Next Article in Journal
A Physical Activity Reference Data-Set Recorded from Older Adults Using Body-Worn Inertial Sensors and Video Technology—The ADAPT Study Data-Set
Next Article in Special Issue
Autonomous Pointing Control of a Large Satellite Antenna Subject to Parametric Uncertainty
Previous Article in Journal
The Smartphone-Based Offline Indoor Location Competition at IPIN 2016: Analysis and Future Work
Previous Article in Special Issue
PDMAA Hydrogel Coated U-Bend Humidity Sensor Suited for Mass-Production
Article Menu
Issue 3 (March) cover image

Export Article

Open AccessArticle
Sensors 2017, 17(3), 558; doi:10.3390/s17030558

Real-Time Digital Signal Processing Based on FPGAs for Electronic Skin Implementation †

1
Department of Electrical, Electronic and Telecommunication Engineering and Naval architecture (DITEN)-University of Genoa, via Opera Pia 11, 16145 Genoa, Italy
2
MECRL Lab, PhD School for Sciences and Technology (EDST)-Lebanese University, AL Hadath, Lebanon
*
Author to whom correspondence should be addressed.
Academic Editors: Stefan Bosse, Ansgar Trächtler, Klaus-Dieter Thoben, Berend Denkena and Dirk Lehmhus
Received: 30 January 2017 / Revised: 2 March 2017 / Accepted: 6 March 2017 / Published: 10 March 2017
(This article belongs to the Special Issue System-Integrated Intelligence and Intelligent Systems)
View Full-Text   |   Download PDF [4536 KB, uploaded 10 March 2017]   |  

Abstract

Enabling touch-sensing capability would help appliances understand interaction behaviors with their surroundings. Many recent studies are focusing on the development of electronic skin because of its necessity in various application domains, namely autonomous artificial intelligence (e.g., robots), biomedical instrumentation, and replacement prosthetic devices. An essential task of the electronic skin system is to locally process the tactile data and send structured information either to mimic human skin or to respond to the application demands. The electronic skin must be fabricated together with an embedded electronic system which has the role of acquiring the tactile data, processing, and extracting structured information. On the other hand, processing tactile data requires efficient methods to extract meaningful information from raw sensor data. Machine learning represents an effective method for data analysis in many domains: it has recently demonstrated its effectiveness in processing tactile sensor data. In this framework, this paper presents the implementation of digital signal processing based on FPGAs for tactile data processing. It provides the implementation of a tensorial kernel function for a machine learning approach. Implementation results are assessed by highlighting the FPGA resource utilization and power consumption. Results demonstrate the feasibility of the proposed implementation when real-time classification of input touch modalities are targeted. View Full-Text
Keywords: electronic skin system; digital signal processing; FPGA implementation; real-time classification; power consumption electronic skin system; digital signal processing; FPGA implementation; real-time classification; power consumption
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Ibrahim, A.; Gastaldo, P.; Chible, H.; Valle, M. Real-Time Digital Signal Processing Based on FPGAs for Electronic Skin Implementation †. Sensors 2017, 17, 558.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top