TY - GEN
T1 - Enhancing Stochastic Resonance by Adaptive Colored Noise and Particle Swarm Optimization
T2 - 2022 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM 2022
AU - Martinez-Garcia, Miguel
AU - Zhang, Yu
AU - Wang, Shuihua
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - In this paper, an intelligent signal processing approach is applied to enhance the detectability of weak signals - i.e., signals which are partially below a theoretical threshold of detection. Mechanical and physiological thresholds limit the capability of humans when manipulating machines via control devices, such as steering wheels. One approach to tackle the shortcomings of lost subthreshold information is stochastic resonance, which consists in adding noise to a signal, to raise its energy content over the threshold of detection. In particular, this paper shows that using adaptive colored can noise improve the detectability of steering control signals recorded from human participants. The approach converts a signal processing task to a machine learning problem; particle swarm optimization is employed to obtain the optimal color (or spectral exponent) of the injected noise, generated through fractional order filters. The results have shown that the proposed method improves the detectability of subthreshold steering control signals, which can be further applicable to many other domains, such as improving tactile sensation or acoustic perception through noise and energy harvesting from vehicle tires.
AB - In this paper, an intelligent signal processing approach is applied to enhance the detectability of weak signals - i.e., signals which are partially below a theoretical threshold of detection. Mechanical and physiological thresholds limit the capability of humans when manipulating machines via control devices, such as steering wheels. One approach to tackle the shortcomings of lost subthreshold information is stochastic resonance, which consists in adding noise to a signal, to raise its energy content over the threshold of detection. In particular, this paper shows that using adaptive colored can noise improve the detectability of steering control signals recorded from human participants. The approach converts a signal processing task to a machine learning problem; particle swarm optimization is employed to obtain the optimal color (or spectral exponent) of the injected noise, generated through fractional order filters. The results have shown that the proposed method improves the detectability of subthreshold steering control signals, which can be further applicable to many other domains, such as improving tactile sensation or acoustic perception through noise and energy harvesting from vehicle tires.
KW - Fractional Calculus
KW - Human-machine systems
KW - Intelligent Signal Processing
KW - Steering Control
KW - Stochastic Resonance
UR - http://www.scopus.com/inward/record.url?scp=85137703698&partnerID=8YFLogxK
U2 - 10.1109/AIM52237.2022.9863271
DO - 10.1109/AIM52237.2022.9863271
M3 - Conference Proceeding
AN - SCOPUS:85137703698
T3 - IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM
SP - 1700
EP - 1705
BT - 2022 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM 2022
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 11 July 2022 through 15 July 2022
ER -