Interpolation and extrapolation of almost periodic data

[Richard Owlett]

I'm working on a "problem" that got me thinking.
As the purpose of attempting the "problem" was education that's good.

I have 96 data points.
I desire:
1. approximation of 97th (but NOT 98th or greater).
2. interpolate 28 intervening data points, including between points
96 and 97.

I know a priori that:
1. the function and ALL derivatives are continuous.
2. the function is almost periodic.
a. 1 period ends ~3/4 ow way between points 96 and 97.
b. The deviations from periodic behavior are small and in
themselves periodic but with periods 30 to 300 times longer
than my total sample time.

Scilab's smooth() function appears to handle the interpolations from
points 1 thru 96.

Can DSP techniques or point of view:
1. get me a better interpolation?
2. get me point 97?

[ The 96 data points are satellite positions in an Earth Centered Earth
Fixed (ECEF) frame of reference from Rinex formated data files running
nominally from midnight to midnight. Unfortunately, the closing
"midnight" is in the next day's data file (which will not always be
accessible). As orbits are well defined, I could convert from ECEF to
inertial frames, interpolate and extrapolate, and convert back to ECEF.
That has it's own set of *MASSIVE* problems for me.]

Comments?

[robert bristow-johnson]

On Apr 12, 9:33*am, Richard Owlett <[email protected]> wrote:
> I'm working on a "problem" that got me thinking.
> As the purpose of attempting the "problem" was education that's good.
>
> I have 96 data points.
> I desire:
> * *1. approximation of 97th (but NOT 98th or greater).
> * *2. interpolate 28 intervening data points, including between points
> * * * 96 and 97.
>
> I know a priori that:
> * *1. the function and ALL derivatives are continuous.
> * *2. the function is almost periodic.
> * * * a. 1 period ends ~3/4 ow way between points 96 and 97.
> * * * b. The deviations from periodic behavior are small and in
> * * * * *themselves periodic but with periods 30 to 300 times longer
> * * * * *than my total sample time.
>
> Scilab's smooth() function appears to handle the interpolations from
> points 1 thru 96.
>
> Can DSP techniques or point of view:
> * *1. get me a better interpolation?
> * *2. get me point 97?
>
> [ The 96 data points are satellite positions in an Earth Centered Earth
> Fixed (ECEF) frame of reference from Rinex formated data files running
> nominally from midnight to midnight. Unfortunately, the closing
> "midnight" is in the next day's data file (which will not always be
> accessible). As orbits are well defined, I could convert from ECEF to
> inertial frames, interpolate and extrapolate, and convert back to ECEF.
> That has it's own set of *MASSIVE* problems for me.]
>
> Comments?

this is the kinda thing i think about regarding synthesis/analysis of
harrmonic (or "quasi-periodic") musical tones. but i usually think i
need about 2 adjacent periods to be able to perform some sorta pitch
detection. if you know the precise period (to less that a sample
precision), you ougta be able to interpolate, but the edge effects are
a problem.

r b-j

[jim]

Richard Owlett wrote:
>
> I'm working on a "problem" that got me thinking.
> As the purpose of attempting the "problem" was education that's good.
>
> I have 96 data points.
> I desire:
> 1. approximation of 97th (but NOT 98th or greater).
> 2. interpolate 28 intervening data points, including between points
> 96 and 97.

I don't understand what number 2 means. Are you increasing the data from
96 evenly spaced samples to 125 evenly spaced samples? Or are you reducing
it to 27 samples?

Anyway if you are using an interpolation method to create a point between
point 95 and 96 that probably means that a 97th point was already invented
to accomplish that. That is, if you are doing anything more than linear
interpolation then to find points in the interval between 95 and 96 you
need points outside that interval.

You say you know the data is periodic. Wouldn't that mean that the
missing point 97 is actually the same as point 1 ?

