Comparison Of Wavelet Research Articles

COMPARISON OF WAVELET TRANSFORM FOR IMAGE RECOGNITION SYSTEM USING LEARNING VECTOR QUANTIZATION

Image is a spatial dimension contains information, color, and not time-dependent. Nowadays image is very important for recognition system as source/data. In order to obtain certain information (features), image transformed or extracted. Wavelet is mathematical function that is able to classify the image energy concentrated on a small group of coefficients (approximation), while other coefficient group (detail) contains very small energy which can be eliminated. This study purpose to compare wavelet family include Haar, Daubechies and Coiflet, then applied to the fingerprint recognition system and face recognition system.There are three major steps in this paper, preprocessing that includes resize normalization, histogram equalization and thresholding, feature extraction using three different wavelet family, learning using Learning Vector Quantization (LVQ) and matching using Euclidean Distance. The experimental result show Haar is the best among the others. In Fingerprint recognition system the accuracy is 85% and 90% for face recognition system.Keywords; Daubichies, Haar, Image Recognition, Learning Vector Quantization, Wavelet Comparison

Comparison of wavelet and empirical mode decomposition hybrid models in drought prediction

Drought is a major area of interest within the field of water resources management, agriculture, energy resources and community health. Recently researchers have examined not only the mathematical expression of drought indices but also statistical predictions. Accordingly, high accuracy results were obtained using stand-alone machine learning techniques such as artificial neural networks (ANN) and support vector machine (SVM). However, lately, hybrid models have been introduced, which are created by integrating different time series decomposition techniques into standalone models, since the accuracy of stand-alone models used in the drought prediction being low particularly for mid-term and long-term drought predictions. In this study, self-calibrated Palmer Drought Severity Index (sc-PDSI) values were predicted by using three different standalone models and six hybrid models which are performed by two different decomposition techniques, such as Empirical mode decomposition (EMD) and Wavelet decomposition (WD). The main purpose of this study is to evaluate the effect of using EMD and WD for decomposing time series into their sub-bands on drought prediction. sc-PDSI time series were used to achieve 1, 3 and 6-month lead time predictions for Adana and Antalya cities located in the southern part of Turkey. Model performance indicators such as mean square error (MSE), Nash-Sutcliffe efficiency coefficient (NSE) and determination coefficient (R2) were employed to compare the proposed models. The results revealed that the accuracy of the stand-alone models, particularly in mid-term sc-PDSI predictions, was unsatisfactory. However, the prediction accuracy has been increased significantly with the introduction of EMD and WD techniques. Considering the Adana region, the hybrid wavelet models outperformed the models hybridized by EMD for not only 1-month lead time (NSEWD-ANFIS = 0.981 and NSEEMD-M5 = 0.890), but also for 3-month (NSEWD-SVM = 0.878 and NSEEMD-ANFIS = 0.811) and 6-month (NSEWD-ANFIS = 0.857 and NSEEMD-ANFIS = 0.783) lead times. According to the obtained results for Antalya region, similar findings were also observed among hybrid models. Thus, it is concluded that the predictions made using WD have higher accuracy than EMD, and the correct wavelet type selection has a significant effect on the results.

Comparison of wavelet and correlation indices of cerebral autoregulation in a pediatric swine model of cardiac arrest

Existing cerebrovascular blood pressure autoregulation metrics have not been translated to clinical care for pediatric cardiac arrest, in part because signal noise causes high index time-variability. We tested whether a wavelet method that uses near-infrared spectroscopy (NIRS) or intracranial pressure (ICP) decreases index variability compared to that of commonly used correlation indices. We also compared whether the methods identify the optimal arterial blood pressure (ABPopt) and lower limit of autoregulation (LLA). 68 piglets were randomized to cardiac arrest or sham procedure with continuous monitoring of cerebral blood flow using laser Doppler, NIRS and ICP. The arterial blood pressure (ABP) was gradually reduced until it dropped to below the LLA. Several autoregulation indices were calculated using correlation and wavelet methods, including the pressure reactivity index (PRx and wPRx), cerebral oximetry index (COx and wCOx), and hemoglobin volume index (HVx and wHVx). Wavelet methodology had less index variability with smaller standard deviations. Both wavelet and correlation methods distinguished functional autoregulation (ABP above LLA) from dysfunctional autoregulation (ABP below the LLA). Both wavelet and correlation methods also identified ABPopt with high agreement. Thus, wavelet methodology using NIRS may offer an accurate vasoreactivity monitoring method with reduced signal noise after pediatric cardiac arrest.

