Wavelet-based Multiresolution Analysis Research Articles

A MODWT-Based Algorithm for the Identification and Removal of Jumps/Short-Term Distortions in Displacement Measurements Used for Structural Health Monitoring

Researchers have made substantial efforts to improve the measurement of structural reciprocal motion using radars in the last years. However, the signal-to-noise ratio of the radar’s received signal still plays an important role for long-term monitoring of structures that are susceptible to excessive vibration. Although the prolonged monitoring of structural deflections may provide paramount information for the assessment of structural condition, most of the existing structural health monitoring (SHM) works did not consider the challenges to handle long-term displacement measurements when the signal-to-noise ratio of the measurement is low. This may cause discontinuities in the detected reciprocal motion and can result in wrong assessments during the data analyses. This paper introduces a novel approach that uses a wavelet-based multi-resolution analysis to correct short-term distortions in the calculated displacements even when previously proposed denoising techniques are not effective. Experimental results are presented to validate and demonstrate the feasibility of the proposed algorithm. The advantages and limitations of the proposed approach are also discussed.

Wavelet-based multiresolution analysis of cavitation-induced loading instabilities under phase effect in a waterjet pump

As observed experimentally and numerically, the blade cavitation in a waterjet pump presents multi-temporal-spatial scales under the inlet guide vanes (IGVs) wake perturbations. Meanwhile, the phase effects on blade cavitation are specifically identified and confirmed to contribute to loadings and force spectrum instabilities. In light of this, the state-of-the-art multiresolution-proper orthogonal decomposition (MRPOD) and dynamic mode decomposition (MRDMD) are employed to separate the cavitating flow scales regarding to tonal- and broadband-spectrum (TS and BS respectively) to perform a space-time-frequency analysis. The results show that the surface cavitation on the adjacent blades globally evolves in a distinct way due to the phase difference between IGVs and rotor blades. As resolved by MRPOD, the TS oscillations are mainly arising around where cavitation dynamically develops, which is majorly attributable to the rotor-stator interference (RSI). The BS oscillations are in general emerging closing to hub regimes wherein usually deviate from the design operating point and perform more flow instabilities. Comparatively, the MRDMD identifies a frequency-jittering spectrum that slightly oscillates around the IGVs passing frequency, which is physically triggered by the cavitation-enhanced irregular loading impingements. The MRDMD relevant TS modes perform similar tendencies as MRPOD but alternatively oscillate with time around the leading edge due to the initial phase difference. The studies on pump cavitation dynamics accounting phase effects via multiresolution analysis (MRA) aim to gain a fundamental understanding of the cavitation-induced vibration to facilitate its control.

Wavelet-based multiresolution analysis coupled with deep learning to efficiently monitor cracks in concrete

This paper proposes an efficient methodology to monitor the formation of cracks in concrete after non-destructive ultrasonic testing of a structure. The objective is to be able to automatically detect the initiation of cracks early enough, i.e. well before they are visible on the concrete surface, in order to implement adequate maintenance actions on civil engineering structures. The key element of this original approach is the wavelet-based multiresolution analysis of the ultrasonic signal received from a sample or a specimen of the studied material subjected to several types of solicitation. This analysis is finally coupled to an automatic identification scheme of the types of cracks based on artificial neural networks (ANNs), and in particular deep learning by convolutional neural networks (CNNs); a technology today at the cutting edge of machine learning, in particular for all applications of pattern recognition. Wavelet-based multiresolution analysis does not add any value in detecting fractures in concrete visible by optical inspection. However, the results of its implementation coupled with different CNN architectures show cracks in concrete can be identified at an early stage with a very high accuracy, i.e. around 99.8%, and a loss function of less than 0.1, regardless of the implemented learning architecture.

