Use Of Wavelet Transformation Research Articles

SEGMENTATION OF DIGITAL IMAGES WITH WAVELET TRANSFORMATION USING MATLAB VERSION R2010B

The purpose of this research is to explore and apply the use of wavelet transformation for the segmentation of digital images, utilizing MATLAB version R2010B. The study aims to analyze how wavelet transform can be used to enhance the accuracy and effectiveness of image segmentation, which is a critical process in image processing and computer vision. The research contributes to the field of digital image processing by demonstrating the application of wavelets transformation for segmenting digital images. In this study, the study provides insights into how wavelets can be utilized to improve the detection of image features, especially in identifying image features more accurately. The experimental results show that only the Canny operator's edge detection method has the best edge detector in detecting the edges of objects in wavelet images. The technique of determining the threshold value (thresholding) can be carried out in two ways, namely, automatic method and technique carried out by trial and error. Finally, improving automatic thresholding techniques using AI-driven algorithms to reduce the reliance on trial-and-error methods could provide more consistent results in varied application areas.

WAVELET DE-NOISING FOR AUTONOMOUS LATITUDE DETERMINATION

Autonomous determination of the latitude of the place of movable and immovable objects is used as an independent task, as well as the task of determination of the initial value of latitude for operation of both platform and platform-free navigation systems. To solve these problems, it is necessary to have an inertial measurement unit (IMU) with at least three gyroscopes and three accelerometers. When using the IMU, executed by MEMS technology, the output signals of micromechanical gyroscope and accelerometers have significant noise components. Kalman filter is usually used to filter such signals. However, for this purpose it is necessary to know, besides the exact mathematical model of sensitive elements, many of their initial random characteristics.
 In the article, the research was conducted in order to investigate the use of wavelet transformation for the filtering of output signals of micromechanical accelerometers and gyroscopes for autonomous determination of the latitude of the place. The peculiarity of using wavelet transform for noisy signals is that due to changing scale, wavelets can detect differences in process characteristics on different scales, and with help of the shift we can analyze process properties at different points on the whole investigated interval. Due to the properties of this system's fullness that it is possible to restore the process by means of inverse wavelet transform. The efficiency of the developed method of increasing the accuracy of the autonomous determination of the latitude of the IMU on the basis of micromechanical gyroscope and accelerometers has been experimentally confirmed. The projections of the angular velocity of Earth rotation and gravitational acceleration were obtained from the IMU made by MEMS technology. After that, the signals of the gyroscopes and accelerometers of the inertial measuring unit were filtered, using the wavelet ‘Daubechies 10’ in decomposition, and averaged. These signals were used in a computational algorithm to determine the latitude. The results showed that, unlike the well-known Kalman filter, which almost did not increase the accuracy of the latitude calculation, wavelet denoising and further averaging reduced calculation error by almost twice.

A hybrid wavelet-Lyapunov exponent model for river water quality forecast

Abstract The use of spectral theory and chaos theory on river water quality modeling is reported in a very limited way. This study proposes a wavelet-maximum Lyapunov exponent (WMLE) hybrid model for river water quality dynamics, combining spectral theory and chaos theory. The methodology involves the following major steps: (1) use of wavelet transformation to filter the noisy signal in the water quality time series; (2) reconstruction of phase space to embed the water quality time series and determine the trajectory of the underlying dynamics; and (3) identification of the presence/absence of chaos and prediction using the largest Lyapunov exponent value. Case studies on the Huaihe River in China and the Potomac River in the United States, as representatives of low-frequency and high-frequency forecast, show average relative errors on weekly dissolved oxygen (DO), chemical oxygen demand (COD), and ammonia nitrogen (NH3-N) data are 2.35%, 4.53%, and 18.85%, and on 15-minute based DO data are 1.185%. It also indicates that the hybrid model performs better to some extent when compared to the purely Lyapunov exponent model, ARMA model, and ANN model. This study is a proof that the combination of spectral theory and chaos theory is promising to describe and predict fluctuation of particular water quality indicators in rivers.

