A little summery of what I am doing

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My current position is developing a computational program that can measure the system of polarized target automatically and repeatedly. The program needs to connect with the microwave generator, voltage supply, power meter, and oscilloscope. That is purely technical and nothing special.

Later, after the program started to collecting data, another program is needed to analysis the data. Although there are many program that can do analysis, but those program are not so easy to use, in both control and display. So, I make an analysis program. The program is simply read the 2-D time-domain signal, and then determine some parameters of a specific function that fit the signal. However, the signal could be very noisy, so FFTW and wavelet analysis were implemented. That’s why the wavelet analysis appeared in this blog.

After that, the sample of polarized target is made from various pentacene derivatives, that no known energy level exist. One way to find out the energy levels of the singlet excited states is to measure the absorption spectrum. However, the energy of the triplet state is difficult to measure. And the energy level of the triplet state is critical for Dynamic Nuclear Polarization to be happened. Thus, one way to find out is doing computation chemistry.

My last chemistry class was like 15 years ago, when I was junior high school. But the basic of computation chemistry is solving the Schrodinger equation. That is what theoretical nuclear physicists do! The variation method, the Hartree-fock method, I heard it and somehow know it, but never do it with my own hand and computer. That is why I revisit the Hartree-fock method, and found out my previous understanding is so naive.

In the course of studying Hartree-Fock method, and one problem is evaluating the overlap integral

\displaystyle \int \psi_a(r_1) \psi_b(r_2) \frac{1}{r_{12}} \psi_c(r_1) \psi_d(r_2) dr_1dr_2

For a 3-D system, the integral involves product of multiple spherical harmonics. That is really troublesome. Therefore, I move to study the spherical harmonics, and the related rotational invariant, Wigner D-matrix, Clebcsh-Gordon series and Fourier series.  The spherical harmonics arises from solving the Laplace equation in spherical coordinate. A general theory of the solution of Lapalace equation involves Legendre polynomial, which is a special case for Hypergeometric function.  And a very interesting connection is that the elliptical function of the 1st and 2nd kind are also two special cases of hypergeometric function, that, the solution of Laplace equation for elliptical boundary condition is elliptical functions. That connects spherical harmonics and elliptical function! Wow!

I am now a bit off-track, that I am very interesting on the function of all functions. We know that there are many elementary functions, such as sin, cos, and Log. And even more kind of special functions, such as

  • Hermite — solving 1-D harmonic oscillator
  • Laguerre — the radial function of hydrogen
  • Legendre — the solution of the “\theta” of  Laplace equation
  • Gamma — a continuation of factorial
  • Bessel — solution of 3-D infinite square well
  • Elliptic — magnetic field of a solenoid
  • Dirac delta
  • Gaussian or Error function

For discrete argument

  • Clebsch-Gordon
  • Factoral
  • Binomial

As far as I know, the Hypergemetric function is like “mother of functions”, although not all special functions can be expressed as it. I am driven by curiosity, so, I am not sure where I will go. For instance, the transformation of Hypergeometric function is very interesting.

So, for now, as an ending, I found one article is very interesting. The History and Future of Special Functions, by Stephen Wolfram.


A wavelet presentation

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Wavelet Transformation

Recently, I gave a mini-lecture on wavelet analysis for my colleagues. This is a 30-min compact lecture for introduction and the application.

Enjoy! and feel free to leave comments if you have questions.

Visualization of wavelet

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Many wavelet does not have functional form, but defined by the MRA coefficient.

The visualization of wavelet can be done by using wavelet construction.

\displaystyle v_{j+1,k} = \sum_{n} g_0(k-2n)v_{j,n} + g_1(k-2n) w_{j,n}

For scaling function, we can define v_0 = {1} and w_0 = {0} .

\displaystyle v_{1,k} = \sum_{n} g_0(k-2n)v_{0,n} = g_0(k)

Similarly, the wavelet can be started with v_0 = {0} and w_0 = {1} .