-jim


>
> I know a priori that:
> 1. the function and ALL derivatives are continuous.
> 2. the function is almost periodic.
> a. 1 period ends ~3/4 ow way between points 96 and 97.
> b. The deviations from periodic behavior are small and in
> themselves periodic but with periods 30 to 300 times longer
> than my total sample time.
>
> Scilab's smooth() function appears to handle the interpolations from
> points 1 thru 96.
>
> Can DSP techniques or point of view:
> 1. get me a better interpolation?
> 2. get me point 97?
>
> [ The 96 data points are satellite positions in an Earth Centered Earth
> Fixed (ECEF) frame of reference from Rinex formated data files running
> nominally from midnight to midnight. Unfortunately, the closing
> "midnight" is in the next day's data file (which will not always be
> accessible). As orbits are well defined, I could convert from ECEF to
> inertial frames, interpolate and extrapolate, and convert back to ECEF.
> That has it's own set of *MASSIVE* problems for me.]
>
> Comments?


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[robert bristow-johnson]

On Apr 12, 6:43*pm, jim <"sjedgingN0sp"@[email protected]> wrote:
>
> * * * * You say you know the data is periodic. Wouldn't that mean that the
> missing point 97 is actually the same as point 1 ?

i thought what happened is that he knows from some other source, what
is the period. and that it has a fractional part, in terms of samples
periods. but it is not sampled synchronously. the period might be
between 95 and 96 samples long.

that's what i thought the problem is. if it is, isn't that a matter
of setting up some Fourier series with the known period and with no
more than 47 harmonics + DC to fit those 96 points?

other than that guess, i'm clueless to what the problem is.

r b-j

[dbd]

On Apr 12, 6:33 am, Richard Owlett <[email protected]> wrote:
> I'm working on a "problem" that got me thinking.
> As the purpose of attempting the "problem" was education that's good.
>
> I have 96 data points.
> I desire:
> 1. approximation of 97th (but NOT 98th or greater).
> 2. interpolate 28 intervening data points, including between points
> 96 and 97.
>
> I know a priori that:
> 1. the function and ALL derivatives are continuous.
> 2. the function is almost periodic.
> a. 1 period ends ~3/4 ow way between points 96 and 97.
> b. The deviations from periodic behavior are small and in
> themselves periodic but with periods 30 to 300 times longer
> than my total sample time.
>
> Scilab's smooth() function appears to handle the interpolations from
> points 1 thru 96.
>
> Can DSP techniques or point of view:
> 1. get me a better interpolation?
> 2. get me point 97?
>
> [ The 96 data points are satellite positions in an Earth Centered Earth
> Fixed (ECEF) frame of reference from Rinex formated data files running
> nominally from midnight to midnight. Unfortunately, the closing
> "midnight" is in the next day's data file (which will not always be
> accessible). As orbits are well defined, I could convert from ECEF to
> inertial frames, interpolate and extrapolate, and convert back to ECEF.
> That has it's own set of *MASSIVE* problems for me.]
>
> Comments?

It's not clear just what you want, but we never let that stop us on
comp.dsp.

Assuming uniformly spaced samples, there are DFT based methods for
decomposing signals into component parts. For signals that are
accurately decomposable into a sum of sinusoids, there are DFT based
decompositions. With the frequency phase and amplitude of all
components, the signal can be calculated for all time subject to
limitations of SNR, stationarity and model match.

If the components are adequately separated, the process works as
follows:

1) Perform DFT of the data set

2) Determine the coarse location of the strongest peak by peak-picking
the magnitude of the DFT

3) Estimate the fine resolution frequency, amplitude and phase of the
peak signal from the DFT coefficients of the bin with greatest
magnitude and the coefficients of the adjacent bins.

4) Use the frequency, amplitude and phase of the peak to remove its
contribution to the signal. In the frequency domain this includes all
leakage effects, in the time domain it is the generation and
subtraction of the estimated signal component.

5) When the residual energy is small continue, else go back to 1) or
2) as required by the domain used for component removal.

6) Use the frequencies, amplitudes and phases of the components to
estimate the value of the components at any desired time and sum the
components for the estimate.

A discussion of the process is found in:
M. D. Macleod, \Fast nearly ML estimation of the parameters of real or
complex single
tones or resolved multiple tones," IEEE Transactions of Signal
Processing, vol. 46,
pp. 141{148, Jan. 1998.

An example of the application of the process to measuring superimposed
tones in an intercept receiver you can download:
ADA426470.pdf
proxy Url: http://handle.dtic.mil/100.2/ADA426470
Title:
Enhancing the Instantaneous Dynamic Range of Electronic
Warfare Receivers Using Statistical Signal Processing
Personal Author(s): Smith, Bryan E.
Report Date: MAR 2004
Media Count: 121 Pages(s)

This thesis includes a scheme for removing leakage errors in the
frequency domain.


