ADC without antialias filter? was: Anti-aliasing ADC samples

[Marte Schwarz]

hello all,

I have a little topic I couldn't understand completely (or as much I want to
;-)

As a rule of thumb it is well known to use a filter to cut all frequencies
above half the sample frequency. But I guess, there are cases that doesn't
need this or where the signals would be more real without this filter than
with such one. For example ECG-signals. A QRS complex has frequencies in the
range of over 100 Hz. But these frequencies where not stationary so one
criteria to construct alias is not present. But filtering these Signals to
avoid frequencies over half the sampling rate may extend the QRS and in this
case changes relevant parameters of the signal. In real world we often have
nonstationary signals in which aliasing would not be created by having
samplerates below the nyquist criteria. OK, we make errors having not enough
sampling rate, sure, but depending on the goal of the signal analysis, may
be it is sometimes better to take these errors and avoid others created from
(analog) filters.

Where is the error in this statement? Or is it correct? What do you think
about?

regards

Marte

 

[Zak]

hello all,

I have a little topic I couldn't understand completely (or as much I want to
;-)

As a rule of thumb it is well known to use a filter to cut all frequencies
above half the sample frequency. But I guess, there are cases that doesn't
need this or where the signals would be more real without this filter than
with such one. For example ECG-signals. A QRS complex has frequencies in the
range of over 100 Hz. But these frequencies where not stationary so one
criteria to construct alias is not present.

Wrong. They can still alias, and perhaps not give a tone but a wrong
waveform.

---_--_--_----

could become

------__------

which is totally incorrect.

But filtering these Signals to
avoid frequencies over half the sampling rate may extend the QRS and in this
case changes relevant parameters of the signal. In real world we often have
nonstationary signals in which aliasing would not be created by having
samplerates below the nyquist criteria. OK, we make errors having not enough
sampling rate, sure, but depending on the goal of the signal analysis, may
be it is sometimes better to take these errors and avoid others created from
(analog) filters.

Where is the error in this statement? Or is it correct? What do you think
about?

I think you need to filter. If you are concerned about the waveform
shape and do not want ringing: there are special filters for that. But
expect that the rolloff is slower.


Thomas

 

[Tim Wescott]

hello all,

I have a little topic I couldn't understand completely (or as much I want to
;-)

As a rule of thumb it is well known to use a filter to cut all frequencies
above half the sample frequency.

Well known, but generally misapplied.

But I guess, there are cases that doesn't
need this or where the signals would be more real without this filter than
with such one.

If the signal comes to you adequately bandlimited then you don't need to
low-pass filter it. There are some signals where anti-alias filtering
would degrade the signal -- specifically, a video signal is not
optically anti-aliased. Instead you try to get the sharpest picture you
can onto the detector, and avoid scenes that have a lot of high
frequency content.

For example ECG-signals. A QRS complex has frequencies in the
range of over 100 Hz. But these frequencies where not stationary so one
criteria to construct alias is not present. But filtering these Signals to
avoid frequencies over half the sampling rate may extend the QRS and in this
case changes relevant parameters of the signal. In real world we often have
nonstationary signals in which aliasing would not be created by having
samplerates below the nyquist criteria. OK, we make errors having not enough
sampling rate, sure, but depending on the goal of the signal analysis, may
be it is sometimes better to take these errors and avoid others created from
(analog) filters.

Where is the error in this statement? Or is it correct? What do you think
about?

I agree with that statement. If you're intentionally sampling below the
frequency content of the parent signal then you _are_ going to degrade
it, you should do so intelligently.

--

Tim Wescott
Wescott Design Services
http://www.wescottdesign.com

Posting from Google? See http://cfaj.freeshell.org/google/

"Applied Control Theory for Embedded Systems" came out in April.
See details at http://www.wescottdesign.com/actfes/actfes.html

 

[Pooh Bear]

hello all,

I have a little topic I couldn't understand completely (or as much I want to
;-)

As a rule of thumb it is well known to use a filter to cut all frequencies
above half the sample frequency. But I guess, there are cases that doesn't
need this

I have indeed omitted an anti-aliasing filter when I'm confident of the
bandwidth of the input signal.

