Wavelet Decomposition Analysis Research Articles

Reduction of lidar ranging error in turbulent water based on WT-ICA method

A new signal-processing method for the underwater lidar system based on wavelet transform (WT) and independent component analysis (ICA) is presented in this paper. Turbulence in water causes stronger scattering in underwater lidar returns, which can reduce the ranging accuracy and spatial resolution of the underwater lidar system. The method we proposed combines wavelet decomposition and independent component analysis (ICA), the data sets from wavelet decomposition are used to form the input matrix of ICA. The new method benefits from adaptive filtering of WT and blind source separation of ICA, therefore, the scattering clutters are further reduced. The WT-ICA signal processing algorithm is tested with experimental data. A mirror and a black Polyvinyl chloride (PVC) plate were used as the targets, respectively. Without using the WT-ICA, the ranging error with the mirror was 10.0 cm at 3 m distance when the attenuation coefficient of the water was 2.0 m-1. A pump was used to generate turbulence in the water tank. When the flow rate of the pump was 200 L/min, after using WT-ICA, the ranging error was reduced from 14.0 cm to 4.0 cm. For the PVC plate, the ranging errors were 4.5 cm and 19.0 cm at 6.0 attenuation length with and without the algorithm respectively. In both cases, applying this algorithm can significantly improve the ranging accuracy. When the algorithm is applied, the ranging error and detection probability of target echoes with and without turbulence were similar. These results indicated that the WT-ICA method had the ability to reduce the influence of turbulence.

Shock wave mitigation and impact resistance response of kevlar fabric with novel shear-stiffening gel core

A novel shear thickening gel with micro-scale hard/soft interface structure and low reaction temperature was proposed in this article. The gel was named TH and used as core material within Kevlar fabrics. Storage modulus of TH was proved to increase by around two orders with increasing of shear frequency, and dynamic properties indicated that TH could dissipate or store more energy. TH can adhere with Kevlar fabrics well and satisfactorily fulfill the gap between fabrics. As a result, shock wave peak pressure and positive impulse decreased markedly. Wavelet decomposition analysis show that high frequency energy was filtered by protection materials. Meanwhile, impact resistance test suggested the existence of TH changed the stress propagation routes and avoided pulling out for Kevlar fabrics. Samples filled with TH demonstrated a better shock wave mitigation ability and impact resistance. Energy dissipation structures with TH have a promising prospect in the application of defending area.

Can geopolitical risk stably predict crude oil prices? A multi-dimensional perspective

This study aims to examine the stability of the lead-lag relationship between geopolitical risk and crude oil prices. To this end, a Granger causality test is conducted, using a lag-augmented vector autoregression model and monthly data from 1997 to 2022. The results show a significant full-sample bilateral Granger causality. The recursive expanding window method and the asymmetric decomposition technique are also used to find that the relationship between geopolitical risk and crude oil prices is complex and time-varying, with asymmetry. The wavelet decomposition analysis shows that this complexity is mainly due to short-term structural changes. When these fluctuations are excluded, the impact of geopolitical risk on crude oil prices (and vice versa) no longer shows time-varying characteristics or asymmetry. This stable relationship provides a new perspective for predicting crude oil prices in the face of geopolitical risk. Given the importance of both geopolitical risk and crude oil prices in the fields of economy, finance, and politics, this research and the accompanying results have implications for informed decision-making by investors and policy makers.

Heart Rate Estimation from Facial Image Sequences of a Dual-Modality RGB-NIR Camera.

This paper presents an RGB-NIR (Near Infrared) dual-modality technique to analyze the remote photoplethysmogram (rPPG) signal and hence estimate the heart rate (in beats per minute), from a facial image sequence. Our main innovative contribution is the introduction of several denoising techniques such as Modified Amplitude Selective Filtering (MASF), Wavelet Decomposition (WD), and Robust Principal Component Analysis (RPCA), which take advantage of RGB and NIR band characteristics to uncover the rPPG signals effectively through this Independent Component Analysis (ICA)-based algorithm. Two datasets, of which one is the public PURE dataset and the other is the CCUHR dataset built with a popular Intel RealSense D435 RGB-D camera, are adopted in our experiments. Facial video sequences in the two datasets are diverse in nature with normal brightness, under-illumination (i.e., dark), and facial motion. Experimental results show that the proposed method has reached competitive accuracies among the state-of-the-art methods even at a shorter video length. For example, our method achieves MAE = 4.45 bpm (beats per minute) and RMSE = 6.18 bpm for RGB-NIR videos of 10 and 20 s in the CCUHR dataset and MAE = 3.24 bpm and RMSE = 4.1 bpm for RGB videos of 60-s in the PURE dataset. Our system has the advantages of accessible and affordable hardware, simple and fast computations, and wide realistic applications.

