Abstract
We discuss the trade-off between sampling and quantization in signal processing for the purpose of minimizing the error of the reconstructed signal subject to the constraint that the digitized signal fits in a given amount of memory. For signals with different regularities, we estimate the intrinsic errors from finite sampling and quantization, and determine the sampling and quantization resolutions.
| Original language | English |
|---|---|
| Pages (from-to) | 359-371 |
| Number of pages | 13 |
| Journal | Journal of Complexity |
| Volume | 3 |
| Issue number | 4 |
| DOIs | |
| State | Published - Dec 1987 |
Bibliographical note
Funding Information:* Research supported by the NSF under Grant IRI-8702168. Parts were done while the author was a consultant with the AT&T Bell Laboratories. t Research supported by the NSF under Grant DCR-86-03674. Current address: Department of Computer Science, University of Kentucky, Lexington, KY 40506.
Funding
* Research supported by the NSF under Grant IRI-8702168. Parts were done while the author was a consultant with the AT&T Bell Laboratories. t Research supported by the NSF under Grant DCR-86-03674. Current address: Department of Computer Science, University of Kentucky, Lexington, KY 40506.
| Funders | Funder number |
|---|---|
| U.S. Department of Energy Chinese Academy of Sciences Guangzhou Municipal Science and Technology Project Oak Ridge National Laboratory Extreme Science and Engineering Discovery Environment National Science Foundation National Energy Research Scientific Computing Center National Natural Science Foundation of China | 8702168, DCR-86-03674, IRI-8702168 |
ASJC Scopus subject areas
- Algebra and Number Theory
- Statistics and Probability
- Numerical Analysis
- General Mathematics
- Control and Optimization
- Applied Mathematics