A General Grid-Less Design Method for Location and Pressure Sensors with High Precision.

Xiaobo Zhu, Xiong Cheng, Weidong Zhang, Jiale Gao, Yijie Dai, Wenhua Gu

Open access · goldAbstract readLetter

In one paragraph

Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing papers in PubMed

0.4field-weighted citation impact, top 40% of its field

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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3 · Its place in the literature

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0 citing papers in PubMed, 8 citations in OpenAlex.

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4 · The record

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5 · Who and what money

Authors and funding

6 authors at 1 institution in 1 country.

Xiaobo ZhuSchool of Electronic and Optical Engineering, Nanjing University of Science and Technology, Xiaolingwei Street 200#, Nanjing 210094, China.

Xiong ChengSchool of Electronic and Optical Engineering, Nanjing University of Science and Technology, Xiaolingwei Street 200#, Nanjing 210094, China.

Weidong ZhangSchool of Electronic and Optical Engineering, Nanjing University of Science and Technology, Xiaolingwei Street 200#, Nanjing 210094, China.

Jiale GaoSchool of Electronic and Optical Engineering, Nanjing University of Science and Technology, Xiaolingwei Street 200#, Nanjing 210094, China.

Yijie DaiSchool of Electronic and Optical Engineering, Nanjing University of Science and Technology, Xiaolingwei Street 200#, Nanjing 210094, China.

Wenhua GuSchool of Electronic and Optical Engineering, Nanjing University of Science and Technology, Xiaolingwei Street 200#, Nanjing 210094, China.

Nanjing University of Science and Technology · CN

Funding

National Natural Science Foundation of China (NSFC) 61627802the Fundamental Research Funds for the Central Universities 30917012202the Fundamental Research Funds for the Central Universities 30920021128the Key Research and Development Plan of Jiangsu Province BE2018728

6 · The paper itself

Abstract

Bionic electronic skin can accurately sense and locate surface pressure, which is widely demanded in many fields. Traditional electronic skin design usually relies on grid-architecture sensor arrays, requiring complex grid and interconnection arrangements as well as high cost. Grid-less planar sensors can solve the problem by using electrodes only at the edges, but they usually require the use of mapping software such as electrical impedance tomography to achieve high precision. In this work, a design method of high-precision grid-less planar pressure sensors based on the back-propagation (BP) neural network is proposed. The measurement precision of this method is demonstrated to be over two orders of magnitude higher than that of a grid-structure sensor array with the same electrode distribution density. Moreover, this method can be used for irregularly-shaped and non-uniform sensors, which further reduces the manufacturing difficulty and increases the application flexibility.

Indexed as

BP neural networkgrid-less planar pressure sensorhigh-precision

Identifiers

PMID33353030

DOI10.3390/s20247286

PMCPMC7766643

OpenAlexW3112228497

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LicenceCC BY

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.