Continuous Wavelet Transformation Continuous Wavelet Transformation Institute for Advanced Studies...

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Continuous Wavelet Transformation

Institute for Advanced Studies Institute for Advanced Studies in Basic Sciences – Zanjanin Basic Sciences – Zanjan

Mahdi Vasighi Mahdi Vasighi

Table of content

Introduction Fourier Transformation Short-Time Fourier Transformation Continuous Wavelet Transformation Applications of CWT

Most of the signals in practice, are TIME-DOMAIN signals in their raw format. It means that measured signal is a function of time.

In many cases, the most distinguished information is hidden in the frequency content of the signal.

Introduction

Why do we need the frequency information?

Frequency content of stationary signals do not change in time.

All frequency components exist at all times

Stationary signal

)2cos(...)2cos()2cos()( 21 tftftftx n

TransformationFourier Transformation (FT) is probably the most popular transform being used (especially in electrical engineering and signal processing), There are many other transforms that are used quite often by engineers and mathematicians:

Hilbert transform Short-Time Fourier transform (STFT) Radon Transform, Wavelet transform, (WT)

Every transformation technique has its own area ofapplication, with advantages and disadvantages.

Fourier Transformation In 19th century, the French mathematician J. Fourier, showed that any periodic function can be expressed as an infinite sum of periodic complex exponential functions.

jft2-ex(t)X(f)

)2sin(.)2cos( ftjft Raw Signal(time domain)

x(t)x(t) cos(2ft)cos(2ft)

5Hz 10Hz

-0.6-0.4

0.40.6

0 0.2 0.4 0.6 0.8 1

x(t).*cos(2ft) = -8.8e-15

-1-0.8-0.6-0.4-0.2

00.20.40.60.8

0 0.2 0.4 0.6 0.8 1

x(t).*cos(2ft) = -5.7e-15

-1-0.8-0.6-0.4-0.2

00.20.40.60.8

0 0.2 0.4 0.6 0.8 1

x(t).*cos(2ft) = -4.6e-14

-1-0.8-0.6-0.4-0.2

00.20.40.60.8

0 0.2 0.4 0.6 0.8 1

x(t).*cos(2ft) = -2.2e-14

-1-0.8-0.6-0.4-0.2

00.20.40.60.8

0 0.2 0.4 0.6 0.8 1

4.8 Hz

x(t).*cos(2ft) = 74.5

-1-0.8-0.6-0.4-0.2

00.20.40.60.8

0 0.2 0.4 0.6 0.8 1

x(t).*cos(2ft) = 100

-1-0.8-0.6-0.4-0.2

00.20.40.60.8

0 0.2 0.4 0.6 0.8 1

5.2 Hz

x(t).*cos(2ft) = 77.5

-1-0.8-0.6-0.4-0.2

00.20.40.60.8

0 0.2 0.4 0.6 0.8 1

x(t).*cos(2ft) = 1.0e-14

-1-0.8-0.6-0.4-0.2

00.20.40.60.8

0 0.2 0.4 0.6 0.8 1

Frequency

20, 80, 120 HzFT

Frequency content of stationary signals change in time.

Non-Stationary signalM

20 Hz 80 Hz 120 Hz

Frequency

So, how come the spectrums of two entirely different signals look very much alike?

Recall that the FT gives the spectral content of the signal, but it gives no information regarding where in time those spectral components appear.

Once again please note that, the FT gives what frequency components (spectral components) exist in the signal. Nothing more, nothing less.

Almost all biological signals are non-stationary. Some of the most famous ones are ECG (electrical activity of the heart , electrocardiograph), EEG (electrical activity of the brain, electroencephalogram), and EMG (electrical activity of the muscles, electromyogram).

Can we assume that , some portion of a non-stationary signal is stationary?

Short-Time Fourier Transformation

The answer is yes.

In STFT, the signal is divided into small enough segments, where these segments (portions) of the signal can be assumed to be stationary. For this purpose, a window function "w" is chosen.

Time stepFrequency

etime-frequency representation (TFR)

Window width = 0.05Time step = 100 milisec

dtex(t)X(f) jft2-

STFT dte)]t'-ω(t[x(t)f)X(t, jft2-

Time stepFrequency

Time step

Frequency

Narrow windows give good time resolution, but poor frequency resolution.

Time stepFrequency

Time step

Frequency

Wide windows give good frequency resolution, but poor time resolution;

What kind of a window to use? The answer, of course, is application dependent:

If the frequency components are well separated from each other in the original signal, than we may sacrifice some

frequency resolution and go for good time resolution, since the spectral components are already well separated from

each other.

The Wavelet transform (WT) solves the dilemma of resolution to a certain extent, as we will see.

Multi Resolution Analysis

MRA, as implied by its name, analyzes the signal at different frequencies with different resolutions. Every spectral component is not resolved equally as was the case in the STFT.

MRA is designed to give good time resolution and poor frequency resolution at high frequencies and good frequency resolution and poor time resolution at low frequencies.

