Name
Title | ||
|---|---|---|
A CS-based acquisition method of acoustic emission signals from distributed SHM systems |
Abstract | ||
|---|---|---|
This paper deals with a preliminary assessment of the performance of four Compressive Sampling (CS) algorithms used for Acoustic Emission (AE) signals delivered by a distributed Structural Health Monitoring (SHM) system. In particular, three random CS-based methods (i.e., Random Demodulation, Gaussian, and Bernoulli), already available in the literature, were evaluated and compared to a deterministic CS-based approach, called Deterministic Binary Block Diagonal (DBBD). The obtained experimental results show that the CS-based method relying on the DBBD outperforms the efficiency of the random CS-based approaches in terms of signal reconstruction quality. In particular, the figure of merit Recovery Error (RE) has been calculated and it is shown that REs are below 20% for compression ratios up to 6 in the case of DBBD CS method. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/I2MTC48687.2022.9806529 | 2022 IEEE INTERNATIONAL INSTRUMENTATION AND MEASUREMENT TECHNOLOGY CONFERENCE (I2MTC 2022) |
Keywords | DocType | ISSN |
Compressive Sampling, Acoustic Emission, Structural Health, Remote Monitoring, IoT Sensor Network | Conference | 1091-5281 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Domenico L. Carni | 1 | 0 | 0.34 |
| Luca De Vito | 2 | 1 | 2.04 |
| Francesco Lamonaca | 3 | 0 | 0.34 |
| Francesco Picariello | 4 | 2 | 7.51 |
| Ioan Tudosa | 5 | 0 | 2.03 |