| Autor: |
Mohamad Alameh, Yahya Abbass, Ali Ibrahim, Maurizio Valle |
| Jazyk: |
angličtina |
| Rok vydání: |
2020 |
| Předmět: |
|
| Zdroj: |
Micromachines, Vol 11, Iss 1, p 103 (2020) |
| Druh dokumentu: |
article |
| ISSN: |
2072-666X |
| DOI: |
10.3390/mi11010103 |
| Popis: |
Embedding machine learning methods into the data decoding units may enable the extraction of complex information making the tactile sensing systems intelligent. This paper presents and compares the implementations of a convolutional neural network model for tactile data decoding on various hardware platforms. Experimental results show comparable classification accuracy of 90.88% for Model 3, overcoming similar state-of-the-art solutions in terms of time inference. The proposed implementation achieves a time inference of 1.2 ms while consuming around 900 μ J. Such an embedded implementation of intelligent tactile data decoding algorithms enables tactile sensing systems in different application domains such as robotics and prosthetic devices. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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