\displaystyle v_{1,k} = \sum_{n} g_1(k-2n)w_{0,n} = g_1(k)

Then build by iteration,

\displaystyle v_{j+1,k} = \sum_{n} g_0(k-2n) v_{j,n}

From last post on the scaling coefficient, i calculated and plot the wavelet for m = 4.


we can see the wavelet becomes the Haar wavelet as the free parameter goes to 1. In fact, it becomes a shifted Haar wavelet when the free parameter goes to 0, as we can imagine.

When the free parameter is 0.683013, it is the Daubechies-2 wavelet. Notes that some people will absorbed a factor $latex 1/ \sqrt{2} $ into the coefficient, so that their free parameter is 0.683013/\sqrt{2} = 0.482963.

Orthonormal Scaling Coefficient

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A multi-resolution analysis is defined by scaling function and the corresponding wavelet. From the scaling relations

\displaystyle \phi_{j,k}(x) = \frac{1}{\sqrt{2}} \sum_{l} g_0(l) \phi_{j+1,2k+l}(x)

\displaystyle \psi_{j,k}(x) = \frac{1}{\sqrt{2}} \sum_{l} g_1(l) \phi_{j+1,2k+l}(x)

the scaling function and wavelet can be defined from the scaling coefficient g_0, g_1

The coefficients are constrained due to the properties of wavelet and scaling function.

\displaystyle \int \phi(x) dx = 1

\displaystyle \int \phi_{j,k}(x) \phi_{j,k'}(x) dx = \delta_{kk'}

\displaystyle \int \psi(x) dx = 0

\displaystyle \int \psi_{j,k}(x) \psi_{j,k'}(x) dx = \delta_{kk'}

\displaystyle \int \psi_{j,k}(x) \phi_{j,k'}(x) dx = 0

These properties lead to

\displaystyle \sum g_0(l) = 2

\displaystyle \sum g_1(l) = 0

\displaystyle \sum_{l,n} g_0(l) g_0(l+2n) = \begin{matrix} 2, & n=0 \\ 0, & else  \end{matrix}

\displaystyle \sum_{l,n} g_1(l) g_1(l+2n) = \begin{matrix} 2, & n=0 \\ 0, & else  \end{matrix}

\displaystyle \sum_{l,n} g_0(l) g_1(l+2n) = 0

The 3rd and 4th constrains requires the numbers of non-zero element in g_0, g_1 are even.

One of the solution is setting

g_1(k) = (-1)^k g_0 (1-k)

so that we don’t need to worry g_1 and the 4th constrain becomes the 3rd constrain, and the 5th constrain is always satisfied. Now, only the 1st, 2nd, and 3rd constrains are needed. This is equivalent to 1+m/2 equations with number of non-zero elements in g_0 is m.

m 1 + \frac{m}{2} Degree of Freedom
2 2 0
4 3 1
6 4 2
8 5 3

For size of 4, the solution is

\displaystyle g_0 = \left(a,  \frac{1-\sqrt{1+4a-4a^2}}{2},  1-a,  \frac{1+\sqrt{1+4a-4a^2}}{2}\right)

In fact, the coefficient for g_0 can be grouped as even and odd, so that

\displaystyle \sum g_0(2k) = \sum g_0(2k+1) = 1

and the constrain 3rd can lead to,

\displaystyle (\sum g_0(2k))^2 + (\sum g_0(2k+1)^2 = 2 ,

which is automatically fulfill.


Hard & soft thresholding

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In usual Fourier transform (FT), the filter is cut-off certain frequency.

This trick is also suitable for wavelet transform (WT). However, there could be some “features” located in high frequency scale (or octave) , a simply cut-off would remove these features.

If the signal to noise level is large, that means the noise has smaller amplitude than that the signal, we can use hard or soft thresholding, which zero any coefficient, which is after the FT or WT,  less then a threshold.