Machine learning of acoustic propagation models for sound aware autonomous systems

Opportunities exist for autonomous underwater vehicles to optimize acoustic communications with knowledge of the sound channel, but implementation has traditionally been hindered by computationally expensive propagation models. This work addresses computational complexity by enabling machine learning to interpret environmental inputs to predict transmission loss (TL) outputs from a physics-based ray tracing model (BELLHOP). Feature representations to reduce bathymetric and sound speed profile (SSP) information through wavelet decomposition and empirical orthogonal analysis are explored, respectively. A decision tree machine learning algorithm is trained to learn the paths (depths and ranges) of acceptable transmission loss for a pair of acoustic modems within a given environment. Our test case is off the coast of Southern California, and SSPs are collected from a 3 year record sampled from an ocean mooring. Bathymetry data is based on high resolution multibeam data. TL field realizations are calculated as training and testing data for environments of varying bathymetry with frequency of 25kHz and range of 1km through the BELLHOP model. Results are shown with stationary and non-stationary emitters to demonstrate the effectiveness of this approach.

Improving the accuracy of bathymetry using the combined neural network and gravity wavelet decomposition method with altimetry derived gravity data

The wide range of bathymetry models can be estimated using the marine gravity information derived from satellite altimetry. However, due to nonlinear factors influences such as isostasy effects, the bathymetry estimated by gravity anomaly and vertical gravity gradient is not satisfactory. Therefore, to improve the accuracy of bathymetry estimation, a combined neural network and gravity information wavelet decomposition (CNNGWD) method is proposed based on wavelet decomposition and correlation analysis. Next, the bathymetry of the Manila Trench area is estimated using the CNNGWD method and multilayer neural network (MNN) method, respectively. Then, the shipborne sounding data and international bathymetric models such as ETOPO1 and GEBCO_2021 are separately used to evaluate the accuracy of the inversion models. The results show that the root mean square errors (RMSE) of the difference between the bathymetric model one (BM1) estimated by CNNGWD method and the shipborne sounding data is 59.90 m, the accuracy is improved by 12.45%, 64.70% and 28.68% compared with the bathymetric model two (BM2) which estimated by MNN, ETOPO1 and GEBCO, respectively. Finally, by analyzing the bathymetric accuracy shift with depth, the BM1 has lower RMSE at depths ranging from 1000 m to 3000 m. Furthermore, BM1 shows dominance in flat troughs and rugged ridge regions.

Influence of boundary conditions on acoustic emission propagation characteristics of Zelkova schneideriana

To study the propagation characteristics of acoustic emission signals in Zelkova schneideriana under different boundary conditions, three types of boundary conditions were generated by applying aluminum plates and sound-absorbing cotton on the surface of Zelkova schneideriana specimens. Firstly, the sudden and continuous acoustic emission (AE) sources were simulated by PLB (pencil–lead break) tests and signal generator on the specimen surface, and the AE signals were collected by 5 sensors equally spaced on the surface of the specimen, and the sampling frequency was set to 500 kHz. Then, the detailed signals of different frequency bands were obtained by wavelet decomposition, and TDOA (the time difference of arrival) and correlation analysis method were used to calculate the time difference of longitudinal wave and surface transverse wave and the corresponding propagation velocity, respectively. Finally, the pulse trains with different energy levels generated by the signal generator were used as AE sources to study the attenuation law of AE signal energy with distance under different boundary conditions. The results show that the boundary changes can lead to a significant increase in the surface transverse wave velocity, and have no significant effect on the longitudinal wave velocity. At the same time, the energy attenuation of surface and longitudinal waves is faster after the aluminum plate and sound-absorbing cotton are affixed, and the distance of longitudinal waves attenuation to 90% is reduced from 186 to 139 mm, and the distance of surface transverse waves propagation is reduced from 312 to 226 mm.