Graham

 

[Mac]

hello all,

I have a little topic I couldn't understand completely (or as much I want to
;-)

As a rule of thumb it is well known to use a filter to cut all frequencies
above half the sample frequency. But I guess, there are cases that doesn't
need this or where the signals would be more real without this filter than
with such one. For example ECG-signals. A QRS complex has frequencies in the
range of over 100 Hz. But these frequencies where not stationary so one
criteria to construct alias is not present. But filtering these Signals to
avoid frequencies over half the sampling rate may extend the QRS and in this
case changes relevant parameters of the signal. In real world we often have
nonstationary signals in which aliasing would not be created by having
samplerates below the nyquist criteria. OK, we make errors having not enough
sampling rate, sure, but depending on the goal of the signal analysis, may
be it is sometimes better to take these errors and avoid others created from
(analog) filters.

Where is the error in this statement? Or is it correct? What do you think
about?

regards

Marte

I don't fully understand your post. In particular, I don't know what QRS
is, and I don't understand what you mean by stationary in this context.

There are a few rules when it comes to sampling. The most strict case is
when you can make no assumptions about the input signal bandwidth. Then
you need to low- or band-pass filter it very well prior to sampling.

Another interesting case would be where you know that the majority of the
signal of interest is below Nyquist, and there is little signal or noise
above Nyquist. In this case, you could omit the filter based on this
a priori knowledge.

I see frequent references to under-sampling in literature about ADC's for
communications applications. In particular, you might see an 80 MHz ADC
with an analog input bandwidth of 200 MHz or something. Why would you feed
such a high BW signal into an 80 MHz ADC?

Well, what is going on there is that they band-pass the input signal
until it has less than 40 MHz of bandwidth, and then they under-sample it.
So it is aliased, but that is OK because you know exactly which alias
region it inhabits (because of the BPF), so you can account for it later.

For example, I was thinking that it would be kind of cool to build a
direct sampling FM radio. You could have an antenna, then a LNA and AGC
circuit followed by an 88-108 MHz bandpass followed by an ADC. If you ran
the ADC at, say, 80 MHz, you could pick up the entire FM band. Sure, it
would be aliased, but since you know the frequency content is between 88
and 108, you don't care, and you can demodulate it digitally in spite of
that.

Another example would be if you have some kind of parametric model of what
you think you are sampling. Then you can just adjust the parameters until
they fit with the samples. By making these kind of assumptions, your
reconstructed signal could conceivably have detail not present in the raw
samples. But there are a lot of assumptions built-in to the process, so it
is important that they be understood and verified.

HTH

--Mac

 

[Steve Burke]

Marte:

I disagree with some of your points. I start by pointing out some sampling
issues and then I'll comment on your main point regarding filters.

I agree with Tim that poor Mr. Nyquist is often misunderstood. Nyquist's
rate has to do with re-constructing a band-limited signal from a sampled
data set. If your intent is to extract signal features without
reconstructing the actual waveform, then you can take all sorts of liberties
with the sample rate, provided you do no violence to the mathematical
properties of the sampled data you want to extract/preserve. A common
application of this is in sampling a radio receiver IF, say for an AM radio
station. The carrier is typically converted to 455 kHz but the actual audio
modulation of that carrier is only about 15 kHz wide. So the sample rate
must be greater than 30 kHz to recover the modulation without introducing
alias products. It doesn't have to be greater than 910 kHz, because we are
not trying to recover the carrier after we digitize. This is often called
"undersampling", but in reality it is still oversampling the bandwidth of
interest. Its undersampling the carrier, because we don't really need it.

The second thing to understand is that alias products are always present in
any sampled data set, no matter if its filtered before digitizing or not.
This is a natural result of the sampling theorem, (when you look at the
entire frequency band, not just the band from 0 to half the sample rate).
What actually has to be avoided is the condition where the alias products
intermix with (or overlap) the desired non-alias products, because then you
don't know which ones you can ignore when you do the digital processing.

If you sample a 45 Hz tone at 100 Hz sample rate, you get a tone at 45 Hz in
the digital domain, and you will also have a tone at (100-45 = ) 55 Hz. You
also have an infinite number of tones at the repeating locations of 145,
155, 245, 255, etc. but I'm going to ignore them for this discussion. As
long as we reject everything above 50 Hz, we can see exactly what we
digitized : 45 Hz. But suppose we digitized 55 Hz at the same 100 Hz rate.
We'd have a tone at 55 Hz, and another at 100-55 = 45 Hz. (and 145, 155....)
Now if we process the lower 0 - 50 Hz band, we are going to think we had 45
Hz coming in when we actually did not. Here the alias product showed up in
the desired signal band.

