then the data generated is transmitted with the MC-CDMA technique over a noisy channel. The results show that image transmissions performed over MC-CDMA using the SPIHT model are better than the traditional approach like MC-CDMA in the AWGN channel.

Time-frequency analysis on gong timor music using short-time fourier transform and continuous wavelet transform

Time-Frequency Analysis on Gong Timor Music has an important role in the application of signal-processing music such as tone tracking and music transcription or music signal notation. Some of Gong characters is heard by different ways of forcing Gong himself, such as how to play Gong based on the Player’s senses, a set of Gong, and by changing the tempo of Gong instruments. Gong's musical signals have more complex analytical criteria than Western music instrument analysis. This research uses a Gong instrument and two notations; frequency analysis of Gong music frequency compared by the Short-time Fourier Transform (STFT), Overlap Short-time Fourier Transform (OSTFT), and Continuous Wavelet Transform (CWT) method. In the STFT and OSTFT methods, time-frequency analysis Gong music is used with different windows and hop size while CWT method uses Morlet wavelet. The results show that the CWT is better than the STFT methods.

Image Compression with the Region Based Easy Path Wavelet Transform

AbstractWe review the Region Based Easy Path Wavelet Transform method in the context of image compression and how it can be used to encode a Region of Interest in the image with higher quality than the complementary region. (© 2017 Wiley‐VCH Verlag GmbH & Co. KGaA, Weinheim)

Improved Partial Fingerprint Recognition by Integration of Minutiae Based and Parallel Division Methods

There are various methods available for the recognition of fingerprints. But in certain cases, the analysis of the fingerprints that are partially available becomes important, as in the cases where fingerprints are overlapped and hence not available completely, or in case of people who do rigorous work with hands like carpentry which can swab their fingerprints due to injuries. This may also happen in case that the fingerprint itself is not completely acquired. The minutiae based methods for fingerprint recognition are widely used. These methods after certain modifications can be used for the recognition of partial fingerprints. But as the complete fingerprint is not available, the number of minutiae points will be comparatively small. Thus there is a need of other methods to confirm this recognition: the ‘Parallel Division’ method and Wavelet Transform method serve this purpose. For these methods, firstly the parameters of the entire fingerprint are stored in the database as a template and then the partial fingerprint is processed to calculate their parameters and finally, the matching of the two templates is done to find if the fingerprints have matched. This approach has been tested on images in FVC 2002 database and it was found to have an accuracy of 92.30%. However this is achieved with a trade-off with execution time as compared to other methods.

Improved Classification of Phonocardiography Signal Using Optimised Feature Selection

Objectives: To propose hybrid feature selection technique based on Particle Swarm Optimization and Genetic Algorithm for phonocardiography. Methods/Statistical Analysis: The system estimated using heart sounds corresponding to different heart conditions like 320 signals out of which 150-normal, 70-Mitral Valve Prolapse, 50-Ventricular Septal Defect and 50-Pulmonary Stenosis. Features are extracted using DWT. Finding: A phonocardiographic signal reflects the health status of the heart. Generally there exists two heart sounds, but further sounds indicate disease. Phonocardiography is non-invasive, low-cost and accurate method to detect heart disease. This work proposes a framework to extract information from phonocardiography signal to classify whether it is proper or improper. Discrete Wavelet Transform method is implemented to extract features followed by Singular value decomposition for feature selection. Applications/Improvements: pplications/Improvements: An experimental result gives the improvement of the proposed method by increasing the efficacy of the corresponding feature selection technique.

An Efficient and Effective Wavelet Transform Methods for OCT, SD-OCT and Secondary Image Compression

An Efficient and Effective Wavelet Transform Methods for OCT, SD-OCT and Secondary Image Compression

Sumerian Character Extraction by Using Discrete Wavelet Transform and Split Region Methods

this paper proposed a new method to extract characters from Sumerian Texts in Sumerian cuneiform tablets from the Ur III period. The work was confronted by the challenges posed by the fact that Sumerian is not a well understood language and it is not similar to any other ancient or modern language, so we offered a new method for extracting characters from Sumerian tablets,it has an accurate results and better time consuming than other methods, taking many tablet images and applying preprocessing methods to enhance and segment the image and then discrete wavelet transformation and we extract characters for each tablet image by split region algorithm, this work will be very helpful to Cuneiforms and scholars in their field.

