# Beating Nyquist?

Started by July 25, 2007
```On 31 Jul, 09:58, Andor <andor.bari...@gmail.com> wrote:
> On 31 Jul., 02:29, glen herrmannsfeldt <g...@ugcs.caltech.edu> wrote:
>
>
>
>
>
> > Fred Marshall wrote:
>
> > (snip)
>
> > > It can be shown that a sinc is just a sum of sinusoids and that sines and
> > > cosines form an equally valid temporal basis set for a finite and regularly
> > > discrete spectrum.  Just being finite and discrete makes it pretty obvious,
> > > eh?
>
> > Any linear, and linearly independent, combination of basis functions
> > can be used as a new basis.  The sinc basis is convenient for uniform
> > spaced samples, as each sample is represented by one basis function
> > in the reconstruction.
>
> > Similarly, there are basis functions that represent the reconstructed
> > samples of a non-linearly sampled signal.  Those are not sinc.
>
> Glen, can you tell me more about those basis functions for non-
> linearly sampled signals?

I would be very surprised if simple analytical expressions
exist for such functions. The statement "a set of basis
functions exists" is a far cry from "this is the set of
basis functions."

The statement "a linear signal can be reconstructed from
any complete set of orthogonal basis functions" can be
proved using the mathematical tools covered in an intro
course on real analysis.

However, the set of sinc(x) functions is the one set of
such functions that has the property that the copntribution
to the reconstructed signal from one sample is represented
by exactly one of the basis functions. As others have
already mentioned, once you throw uniform sampling
out the window, you throw all the convenient, well
understood easy-to-use tools with it.

Rune

```
```Rune Allnor wrote:
(snip)

>>Glen, can you tell me more about those basis functions for non-
>>linearly sampled signals?

> I would be very surprised if simple analytical expressions
> exist for such functions. The statement "a set of basis
> functions exists" is a far cry from "this is the set of
> basis functions."

Simpler the sample spacing will make simpler basis
functions.  Next to uniform sampling, shifting every
other sample over, (that is, uniformly spaced pairs)
shouldn't be so hard.  Uniformly spaced triplets will
be a little harder.

> The statement "a linear signal can be reconstructed from
> any complete set of orthogonal basis functions" can be
> proved using the mathematical tools covered in an intro
> course on real analysis.

(snip)

-- glen

```
```On 31 Jul., 22:08, glen herrmannsfeldt <g...@ugcs.caltech.edu> wrote:
> Rune Allnor wrote:
>
> (snip)
>
> >>Glen, can you tell me more about those basis functions for non-
> >>linearly sampled signals?
> > I would be very surprised if simple analytical expressions
> > exist for such functions. The statement "a set of basis
> > functions exists" is a far cry from "this is the set of
> > basis functions."
>
> Simpler the sample spacing will make simpler basis
> functions.  Next to uniform sampling, shifting every
> other sample over, (that is, uniformly spaced pairs)
> shouldn't be so hard.

What exactly would they look like? How would you go about computing
them?

Regards,
Andor

```
```"Andor" <andor.bariska@gmail.com> wrote in message
> On 31 Jul., 22:08, glen herrmannsfeldt <g...@ugcs.caltech.edu> wrote:
>> Rune Allnor wrote:
>>
>> (snip)
>>
>> >>Glen, can you tell me more about those basis functions for non-
>> >>linearly sampled signals?
>> > I would be very surprised if simple analytical expressions
>> > exist for such functions. The statement "a set of basis
>> > functions exists" is a far cry from "this is the set of
>> > basis functions."
>>
>> Simpler the sample spacing will make simpler basis
>> functions.  Next to uniform sampling, shifting every
>> other sample over, (that is, uniformly spaced pairs)
>> shouldn't be so hard.
>
> What exactly would they look like? How would you go about computing
> them?
>
> Regards,
> Andor
>

I'm a bit unclear with Glen's specifications for such a basis set.

Here's a guess:
The basis set has to be 1.0 at the intended sample time *and* zero at all
the other known sample times (?).

So, knowing the sample times, one might think one could construct a
polynomial that fits all those points I suppose.  I'd expect to see some
weird functions if the sample times are bunched and sparse.  In fact, I
expect it can be proven that zeros can't be bunched too closely together
without the functions "blowing up" outside.  Bandwidth limitations limit the
regular spacing of zeros .. as with the sinc.

This makes me think that my "guess" above isn't what Glen had in mind.

Fred

