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the original data into some appropriate data sets that contributes to some frequency bands. Basically the EMD is described by a sifting function defined as $$ x^2 $$ the original data into some appropriate data sets that contributes to some frequency bands. Basically the EMD is described by a sifting function defined as {{<a href="http://latex.codecogs.com/png.latex?u%28t%29%20%3D%20%7B%5Cmathcal%20R%7D%5Csum_%7Bj%7D%20a_j%20e%5E%7Bi%5Comega_j%20t%7D"}}

HHT based Instrumental Glitch Trigger Generation

Project Description

  • Introduction: Hilbert-Huang Transform (HHT)

HHT is a recently suggested empirical data transform based on adaptive bases. So it is very useful for analyzing non-linear and/or non-stationary data. The HHT consists of two main parts: 1) empirical mode decomposition (EMD) 2) Hilbert transform. The EMD can decompose the original data into some appropriate data sets that contributes to some frequency bands. Basically the EMD is described by a sifting function defined as <a href="http://latex.codecogs.com/png.latex?u(t) = {\mathcal R}\sum_{j} a_j e^{i\omega_j t}"

Goal

Preliminary Analysis of HHT

Trigger Generation Method

Simulation Results

Comparison to Klein-Welle and Other Methods

HHT based Instrumental Glitch Trigger Generation (last edited 2014-01-09 12:58:08 by johnoh)