Results 51 to 60 of about 197,948 (294)
Sparse Reconstruction for Enhancement of the Empirical Mode Decomposition-Based Signal Denoising
IEEE Access, 2020 Effective signal denoising methods are essential for science and engineering. In general, denoising algorithms may be either linear or non-linear. Most of the linear ones are unable to remove the noise from the real-world measurements.
Krzysztof Brzostowski
doaj +1 more source
Noise Corruption of Empirical Mode Decomposition and Its Effect on Instantaneous Frequency
, 2010 Huang's Empirical Mode Decomposition (EMD) is an algorithm for analyzing nonstationary data that provides a localized time-frequency representation by decomposing the data into adaptively defined modes.
Kaslovsky, Daniel N., Meyer, Francois G.
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Seizure detection from EEG signals using Multivariate Empirical Mode Decomposition [PDF]
, 2017 We present a data driven approach to classify ictal (epileptic seizure) and non-ictal EEG signals using the multivariate empirical mode decomposition (MEMD) algorithm.
Ehsan, Shoaib +4 more
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Inspection of Methods of Empirical Mode Decomposition
5th International Conference on Computer Science, Information Technology (CSITEC 2019), 2019 11 pages, 6 figures.
Santander, Roberto Hernández +1 more
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Elevated Connectivity During Language Processing Is Associated With Cognitive Performance in SeLECTS
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Objective Self‐Limited Epilepsy with Centrotemporal Spikes (SeLECTS) is associated with language impairments despite seizures originating in the motor cortex, suggesting aberrant cross‐network interactions. Here we tested whether functional connectivity in SeLECTS during language tasks predicts language performance.
Wendy Qi +8 more
wiley +1 more source
An Alternative Formulation for the Empirical Mode Decomposition [PDF]
IEEE Transactions on Signal Processing, 2012 The Empirical Mode Decomposition (EMD) is a relatively new adaptive method for multicomponent signal representation which allows for analyzing nonlinear and nonstationary signals. In spite of its lack of mathematical foundations, very few papers are dedicated to defining new decompositions that would preserve the interesting properties of the EMD while
Thomas Oberlin +2 more
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Additive Gaussian Process Regression for Predictive Design of High‐Performance, Printable Silicones
Advanced Engineering Materials, EarlyView.A chemistry‐aware design framework for tuning printable polydimethylsiloxane (PDMS) for vat photopolymerization (VPP) is developed using additive Gaussian process (GP) modeling. Polymer network mechanics informs variable groupings, feasible formulation constraints, and interaction variables.
Roxana Carbonell +3 more
wiley +1 more source
Journal of Low Frequency Noise, Vibration and Active Control, 2021 Technology that measures bridge responses when a vehicle is crossing over it for structural health monitoring has been under development for approximately a decade.
Feng Xiao +3 more
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Screening of Obstructive Sleep Apnea with Empirical Mode Decomposition of Pulse Oximetry
, 2014 Detection of desaturations on the pulse oximetry signal is of great importance for the diagnosis of sleep apneas. Using the counting of desaturations, an index can be built to help in the diagnosis of severe cases of obstructive sleep apnea-hypopnea ...
Di Persia, Leandro E. +3 more
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Surface Tension Measurement of Ti‐6Al‐4V by Falling Droplet Method in Oxygen‐Free Atmosphere
Advanced Engineering Materials, EarlyView.In this article, the temperature‐dependent surface tension of free falling, oscillating Ti‐6Al‐4V droplets is investigated in both argon and monosilane doped, oxygen‐free atmosphere. Droplet temperature and oscillation are captured with one single high‐speed camera, and the surface tension is calculated with Rayleigh's formula.
Johannes May +9 more
wiley +1 more source