Signal decomposition (SD) approaches aim to decompose non-stationary signals into their
constituent amplitude-and frequency-modulated components. This represents an important …

In this paper, a novel Successive Multivariate Variational Mode Decomposition (SMVMD) is
presented. Different from most existing multichannel signal decomposition approaches, the …

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 …

As a common appearance defect of concrete bridges, cracks are important indices for bridge
structure health assessment. Although there has been much research on crack identification …

Model-based image denoising methods are well suited for use as image processors in
remote sensing systems such as satellites due to their well-developed mathematical theory …

Image denoising is an important topic in image processing. This paper proposes a novel
approach to speckle noise removal using a combination of nonlocal and local variable p …

This study presents a near-field beamforming method for the moving swarm composed of
unmanned aerial vehicles (UAVs) to address the challenges of position errors that hinder …

Denoising is a significant preprocessing process, garnering substantial attention across
various signal-processing domains. Many traditional denoising methods assume signal …

We propose a novel multivariate signal denoising method that performs long-range
correlation analysis of multiple modes in input data by considering inherent inter-channel …

Spatiotemporal (ST) climatic signals are used exclusively in the analysis and prediction of
weather and climate. These signals are prone to noise due to sensor defects, environmental …