Towards automated eye diagnosis: an improved retinal vessel segmentation framework using ensemble block matching 3D filter
Automated detection of vision threatening eye disease based on high resolution retinal
fundus images requires accurate segmentation of the blood vessels. In this regard, detection …
fundus images requires accurate segmentation of the blood vessels. In this regard, detection …
Data-driven nonstationary signal decomposition approaches: a comparative analysis
T Eriksen, N Rehman - Scientific Reports, 2023 - nature.com
Signal decomposition (SD) approaches aim to decompose non-stationary signals into their
constituent amplitude-and frequency-modulated components. This represents an important …
constituent amplitude-and frequency-modulated components. This represents an important …
Multivariate intrinsic chirp mode decomposition
A multivariate intrinsic chirp mode decomposition (MICMD) algorithm is proposed to process
multivariate/multichannel signals. In contrast to most existing multivariate time-frequency …
multivariate/multichannel signals. In contrast to most existing multivariate time-frequency …
Enhanced symplectic geometry mode decomposition and its application to rotating machinery fault diagnosis under variable speed conditions
Tacholess order tracking (TLOT) has been one of the most powerful and applicable rotating
machinery fault diagnosis methods. However, it is still a challenge to accurately estimate the …
machinery fault diagnosis methods. However, it is still a challenge to accurately estimate the …
An adaptive CEEMDAN thresholding denoising method optimized by nonlocal means algorithm
S Zhang, H Liu, M Hu, A Jiang, L Zhang… - IEEE Transactions …, 2020 - ieeexplore.ieee.org
A complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN)
thresholding denoising method optimized by nonlocal means (NLM) algorithm is proposed …
thresholding denoising method optimized by nonlocal means (NLM) algorithm is proposed …
Fault detection, identification and reconstruction of sensors in nuclear power plant with optimized PCA method
W Li, M Peng, Y Liu, N Jiang, H Wang, Z Duan - Annals of Nuclear Energy, 2018 - Elsevier
Principal component analysis (PCA) is applied for fault detection, identification and
reconstruction of sensors in a nuclear power plant (NPP) in this paper. Various methods are …
reconstruction of sensors in a nuclear power plant (NPP) in this paper. Various methods are …
A statistical approach to signal denoising based on data-driven multiscale representation
We develop a data-driven approach for signal denoising that utilizes variational mode
decomposition (VMD) algorithm and Cramer Von Misses (CVM) statistic. In comparison with …
decomposition (VMD) algorithm and Cramer Von Misses (CVM) statistic. In comparison with …
Data-driven multivariate signal denoising using mahalanobis distance
A novel multivariate signal denoising method is presented that computes Mahalanobis
distance measure at multiple data scales obtained from multivariate empirical mode …
distance measure at multiple data scales obtained from multivariate empirical mode …
Wavelet based multivariate signal denoising using mahalanobis distance and edf statistics
A multivariate signal denoising method is proposed which employs a novel multivariate
goodness of fit (GoF) test that is applied at multiple data scales obtained from discrete …
goodness of fit (GoF) test that is applied at multiple data scales obtained from discrete …
Multiscale image denoising using goodness-of-fit test based on EDF statistics
Two novel image denoising algorithms are proposed which employ goodness of fit (GoF)
test at multiple image scales. Proposed methods operate by employing the GoF tests locally …
test at multiple image scales. Proposed methods operate by employing the GoF tests locally …