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UHF-RFID smart gate: Tag action classifier by artificial neural networks
- Source :
- RFID-TA
- Publication Year :
- 2017
- Publisher :
- IEEE, 2017.
-
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.
- Subjects :
- Artificial neural network
Computer science
Computer Science::Neural and Evolutionary Computation
010401 analytical chemistry
Feature extraction
Computer Science::Computation and Language (Computational Linguistics and Natural Language and Speech Processing)
02 engineering and technology
Perceptron
computer.software_genre
01 natural sciences
UHF-RFID gate
UHF-RFID tag action discrimination
Artificial Neural Networks
0104 chemical sciences
ComputingMethodologies_PATTERNRECOGNITION
Computer Science::Emerging Technologies
Signal strength
Ultra high frequency
020204 information systems
Logic gate
0202 electrical engineering, electronic engineering, information engineering
Data mining
computer
Classifier (UML)
Subjects
Details
- Database :
- OpenAIRE
- Journal :
- 2017 IEEE International Conference on RFID Technology & Application (RFID-TA)
- Accession number :
- edsair.doi.dedup.....7f0aefc3a5fc3d58c3ecdc514fe77d0a