Wavelet Filter Research Articles

An online identification approach for a nonlinear ship motion model based on a receding horizon

The ship motion system is a nonlinear control object, and its system parameters exhibit time-varying characteristics with the ship motion state, which increases the difficulty of control. Therefore, parameter identification has an important significance for the stability of ship motion control. Aiming at the real-time identification problem of the nonlinear and time-varying ship motion system during movement, this paper reconstructs the ship motion system with the propeller speed and rudder angle as control variables and designs an online identification algorithm with the sliding time window method based on the extended Kalman filter algorithm. In addition, to solve the problem of noise in ship motion data collected in real-time, a real-time wavelet filter is developed to perform online preprocessing of the input data of the identification algorithm. The applicability of the method is further demonstrated via a model-scale Korea Research Institute of Ships and Ocean Engineering container ship free-running experiments in a basin.

Structural subgrid scale model based on wavelet filter for large eddy simulation of particle-laden turbulence

Coherent vortex structures in subgrid scale (SGS) motions are important for preferential concentration and collision of particles with small and intermediate Stokes number (Xiong et al., 2019). In this study, a new large eddy simulation (LES) strategy that combines wavelet filtered large eddy simulation with a differential filter SGS model is proposed. First, a wavelet filtered direct numerical simulation (WFDNS) enables good preservation of SGS structures due to the high compressibility and local fidelity of the wavelet filter. Second, a differential filter (DF) model, containing only a parameter related to the nominal filter width, is used to dynamically reconstruct unresolved eddies. The SGS model presented here is verified using direct numerical simulation (DNS) data for particle-laden homogenous isotropic turbulence. Compared to the classical spectral-filtered DNS (FDNS) model, the new SGS model enables to achieve better agreement with DNS results related to the dispersed-phase statistics, such as particle acceleration, particle-seen fluid kinetic energy, particle-seen Lagrangian integral time etc. Furthermore, obtained results also exhibit the advantages of a proposed model over the stochastic Langevin model in the prediction of particle dynamics, especially collision-related statistics such as radial distribution function, radial relative velocities, and particle collision rates. Moreover, the model also demonstrates good performance in application to particle-pair related statistics at different Reynolds numbers and different filter depths. This is attributed to the ability of this model to multispectrally enhance coherent vortex structures in SGS, which is promising to apply for recovering SGS effects in the LES of turbulent particle-laden flows.

Cepstrum-assisted empirical wavelet transform (CEWT)-based improved demodulation analysis for fault diagnostics of planetary gearboxes

Demodulation analysis is a widely used approach for fault diagnostics of planetary gearboxes by identifying the fault-induced modulation effect buried in noise with complicated characteristics. To enhance the performance of demodulation analysis, previous studies have employed signal decomposition, including empirical wavelet transform (EWT), to decompose a signal with a clear modulation effect. However, EWT requires a physical understanding of the modulation effect to isolate the fault-related signals. To solve this challenge, we propose a cepstrum-assisted empirical wavelet transform (CEWT). In the proposed method, the vibration signal is decomposed using empirical wavelet filters designed based on the smoothed spectrum from cepstrum analysis. To further enhance the fault-related signal, the proposed method employs averaging for the envelopes of the decomposed signals for the demodulation analysis. The proposed method is validated by examining numerical simulation and experiment. The results show that the proposed method improves fault diagnostic performance, as compared to existing methods.

A 392-pW 42.7-dB Gm-C wavelet filter for low-frequency feature extraction used for wearable sensor

A 392-pW 42.7-dB Gm-C wavelet filter for low-frequency feature extraction used for wearable sensor

A maximal overlap discrete wavelet packet transform integrated approach for rainfall forecasting – A case study in the Awash River Basin (Ethiopia)

This study introduces the maximal overlap discrete wavelet packet transform (MODWPT) for forecasting hydrological variables that exhibit change over multiple timescales (e.g., rainfall, streamflow). The advantage of the MODWPT over other recent wavelet decomposition methods (à trous algorithm (AT) and the maximal overlap discrete wavelet transform (MODWT)) is that it can extract finer scale information that may be important for improving forecast performance. Multiple wavelet decomposition methods (MODWPT, AT, MODWT) are integrated within the Wavelet Data-Driven Forecasting Framework (WDDFF), applied for forecasting monthly rainfall at six meteorological stations in the Awash River Basin (Ethiopia), and compared using eight statistical performance metrics. Results demonstrate that the MODWPT can be used to generate more accurate forecasts than the AT and MODWT for the majority of stations and performance metrics. Certain settings within the WDDFF (decomposition level, wavelet filter, input variable method, and data-driven model) lead to improved performance more often than others.