Lets X be the coefficient. The hard thresholding is

Y=\begin{cases} 0, & |X| <\sigma \\ X, & \mbox{else} \end{cases}


The soft thresholding is

Y = \begin{cases} 0, & |X| < \sigma \\ sign(X) f(|X|, \sigma), & \mbox{else} \end{cases}

A popular function

\displaystyle f(x, \sigma) = \frac{x - \sigma}{ X_{max} - \sigma } X_{max}



\displaystyle f(x, \sigma) = x - \sigma


Algorithm of Wavelet Transform (with Qt class)

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There are many kind of wavelet transform, and I think the names are quite confusing.

For instance, there are continuous and discrete wavelet transforms, in which, the “continuous” and “discrete” are for the wavelet parameters, not for the “data” itself. Therefore, for discrete data, there are “continuous” and “discrete” wavelet transforms, and for function, there are also “continuous” and “discrete” wavelet transforms.

In here, we will focus on discrete wavelet transform for function first. This discrete wavelet transform is also called as wavelet series, which express a compact support function into series of wavelet.

For simplicity, we also focus on orthonormal wavelet.

As the wavelet span the entire space, any compact function can be expressed as

\displaystyle f(t) = \sum_{j,k} \left<f(t)|\psi_{j,k}(t)\right> \psi_{j,k}(t)

\psi_{j,k}(t) = 2^{j/2} \psi(2^j t - k)

where j, k are integer.

Now, we move to discrete data discrete wavelet transform. The data is discrete, we can imagine only t_n = t_0 + n \Delta points are known with finite n .

\displaystyle f_n = f(t_n) = \sum_{j,k} \left<f_n|\psi_{j,k}(t_n) \right> \psi_{j,k}(t_n)

the integration becomes a finite sum.

Without loss of generality, we can set t_0 = 0, \Delta = 1, and then the time axis becomes an integer number axis. We found that j  \leq 0 as the wavelet can only be expand, not shrink. Because there are finite number of data point, i.e. n < \infty, -Log_2(n) < j \leq 0 .

However, this double summation for each f_n is very time consuming. There is a Fast Discrete Wavelet Transform. Before we continuous, we must study the wavelet.

From the last post, we know that the scaling function that generate a MRA must be:

\displaystyle \phi(t) = \sum_{k} g_0(k) \phi(2t-k)

\left<\phi(t-k) | \phi(t-k') \right> = \delta_{kk'}

, where k are integer. The set of shifted scaling function span a space V_0 . For the wavelet,

\displaystyle \psi(t) = \sum_{k} g_1(k) \psi(2t-k)

\left<\psi(t-k) | \psi(t-k') \right> = \delta_{kk'}

The set of shifted wavelet span a space W_0, so that W_0 \perp V_0, so that

\left<\phi(t-k)|\psi(t-k') \right> = 0

Since the wavelet is generated from the scaling function, we expect the coefficient of g_0(k) and g_1(k) are related. In fact, the relationship for orthonormal scaling function and wavelet is

g_1(k) = (-1)^k g_0(1-k)

For discrete data x_i , it can be decomposed into the MRA space. We start by the largest V_0 space, where the wavelet is most shrunken.

\displaystyle x_i = \sum_{k} v_{0,k} \phi(i-k)

to decompose to the V_{-1} and W_{-1} space. We can use the nested property of the MRA space, \phi(2t) can be decomposed into \phi(t-k) and \psi(t-k) ,

\displaystyle \psi(2t-l) = \sum_{k} h_0(2k-l) \phi(t-k) + h_1(2k-l) \psi(t-k)

where (given that \phi(t) and $\latex \psi(t)$ are orthonormal ),

h_0(2k-l) = \left< \phi(2t-l) | \phi(t-k) \right>

h_1(2k-l) = \left< \phi(2t-l) | \psi(t-k) \right>

Therefore, using the coefficient of h_0 and h_1, the wavelet coefficient v_{0,k} can be decomposed to

\displaystyle v_{s-1,k} = \sum_{l} h_0(2k-l) v_{s,l}  

\displaystyle w_{s-1,k} = \sum_{l} h_1(2k-l) v_{s,l}

in graphic representation


This is a fast discrete wavelet transform.