Analysing Time-frequency Relationship between Oil price and Sectoral Indices in India using Wavelet Techniques

Oil is considered an essential factor of any economy. This paper examines the time-varying correlation between oil price return, BSE SENSEX, and 14 sectoral indexes in India using multiscale wavelet decomposition and wavelet coherence analysis. The maximal wavelet discrete wavelet transform analysis shows a feedback relationship between 13 sectors at higher time horizons (dC4, dC5, and dC6). Based on the wavelet coherence plot, the oil price and sectoral index return show a high co-movement at 32 to 128 weeks. The wavelet coherence plot shows that the oil price and sectoral index return show a high co-movement during the period of Mar-May 2020 (especially during the period of financial crisis widely spread due to the COVID19 pandemic and nationwide lockdown notification announced by the Government of India). We discuss the implications of these studies in detail.

Abstract 543: Characterization of extravascular extracellular space of rat brain tumors using wavelet-based radiomics analysis of dynamic contrast enhanced MRI

Abstract Introduction: Research studies have already shown that tumor aggressiveness and response to chemical and radiation therapies are influenced by the extravascular extracellular space (VEES) of the tumor microenvironment. Assessment of VEES has been reported to be fundamental to understanding tumor response to treatment and probability of recurrence. Purpose: This pilot study investigates the association between wavelet-based radiomic features extracted from dynamic contrast-enhanced magnetic resonance images (DCE-MRI) of rat brain tumors against VEES estimated by pharmacokinetic modeling. Methods: Eight immune-compromised-RNU/RNU rats were implanted with human U251n cancer cells to form an orthotopic glioma (IACUC #1509). For each rat, two DCE-MRI studies (multi slice/echo GE, 3 slice(2mm),128x64, FOV:32x32mm2, TR/(TE1-TE2)=24ms/(2ms-4ms), flip angle=18º, 400 acquisitions, 1.55 sec interval, Magnevist was injected at acquisition no. 15) were performed (24h apart) using a 7T Varian (Agilent, 20cm bore) scanner. A single 20Gy stereotactic radiation exposure was performed before the second study. The post treatment MRIs were taken a range of 1-6.5 hrs post radiation. The time trace of relaxivity change (ΔR1) in all the voxels of the animal’s brain for all studies were calculated. Wavelet decomposition analysis was performed on the ΔR1 for each voxel and frequency-based localized approximations with 4 degrees of regularities were estimated. The VEES map was estimated from ΔR1 by the pharmacokinetic (modified Toft’s) model and a nested model selection technique. Finally, the Pearson correlation coefficients between the VEES map and corresponding wavelet coefficient maps in the tumor region were calculated. Results: The average voxel-wise Pearson correlation coefficients between the VEES maps (averaged for all animals) and their corresponding wavelet-based, radiomics coefficient maps were: r= -0.680, r= -0.802, r= -0.813, and r= -0.791 with p<0.0001 for the 4 wavelet coefficients (from higher to lower frequencies), respectively. Discussion & Conclusion: This pilot study suggests that wavelet based radiomic analysis has potential to provide information pertinent to the tumor microenvironment, which correlates well with pharmacokinetic modeling. As such, this work represents an important first step toward potentially connecting radiomics with underlying biological mechanisms. Citation Format: Hassan Bagher-Ebadian, Stephen L. Brown, Olivia Valadie, Julian A. Rey, Ning Wen, Malisa Sarntinoranont, James R. Ewing, Indrin J. Chetty. Characterization of extravascular extracellular space of rat brain tumors using wavelet-based radiomics analysis of dynamic contrast enhanced MRI [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 543.