If you want to do more computation and claim a more exact maximum
likelihood approach download:
ADA310879.pdf
proxy Url: http://handle.dtic.mil/100.2/ADA310879
Title:
Parameter Estimation for Superimposed Weighted Exponentials.
Fields and Groups :
120300 - STATISTICS AND PROBABILITY
120400 - OPERATIONS RESEARCH
201400 - RADIOFREQUENCY WAVE PROPAGATION
Corporate Author:
AIR FORCE INST OF TECH WRIGHT-PATTERSON AFB OH SCHOOL OF
ENGINEERING
Personal Author(s): Ingham, Edwards A.
Report Date: JUL 1996
Media Count: 212 Pages(s)


If constant frequency sinusoids are not a good match for your signal
(if the residuals in step 5 don't get small it's a hint) you can use
other signal models such as linear FMs or polynomial phase signals at
greater computational cost.

Accuracy can be improved by reestimating the parameters of the larger
peaks with the other peaks removed, it's a just a lot more
computation. Some signals may require it.

There is no need to limit the decomposition to DFT based methods, but
if they work they may be computationally advantageous.

If the samples are not uniformly spaced in time the Lomb-Scargle
periodogram algorithms can be used to generate Fourier coefficients
that are at equi-spaced frequencies.

Dale B. Dalrymple
http://dbdimages.com

[Ron N.]

On Apr 12, 6:33 am, Richard Owlett <[email protected]> wrote:
> I'm working on a "problem" that got me thinking.
> As the purpose of attempting the "problem" was education that's good.
>
> I have 96 data points.
> I desire:
> 1. approximation of 97th (but NOT 98th or greater).
> 2. interpolate 28 intervening data points, including between points
> 96 and 97.
>
> I know a priori that:
> 1. the function and ALL derivatives are continuous.

Do you have a model of the system producing the signal
or function?

Was the function or signal band-limited before the
sampling? If so, what was the bandwidth?

Do you know enough about the function to estimate
what portion of the sample content is noise vs. signal?

> 2. the function is almost periodic.
> a. 1 period ends ~3/4 ow way between points 96 and 97.

How many total periods do you have represented in
the 96 point vector?

> b. The deviations from periodic behavior are small and in
> themselves periodic but with periods 30 to 300 times longer
> than my total sample time.
>
> Scilab's smooth() function appears to handle the interpolations from
> points 1 thru 96.
>
> Can DSP techniques or point of view:
> 1. get me a better interpolation?
> 2. get me point 97?

If the noise level of the signal and it's derivatives
are zero, then you might be able to use some number of
finite differences (or other polynomial method) to
interpolate forward.

If the signal is noisy but all in narrow band(s), you
might be able to filter it, or model it with an "adapted"
all pole filter.

If you have a parametric model of the system producing
the signal, then you might be able to do some sort of
linear (least squares?) or non-linear (genetic evolution?)
best fit of the parameters to the samples, and then run
the model forward for one more time step.

If the signal consists of only one fundamental period,
but has some rich harmonic content, then you could try
a combination of changing the fft size and resampling
the signal until the fundamental and every harmonic all
fall at exact bin centers. (If the harmonics are known
to be exact, then this may converge faster than dbd's
suggestion of trying to remove each frequency peak
one-at-a-time, since you will be using the information
from several peaks to estimate only two parameters.)



IMHO. YMMV.
--
rhn A.T nicholson d.0.t C-o-M

[Richard Owlett]

robert bristow-johnson wrote:

> On Apr 12, 6:43 pm, jim <"sjedgingN0sp"@[email protected]> wrote:
>
>> You say you know the data is periodic. Wouldn't that mean that the
>>missing point 97 is actually the same as point 1 ?
>
>
> i thought what happened is that he knows from some other source, what
> is the period. and that it has a fractional part, in terms of samples
> periods. but it is not sampled synchronously. the period might be
> between 95 and 96 samples long.

Yes to knowing period from other sources.
Points 1 -> 97 cover exactly 24 hours.
The period is 23 hours 56 minutes and some seconds.
Point 97 is "available" in another file, but that file may not be
accessible.

There are workarounds for my specific case.
*BUT* the problem got me thinking about the general case.

