Wavelet-based Data Research Articles

Multifield intermittency of dust storm turbulence in the atmospheric surface layer

Dust storms are typical dispersed two-phase atmospheric turbulence involving electrified charged dust particles. Previous observations have demonstrated that clean-air atmospheric turbulence is strongly intermittent. However, the intermittency of the wind velocity, concentration of dust particles with a diameter smaller than $10\ \mathrm {\mu }{\rm m}$ (PM10) and electric fields, known as multifield intermittency, has not been reported or characterized yet. Here, we quantify the small-scale multifield intermittency of dust storms using datasets obtained from the Qingtu Lake Observation Array and a wavelet-based data analysis technique. The results indicate that the probability density functions of the multifield increments are scale dependent, and the scaling exponents of the multifield structure functions exhibit anomalous scaling, suggesting that the multiple fields in dust storms are also highly intermittent. Specifically, the wind velocity during dust storms appears to be more intermittent as compared with clean-air conditions. Among the multiple fields, the small-scale intermittency is strongest for PM10 dust concentration, moderate for electric fields and weakest for wind velocity. Furthermore, the anomalous scaling of multiple fields is well described by the hierarchical structure theory of turbulence. It is theoretically predicted that the wind velocity displays a one-dimensional filamentary structure, while the PM10 dust concentration and electric fields display two-dimensional sheet-like structures. Finally, after removing the coherent components of the observed time series by the proposed wavelet conditioning statistics, Kolmogorov linear scaling is recovered for the multiple fields, suggesting that small-scale multifield intermittency is caused by the presence of small-scale coherent structures.

A Spatial-Temporal Analysis of Cellular Biopolymers on Leaf Blight-Infected Tea Plants Using Confocal Raman Microspectroscopy.

The objective of the present study was to characterize the temporal and spatial variation of biopolymers in cells infected by the tea leaf blight using confocal Raman microspectroscopy. We investigated the biopolymers on serial sections of the infection part, and four sections corresponding to different stages of infection were obtained for analysis. Raman spectra extracted from four selected regions (circumscribing the vascular bundle) were analyzed in detail to enable a semi-quantitative comparison of biopolymers on a micron-scale. As the infection progressed, lignin and other phenolic compounds decreased in the vascular bundle, while they increased in both the walls of the bundle sheath cells as well as their intracellular components. The amount of cellulose and other polysaccharides increased in all parts as the infection developed. The variations in the content of lignin and cellulose in different tissues of an individual plant may be part of the reason for the plant’s disease resistance. Through wavelet-based data mining, two-dimensional chemical images of lignin, cellulose and all biopolymers were quantified by integrating the characteristic spectral bands ranging from 1,589 to 1,607 cm–1, 1,087 to 1,100 cm–1, and 2,980 to 2,995 cm–1, respectively. The chemical images were consistent with the results of the semi-quantitative analysis, which indicated that the distribution of lignin in vascular bundle became irregular in sections with severe infection, and a substantial quantity of lignin was detected in the cell wall and inside the bundle sheath cell. In serious infected sections, cellulose was accumulated in vascular bundles and distributed within bundle sheath cells. In addition, the distribution of all biopolymers showed that there was a tylose substance produced within the vascular bundles to prevent the further development of pathogens. Therefore, confocal Raman microspectroscopy can be used as a powerful approach for investigating the temporal and spatial variation of biopolymers within cells. Through this method, we can gain knowledge about a plant’s defense mechanisms against fungal pathogens.

WaveRange: wavelet-based data compression for three-dimensional numerical simulations on regular grids

A wavelet-based method for compression of three-dimensional simulation data is presented and its software framework is described. It uses wavelet decomposition and subsequent range coding with quantization suitable for floating-point data. The effectiveness of this method is demonstrated by applying it to example numerical tests, ranging from idealized configurations to realistic global-scale simulations. The novelty of this study is in its focus on assessing the impact of compression on post-processing and restart of numerical simulations.Graphical abstract

Data-Driven Approach for Rainfall-Runoff Modelling Using Equilibrium Optimizer Coupled Extreme Learning Machine and Deep Neural Network