Simulation of response spectrum-compatible ground motions using wavelet-based multi-resolution analysis

This study proposed a simple and effective response spectrum-compatible ground motions simulation method to mitigate the scarcity of ground motions on seismic hazard analysis base on wavelet-based multi-resolution analysis. The feasibility of the proposed method is illustrated with two recorded ground motions in El Mayor-Cucapah earthquake. The results show that the proposed method enriches the ground motions exponentially. The simulated ground motions agree well with the attenuation characteristics of seismic ground motion without modulating process. Moreover, the pseudo-acceleration response spectrum error between the recorded ground motion and the average of the simulated ground motions is 5.2%, which fulfills the requirement prescribed in Eurocode 8 for artificially simulated ground motions. Besides, the cumulative power spectra between the simulated and recorded ground motions agree well on both high- and low-frequency regions. Therefore, the proposed method offers a feasible alternative in enriching response spectrum-compatible ground motions, especially on the regions with insufficient ground motions.

Effects of Heat Treatment of Selective Laser Melting Printed Ti-6Al-4V Specimens on Surface Texture Parameters and Cell Attachment

Selective laser melting (SLM) is extensively used for fabricating metallic biomedical products. After 3D printing, it is almost always advisable to apply a heat treatment to release the internal tensions or optimize the mechanical properties of the printed parts. The aim of this paper is to investigate the effects of heat treatment of SLM printed Ti-6Al-4V (Ti64) circular specimens on the areal surface texture parameters and cell attachment. Areal surface texture parameters, including the arithmetic mean height (Sa), root-mean-square height (Sq), skewness (Ssk), and kurtosis (Sku) were characterized. In addition, wavelet-based multi-resolution analysis was applied to investigate the characteristic length scales of untreated and heat-treated Ti64 specimens. In this study, the vertical distance between the highest and lowest position of cell attachment for each sampling area was defined as ΔH. Results showed that an increase in the periodic characteristic length scale was primarily due to the formation of large-scale aggregations of Ti64 metal powder particles on the heat-treated surface. In addition, MG-63 cells preferred lying in concave hollows; in heat-treated specimens, values of ΔH statistically significantly decreased from 31.6 ± 4.2 to 8.8 ± 2.8 μm, while Sku decreased from 3.3 ± 1.4 to 2.6 ± 0.6, indicating a strong influence of Sku on cell attachment.

Open-Circuit Fault Diagnosis in Power Inverters Through Currents Analysis in Time Domain

Power inverters are essential components in modern electrical distribution systems. Switches are key elements of a power inverter, and they are the more affected components when a fault occurs. Breakdown of power-semiconductor devices represents around 38% of a power inverter fault. An open-circuit fault will keep the inverter working, jeopardizing its components and degrading its performance. The diagnosis and classification of power inverter faults have become important research subjects to avoid malfunctioning or damage of other components in an electric system. The Fourier transform and the wavelet-based multiresolution analysis (MRA) are the most applied signal processing techniques for identifying open-circuit faults in these devices. They are usually combined with other methods for increasing its effectiveness, which demands a considerable use of computational resources. This work introduces a novel low-computational-cost technique that directly analyzes the electric phase currents from a power inverter, in the time domain, utilizing three rotatory reference systems and basic arithmetic operations to identify open-circuit faults. The proposed method allows determining the specific power supply phase and the faulty switch, or switches, with high certainty. The obtained results are validated through exhaustive analyses against the Fourier transform and the wavelet-based MRA, demonstrating the proposed method suitability for online-monitoring applications, ensuring high sensitivity and accuracy on detecting and isolating open-circuit faults with low computation cost.

