In this paper a data-driven approach for signal separation over the digital domain is discussed. The proposed approach solves the problem as a classification task and it is widely experimented over electromagnetic signals in open scenarios. Results show that high levels of accuracy are reachable through a relatively easy learning method over simulated data. © 2013 Springer-Verlag.
Filice, S., Croce, D., Basili, R. (2013). A robust machine learning approach for signal separation and classification. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp.749-757) [10.1007/978-3-642-38628-2_89].
A robust machine learning approach for signal separation and classification
CROCE, DANILO;BASILI, ROBERTO
2013-05-01
Abstract
In this paper a data-driven approach for signal separation over the digital domain is discussed. The proposed approach solves the problem as a classification task and it is widely experimented over electromagnetic signals in open scenarios. Results show that high levels of accuracy are reachable through a relatively easy learning method over simulated data. © 2013 Springer-Verlag.| File | Dimensione | Formato | |
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