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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 {{https://upload.wikimedia.org/math/e/e/4/ee4471f673afaddfc791f692cf250f6b.png}}, which shows that the EMD performs the subtraction between the original data (or the previously generated data) and its average of envelopes, repeatedly, called '''''intrinsic mode functions (IMF)''''', expressed by {{https://upload.wikimedia.org/math/7/e/c/7ec2bae54bcdd79fab725ef8d6c5314c.png}} and {{https://upload.wikimedia.org/math/6/5/3/65349fc2c693c1b809562b59f4b5e172.png}}. The Hilbert transform of the ''EMDed data'' consists of the amplitude and phase parts and we see that the original data can be expressed by the summation of the whole IMFs, which is
{{https://upload.wikimedia.org/math/9/d/e/9de1f9c2b24c4c2b3bab29243d825bc3.png}}.
the original data into some appropriate data sets that contributes to some frequency bands. Basically the EMD is described by a sifting processes with {{https://upload.wikimedia.org/math/e/e/4/ee4471f673afaddfc791f692cf250f6b.png}}, which shows that the EMD performs the subtraction between the original data (or the previously generated data) and its average of envelopes, repeatedly, called '''''intrinsic mode functions (IMF)''''', expressed by {{https://upload.wikimedia.org/math/7/e/c/7ec2bae54bcdd79fab725ef8d6c5314c.png}} and {{https://upload.wikimedia.org/math/6/5/3/65349fc2c693c1b809562b59f4b5e172.png}}. The Hilbert transform of the ''EMDed data'' consists of the amplitude and phase parts and we see that the original data can be expressed by the summation of the whole IMFs, which is
{{https://upload.wikimedia.org/math/6/f/d/6fdad0aefbac705612d1e8541fcd9eef.png}}.
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Comparing to the (Fast) Fourier Transform (FFT), {{https://upload.wikimedia.org/math/2/7/7/277cc4bee5968c6ca2d4c78747d18689.png}}, we easily see that the HHT deals with the time-variant amplitude and phase data with adaptive bases.
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||<bgcolor="#E0E0FF"> ||<bgcolor="#E0E0FF"> Fourier ||<bgcolor="#E0E0FF"> Wavelet ||<bgcolor="#E0E0FF"> Hilbert-Huang ||
||<bgcolor="#E0E0FF">Basis || a priori || a priori || Adaptive ||
||<bgcolor="#E0E0FF">Frequency || Integral transform: Global || Integral transform: Regional || Differentiation: Local||
||<bgcolor="#E0E0FF">Presentation || Energy-Frequency || Energy-Time-Frequency || Energy-Time-Frequency||
||<bgcolor="#E0E0FF"> Nonlinearity || No || No || Yes ||
||<bgcolor="#E0E0FF"> Non-stationarity || No || Yes || Yes ||
||<bgcolor="#E0E0FF"> Uncertainty || Yes || Yes || No ||
||<bgcolor="#E0E0FF">Harmonics || Yes || Yes || No ||

HHT based Instrumental Glitch Trigger Generation

Project Description

  • Introduction: Hilbert-Huang Transform (HHT)

emd2.png 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 processes with https://upload.wikimedia.org/math/e/e/4/ee4471f673afaddfc791f692cf250f6b.png, which shows that the EMD performs the subtraction between the original data (or the previously generated data) and its average of envelopes, repeatedly, called intrinsic mode functions (IMF), expressed by https://upload.wikimedia.org/math/7/e/c/7ec2bae54bcdd79fab725ef8d6c5314c.png and https://upload.wikimedia.org/math/6/5/3/65349fc2c693c1b809562b59f4b5e172.png. The Hilbert transform of the EMDed data consists of the amplitude and phase parts and we see that the original data can be expressed by the summation of the whole IMFs, which is https://upload.wikimedia.org/math/6/f/d/6fdad0aefbac705612d1e8541fcd9eef.png.

Comparing to the (Fast) Fourier Transform (FFT), https://upload.wikimedia.org/math/2/7/7/277cc4bee5968c6ca2d4c78747d18689.png, we easily see that the HHT deals with the time-variant amplitude and phase data with adaptive bases.

Fourier

Wavelet

Hilbert-Huang

Basis

a priori

a priori

Adaptive

Frequency

Integral transform: Global

Integral transform: Regional

Differentiation: Local

Presentation

Energy-Frequency

Energy-Time-Frequency

Energy-Time-Frequency

Nonlinearity

No

No

Yes

Non-stationarity

No

Yes

Yes

Uncertainty

Yes

Yes

No

Harmonics

Yes

Yes

No

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)