Frequency splitting approach using wavelet for energy management strategies in fuel cell ultra-capacitor hybrid system

Using frequency splitting, two energy management strategies (EMS) based on Haar wavelet decomposition and Fourier analysis for fuel cell hybrid vehicle (FCHV) are proposed to manage efficiently the power flow between components. The paper aims to discuss the performances of the proposed EMS in terms of dynamic behavior, robustness operation, real time application and fuel economy. For apply this methodology, two EMS approaches are elaborated and successfully tested for parallel Fuel Cell/UC: conventional approach using Fourier Transform analysis (FT) and Wavelet analysis approach allowing natural frequency splitting. Finally, and to evaluate the performance and relevance of the developed approach, a comparison analysis were conducted. The simulation results exhibit the effectiveness of both strategies. Indeed, Wavelet analysis leads to better results in terms of energy flow and dynamic behavior, excellent robustness and stability of system, as well as energy economy improvement. A very relevant strategy is proposed based on Wavelet analysis using digital filtering techniques, which enables a natural frequency splitting to ensure the best global performances. In addition, the approach remains simple and suitable for real time operation.

A Microwave Time Domain Reflectometry Technique Combining the Wavelet Decomposition Analysis and Artificial Neural Network for Detection of Defects in Dielectric Structures

The microwave nondestructive testing (MNDT) technique is an attractive candidate for defect detection in the dielectric structures as microwave signals can penetrate inside and interact with the insides of the dielectric material. In this research, a novel MNDT technique is presented for defect detection combining the time domain reflectometry (TDR) technique, the wavelet decomposition analysis (WDA) technique, and an artificial neural network (ANN), which has good generality and can be applied to various dielectric materials with different thicknesses. This method is based on scanning the dielectric material with an open-ended rectangular waveguide (OERW) operating from 18 to 26.5 GHz with 201 frequency points and analyzing its reflections using the proposed algorithm. The method was validated by simulation and experiments successfully. In the simulations, two typical kinds of dielectric materials were used to verify the generality and accuracy of this proposed method. The accuracies of these two dielectric materials were 96% and 90%, respectively. A 2-D image of detection can be constructed by performing repetition of the process with a certain step on the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">xy</i> plane of a material, which is illustrated by simulations. Meanwhile, the sensitivity analysis was performed by studying the similarity quantified using the Pearson’s coefficient correlation. Eventually, some FR4 samples were machined to demonstrate the feasibility of this method. The acceptable accuracy is 86% of the experimental result.

ENERGY MARKET RISK MANAGEMENT UNDER UNCERTAINTY: A VAR BASED ON WAVELET APPROACH

This study contributes to the literature on energy market risk management and portfolio management by examining co-movements between several energy commodities in a portfolio context in light of the impact of several types of uncertainty over time and under high, medium, and low frequencies. Using of wavelet decomposition analysis, we first investigate the lead-lag relationship together with the power of the correlation over time between major renewable and non-renewable energy indexes and uncertainty indexes. Second, we explore the contribution of uncertainty to the energy portfolio. Our procedure reveals that a dependent relationship generally exists between energy returns and changes in uncertainty. The risks of clean energy and crude oil returns are more sensitive to financial uncertainties, whereas investing in GAS markets offers market diversification opportunities during periods of energy uncertainty. Keywords: VaR Based on Wavelet Approach; Energy market, UncertaintyJEL Classifications: C580; G15; E440.DOI: https://doi.org/10.32479/ijeep.11404

EEMD and Multiscale PCA-Based Signal Denoising Method and Its Application to Seismic P-Phase Arrival Picking.

Signal denoising is one of the most important issues in signal processing, and various techniques have been proposed to address this issue. A combined method involving wavelet decomposition and multiscale principal component analysis (MSPCA) has been proposed and exhibits a strong signal denoising performance. This technique takes advantage of several signals that have similar noises to conduct denoising; however, noises are usually quite different between signals, and wavelet decomposition has limited adaptive decomposition abilities for complex signals. To address this issue, we propose a signal denoising method based on ensemble empirical mode decomposition (EEMD) and MSPCA. The proposed method can conduct MSPCA-based denoising for a single signal compared with the former MSPCA-based denoising methods. The main steps of the proposed denoising method are as follows: First, EEMD is used for adaptive decomposition of a signal, and the variance contribution rate is selected to remove components with high-frequency noises. Subsequently, the Hankel matrix is constructed on each component to obtain a higher order matrix, and the main score and load vectors of the PCA are adopted to denoise the Hankel matrix. Next, the PCA-denoised component is denoised using soft thresholding. Finally, the stacking of PCA- and soft thresholding-denoised components is treated as the final denoised signal. Synthetic tests demonstrate that the EEMD-MSPCA-based method can provide good signal denoising results and is superior to the low-pass filter, wavelet reconstruction, EEMD reconstruction, Hankel–SVD, EEMD-Hankel–SVD, and wavelet-MSPCA-based denoising methods. Moreover, the proposed method in combination with the AIC picking method shows good prospects for processing microseismic waves.