Wavelet based simulation of thermal regenerator

Thermal regenerators are the heat exchanging devices used for transferring heat from hot fluid to cold fluid via porous matrix of solid material. The transfer of heat energy in the thermal regenerator can be expressed by coupled partial differential equations (solid phase and fluid phase). In the present work, the governing equations for thermal regenerators are solved and compared by the finite difference method and wavelet optimized finite difference method. Validation of previous experimental results has also been done by using the present simulation. A methodology based on Sparse Point Representation (SPR) is used to adapt the effective grid pattern using wavelet transformation. Temperature profiles along the bed length are shown for the heating process by both the methods. The modified grid patterns based upon SPR, at different time levels are represented with the help of appropriate figures, and it is observed that the use of wavelet transformation in numerical solution increases the efficiency of the method by reducing CPU time.

Harmonic and pulsed eddy current testing methods as tools for surface and subsurface defect evaluation

Abstract In most publications eddy current testing (ECT) methods are said to be suitable for surface defects up to several millimeters. This paper aims to evaluate the detection capabilities of eddy current testing based on two examples of modern approach. One of them is a harmonic eddy current method with use of wavelet transformation and neural network. The second method is pulsed eddy current testing (PECT) with use of evaluation script based on cross-correlation of measured data with the set of known reference signals. Two samples with five artificial defects each were investigated from the near and far side. Geometric parameters of every defect were then estimated. Results show that both methods are limited by the skin effect. However, the PECT with use of cross-correlation exhibits detection capabilities of subsurface defects in significant depth. On the other hand, harmonic eddy current testing shows lower errors in dimensions estimation for set of surface defects.

Streamflow Forecasting Using Four Wavelet Transformation Combinations Approaches with Data-Driven Models: A Comparative Study

This study investigates the use of wavelet transformation (WT) as preprocessing tool in data-driven models (DDMs) for forecasting streamflow 7 days ahead. WT used are Continuous wavelet transformation (CWT), discrete wavelet transformation (DWT), and a new proposed combination of CWT and DWT, namely discrete continuous wavelet transformation (DCWT). In addition to these three different WTs, the single DDMs were used also to create four different schematic layouts. The DDMs applied were artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), and support vector machines (SVM). The lagged rainfall, temperature, and streamflow were incorporated as inputs into the WT-DDMs. It was found that CWT improved the forecasting accuracy of models which only included the rainfall and temperature but not the streamflow. Moreover, DWT improved the performance dramatically for the models with streamflow. Notably, DWT layout outperformed CWT layout in general while CWT layouts resulted in higher improvement to the models with rainfall and temperature only. The proposed DCWT in which CWT applied on the rainfall and temperature variables and DWT applied on the streamflow improved the forecasting ability in several models combinations when ANN was applied. Nevertheless, improvement in the forecasting accuracy was deteriorated in those with SVM while no improvement was observed with ANFIS. ANN outperformed both ANFIS and SVM while ANFIS performed better than SVM.

Rolling bearings control by comparing the wavelet of scaling

The control of production processes, the quality of machine and mechanism assembly during their manufacture and during the repair period saves labor time and labor costs. Analysis of statistical data on equipment failures in various industries has shown that a large number of failures is conditioned by rolling bearings. The solution of bearing early diagnosis problem will predict reasonably the failure-free operation of the equipment in accordance with the actual condition of the bearing units. The purpose of the study is to develop the method and an information and measuring system to monitor the technical condition of bearings based on vibration parameters based the comparison of scaling graphs of continuous wavelet analysis. The results of vibration acoustic signals of rolling bearings are presented with the use of wavelet transformations, the scaling graphs of signals from rolling bearings with various defects are obtained and analyzed experimentally. The method for bearing monitoring is proposed, which makes it possible to automate the process of defect detection and to increase the resolving power during vibration-acoustic control performance. The result of the study showed that the application of analysis algorithms with the use of wavelet transformation allows to detect the defects of rolling bearings at an early stage of nucleation. Implemented by the proposed control methodology, the measuring system can be used both as in-built into a monitored system and in a mobile version.