```
```Fred Marshall wrote:

...

> Here's a guess:
> The basis set has to be 1.0 at the intended sample time *and* zero at all
> the other known sample times (?).

Suppose that two of the manufactured basis functions were each .5 at one
of the sample points? Suppose that instead of being individually zero at
all other sample points, the *sum* of all functions not specific to a
point is zero? Suppose, suppose.... It makes my head spin.

Jerry
--
Engineering is the art of making what you want from things you can get.
&macr;&macr;&macr;&macr;&macr;&macr;&macr;&macr;&macr;&macr;&macr;&macr;&macr;&macr;&macr;&macr;&macr;&macr;&macr;&macr;&macr;&macr;&macr;&macr;&macr;&macr;&macr;&macr;&macr;&macr;&macr;&macr;&macr;&macr;&macr;&macr;&macr;&macr;&macr;&macr;&macr;&macr;&macr;&macr;&macr;&macr;&macr;&macr;&macr;&macr;&macr;&macr;&macr;&macr;&macr;&macr;&macr;&macr;&macr;&macr;&macr;&macr;&macr;&macr;&macr;&macr;&macr;&macr;&macr;&macr;&macr;
```
```"Jerry Avins" <jya@ieee.org> wrote in message
news:JuidndhNMfkEiS_bnZ2dnUVZ_uKpnZ2d@rcn.net...
> Fred Marshall wrote:
>
>   ...
>
>> Here's a guess:
>> The basis set has to be 1.0 at the intended sample time *and* zero at all
>> the other known sample times (?).
>
> Suppose that two of the manufactured basis functions were each .5 at one
> of the sample points? Suppose that instead of being individually zero at
> all other sample points, the *sum* of all functions not specific to a
> point is zero? Suppose, suppose.... It makes my head spin.
>
> Jerry

Jerry,

There aren't that many choices for criteria.  I guessed that Glen wanted
zero at each "other" sample instant and 1.0 at the "designated" sample
instant - the same criterion as when one uses a sinc on a regular grid.

Otherwise, e.g. when using when using sinusoids, the only criterion is that
the sums equal the sample values at the sample points.  How's that for a
general requirement? errrr... that's the Fourier Transform or Series
depending .....

But, since you mentioned it, if one uses the sum of three adjacent sincs
with weights .5 1.0 .5 as the basis set then the summation is a bit more
involved as three samples determine the weight for each basis and the payoff
is that the "tails" decay much more rapidly than a sinc.  Sometimes that's
handy.  In the frequency domain it's the difference between a gate and a
raised cosine.

Forcing the sum to be zero instead of forcing the basis functions to be
individually zero is doable as long as the number of such points is limited.
For example, if doing a minimax approximation like Parks McClellan does, one
can force zeros (or other equalities) in the approximant.  So, with the
Remez algorithm, doing this causes the error to skip a sign alternation - as
it uses up one degree of freedom for each such point.  Specifically, the
zeros need to be in the error so they would also be response zeros in a
stopband.  Passband forced zero error points would be where the approximant
is equal to the desired passband response.

Fred

```
```Fred Marshall wrote:
(snip of discussion on basis function for non-uniform sampling)

> Here's a guess:
> The basis set has to be 1.0 at the intended sample time *and* zero at all
> the other known sample times (?).

> So, knowing the sample times, one might think one could construct a
> polynomial that fits all those points I suppose.  I'd expect to see some
> weird functions if the sample times are bunched and sparse.  In fact, I
> expect it can be proven that zeros can't be bunched too closely together
> without the functions "blowing up" outside.  Bandwidth limitations limit the
> regular spacing of zeros .. as with the sinc.

If by "blowing up" you mean infinite, no, it shouldn't do that.

As the spacing gets less uniform, though, the basis functions can
get large, which is the cause for the sensitivity of the reconstruction
to the accuracy of the samples, including any noise.

Consider sampling at points ..., -3, -3+a, -2, -2+a, -1, -1+a, 0, a, 1,
1+a, 2, 2+a, ...  That is, all integers and integers plus a constant a.
What do the functions look like when a=0.5?  They must be sinc(2x).
But sinc(2x)=sin(2pi x)/(2 pi x) = cos(pi x)sin(pi x)/(pi x) where
the cos(pi x) adds the extra zeros where they are needed.

If you put two samples between each integer, at a and b, and
have a=1/3 and b=2/3, then
3 sinc(3x)=sin(3 pi x)/(pi x)=(4 cos(pi x)**2 -1) sin(pi x)/(3 pi x)
or  (2 cos(pi x)-1) (2 cos(pi x)+1) sin(pi x) / (3 pi x)

where the cos terms supply the new zeros.

This gives some hint as to what the functions will look like
for other a and b in factored form.

For samples at integers and integers+a, the function
for x=0 (and shifted, for other integers)

f(x)=sin(pi x)sin(pi (x-a))/sin(-pi a)/(pi x)

the function for the sample at a will be the mirror image around a,
g(x)=f(a-x)

g(x)=sin(pi (a-x)) sin(-x)/sin(-pi a)/(pi (a-x))

as a gets close to 0 or 1 the peak gets larger as
abs(sin(pi a)) gets smaller.

-- glen