ASIC Implementation of Finger Print Recognition Using Overlap-Add and Integer Wavelet Transform Methods

ASIC Implementation of Finger Print Recognition Using Overlap-Add and Integer Wavelet Transform Methods

Influence of soil-structure interaction on seismic responses of offshore wind turbine considering earthquake incident angle

Displacement response and corresponding maximum response energy of structures are key parameters to assess the dynamic effect or even more destructive structural damage of the structures. By employing them, this research has compared the structural responses of jacket supported offshore wind turbine (OWT) subjected to seismic excitations apprehending earthquake incidence, when (a) soil-structure interaction (SSI) has been ignored and (b) SSI has been considered. The effect of earthquakes under arbitrary angle of excitation on the OWT has been investigated by means of the energy based wavelet transformation method. Displacement based fragility analysis is then utilized to convey the probability of exceedance of the OWT at different soil site conditions. The results show that the uncertainty arises due to multi-component seismic excitations along with the diminution trend of shear wave velocity of soil and it tends to reduce the efficiency of the OWT to stand against the ground motions.

Wavelet Transform Method in Applications to the Analysis of the Nonlinear Control Systems

The article is devoted to the theory of Wavelet-transformations with reference to the analysis and synthesis of the dynamic characteristics of the control systems containing an essential nonlinearity of the type of the zone of tolerance, restriction, backlash, etc. of nonlinearity. In the presented work a possibility of application of a device of the theory of Wavelet-transformations for the analysis and synthesis of the dynamic characteristics of the control systems containing essential nonlinearity is considered. A scaling function and wavelet potential function are proposed. The given device has an advantage in comparison with the frequency methods, because it takes into account the processes in the time area. Reactions of the linear systems, including essential nonlinearity, to the influence of a kind of potential function and wavelet are investigated. Errors in the established mode, because of the essential nonlinearities in the tracking system are considered under the influence of the signals representing a Wavelet-number, made of potential functions and wavelet. The condition of stability of the closed-loop system to reaction of the open-loop on the typical influence in the form of a half wave is formulated. The given condition allows us to analyze the stability of the tracking system containing an essential nonlinearity. For this purpose the dependence of the factor of transfer K (j) and the relative delay T 3 (j) for the open-loop system, where parameter j characterizes the width of a half wave, is formed. Application of the theory of Wavelet-transformations for the analysis of the nonlinear systems allows us to investigate the complex systems containing the essential nonlinearity as similarly linear systems. The proposed technique of analysis of the nonlinear systems has an advantage in comparison with the frequency methods, because it considers the processes in the time area and allows us to investigate analytically the errors and stability of the tracking systems.

3D surface profilometry based on 2D S-Transform method with optimized window

S-Transform method can be regarded as an extension to the ideas of the Windowed Fourier Transform (WFT) method and the Wavelet Transform (WT) method, which gives consideration to both local frequency concept of WFT and the multi-resolution strategy of WT. It has been introduced in optical 3D shape measurement recently. However, the S-Transform method suffers from poor energy concentration when some kind of signal is treated with. In order to enhance energy concentration to provide more accurate estimation of the instantaneous frequency, a modification to the existing 2D S-Transform is proposed in this paper. Two additional parameters are introduced to respectively optimize the window width in x and y direction. The paper gives the expression of 2D S-Transform with optimized window and its performance in 3D surface reconstruction. The application of proposed method is demonstrated on both simulation and experiment by comparing with traditional 2D S-Transform method. The improved performance of optimized 2D S-Transform for 3D surface shape measurement shows its feasibility and veracity.

Particulate material fabric characterization by rotational haar wavelet transform

A Rotational Haar Wavelet Transform (RHWT) method is developed to characterize the fabric of particulate assemblies from two-dimensional images. A Maximum Energy Ratio Ψ reveals the fabric direction and its intensity. The method is implemented on 12 sand and 3 rice specimens of various shapes. It was shown that Ψ may be expressed in terms of a material's aspect ratio and relative density. A material fabric classification system based on Ψ is proposed. The parameter also defines the fabric tensor for cross anisotropic material. Scanning electron microscope images of kaolinite clay and several rock images are also analyzed.

Multifractal surrogate-data generation algorithm that preserves pointwise Hölder regularity structure, with initial applications to turbulence.

An algorithm is described that can generate random variants of a time series while preserving the probability distribution of original values and the pointwise Hölder regularity. Thus, it preserves the multifractal properties of the data. Our algorithm is similar in principle to well-known algorithms based on the preservation of the Fourier amplitude spectrum and original values of a time series. However, it is underpinned by a dual-tree complex wavelet transform rather than a Fourier transform. Our method, which we term the iterated amplitude adjusted wavelet transform can be used to generate bootstrapped versions of multifractal data, and because it preserves the pointwise Hölder regularity but not the local Hölder regularity, it can be used to test hypotheses concerning the presence of oscillating singularities in a time series, an important feature of turbulence and econophysics data. Because the locations of the data values are randomized with respect to the multifractal structure, hypotheses about their mutual coupling can be tested, which is important for the velocity-intermittency structure of turbulence and self-regulating processes.

A Simple Continuous Wavelet Transform Method for Detection of Rolling Element Bearing Faults and its Comparison with Envelope Detection

A Simple Continuous Wavelet Transform Method for Detection of Rolling Element Bearing Faults and its Comparison with Envelope Detection