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

Position Vectors01:29

Position Vectors

1.1K
A position vector is a fundamental concept in mathematics that helps determine the position of one point with respect to another point in space. It is a vector that describes the direction and distance between two points. Position vectors are highly useful in the field of math and science, as they help represent spatial relationships and make calculations easier.
For instance, we want to locate a point P(x, y, z) relative to the origin of coordinates O. In that case, we can define a position...
1.1K
Relative Motion Analysis using Rotating Axes-Problem Solving01:29

Relative Motion Analysis using Rotating Axes-Problem Solving

437
Consider a crane whose telescopic boom rotates with an angular velocity of 0.04 rad/s and angular acceleration of 0.02 rad/s2. Along with the rotation, the boom also extends linearly with a uniform speed of 5 m/s. The extension of the boom is measured at point D, which is measured with respect to the fixed point C on the other end of the boom. For the given instant, the distance between points C and D is 60 meters.
Here, in order to determine the magnitude of velocity and acceleration for point...
437
Velocity and Position by Integral Method01:13

Velocity and Position by Integral Method

6.2K
If acceleration as a function of time is known, then velocity and position functions can be derived using integral calculus. For constant acceleration, the integral equations refer to the first and second kinematic equations for velocity and position functions, respectively.
Consider an example to calculate the velocity and position from the acceleration function. A motorboat is traveling at a constant velocity of 5.0 m/s when it starts to decelerate to arrive at the dock. Its acceleration is...
6.2K
Position and Displacement Vectors01:00

Position and Displacement Vectors

10.0K
To describe the motion of an object, one should first be able to describe its position (where it is at any particular time). More precisely, the position needs to be specified relative to a convenient frame of reference. A frame of reference is an arbitrary set of axes from which the position and motion of an object are described. Earth is often used as a frame of reference to describe the position of an object in relation to stationary objects on Earth.
Further, several important kinds of...
10.0K
Velocity and Position by Graphical Method01:34

Velocity and Position by Graphical Method

7.6K
Velocity and position can be calculated from the known function of acceleration as a function of time. The total area under the acceleration-time graph and the velocity-time graph gives the change in velocity and position, respectively. In the case of an airplane, its acceleration is tracked using the inertial navigation system. The pilot provides the input of the airplane's initial position and velocity before takeoff. The inertial navigation system then uses the acceleration data to...
7.6K
Relative Motion Analysis using Rotating Axes01:25

Relative Motion Analysis using Rotating Axes

505
Consider a component AB undergoing a linear motion. Along with a linear motion, point B also rotates around point A. To comprehend this complex movement, position vectors for both points A and B are established using a stationary reference frame.
However, to express the relative position of point B relative to point A, an additional frame of reference, denoted as x'y', is necessary. This additional frame not only translates but also rotates relative to the fixed frame, making it...
505

You might also read

Related Articles

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

Sort by
Same journal

RETRACTED: Zhang et al. A Novel Framework for Reconstruction and Imaging of Target Scattering Centers via Wide-Angle Incidence in Radar Networks. <i>Sensors</i> 2025, <i>25</i>, 6802.

Sensors (Basel, Switzerland)·2026
Same journal

Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation.

Sensors (Basel, Switzerland)·2026
Same journal

Development of an Instrumented Glove for Palmar Pressure Assessment in Kayakers.

Sensors (Basel, Switzerland)·2026
Same journal

Development and Experimental Validation of an Autonomous IoT-Based Monitoring System for Real-Time Water Quality Assessment in the Amazon River.

Sensors (Basel, Switzerland)·2026
Same journal

Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection.

Sensors (Basel, Switzerland)·2026
Same journal

Smart Optimization Method for Safety Signs in Innovative Manufacturing Environments Integrating Industrial Field IoT Sensors and Knowledge Graphs.

Sensors (Basel, Switzerland)·2026

Related Experiment Video

Updated: Aug 22, 2025

Novel 3D/VR Interactive Environment for MD Simulations, Visualization and Analysis
11:29

Novel 3D/VR Interactive Environment for MD Simulations, Visualization and Analysis

Published on: December 18, 2014

12.0K

AI-Based Positioning with Input Parameter Optimization in Indoor VLC Environments.

Sung-Hyun Oh1, Jeong-Gon Kim1

  • 1Department of Electronic Engineering, Korea Polytechnic University, Siheung-si 15297, Korea.

Sensors (Basel, Switzerland)
|November 11, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces visible-light communication (VLC) for precise indoor positioning, overcoming radio-frequency limitations. A deep neural network (DNN) optimizes accuracy and speed for reliable location-based services (LBS).

Keywords:
artificial Intelligence (AI)deep neural network (DNN)indoor positioninglocalizationvisible light communication (VLC)weighted k-nearest neighbor (WKNN)

More Related Videos

Virtual Prism Adaptation Therapy: Protocol for Validation in Healthy Adults
06:12

Virtual Prism Adaptation Therapy: Protocol for Validation in Healthy Adults

Published on: February 12, 2020

6.9K
Integrating Visual Psychophysical Assays within a Y-Maze to Isolate the Role that Visual Features Play in Navigational Decisions
07:09

Integrating Visual Psychophysical Assays within a Y-Maze to Isolate the Role that Visual Features Play in Navigational Decisions

Published on: May 2, 2019

6.2K

Related Experiment Videos

Last Updated: Aug 22, 2025

Novel 3D/VR Interactive Environment for MD Simulations, Visualization and Analysis
11:29

Novel 3D/VR Interactive Environment for MD Simulations, Visualization and Analysis

Published on: December 18, 2014

12.0K
Virtual Prism Adaptation Therapy: Protocol for Validation in Healthy Adults
06:12

Virtual Prism Adaptation Therapy: Protocol for Validation in Healthy Adults

Published on: February 12, 2020

6.9K
Integrating Visual Psychophysical Assays within a Y-Maze to Isolate the Role that Visual Features Play in Navigational Decisions
07:09

Integrating Visual Psychophysical Assays within a Y-Maze to Isolate the Role that Visual Features Play in Navigational Decisions

Published on: May 2, 2019

6.2K

Area of Science:

  • Electrical Engineering
  • Computer Science
  • Signal Processing

Background:

  • Indoor location-based services (LBS) are crucial but challenged by imprecise radio-frequency (RF) positioning.
  • Existing RF technologies like Wi-Fi suffer from signal fluctuations due to indoor environmental factors.
  • Need for accurate and efficient indoor positioning systems is a significant research area.

Purpose of the Study:

  • To propose and evaluate a novel indoor positioning system utilizing visible-light communication (VLC).
  • To enhance positioning accuracy and reduce processing time using a deep neural network (DNN).
  • To optimize DNN hyperparameters for superior indoor positioning performance.

Main Methods:

  • Employed visible-light communication (VLC) technology, leveraging light-emitting diodes (LEDs) for indoor positioning.
  • Developed and trained a deep neural network (DNN) model to predict user's 3D position.
  • Optimized DNN hyperparameters to maximize positioning accuracy and minimize computational overhead.

Main Results:

  • Achieved a highly precise positioning error of 0.0898 meters.
  • Demonstrated a significantly reduced processing time of 0.5 milliseconds.
  • The proposed VLC-based DNN method outperformed existing indoor positioning techniques.

Conclusions:

  • Visible-light communication (VLC) offers a viable and accurate alternative for indoor positioning.
  • Deep neural networks (DNNs) effectively enhance the precision and speed of VLC-based positioning.
  • The optimized DNN model provides a robust solution for next-generation indoor location-based services (LBS).