THEREFORE I posed the question in my original post with NO explicit
reference to the specific case.

The *PURPOSE* was to educate me on how to _THINK_ about DSP problems.


>
> that's what i thought the problem is. if it is, isn't that a matter
> of setting up some Fourier series with the known period and with no
> more than 47 harmonics + DC to fit those 96 points?
>
> other than that guess, i'm clueless to what the problem is.
>
> r b-j

[Richard Owlett]

The bon-n-n-g-g-g you heard this morning was from your 100 lb sledge
shakin out some cobwebs ;) In the mid 60's I maintained what might be
considered a crude analog computer that used essentially your approach.


dbd wrote:

> On Apr 12, 6:33 am, Richard Owlett <[email protected]> wrote:
>
>>I'm working on a "problem" that got me thinking.
>>As the purpose of attempting the "problem" was education that's good.
>>
>>I have 96 data points.
>>I desire:
>> 1. approximation of 97th (but NOT 98th or greater).
>> 2. interpolate 28 intervening data points, including between points
>> 96 and 97.
>>
>>I know a priori that:
>> 1. the function and ALL derivatives are continuous.
>> 2. the function is almost periodic.
>> a. 1 period ends ~3/4 ow way between points 96 and 97.
>> b. The deviations from periodic behavior are small and in
>> themselves periodic but with periods 30 to 300 times longer
>> than my total sample time.
>>
>>Scilab's smooth() function appears to handle the interpolations from
>>points 1 thru 96.
>>
>>Can DSP techniques or point of view:
>> 1. get me a better interpolation?
>> 2. get me point 97?
>>
>>[ The 96 data points are satellite positions in an Earth Centered Earth
>>Fixed (ECEF) frame of reference from Rinex formated data files running
>>nominally from midnight to midnight. Unfortunately, the closing
>>"midnight" is in the next day's data file (which will not always be
>>accessible). As orbits are well defined, I could convert from ECEF to
>>inertial frames, interpolate and extrapolate, and convert back to ECEF.
>>That has it's own set of *MASSIVE* problems for me.]
>>
>>Comments?
>
>
> It's not clear just what you want, but we never let that stop us on
> comp.dsp.

So consider the OP a unknown N-port device.
So instead of hitting it with a pure impulse to get total transfer
function, you hit it with a finite set of ideas.

>
> Assuming uniformly spaced samples,

They are

> there are DFT based methods for
> decomposing signals into component parts. For signals that are
> accurately decomposable into a sum of sinusoids, there are DFT based
> decompositions. With the frequency phase and amplitude of all
> components, the signal can be calculated for all time subject to
> limitations of SNR, stationarity and model match.

The system has 4 independent signal sources. Two of which are comparable
in magnitude of their fundamentals. Their frequencies differ by ~.2% .
The other are 30-300+ times slower and much weaker. But ALL 4
fundamental frequencies are known very accurately.



>
> If the components are adequately separated, the process works as
> follows:
>
> 1) Perform DFT of the data set
>
> 2) Determine the coarse location of the strongest peak by peak-picking
> the magnitude of the DFT
>
> 3) Estimate the fine resolution frequency, amplitude and phase of the
> peak signal from the DFT coefficients of the bin with greatest
> magnitude and the coefficients of the adjacent bins.
>
> 4) Use the frequency, amplitude and phase of the peak to remove its
> contribution to the signal. In the frequency domain this includes all
> leakage effects, in the time domain it is the generation and
> subtraction of the estimated signal component.
>
> 5) When the residual energy is small continue, else go back to 1) or
> 2) as required by the domain used for component removal.
>
> 6) Use the frequencies, amplitudes and phases of the components to
> estimate the value of the components at any desired time and sum the
> components for the estimate.
> [snip references and discussion of non-uniform sampling and varying
signal frequencies]

Got to get dressed for church. More later.

[[email protected]]

> Yes to knowing period from other sources.
> Points 1 -> 97 cover exactly 24 hours.
> The period is 23 hours 56 minutes and some seconds.

Aha a sidereal period. Richard, there are quite a few sources that
will let you find the osculating (not oscillating!) coefficients. A
good book on orbital mechanics will likely prove useful.

Given your simple case of data and limited interval for extrapolation,
you can try a simple LPC method. It should be good enough.