Rainfall-runoff (R-R) modelling is used to study the runoff generation of a catchment. The quantity or rate of change measure of the hydrological variable, called runoff, is important for environmental scientists to accomplish water-related planning and design. This paper proposes (i) an integrated model namely EO-ELM (an integration of equilibrium optimizer (EO) and extreme learning machine (ELM)) and (ii) a deep neural network (DNN) for one day-ahead R-R modelling. The proposed R-R models are validated at two different benchmark stations of the catchments, namely river Teifi at Glanteifi and river Fal at Tregony in the UK. Firstly, a partial autocorrelation function (PACF) is used for optimal number of lag inputs to deploy the proposed models. Six other well-known machine learning models, called ELM, kernel ELM (KELM), and particle swarm optimization-based ELM (PSO-ELM), support vector regression (SVR), artificial neural network (ANN) and gradient boosting machine (GBM) are utilized to validate the two proposed models in terms of prediction efficiency. Furthermore, to increase the performance of the proposed models, paper utilizes a discrete wavelet-based data pre-processing technique is applied in rainfall and runoff data. The performance of wavelet-based EO-ELM and DNN are compared with wavelet-based ELM (WELM), KELM (WKELM), PSO-ELM (WPSO-ELM), SVR (WSVR), ANN (WANN) and GBM (WGBM). An uncertainty analysis and two-tailed t-test are carried out to ensure the trustworthiness and efficacy of the proposed models. The experimental results for two different time series datasets show that the EO-ELM performs better in an optimal number of lags than the others. In the case of wavelet-based daily R-R modelling, proposed models performed better and showed robustness compared to other models used. Therefore, this paper shows the efficient applicability of EO-ELM and DNN in R-R modelling that may be used in the hydrological modelling field.

A Comparative Assessment of Metaheuristic Optimized Extreme Learning Machine and Deep Neural Network in Multi-Step-Ahead Long-term Rainfall Prediction for All-Indian Regions

Prediction of long-term rainfall patterns is a highly challenging task in the hydrological field due to random nature of rainfall events. The contribution of monthly rainfall is important in agriculture and hydrological tasks. This paper proposes two data-driven models, namely biogeography-based extreme learning machine (BBO-ELM) and deep neural network (DNN), to predict one, two, and three month-ahead rainfall over India (All-India and six other homogeneous regions). Three other data-driven models called ELM, genetic algorithm (GA)-based ELM, and particle swarm optimization (PSO)-based ELM are used to compare the performance of the proposed models. Firstly, partial autocorrelation function (PACF) is applied in all datasets to select the optimal number of lags for input to the models. Secondly, the wavelet-based data pre-processing technique is applied in selected optimal lags and feed to the proposed models for achieving higher prediction performance. To investigate the performance of proposed models, a non-parametric statistical test, Anderson–Darling’ Normality test, is performed in all India dataset. The wavelet-based proposed hybrid models show better prediction capability compared to optimal lag-based proposed models. This study shows the successful application of time-series data using proposed techniques (optimal lags-based BBO-ELM and wavelet-based DNN) in the hydrological field which may be used for risk mitigation from dreadful natural events.

Wavelet-Based Dimensionality Reduction for Multiple Sets of Complicated Functional Data

Multiple sets of complicated functional data with sharp changes have appeared in many engineering studies for such purposes as monitoring the quality and detecting faults in manufacturing processes. Some of the data curves in these studies exhibit large variations in local regions. This paper present a wavelet-based data reduction procedure to reduce high dimensional functional data from manufacuring processes. The proposed method can characterize the variations of multiple curves at certain local regions. In addition, unlike existing methods, which is based on a single curve, the method can handle with multiple curves together for the reduction of high dimensional data having distinct structures. Evaluation with real-life data sets shows that the proposed procedure performs better than several techniques extended from methods based on single-curve-based data reduction.

Surface-Enhanced Raman Spectroscopy for Monitoring Extravirgin Olive Oil Bioactive Components

Olive oil is the main fat source of the Mediterranean diet. This seasoning ingredient is highly appreciated for its unique taste, functional properties, and positive impact on human health. The determination of chemical composition is a demanding task in order to fully characterize this precious food product, ensure its quality, and prevent fraudulent practices. Among innovative techniques proposed for the oil analysis, surface-enhanced Raman spectroscopy (SERS) can be an extremely useful tool for olive oil characterization. In this frame, we have investigated five noncommercial olive oils produced in different parts of South Italy by using a commercial Raman microspectroscopy apparatus and home-made signal-enhancing SERS substrates. A wavelet-based data analysis has allowed us to efficiently remove the background and the noise from the acquired spectra. The analysis of these SERS spectra has enabled the quantification of the relative contents of carotene, oleic acid, and phenols. These relative contents differ in the examined samples. In addition, SERS response in the lipid region has indicated differences in the relative abundance of saturated fatty acids. The present results confirm the validity of the SERS technique as a rapid, nondestructive, and reliable analytical technique for identifying olive oil bioactive components.

