Smart Tactile Sensing Systems Based on Embedded CNN Implementations.

Clicks: 276
ID: 90639
2020
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Emerging

Ranked #45 of 128 articles by views in micromachines

Most read Least read

Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 128 in total.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
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.
Reference Key
alameh2020smartmicromachines Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Alameh, Mohamad;Abbass, Yahya;Ibrahim, Ali;Valle, Maurizio;
Journal micromachines
Year 2020
DOI
E103
URL
Keywords
Convolutional neural network embedding intelligence tactile sensing systems

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.