Application Of Discrete Wavelet Transform Research Articles

Hydroclimatic Trends and Streamflow Response to Recent Climate Change: An Application of Discrete Wavelet Transform and Hydrological Modeling in the Passaic River Basin, New Jersey, USA

The exigency of the current climate crisis demands a more comprehensive approach to addressing location-specific climate impacts. In the Passaic River Basin (PRB), two bodies of research—hydroclimatic trend detection and hydrological modeling—have been conducted with the aim of revealing the basin’s hydroclimate patterns as well as the hydrologic response to recent climate change. In a rather novel application of the wavelet transform tool, we sidelined the frequently used Mann–Kendal (MK) trend test, to identify the hidden monotonic trends in the inherently noisy hydroclimatic data. By this approach, the use of MK trend test directly on the raw data, whose results are almost always ambiguous and statistically insignificant in respect of precipitation data, for instance, no longer poses a challenge to the reliability of trend results. Our results showed that, whereas trends in temperature and precipitation are increasing in the PRB, streamflow trends are decreasing. Based on results from the hydrological modeling, streamflow is more sensitive to actual evapotranspiration (ET) than it is to precipitation. In periods spanning decades with sufficient water availability, energy governs actual evapotranspiration rates, rendering streamflow more sensitive to increases in precipitation. Conversely, during meteorologically stressed decades, water availability dictates actual evapotranspiration, consequently amplifying streamflow sensitivity to fluctuations in actual evapotranspiration. We found that the choice of baseline condition constitutes an important source of uncertainty in the sensitivities of streamflow to precipitation and evapotranspiration changes and should routinely be considered in any climate impact assessment.

Drought trend and variability based wavelet transform in Euphrates-Tigris Basin, Türkiye

This paper presents an analysis of trends in drought variables at 29 stations across the Euphrates-Tigris Basin over the period 1965–2022. Drought variables were calculated using the Standardized Precipitation Index (SPI) for 1-, 3-, 6- and 12-month scales. The drought values have been decomposed for these time scales through application of Discrete Wavelet Transform (DWT). The components are defined as short, medium and long periodicities. DWT-based trend analysis was computed using Mann-Kendall (MK) and Innovative Trend Analysis (ITA) methods. As the time scale and the periodicity of drought increase, a strong increase in drought occurs. It was revealed that drought increased significantly at all time scales in almost all stations, especially in the long periodicity, according to both MK and ITA. The droughts and the serious increasing trend of droughts in this basin constitute a significant threat to water resources.

Application of Discrete Wavelet Transform and Chebyshev Neural Network for Differential Protection of Indirect Symmetrical Phase Shift Transformer

The Indirect Symmetrical Phase Shift Transformer (ISPST) stands out from a power transformer due to its combination of electrically connected and magnetically coupled circuits. Hence in this work, an intelligent differential protection algorithm, based on Discrete Wavelet Transform (DWT) and Chebyshev Neural Network (ChNN), is proposed as main classifier to discriminate internal fault and inrush. Half cycle thee phase post fault differential current is considered for the proposed algorithm. PSCAD/EMTDC software is utilized to simulate different operating conditions of ISPST, resulting in the simulation of a significant amount of internal faults and inrush cases. The algorithm under consideration has undergone extensive evaluation across numerous cases, resulting in an accuracy rate exceeding 99%. The results indicate that the classifier based on DWT - ChNN yields extremely promising results, even when dealing with a noisy signal, current transformer (CT) saturation, and varying ISPST ratings. Superiority of the proposed algorithm is also compared with Multilayer Perceptron (MLP), Radial Basis Function Neural Network (RBFNN) and Probabilistic Neural Network (PNN) based approaches under the same conditions and it is found that proposed classifier is the most efficient and rapid among all alternative classifiers for the differential protection scheme of an ISPST under the considered conditions.