Thermography data fusion and nonnegative matrix factorization for the evaluation of cultural heritage objects and buildings

The application of the thermal and infrared technology in different areas of research is considerably increasing. These applications involve nondestructive testing, medical analysis (computer aid diagnosis/detection—CAD), and arts and archeology, among many others. In the arts and archeology field, infrared technology provides significant contributions in terms of finding defects of possible impaired regions. This has been done through a wide range of different thermographic experiments and infrared methods. The proposed approach here focuses on application of some known factor analysis methods such as standard nonnegative matrix factorization (NMF) optimized by gradient-descent-based multiplicative rules (SNMF1) and standard NMF optimized by nonnegative least squares active-set algorithm (SNMF2) and eigen-decomposition approaches such as principal component analysis (PCA) in thermography, and candid covariance-free incremental principal component analysis in thermography to obtain the thermal features. On the one hand, these methods are usually applied as preprocessing before clustering for the purpose of segmentation of possible defects. On the other hand, a wavelet-based data fusion combines the data of each method with PCA to increase the accuracy of the algorithm. The quantitative assessment of these approaches indicates considerable segmentation along with the reasonable computational complexity. It shows the promising performance and demonstrated a confirmation for the outlined properties. In particular, a polychromatic wooden statue, a fresco, a painting on canvas, and a building were analyzed using the above-mentioned methods, and the accuracy of defect (or targeted) region segmentation up to 71.98%, 57.10%, 49.27%, and 68.53% was obtained, respectively.

Wavelet-based data compression for wide-area measurement data of oscillations

This paper proposes a wavelet-based data compression method to compress the recorded data of oscillations in power systems for wide-area measurement systems. Actual recorded oscillations and simulated oscillations are compressed and reconstructed by the wavelet-based data compression method to select the best wavelet functions and decomposition scales according to the criterion of the minimum compression distortion composite index, for a balanced consideration of compression performance and reconstruction accuracy. Based on the selections, the relationship between the oscillation frequency and the corresponding optimal wavelet and scale is discussed, and a piecewise linear model of the base-2 logarithm of the frequency and the order of the wavelet is developed, in which different pieces represent different scales. As a result, the wavelet function and decomposition scale can be selected according to the oscillation frequency. Compared with the wavelet-based data compression method with a fixed wavelet scale for disturbance signals and the real-time data compression method based on exception compression and swing door trending for oscillations, the proposed method can provide high compression ratios and low distortion rates.

The need to generate realistic strain signals at an automotive coil spring for durability simulation leading to fatigue life assessment

This study aims to accelerate fatigue tests using simulated strain signals. Strain signals were acquired from a coil spring involving car movements. Using a mathematical expression, the measured strain signals yielded acceleration signals, and were considered as disturbances on generating strain signals. The simulated strain signals gave the testing time deviation by only 1.5%. The wavelet-based data editing was applied to shorten the strain signals time up to 36.7% and reduced the testing time up to 33.9%. In conclusion, the simulated strain signals were able to maintain the majority of fatigue damage and decreased the testing time.

Modelling Animal Activity as Curves: An Approach Using Wavelet-Based Functional Data Analysis

Temporal activity patterns in animals emerge from complex interactions between choices made by organisms as responses to biotic interactions and challenges posed by external factors. Temporal activity pattern is an inherently continuous process, even being recorded as a time series. The discreteness of the data set is clearly due to data-acquisition limitations rather than a true underlying discrete nature of the phenomenon itself. Therefore, curves are a natural representation for high-frequency data. Here, we fully model temporal activity data as curves integrating wavelets and functional data analysis, allowing for testing hypotheses based on curves rather than on scalar and vector-valued data. Temporal activity data were obtained experimentally for males and females of a small-bodied marsupial and modelled as wavelets with independent and identically distributed errors and dependent errors. The null hypothesis of no difference in temporal activity pattern between male and female curves was tested with functional analysis of variance (FANOVA). The null hypothesis was rejected by FANOVA and we discussed the differences in temporal activity pattern curves between males and females in terms of ecological and life-history attributes of the reference species. We also performed numerical analysis that shed light on the regularity properties of the wavelet bases used and the thresholding parameters.