Clay

[Richard Owlett]

[email protected] wrote:
>>Yes to knowing period from other sources.
>>Points 1 -> 97 cover exactly 24 hours.
>>The period is 23 hours 56 minutes and some seconds.
>
>
> Aha a sidereal period. Richard, there are quite a few sources that
> will let you find the osculating (not oscillating!) coefficients. A
> good book on orbital mechanics will likely prove useful.

I've a limited budget (read as ~null ;)
What would be good keywords for Google searching?

>
> Given your simple case of data and limited interval for extrapolation,
> you can try a simple LPC method. It should be good enough.
>

Yep. And Dale used a large enough hammer to wake me up to already
"knowing" the answer ;)

> Clay
>

[dbd]

On Apr 13, 7:52 am, Richard Owlett <[email protected]> wrote:
> [email protected] wrote:
> >>Yes to knowing period from other sources.
> >>Points 1 -> 97 cover exactly 24 hours.
> >>The period is 23 hours 56 minutes and some seconds.
>
> > Aha a sidereal period. Richard, there are quite a few sources that
> > will let you find the osculating (not oscillating!) coefficients. A
> > good book on orbital mechanics will likely prove useful.
>
> I've a limited budget (read as ~null ;)
> What would be good keywords for Google searching?
>
>
>
> > Given your simple case of data and limited interval for extrapolation,
> > you can try a simple LPC method. It should be good enough.
>
> Yep. And Dale used a large enough hammer to wake me up to already
> "knowing" the answer ;)
>
> > Clay

Richard

Given the additional information you have provided about the signals
in your samples, I doubt that the DFT decomposition approach for your
sample size will prove appropriate. The maximum likelihood approach
might. Knowing the number of expected signals is an advantage. If you
are interested in it try reading the second section of the last
reference I provided. The last two references are theses and one of
the advantages of theses over transactions papers is that they provide
a description of the technical background instead of just references
to it. Also the cost of the material from the source I cited is within
your budget.

An interesting follow up on your effort might be to subtract the model
of your signal (however obtained) from your samples and look for the
frequencies that have peaks in the residual. Are there effects where
you might expect them?

Dale B. Dalrymple

[Andor]

Hi Dale

As usual from you, an exemplary and highly useful reference list.
Thanks!

Regards,
Andor

[Richard Owlett]

dbd wrote:
> On Apr 13, 7:52 am, Richard Owlett <[email protected]> wrote:
>
>>[email protected] wrote:
>>
>>>>Yes to knowing period from other sources.
>>>>Points 1 -> 97 cover exactly 24 hours.
>>>>The period is 23 hours 56 minutes and some seconds.
>>
>>>Aha a sidereal period. Richard, there are quite a few sources that
>>>will let you find the osculating (not oscillating!) coefficients. A
>>>good book on orbital mechanics will likely prove useful.
>>
>>I've a limited budget (read as ~null ;)
>>What would be good keywords for Google searching?
>>
>>
>>
>>
>>>Given your simple case of data and limited interval for extrapolation,
>>>you can try a simple LPC method. It should be good enough.
>>
>>Yep. And Dale used a large enough hammer to wake me up to already
>>"knowing" the answer ;)
>>
>>
>>>Clay
>
>
> Richard
>
> Given the additional information you have provided about the signals
> in your samples, I doubt that the DFT decomposition approach for your
> sample size will prove appropriate. The maximum likelihood approach
> might. Knowing the number of expected signals is an advantage. If you
> are interested in it try reading the second section of the last
> reference I provided. The last two references are theses and one of
> the advantages of theses over transactions papers is that they provide
> a description of the technical background instead of just references
> to it. Also the cost of the material from the source I cited is within
> your budget.
>
> An interesting follow up on your effort might be to subtract the model
> of your signal (however obtained) from your samples and look for the
> frequencies that have peaks in the residual. Are there effects where
> you might expect them?
>
> Dale B. Dalrymple

Downloaded both. Have some reading do ;)
May have more than enough reading time, retirement may come earlier than
planned.

[glen herrmannsfeldt]

Richard Owlett wrote:

(snip)

> The system has 4 independent signal sources. Two of which are comparable
> in magnitude of their fundamentals. Their frequencies differ by ~.2% .
> The other are 30-300+ times slower and much weaker. But ALL 4
> fundamental frequencies are known very accurately.

