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From: Erwin on 8 Jun 2010 21:12
dbd <dbd(a)ieee.org> wrote in message <a3e6aaba-382d-46fb-8898-230d66fefba8(a)6g2000prg.googlegroups.com>...
> Erwin
>
> I'm not going to waste time trying to keep up with a constantly moving
> target when you fail to even clean up the problems people help you
> with and persist in using function calls I don't have the toolbox for.
> Here is an example of what I have said with vectors usage added and
> Fortran-like indexing removed:
>
> %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
> clear all
> beta=2; %slope of the log-log power law
> a=500; %amplitude
> N = 500; % half the number of samples
> Fs = 1/5;
> f = 2*(1:N)/Fs; % generated frequency space, includes no DC term
>
> S = (1./f).^(beta); %power function S(w)=(1/w)^beta
> Y = randn(1,N).*S + i*randn(1,N).*S;
> newY = [0, Y(1:N-1) 0 conj(Y(N-1:-1:1))]; % As I said
>
> figure
> subplot(2,1,1)
> loglog(abs(Y(1:N-1)))
> grid on
> title('Generated Spectrum Y')
>
> timecx = ifft(newY);
> sum(abs(real(timecx)))
> sum(abs(imag(timecx)))
> timerl = real(timecx);
> ftest = abs(fft(timerl));
>
> subplot(2,1,2)
> loglog(abs(ftest(2:N-1)))
> grid on
> title('Spectrum calculated from time domain real samples')
> %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
>
> That leaves you with the exercises of determining unit scaling and of
> controlling the bandwidth you wish to generate. Then you can try to
> figure what you want about Parseval.
>
> Dale B. Dalrymple

Hello Dale,

Thanks for all the support.
I didn't mean to be persistent, I'm just new to Matlab and to signal processing... Sorry.

I think that the algorithm finally gives me credible results! :)

Thanks a lot.
From: dbd on 8 Jun 2010 23:30
On Jun 8, 6:12 pm, "Erwin " <car...(a)gmail.com> wrote:
....
>
> Hello Dale,
>
> Thanks for all the support.
> I didn't mean to be persistent, I'm just new to Matlab and to signal processing... Sorry.
>
> I think that the algorithm finally gives me credible results! :)
>
> Thanks a lot.

Erwin

Persistent is fine. But try to get one code fixed or question answered
before adding new features to broken code. It's easier to follow and
fix/answer.

For the advocates of phase randomization, Erwin's cited paper
appropriately dismisses phase randomization for it's failure to
generate data with a proper variance (non-zero) and distribution (chi-
squared) for the periodogram.

Dale B. Dalrymple
From: Greg Heath on 10 Jun 2010 04:03
On Jun 8, 11:30 pm, dbd <d...(a)ieee.org> wrote:
> On Jun 8, 6:12 pm, "Erwin " <car...(a)gmail.com> wrote:
> ...
> > Hello Dale,
>
> > Thanks for all the support.
> > I didn't mean to be persistent, I'm just new to Matlab and to signal
>> processing... Sorry.
>
> > I think that the algorithm finally gives me credible results! :)
>
> > Thanks a lot.
>
> Erwin
>
> Persistent is fine. But try to get one code fixed or question answered
> before adding new features to broken code. It's easier to follow and
> fix/answer.
>
> For the advocates of phase randomization, Erwin's cited paper
> appropriately dismisses phase randomization for it's failure to
> generate data with a proper variance (non-zero) and distribution (chi-
> squared) for the periodogram.

A phase randomization model had been used in an
attempt to model radar signal perturbations caused
by ionospheric scintillation. However, predictions
were incompatible with measurements.

As a result, I was tasked to constuct a model with
the following characteristics specified:

Amplitude;
Rayleigh distributed
Decorrelation time, taua
Asymptotic power law spectrum 1/f^1.5
Phase
Gaussian distributed
Decorrelation time, taup
Correlation
Crosscorrelation coefficient Cap

I posted the reults but haven't been able to
Google them up.

My memory ain't that good these days. However,
I remember the key to the solution was to use
copulas and rank correlation coefficients.

If I find the threads I will post the links.

Hope this helps.

Greg
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