```
```glen herrmannsfeldt wrote:
> Fred Marshall wrote:
>
> (snip of discussion on basis function for non-uniform sampling)
>
> > Here's a guess:
> > The basis set has to be 1.0 at the intended sample time *and* zero at all
> > the other known sample times (?).
> > So, knowing the sample times, one might think one could construct a
> > polynomial that fits all those points I suppose.

In general, there are an infinite number of sample points, and the
polynomial becomes a power series. But the idea is correct.

> > I'd expect to see some
> > weird functions if the sample times are bunched and sparse.  In fact, I
> > expect it can be proven that zeros can't be bunched too closely together
> > without the functions "blowing up" outside.  Bandwidth limitations limit the
> > regular spacing of zeros .. as with the sinc.
>
> If by "blowing up" you mean infinite, no, it shouldn't do that.

If by "blowing up" Fred means growing without bound, then that seems
very good intuition to me. You can see it in your functions below if
you let a->0 or 1.

>
> As the spacing gets less uniform, though, the basis functions can
> get large, which is the cause for the sensitivity of the reconstruction
> to the accuracy of the samples, including any noise.
>
> Consider sampling at points ..., -3, -3+a, -2, -2+a, -1, -1+a, 0, a, 1,
> 1+a, 2, 2+a, ...  That is, all integers and integers plus a constant a.
> What do the functions look like when a=0.5?  They must be sinc(2x).
> But sinc(2x)=sin(2pi x)/(2 pi x) = cos(pi x)sin(pi x)/(pi x) where
> the cos(pi x) adds the extra zeros where they are needed.
>
> If you put two samples between each integer, at a and b, and
> have a=1/3 and b=2/3, then
> 3 sinc(3x)=sin(3 pi x)/(pi x)=(4 cos(pi x)**2 -1) sin(pi x)/(3 pi x)
> or  (2 cos(pi x)-1) (2 cos(pi x)+1) sin(pi x) / (3 pi x)
>
> where the cos terms supply the new zeros.
>
> This gives some hint as to what the functions will look like
> for other a and b in factored form.
>
> For samples at integers and integers+a, the function
> for x=0 (and shifted, for other integers)
>
> f(x)=sin(pi x)sin(pi (x-a))/sin(-pi a)/(pi x)

I'm not quite sure how you arrived at that function. Does that follow
from what you wrote above?

>
> the function for the sample at a will be the mirror image around a,
> g(x)=f(a-x)
>
> g(x)=sin(pi (a-x)) sin(-x)/sin(-pi a)/(pi (a-x))

If you compare g and f with the kernels given in [1], these certainly
seem to be the correct interpolation functions in the case where every
second sample shifted.

>
> as a gets close to 0 or 1 the peak gets larger as
> abs(sin(pi a)) gets smaller.

Yup.

Regards,
Andor

[1] J. L. Yen, "On Nonuniform Sampling of Bandwidth-Limited Signals,"
IRE Trans. Circuit Theory, vol. 3, pp. 251-257, Dec. 1956.

