Search Results
| Search type | Search syntax |
|---|---|
| Tags | [tag] |
| Exact | "words here" |
| Author |
user:1234 user:me (yours) |
| Score |
score:3 (3+) score:0 (none) |
| Answers |
answers:3 (3+) answers:0 (none) isaccepted:yes hasaccepted:no inquestion:1234 |
| Views | views:250 |
| Code | code:"if (foo != bar)" |
| Sections |
title:apples body:"apples oranges" |
| URL | url:"*.example.com" |
| Saves | in:saves |
| Status |
closed:yes duplicate:no migrated:no wiki:no |
| Types |
is:question is:answer |
| Exclude |
-[tag] -apples |
| For more details on advanced search visit our help page | |
Results tagged with quantization
Search options not deleted
user 42885
Quantization, in mathematics and digital signal processing, is the process of mapping input values from a large set (often a continuous set) to output values in a (countable) smaller set, often with a finite number of elements. Rounding and truncation are typical examples of quantization processes.
0
votes
1
answer
143
views
AWGN model of Quantization Noise
I'm afraid I'm missing big points about quantization noise. … Note that for any value of alpha I can observe quantization noise as 1/sqrt(12) by just taking: std(I-Iq)
It seems I can't measure the final noise as Iq_std_meas = std(Iq). …
CommunityBot
- 1
0
votes
Accepted
AWGN model of Quantization Noise
I'm expecting to be able to model the output noise as Iq_std_model = alpha+1/sqrt(12)
That's the big fundamental point that I was missing.
Standard deviation sums in quadrature, duh!
Using Iq_std_mo …
- 51