www.piclist.com/techref/io/atod.htm?key=a%2Fd

In regards to adding more noise, remember, you're not actually adding any

accuracy (repeatability), in fact you're decreasing the accuracy of the

individual readings as succesive readings of the same value (without noise)

will have more variation. What you are doing is adding precision. The

averaged result is now closer to the 'true' value.

However, I think this would only apply with analog noise. You need

fractional noise or it falls apart. Adding 1LSB of digital noise would just

give you 1\3rd correct values, 1\3rd up 1 LSB and 1\3rd down 1LSB (assuming

true Gaussian noise). Average that out and it just returns the correct

value.

Yes, averaging the values would reduce the noise. If it's natural noise then

it's Gaussian, thus it should average out to 0 over multiple samples. But

the digital noise is irrelevant. Assuming the digital noise is also Gaussian

it too will be averaged out, but it will not increase accuracy or precision

(but as suggested it may speed the averaging).

In terms of how you 'add' the random noise, you don't. Ever designed a

(mixed signal) circuit with 0 noise? You don't add analog noise, you just

use the existing noise (assuming it's Gaussian, which almost all naturally

occuring noise is). You could digitally produce the noise and then subtract

it out for even better accuracy but this is rarely used as it isn't

justified. After spending 6months trying to eliminate the #$%$ing analog

noise, it's nice to be able to put it to good use.

In terms of digitally producing Gaussian noise, you just apply the Central

Limits Theroem. It states that:

A sum of random numbers becomes normally distributed as more and more of the

random numbers are added together. The Central Limit Theorem does not

require the individual random numbers be from any particular distribution,

or even that the random numbers be from the same distribution.

This is why Gaussian noise is so common. Whenever multiple random processes

(any distribution including pseudorandom computer generated numbers)

interact, the overall output will show Gaussian distribution.

Tom.

-----Original Messages-----

>From Andy:

<SNIP>

It worked much better than w/o the noise correction.

Andy

>From Scott:

<SNIP>

I suspect if you go back and repeat your experiments with the "digital

random

number" stuff removed that you'll get the same results - perhaps even

improved

results.

<SNIP>

Scott

>From KŸbek Tony:

Hi,

I'm intrigued by this discussion, and I'm probably

in over my head here :-)

Anyway regarding bit jittering, increasing accuracy etc.

If I understand correctly if one adds a random bit to the

reading then one possibly ( likely ? ) could increase the accuracy ?

Is this vaild for, let's say one has 6 bit's of noice ?

Would one then add several random bit's ?

Ore are this only valid for the LSB ?

Further how does one accomplish this 'random bit' ?

to be truly random I guess it would be very hard,

I've read the last month disscussion regarding

an random byte generation, but for a single bit

there must be an easier way.

Anyway very interesting thread, nice to read.

BTW Just 'playing' with the 24 bit AD7730 ;-) DS

/Tony

Tony KŸbek, Flintab AB

ÓÓÓÓÓÓÓÓÓÓÓÓÓÓÓÓÓÓÓÓÓÓÓÓÓÓÓÓÓÓÓÓÓÓÓÓÓÓÓÓÓÓÓÓÓÓÓÓÓÓÓÓÓÓÓÓÓÓÓÓ

E-mail: KILLspamtony.kubekRemoveME@spam@flintab.com

ÓÓÓÓÓÓÓÓÓÓÓÓÓÓÓÓÓÓÓÓÓÓÓÓÓÓÓÓÓÓÓÓÓÓÓÓÓÓÓÓÓÓÓÓÓÓÓÓÓÓÓÓÓÓÓÓÓÓÓÓ

See also: www.piclist.com/techref/io/atod.htm?key=a%2Fd