Spatiotemporal variability of solar radiation introduced by clouds over Arctic sea ice

Abstract. The role of clouds in recent Arctic warming is not fully understood, including their effects on the solar radiation and the surface energy budget. To investigate relevant small-scale processes in detail, the intensive Physical feedbacks of Arctic planetary boundary layer, Sea ice, Cloud and AerosoL (PASCAL) drifting ice floe station field campaign was conducted during early summer in the central arctic. During this campaign, the small-scale spatiotemporal variability of global irradiance was observed for the first time on an ice floe with a dense network of autonomous pyranometers. A total of 15 stations were deployed covering an area of 0.83 km×1.59 km from 4–16 June 2017. This unique, open-access dataset is described here, and an analysis of the spatiotemporal variability deduced from this dataset is presented for different synoptic conditions. Based on additional observations, five typical sky conditions were identified and used to determine the values of the mean and variance of atmospheric global transmittance for these conditions. Overcast conditions were observed 39.6 % of the time predominantly during the first week, with an overall mean transmittance of 0.47. The second most frequent conditions corresponded to multilayer clouds (32.4 %), which prevailed in particular during the second week, with a mean transmittance of 0.43. Broken clouds had a mean transmittance of 0.61 and a frequency of occurrence of 22.1 %. Finally, the least frequent sky conditions were thin clouds and cloudless conditions, which both had a mean transmittance of 0.76 and occurrence frequencies of 3.5 % and 2.4 %, respectively. For overcast conditions, lower global irradiance was observed for stations closer to the ice edge, likely attributable to the low surface albedo of dark open water and a resulting reduction of multiple reflections between the surface and cloud base. Using a wavelet-based multi-resolution analysis, power spectra of the time series of atmospheric transmittance were compared for single-station and spatially averaged observations and for different sky conditions. It is shown that both the absolute magnitude and the scale dependence of variability contains characteristic features for the different sky conditions.

Time-varying impact of snow depth on tourism in selected regions

This study uses a time-varying model that provides new evidence on the changing relationship between domestic overnight stays of selected winter sport destinations and natural snow conditions. A Kalman filter method combined with wavelet-based multiresolution analysis (MRA) is employed to investigate the relationships in intervals between 2 and 4 and up to 16–32 months. The model is applied to domestic overnight stays for selected mountain regions in Sweden (Dalarna and Jämtland), Norway (Buskerud, Hedmark, Hordaland and Oppland) and Austria (Salzburg and Tyrol). Results show that the sensitivity of domestic overnight stays on natural snow conditions varies markedly depending on location, time period and frequency band window used in the estimation. The medium-run relation for Tyrol and Salzburg is significantly declining over time, while in Norway and Sweden, the same relationship is generally volatile and not significant at the end of the sample period. In the short run, none of the regions exhibits a link between domestic overnight stays and snow depth fluctuations.

Combining ensemble Kalman filter and multiresolution analysis for efficient assimilation into adaptive mesh models

A new approach is developed for data assimilation into adaptive mesh models with the ensemble Kalman filter (EnKF). The EnKF is combined with a wavelet-based multiresolution analysis (MRA) scheme to enable robust and efficient assimilation in the context of reduced-complexity adaptive spatial discretization. The wavelet representation of the solution enables the use of different meshes that are individually adapted to the corresponding members of the EnKF ensemble. The analysis step of the EnKF is then performed by involving coarsening, refinement, and projection operations on its ensemble members. Depending on the choice of these operations, five variants of the MRA-EnKF are introduced, and tested on the one-dimensional Burgers equation with periodic boundary condition. The numerical results suggest that, given an appropriate tolerance value for the coarsening operation, four out of the five proposed schemes significantly reduce the computational complexity of the assimilation system, with marginal accuracy loss compared to the reference, full resolution, and EnKF solution. Overall, the proposed framework offers the possibility of capitalizing on the advantages of adaptive mesh techniques, and the flexibility of choosing suitable context-oriented criteria for efficient data assimilation.

