Open AccessOne-dimensional sensor learns to sense three-dimensional space
Author(s) -
Chen Zhu,
Rex E. Gerald,
Yizheng Chen,
Jie Huang
Publication year - 2020
Publication title -
optics express
Language(s) - Uncategorized
Resource type - Journals
SCImago Journal Rank - 1.394
H-Index - 271
ISSN - 1094-4087
DOI - 10.1364/oe.395282
Subject(s) - computer science , noise (video) , dimension (graph theory) , sensitivity (control systems) , signal (programming language) , signal processing , artificial intelligence , transformative learning , diagonal , electronic engineering , telecommunications , mathematics , engineering , psychology , pedagogy , radar , geometry , pure mathematics , image (mathematics) , programming language
A sensor system with ultra-high sensitivity, high resolution, rapid response time, and a high signal-to-noise ratio can produce raw data that is exceedingly rich in information, including signals that have the appearances of "noise". The "noise" feature directly correlates to measurands in orthogonal dimensions, and are simply manifestations of the off-diagonal elements of 2 nd -order tensors that describe the spatial anisotropy of matter in physical structures and spaces. The use of machine learning techniques to extract useful meanings from the rich information afforded by ultra-sensitive one-dimensional sensors may offer the potential for probing mundane events for novel embedded phenomena. Inspired by our very recent invention of ultra-sensitive optical-based inclinometers, this work aims to answer a transformative question for the first time: can a single-dimension point sensor with ultra-high sensitivity, fidelity, and signal-to-noise ratio identify an arbitrary mechanical impact event in three-dimensional space? This work is expected to inspire researchers in the fields of sensing and measurement to promote the development of a new generation of powerful sensors or sensor networks with expanded functionalities and enhanced intelligence, which may provide rich n-dimensional information, and subsequently, data-driven insights into significant problems.