FFTs On Excel

On Sun, 14 Nov 2010 12:26:20 -0500, Nancy Norelli <ms.n@hushmail.com>
wrote:


Look here boyo...if you or anyone else puts a muslim propaganda post
here, I'm gonna put 10 posts against it. If you put ten, I'll put a
hundred. If you put a hundred, I'll put a thousand. So tell your
sandnigger friends they're responsible for the ANTI campaign that
follows.
---
Nancy,

Which newsgroup are you posting from?

---
JF
 
On Sun, 14 Nov 2010 12:26:20 -0500, Nancy Norelli <ms.n@hushmail.com>
wrote:

On Sun, 14 Nov 2010 15:34:04 GMT, Eric Jacobsen wrote:

On Fri, 12 Nov 2010 18:07:41 -0500, Nancy Norelli <ms.n@hushmail.com
wrote:

On Fri, 12 Nov 2010 16:51:52 -0500, Randy Yates wrote:

Nancy Norelli <ms.n@hushmail.com> writes:
[...]
The exact details will have to remain a secret, but I can tell you
that it involves fibonacci sequences, monte carlo methods,
differential equations, recursion, neural networks, priority queues,
referential transparency, lambda calculus, bipartite graphs, the phase
of the moon and the tire pressure of John Cleese's bicycle.

Nancy, I can understand the other factors, but what do neural networks
have to do with it?

ha ha ha a noob would ask that question. You need to draw the
attention of educators.

I know what it is to draw the attention of educators, and be selected
for I.Q. testing and advanced placement. I don't have a degree in
physics, so maybe if I did then this "genius" thing which I've been
assigned might be put to better use on this subject.

I have a gift for looking past the obvious and discussing the
abstract. I saved a lot of people of many different ethnicities. You
did know that in Junior high school I memorized the Latin genus of
all the snakes in North America? I was a herpetologist by age 15. I
am a master chess player.

You can call yourself a creative genius when you've done what I've
done. My walls are covered with the first run editions of /intricate
valuable artwork. I have the originals safely stored away too. All
these pieces were commissioned before they were started, and they
represent thousands of dollars in revenue. They were all done by the
same artist.

*Me*
--
Don't FUCK with me. I'm tuff. And stupid but don't dare FUCK with me.

This guy:

http://otrsportsonline.com/wp-content/uploads/2010/05/Jayson-Werth.jpg

shook my hand and said he admired me.

Look here boyo...if you or anyone else puts a muslim propaganda post
here, I'm gonna put 10 posts against it. If you put ten, I'll put a
hundred. If you put a hundred, I'll put a thousand. So tell your
sandnigger friends they're responsible for the ANTI campaign that
follows.
--
Don't FUCK with me. I'm tuff. And stupid but don't dare FUCK with me.
You don't like Dos Equis?

And for a while there we thought you might be smart.


Eric Jacobsen
Minister of Algorithms
Abineau Communications
http://www.abineau.com
 
On 14/11/2010 15:16, John Ferrell wrote:
On Sat, 13 Nov 2010 21:23:09 +0000, Gareth <me@privacy.net> wrote:


On 12/11/2010 15:57, Bret Cahill wrote:
Has anyone heard about Fourier transforms on Excel?

Yes and I've used it. In my opinion it isn't very good due to the messy
way Excel deals with complex numbers, but useful if you haven't got any
better software available.

Google didn't have any quick obvious hits.

Have you tried the Excel help?

Excel would be a cheap easy way demonstrate FFTs in the classroom.


There are plenty of free calculators on the web, for example:

http://www.random-science-tools.com/maths/FFT.htm

Keep them coming, I may eventually understand this subject ....
What is it that you don't understand but want to?

A Fourier transform is a mathematical technique to transform from time
domain (what you would see on an oscilloscope) to the frequency domain
(what you would see on a spectrum analyzer).

An FFT (Fast Fourier Transform) is a very efficient algorithm for doing
a Fourier transform on sampled data.
 
On Mon, 15 Nov 2010 20:07:07 +0000, Gareth <me@privacy.net> wrote:

On 14/11/2010 15:16, John Ferrell wrote:
On Sat, 13 Nov 2010 21:23:09 +0000, Gareth <me@privacy.net> wrote:


On 12/11/2010 15:57, Bret Cahill wrote:
Has anyone heard about Fourier transforms on Excel?

Yes and I've used it. In my opinion it isn't very good due to the messy
way Excel deals with complex numbers, but useful if you haven't got any
better software available.

Google didn't have any quick obvious hits.

Have you tried the Excel help?

Excel would be a cheap easy way demonstrate FFTs in the classroom.


There are plenty of free calculators on the web, for example:

http://www.random-science-tools.com/maths/FFT.htm

Keep them coming, I may eventually understand this subject ....

What is it that you don't understand but want to?

A Fourier transform is a mathematical technique to transform from time
domain (what you would see on an oscilloscope) to the frequency domain
(what you would see on a spectrum analyzer).

