Cecil Moore wrote:
Jim Kelley wrote:

Chances are fair that something is doing some rectifying somewhere.


It later occurred to me that wind/snow noise is
carried by static DC charged particles.

DC stands for Direct Current. What is a static, direct current, charged
particle?

73,
Tom Donaly, KA6RUH

Tom Donaly wrote:
DC stands for Direct Current. What is a static, direct current, charged
particle?

A particle possessing a DC potential with respect to
the antenna. The mechanism of charge transfer from the
charged particle to the antenna is DC.
--
73, Cecil http://www.qsl.net/w5dxp


----== Posted via Newsfeeds.Com - Unlimited-Uncensored-Secure Usenet News==----
http://www.newsfeeds.com The #1 Newsgroup Service in the World! 100,000 Newsgroups
---= East/West-Coast Server Farms - Total Privacy via Encryption =---

Tom Donaly wrote:
"DC stands for Direct Current,. What is a static direct current, charged
particle?"

My dictionary says d-c: An essentially constant-value current that flows
in only one direction." Another definition says: "It may be continuous
or discontinuous. It may be constant or varying."

Static means stationary or fixed. A static charge is the accumulated
electrical charge on an object.

Terman writes on page 288 of his 1955 edition:
"An amplifier having a frequency range extending from a low value up to
the order of a megacycle or higher is termed a video amplifier."

Black level is that level of the picture signal corresponding to the
maximum limit of black peaks. White level is the carrier-signal level
which corresponds to maximum TV picture brightness.

Alternating current cycles, regularly, increasing and decreasing
periodically.

An alternating current is assumed to be sinusoidal unless otherwise
specified, but it could, for example, be triangular, square, or it could
have some other form. If the wave is symmetrical about the zero axis,
any number of complete cycles will have zero as their average value.
Most amplifiers and transmission systems are not directly coupled. They
use capacitors or transformers between stages. These can`t pass direct
current, so this reference is lost in transmission unless other steps
are taken for its replacement.

The effective value of a sine wave thet does the same work as direct
current does is 0.707 x the peak value of the sine wave. Average of the
sine wave is different. It is the average of many equally spaced values
taken along the course, from awro to zero through one complete HALF
cycle of the wave. This works out to 0.636 x the peak value of the sine
wave (0.9 x 0.707).

Best regards, Richard Harrison, KB5WZI

Richard,

It is clear that you are not discussing a general Fourier Transform.
Everything you state below, while correct, refers to Discrete Fourier
Series analysis and Discrete Fourier Transforms, including FFT.

More generally, integral Fourier Transforms are widely, rigorously, and
correctly used to analyze pulse phenomena that are definitely not periodic.

The original spark for this thread was an aperiodic pulse. You chose to
"mandate" all sorts of conditions that may be useful in a particular
case but are not required for theoretical correctness.

73,
Gene
W4SZ




Richard Clark wrote:
On Mon, 07 Feb 2005 14:34:38 GMT, Gene Fuller
wrote:


I can ignore the name-dropping, but I cannot ignore the incorrect
statement about "Pure Fourier". There is no mandate of constancy even
for the purest Fourier transform. The function needs only to be
moderately well-behaved, including single valued and integrable.


Hi Gene,

In this case you are seriously wrong. There are no IFs ANDs or BUTs.
The loop hole of well-behaved is not enough with it being far too
inspecific.

The ONLY case where the Fourier Series resolves a correct
transformation is if you limit your data set (or for an Integration,
you define your limits) over an interval of n · 2 · PI for a periodic
function where n is an integer from 1..m. Further, you are resolution
limited if you fail to observe Nyquist's laws and under sample, or
fail to frequency limit your real data. This also segues into
Shannon's laws where you can observe the S+N/N in the transform
(discussed below). These concerns are EXTERNAL to the simple act of
transforming data, but are necessary correlatives that MUST be taken
into account.

If you fail even in this simple regard for periodicity (say looking at
only 359 degrees of the periodic function), the result is quite
dramatically different in the Fourier output. Even the casual
observer can immediately see the difference between the correct and
incorrect results, there is nothing ambiguous about it at all.


Perhaps you are confusing Fourier series analysis with Fourier transform
analysis?