This approach makes sense especially when the signal at hand has high frequency components for short durations and low frequency components for long durations.

Continuous Wavelet TransformationContinuous Wavelet Transformation

The continuous wavelet transform was developed as an alternative approach to the short time Fourier transform to overcome the resolution problem. The wavelet analysis is done in a similar way to the STFT analysis, in the sense that the signal is multiplied with a function, wavelet, similar to the window function in the STFT, and the transform is computed separately for different segments of the time domain signal.

waveletwavelet

Mexican hatMexican hat MorletMorlet

iateet

t = 0Scale = 1

Inner productInner product

t = 50Scale = 1

t = 100Scale = 1

t = 150Scale = 1

t = 200Scale = 1

t = 0Scale = 10

t = 50Scale = 10

t = 100Scale = 10

t = 150Scale = 10

t = 200Scale = 10

Scale = 10

Scale = 20

Scale = 30

Scale = 40

Scale = 50

τt(ψx(t)

1s),(CWTψ

As seen in the above equation , the transformed signal is a function of two variables, and s , the translation and scale parameters, respectively. (t) is the transforming function, and it is called the mother wavelet.

If the signal has a spectral component that corresponds to the value of s, the product of the wavelet with the signal at the location where this spectral component exists gives a relatively large value.

20 Hz 50 Hz 120 Hz

Translation increment=50 milisecondScale inc.=0.5

10 Hz 20 Hz 60 Hz 120 Hz

CWT Applications

Identifying time-scale (time-frequency) scheme Frequency filtering (Noise filtering)

Wavelet SynthesisReconstructing signal using selected range of scales

CWT result for non-stationary signal (10 & 20 Hz )

CWT Applications

Solving peak overlapping problem in different analytical techniques (simultaneous determination)

Journal of Pharmaceutical and Biomedical Analysis (2007) in press

Continuous wavelet and derivative transforms for the simultaneous quantitative analysis and dissolution test of levodopa–benserazide tablets

Erdal Dinc et.al.

Simultaneous analyses of levodopa–benserazide tablets were carried out by continuous wavelet transform (CWT) without using any chemical separation step. The developed spectrophotometric resolution is based on the transformation of the original UV spectra.

Conc. A Conc. B

CWT sym6 (s = 128)

Unknown mixture spectrum

CWT sym6 (s = 128)

Calibration modelCalibration model

Prediction

Determination of bismuth and copper using adsorptive stripping voltammetry couple with continuous wavelet transform

Shokooh S. Khaloo, Ali A. Ensafi, T. KhayamianTalanta 71 (2007) 324–332

A new method is proposed for the determination of bismuth and copper in the presence of each other based on adsorptive stripping voltammetry of complexes of Bi(III)-chromazorul-S and Cu(II)-chromazorul-S at a hanging mercury drop electrode (HMDE). Copper is an interfering element for the determination of Bi(III) because, the voltammograms of Bi(III) and Cu(II) overlapped with each other. Continuous wavelet transform (CWT)was applied to separate the voltammograms.

The method was used for determination of these two cations in water and human hair samples. The results indicate the ability of method for the determination of these two elements in real samples.

The combination of both continuousThe combination of both continuouswavelet and chemometrics techniqueswavelet and chemometrics techniques

Spectrophotometric Multicomponent Determination of Tetramethrin, Propoxur and Piperonyl Butoxide in Insecticide Formulation by

Principal Component Regression and Partial Least Squares Techniques with Continuous Wavelet Transform

Canadian Journal of Analytical Sciences and Spectroscopy 49 (2004) 218

A continuous wavelet transform (CWT) followed by a principal component regression (PCR) and partial least squares (PLS) were applied for the

quantitative determination of tetramethrin (TRM), propoxur (PPS) and piperonil butoxide (PPR) in their formulations. A CWT was applied to the absorbance

data. The resulting CWT-coefficients (xblock) and concentration set (y-block) were used for the construction of CWT-PCR and CWT-PLS calibrations. The

combination of both continuous wavelet and chemometrics techniques indicates good results for the determination of insecticide in synthetic mixtures and

commercial formulation.

References Mathworks, Inc. Wavelet Toolbox Help

Robi Polikar, The Wavelet Tutorial

Multiresolution Wavelet Analysis of Event Related Potentials for the Detection of Alzheimer's Disease, Iowa State University, 06/06/1995 Robi Polikar

An Introduction to Wavelets, IEEE Computational Sciences and Engineering, Vol. 2, No 2, Summer 1995, pp 50-61.

Continuous wavelet and derivative transforms for the simultaneous quantitative analysis and dissolution test of levodopa–benserazide tablets, Journal of Pharmaceutical and Biomedical Analysis (2007) In press.

Determination of bismuth and copper using adsorptive strippingvoltammetry couple with continuous wavelet transform, Talanta 71 (2007) 324–332

Canadian Journal of Analytical Sciences and Spectroscopy 49 (2004) 218

Thanks for your attention

Continuous Wavelet Transformation Continuous Wavelet Transformation Institute for Advanced Studies...

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