A Bio-inspired Redundant sensing architecture

Anh Tuan Nguyen, Jian Xu, Zhi Yang

Research output: Contribution to journalConference article

2 Citations (Scopus)

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 [1]. 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.

Original languageEnglish (US)
Pages (from-to)2387-2395
Number of pages9
JournalAdvances in Neural Information Processing Systems
StatePublished - Jan 1 2016
Event30th Annual Conference on Neural Information Processing Systems, NIPS 2016 - Barcelona, Spain
Duration: Dec 5 2016Dec 10 2016

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Binocular vision
Eye movements
Digital to analog conversion
Redundancy
Demonstrations
Processing
Monte Carlo simulation

Cite this

A Bio-inspired Redundant sensing architecture. / Nguyen, Anh Tuan; Xu, Jian; Yang, Zhi.

In: Advances in Neural Information Processing Systems, 01.01.2016, p. 2387-2395.

Research output: Contribution to journalConference article

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