WearAuth: Wristwear-Assisted User Authentication for Smartphones Using Wavelet-Based Multi-Resolution Analysis

WearAuth: Wristwear-Assisted User Authentication for Smartphones Using Wavelet-Based Multi-Resolution Analysis

Textural segmentation of remotely sensed images using multiresolution analysis for slum area identification

ABSTRACTMany cities in developing countries are facing rapid growth of dynamic slum areas but often lack detailed information and analysis on these informal settlements. Multiresolution analysis (MRA) has been successfully used in texture analysis. Texture analysis is widely discussed in literature, but most of the methods which do not employ multiresolution strategy cannot exploit the fact that texture occurs at various spatial scales. This paper proposes a texture-based segmentation scheme using newly developed multiresolution methods for slum area identification. The proposed method is tested on remotely sensed images where textural information in terms of statistical moments and energy are extracted at various scales and in different directions with the help of curvelet and contourlet transforms. The results are compared with wavelet-based MRA method of segmentation. Accuracy assessment is performed for segmented images, and comparative analysis is carried out in terms of class-wise and overall accuracies. It is found that the proposed method shows better class-discriminating power as compared to existing methods and overall classification accuracy of 91.4–95.4%.

A Distributed Flow Correlation Attack to Anonymizing Overlay Networks Based on Wavelet Multi-resolution Analysis

Government agencies rely more and more heavily on the availability of flexible and intelligent solutions for the interception and analysis of Internet-based telecommunications. Unfortunately, the global lawful interception market has been recently put into a corner by the emerging sophisticated encryption, obfuscation and anonymization technologies provided by modern overlay communication infrastructures. To face this challenge, this work proposes a novel strategy for defeating the anonymity of traffic flows, collected within and at the exit of these anonymizing networks, relying on distributed flow-capture, characterization and correlation attacks driven by wavelet-based multi-resolution analysis. Such a strategy, starting from a properly formalized attack model, results in an effective and promising framework that can be easily deployed on real-life network equipment and can potentially scale by working according to different distribution/parallelization scenarios.

Wavelet Based Video Denoising using Probabilistic Models

Wavelet based image processing techniques do not strictly follow the conventional probabilistic models that are unrealistic for real world images. However, the key features of joint probability distributions of wavelet coefficients are well captured by HMT (Hidden Markov Tree) model. This paper presents the HMT model based technique consisting of Wavelet based Multiresolution analysis to enhance the results in image processing applications such as compression, classification and denoising. The proposed technique is applied to colored video sequences by implementing the algorithm on each video frame independently. A 2D (Two Dimensional) DWT (Discrete Wavelet Transform) is used which is implemented on popular HMT model used in the framework of Expectation-Maximization algorithm. The proposed technique can properly exploit the temporal dependencies of wavelet coefficients and their non-Gaussian performance as opposed to existing wavelet based denoising techniques which consider the wavelet coefficients to be jointly Gaussian or independent. Denoised frames are obtained by processing the wavelet coefficients inversely. Comparison of proposed method with the existing techniques based on CPSNR (Coloured Peak Signal to Noise Ratio), PCC (Pearson’s Correlation Coefficient) and MSSIM (Mean Structural Similarity Index) has been carried out in detail.The proposed denoising method reveals improved results in terms of quantitative and qualitative analysis for both additive and multiplicative noise and retains nearly all the structural contents of a video frame.

Optimized Coherent Integration-Based Radio Frequency Fingerprinting in Internet of Things

This paper focuses on the security risks in the access authentication of Internet of Things, to provide an optimized classification algorithm of radio frequency fingerprinting (RFF). The novel method is based on coherent integration, multiresolution analysis, and Gaussian support vector machine (SVM). First, we proposed a de-noising algorithm for RFF as the present performance of RFF is seriously affected by the noise. The optimized coherent integration first developed in this paper effectively improves the signal-to-noise ratio (SNR) of the waveform without increasing the number of required signals, by which a de-noising processing is performed and the identification accuracy is improved. Then a wavelet-based multiresolution analysis is applied to extract feature points in the waveform that has passed the de-noising optimizer, because the less sample points are needed for the SVM classification processing, which reduces the computational complexity of SVM compared to the classical SVM classification methods where massive sample points are necessary. Extensive experiments are performed. Simulation result shows that the optimized classification algorithm achieves a high accuracy (exceed 99%) at a relatively low SNR (0 dB), which is the best result compared with other existing methods.

