Name
Abstract | ||
|---|---|---|
We propose an adaptive signal sampling approach that dynamically adjusts the sampling rate to approximate the local Nyquist rate of the signal. The proposed adaptive sampling approach consists of a recurrent neural network-based change detector that detects the point of frequency change and a local Nyquist rate estimator based on a multi-rate signal processing scheme. We empirically demonstrate that our adaptive sampling approach significantly reduces the overall sampling rate for various types of signals and therefore improves the computational efficiency of subsequent signal processing. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/BigData47090.2019.9006504 | 2019 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA) |
Keywords | Field | DocType |
Adaptive sampling, approximate computing, neural networks, machine learning, signal processing | Data mining,Signal processing,Computer science,Adaptive sampling,Sampling (signal processing),Algorithm,Recurrent neural network,Sampling (statistics),Artificial neural network,Nyquist rate,Estimator | Conference |
ISSN | Citations | PageRank |
2639-1589 | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yi Feng | 1 | 3 | 1.74 |
| Yi Zhou | 2 | 65 | 17.55 |
| Vahid Tarokh | 3 | 10373 | 1461.51 |