Wavelet Denoising Procedure Research Articles

Enhanced winter snowmelt in the Antarctic Peninsula: Automatic snowmelt identification from radar scatterometer

Surface snowmelt affects the energy balance through melt-albedo feedback and may endanger the ice shelves in the Antarctic Peninsula (AP) through hydrofracture. Here, we introduce an automatic snowmelt identification algorithm based on Quick Scatterometer and Advanced Scatterometer. The proposed method can provide self-adaptive thresholds for snowmelt detection based on Rosin thresholding, and is able to detect weak melt signals with a wavelet denoising procedure. Results suggest the AP surface snowmelt has slightly declined during 1999–2018 in the context of recent cooling. However, winter melt index (i.e., melt days times the area of melting) has significantly increased (above the 99% confidence level) with a rate of 83% decade−1. Unprecedented winter snowmelt was found in 2015/2016 when about one-third of the AP experienced snowmelt in late May. This may be attributed to the intrusions of marine air and the enhanced föhn-driven warming resulted from the abnormal northwesterly flow driven by a significant deepening of the Amundsen Sea Low.

Range Versus Surface Denoising of Terrestrial Laser Scanning Data for Rock Discontinuity Roughness Estimation

Surface roughness represents a major component of rock discontinuity shear strength. To achieve comprehensive, accurate, and efficient estimates of in situ discontinuity roughness, the traditional contact measuring methods are being replaced by advanced remote-sensing technologies. Terrestrial laser scanner (TLS) is well suited for measuring large inaccessible discontinuities; however, inherent TLS range noise strongly influences the surface details and roughness estimation. The aim of this research is to establish an optimal wavelet-denoising procedure for the TLS data acquired with different scanning configurations (range and incidence angle), and for rock discontinuities having different roughness characteristics and surface reflectivity. The conventional discrete wavelet transform and stationary wavelet transform in combination with four threshold selection methods are applied on TLS data in the direction of range measurements (range denoising) and in the direction perpendicular to the best-fit plane (surface denoising). The performance of the denoising procedures is assessed by comparing the range and surface-denoised TLS surfaces with reference surfaces acquired with the Advanced TOpometric Sensor. Comparative analyses of the roughness calculated according to the angular thresholding method (Grasselli, in Shear strength of rock joints based on quantified surface description, Ph.D. thesis. EPF Lausanne, Lausanne; Grasselli, Shear strength of rock joints based on quantified surface description, Ph.D. thesis, EPF Lausanne, Lausanne, 2001) indicate that all the denoising methods improve the roughness estimated from the TLS data appreciably; however, the level of improvement depends intrinsically on geometrical characteristics of the rock surface and scanning configuration. Range denoising has been found to provide more reliable noise estimations.

On the wavelet analysis of cutting forces for chatter identification in milling

Chatter vibrations in machining operations affect surface finishing and tool behaviour, particularly in the end-milling of aluminum parts for the aerospace industry. This paper presents a methodological approach to identify chatter vibrations during manufacturing processes. It relies on wavelet analyses of cutting force signals during milling operations. The cutting-force signal is first decomposed into an approximation/trend sub-signal and detailed sub-signals, and it is then re-composed using modified sub-signals to reduce measurement noise and strengthen the reference peak forces. The reconstruction of the cutting-force signal is performed using a wavelet denoising procedure based on a hard-thresholding method. Four experimental configurations were set with specific cutting parameters using a workpiece specifically designed to allow experiments with varying depths of cut. The experimental results indicate that resultant force peaks (after applying the threshold to the detailed sub-signals) are related to the presence of chatter, based on the increased correlation of such peaks and the surface roughness profiles, thereby reinforcing the applicability of the proposed method. The results can be used to control the online occurrence of chatter in end-milling processes, as the method does not depend on the knowledge of cutting geometry nor dynamic parameters.

Flow artifact removal in carotid wall imaging based on black and gray-blood dual-contrast images subtraction

To develop and validate a dual-contrast image subtraction (DCIS) strategy for eliminating the flow artifacts in black-blood carotid MRI. Twelve patients with carotid stenosis and eight healthy volunteers were imaged using the black and gray-blood dual-contrast imaging based on the relaxation-enhanced compressed sensing three-dimensional motion-sensitizing driven equilibrium prepared rapid-gradient-echo (RECS-3D MERGE) sequence. Subtraction of black-blood images (BBIs) and gray-blood images (GBIs), together with a preweighting procedure, was performed to eliminate the residual blood signal in BBIs. A wavelet denoising procedure was applied to offset the noise amplification. In addition to the lumen signal-to-noise ratio (SNR) and wall-lumen contrast-to-noise ratio (CNR), the signal variance ratio (SVR) and contrast variance ratio (CVR) were also used to evaluate the blood suppression efficiency. By choosing the weighting factor of one, the lumen SNR of DCIS images was approximately 1% of that of the original BBIs, and the CNR showed a 91.4% improvement as compared with the BBIs. The median of the lumen SVR decreased to zero, and the CVR increased to 123% of that of the BBIs. DCIS is demonstrated to be an effective strategy for sufficiently removing the residual flow signal from black-blood carotid MRI. Magn Reson Med 77:1612-1618, 2017. © 2016 International Society for Magnetic Resonance in Medicine.

Hardware design and implementation of a wavelet de-noising procedure for medical signal preprocessing.