A novel wavelet-based approach to characterize dynamic environmental factors controlling short-term soil surface CO2 flux: Application to a controlled CO2 release test site (EIT) in South Korea

Soil surface CO2 flux (FCO2) fluctuates complicatedly in time and space according to hydro-meteorological and atmospheric conditions as well as physicochemical and biological conditions of soil. In this study, we developed a novel approach to identify and quantitatively evaluate the driving forces (DFs) that control FCO2 fluctuations in the near-surface environment with the combined use of dynamic factor analysis (DFA) and wavelet-based multiresolution analysis (WMRA). We focused on short-term (<16 days) periodic DFs using 6-hourly data blocks observed for 107 days. The procedures were as follows: first, potential DFs (PDFs) were examined using DFA for 11 types of data (i.e., solar radiation, air temperature, relative humidity, atmospheric pressure, wind speed, soil water content, soil temperature, soil EC, water vapor, and the concentration and flux of CO2). Then, major DFs (MDFs) were identified using multiresolution correlation analysis (MODCA) and wavelet transform coherence (WTC). MODCA, with the support of WTC, quantitatively estimated the multiscale similarities and lagged phase responses between MDFs and observations including precipitation by using a scale-localized maximum correlation coefficient (rmax). Finally, the contributions of MDFs to FCO2 were evaluated by calculating the effective dynamic efficiency (Def) based on dynamic factor loadings (α) of MDFs to FCO2 and cumulative wavelet energy ratios at main time-frequency scales. As a result, four MDFs and their maximum contributions to baseline FCO2 were characterized as follows: MDF1 represented irregular precipitation (scales of 1/2 to 2 days; α = 0.38; rmax = |−0.36|; Def ≤ 7.58%) and the effect of evapotranspiration (scales of 1/2 to 2 days; α = 0.38; rmax = 0.58; Def ≤ 21.57%); MDF2 addressed seasonal precipitation (scales of 8 to 16 days; α = 0.53; rmax = 0.91; Def ≤38.33%) and the soil moisture effect (scales of 8 to 16 days; α = 0.53; rmax = 0.79; Def ≤52.86%); MDF3 and MDF4 were the surface atmospheric stability effect (scales of 8 to 16 days; α = 0.09, rmax = |−0.85|; Def ≤ 8.25%) and the soil temperature effect (scales of 8 to 16 days; α = 0.44; rmax = 0.29; Def ≤41.79%), respectively. A comparison with conventional multiple regression showed that the combined application of DFA and WMRA supplements the conventional method by considering the dynamic properties of DFs and their contributions and by analyzing correlations at multiresolution time-frequency scales. The present method is expected to better characterize MDFs for other complex physicochemical and biological interactions in the environmental system.

Memories of the Gold Foreign Exchange Market Based on a Moving V-Statistic and Wavelet-Based Multiresolution Analysis

Memory in finance is the foundation of a well-established forecasting model, and new financial theory research shows that the stochastic memory model depends on different time windows. To accurately identify the multivariate long memory model in the financial market, this paper proposes the concept of a moving V-statistic on the basis of a modified R/S method to determine whether the time series has a long-range dependence and subsequently to apply wavelet-based multiresolution analysis to study the multifractality of the financial time series to determine the initial data windows. Finally, we check the moving V-statistic estimation in wavelet analysis in the same condition; the paper selects the volatilities of the gold foreign exchange rates to evaluate the moving V-statistic. According to the results, the method of testing memory established in this paper can identify the breakpoint of the memories effectively. Furthermore, this method can provide support for forecasting returns in the financial market.