How about a least-squares fit to the four sines of known frequency
but unknown phase and amplitude?

Though it might be that a linear least-squares fit to a low order
polynomial would work, too. Or maybe two sines plus a polynomial,
where the sines do the higher frequencies and the polynomial the
low frequencies.

-- glen

[Richard Owlett]

glen herrmannsfeldt wrote:
> Richard Owlett wrote:
>
> (snip)
>
>> The system has 4 independent signal sources. Two of which are
>> comparable in magnitude of their fundamentals. Their frequencies
>> differ by ~.2% .
>> The other are 30-300+ times slower and much weaker. But ALL 4
>> fundamental frequencies are known very accurately.
>
>
> How about a least-squares fit to the four sines of known frequency
> but unknown phase and amplitude?
>
> Though it might be that a linear least-squares fit to a low order
> polynomial would work, too. Or maybe two sines plus a polynomial,
> where the sines do the higher frequencies and the polynomial the
> low frequencies.
>
> -- glen
>

I asked a different form of the question on sci.geo.satellite-nav. I
eventually got a reply that one person uses Lagrange or Hermite
interpolators depending on whether or not he has velocity data as well
as position data. I have had a chance to look only at couple of
Wikipedia articles. I've more reading to do. Any recommended sites - esp
with worked examples?

I asked here with a slightly different purpose in mind.

Given a limited amount of data and some understanding of what the
underlying process "looks like":
1. How much/what can we extrapolate/interpolate/infer/... ?
2. How do you go about solving the problem?

I've learned to be very careful in phrasing my questions on this group.
If I give too many details of the _specific_ instance that triggered the
question, I'll get a fish rather than learning to fish ;)

[dbd]

On Apr 15, 3:35 am, Richard Owlett <[email protected]> wrote:
> glen herrmannsfeldt wrote:
> > Richard Owlett wrote:
>
> > (snip)
>
> >> The system has 4 independent signal sources. Two of which are
> >> comparable in magnitude of their fundamentals. Their frequencies
> >> differ by ~.2% .
> >> The other are 30-300+ times slower and much weaker. But ALL 4
> >> fundamental frequencies are known very accurately.
>
> > How about a least-squares fit to the four sines of known frequency
> > but unknown phase and amplitude?
>
> > Though it might be that a linear least-squares fit to a low order
> > polynomial would work, too. Or maybe two sines plus a polynomial,
> > where the sines do the higher frequencies and the polynomial the
> > low frequencies.
>
> > -- glen
>
> I asked a different form of the question on sci.geo.satellite-nav. I
> eventually got a reply that one person uses Lagrange or Hermite
> interpolators depending on whether or not he has velocity data as well
> as position data. I have had a chance to look only at couple of
> Wikipedia articles. I've more reading to do. Any recommended sites - esp
> with worked examples?
>
> I asked here with a slightly different purpose in mind.
>
> Given a limited amount of data and some understanding of what the
> underlying process "looks like":
> 1. How much/what can we extrapolate/interpolate/infer/... ?
> 2. How do you go about solving the problem?


> I've learned to be very careful in phrasing my questions on this group.
> If I give too many details of the _specific_ instance that triggered the
> question, I'll get a fish rather than learning to fish ;)

You don't leave out just specifics, you leave out any clear statement
of purpose or goal. You keep fuzzy logic alive at comp.dsp. We've
grown accustomed to your inability to define a problem when you hand
wave about your "problem". Fortunately at comp.dsp we are so used to
amusing ourselves talking about fishing that it doesn't matter that
you never know whether to fish or cut bait:,)

Dale B. Dalrymple

[Richard Owlett]

dbd wrote:
> [snip]
>>
>>I asked here with a slightly different purpose in mind.
>>
>>Given a limited amount of data and some understanding of what the
>>underlying process "looks like":
>>1. How much/what can we extrapolate/interpolate/infer/... ?
>>2. How do you go about solving the problem?
>
>
>
>>I've learned to be very careful in phrasing my questions on this group.
>>If I give too many details of the _specific_ instance that triggered the
>>question, I'll get a fish rather than learning to fish ;)
>
>
> You don't leave out just specifics, you leave out any clear statement
> of purpose or goal. You keep fuzzy logic alive at comp.dsp. We've
> grown accustomed to your inability to define a problem when you hand
> wave about your "problem". Fortunately at comp.dsp we are so used to
> amusing ourselves talking about fishing that it doesn't matter that
> you never know whether to fish or cut bait:,)
>
> Dale B. Dalrymple

ARRRGHH ;)

This group thinks my questions are more subtle than they are.
Although I haven't been in a classroom for 40 years, I'd guess my level
of comprehension is that of a college sophomore or junior.

