Name
Abstract | ||
|---|---|---|
The application of Artificial Neural Networks (ANNs) to discriminate tag actions in UHF-RFID gate is presented in this paper. By exploiting Received Signal Strength Indicator values acquired in a real experimental scenario, a multi-layer perceptron neural network is trained to distinguish among tags incoming, outgoing or passing the RFID gate. A 99% accuracy can be obtained in tag classification by employing only one reader antenna and independently from tag orientation and typology. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/RFID-TA.2017.8098900 | 2017 IEEE International Conference on RFID Technology & Application (RFID-TA) |
Keywords | Field | DocType |
UHF-RFID gate,UHF-RFID tag action discrimination,Artificial Neural Networks | Data mining,Logic gate,Computer science,Feature extraction,Signal strength,Artificial neural network,Classifier (linguistics),Perceptron,Ultra high frequency | Conference |
ISBN | Citations | PageRank |
978-1-5386-1834-9 | 3 | 0.48 |
References | Authors | |
5 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| alice buffi | 1 | 34 | 8.14 |
| Eleonora D'Andrea | 2 | 51 | 6.19 |
| Beatrice Lazzerini | 3 | 715 | 45.56 |
| Paolo Nepa | 4 | 86 | 16.00 |