Utilizing the cochlea as a bio-inspired compressive sensing technique

CA Peckens, JP Lynch - Smart Materials and Structures, 2013 - iopscience.iop.org
Smart Materials and Structures, 2013iopscience.iop.org
Structural monitoring for civil infrastructure is a rapidly developing field that has made
significant advancements over the last decade. However, a number of performance
bottlenecks remain including challenges with cost-effectively scaling monitoring systems up
to large nodal counts. Due to the many parallels between biological sensory systems and
engineered sensing systems, the biological nervous system can offer potential solutions to
the current deficiencies of structural monitoring systems. The nervous system is capable of …
Abstract
Structural monitoring for civil infrastructure is a rapidly developing field that has made significant advancements over the last decade. However, a number of performance bottlenecks remain including challenges with cost-effectively scaling monitoring systems up to large nodal counts. Due to the many parallels between biological sensory systems and engineered sensing systems, the biological nervous system can offer potential solutions to the current deficiencies of structural monitoring systems. The nervous system is capable of real-time processing and data transmission of external stimuli through an extremely condensed format with very basic processing units. This study explores the mammalian auditory system for inspiration because it achieves efficient data acquisition processes that outperform existing engineered sensing systems. Specifically, the auditory system realizes this through three steps:(1) real-time decomposition of a convoluted time-based signal into frequency components,(2) information compression for each component, and (3) efficient high-speed data transmission to the auditory cortex. In this paper, these three main mechanisms are explored and a bio-inspired structural monitoring system is proposed. The functionality of the proposed system is compared to traditional data compression techniques (wavelet transforms and compressed sensing) on various vibratory signals. While the wavelet transform is able to outperform the proposed sensor by minimizing signal reconstruction errors, the proposed bio-inspired sensor achieves similar compression rates but, unlike the others, does so using real-time processing.
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