Comparison of Wavelet and RID-Rihaczek Based Methods for Phase-Amplitude Coupling

Oscillatory phenomena and linear/nonlinear interactions across different oscillatory frequencies are commonly seen between the elements of complex systems. One particular form of interaction that is widely encountered is the modulation of the amplitude of broadband high-frequency oscillations by the phase of low-frequency oscillations, defined as phase-amplitude coupling (PAC). Conventional methods for assessing PAC mostly rely on the Hilbert transform. Previous studies have shown that this method produces biased or spurious PAC estimates as it is highly dependent on the parameters of the bandpass filter used to extract narrowband signals from broadband signals. Recently, wavelet-based methods have been proposed to address some of these limitations. In this letter, we provide a thorough comparison between wavelet-based PAC measures and our recently introduced Reduced Interference Rihaczek (RID-Rihaczek) distribution based PAC both analytically and through simulations. The proposed method is shown to yield higher frequency resolution PAC estimates compared to wavelet-based methods.

СТАТИСТИЧЕСКОЕ СРАВНЕНИЕ ВЕЙВЛЕТ-ПРЕОБРАЗОВАНИЙ В ОБРАБОТКЕ СИГНАЛОВ НОРМАЛЬНЫХ ЭЛЕКТРОКАРДИОГРАММ

СТАТИСТИЧЕСКОЕ СРАВНЕНИЕ ВЕЙВЛЕТ-ПРЕОБРАЗОВАНИЙ В ОБРАБОТКЕ СИГНАЛОВ НОРМАЛЬНЫХ ЭЛЕКТРОКАРДИОГРАММ

Comparison of Wavelet and Shearlet Transforms for Medical Images

Comparison of Wavelet and Shearlet Transforms for Medical Images

Interdependence between Greece and other European stock markets: A comparison of wavelet and VMD copula, and the portfolio implications

The interdependence of Greece and other European stock markets and the subsequent portfolio implications are examined in wavelet and variational mode decomposition domain. In applying the decomposition techniques, we analyze the structural properties of data and distinguish between short and long term dynamics of stock market returns. First, the GARCH-type models are fitted to obtain the standardized residuals. Next, different copula functions are evaluated, and based on the conventional information criteria and time varying parameter, Joe–Clayton copula is chosen to model the tail dependence between the stock markets. The short-run lower tail dependence time paths show a sudden increase in comovement during the global financial crises. The results of the long-run dependence suggest that European stock markets have higher interdependence with Greece stock market. Individual country’s Value at Risk (VaR) separates the countries into two distinct groups. Finally, the two-asset portfolio VaR measures provide potential markets for Greece stock market investment diversification.

Compression of Satellite Imagery Sequences Using Wavelet for Detection of Natural Disaster

Indonesia, geographically and geologically, is potentially encounter natural disasters. One of the tools used to early detect disaster is sensor of ocean waves change, but it has drawbacks including the time difference between information/warnings obtained with the disaster event is very short, less than 30 minutes. The faster detector is required, so the time difference will be longer. For example, early detection of natural disasters information system, which can be made with the pattern recognition of satellite imagery sequences of before and during natural disaster images. This study was conducted to determine the right wavelet to compress the satellite image sequences. The compressed images will be used to perform the pattern recognition of natural disaster using artificial neural network. This study use satellite imagery sequences of tornadoes and hurricanes. The eight wavelets used are Haar, Coiflet 1, Coiflet 3, Symlet 2, Symlet 5, 1 AJS, AJS 2, and AJS 3. The test results are then compared with the compression ratio. Result of this study are the comparison of wavelet used to compress satellite imagery sequences, which is save the storage space, access time, processing time and delivery time.

