Results 111 to 120 of about 31,823 (308)
PERFORMANCE EVALUATION OF ENSEMBLE EMPIRICAL MODE DECOMPOSITION
Advances in Adaptive Data Analysis, 2009 Empirical mode decomposition (EMD) is an adaptive, data-driven algorithm that decomposes any time series into its intrinsic modes of oscillation, which can then be used in the calculation of the instantaneous phase and frequency. Ensemble EMD (EEMD), where the final EMD is estimated by averaging numerous EMD runs with the addition of noise, was an ...
Rami K. Niazy +3 more
openaire +2 more sources
Development of Empirical Mode Decomposition
, 2020 In this paper, one of the tasks for which empirical mode decomposition is potentially useful is nonparametric signal denoising, an area for which wavelet thresholding has been the dominant technique for many years. In this paper, the wavelet thresholding
Nayak, R. samba Siva
core +1 more source
Tumbling Magnetic Microrobots for Targeted In Vivo Drug Delivery in the GI Tract
Advanced Robotics Research, EarlyView.We introduce a microrobot design and integrated system for on‐demand targeted drug release in the gastrointestinal tract. The microrobot has an embedded magnet for actuation with external magnetic fields and is visualized in real time using ultrasound. It has two drug release ports sealed with a thermally sensitive wax. Local heating of the wax using a
Aaron C. Davis +7 more
wiley +1 more source
, 2018 Time series modelling and forecasting has fundamental importance to various practical domains. Thus, a lot of active research works is going on in this subject during several years. Many important models have been proposed in literature for improving the
A. Shabri, A. Rafidah, (UniKL MITEC)
core
Advanced Robotics Research, EarlyView.This work presents a state‐adaptive Koopman linear quadratic regulator framework for real‐time manipulation of a deformable swab tool in robotic environmental sampling. By combining Koopman linearization, tactile sensing, and centroid‐based force regulation, the system maintains stable contact forces and high coverage across flat and inclined surfaces.
Siavash Mahmoudi +2 more
wiley +1 more source
Empirical Blaschke Mode decomposition: Algorithm and application.
PLoS ONEPeriodic pulse components are common in complex signals across fields like biomedical and aerospace engineering, and their accurate extraction is crucial for monitoring.
Sainan Li, Jing Wu
doaj +1 more source
The research progress of empirical mode decomposition and mode mixing elimination
Dianzi Jishu Yingyong, 2019 The Empirical Mode Decomposition(EMD) algorithm proposed by Huang is a data driven adaptive analysis method for nonlinear time-varying signals. The signals can be decomposed into a few Intrinsic Mode Functions(IMFs) components with physical meaning ...
Dai Ting +5 more
doaj +1 more source
Ensemble empirical mode decomposition for high frequency ECG noise reduction
, 2012 [[abstract]]An electrocardiogram (ECG) is measured from the body surface and is often corrupted by various noises, such as high-frequency muscle contraction.
張剛鳴;Chang, Kang-Ming
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Accounting for nonstationarity in the condition monitoring of wind turbine gearboxes [PDF]
, 2013 Increasing growth of wind turbine systems suggests a more systematic research around their design, operation and maintenance is needed. These systems operate under challenging enviromental conditions and failure of some of their parts, for the time being,
Antoniadou, Ifigeneia
core
Advanced Robotics Research, EarlyView.This study explores how information processing is distributed between brains and bodies through a codesign approach. Using the “backpropagation through soft body” framework, brain–body coupling agents are developed and analyzed across several tasks in which output is generated through the agents’ physical dynamics.
Hiroki Tomioka +3 more
wiley +1 more source