Fault Feature Extraction for Anchor Bolt Loosening of Escalator Based on EWT and Bispectrum Analysis

Escalator is an essential large-scale public transportation equipment. Once the failure occurs, it will inevitably affect the operation and even cause safety accidents. As an important part of the structure of escalator, the loosening of the anchor bolt will lead to abnormal operation of escalator. Aiming at the current difficultyin extracting the fault features of anchor bolt loosening, a fault feature extraction method of escalator anchor loosening is constructed based on empirical wavelet transform (EWT) and bispectrum analysis. First, perform EWT decomposition of the original footing vibration acceleration signal to obtain a series of empirical mode functions(EMFs).Then, for each empirical mode function, the bispectrum was calculated by using bispectrum analysis method, and six texture features of the bispectrum were extracted as fault feature vectors by means of gray-gradient co-occurrence matrix. Finally, the extracted multi-scale fault feature vectors and bi-directional longshort-term memory (BI-LSTM) were used to classify and identify the four types of fault signals with different degrees of foot loosening, and the fault types of foot loosening were determined. The results show that the feature extraction method based on empirical wavelet decomposition and bispectrum analysis can more effectively identify the loosening level of anchor bolts.

EEG analysis of mathematical cognitive function and startle response using single channel electrode

Electroencephalographic (EEG) signals are non-invasive means of measuring brain functions. EEG has been used in areas ranging from analysis of neurological disorders, emotional states and sleep pattern to Brain Machine Interface. Here we study cognitive functions from a limited set of daily activities of normal human subjects. For this purpose, we utilize EEG signal obtained from prefrontal cortex, specifically Brodmann Area 10L, which is known to play an important role in these functions. In this work, we study characteristics of a task requiring focussed attention (Mathematical Cognition (T3)), an involuntary response to external stimuli (Startle Response (T2)) and the state of rest (Relax (T1)). The single channel EEG is preprocessed and statistical features are extracted from (i) preprocessed data, (ii) wavelet decomposition, and (iii) cepstral analysis. These features are then separately input to various classifiers. Results are reported for 2 class (T1 vs T2, T1 vs T3, T2 vs T3) and 3 class (T1 vs T2 vs T3) classification framework. Experimental results reveal that cepstral analysis is most effective for classification across both frameworks. In 3-class classification, cepstral analysis results in a mean accuracy of 96.61% across classifiers. This framework is shown to be effective in classifying the chosen set of cognitive states using EEG and can be extended to broader classes for more conclusive inferences. In addition, gender differences peak at 81.7%, 77.09% and 77.04% for T1, T2 and T3 respectively, indicating that there are significant differences between the genders, although performing the same cognitive task.

Stochastic fracture simulation using pixel-based multiple-point geostatistics by integrating seismic radial anisotropy and well data: applications in two hydrology sites

We propose a pixel-based multiple-point simulation algorithm for stochastic simulation of near surface fractures using seismic radial anisotropy as secondary information. The method borrows information from the training image of the fracture and anchoring within the secondary radial anisotropy data using wavelet-based multiple-point simulation framework. The simulation was performed sequentially, by sampling from the conditional cumulative density function (ccdf) of fracture, conditioning to radial anisotropy. For faster computation, instead of calculating ccdf at each unknown location, a limited number of ccdfs are calculated. The patterns of the multiple-point representation are extracted from the training image and are classified into multiple classes using the wavelet decomposition analysis. Each class is represented by a class prototype and ccdf. During simulation, the conditioning data are compared with the class prototypes, and a random sample is generated from the ccdf of the best match class. We validated the proposed simulation method using a synthetic fracture dataset. A sensitivity analysis was also performed to show the impact of correlation between fracture and radial anisotropy, and the resolution of the radial anisotropy. Results demonstrated that the reproduction of the fracture depends on its correlation with the radial anisotropy. It was also observed that fracture reproduction is proportional to the resolution of the radial anisotropy. We successfully applied our proposed model to two hydrogeological sites in which seismic radial anisotropy was obtained using the dispersion analysis of the surface wave.