I apologize if you understand all this already, but I wanted to set up a
numerical case to disagree with some of your assertions, because numbers
help make things clearer. I've already spoken to your statement that:I assume you mean that because the pulse is transient, its not stationary in
the mathematical sense. That doesn't really matter here - the fact that we
sample it at all means that we have to concern ourselves with alias issues.
The duration of the alias energy is exactly as long as the duration of the
digitized signal whether it's transient or continuous, and its always there.

Your primary concern seems to be in these statements:

First, if the QRS signal changes in any relevant way due to the filter, then
either there is really higher frequency energy than you think there is, or
the filter is not as good as it needs to be. Building a good anti-alias
filter can be difficult or impossible, if the design tries to use a sampling
rate that's very close to the minimum (2x). The biggest reason for raising
the sample rate above 2x is to relax the analog filter specs. Its usually
much easier to process more data digitally than to build a sharper analog
filter. In my example, if the highest component to preserve is 45 Hz, then
the analog filter has to pass 45 Hz with no loss or phase perturbations, and
reject everything above 50 Hz. That is a 50/45 = 1.1 to 1 shape factor. It
is almost impossible to build a low pass filter that steep, and even harder
to keep it stable over temperature and time.

But if my digital processing can reject everything above 45 Hz in the
digital spectrum, then I don't really have to worry about a 51 Hz analog
signal causing a problem. Its digital alias will show up at 49 Hz and the
digital processing will reject it. But if an analog signal arrives at 55 Hz,
its going to create an alias at 45 and my digital processing won't be able
to reject that. The digital filtering does allow the cutoff for the analog
filter to be extended from 50 to 55 Hz though. This changes the shape factor
to 55/45 = 1.2 to 1. Still hard to build, but better than before. Moving
the sample rate up to 120 Hz will buy big improvement. An input at 75 Hz
will alias to 45 Hz when sampled at 120 Hz. Now the filter shape decreased
to 75/45 = 1.6 to 1. Here we get to practical shape for an analog filter.
The farther I'm willing to raise that sample frequency, the easier to ensure
clean digitizing with no alias issues and no in-band distortion from the
filter.

My advice is to not get hung up on squeezing your signal into the minimum
number of samples per second. Make your life easier by giving margin for
your filters. ADC's without filters mean you can never be certain that the
digital data stream really represents what appeared at the analog input.
Noise, static electricity pops, ground loops, etc are all facts of life and
analog filters are a necessity for signal integrity.

I agree with Tim that if the filtering is done elsewhere, you don't need to
do it twice. But it must be done. Avoiding filters because they seem to
degrade the signal indicates you are actually interested in more of the
signal than you designed for.

A great book on this topic is Richard Lyons "Understanding Digital Signal
Processing". His graphical illustrations of many of these principles are
very unique and bring a lot of clarity without a pile of obscure math.

Steve

 

[Ban]

hello all,

I have a little topic I couldn't understand completely (or as much I
want to ;-)

As a rule of thumb it is well known to use a filter to cut all
frequencies above half the sample frequency. But I guess, there are
cases that doesn't need this or where the signals would be more real
without this filter than with such one. For example ECG-signals. A
QRS complex has frequencies in the range of over 100 Hz. But these
frequencies where not stationary so one criteria to construct alias
is not present. But filtering these Signals to avoid frequencies over
half the sampling rate may extend the QRS and in this case changes
relevant parameters of the signal. In real world we often have
nonstationary signals in which aliasing would not be created by
having samplerates below the nyquist criteria. OK, we make errors
having not enough sampling rate, sure, but depending on the goal of
the signal analysis, may be it is sometimes better to take these
errors and avoid others created from (analog) filters.
Where is the error in this statement? Or is it correct? What do you
think about?

Marte,
first of all: analog filters work with known parameters. Whatever change
they induce is linear and can be compensated for during the digital
processing if needed.
Aliasing on the other hand is not a linear phenomenon. When the signal is
digitized you cannot differ between the folded back alias and the original
signal, because the frequency is no more related to the signal frequency.
Additionally there are timing errors, because the remaining analog filters
will delay the aliasing signal much less than the original.
An example:
The probes pick up the heart muscle signal plus some mains-, motor-
cellphone- radiation. Later the doctor wants to see the heart signal with a
resolution of 10cm/beat and is used to a resolution of 1mm. this means a
signal range of 2Hz*100=200Hz, so if you digitize with 1000Hz and 12bit,
your analog filter has to attenuate all signals above 800Hz by at least
74dB. a 700Hz disturbance will fold back to 300 hz and can be filtered out
digitally, but not one at 900Hz, that folds back to 100Hz. So your analog
filter has to be quite steep and complicated.
Much better is to oversample and use a simple analog filter. So with 12800Hz
sample rate you just need a 3rd order filter, easy to implement and no
adjustment needed.
The most important thing is to shield the analog processing to eliminate the
induced undesired crap, then the filter might even be just a first order
lowpass.