Application of Discrete Wavelet Transform for Condition Monitoring and Fault Detection in Wind Turbine Blades: An Experimental Study

Application of Discrete Wavelet Transform for Condition Monitoring and Fault Detection in Wind Turbine Blades: An Experimental Study

The Application Of Discrete Wavelet Transform In Medical Image Compression

The Application Of Discrete Wavelet Transform In Medical Image Compression

AN ENHANCED IMAGE STEGANOGRAPHY TECHNIQUE FOR HIGH-SECURITY COMMUNICATION

Purpose of the study: We propose an approach to hide data in an image with minimum Mean Squared Error (MSE) and maximum Signal-to-Noise ratio (SNR) using Discrete Wavelet Transform (DWT).
 Methodology: The methodology used by us considers the application of Discrete Wavelet transform to transform the values of the image into a different domain for embedding the information to be hidden in the image and then using Singular Value decomposition we decomposed the matrix values of the image for better data hiding.
 Main Findings: The application of the SVD function gave the model a better performance and also RED pixel values with the High-High frequency domain are a better cover for hiding data.
 Applications of this study: This article can be used for further research on applications of mathematical and frequency transformation functions on data hiding. It can also be used to implement a highly secure image steganography model.
 Novelty/Originality of this study: The application of Discrete Wavelet Transform has been used before but the application of SVD and hiding data in the H-H domain to obtain better results is original.

Application of Discrete Wavelet Transform in Shapelet-Based Classification

Recently, several shapelet-based methods have been proposed for time series classification, which are accomplished by identifying the most discriminating subsequence. However, for time series datasets in some application domains, pattern recognition on the original time series cannot always obtain ideal results. To address this issue, we propose an ensemble algorithm by combining time frequency analysis and shape similarity recognition of time series. Discrete wavelet transform is used to decompose the time series into different components, and the shapelet features are identified for each component. According to the different correlations between each component and the original time series, an ensemble classifier is built by weighted majority voting, and the Monte Carlo method is used to search for optimal weight vector. The comparative experiments and sensitivity analysis are conducted on 25 datasets from UCR Time Series Classification Archive, which is an important open dataset resource in time series mining. The results show the proposed method has a better performance in terms of accuracy and stability than the compared classifiers.

The Application of Discrete Wavelet Transform with Improved Partial Least-Squares Method for the Estimation of Soil Properties with Visible and Near-Infrared Spectral Data

This study evaluated whether wavelet functions (Bior1.3, Bior2.4, Db4, Db8, Haar, Sym4, and Sym8) and decomposition levels (Levels 3–8) can estimate soil properties. The analysis is based on the discrete wavelet transform with partial least-squares (DWT–PLS) method, incorporated into a visible and near-infrared reflectance analysis. The improved DWT–PLS method (called DWT–Stepwise-PLS) enhances the accuracy of the quantitative analysis model with DWT–PLS. The cation exchange capacity (CEC) was best estimated by the DWT–PLS model using the Haar wavelet function. This model yielded the highest coefficient of determination (Rv2 = 0.787, p < 0.001), with the highest relative percentage deviation (RPD = 2.047) and lowest root mean square error (RMSE = 4.16) for the validation data set of the CEC. The RPD of the SOM predictions by DWT–PLS using the Bior1.3 wavelet function was maximized at 1.441 (Rv2 = 0.642, RMSE = 5.96), highlighting the poor overall predictive ability of soil organic matter (SOM) by DWT–PLS. Furthermore, the best performing decomposition levels of the wavelet function were distributed in the fifth, sixth, and seventh levels. For various wavelet functions and decomposition levels, the DWT–Stepwise-PLS method more accurately predicted the quantified soil properties than the DWT–PLS model. DWT–Stepwise-PLS using the Haar wavelet function remained the best choice for quantifying the CEC (Rv2 = 0.92, p < 0.001, RMSE = 4.91, and RPD = 3.57), but the SOM was better predicted by DWT–Stepwise-PLS using the Bior2.4 wavelet function (Rv2 = 0.8, RMSE = 5.34, and RPD = 2.24) instead of the Bior1.3 wavelet function. However, the performance of the DWT–Stepwise-PLS method tended to degrade at high and low decomposition levels of the DWT. These degradations were attributed to a lack of sufficient information and noise, respectively.