Wavelet Filtering by Using Nonthreshold Method and Example of Model Doppler Function

Wavelet Filtering by Using Nonthreshold Method and Example of Model Doppler Function

Novel Architecture for Lifting Discrete Wavelet Packet Transform With Arbitrary Tree Structure

This brief presents a novel pipelined VLSI architecture for computing discrete wavelet packet transform (DWPT) with an arbitrary wavelet tree. Coefficients for different levels are computed in a series of stages. Each stage consists of a bypassed wavelet filter and circuit for reordering intermediate coefficients. The proposed lifting-based wavelet filter computes high- and low-pass coefficients in series. In order to accommodate the arbitrary tree structure, the filter either computes the coefficients or bypass the samples. The reordering of intermediate coefficients forms a subband required for next-level computation. The coefficients are computed in a serial manner and reordering of intermediate coefficients reduce not only the memory elements but also the circuit complexity. The proposed pipelined architecture reduces the requirement of memory elements by 50%. Furthermore, the hardware implementation results show that the area and power requirement are reduced by 33% and 20%, respectively.

Segmentation Based Biomedical Image Retrieval with Low-Level Feature Extraction

Content based image retrieval (CBIR) is an imperative and testing errand in numerous fields, for example, military, common, restorative and even in web applications. Here, we implemented medical image retrieval focused on novel material with decimated bi-orthogonal spline wavelet filter banks. Proposed algorithm is an extension for the existing low-level feature extraction method done by using spline wavelet filters, also utilized iterative partitioning (IP) for extracting the similar patterns of Pictures of query & database. In terms of consistency & memory, simulation results indicate that the approach suggested was better than the current scheme.

Machine learning-based CT radiomics features for the prediction of pulmonary metastasis in osteosarcoma.

This study aims to build machine learning-based CT radiomic features to predict patients developing metastasis after osteosarcoma diagnosis. This retrospective study has included 81 patients with a histopathological diagnosis of osteosarcoma. The entire dataset was divided randomly into training (60%) and test sets (40%). A data augmentation technique for the minority class was performed in the training set, along with feature's selection and model's training. The radiomic features were extracted from CT's image of the local osteosarcoma. Three frequently used machine learning models tried to predict patients with lung metastases (MT) and those without lung metastases (non-MT). According to the higher area under the curve (AUC), the best classifier was chosen and applied in the testing set with unseen data to provide an unbiased evaluation of the final model. The best classifier for predicting MT and non-MT groups used a Random Forest algorithm. The AUC and accuracy results of the test set were bulky (accuracy of 73% [ 95% coefficient interval (CI): 54%; 87%] and AUC of 0.79 [95% CI: 0.62; 0.96]). Features that fitted the model (radiomics signature) derived from Laplacian of Gaussian and wavelet filters. Machine learning-based CT radiomics approach can provide a non-invasive method with a fair predictive accuracy of the risk of developing pulmonary metastasis in osteosarcoma patients. Models based on CT radiomic analysis help assess the risk of developing pulmonary metastases in patients with osteosarcoma, allowing further studies for those with a worse prognosis.

A sinusoidal current generator and a TEB decomposer for measuring bioimpedance in a cardiac pacemaker, using an analog wavelet filter

A sinusoidal current generator and a TEB decomposer for measuring bioimpedance in a cardiac pacemaker, using an analog wavelet filter

Modelling Price Volatility in Onion using Wavelet based Hybrid Models

The performance of wavelet-based hybrid models using different combinations of wavelet filters was compared to that of other conventional models to model volatility in the onion prices and arrivals at the Lasalgaon market of Maharashtra, which is known to be one of the largest markets in terms of arrivals. Monthly data of more than twenty-three years from 1996 onwards were taken into account. The results of hybrid models were compared to that of the ARIMA model. A normality test was conducted for both data series, and both of them were found to be non-normal. Therefore, a suitable nonparametric approach, namely wavelet decomposition of the data, was called for. For the price data, too, the wavelet- GARCH model with LA8 filter at five-level decomposition performed best for single value forecast, whereas the ARIMA performed well at expanded horizons. For the arrivals data, the Wavelet-GARCH model with LA8 filter at four level decomposition outperformed all models for single value forecasts. However, the wavelet-ANN model was able to perform better as the horizon expanded to twelve months. The study concluded that the wavelet hybrid models do pretty well for single value forecast, but as the horizon expands, the accuracy of the models decreases.