Due to the nested space of MRA, we also expect that the coefficient h_0 and h_1 are related to g_0 . For orthonormal wavelet,

\displaystyle h_0(k) = \frac{1}{2} g_0(-k)

\displaystyle h_1(k) = \frac{1}{2} (-1)^{k} g_0 (k+1)

Since the g_0 is finite, the g_1, h_0, h_1 are all finite. That greatly reduce the computation cost of the discrete wavelet transform.

To reconstruct the discrete data x_i, we don’t need to use

\displaystyle v_{s+1,l} = \sum_{k} v_{s,k} \phi(l - k) + w_{s,k} \psi(l-k)

using the nested space of MRA, \psi(t) = \sum_{k} g_1(k) \psi(2t-k) ,

\displaystyle v_{s+1,l} = \sum_{k} g_0(l-2k) v_{s,k} + g_1(l-2k) w_{s,k}

in graphical representation,


I attached the wavelet transfrom class for Qt, feel free to modify.



in the code, the data did not transform to MRA space. The code treats the data already in the MRA space. Some people said this is a “crime”. But for the seek of “speed”, it is no need to map the original discrete data into MRA space. But i agree, for continuous function, we must map to MRA space.



Wavelet Analysis or MRA


Although the Fourier transform is a very powerful tool for data analysis, it has some limit due to lack of time information. From physics point of view, any time-data should live in time-frequency space. Since the Fourier transform has very narrow frequency resolution, according to  uncertainty principle, the time resolution will be very large, therefore, no time information can be given by Fourier transform.

Usually, such limitation would not be a problem. However, when analysis musics, long term performance of a device, or seismic survey, time information is very crucial.

To over come this difficulty, there a short-time Fourier transform (STFT) was developed. The idea is the applied a time-window (a piecewise uniform function, or Gaussian) on the data first, then FT. By applying the time-window on difference time of the data (or shifting the window), we can get the time information. However, since the frequency range of the time-window  always covers the low frequency, this means the high frequency  signal is hard to extract.

To improve the STFT, the time-window can be scaled (usually by 2). When the time window is shrink by factor of 2, the frequency range is expanded by factor of 2. If we can subtract the frequency ranges for the time-window and the shrink-time-window, the high frequency range is isolated.

To be more clear, let say the time-window function be

\phi_{[0,1)}(t) = 1 , 0 \leq t < 1

its FT is

\hat{\phi}(\omega) = sinc(\pi \omega)

Lets also define a dilation operator

Df(t) = \sqrt{2} f(2t)

the factor \sqrt{2} is for normalization.

The FT of D\phi(t) has smaller frequency range, like the following graph.


We can subtract the orange and blue curve to get the green curve. Then FT back the green curve to get the high-frequency time-window.

We can see that, we can repeat the dilation, or anti-dilation infinite time. Because of this, we can drop the FT basis Exp(-2\pi i t \omega), only use the low-pass time-window to see the low-frequency behaviour of the data, and use the high-pass time-window to see the high-frequency behaviour of the data. Now, we stepped into the Multi-resolution analysis (MRA).

In MRA, the low-pass time-window is called scaling function \phi(t) , and the high-pass time-window is called wavelet \psi(t).

Since the scaling function is craetd by dilation, it has the property

\phi(t) = \sum_{k} g_{0}(k) \phi(2t-k)

where k is integer. This means the vector space span by {\phi(t-k)}_{k}=V_0 is a subspace of the dilated space DV_0 =V_1. The dilation can be go one forever, so that the whole frequency domain will be covered by V_{\infty}.

Also, the space span by the wavelet, {\psi(t-k)}=W_0, is also a subspace of V_1. Thus, we can imagine the structure of MRA is:


Therefore, any function f(t) can also be expressed into the wavelet spaces. i.e.

f(t) = \sum_{j,k} w_{j,k} 2^{j/2}\psi(2^j t - k)

where j, k are integers.

I know this introduction is very rough, but it gives a relatively smooth transition from FT to WT (wavelet transform), when compare to the available material on the web.





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