No, I have done both, and I will drop the name again, at HP with their
work on Fourier Analysis equipment where I tested their FFT algorithms
(call them what you may, the basic underlying requirements do not
change). I was working with 24 Mathematicians AND Engineers - there
was nothing sloppy about the quality of up-front preparation. This
was a project 5 years in the making. They even wrote their own Pascal
compiler for 1000000 lines of code. I have also done IIRs and FIRs,
Wavelets, and a host of other frequency/time series decimation
analysis.

ALL Fourier techniques have requirements that go beyond the Fourier
math. These requirements (if you have any interest in accuracy)
cannot be ignored. If you have no interest in accuracy, you still
have to perform some of them, which is to say there are trade offs as
I mentioned previously. Ignoring them all simply reduces real data
into transformed garbage.

I have written FFT software that has resolved pure sine waves into a
transformation to a single bin with a statistical noise floor and ALL
spurious response down 200dB. To give an example of what 1° of
decimation error will do, it will inject 120dB of noise into the
product and spurs that are barely 10 to 20 dB down from the principle
bin (which also exhibits about 3dB error).

Much of what is available through college texts and on the web are
seriously under powered in their scope. College is not very
interested in scope, simply introduction. That is, unless you find
yourself in a undergrad (more probably grad school with the additional
considerations taken into account) engineering course dedicated to
modern implementations (practical Fourier) now largely focused on DSP
(which had its genesis in the IIR and FIR earlier implementations).

73's
Richard Clark, KB7QHC

On Tue, 08 Feb 2005 23:45:52 GMT, Gene Fuller
wrote:

Richard,

It is clear that you are not discussing a general Fourier Transform.
Everything you state below, while correct, refers to Discrete Fourier
Series analysis and Discrete Fourier Transforms, including FFT.

More generally, integral Fourier Transforms are widely, rigorously, and
correctly used to analyze pulse phenomena that are definitely not periodic.

The original spark for this thread was an aperiodic pulse. You chose to
"mandate" all sorts of conditions that may be useful in a particular
case but are not required for theoretical correctness.

73,
Gene
W4SZ

Hi Gene,

The simple truth to the matter would be resolved in your offering the
"general" Fourier Transform that could accomplish this feat of
rendering the spectrum of an aperiodic pulse without having to tailor
the waveform. I will lead the way instead.

I would note, ironically, that this data would be discrete (not
continuous) and would necessarily drive peripheral processes to
approach this "general" Fourier Transform. In other words, to
accomplish this generality you would be required to describe the
aperiodic function mathematically from discrete data. I've done tons
of multivariate regressions, and there are any number of "solutions"
that each would exhibit quite different Fourier results. Nearly every
regression suffers from the same issues I've already discussed for
Fourier analysis - aperiodicity. Frankly such an approach would be
inferior to rather simple windowing and performing standard FFTs.
This, of course, strips D.C. from the data set.

Windowing has been studied for its qualities since Blackman and
Tukey's seminal work "The Measurement of Power Spectra" written in
1958. This is the mathematical work that predates FFTs. Every
constraint and reservation that I have describe arises from the pages
of this slim volume in terms of what you describe as "general"
Fourier. As I've said, I have worked with both the Integral solutions
to Fourier Analysis and discrete FFTs for some 20 years. The cautions
and constraints are absolutely identical. Nyquist teaches us this,
Shannon further instructs us.

The authors offer three methods to perform Fourier analysis: Spaced,
Mixed, and Continuous. They report:
"The choice among these types will depend on their particular
advantages and disadvantages, and on the availability of
equipment, both for recording and analysis. In almost every case,
however, the detailed problems will be surprisingly similar."
pg. 55

The concepts of windowing are revealed by Blackman and Tukey; and
their necessity described at great length - per my summarized
cautions. The notion of "prewhitening" is discussed so that a finite
data record can be transformed (no practical Fourier analysis consists
of an infinite record). Aliasing is revealed as a problem as a
consequence of sampling (I cannot imagine our correspondent made a
continuous recording with a 2.5GHz baseband recorder). Windowing is
described in "general" Fourier (and Laplace) math (no one here wants
to deal with these abstractions). Impulse data (Dirac functions)
litter the pages. Analysis goes miles beyond power spectra to include
autocorrelation math- all done in integral calculus. Should we go
into covariability? How about Coherence? This last would be useful
to prove that the data is even real (and likely as not, it fails this
transform).

