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New Technology and Application of Optic Flow Sensors
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New Technology and Application of Optic Flow Sensors

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

Deadline for manuscript submissions: closed (30 April 2022) | Viewed by 5986

Special Issue Editors

Dr. Laura Fabbiano

E-Mail Website
Guest Editor
Department of Mechanics Mathematics and Management, Politecnico di Bari, University, 70100 Bari, Italy
Interests: metrological characterization of measurement systems; analysis of uncertainty; statistical quality control; processing of images and measurement procedures applied to biometric measurements; thermofluid dynamics measurements; vibrational measurements
Special Issues, Collections and Topics in MDPI journals
Dr. Aimé Lay-Ekuakille

E-Mail Website
Guest Editor
Department of Innovation Engineering, University of Salento, 73100 Lecce, Italy
Interests: instrumentation; measurement and sensors; biomedicine; environment; industry; nanotechnology; machine learning; photovoltaic panel aging
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Ramiro Velázquez

E-Mail Website
Guest Editor
Facultad de Ingeniería, Universidad Panamericana, Aguascalientes 20296, Mexico
Interests: mechatronic systems; assistive technology; haptic and tactile devices; human perception
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Paolo Oresta

E-Mail Website
Guest Editor
Department of Mechanics Mathematics and Management, Politecnico di Bari, University, 70100 Bari, Italy
Interests: thermal flow; multiphase flow; fluid dynamics measurements; combustion; aerodynamics/hydrodynamics of robots

Special Issue Information

Dear Colleagues,

Optical flow sensors are experiencing a period of strong growth and interest thanks to the scientific efforts of researchers who have made optical technology widely versatile, obtaining great results whose implications fall within the most disparate sectors, such as medicine, aerospace, transport, telecommunications, energy, mechanics, chemistry, and making optical flow sensors reliable and with high metrological performance.

This Special Issue is devoted to frontier applications and technologies, original and unconventional approaches to optic flow sensors used to improve the performance of physical quantity measurements compared to conventional sensors, and the development of more and more innovative and performant measurement system technologies.

Our aim is to present and highlight advancements and the latest technologies, implementations, and new and emerging applications in the field of optic flow sensors. We intend to collect manuscripts on the design and development of new instrumentation and the definition of innovative measurement methodologies and procedure progress regarding their characterization and applications.

Potential topics include but are not limited to:

Sensor design, construction, and testing

Metrological aspects

Modeling and statistics

Cyberphysical systems and IoT

Fiber optic devices

Object detection and tracking

Image dominant plane extraction

Machine vision

Movement detection

Robot navigation

Visual odometry

Control of micro air vehicles

Drone

Prof. Dr. Laura Fabbiano
Prof. Dr. Aime' Lay-Ekuakille
Prof. Dr. Ramiro Velázquez
Prof. Dr. Paolo Oresta
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

  • sensors technologies, sensor systems for water flow, signal processing
  • remote sensing and signal transmission
  • cyber-physical systems and environmental monitoring
  • modeling
  • water infrastructure degradation monitoring
  • vibration

Published Papers (2 papers)

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Research

17 pages, 5027 KiB  
Article
About 3D Incompressible Flow Reconstruction from 2D Flow Field Measurements
by Laura Fabbiano, Paolo Oresta, Aimé Lay-Ekuakille and Gaetano Vacca
Sensors 2022, 22(3), 958; https://doi.org/10.3390/s22030958 - 26 Jan 2022
Viewed by 2334
Abstract
In this paper, an assessment of the uncertainty affecting a hybrid procedure (experimental/numerical) is carried out to validate it for industrial applications, at the least. The procedure in question serves to depict 3D incompressible flow fields by using 2D measurements of it and [...] Read more.
In this paper, an assessment of the uncertainty affecting a hybrid procedure (experimental/numerical) is carried out to validate it for industrial applications, at the least. The procedure in question serves to depict 3D incompressible flow fields by using 2D measurements of it and computing the third velocity component by means of the continuity equation. A quasi-3D test case of an incompressible flow has been inspected in the wake of a NACA 0012 airfoil immersed in a forced flow of water running in a rectangular open channel. Specifically, starting from a 2D measurement data in planes orthogonal to the stream-wise direction, the computational approach can predict the third flow velocity component. A 3D ADV instrument has been utilized to measure the flow field, but only two velocity components have been considered as measured quantities, while the third one has been considered as reference with which to compare the computed component from the continuity equation to check the accuracy and validity of the hybrid procedure. At this aim, the uncertainties of the quantities have been evaluated, according to the GUM, to assess the agreement between experiments and predictions, in addition to other metrics. This aspect of uncertainty is not a technical sophistication but a substantial way to bring to the use of a 1D and 2D measurement system in lieu of a 3D one, which is costly in terms of maintenance, calibration, and economic issues. Moreover, the magnitude of the most relevant flow indicators by means of experimental data and predictions have been estimated and compared, for further confirmation by means of a supervised learning classification. Further, the sensed data have been processed, by means of a machine learning algorithm, to express them in a 3D way along with accuracy and epoch metrics. Two additional metrics have been included in the effort to show paramount interest, which are a geostatistical estimator and Sobol sensitivity. The statements of this paper can be used to design and test several devices for industrial purposes more easily. Full article
(This article belongs to the Special Issue New Technology and Application of Optic Flow Sensors)
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19 pages, 8040 KiB  
Article
ARTFLOW: A Fast, Biologically Inspired Neural Network that Learns Optic Flow Templates for Self-Motion Estimation
by Oliver W. Layton
Sensors 2021, 21(24), 8217; https://doi.org/10.3390/s21248217 - 08 Dec 2021
Cited by 5 | Viewed by 2608
Abstract
Most algorithms for steering, obstacle avoidance, and moving object detection rely on accurate self-motion estimation, a problem animals solve in real time as they navigate through diverse environments. One biological solution leverages optic flow, the changing pattern of motion experienced on the eye [...] Read more.
Most algorithms for steering, obstacle avoidance, and moving object detection rely on accurate self-motion estimation, a problem animals solve in real time as they navigate through diverse environments. One biological solution leverages optic flow, the changing pattern of motion experienced on the eye during self-motion. Here I present ARTFLOW, a biologically inspired neural network that learns patterns in optic flow to encode the observer’s self-motion. The network combines the fuzzy ART unsupervised learning algorithm with a hierarchical architecture based on the primate visual system. This design affords fast, local feature learning across parallel modules in each network layer. Simulations show that the network is capable of learning stable patterns from optic flow simulating self-motion through environments of varying complexity with only one epoch of training. ARTFLOW trains substantially faster and yields self-motion estimates that are far more accurate than a comparable network that relies on Hebbian learning. I show how ARTFLOW serves as a generative model to predict the optic flow that corresponds to neural activations distributed across the network. Full article
(This article belongs to the Special Issue New Technology and Application of Optic Flow Sensors)
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