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

Ogive Graph01:07

Ogive Graph

6.8K
An ogive graph is sometimes called a cumulative frequency polygon. It is one type of frequency polygon that shows cumulative frequency. In other words, the cumulative percentages are added to the graph from left to right. An ogive graph plots cumulative frequency on the vertical y-axis and class boundaries along the horizontal x-axis. It’s very similar to a histogram; only instead of rectangles, an ogive displays a single point where the top right of the rectangle would be. Creating this...
6.8K
Graphing Antiderivatives01:30

Graphing Antiderivatives

69
The concept of an antiderivative is fundamental in calculus, describing how a function's values accumulate over time. This process is closely related to physical motion, such as the movement of a rolling ball. As the ball progresses, its position changes in response to variations in velocity, just as an antiderivative graph reflects the cumulative effect of the original function's values.Graphing an antiderivative requires interpreting how a function's values influence the shape of its...
69
Bar Graph01:07

Bar Graph

22.1K
A bar graph is also called a bar chart and consists of bars that are separated from each other. It either uses horizontal or vertical bars to show comparisons among categories. The bars can be rectangles, or they can be rectangular boxes (used in three-dimensional plots). One axis of the graph represents the specific categories being compared, and the other axis shows a discrete value. In this graph, the length of the bar for each category is proportional to the number or percent of individuals...
22.1K
Graphs of Functions01:30

Graphs of Functions

331
Graphs of functions provide a visual representation of how output values change in response to varying inputs. Each point on the graph corresponds to an ordered pair, where the x-coordinate (independent variable) determines the horizontal position and the y-coordinate (dependent variable) determines the vertical position. Linear functions like y = x give a straight line, indicating a constant rate of change.Nonlinear functions display more complex behaviors. Even power functions generate...
331
Time-Series Graph00:54

Time-Series Graph

5.1K
A time-series graph is a line graph with repeated measurements taken at successive intervals of time. It is also called a time series chart. To construct a time-series graph, one must look at both pieces of a paired data set. The horizontal axis is used to plot the time increments, and the vertical axis is used to plot the values of the variable that one is measuring. By using the axes in this way, each point on the graph will correspond to time and a measured quantity. The points on the graph...
5.1K
Multiple Bar Graph01:07

Multiple Bar Graph

9.3K
As the name suggests, a multiple bar graph is the same as a bar graph but has multiple bars to depict relationships between different data values. One can include as many parameters as possible. However, each parameter must have the same unit of measurement.
Each bar or column in the multiple bar graph represents a data value. These graphs are used primarily in interrelating two or more sets of data. The categories of different kinds of data are listed along the horizontal or x-axis, whereas...
9.3K

You might also read

Related Articles

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

Sort by
Same author

Ligand identification in CryoEM and X-ray maps using deep learning.

Bioinformatics (Oxford, England)·2024
Same author

Ligand Identification in CryoEM and X-ray Maps Using Deep Learning.

bioRxiv : the preprint server for biology·2024
Same author

Optimizing Appearance-Based Localization with Catadioptric Cameras: Small-Footprint Models for Real-Time Inference on Edge Devices.

Sensors (Basel, Switzerland)·2023
Same author

Adopting the YOLOv4 Architecture for Low-Latency Multispectral Pedestrian Detection in Autonomous Driving.

Sensors (Basel, Switzerland)·2022
Same author

Real-Time Detection of Non-Stationary Objects Using Intensity Data in Automotive LiDAR SLAM.

Sensors (Basel, Switzerland)·2021
Same author

Large-Scale LiDAR SLAM with Factor Graph Optimization on High-Level Geometric Features.

Sensors (Basel, Switzerland)·2021
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
See all related articles

Related Experiment Video

Updated: Jan 31, 2026

Personalized 3D-printed Headgear for Multi-electrode Transcranial Electrical Stimulation
07:47

Personalized 3D-printed Headgear for Multi-electrode Transcranial Electrical Stimulation

Published on: September 9, 2025

748

A Multi-User Personal Indoor Localization System Employing Graph-Based Optimization.

Michał R Nowicki1, Piotr Skrzypczyński2

  • 1Institute of Control, Robotics and Information Engineering, Poznan University of Technology, 60-965 Poznan, Poland. michal.nowicki@put.poznan.pl.

Sensors (Basel, Switzerland)
|January 10, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces a graph-based optimization system for smartphone indoor localization. It improves accuracy, especially in multi-user scenarios, by fusing WiFi and dead reckoning data.

Keywords:
WiFi fingerprintinggraph-based optimizationindoor positioning

More Related Videos

Optimized PCR-based Detection of Mycoplasma
06:01

Optimized PCR-based Detection of Mycoplasma

Published on: June 20, 2011

55.1K
Lensfree On-chip Tomographic Microscopy Employing Multi-angle Illumination and Pixel Super-resolution
08:41

Lensfree On-chip Tomographic Microscopy Employing Multi-angle Illumination and Pixel Super-resolution

Published on: August 16, 2012

12.0K

Related Experiment Videos

Last Updated: Jan 31, 2026

Personalized 3D-printed Headgear for Multi-electrode Transcranial Electrical Stimulation
07:47

Personalized 3D-printed Headgear for Multi-electrode Transcranial Electrical Stimulation

Published on: September 9, 2025

748
Optimized PCR-based Detection of Mycoplasma
06:01

Optimized PCR-based Detection of Mycoplasma

Published on: June 20, 2011

55.1K
Lensfree On-chip Tomographic Microscopy Employing Multi-angle Illumination and Pixel Super-resolution
08:41

Lensfree On-chip Tomographic Microscopy Employing Multi-angle Illumination and Pixel Super-resolution

Published on: August 16, 2012

12.0K

Area of Science:

  • Computer Science
  • Robotics
  • Ubiquitous Computing

Background:

  • Personal indoor localization using smartphones is established but often treats users individually.
  • Current methods like particle filters struggle to fuse unsynchronized multi-user trajectory data.
  • Existing approaches neglect the benefits of simultaneous multi-user localization.

Purpose of the Study:

  • To develop an improved indoor localization system leveraging graph-based optimization.
  • To enhance localization accuracy by fusing WiFi and dead reckoning data.
  • To demonstrate the system's effectiveness in both single-user and multi-user environments.

Main Methods:

  • Utilizing graph-based optimization, a technique common in robotics.
  • Fusing sensor data from WiFi and dead reckoning (DR).
  • Implementing a system capable of processing multiple user trajectories simultaneously.

Main Results:

  • Graph-based optimization provides accurate trajectory estimates.
  • The system shows significant improvements in multi-user indoor localization.
  • Experiments conducted in an office building validate the approach.

Conclusions:

  • Graph-based optimization is an efficient fusion mechanism for indoor localization.
  • The proposed system enhances accuracy for both single and multiple users.
  • Future work will involve releasing the system code and dataset.