Name
Abstract | ||
|---|---|---|
Compressed sensing (CS) is often applied at the digital level. We consider the case where CS follows a $\Delta \Sigma$ data converter and we show that CS can be practiced directly on the $\Delta \Sigma$ stream. In the proposed scheme, an appropriate sensing matrix incorporates the ability to get rid of the quantization noise from the $\Delta \Sigma$ modulator. We also show that a suitable sparsity basis enables the CS information recovery to be practiced directly at the Nyquist rate and that decimation, which is typically inherent in $\Delta \Sigma$ data acquisition, is not needed. Furthermore, the low depth of $\Delta \Sigma$ streams allows CS measures to be taken without multipliers, streamlining arithmetic blocks. A test case based on electrocardiograms is used to validate the approach. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/ICECS46596.2019.8964730 | ICECS |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sergio Callegari | 1 | 112 | 18.52 |
| Mauro Mangia | 2 | 125 | 20.94 |
| Riccardo Rovatti | 3 | 377 | 54.32 |
| Gianluca Setti | 4 | 478 | 71.19 |