A Bio-inspired Redundant Sensing Architecture

TL;DR

This paper introduces a bio-inspired redundant sensing architecture that enhances precision and error correction in signal acquisition, approaching Shannon limits and providing insights into biological visual processing.

Contribution

It proposes a novel redundant sensing architecture inspired by biological systems, demonstrating improved accuracy and error correction in analog-to-digital conversion.

Findings

Achieved at least 2-bit extra precision in measurements.

Demonstrated performance approaching Shannon limit via simulation.

Provided insights into biological visual mechanisms like binocular vision.

Abstract

Sensing is the process of deriving signals from the environment that allows artificial systems to interact with the physical world. The Shannon theorem specifies the maximum rate at which information can be acquired. However, this upper bound is hard to achieve in many man-made systems. The biological visual systems, on the other hand, have highly efficient signal representation and processing mechanisms that allow precise sensing. In this work, we argue that redundancy is one of the critical characteristics for such superior performance. We show architectural advantages by utilizing redundant sensing, including correction of mismatch error and significant precision enhancement. For a proof-of-concept demonstration, we have designed a heuristic-based analog-to-digital converter - a zero-dimensional quantizer. Through Monte Carlo simulation with the error probabilistic distribution as a priori, the performance approaching the Shannon limit is feasible. In actual measurements without knowing the error distribution, we observe at least 2-bit extra precision. The results may also help explain biological processes including the dominance of binocular vision, the functional roles of the fixational eye movements, and the structural mechanisms allowing hyperacuity.