For someone with engineering interests, my math aptitude is below average.

I work on a problem of interest, and it triggers a *GENERAL* question.
There are usually multiple ways out of whatever bind I'm in.

But the question remains.

P.s. Re "You keep fuzzy logic alive at comp.dsp." I've stayed out of
that thread cause the closest I've come to control systems is
maintaining L&N chart recorders with chopper stabilized vacuum tube
amplifiers in the loop. (so i show my age)

[Jerry Avins]

Richard Owlett wrote:

...

> ARRRGHH ;)
>
> This group thinks my questions are more subtle than they are.
> Although I haven't been in a classroom for 40 years, I'd guess my level
> of comprehension is that of a college sophomore or junior.

Maybe you could word your questions more cautiously, so as not to evoke
images od subtlty in the reader. (More easily said than done)

...

> P.s. Re "You keep fuzzy logic alive at comp.dsp." I've stayed out of
> that thread cause the closest I've come to control systems is
> maintaining L&N chart recorders with chopper stabilized vacuum tube
> amplifiers in the loop. (so i show my age)

Dale was gently implying that your thinking is fuzzy. Sometimes it is.
Sometimes mine is too. I don't mind getting called on it now and then.

Jerry
--
Engineering is the art of making what you want from things you can get.
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ ¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯

[Richard Owlett]

Jerry Avins wrote:
> Richard Owlett wrote:
>
> ...
>
>> ARRRGHH ;)
>>
>> This group thinks my questions are more subtle than they are.
>> Although I haven't been in a classroom for 40 years, I'd guess my
>> level of comprehension is that of a college sophomore or junior.
>
>
> Maybe you could word your questions more cautiously, so as not to evoke
> images od subtlty in the reader. (More easily said than done)
>
> ...
>
>> P.s. Re "You keep fuzzy logic alive at comp.dsp." I've stayed out of
>> that thread cause the closest I've come to control systems is
>> maintaining L&N chart recorders with chopper stabilized vacuum tube
>> amplifiers in the loop. (so i show my age)
>
>
> Dale was gently implying that your thinking is fuzzy. Sometimes it is.
> Sometimes mine is too. I don't mind getting called on it now and then.
>
> Jerry

RAFL (similar to ROFL, physical therapist objects to reports of me being
"on floor", so wheelchair has me "Rolling ABOVE Floor Laughing")

To take the second point first - my thinking isn't "fuzzy".
It's frequently totally disoriented and/or lost ;)

More seriously, this is primarily a group of engineers. And, although I
completed 3 years towards a BSEE, my aptitude/interest profile just does
not match a typical/average/"statistically normal" engineer.

This group is not just "application oriented" but " _specific_
application oriented" by mindset. That does *NOT* mean an individual
deals with ONLY "speech recognition" OR "control systems". He may do
both. !!!! *BUT* !!! he is most comfortable with a specific case.
[I hope that came out somewhere near right.]

As to the first, I can't win.

If I pare it down to a bare bones math question I'm assaulted with
request for the application details.

If I give "details", either it generates "rabbit trail" subthreads or to
paraphrase somebody's tag line, "Universal advice 'DON'T DO DAT' "

For example (snicker snicker LOL)

About two years ago I basically wanted to ask how to determine the
average time of a process measured by a clock having femto-second
accuracy *BUT* quarter HOUR resolution and report in a form grasped by
liberal ARTS majors. Having been recently severely *CHASTISED* for lack
of "detail",
http://groups.google.com/groups/search?q=%22Time+cards+and+sampling+theorems%22
was the result. Not only did I not get an answer, but in a related
thread or a personal email I was told "don't do that you will jeporadize
your job"


So, i'm frustrated
And even if the group "dives me up a wall" I do appreciate the attempts
to answer the questions that many think I should be asking. Even if they
are not the questions I am asking.



http://groups.google.com/groups/search?q=%22Time+cards+and+sampling+theorems%22
Time cards and sampling theorems

[dbd]

On Apr 16, 10:45 am, Richard Owlett <[email protected]> wrote:

When you are in a room full of people with hammers, why are you
surprised when they respond to your questions with nails?

