Functional connectivity has been widely used to reveal the dependencies between signals in complex networks such as neural networks observed from electroencephalogram (EEG) data. ...
In order to extrapolate a signal, Empirical Mode Decomposition is used to decompose it into simpler components. Each component is individually extrapolated linearly, and the fina...
Nikolaos Tsakalozos, Konstantinos Drakakis, Scott ...
In this paper an algorithm based on the ensemble empirical mode decomposition (EEMD) is presented. The key idea on the EEMD relies on averaging the modes obtained by EMD applied t...
An analysis of quaternion-valued intrinsic mode functions (IMFs) within three dimensional empirical mode decomposition is presented. This is achieved by using the delay vector var...
Empirical mode decomposition (EMD) is an algorithm for signal analysis recently introduced by Huang. It is a completely datadriven non-linear method for the decomposition of a sign...
This paper demonstrates the feasibility and potential of applying empirical mode decomposition (EMD) to forecast the arrival time behaviors in a parallel batch system. An analysis...
The empirical mode decomposition (EMD) has seen widespread use for analysis of nonlinear and nonstationary time-series. Despite some practical success, it lacks a firm theoretica...
Stephen D. Hawley, Les E. Atlas, Howard J. Chizeck
In this paper a framework for defining scale-spaces, based on the computational geometry concepts of α-shapes, is proposed. In this approach, objects (curves or surfaces) of incr...
For the first time, a proof of the sifting process (SP) and so the empirical mode decomposition (EMD), is given. For doing this, lower and upper envelopes are modeled in a more c...
El-Hadji Samba Diop, R. Alexandre, Abdel-Ouahab Bo...
Here, a versatile data-driven application independent method to extend the depth of field is presented. The principal contribution in this effort is the use of features extracted ...
Andreas Koschan, Harishwaran Hariharan, Mongi A. A...