In this paper, a discrete wavelet transform (DWT) based de-noising with its applications into the noise reduction for medical signal preprocessing is introduced. This work focuses on the hardware realization of a real-time wavelet de-noising procedure. The proposed de-noising circuit mainly consists of three modules: a DWT, a thresholding, and an inverse DWT (IDWT) modular circuits. We also proposed a novel adaptive thresholding scheme and incorporated it into our wavelet de-noising procedure. Performance was then evaluated on both the architectural designs of the software and. In addition, the de-noising circuit was also implemented by downloading the Verilog codes to a field programmable gate array (FPGA) based platform so that its ability in noise reduction may be further validated in actual practice. Simulation experiment results produced by applying a set of simulated noise-contaminated electrocardiogram (ECG) signals into the de-noising circuit showed that the circuit could not only desirably meet the requirement of real-time processing, but also achieve satisfactory performance for noise reduction, while the sharp features of the ECG signals can be well preserved. The proposed de-noising circuit was further synthesized using the Synopsys Design Compiler with an Artisan Taiwan Semiconductor Manufacturing Company (TSMC, Hsinchu, Taiwan) 40 nm standard cell library. The integrated circuit (IC) synthesis simulation results showed that the proposed design can achieve a clock frequency of 200 MHz and the power consumption was only 17.4 mW, when operated at 200 MHz.

A-priori dynamic test for deterministic/stochastic modeling in large-eddy simulation of turbulent flow

The coherent/incoherent decomposition of the subgrid-scale stresses based on the wavelet de-noising procedure is exploited in the framework of large-eddy simulation of turbulence. Dynamic a-priori tests based on the perfect modeling approach are performed for decaying isotropic turbulence. The theoretical performances of deterministic/stochastic subgrid-scale models are evaluated during the simulation. The main result is that in large-eddy simulations low order statistics can be almost exactly reproduced when only the effect of the coherent subgrid-scale modes is accounted for, while the incoherent subgrid-scale stresses not affecting the energy transfer.

On the role of subgrid-scale coherent modes in large-eddy simulation

The role of coherent and incoherent subgrid-scale modes in large-eddy simulation modelling is examined. The coherent/incoherent decomposition of the subgrid-scale stresses based on the wavelet de-noising procedure is introduced. A priori dynamical tests based on the perfect modelling approach are performed for decaying isotropic turbulence. The theoretical effects of coherent and incoherent subgrid-scale forces are dynamically evaluated during the simulation. The relation between deterministic/stochastic subgrid-scale models and coherent/incoherent subgrid-scale stresses is discussed. The main result is that in large-eddy simulations low-order statistics can be almost exactly reproduced when only the effect of the coherent subgrid-scale modes is accounted for, while the incoherent subgrid-scale modes have a negligible effect upon the large-scale dynamics and the energy transfer.

Bearing Fault Diagnostic Using Wavelet Transforms (2nd Report, Application of the Wavelet De-Noising for Fault Diagnostic)

Extracting symptom of bearing faults from noisy signal is a key problem to detect early faults of machinery. An effective method is presented for improving the signal to noise ratio by the wavelet transforms. The paper, which is Part II of a pair, illustrates the advantage of the wavelet de-noising procedure on a range of practical examples. We have made a comparison of failure detection-ability between envelope estimator and wavelet-based estimator under the noise condition of machinery. Experiment results have shown that using wavelet de-noising procedure is an effective means to extract early symptoms of machine fault form the heavy noise condition of machinery.

Bearing Fault Diagnostic Using Wavelet Transforms (1st Report, Analyses on the Noise Cancelling by Wavelet Transforms)

Extracting symptom of bearing faults from noisy signal is a key problem to detect early faults of machinery. An effective method is presented for improving the signal to noise ratio by the wavelet transforms. The paper, which is Part I of a pair, describes wavelet de-noising algorithms and their properties. The relationship between the thresholds for wavelet coefficients and the ability of wavelet de-noise under Gaussian noise is discussed. The advantage of the wavelet de-noising procedure on simulated data is illustrated. Simulated results have shown that using wavelet de-noising procedure is an effective means to extract the signal from noisy data.

Turbulent Patch Identification in Microstructure Profiles: A Method Based on Wavelet Denoising and Thorpe Displacement Analysis

Abstract A new method based on wavelet denoising and the analysis of Thorpe displacements dT profiles is presented for turbulent patch identification. Thorpe profiles are computed by comparing the observed density profile ρ(z) and the monotonic density profile ρm(z), which is constructed by reordering ρ(z) to make it gravitationally stable. This method is decomposed in two main algorithms. The first, based on a wavelet denoising procedure, reduces most of the noise present in the measured profiles. This algorithm has been tested from theoretical profiles and has demonstrated a high efficiency in noise reduction, only some limitations were detected in very low-density gradient conditions. The second algorithm is based on a semiquantitative analysis of the Thorpe displacements. By comparing each displacement dT with its potential error EdT, it is possible to classify samples in three possible states: Z (dT = 0), U (dT EdT). This classification makes it possible to compute two statistical...

A fuzzy shell clustering approach to recognize hyperbolic signatures in subsurface radar images

The authors propose a pattern recognition approach to analyze subsurface radar images and recognize the hyperbolic signatures produced by targets. After enhancing these signatures using a wavelet denoising procedure, pixels are grouped into significant hyperbolic shapes by fuzzy clustering. The approach also provides a validation measure for each recognized shape using so-called "shell thickness". Excellent experimental results justify the use of this algorithm for automatic interpretation of subsurface radar images.