 

[Marte Schwarz]

Hi all,

An example:
The probes pick up the heart muscle signal plus some mains-, motor-
cellphone- radiation.

This is the one case that I don't want to take in account. Just let's take
the heart-signal as it is. For those who don't know I try to simplify it:

_____ _____ _____
| | | | | |
| | | | | |
| | | | | |
_________| |________________| |________________________| |_________

t1 t2 t3 t4 t5 t6

Look @ this: Really we have (nearly) infinite frequencies in this signal. So
we have to decide whether we have to filter or not.
I pointed, that as long as the distances t3-t1, t5-t3... are not equal (in
case of ECG varying randomly from 0.3 to 1 s) I do not get aliasing
frequencies if i use a sampling rate from lets say 100 Sps. Well, it is
obvious, that the time-resolution won't be more than 10 ms but I can't see,
where in this example would be any chance for alias errors.

digitally, but not one at 900Hz, that folds back to 100Hz. So your analog
filter has to be quite steep and complicated.

Right, but there you have stationary signals in contrast to the upper
situation, where the higher frequencies exists only a very short time and
because of the nonstationarity there is no chance to create alias effects.

Much better is to oversample and use a simple analog filter. So with
12800Hz sample rate you just need a 3rd order filter, easy to implement
and no adjustment needed.

but guess, if you have 8 channels with 24 Bit resolution @ 12800 Sps that
creates a dataflow that needs too much µC-power for a small, and portable
system.

Marte

 

[Rich Grise]

I don't fully understand your post. In particular, I don't know what QRS
is, and I don't understand what you mean by stationary in this context.

The QRS wave is a sample of an EKG (electrocardiogram) waveform, that has
segments known as P, Q, R, S, and T. "QRS" is the "important" part". The
waveform can tell a lot about the health of the heart, but I can't imagine
anything anywhere near 100 Hz being meaningful - the fundamental is about
1 Hz, after all. ;-)

I once worked with a unit that did that very thing, but I was on the
software side, so just got a data stream - I have no idea if they used
antialiasing filters, but since it was an FM RF link, it was probably
pretty low. Just for perspective, if you listen to the FM, it goes
wuooooooWEEEoowuoooooWEEoowuoooooWEEoowuooo....., then that goes to an
F/V converter and gets ADC'd.

Cheers!
Rich

Cheers!
Rich

 

[Ban]

Hi all,


This is the one case that I don't want to take in account. Just let's
take the heart-signal as it is. For those who don't know I try to
simplify it:
_____ _____ _____
| | | | | |
| | | | | |
| | | | | |
_________| |________________| |________________________| |_________
t1 t2 t3 t4 t5 t6

Look @ this: Really we have (nearly) infinite frequencies in this
signal. So we have to decide whether we have to filter or not.
I pointed, that as long as the distances t3-t1, t5-t3... are not
equal (in case of ECG varying randomly from 0.3 to 1 s) I do not get
aliasing frequencies if i use a sampling rate from lets say 100 Sps.
Well, it is obvious, that the time-resolution won't be more than 10
ms but I can't see, where in this example would be any chance for
alias errors.

Have you ever seen an ECG? What you have drawn has only timing information
and a lot of redundancy. You better take a logic analyzer for that kind of
signal.
And you should study again the sampling theorem. It actually doesn't talk
about aliasing, but that any waveform can be perfectly reconstructed, if the
highest frequency is below half the sampling frequency. If your heartbeats
are equal or not is irrelevant.

Right, but there you have stationary signals in contrast to the upper
situation, where the higher frequencies exists only a very short time
and because of the nonstationarity there is no chance to create alias
effects.