Assessing Autonomic Function from Electrodermal Activity and Heart Rate Variability During Cold-Pressor Test and Emotional Challenge

Standard functional assessment of autonomic nervous system (ANS) activity on cardiovascular control relies on spectral analysis of heart rate variability (HRV) series. However, difficulties in obtaining a reliable measure of sympathetic activity from HRV spectra limits the exploitation of sympatho-vagal metrics. On the other hand, measures of electrodermal activity (EDA) have been demonstrated to provide a reliable quantifier of sympathetic dynamics. In this study we propose novel indices of phasic autonomic regulation mechanisms by combining HRV and EDA correlates and thoroughly investigating their time-varying dynamics. HRV and EDA series were gathered from 26 healthy subjects during a cold-pressor test and emotional stimuli. Instantaneous linear and nonlinear (bispectral) estimates of vagal dynamics were obtained from HRV through inhomogeneous point-process models, and combined with a sensitive maker of sympathetic tone from EDA spectral power. A wavelet decomposition analysis was applied to estimate phasic components of the proposed sympatho-vagal indices. Results show significant statistical differences for the proposed indices between the cold-pressor elicitation and previous resting state. Furthermore, an accuracy of 73.08% was achieved for the automatic emotional valence recognition. The proposed nonlinear processing of phasic ANS markers brings novel insights on autonomic functioning that can be exploited in the field of affective computing and psychophysiology.

Aircraft classification method based on the kurtosis – skewness feature and wavelet decomposition and linear discriminant analysis

Aircraft classification method based on the kurtosis <i>–</i> skewness feature and wavelet decomposition and linear discriminant analysis

Tree-stem Volume Calculation from SfM-MVS to Improve Estimates in the Timber Industry in Indonesia

Spatial Analysis is often thought through the cartographic two dimensions of space, with eventually a 3D component and any form of data associated to it. This concept inherited from eras from data mapping on paper has been driving recent computational development in spatial analysis, however spatial analysis can free itself from this virtual plane, we human evolve on and use any space vertical, reversed, spherical, tubular to investigates all sorts of issues, like the production of timber from trees for instance. As Indonesia is a forestry nation, being able to effectively assess the usable amount of timber available. In the present contribution we aimed to show the loss that can be miscalculated using DBH traditional measurement, and to do so we used a photographic based Structure from Motion method combined with Wavelet Decomposition Analysis. The data shows that the DBH method is correct between 60 to 80%, when considering large-scale variations between DBH and SfM-obtained data, showing that the estimates for the timber industry could be improved, even without the use of expensive laser-based equipment.

Ultra short‐term probability prediction of wind power based on LSTM network and condition normal distribution

AbstractConsidering the inevitable prediction errors in the traditional point predictions of wind power, in this paper, a new ultra short‐term probability prediction method for wind power is proposed, in which the long short‐term memory (LSTM) network, wavelet decomposition (WT), and principal component analysis (PCA) are combined together for ultra short‐term probability prediction of wind power, a conditional normal distribution model that is developed to describe the uncertainty of prediction errors. First, WT and PCA are jointly used to smooth the original time series, then the point prediction model for subsequence data based on LSTM network is proposed. It is worth pointing out that the input matrix of the model includes many features, such as wind power and wind speed, which will be helpful for improving prediction performance. After optimizing the index of the ultra short‐term probability prediction interval (PI) of wind power by particle swarm optimization (PSO), the conditional normal distribution model of prediction errors is developed. Thus, the ultra short‐term PIs for wind power are obtained. Finally, based on the data of two wind farms in China, simulation results are provided to illustrate the usefulness of the proposed prediction model. It follows from those results that the proposed method can improve the accuracy of prediction, and the reliability of probability prediction for wind power is also improved.