An FFT (Fast Fourier Transform) is a very efficient algorithm for doing
a Fourier transform on sampled data.
Thank you, I am beginning to understand!

John Ferrell W8CCW
 
Has anyone heard about Fourier transforms on Excel?  

Yes and I've used it.  In my opinion it isn't very good due to the messy
way Excel deals with complex numbers, but useful if you haven't got any
better software available.

Google didn't have any quick obvious hits.

Have you tried the Excel help?

Excel would be a cheap easy way demonstrate FFTs in the classroom.

There are plenty of free calculators on the web, for example:

http://www.random-science-tools.com/maths/FFT.htm

Keep them coming, I may eventually understand this subject ....

What is it that you don't understand but want to?

A Fourier transform is a mathematical technique to transform from time
domain (what you would see on an oscilloscope) to the frequency domain
(what you would see on a spectrum analyzer).

An FFT (Fast Fourier Transform) is a very efficient algorithm for doing
a Fourier transform on sampled data.

Thank you, I am beginning to understand!
It's not necessary to know everything about the Excel application to
learn a lot about transforms.

Make up a function and take the transform

If the original time domain function was just sine curves with 0 phase
angle, take the IMAGINARY() part of the transform to see what
amplitude ends up in each frequency.

Make up something simple in the frequency domain, say just one
frequency, and take the inverse.

To see the inverse transformed graph you may need to transfer the data
to another column and add zero.


Bret Cahill
 
On Nov 12, 4:09 pm, Bret Cahill <Bret_E_Cah...@yahoo.com> wrote:
Has anyone ever heard about Fourier transforms on Excel?  Google
didn't
have any quick obvious hits.

Excel would be a cheap easy way demonstrate FFTs in the classroom.

The first column would be phase angle and 2nd column would calculate
A1sin(w1*t + phi1) + A2sin(w2*t + phi2) etc. for a time domain
signal.

Go down 256 cells and start something like the divide and conquer
approach.

A1, A2, A3 . . . An and phi1, phi2, phi3, . . . phin would output
in two column of cells dedicated to various frequency ranges.

Bret Cahill
 
On Nov 12, 4:09 pm, Bret Cahill <Bret_E_Cah...@yahoo.com> wrote:
Has anyone ever heard about Fourier transforms on Excel?  Google
didn't
have any quick obvious hits.

How about this:
http://www.ehow.com/how_4670778_fourier-transform-fft-microsoft-excel.html
seems to explain it clearly enough.

Good luck

Brian
Ancient and Modern Optics
 
How about this:http://www.ehow.com/how_4670778_fourier-transform-fft-microsoft-excel...
seems to explain it clearly enough.
I've been trying to recover a signal, a polynomial, with match
filtering on Excel but keep getting something that positively reeks of
sinusoids.

OK, the "positively reeking of sinusoids" was just a really bad
Fourier joke but I should get be able to recover the original
polynomial made up of a lot of sine curves w/ phase angles or a lot of
real and imaginary components..

Here's what do:

First make up a 2^n list of counting numbers in A0 - AN, the time
domain. Then make some polynomial curve. Then add some not too very
correlated noise. Then take the transform. The take the dot product
with a reference that has been similarly transformed. Then take the
inverse transform and the noise should be somewhat attenuated.

I don't want to see a sin or cosine curve if the original curve was a
polynomial.


Bret Cahill
 
On Dec 3, 6:58 am, Bret Cahill <Bret_E_Cah...@yahoo.com> wrote:

I don't want to see a sin or cosine curve if the original curve was a
polynomial.
How do you expect to tell the difference?

What you ask for is similar to be given a number, say, 1,
and determine in what context it originated: DSP? Physics?
Economy? Schoolgrades? Counting socks in the drawer?

A number is just that, a number. A curve is a curve.
And nothing more.

Rune
 
I don't want to see a sin or cosine curve if the original curve was a
polynomial.

How do you expect to tell the difference?

I don't want to see a sin or cosine curve if the original curve was a
polynomial.

How do you expect to tell the difference?
Normalized versions of the original and the final can appear together
on the same graph.


Bret Cahill
 
Make up a clean signal, add noise without too much correlation -- you
can check this with the CORREL function -- and then take the Fourier
transform of both the noisy signal and the original signal which will
serve as the reference.

Then take IMREAL and IMAGINARY of the noisy signal in the frequency
domain and do the same with the ref.

Then take the dot product. Multiply the real of the transformed noisy
signal with the real ref and add to the imaginary of the noisy signal
times the imaginary of the ref. Do this for each frequency.

Since the noise doesn't correlate it will tend to be attenuated, at
least according to match filter theory.

These sums of products should be scalars or real numbers but you can
still take the inverse Fourier transform to (supposedly) recover the
filtered function.

Alternatively you could just combine the real and imaginary with
COMPLEX and take the inverse Fourier transform of the complex number
to (maybe) recover the original signal.

Polynomials like (255-a1)*(127-a1)*a1 for n= 256 do not reappear after
the processing above with their original characteristic asymmetrical
triangularish shape.

What gives?


Bret Cahill
 

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