Neuromorphic Signal Filter for Robot Sensoring - García-Sebastián, Luis M.; Ponce-Ponce, Victor H.; Sossa, Humberto; Rubio-Espino, Elsa; Martínez-Navarro, José A. (Frontiers in Neurorobotics, 2022)
Noise management associated with input signals in sensors devices arises as one of the main problems limiting robot control performance. This paper 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 a clean output signal 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 to 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.