Don't mix Shannon and Fourier.
Of course there is aliasing. Lets zoom into the rising edge. There is a
+/-10ms uncertainty when it happened, so only a 50Hz signal can be exactly
reconstructed. And what happens when you sample in the moment it rises?
There is a random step, which is the higher frequencies folding back into
your signal.

but guess, if you have 8 channels with 24 Bit resolution @ 12800 Sps
that creates a dataflow that needs too much µC-power for a small, and
portable system.

You seem to know too little about digital processing. And if you have a
24bit A/D, it must be a Delta/Sigma converter, which will work completely
different than you thought. BTW these converters do a similar data reduction
that I was proposing. And they *are* small.

 

[GregS]

The QRS wave is a sample of an EKG (electrocardiogram) waveform, that has
segments known as P, Q, R, S, and T. "QRS" is the "important" part". The
waveform can tell a lot about the health of the heart, but I can't imagine
anything anywhere near 100 Hz being meaningful - the fundamental is about
1 Hz, after all. ;-)

I once worked with a unit that did that very thing, but I was on the
software side, so just got a data stream - I have no idea if they used
antialiasing filters, but since it was an FM RF link, it was probably
pretty low. Just for perspective, if you listen to the FM, it goes
wuooooooWEEEoowuoooooWEEoowuoooooWEEoowuooo....., then that goes to an
F/V converter and gets ADC'd.

We are looking at heart surface being fluoresced by heart signals, looking at it with a phodiode
camera and the bandwidth needs to be high, but I figure most of the signal is below
100 Hz. The bandwith of the system is several hundred Hz. There is some other stuff
apparently as they wish to get a 10 kHz bandwidth.

A chart of collected data of signals propagating the heart surface. I could not find the movie
I was looking for.
http://ajpheart.physiology.org/content/vol280/issue4/images/large/h40410665005.jpeg



greg

 

[Marte Schwarz]

Hi Rich,

The QRS wave is a sample of an EKG (electrocardiogram) waveform, that has
segments known as P, Q, R, S, and T. "QRS" is the "important" part". The
waveform can tell a lot about the health of the heart, but I can't imagine
anything anywhere near 100 Hz being meaningful - the fundamental is about
1 Hz, after all. ;-)

The range of heartbeats is about 50 to 180 bpm, that's true. But ig you want
to create a QRS which usualy looks like a triangle you need a frequency
range up to 200 Hz. IMHO IEC and AAMI says about 100 Hz is needed for
diagnostic ECG.

pretty low. Just for perspective, if you listen to the FM, it goes
wuooooooWEEEoowuoooooWEEoowuoooooWEEoowuooo....., then that goes to an
F/V converter and gets ADC'd.

That is not the point I mean.

Marte

 

[Marte Schwarz]

Hi Ban,

Have you ever seen an ECG?

Guess, I say a lot of EGCs. As I wrote: It is simplified and while I didn't
want to "draw" real looking ECG signals, the drawn signal is very good to
explain what I want to know.

What you have drawn has only timing information and a lot of redundancy.

In face of the postulation I did i can not see the relevance of real
amplitude or something else. The question is the same with such a signal:

_____
_____ | |
| | | | _____
| | | | | |
_________| |________________| |________________________| |_________
t1 t2 t3 t4 t5 t6

I know, this is not a ECG signal but is very pretty to explain, why I think,
a filter derates my signal more than it would help.

Don't mix Shannon and Fourier.
Of course there is aliasing. Lets zoom into the rising edge. There is a
+/-10ms uncertainty when it happened, so only a 50Hz signal can be exactly
reconstructed. And what happens when you sample in the moment it rises?
There is a random step, which is the higher frequencies folding back into
your signal.

I this special case ther is no sampling point in the moment of rising
possible (infinity small). That would mean, I only get a jitter of 10 ms and
gan reconstruct the Signal as is. I gan not see any mirroring frequencies
created by aliasing.

You seem to know too little about digital processing. And if you have a
24bit A/D, it must be a Delta/Sigma converter, which will work completely
different than you thought.

The AD we uses at the moment (AD1254) gives us 4 ksps when multiplexing the
channels. I get the sample values like with every other AD, isn't it?

BTW these converters do a similar data reduction that I was proposing.

Clock frequency is not the same than sample rate here.

And they *are* small.

Yes, and now what do you want to say me with this?

Marte

 

[Rich Grise]

Hi Rich,


The range of heartbeats is about 50 to 180 bpm, that's true. But ig you want
to create a QRS which usualy looks like a triangle you need a frequency
range up to 200 Hz. IMHO IEC and AAMI says about 100 Hz is needed for
diagnostic ECG.