Wavelet/PSO-Based Segmentation and Marker-Less Tracking of the Gallbladder in Monocular Calibration-free Laparoscopic Cholecystectomy

This paper presents an automatic segmentation and monocular marker-less tracking method of the gallbladder in minimally invasive laparoscopic cholecystectomy intervention that can be used for the construction of an adaptive calibrationfree medical augmented reality system. In particular, the proposed method consists of three steps, namely, a segmentation of 2D laparoscopic images using a combination of photometric population-based statistical approach and edge detection techniques, a PSO-based detection of the targeted anatomical structure (the gallbladder) and, finally, the 3D model waveletbased multi-resolution analysis and adaptive 2D/3D registration. The proposed population-based statistical segmentation approach of 2D laparoscopic images differs from classical approaches (histogram thresholding), in that we consider anatomical structures and surgical instruments in terms of distributions of RGB color triples. This allows an efficient handling, superior robustness and to readily integrate current intervention information. The result of this step consists in a set of point clouds with a loosely gradient information that can cover various anatomical structures. In order to enhance both sensitivity and specificity, the detection of the targeted structure (the gallbladder) is based on a modified PSO (particles swarm optimization) scheme which maximizes both internal features density and the divergence with neighboring structures such as, the liver. Finally, a multi-particles based representation of the targeted structure is constructed, thanks to a proposed wavelet-based multi-resolution analysis of the 3D model of the targeted structure which is registered adaptively with the 2D particles generated during the previous step. Results are shown on both synthetic and real data. © 2018, (IJACSA) International Journal of Advanced Computer Science and Applications.

Multi-resolution privacy-enhancing technologies for smart metering

The availability of individual load profiles per household in the smart grid end-user domain combined with non-intrusive load monitoring to infer personal data from these load curves has led to privacy concerns. Privacy-enhancing technologies have been proposed to address these concerns. In this paper, the extension of privacy-enhancing technologies by wavelet-based multi-resolution analysis (MRA) is proposed to enhance the options available on the user side. For three types of privacy methods (secure aggregation, masking and differential privacy), we show that MRA not only enhances privacy, but also adds additional flexibility and control for the end-user. The combination of MRA and PETs is evaluated in terms of privacy, computational demands, and real-world feasibility for each of the three method types.

A multiresolution ensemble Kalman filter using the wavelet decomposition

We present a method of using classical wavelet-based multiresolution analysis to separate scales in model and observations during data assimilation with the ensemble Kalman filter. In many applications, the underlying physics of a phenomena involve the interaction of features at multiple scales. Blending of observational and model error across scales can result in large forecast inaccuracies since large errors at one scale are interpreted as inexact data at all scales due to the misrepresentation of observational error. Our method uses a partitioning of the range of the observation operator into separate observation scales. This naturally induces a transformation of the observation covariance and we put forward several algorithms to efficiently compute the transformed covariance. Another advantage of our multiresolution ensemble Kalman filter is that scales can be weighted independently to adjust each scale’s affect on the forecast. To demonstrate feasibility, we present applications to a one-dimensional Kuramoto-Sivashinsky (K–S) model with scale-dependent observation noise and an application involving the forecasting of solar photospheric flux. The solar flux application uses the Air Force Data Assimilative Photospheric Transport (ADAPT) model which has model and observation error exhibiting strong scale dependence. Results using our multiresolution ensemble Kalman filter show significant improvement in solar forecast error compared to traditional ensemble Kalman filtering.

Wavelet-based multi-scale finite element modeling and modal identification for structural damage detection

Wavelet techniques enable multi-resolution analysis that can represent a function (either field or signal function) in a multi-scale manner. This article presents a damage detection method with dynamically changed scales in both temporal and spatial domains, by taking advantage of the wavelet-based multi-resolution analysis. This method combines a wavelet-based finite element model (WFEM) that employs B-spline wavelet as shape functions and wavelet-based modal identification method to detect structural damage progressively. High-fidelity modal information can be computed or identified with minimized computation cost by lifting the wavelet scales in the wavelet-based finite element model and in signal processing individually according to the actual requirements. Numerical examples demonstrate that the accuracy of damage detection is improved considerably by this lifting strategy during the damage detection process. Besides, fewer degrees of freedom are involved in the wavelet-based finite element model than those of traditional finite element method. The computational efficiency can be improved to large extent and computation resources can be utilized more rationally using the proposed multi-scale approach.