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

Ali Ibrahim1,2, Paolo Gastaldo3, Hussein Chible4

  • 1Department of Electrical, Electronic and Telecommunication Engineering and Naval architecture (DITEN)-University of Genoa, via Opera Pia 11, 16145 Genoa, Italy. ali.ibrahim@edu.unige.it.

Sensors (Basel, Switzerland)
|March 14, 2017
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Summary

This study implements Field-Programmable Gate Arrays (FPGAs) for processing electronic skin tactile data using machine learning. The system efficiently processes touch information for real-time applications like robotics and prosthetics.

Keywords:
FPGA implementationdigital signal processingelectronic skin systempower consumptionreal-time classification

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Area of Science:

  • Robotics and Artificial Intelligence
  • Biomedical Engineering
  • Materials Science

Background:

  • Electronic skin is crucial for robots, prosthetics, and biomedical devices, requiring local tactile data processing.
  • Current electronic skin systems need efficient methods to extract meaningful information from raw sensor data for real-time applications.
  • Machine learning shows promise for analyzing tactile sensor data, but efficient hardware implementation is key.

Purpose of the Study:

  • To implement digital signal processing using FPGAs for tactile data processing in electronic skin systems.
  • To develop and integrate a tensorial kernel function for a machine learning approach on FPGAs.
  • To evaluate the feasibility and performance of the FPGA-based implementation for real-time touch modality classification.

Main Methods:

  • Implementation of digital signal processing algorithms on FPGAs for tactile data acquisition and processing.
  • Integration of a tensorial kernel function tailored for machine learning on the FPGA platform.
  • Assessment of FPGA resource utilization and power consumption for the developed system.

Main Results:

  • Demonstrated the feasibility of using FPGAs for real-time tactile data processing in electronic skin applications.
  • Successfully implemented a machine learning approach with a tensorial kernel function on FPGA hardware.
  • Quantified FPGA resource utilization and power consumption, indicating efficiency for targeted applications.

Conclusions:

  • FPGA-based digital signal processing offers an efficient solution for real-time tactile data analysis in electronic skin.
  • The implemented tensorial kernel function on FPGA enables effective machine learning for touch modality classification.
  • The proposed system is suitable for applications demanding rapid and accurate interpretation of tactile interactions.