Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Depth Perception and Spatial Vision01:15

Depth Perception and Spatial Vision

601
Depth perception is the ability to perceive objects three-dimensionally. It relies on two types of cues: binocular and monocular. Binocular cues depend on the combination of images from both eyes and how the eyes work together. Since the eyes are in slightly different positions, each eye captures a slightly different image. This disparity between images, known as binocular disparity, helps the brain interpret depth. When the brain compares these images, it determines the distance to an object.
601

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Design of a Remote, Multi-Range Conductivity Sensor.

Sensors (Basel, Switzerland)·2023
Same author

Tailoring the dispersion characteristics in planar arrays of discrete and coalesced split ring resonators.

Scientific reports·2023
Same author

3D Printing of Functional Metal and Dielectric Composite Meta-Atoms.

Small (Weinheim an der Bergstrasse, Germany)·2022
Same author

Core Temperature Responses to Elite Racewalking Competition.

International journal of sports physiology and performance·2020
Same author

Endurance Performance is Influenced by Perceptions of Pain and Temperature: Theory, Applications and Safety Considerations.

Sports medicine (Auckland, N.Z.)·2017
Same author

Ice slurry ingestion during cycling improves Olympic distance triathlon performance in the heat.

Journal of sports sciences·2013

Related Experiment Video

Updated: Jun 10, 2025

Demonstration of Equal-Intensity Beam Generation by Dielectric Metasurfaces
09:33

Demonstration of Equal-Intensity Beam Generation by Dielectric Metasurfaces

Published on: June 7, 2019

6.3K

Spatial localisation and sensing in two dimensions via metasurfaces.

Georgiana Dima1, Christopher John Stevens2

  • 1Department of Engineering Science, University of Oxford, Parks Road, Oxford, OX1 3PJ, UK. georgiana.dima@eng.ox.ac.uk.

Scientific Reports
|October 15, 2024
PubMed
Summary
This summary is machine-generated.

This study presents a metasurface sensor for object detection, localization, and identification using a neural network. The sensor achieves high precision in pinpointing object locations and distinguishing between them, opening doors for advanced applications.

More Related Videos

Demonstration of Spin-Multiplexed and Direction-Multiplexed All-Dielectric Visible Metaholograms
08:48

Demonstration of Spin-Multiplexed and Direction-Multiplexed All-Dielectric Visible Metaholograms

Published on: September 25, 2020

5.7K
Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform
06:25

Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform

Published on: February 12, 2014

8.4K

Related Experiment Videos

Last Updated: Jun 10, 2025

Demonstration of Equal-Intensity Beam Generation by Dielectric Metasurfaces
09:33

Demonstration of Equal-Intensity Beam Generation by Dielectric Metasurfaces

Published on: June 7, 2019

6.3K
Demonstration of Spin-Multiplexed and Direction-Multiplexed All-Dielectric Visible Metaholograms
08:48

Demonstration of Spin-Multiplexed and Direction-Multiplexed All-Dielectric Visible Metaholograms

Published on: September 25, 2020

5.7K
Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform
06:25

Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform

Published on: February 12, 2014

8.4K

Area of Science:

  • Metasurface technology
  • Sensor development
  • Machine learning applications

Background:

  • Metasurfaces offer unique electromagnetic properties for sensing.
  • Near-field interactions with metasurfaces can be analyzed for object detection.
  • Existing methods may lack precision in localization and identification.

Purpose of the Study:

  • To develop a two-dimensional metasurface sensor for detecting, locating, and distinguishing objects.
  • To demonstrate the capability of a single observation point for unambiguous object analysis.
  • To explore the potential of metasurface sensors in various applications, including wireless power transfer and human-computer interfaces.

Main Methods:

  • Designing a two-dimensional metasurface sensor capable of detecting local changes in meta-atoms.
  • Utilizing changes in meta-atom inductance and overall input impedance to characterize object interactions.
  • Employing a neural network machine learning algorithm for object localization and identification based on impedance changes.
  • Deriving metasurface properties and behavior using superposition principles.

Main Results:

  • Accurate localization of studied objects with precision exceeding .
  • Successful separation and identification of distinct objects with accuracy over .
  • Demonstration that single-point observation is sufficient for unambiguous object analysis.

Conclusions:

  • The developed metasurface sensor effectively detects, localizes, and identifies objects in its near field.
  • The neural network approach provides high accuracy in both localization and identification tasks.
  • Potential applications include foreign object detection for wireless power transfer and novel touchscreen interfaces.