The long and short of this obviates the hugely erroneous report of
measuring DC at the terminals of an antenna. There is absolutely no
Fourier Cavalry coming to the rescue of a poorly stated problem with
equally problematic data.

73's
Richard Clark, KB7QHC

Tom Donaly, KA6RUH wrote:
"DC stands for Direct Current. What is static, direct current, charged
particle?

Tom addressed Cecil Moore, W5DXP. I`ll risk a breach of protocol and
respond though I was not addressed.

Put a charge on a speck of dust, snow flake, or rain drop. Propel it
through space by any means. The moving charge is an electric current.
What kind of current depends on its trajectory. A unidirectional
atraight trip is without doubt a d-c flow.

If the chrge is propelled regularly back and forth, it possibly
qualifies as a-c.

If the charge lands on a bare antenna wire, it likely will abruptly give
some of its charge to the antenna wire. In this case, the singular event
is a pulse and a static discharge.

Best regards, Richard Harrison, KB5WZI

Richard Clark wrote:

On Tue, 08 Feb 2005 23:45:52 GMT, Gene Fuller
wrote:


Richard,

It is clear that you are not discussing a general Fourier Transform.
Everything you state below, while correct, refers to Discrete Fourier
Series analysis and Discrete Fourier Transforms, including FFT.

More generally, integral Fourier Transforms are widely, rigorously, and
correctly used to analyze pulse phenomena that are definitely not periodic.

The original spark for this thread was an aperiodic pulse. You chose to
"mandate" all sorts of conditions that may be useful in a particular
case but are not required for theoretical correctness.

73,
Gene
W4SZ


Hi Gene,

The simple truth to the matter would be resolved in your offering the
"general" Fourier Transform that could accomplish this feat of
rendering the spectrum of an aperiodic pulse without having to tailor
the waveform. I will lead the way instead.

I would note, ironically, that this data would be discrete (not
continuous) and would necessarily drive peripheral processes to
approach this "general" Fourier Transform. In other words, to
accomplish this generality you would be required to describe the
aperiodic function mathematically from discrete data. I've done tons
of multivariate regressions, and there are any number of "solutions"
that each would exhibit quite different Fourier results. Nearly every
regression suffers from the same issues I've already discussed for
Fourier analysis - aperiodicity. Frankly such an approach would be
inferior to rather simple windowing and performing standard FFTs.
This, of course, strips D.C. from the data set.

Windowing has been studied for its qualities since Blackman and
Tukey's seminal work "The Measurement of Power Spectra" written in
1958. This is the mathematical work that predates FFTs. Every
constraint and reservation that I have describe arises from the pages
of this slim volume in terms of what you describe as "general"
Fourier. As I've said, I have worked with both the Integral solutions
to Fourier Analysis and discrete FFTs for some 20 years. The cautions
and constraints are absolutely identical. Nyquist teaches us this,
Shannon further instructs us.

The authors offer three methods to perform Fourier analysis: Spaced,
Mixed, and Continuous. They report:
"The choice among these types will depend on their particular
advantages and disadvantages, and on the availability of
equipment, both for recording and analysis. In almost every case,
however, the detailed problems will be surprisingly similar."
pg. 55

The concepts of windowing are revealed by Blackman and Tukey; and
their necessity described at great length - per my summarized
cautions. The notion of "prewhitening" is discussed so that a finite
data record can be transformed (no practical Fourier analysis consists
of an infinite record). Aliasing is revealed as a problem as a
consequence of sampling (I cannot imagine our correspondent made a
continuous recording with a 2.5GHz baseband recorder). Windowing is
described in "general" Fourier (and Laplace) math (no one here wants
to deal with these abstractions). Impulse data (Dirac functions)
litter the pages. Analysis goes miles beyond power spectra to include
autocorrelation math- all done in integral calculus. Should we go
into covariability? How about Coherence? This last would be useful
to prove that the data is even real (and likely as not, it fails this
transform).

The long and short of this obviates the hugely erroneous report of
measuring DC at the terminals of an antenna. There is absolutely no
Fourier Cavalry coming to the rescue of a poorly stated problem with
equally problematic data.

Right. It's probably just a rusty bolt somewhere.

ac6xg