Perhaps you have unrealistic expectations that have led you to an
unproductive approach.

> ...
>
> If I pare it down to a bare bones math question I'm assaulted with
> request for the application details.

Yes, what's wrong with that? This is an engineering sort of forum and
that is how the engineering mind proceeds when you say you have a
problem. That's how we evaluate to arrive at solutions and how we know
when we are done.

You began this discussion with a request for a 'better interpolation'.
The 'better' can only be understood in some context, but you complain
when someone asks for that context! That's either fuzzy or trollish.

>
> If I give "details", either it generates "rabbit trail" subthreads ...

Yes, what's wrong with that? This is an engineering sort of forum.

If the subthreads aren't doing what you want, post. Say so and say
why. If you don't keep playing, why shouldn't we continue to discuss
points that interest us? Of course, those subthreads will continue
whatever you do, but if you don't keep giving feedback towards what
you want to hear about, we can't respond with anything about the
interests you have chosen to leave vague.

> ...
>
> So, i'm frustrated
> And even if the group "dives me up a wall" I do appreciate the attempts
> to answer the questions that many think I should be asking. Even if they
> are not the questions I am asking.
> ...

You are getting better than you are asking for. If you want something
different, change what you ask for and how you ask for it or who you
ask. If all you want is a warm fuzzy feeling, don't feed a bunch of
technical people any suggestion of a "problem" and expect them to
respond by trying to hold your hand.

Dale B. Dalrymple

[Richard Owlett]

dbd wrote:
> On Apr 16, 10:45 am, Richard Owlett <[email protected]> wrote:
>
> When you are in a room full of people with hammers, why are you
> surprised when they respond to your questions with nails?
>
> Perhaps you have unrealistic expectations that have led you to an
> unproductive approach.
>
>
>>...
>>
>>If I pare it down to a bare bones math question I'm assaulted with
>>request for the application details.
>
>
> Yes, what's wrong with that? This is an engineering sort of forum and
> that is how the engineering mind proceeds when you say you have a
> problem. That's how we evaluate to arrive at solutions and how we know
> when we are done.
>
> You began this discussion with a request for a 'better interpolation'.
> The 'better' can only be understood in some context, but you complain
> when someone asks for that context! That's either fuzzy or trollish.
>
>
>>If I give "details", either it generates "rabbit trail" subthreads ...
>
>
> Yes, what's wrong with that? This is an engineering sort of forum.
>
> If the subthreads aren't doing what you want, post. Say so and say
> why. If you don't keep playing, why shouldn't we continue to discuss
> points that interest us? Of course, those subthreads will continue
> whatever you do, but if you don't keep giving feedback towards what
> you want to hear about, we can't respond with anything about the
> interests you have chosen to leave vague.
>
>
>>...
>>
>>So, i'm frustrated
>>And even if the group "dives me up a wall" I do appreciate the attempts
>>to answer the questions that many think I should be asking. Even if they
>>are not the questions I am asking.
>>...
>
>
> You are getting better than you are asking for. If you want something
> different, change what you ask for and how you ask for it or who you
> ask. If all you want is a warm fuzzy feeling, don't feed a bunch of
> technical people any suggestion of a "problem" and expect them to
> respond by trying to hold your hand.
>
> Dale B. Dalrymple
>

*LOL*

I ask for basic math
I get advanced engineering

OK so we are of different mindsets

b-b-b-b-b-b ut u got info i need/want ;/

[glen herrmannsfeldt]

Richard Owlett wrote:
(snip)

> So, i'm frustrated
> And even if the group "dives me up a wall" I do appreciate the attempts
> to answer the questions that many think I should be asking. Even if they
> are not the questions I am asking.

Sometimes people don't ask the right question, so the only possibility
is to answer the question that they seem to be asking.

This happens a lot in comp.lang.* newsgroups, where someone posts
about a program that doesn't work (possibly in the language of
the newsgroup) but shows no code from the program. One can then
guess what might have caused the given problem.

Sometimes a post reminds one of a question that should have
been asked but wasn't actually asked.

-- glen