ECG compression using non-recursive wavelet transform with quality control

ABSTRACTWhile wavelet-based electrocardiogram (ECG) data compression using scalar quantisation (SQ) yields excellent compression performance, a wavelet’s SQ scheme, however, must select a set of multilevel quantisers for each quantisation process. As a result of the properties of multiple-to-one mapping, however, this scheme is not conducive for reconstruction error control. In order to address this problem, this paper presents a single-variable control SQ scheme able to guarantee the reconstruction quality of wavelet-based ECG data compression. Based on the reversible round-off non-recursive discrete periodised wavelet transform (RRO-NRDPWT), the SQ scheme is derived with a three-stage design process that first uses genetic algorithm (GA) for high compression ratio (CR), followed by a quadratic curve fitting for linear distortion control, and the third uses a fuzzy decision-making for minimising data dependency effect and selecting the optimal SQ. The two databases, Physikalisch-Technische Bundesanstalt (PTB) and Massachusetts Institute of Technology (MIT) arrhythmia, are used to evaluate quality control performance. Experimental results show that the design method guarantees a high compression performance SQ scheme with statistically linear distortion. This property can be independent of training data and can facilitate rapid error control.

Fusion of Thresholding Rules During Wavelet-Based Noisy Image Compression

The new method for combining semisoft thresholding rules during wavelet-based data compression of images with multiplicative noise is suggested. The method chooses the best thresholding rule and the threshold value using the proposed criteria which provide the best nonlinear approximations and take into consideration errors of quantization. The results of computer modeling have shown that the suggested method provides relatively good image quality after restoration in the sense of some criteria such as PSNR, SSIM, etc.

Onboard joint nondestructive despeckling and wavelet-based data compression of multilook synthetic aperture radar images using reordered spatial oriented trees

The suggested method of joint despeckling and set partitioning in hierarchical trees–based compression for degraded multilook synthetic aperture radar (SAR) images uses a fused image as a reference during compression. The fused image itself is formed from intermediate frames (looks) by the logical operation on wavelet coefficients, which are presented in the form of spatial oriented trees (SOTs) of wavelet decomposition. This decomposition is calculated for one and the same region of the Earth’s surface during SAR spacecraft flight. The high-frequency subbands (details) of the fused images contain the smallest wavelet coefficients from different SOTs. The wavelet coefficients related to the low-frequency subband (approximation) are processed by another special logical operation providing a good smoothing. Based on the improved fused image, the best bit rate (optimal operation point) is found by observing the output mean square errors and cross-correlation coefficients between the compressed and reference images. The results of computer modeling and comparison with a few well-known despeckling and compression procedures have shown the superb quality of the proposed method in the sense of different criteria such as peak signal-to-noise ratio, structural similarity, etc.

Application of a Real-Time Data Compression and Adapted Protocol Technique for WAMS

This paper proposes a real-time data compression and adapted protocol technique for wide-area measurement systems (WAMS). The compression algorithm combines exception compression (EC) with swing door trending (SDT) compression. The compression logic is designed to perform this algorithm in real time. Selection of compression parameters and data reconstruction are presented. An adapted protocol is introduced by improving the format of data frames defined by IEEE standard C37.118 for compressed data packets. The proposed compression technique and protocol were applied to the phasor measurement units (PMUs) of a hydropower plant in Guizhou Power Grid in Southwest China. A low-frequency oscillation incident was recorded by this technique. The raw, compressed and reconstructed data were analyzed to verify the compression and determine the accuracy of the proposed technique. Also, the wavelet-based data compression, the standalone EC and SDT are compared with the proposed compression technique. Our results demonstrated that this compression can reach the compression ratios in the range of 6 to 11. Also, this compression and adapted protocol technique can reduce the size of data packets by approximately 75% with high accuracy in both dynamic and steady states.