The algorithm for contour segmentation of images with hierarchical objects on the basis of the Canny method with use of wavelet-transformation

The algorithm for contour segmentation of images with hierarchical objects on the basis of the Canny method with use of wavelet-transformation

The use of wavelet transformation in conjunction with a heuristic algorithm as a tool for feature extraction from signals

The use of algorithms is helpful in analysis of various data samples. Examples of these applications are sound and graphics processing that are used in authentication and analysis of images. In this work, we propose technique of extracting data from image file created based on voice sample. Proposed method makes use of mathematical model of wavelet transformation and it’s graphical visualization like scaleogram combined with computational intelligence methods like neural network and heuristic algorithm. In order to verify operation of proposed technique we present experimental results. DOI: http://dx.doi.org/10.5755/j01.itc.46.3.17582

Outlier correction method of telemetry data based on wavelet transformation and Wright criterion

An outlier revision method is proposed based on Wright criterion, quadratic difference prediction and wavelet transformation. Making use of wavelet transformation, the original telemetry data is decomposed into multi-level detail and approximation components. In detail component of every level, outliers are distinguished by Wright criterion, and which are corrected by quadratic difference prediction method. After wavelet reconstruction, the revised data will be obtained. This method not only can revise the outliers effectively, but also can reserve the key information in original data. Finally, this method has been proved efficacious and feasible for revising the outliers of telemetry data.

Comparative study of different wavelet based neural network models for rainfall–runoff modeling

The use of wavelet transformation in rainfall–runoff modeling has become popular because of its ability to simultaneously deal with both the spectral and the temporal information contained within time series data. The selection of an appropriate wavelet function plays a crucial role for successful implementation of the wavelet based rainfall–runoff artificial neural network models as it can lead to further enhancement in the model performance. The present study is therefore conducted to evaluate the effects of 23 mother wavelet functions on the performance of the hybrid wavelet based artificial neural network rainfall–runoff models. The hybrid Multilayer Perceptron Neural Network (MLPNN) and the Radial Basis Function Neural Network (RBFNN) models are developed in this study using both the continuous wavelet and the discrete wavelet transformation types. The performances of the 92 developed wavelet based neural network models with all the 23 mother wavelet functions are compared with the neural network models developed without wavelet transformations. It is found that among all the models tested, the discrete wavelet transform multilayer perceptron neural network (DWTMLPNN) and the discrete wavelet transform radial basis function (DWTRBFNN) models at decomposition level nine with the db8 wavelet function has the best performance. The result also shows that the pre-processing of input rainfall data by the wavelet transformation can significantly increases performance of the MLPNN and the RBFNN rainfall–runoff models.

Precision Parts Parallelism Error on-Line Detection Based on the CCD Imaging System

In order to meet the requirement of high precision and high efficiency in parts detection, the paper made the research on parts shape-position error on-line detection, put forward a new detection method about parts detection, applied the CCD imaging techniques into the machining quality on-line detection of parts, detected the edge points of parts image by making use of wavelet transformation and Hough transformation, fit the points using least-squares procedure and got the equation of line accurately, realized the non-contact detection of surface quality of parts. Experiment shows that the new method can detect parallelism error more accurately and effectively compared to the artificial detection method.

An Identification System for Classifying Nonwoven Quality

Fabric defect detection and classification plays a very import role in automatic inspection for textile. With the use of wavelet transformation and gray-level co-occurrence matrix to extract feature value of nonwoven image, then grey relational analysis of grey theory is availed to conduct the classification of uniformity of nonwoven. Of these processes, they care carried out mainly to discover uneven images for nonwoven, and proceed with classification study. Grey relation analysis can be used to find out the relation with selection feature indicators upon each factor to be studied and analyzed through some images processing through random factor sequence, and the highest relation is followed to determine the uneven images for nonwoven.

Spectral information enhancement using wavelet-based iterative filtering for in vivo gamma spectrometry

Use of wavelet transformation in stationary signal processing has been demonstrated for denoising the measured spectra and characterisation of radionuclides in the in vivo monitoring analysis, where difficulties arise due to very low activity level to be estimated in biological systems. The large statistical fluctuations often make the identification of characteristic gammas from radionuclides highly uncertain, particularly when interferences from progenies are also present. A new wavelet-based noise filtering methodology has been developed for better detection of gamma peaks in noisy data. This sequential, iterative filtering method uses the wavelet multi-resolution approach for noise rejection and an inverse transform after soft 'thresholding' over the generated coefficients. Analyses of in vivo monitoring data of (235)U and (238)U were carried out using this method without disturbing the peak position and amplitude while achieving a 3-fold improvement in the signal-to-noise ratio, compared with the original measured spectrum. When compared with other data-filtering techniques, the wavelet-based method shows the best results.

