Name
Abstract | ||
|---|---|---|
In this paper, we present a novel deep neural network (DNN) dedicated to dynamic gesture control based on a radio-frequency (RF) type sensors. The proposed network is based on auto-encoder (AE) and long short-term memory recurrent neural network (LSTM-RNN). The encoder for reduction and effective feature extraction is obtained by unsupervised training an AE with a large untagged RF database. Furth... |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/ISCAS51556.2021.9401065 | 2021 IEEE International Symposium on Circuits and Systems (ISCAS) |
Keywords | DocType | ISSN |
Radio frequency,Training,Human computer interaction,Supervised learning,Gesture recognition,Sensor systems,Intelligent sensors | Conference | 0271-4302 |
ISBN | Citations | PageRank |
978-1-7281-9201-7 | 0 | 0.34 |
References | Authors | |
0 | 4 | |