25 papers · ranked by Valyu relevance
Yanwen Wang, Peng Chen, Yongmei Zhao, Yanying Sun + 1 more
'Manuel José Cabral dos Santos Reis'] When the pulse current method is used for partial discharge (PD) monitoring of mining cables, the detected PD signals are seriously disturbed by the field noise, which are easily submerged in the noise and cannot be extracted. In order to realize the effective separation of the PD…
Ting-Hua Yi, Hong-Nan Li, Xiao-Yan Zhao
In structural vibration tests, one of the main factors which disturb the reliability and accuracy of the results are the noise signals encountered. To overcome this deficiency, this paper presents a discrete wavelet transform (DWT) approach to denoise the measured signals. The denoising performance of DWT is discussed…
Shuxun Li, Qian Zhao, Jinwei Liu, Xuedong Zhang + 2 more
'Jiawei Xiang'] The performance of steam traps plays an important role in the normal operation of steam systems. It also contributes to the improvement of thermal efficiency of steam-using equipment and the rational use of energy. As an important component of the steam system, it is crucial to monitor the state of the…
Kelly McGinnity, Roumen Varbanov, Eric Chicken
Wavelet thresholding generally assumes independent, identically distributed normal errors when estimating functions in a nonparametric regression setting. VisuShrink and SureShrink are just two of the many common thresholding methods based on this assumption. When the errors are not normally distributed, however, few…
Ananias Pereira Neto, Fabrício J. B. Barros
Introduction Wavelet thresholding techniques are crucial in mitigating noise in data communication and storage systems. In image processing, particularly in medical imaging like MRI, noise reduction is vital for improving visual quality and accurate analysis. While existing methods offer noise reduction, they often…
Szi-Wen Chen, Yuan-Ho Chen, Steffen Leonhardt, Daniel Teichmann
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…
Shanfeng Gao, Lei Xu, Yongkang Li, Jiwen Ji + 1 more
To meet the high thickness accuracy requirements in cold-rolling processes, a roll eccentricity signal extraction method based on modified particle swarm optimization and wavelet threshold denoising (MPSO-WTD) with intrinsic time-scale decomposition (ITD) is proposed. The strong denoising ability of the wavelet is…
Nilanjan Dey, Pradipti Nandi, Nilanjana Barman
In this paper we propose a method of corner detection for obtaining features which is required to track and recognize objects within a noisy image. Corner detection of noisy images is a challenging task in image processing. Natural images often get corrupted by noise during acquisition and transmission. Though Corner…
Vijay S. Chourasia, Anil Kumar Tiwari
Fetal phonocardiography (fPCG) based antenatal care system is economical and has a potential to use for long-term monitoring due to noninvasive nature of the system. The main limitation of this technique is that noise gets superimposed on the useful signal during its acquisition and transmission. Conventional filtering…
Mohd Rozni Md Yusof, Ahmad Kamal bin Ariffin
A method of determining the optimum number of levels of decomposition in soft-thresholding wavelet denoising using Stationary Wavelet Transform (SWT) is presented here. The method calculates the risk at each level of decomposition using Steins Unbiased Risk Estimate (SURE), analogous to calculating the sum square error…
Nilanjan Dey, Pradipti Nandi, Nilanjana Barman, Debolina Das + 1 more
'Subhabrata Chakraborty'] In this paper a comparative study between Moravec and Harris Corner Detection has been done for obtaining features required to track and recognize objects within a noisy image. Corner detection of noisy images is a challenging task in image processing. Natural images often get corrupted by…
Bowen Ni, Fei Song, Liguo Zhao, Zhipeng Fu + 1 more
To address the noise issue in fiber optic monitoring signals in frozen soil areas, this study employs wavelet denoising techniques to process the fiber optic signals. Since existing parameter choices for wavelets are typically based on conventional environments, selecting suitable parameters for frozen soil regions…
Priyalakshmi Sheela, Indrakshi Dey
Wavelet denoising suppresses nonstationary, impulsive, and interference-like disturbances in communication signals, but its effectiveness depends on jointly selecting the transform family, mother wavelet, decomposition level, thresholding rule, and shrinkage function. This review synthesises studies published during…