3323 Image based defect detection Algorithm by use of wavelet transformation

3323 Image based defect detection Algorithm by use of wavelet transformation

The use of the wavelet transformation for simulating certain characteristics of optoelectronic devices

The use of the discrete wavelet transformation for the purpose of simulation and analysis of the characteristics of signals in optoelectronic devices with multi-element radiation detectors for the measurement of the linear motions of objects is discussed. A comparison of the wavelet transformation to the Fourier transformation, a method that is in widespread use in practical applications, is presented and its information efficiency in the solution of analogous problems is demonstrated.

Enhancing the reconstruction of acoustic source field using wavelet transformation

This paper shows the use of wavelet transformation combined with inverse acoustics to reconstruct the surface velocity of a noise source. This approach uses the boundary element analysis based on the measured sound pressure at a set of field points, the Helmholtz integral equations and wavelet transformation for reconstructing the normal surface velocity field. The reconstructed field can be diverged due to the small measurement errors in the case of nearfield acoustic holography (NAH) using an inverse boundary element method. In order to avoid this instability in the inverse problem, the reconstruction process should include some form of regularization for enhancing the resolution of source images. The usual method of regularization has been the truncation of wave vectors associated with small singular values, although the order of an optimal truncation is difficult to determine. In this paper, a wavelet transformation is applied to reduce the computation time for inverse acoustics and to enhance the reconstructed vibration field. The computational speed-up is achieved, with solution time being reduced to 14.5%.

Universal approach to overcoming nonstationarity, unsteadiness and non-Markovity of stochastic processes in complex systems

In the present paper, we suggest a new universal approach to study complex systems by microscopic, mesoscopic and macroscopic methods. We discuss new possibilities of extracting information on nonstationarity, unsteadiness and non-Markovity of discrete stochastic processes in complex systems. We consider statistical properties of the fast, intermediate and slow components of the investigated processes in complex systems within the framework of microscopic, mesoscopic and macroscopic approaches separately. Among them theoretical analysis is carried out by means of local noisy time-dependent parameters and the conception of a quasi-Brownian particle (QBP) (mesoscopic approach) as well as the use of wavelet transformation of the initial row time series. As a concrete example we examine the seismic time series data for strong and weak earthquakes in Turkey (1998,1999) in detail, as well as technogenic explosions. We propose a new possible solution to the problem of forecasting strong earthquakes. Besides we have found out that an unexpected restoration of the first two local noisy parameters in weak earthquakes and technogenic explosions is determined by exponential law. In this paper we have also carried out the comparison and have discussed the received time dependence of the local parameters for various seismic phenomena.

Universal approach to overcoming nonstationarity, unsteadiness and non-Markovity of stochastic processes in complex systems

In the present paper, we suggest a new universal approach to study complex systems by microscopic, mesoscopic and macroscopic methods. We discuss new possibilities of extracting information on nonstationarity, unsteadiness and non-Markovity of discrete stochastic processes in complex systems. We consider statistical properties of the fast, intermediate and slow components of the investigated processes in complex systems within the framework of microscopic, mesoscopic and macroscopic approaches separately. Among them theoretical analysis is carried out by means of local noisy time-dependent parameters and the conception of a quasi-Brownian particle (QBP) (mesoscopic approach) as well as the use of wavelet transformation of the initial row time series. As a concrete example we examine the seismic time series data for strong and weak earthquakes in Turkey ( 1998 , 1999 ) in detail, as well as technogenic explosions. We propose a new possible solution to the problem of forecasting strong earthquakes. Besides we have found out that an unexpected restoration of the first two local noisy parameters in weak earthquakes and technogenic explosions is determined by exponential law. In this paper we have also carried out the comparison and have discussed the received time dependence of the local parameters for various seismic phenomena.

Improving the sharpness of an image with non-uniform illumination

We demonstrate a new method to sharpen a non-uniformly illuminated image by use of wavelet transformation employing Harr function. Both the fine characteristics and object edges in the image are well enhanced without degrading the contrast. This method provides some superiority compared to the conventional algorithms.