Performance Comparison of Wavelets Generated from Four Different Orthogonal Transforms for Watermarking With Various Attacks

This paper proposes a watermarking technique using different orthogonal wavelet transforms like Hartley wavelet, Kekrewavelet, Slant wavelet and Real Fourier wavelet transform generated from corresponding orthogonal transform. Theseorthogonal wavelet transforms have been generated using different sizes of component orthogonal transform matrices.For example 256*256 size orthogonal wavelet transform can be generated using 128*128 and 2*2 size componentorthogonal transform. It can also be generated using 64*64 and 4*4, 32*32 and 8*8, 16*16 and 16*16 size componentorthogonal transform matrices. In this paper the focus is to compare the performance of above mentioned transformsgenerated using 128*128 and 2*2 size component orthogonal transform and 64*64 and 4*4 size component orthogonaltransform in digital image watermarking. The other two combinations are not considered as their performance iscomparatively not as good. Comparison shows that wavelet transforms generated using (128,2) combination of orthogonal transform give better performances than wavelet transforms generated using (64,4) combination of orthogonaltransformfor contrast stretching, cropping, Gaussian noise, histogram equalization and resizing attacks. Real Fourierwavelet and Slant wavelet prove to be better for histogram equalization and resizing attack respectively than DCT waveletand Walsh wavelet based watermarking presented in previous work.

Comparison of Wavelet Denoising with Independent Component Analysis Denoising in Deformation Analysis

Comparison of Wavelet Denoising with Independent Component Analysis Denoising in Deformation Analysis

Performance comparison of wavelet denoising based fast DOA estimation of MIMO OFDM system over Rayleigh Fading Channel

This paper presents a tool for the analysis, and simulation of direction-of-arrival estimation for MIMO OFDM signal over the Rayleigh fading channel. The performance of the proposed technique is tested for wavelet denoising based CYCLIC MUSIC algorithm. Simulation results demonstrate that the proposed system not only has good ability of suppressing interference, but also significantly improves the DOA estimation of the system. In this paper, it is proposed to find DOA of the received MIMO OFDM signal, and the performances are analyzed using matlab simulation by the Monte Carlo computer iteration. This paper provides a fairly complete image of the performance and statistical efficiency with QPSK signal model for coherent system at a lower SNR(18dB) and interference environment.

New Rothe-wavelet method for solving telegraph equations

In this article, we present a new competitive scheme to solve one of the most important cases in hyperbolic partial differential equations which is called telegraph equations. This method is based on Rothe's approximation in time discretisation and on the wavelet-Galerkin in the spatial discretisation. For comparison of wavelets, we use sin–cos, Legendre and Daubechies wavelets as basis in projection methods. For showing efficiency of method, a numerical experiment, for which the exact solution is known, is considered.

Performance comparison of wavelet based and conventional OFDM systems in multipath Rayleigh fading channels

In this work, the performance of wavelet based OFDM (WOFDM) is studied and compared to that of conventional OFDM over multipath Rayleigh fading channels with exponential power delay profile. Results show that WOFDM has better bit error rate (BER) performance than conventional OFDM without cyclic prefix (CP) for all signal-to-noise ratios (SNRs) and also outperforms OFDM with CP for low SNR values in indoor and outdoor environments. Therefore, WOFDM might be an alternative to conventional OFDM since it also has better bandwidth efficiency.

