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Bioinspired Flow Sensing

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Physical Sensors".

Deadline for manuscript submissions: closed (30 August 2020) | Viewed by 137

Special Issue Editors


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Guest Editor
University of Groningen, Groningen, Netherlands
Interests: mechano-detection; hair cell transduction; lateral line; neural networks; artificial intelligence
University of Groningen, Groningen, Netherlands
Interests: Artificial Intelligence; Robotics; Flow Sensing

Special Issue Information

Dear Colleagues,

Fluid flow sensing forms the basis of several sensory modalities in humans and animals. For sensing external flow in their aquatic habitat, fish and amphibians rely on their so-called lateral line organ, consisting of arrays of flow detectors. Their concatenated spatio-temporal signals bear information on location, size and shape of relatively close-by moving objects and structures via their induced near-field flow. This so called hydrodynamic imaging favorably complements acoustic information from the far field.

Over the past several decades, a diversity of  lateral-line-inspired sensing systems have been investigated and shown capable of addressing one or more hydrodynamic imaging tasks. Important issues in developing this technology are the optimization of physical and technical parameters such as the array’s dimensions, its fluid–structure interaction and noise characteristics, and temporal and spatial sampling. Together these govern the range and accuracy of detection both in terms of localization and identification.

For this Special Issue we invite researchers to submit unpublished manuscripts describing original research on, or related to, the following topics:

  • Fluid flow sensors for hydrodynamic imaging;
  • Localization, size, and shape detection and identification of objects based on induced fluid flow;
  • Signal processing of flow signals used for hydrodynamic imaging and/or object detection, including template-based, non-linear estimation methods, inverse methods and neural networks (machine learning);
  • Hydrodynamic fluid–structure interactions for practical use in hydrodynamic imaging;
  • Optimization principles for designing fluid flow sensors for hydrodynamic imaging and/or object detection;
  • Applications of hydrodynamic imaging.

Dr. Sietse Van Netten
Dr. Ben Wolf
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • artificial lateral line
  • near field
  • hydrodynamic imaging
  • neural networks
  • signal processing
  • bio-inspired hydrodynamic sensors
  • hydrodynamic inverse methods
  • sensor array processing.

Published Papers

There is no accepted submissions to this special issue at this moment.
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