Numerical and experimental diagnosis of complex rotor system by time-frequency techniques

This paper describes the application of Discrete Wavelet Transform (DWT) to identify various types of nonlinear damage caused by, unbalance, rotor-stator contact and a breathing crack in rotating machinery. Multiple faults have been investigated based on numerical and experimental signal analysis using Fast Fourier Transform (FFT) and DWT. A four degree of freedom fully coupled model of the rotor-stator system that includes the nonlinear damage in the rotor vibrations was established using Energy principles. Existence of high system nonlinearity could not allow exhaustive discrimination of rub and crack by classical FFT. Therefore, the DWT was employed. The results provide detailed feature analysis of the fault signals. Practical vibration measurements through a data acquisition system interfaced with Rotor Kit-4 and crack simulator provided the test data. Experimental Time-Frequency analysis gave more realistic faults responses with variable faults features. Irregularity of orbit, harmonic peaks in the presence of rub and crack were unique and distinguished periodic motion from other types of motion. The presence of a crack shifted the critical speed location and exhibited sub-harmonic components, which were more prominent with rub in vibration response. The detailed decomposition signal by DWT method established inherent feature patterns that effectively discriminated the multiple faults.

Mining Data of Noisy Signal Patterns in Recognition of Gasoline Bio-Based Additives using Electronic Nose

Abstract The paper analyses the distorted data of an electronic nose in recognizing the gasoline bio-based additives. Different tools of data mining, such as the methods of data clustering, principal component analysis, wavelet transformation, support vector machine and random forest of decision trees are applied. A special stress is put on the robustness of signal processing systems to the noise distorting the registered sensor signals. A special denoising procedure based on application of discrete wavelet transformation has been proposed. This procedure enables to reduce the error rate of recognition in a significant way. The numerical results of experiments devoted to the recognition of different blends of gasoline have shown the superiority of support vector machine in a noisy environment of measurement.

Chaotic Image Encryption Technique based on IDEA and Discrete Wavelet Transformation

Objective: To provide secure image to the user within less processing time. Methods: Due to increase in multimedia traffic the intruders become more active to misuse the personal data including images. Therefore it is important to protect the images being shared. Hence in this research, image is encrypted using International Data Encryption Algorithm (IDEA) known as A-IDEA. First the image is divided into different parts and then DWT is applied. Application of Discrete Wavelet Transformation (DWT) results in confusion of pixels i.e. pixels get jumbled due to which it is impossible to depict the original image. Results: This procedure increases security of the image transmission and helps to reduce the computational delay. Conclusion: A new algorithm based on DWT and IDEA for image encryption is proposed which results in less computational and processing delay.

Application of Discrete Wavelet Transform with Changing Presentation of Coefficients in Data Compression in Mobile ECG Monitoring Systems

The article studies the problem of compression of data recorded by ECG sensors of mobile heart activity monitoring systems. An ECG data compression algorithm is presented which is based on transformation of coefficients of a discrete wavelet transform implemented using a computing device with limited processing power and low power consumption. The algorithm was tested on real signals and demonstrated its effectiveness.

On the Design and Performance Evaluation of DWT based Compressed Speech Transmission with Convolutional Coding

With the recent growth and advancement in multimedia based applications, many new techniques capable of producing good quality of compressed speech have been developed. Thus over the past few years there has been enough work done on compression and enhancement of speech signals. In this paper, the application of Discrete Wavelet Transform for speech compression along with the Convolutional Coding for error detection and correction has been described. A convolutionally encoded 8-DPSK modulated bit stream is transmitted through an AWGN channel. At the decoder the received binary bit stream is demodulated and decoded using Viterbi decoder. The compression is performed using Db10 Wavelets with Hard and Soft Thresholding algorithms. To evaluate the performance of the proposed technique, original and the reconstructed signal at the decoder are compared and various performance parameters in terms of Mean Square Error, Peak signal to Noise Ratio, Retained Signal Energy and Compression Ratio have been calculated. From the results obtained, it is observed that speech compression using Discrete Wavelet Transform along with Convolution Coding, shows better performance in terms of PSNR and MSE (high PSNR and low MSE) as compared to speech compression without convolution Coding. The proposed technique has a lot of scope in wireless communications where bandwidth and Quality of Service (QOS) are two main important factors that are taken into considerations. General Terms Speech compression, discrete wavelet transform, convolutional coding, hard thresholding, soft thresholding

Unsupervised machine-learning method for improving the performance of ambulatory fall-detection systems