```
```"Andor" <andor.bariska@gmail.com> wrote in message
> glen herrmannsfeldt wrote:
>> Fred Marshall wrote:
>>
>> (snip of discussion on basis function for non-uniform sampling)
>>
>> > Here's a guess:
>> > The basis set has to be 1.0 at the intended sample time *and* zero at
>> > all
>> > the other known sample times (?).
>> > So, knowing the sample times, one might think one could construct a
>> > polynomial that fits all those points I suppose.
>
> In general, there are an infinite number of sample points, and the
> polynomial becomes a power series. But the idea is correct.
>
>> > I'd expect to see some
>> > weird functions if the sample times are bunched and sparse.  In fact, I
>> > expect it can be proven that zeros can't be bunched too closely
>> > together
>> > without the functions "blowing up" outside.  Bandwidth limitations
>> > limit the
>> > regular spacing of zeros .. as with the sinc.
>>
>> If by "blowing up" you mean infinite, no, it shouldn't do that.
>
> If by "blowing up" Fred means growing without bound, then that seems
> very good intuition to me. You can see it in your functions below if
> you let a->0 or 1.
>
>>
>> As the spacing gets less uniform, though, the basis functions can
>> get large, which is the cause for the sensitivity of the reconstruction
>> to the accuracy of the samples, including any noise.
>>
>> Consider sampling at points ..., -3, -3+a, -2, -2+a, -1, -1+a, 0, a, 1,
>> 1+a, 2, 2+a, ...  That is, all integers and integers plus a constant a.
>> What do the functions look like when a=0.5?  They must be sinc(2x).
>> But sinc(2x)=sin(2pi x)/(2 pi x) = cos(pi x)sin(pi x)/(pi x) where
>> the cos(pi x) adds the extra zeros where they are needed.
>>
>> If you put two samples between each integer, at a and b, and
>> have a=1/3 and b=2/3, then
>> 3 sinc(3x)=sin(3 pi x)/(pi x)=(4 cos(pi x)**2 -1) sin(pi x)/(3 pi x)
>> or  (2 cos(pi x)-1) (2 cos(pi x)+1) sin(pi x) / (3 pi x)
>>
>> where the cos terms supply the new zeros.
>>
>> This gives some hint as to what the functions will look like
>> for other a and b in factored form.
>>
>> For samples at integers and integers+a, the function
>> for x=0 (and shifted, for other integers)
>>
>> f(x)=sin(pi x)sin(pi (x-a))/sin(-pi a)/(pi x)
>
> I'm not quite sure how you arrived at that function. Does that follow
> from what you wrote above?
>
>>
>> the function for the sample at a will be the mirror image around a,
>> g(x)=f(a-x)
>>
>> g(x)=sin(pi (a-x)) sin(-x)/sin(-pi a)/(pi (a-x))
>
> If you compare g and f with the kernels given in [1], these certainly
> seem to be the correct interpolation functions in the case where every
> second sample shifted.
>
>>
>> as a gets close to 0 or 1 the peak gets larger as
>> abs(sin(pi a)) gets smaller.
>
> Yup.
>
> Regards,
> Andor
>
> [1] J. L. Yen, "On Nonuniform Sampling of Bandwidth-Limited Signals,"
> IRE Trans. Circuit Theory, vol. 3, pp. 251-257, Dec. 1956.

Andor and Glen,

Well, it wasn't *entirely* intuition but close enough.  I was thinking of
supergained functions which "blow up" in the sense that the approximated
function gets very large  - perhaps not infinite - outside the region of
interest (and sometimes in the "invisible" region to use antenna pattern
language).  This occurs when you push the approximant between zero and 2pi
without bounding what happens beyond 2pi.

A limiting case for supergaining is the vanDerMaas antenna pattern function
that has perfectly flat sidelobes / sinc-like functions with non-decaying
tails / extending beyond 2pi and to infinity (this is seen if the
independent variable is taken as the angle and not the cosine of the angle),
and has a window transform that *is* necessarily infinite at the edges to
produce the never-edning sinusoidal component.

Andor interprets what I called "bunched up" as the separation between
samples approaches zero.  That's a good way of looking at it.  You might
ask: What happens to the implied bandwidth when that happens?  I think it
must go up.

There's another way of looking at this: if we assume a strictly bandlimited
function is sampled irregularly.  Reconstruction can happen by convolving
the samples with a sinc / i.e. passing them through a perfect lowpass
filter.  Looking at it that way, the basis set doesn't change from the most
familiar one - but the construction expressions are more complicated is all.
Glen's construction is interesting nonetheless!

Fred

```