OK, so find a filter design with a 300 Hz cutoff, that's flat in the
passband. Or am I missing something?

That is not the point I mean.

Yeah, but was it cute? ;-)

Cheers!
Rich

 

[Marte Schwarz]

Hi Rich,

OK, so find a filter design with a 300 Hz cutoff, that's flat in the
passband. Or am I missing something?

Yes, may be I want to sample with 100 Sps. There are really higher
frequencies
in the ECG signal as well as in the exsample signal above.
Normally we have to built an antialias filter with cutoff under 50 Hz. The
obvious effect then is, that the signal without filter is much more like the
original signal (sampled with >1 kSps) than the (correct) filtered signal.
Theory says the filter should be there to reduce unwanted / unreal signals
that are produced via aliasing. Fact is, we do not see such unwanted signal
parts but we realise degraded quality with filters.

Marte

 

[Ban]

I this special case ther is no sampling point in the moment of rising
possible (infinity small). That would mean, I only get a jitter of 10
ms and gan reconstruct the Signal as is. I gan not see any mirroring
frequencies created by aliasing.


The AD we uses at the moment (AD1254) gives us 4 ksps when
multiplexing the channels. I get the sample values like with every
other AD, isn't it?

I didn't find that one on AD, but the 7718. These converters work completely
different. They integrate the incoming analog signals and are open as long
as the channel is active. Which means you get an ugly step instead of your
square edge and anyway you want that step only on rising or falling edges,
not when the signal is almost constant. You are describing a sampling
converter, using much more current and having much less resolution.

Clock frequency is not the same than sample rate here.


Yes, and now what do you want to say me with this?

You can pretty easily downsample with a small FIR filter, but if you do not
know that, I would recommend to read your script about A/D converters again.
Then it would be better to simulate the whole project with Matlab. Record
some real probe signal with a digital scope and use that wav file for your
simulation. You can also simulate analog filters with CoolEdit and the
digital part with Matlab DSP toolbox and filter toolbox. When this works you
should only start with the hardware. This is also tricky as the converter is
extremely delicate and has to be protected from blowout. We can give you
some hand there, but we cannot do the understanding for you.

 

[Rich Grise]

Hi Rich,



Yes, may be I want to sample with 100 Sps. There are really higher
frequencies
in the ECG signal as well as in the exsample signal above.
Normally we have to built an antialias filter with cutoff under 50 Hz. The
obvious effect then is, that the signal without filter is much more like the
original signal (sampled with >1 kSps) than the (correct) filtered signal.
Theory says the filter should be there to reduce unwanted / unreal signals
that are produced via aliasing. Fact is, we do not see such unwanted signal
parts but we realise degraded quality with filters.

That's probably because in an EKG signal there probably aren't very many
harmonics above 50 Hz to _do_ any aliasing. As long as you can follow
the slew rate, you should be fine as is.

Good Luck!
Rich

 

[Marte Schwarz]

Hi,

I didn't find that one on AD
www.ti.com

some hand there, but we cannot do the understanding for you.

Thank you, but as long as I have the feeling that you didn't understand the
topic here I won't miss your help

Have a nice weekend

Marte

 

[Ban]

Hi,


Thank you, but as long as I have the feeling that you didn't
understand the topic here I won't miss your help

Have a nice weekend

Marte

Marte, the part is called ADS1254, that's why I didn't find it.

Please read about Sigma/Delta converters. You will see that you are just
dreaming what you wrote. And I'm sure your professor will ask you as well.
So you should be a little more humble if someone points out your
misunderstanding. This is not only true for the principle of operation, but
also about the sampling theorem. This is one of the fundamentals for any
engineer, which you will be asked about. And I can see you know deep down
that your understanding is missing: "I get the sample values like with every
other AD, isn't it?"
No, it is integrating and *not* sampling. And maybe you know that this is
equivalent to a lowpass filter. So the whole question is bogus, you are
already filtering with this converter and maybe you do not need an
additional analog filter, if you choose the right parameters, and you will
loose many details and you won't get any square edges, they are also not
needed anyway.
But I think Karlsruhe is not a bad place to study, so you seem to be still
in the beginning. Talk with some tutor about the project, is it for the
"Studienarbeit?"
And why don't you ask the question in de.sci.electronics, where you
participate with some arrogant answers:I'm sure Mawin will put you right.