BackgroundFalls can cause trauma, disability and death among older people. Ambulatory accelerometer devices are currently capable of detecting falls in a controlled environment. However, research suggests that most current approaches can tend to have insufficient sensitivity and specificity in non-laboratory environments, in part because impacts can be experienced as part of ordinary daily living activities.MethodWe used a waist-worn wireless tri-axial accelerometer combined with digital signal processing, clustering and neural network classifiers. The method includes the application of Discrete Wavelet Transform, Regrouping Particle Swarm Optimization, Gaussian Distribution of Clustered Knowledge and an ensemble of classifiers including a multilayer perceptron and Augmented Radial Basis Function (ARBF) neural networks.ResultsPreliminary testing with 8 healthy individuals in a home environment yields 98.6% sensitivity to falls and 99.6% specificity for routine Activities of Daily Living (ADL) data. Single ARB and MLP classifiers were compared with a combined classifier. The combined classifier offers the greatest sensitivity, with a slight reduction in specificity for routine ADL and an increased specificity for exercise activities. In preliminary tests, the approach achieves 100% sensitivity on in-group falls, 97.65% on out-group falls, 99.33% specificity on routine ADL, and 96.59% specificity on exercise ADL.ConclusionThe pre-processing and feature-extraction steps appear to simplify the signal while successfully extracting the essential features that are required to characterize a fall. The results suggest this combination of classifiers can perform better than MLP alone. Preliminary testing suggests these methods may be useful for researchers who are attempting to improve the performance of ambulatory fall-detection systems.

Plasma Plume Oscillations Monitoring during Laser Welding of Stainless Steel by Discrete Wavelet Transform Application

The plasma optical radiation emitted during CO2 laser welding of stainless steel samples has been detected with a Si-PIN photodiode and analyzed under different process conditions. The discrete wavelet transform (DWT) has been used to decompose the optical signal into various discrete series of sequences over different frequency bands. The results show that changes of the process settings may yield different signal features in the range of frequencies between 200 Hz and 30 kHz. Potential applications of this method to monitor in real time the laser welding processes are also discussed.

Electrical monitoring for fault detection in an EMA

The concept of a More Electric Aircraft (MEA) leads, among other actions, to the substitution of hydraulic-based actuators for Electro-Mechanical Actuators (EMA). Removal of the engine hydraulic pumps requires fully-operative electrical power actuators and mastery of the flight control architecture. However, unexpected faults and lack of safety hinder the massive use of EMAs in flight control actuators and force the development of new systems and methods for supervision in aircrafts actuators. On-line supervision of the motor drive, by means of electrical signature and harmonics analysis, can detect motor faults in its most preliminary state, thus allowing increasing the EMA reliability. However, for typical non-stationary conditions in EMA operation, classical Fourier transform cannot be applied, and other time-frequency decompositions must be explored. This presents an application of Discrete Wavelet Transform to electrical and mechanical Permanent Magnet Synchronous Motor fault detection. Experimental results are shown and prove that affordable Predictive Testing and Inspection technologies can be used to assure the reliability of the new EMAs.

Reconstruction of surface velocity field using wavelet transformation and boundary-element method

This paper shows the application of discrete wavelet transformation (DWT) to inverse acoustics for reconstructing 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 to reconstruct the normal surface velocity field. The reconstructed velocity field can be diverged due to the small measurement errors in the case of nearfield acoustic holography using an inverse boundary-element method. In order to bypass the 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 DWT is applied to reduce the computation time for inverse acoustics and to enhance the reconstructed surface velocity field. The computational speed-up is achieved, with solution time being reduced to 14.3 per cent.

Application of Discrete Wavelet Transform in the Analysis of Nonlinear Chemical System

Oscillations of species concentration are phenomena accompanying several types of electrochemical and purely chemical processes. Importance of analyzing of such processes results from both scientific and practical reasons. In the paper the authors deal with investigation of time dependent spectra of various stages of Belousov-Zhabotinsky type chemical oscillations. As the analytical tool the discrete wavelet transform is proposed.

Application of Discrete Wavelet Transform in a Combustion Chamber to Turbulence Measurement with LDV (Removing Noise in Measurement Data Using Wavelet Shrinkage)

In this study, the influence of the measurement noise in LDV measurements were considered, and how to remove the noise using Complex Discrete Wavelet Transform (CDWT) was proposed. Then turbulent velocity in the combustion chamber measured with an LDV was analyzed. The major results can be summarized as follows : 1) The influence of the noise in the turbulent velocity becomes large as frequency increases. Therefore, a setup of cutoft frequency is difficult when removing the noise with a lowpass filter. 2) In this case, it is effective to remove the noise using the CDWT because of improvement of translation invariance.