OP27.09: Comparison of wavelet and conventional analysis of uterine artery Doppler waveforms in pre‐eclampsia screening

OP27.09: Comparison of wavelet and conventional analysis of uterine artery Doppler waveforms in pre‐eclampsia screening

Intelligent Hybrid Wavelet Models for Short-Term Load Forecasting

A wavelet decomposition based load forecast approach is proposed for 24-h and 168-h ahead short-term load forecasting. The proposed approach is applied to and compared with representative load forecasting methods such as: time series in traditional approaches and RBF neural network and neuro-fuzzy forecaster in nontraditional approaches. The other forecasters, such as multiple linear regression (MLR), time series, feed forward neural network (FFNN), radial basis function neural network (RBFNN), clustering, and fuzzy inference neural network (FINN), reported in the literature are also compared with the present approach. The process of the proposed wavelet decomposition approach is that it first decomposes the historical load and weather variables into an approximate part associated with low frequencies and several detail parts associated with high frequencies components through the wavelet transform. The historical data are smoothened by deleting the high frequency components and fed as input to the proposed models for the prediction. A comparison of wavelet and non-wavelet based approaches shows the superiority of proposed wavelet based approach over non-wavelet methods for the same set of data of the same utility.

A comparison of wavelet and curvelet for breast cancer diagnosis in digital mammogram

This paper presents a comparative study between wavelet and curvelet transform for breast cancer diagnosis in digital mammogram. Using multiresolution analysis, mammogram images are decomposed into different resolution levels, which are sensitive to different frequency bands. A set of the biggest coefficients from each decomposition level is extracted. Then a supervised classifier system based on Euclidian distance is constructed. The performance of the classifier is evaluated using a 2×5-fold cross validation followed by a statistical analysis. The experimental results suggest that curvelet transform outperforms wavelet transform and the difference is statistically significant.

Comparison of Wavelet and Short Time Fourier Transform Methods in the Analysis of EMG Signals

The electromyographic (EMG) signal observed at the surface of the skin is the sum of thousands of small potentials generated in the muscle fiber. There are many approaches to analyzing EMG signals with spectral techniques. In this study, the short time Fourier Transform (STFT) and wavelet transform (WT) were applied to EMG signals and coefficients were obtained. In these studies, MATLAB 7.01 program was used. According to obtained results, it was determined that WT is more useful than STFT in the fields of eliminating of resolution problem and providing of changeable resolution during analyze.

A comparison of wavelet, ridgelet, and curvelet-based texture classification algorithms in computed tomography

The research presented in this article is aimed at the development of an automated imaging system for classification of normal tissues in medical images obtained from computed tomography (CT) scans. This article focuses on comparing the discriminating power of several multi-resolution texture analysis techniques using wavelet, ridgelet, and curvelet-based texture descriptors. The approach consists of two steps: automatic extraction of the most discriminative texture features of regions of interest and creation of a classifier that automatically identifies the various tissues. The algorithms are extensively tested and results are compared with standard texture classification algorithms. Tests indicate that using curvelet-based texture features significantly improves the classification of normal tissues in CT scans.

Comparison of wavelets for multiresolution motion estimation

The performance of various wavelets, including those known to be well suited for the coding of still images, has been evaluated for the multiresolution motion estimation of video sequences. The multiresolution motion estimation scheme proposed by Zhang and Zafar, which has been widely cited in the literature, is used as the simulation scheme in this study. In our study, the prediction mean square error in the wavelet transform coefficient domain is used as the measure for prediction performance. In order to show the overall rate distortion performance, the number of bits needed to encode the motion vectors is also calculated. Simulation results show that the 7/9 biorthogonal wavelet, one of the best wavelets for the coding of still images, is the best wavelet for the task of multiresolution motion estimation among the wavelets evaluated in this study.

Comparison ofWavelet and PCA Compression Methods for Spectral Images

In this paper, we study and compare wavelet and PCA compression methods for spectral images. By spectral images we mean any kind of multi-, hyper-, or ultraspectral images. In our study, we handle spectral images by channels or slices. We clarify advantages and disadvantages of wavelet based compression methods. If spectra of spectral images are smooth then wavelets can compress the spectral images quite well. We use two dimensional wavelet transforms for both spatial directions and spectrum directions. We calculate the transform using different wavelets and try several threshold values and techniques. The tests were done using 14 different natural spectral images from our spectral image database and the results were compared to each other. In judging the quality of a compression method we used three different criteria: compression time, error (measured in various ways) and compression ratio.