Loading…

Neuromorphic Signal Filter for Robot Sensoring

Noise management associated with input signals in sensor devices arises as one of the main problems limiting robot control performance. This article introduces a novel neuromorphic filter model based on a leaky integrate and fire (LIF) neural model cell, which encodes the primary information from a...

Full description

Saved in:
Bibliographic Details
Published in:Frontiers in neurorobotics 2022-06, Vol.16, p.905313-905313
Main Authors: García-Sebastián, Luis M., Ponce-Ponce, Victor H., Sossa, Humberto, Rubio-Espino, Elsa, Martínez-Navarro, José A.
Format: Article
Language:English
Subjects:
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Noise management associated with input signals in sensor devices arises as one of the main problems limiting robot control performance. This article introduces a novel neuromorphic filter model based on a leaky integrate and fire (LIF) neural model cell, which encodes the primary information from a noisy input signal and delivers an output signal with a significant noise reduction in practically real-time with energy-efficient consumption. A new approach for neural decoding based on the neuron-cell spiking frequency is introduced to recover the primary signal information. The simulations conducted on the neuromorphic filter demonstrate an outstanding performance of white noise rejecting while preserving the original noiseless signal with a low information loss. The proposed filter model is compatible with the CMOS technology design methodologies for implementing low consumption smart sensors with applications in various fields such as robotics and the automotive industry demanded by Industry 4.0.
ISSN:1662-5218
1662-5218
DOI:10.3389/fnbot.2022.905313