Evidence map›Paper›PMID 31640187›Full record

ArticleSensors (Basel, Switzerland)2019

Dropping Counter: A Detection Algorithm for Identifying Odour-Evoked Responses from Noisy Electroantennograms Measured by a Flying Robot.

Bluest Lan, Ryohei Kanzaki, Noriyasu Ando

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
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

The trial behind it

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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Bluest LanResearch Center for Advanced Science and Technology, The University of Tokyo, 4-6-1 Meguro-ku, Komaba, Tokyo 153-8904, Japan. bluest@engineer.com.ORCID 0000-0002-4419-7760
Ryohei KanzakiResearch Center for Advanced Science and Technology, The University of Tokyo, 4-6-1 Meguro-ku, Komaba, Tokyo 153-8904, Japan. kanzaki@rcast.u-tokyo.ac.jp.
Noriyasu AndoResearch Center for Advanced Science and Technology, The University of Tokyo, 4-6-1 Meguro-ku, Komaba, Tokyo 153-8904, Japan. ando@maebashi-it.ac.jp.ORCID 0000-0002-7216-9580

Funding

Human Frontier Science Program 2015NEC Corporation (R.K.)
6 · The paper itself

Abstract

The electroantennogram (EAG) is a technique used for measuring electrical signals from the antenna of an insect. Its rapid response time, quick recovery speed, and high sensitivity make it suitable for odour-tracking tasks employing mobile robots. However, its application to flying robots has not been extensively studied owing to the electrical and mechanical noises generated. In this study, we investigated the characteristics of the EAG mounted on a tethered flying quadcopter and developed a special counter-based algorithm for detecting the odour-generated responses. As the EAG response is negative, the algorithm creates a window and compares the values inside it. Once a value is smaller than the first one, the counter will increase by one and finally turns the whole signal into a clearer odour stimulated result. By experimental evaluation, the new algorithm gives a higher cross-correlation coefficient when compared with the fixed-threshold method. The result shows that the accuracy of this novel algorithm for recognising odour-evoked EAG signals from noise exceeds that of the traditional method; furthermore, the use of insect antennae as odour sensors for flying robots is demonstrated to be feasible.

Indexed as

AlgorithmsNoiseRoboticsAnimalsArthropod AntennaeBiosensing TechniquesMaleMothsOdorantsSignal Processing, Computer-Assisteddroneelectroantennograminsectodourrobotsignal processing

Identifiers

PMID31640187
PMCPMC6832354

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.