Madhur Srivastava, Yashwant Yashu, Satish Kumar Singh, Prasanta K. Panigrahi
'Prasanta K. Panigrahi'] Abstract--- In this paper, we carry out a comparative study of the efficacy of wavelets belonging to Daubechies and Coiflet family in achieving image segmentation through a fast statistical algorithm.The fact that wavelets belonging to Daubechies family optimally capture the polynomial trends…
Mario Mastriani
—We describe a new filtering approach in the wavelet domain for image denoising and compression, based on the projections of details subbands coefficients (resultants of the splitting procedure, typical in wavelet domain) onto the approximation subband coefficients (much less noisy). The new algorithm is called…
Masoud Hashemi, Soosan Beheshti
—Optimum Bayes estimator for General Gaussian Distributed (GGD) data in wavelet is provided. The GGD distribution describes a wide class of signals including natural images. A wavelet thresholding method for image denoising i s proposed. Interestingly, we show that the Bayes estimator for this class of signals is well…
William Denault, Håkon K. Gjessing, Julius Juodakis, Bo Jacobsson + 1 more
We present here an alternative method for genome-wide association study (GWAS) that is more powerful than traditional GWAS methods for locus detection. Single-variant GWAS methods incur a substantial multiple-testing burden because of the vast number of single nucleotide polymorphisms (SNPs) being tested…
František Váša, Edward T. Bullmore, Ameera X. Patel
Functional connectomes are commonly analysed as sparse graphs, constructed by thresholding cross-correlations between regional neurophysiological signals. Thresholding generally retains the strongest edges (correlations), either by retaining edges surpassing a given absolute weight, or by constraining the edge density.…
Zheng Zhang, Timothy G. Constandinou
This paper assesses and challenges whether commonly used methods for defining amplitude thresholds for spike detection are optimal. This is achieved through empirical testing of single amplitude thresholds across multiple recordings of varying SNR levels. Our results suggest that the most widely used…
Ambra Ferrari, Luca Filippin, Marco Buiatti, Eugenio Parise
Electroencephalography (EEG) is an established method for investigating neurocognitive functions during human development. In cognitive neuroscience, time-frequency analysis of the EEG is a widely used analytical approach. This paper introduces WTools, a new MATLAB-based toolbox capable of performing time-frequency…
Michael X Cohen
Morlet wavelets are frequently used for time-frequency analysis of non-stationary time series data, such as neuroelectrical signals recorded from the brain. The crucial parameter of Morlet wavelets is the width of the Gaussian that tapers the sine wave. This width parameter controls the trade-off between temporal…
Authors not listed
This report compares various simulation and data analysis methods for free induction decay (FID) signals in Nuclear Magnetic Resonance (NMR) Spectroscopy. The methods discussed include discrete fast Fourier transformation (FFT), least squares fitting (LSF), short-time Fourier transformation (STFT), and wavelet…
Mauro Silberberg, Hernán E. Grecco
Quantitative analysis of high-throughput microscopy images requires robust automated algorithms. Background estimation is usually the first step and has an impact on all subsequent analysis, in particular for foreground detection and calculation of ratiometric quantities. Most methods recover only a single background…
Authors not listed
The global drive towards net-zero has accelerated the adoption of carbon fibre reinforced polymers (CFRP) for lightweight structures in various sectors such as aerospace, automotive, energy and biomedical. Mechanical machining of CFRP is often necessary to meet dimensional or assembly-related requirements. However…
Csaba Dávid, Kristóf Giber, Katalin Kerti-Szigeti, Mihaly Kollo + 2 more
Unsupervised segmentation in biological and non-biological images is only partially resolved. Segmentation either requires arbitrary thresholds or large teaching datasets. Here we propose a spatial autocorrelation method based on Local Moran’s I coefficient to differentiate signal, background and noise in any type of…