Performance Evaluation of Change Detection in SAR Images Based on Hybrid Antlion DWT Fuzzy c-Means Clustering

Abstract In this paper, the main objective is to detect changes in the geographical area of Ottawa city in Canada due to floods. Two multi-temporal Synthetic Aperture Radar (SAR) images have been taken to evaluate the un-supervised change detection process. In this process, two ratio operators named as Log-Ratio and Mean-Ratio are used to generate a difference image. Performing image fusion based on DWT by selecting optimum filter coefficients by satisfying the wavelet filter coefficient properties through a novel image fusion technique is named as ADWT. GA, PSO, AntLion Optimization algorithms (ALO) and Hybridized AntLion Algorithm (HALO) have been adapted to perform the ADWT based image fusion. Segmentation has been performed based on fuzzy c-Means clustering to detect changed and unchanged pixels. Finally, the performance of the proposed method will be analysed by comparing the segmented image with the ground truth image in terms of sensitivity, accuracy, specificity, precision, F1-score.

Effect of De-noising by Wavelet Filtering and Data Augmentation by Borderline SMOTE on the Classification of Imbalanced Datasets of Pig Behavior

Classification of imbalanced datasets of animal behavior has been one of the top challenges in the field of animal science. An imbalanced dataset will lead many classification algorithms to being less effective and result in a higher misclassification rate for the minority classes. The aim of this study was to assess a method for addressing the problem of imbalanced datasets of pigs' behavior by using an over-sampling method, namely Borderline-SMOTE. The pigs' activity was measured using a triaxial accelerometer, which was mounted on the back of the pigs. Wavelet filtering and Borderline-SMOTE were both applied as methods to pre-process the dataset. A multilayer feed-forward neural network was trained and validated with 21 input features to classify four pig activities: lying, standing, walking, and exploring. The results showed that wavelet filtering and Borderline-SMOTE both lead to improved performance. Furthermore, Borderline-SMOTE yielded greater improvements in classification performance than an alternative method for balancing the training data, namely random under-sampling, which is commonly used in animal science research. However, the overall performance was not adequate to satisfy the research needs in this field and to address the common but urgent problem of imbalanced behavior dataset.

Applying Wavelet Filters in Wind Forecasting Methods

Wind is a physical phenomenon with uncertainties in several temporal scales, in addition, measured wind time series have noise superimposed on them. These time series are the basis for forecasting methods. This paper studied the application of the wavelet transform to three forecasting methods, namely, stochastic, neural network, and fuzzy, and six wavelet families. Wind speed time series were first filtered to eliminate the high-frequency component using wavelet filters and then the different forecasting methods were applied to the filtered time series. All methods showed important improvements when the wavelet filter was applied. It is important to note that the application of the wavelet technique requires a deep study of the time series in order to select the appropriate family and filter level. The best results were obtained with an optimal filtering level and improper selection may significantly affect the accuracy of the results.

DATA REDUCTION BY THE HAAR FUNCTION: A CASE STUDY OF THE PHILLIPS CURVE

The unorthodox estimation procedure, which Phillips (1958) adopted in his original paper, is examined using the Haar wavelet filter. The application of the Haar wavelet transform to Phillips’ original data shows that Phillips’ six pairs of mean coordinates display a striking similarity with the Haar scaling coefficients that represent averages with a period greater than 16 years. This is consistent with Desai’s (1975) intuition on the interpretation of the Phillips Curve. We show that the choice of sorting observations by ascending values of the unemployment rate is crucial for reaching the goal of estimating the eye-catching nonlinear hyperbolic shape of the wage–unemployment relationship that would be otherwise linear. Interestingly, the Haar filter can account not only for the facts characterizing the Phillips’ relationship up to the early 1960s but also for two important facts mostly debated among policymakers: the downward shift of the Phillips Curve and its flattening over time.

Complex wavelet filter improves FLIM phasors for photon starved imaging experiments.