A GA-based quantisation design scheme for ECG data compression with linear distortion control

Wavelet-based ECG data compression with multi-level adjustment mechanism is disadvantageous for reconstruction error control due to multi-toone mapping between multi-level quantisation scales and distortion. Linear distortion compression performance with easy error control is crucial for quality guarantee desired in diagnosis use. In this paper, a systematic approach is proposed for wavelet-based quantisation scheme design. This approach combines a Genetic Algorithm (GA) and Three-Dimensional (3-D) curvefitting technology to form a cascaded design process. The former is used to obtain high Compression Ratio (CR) and the latter aims to design a linear distortion control. Two kinds of ECG signals saved in MIT arrhythmia and ST change databases were used for the evaluation of compression performance and distortion control. The experimental results show that the design method can obtain linear distortion characteristic with high compression performance being kept.

Determination of dry matter content of tea by near and middle infrared spectroscopy coupled with wavelet-based data mining algorithms

To explore the potential of near and middle infrared spectroscopy application in fast determination of dry matter content (DMC) of tea through the whole process from fresh tea leaf, semi-manufactured tea and to finished tea, samples from seven stages of the tea process were collect and the research was conducted based on data mining algorithms. Kubelka–Munk transform and spectral pre-treatment were adopted for elimination of disturbances caused by irregular appearance of intact tea in diffuse reflectance mode. A wavelet-based data mining algorithm composed of wavelet packet transform and statistical analysis (WPT–SA) was proposed to extract and optimize spectral feature from full-spectrum data. Another data mining algorithm of kernel principal component analysis (KPCA) was also employed for a performance comparison. Regression models were respectively established based on the full-spectrum data, wavelet spectral feature and kernel principal component. Statistical analysis revealed that the wavelet parameters (basis function and scale) were significant for these R2 and RMSE of determination model and the optimization of wavelet parameters were vital for application of WPT. Modeling results showed that the regression model based on the wavelet spectral feature outperformed the other models, and the optimal regression model obtained a high R2 of 0.9556, and a low root mean square error of 0.0501. These results indicate that it is feasible to measure DMC of tea in different processing procedures using near and middle infrared spectroscopy, and the proposed feature optimization algorithm (WPT–SA) is an effective data mining approach for enhancing the capability of spectral measurement.

Wavelet-based data and solution compression for efficient image reconstruction in fluorescence diffuse optical tomography

Current fluorescence diffuse optical tomography (fDOT) systems can provide large data sets and, in addition, the unknown parameters to be estimated are so numerous that the sensitivity matrix is too large to store. Alternatively, iterative methods can be used, but they can be extremely slow at converging when dealing with large matrices. A few approaches suitable for the reconstruction of images from very large data sets have been developed. However, they either require explicit construction of the sensitivity matrix, suffer from slow computation times, or can only be applied to restricted geometries. We introduce a method for fast reconstruction in fDOT with large data and solution spaces, which preserves the resolution of the forward operator whilst compressing its representation. The method does not require construction of the full matrix, and thus allows storage and direct inversion of the explicitly constructed compressed system matrix. The method is tested using simulated and experimental data. Results show that the fDOT image reconstruction problem can be effectively compressed without significant loss of information and with the added advantage of reducing image noise.

Multiple-view fluorescence optical tomography reconstruction using compression of experimental data

We report on the experimental demonstration of a fast reconstruction method for multiview fluorescence diffuse optical tomography by using a wavelet-based data compression. We experimentally demonstrate that the use of data compression combined with the multiview approach makes it possible to perform a fast reconstruction of high quality. A structured illumination approach, guided by the compression scheme, has been adopted to further reduce the acquisition time. The reconstruction algorithm is based on the finite element method, and hence is suitable for samples of any arbitrary shape.

A Wavelet-Based Data Compression Technique for Smart Grid

This paper proposes a wavelet-based data compression approach for the smart grid (SG). In particular, wavelet transform (WT)-based multiresolution analysis (MRA), as well as its properties, are studied for its data compression and denoising capabilities for power system signals in SG. Selection of the Order 2 Daubechies wavelet and scale 5 as the best wavelet function and the optimal decomposition scale, respectively, for disturbance signals is demonstrated according to the criterion of the maximum wavelet energy of wavelet coefficients (WCs). To justify the proposed method, phasor data are simulated under disturbance circumstances in the IEEE New England 39-bus system. The results indicate that WT-based MRA can not only compress disturbance signals but also depress the sinusoidal and white noise contained in the signals.