Fluorescence lifetime imaging microscopy (FLIM) with phasor analysis provides easy visualization and analysis of fluorophores' lifetimes which is valuable for multiple applications including metabolic imaging, STED imaging, FRET imaging and functional imaging. However, FLIM imaging typically suffers from low photon budgets, leading to unfavorable signal to noise ratios which in many cases prevent extraction of information from the data. Traditionally, median filters are applied in phasor analysis to tackle this problem. This unfortunately degrades high spatial frequency FLIM information in the phasor analysis. These high spatial frequency components are typically edges of features and puncta, which applies to membranes, mitochondria, granules and small organelles in a biological sample. To tackle this problem, we propose a filtering strategy with complex wavelet filtering and Anscombe transform for FLIM phasor analysis. This filtering strategy preserves fine structures and reports accurate lifetimes in photon starved FLIM imaging. Moreover, this filter outperforms median filters and makes FLIM imaging with lower laser power and faster imaging possible.

Data Processing in Functional Near-Infrared Spectroscopy (fNIRS) Motor Control Research.

FNIRS pre-processing and processing methodologies are very important—how a researcher chooses to process their data can change the outcome of an experiment. The purpose of this review is to provide a guide on fNIRS pre-processing and processing techniques pertinent to the field of human motor control research. One hundred and twenty-three articles were selected from the motor control field and were examined on the basis of their fNIRS pre-processing and processing methodologies. Information was gathered about the most frequently used techniques in the field, which included frequency cutoff filters, wavelet filters, smoothing filters, and the general linear model (GLM). We discuss the methodologies of and considerations for these frequently used techniques, as well as those for some alternative techniques. Additionally, general considerations for processing are discussed.

High Throughput JPEG 2000 for Broadcast and IP-Based Applications

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">High-Throughput JPEG 2000 (HTJ2K) is a royalty-free image compression standard published in 2019 that enhances JPEG 2000 by replacing its slow block coder with a fast block coder. The resulting speedup (e.g., &gt;30× for lossless coding) accelerates the encoding and decoding of images, which can have a great impact on users. A less obvious benefit of the speedup is the enabling of low-latency applications, which was not possible with the original JPEG 2000 standard. Examples of these new potential applications are live, high-quality, low-latency broadcast video contribution, remote production, and Internet Protocol (IP)-based production on-premises and on cloud. This paper examines various HTJ2K encoding parameters and their impact on quality, bitrate, latency, and multigeneration encode/decode cycle performance in the context of requirements of broadcast and IP-based applications. Configurations include a number of different wavelet filters, code-block sizes, and wavelet decomposition structures that are available with HTJ2K. The performance of low-latency HTJ2K is also compared to other low-latency wavelet codecs like VC-2, JPEG XS, and JPEG 2000 Part-1. The JPEG 2000 Part-1 comparisons use the full-frame broadcast profile as well as the ultra-low latency (ULL) configuration as per Video Services Forum (VSF) Technical Recommendation (TR)-01:2018. A tradeoff between latency and compression quality is discussed</i> .

Discrete wavelet based estimator for the Hurst parameter of multivariate fractional Brownian motion

In this paper, wavelets were used to study the multivariate fractional Brownian motion through the deviations of the random process to find an efficient estimation of Hurst exponent. The results of simulations experiments were shown that the performance of the proposed estimator was efficient. The estimation process was made by taking advantage of the detail coefficients stationarity from the wavelet transform, as the variance of this coefficient showed the power-low behavior. We use two wavelet filters (Haar and db5) to manage minimizing the mean square error of the model.

A Novel Design of Dyadic db3 Orthogonal Wavelet Filter Bank for Feature Extraction

The irrational coefficients of orthogonal wavelet filters require a large amount of hardware for their implementation. This results in huge memory requirement, increased power dissipation and reduction in speed of operation. This paper presents a new class of multiplier free db3 orthogonal wavelet filter bank with significantly reduced power dissipation, adders and shifters. In this paper, dyadic filter coefficients of db3 have been obtained by slightly altering double-shifting orthogonality property for perfect reconstruction. The proposed db3 wavelet filter improved time–frequency product, Sobolev regularity and frequency selectivity. Also, the VLSI architecture is suggested for the proposed db3 wavelet filter banks to analyse the hardware computational complexity. The performance of the proposed db3 wavelets achieved satisfactory performance for feature extraction in medical image retrieval and fingerprint recognition of infants and toddlers.