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

Improving Translational Accuracy02:07

Improving Translational Accuracy

14.1K
Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
14.1K
Improving Translational Accuracy02:07

Improving Translational Accuracy

3.6K
3.6K
Velocity and Position by Integral Method01:13

Velocity and Position by Integral Method

7.4K
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...
7.4K
Velocity and Position by Graphical Method01:34

Velocity and Position by Graphical Method

9.5K
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...
9.5K
Uncertainty in Measurement: Accuracy and Precision03:37

Uncertainty in Measurement: Accuracy and Precision

100.2K
Scientists typically make repeated measurements of a quantity to ensure the quality of their findings and to evaluate both the precision and the accuracy of their results. Measurements are said to be precise if they yield very similar results when repeated in the same manner. A measurement is considered accurate if it yields a result that is very close to the true or the accepted value. Precise values agree with each other; accurate values agree with a true value. 
100.2K
Protein Networks02:26

Protein Networks

4.5K
An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
4.5K

You might also read

Related Articles

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

Sort by
Same author

A Framework for the Optimization of Complex Cyber-Physical Systems via Directed Acyclic Graph.

Sensors (Basel, Switzerland)·2022
Same author

Digital Twin for Automatic Transportation in Industry 4.0.

Sensors (Basel, Switzerland)·2021
Same author

Memetic Chains for Improving the Local Wireless Sensor Networks Localization in Urban Scenarios.

Sensors (Basel, Switzerland)·2021
Same author

Hybrid Memetic Algorithm for the Node Location Problem in Local Positioning Systems.

Sensors (Basel, Switzerland)·2020
Same author

Local Wireless Sensor Networks Positioning Reliability Under Sensor Failure.

Sensors (Basel, Switzerland)·2020
Same author

Genetic Algorithm Approach to the 3D Node Localization in TDOA Systems.

Sensors (Basel, Switzerland)·2019
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 22, 2026

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

1.1K

Accuracy Analysis in Sensor Networks for Asynchronous Positioning Methods.

Rubén Álvarez1, Javier Díez-González2, Efrén Alonso1

  • 1Positioning Department, Drotium, Universidad de León, 24071 León, Spain.

Sensors (Basel, Switzerland)
|July 21, 2019
PubMed
Summary
This summary is machine-generated.

New sensor network positioning systems, Asynchronous Time Difference of Arrival (A-TDOA) and Difference-Time Difference of Arrival (D-TDOA), offer improved accuracy. A-TDOA reduces positioning uncertainty, while D-TDOA enhances stability for vehicle and robot applications.

Keywords:
Cramér–Rao lower boundTDOAasynchronousheteroscedasticitysensor networks

More Related Videos

A Method for Evaluating Timeliness and Accuracy of Volitional Motor Responses to Vibrotactile Stimuli
07:28

A Method for Evaluating Timeliness and Accuracy of Volitional Motor Responses to Vibrotactile Stimuli

Published on: August 2, 2016

7.6K
Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy
12:09

Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy

Published on: August 5, 2014

18.5K

Related Experiment Videos

Last Updated: Jan 22, 2026

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

1.1K
A Method for Evaluating Timeliness and Accuracy of Volitional Motor Responses to Vibrotactile Stimuli
07:28

A Method for Evaluating Timeliness and Accuracy of Volitional Motor Responses to Vibrotactile Stimuli

Published on: August 2, 2016

7.6K
Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy
12:09

Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy

Published on: August 5, 2014

18.5K

Area of Science:

  • Robotics and Autonomous Systems
  • Sensor Networks
  • Signal Processing

Background:

  • Increasing demand for high-precision positioning in vehicle and robot applications.
  • Traditional sensor synchronization introduces time determination errors.
  • Centralized measurement with a single clock in coordinate sensors offers a solution.

Purpose of the Study:

  • Evaluate the suitability of Asynchronous Time Difference of Arrival (A-TDOA) and Difference-Time Difference of Arrival (D-TDOA) systems.
  • Analyze system performance in various 3D real environments with multiple sensor locations.
  • Compare A-TDOA and D-TDOA based on Cramér-Rao Lower Bound (CRLB) evaluation.

Main Methods:

  • Cramér-Rao Lower Bound (CRLB) evaluation for positioning accuracy.
  • Heteroscedastic noise variance modeling.
  • Distance-dependent Log-normal path loss propagation model.
  • Analysis across diverse 3D environments and sensor configurations.

Main Results:

  • A-TDOA demonstrated less uncertainty in root mean square error (RMSE) for positioning.
  • D-TDOA exhibited reduced standard deviation and increased overall stability.
  • Both systems were evaluated for their performance under specific noise and propagation conditions.

Conclusions:

  • A-TDOA and D-TDOA are suitable for high-accuracy sensor network positioning.
  • The choice between A-TDOA and D-TDOA depends on whether minimizing RMSE or maximizing stability is prioritized.
  • The study provides a novel CRLB evaluation for these systems in realistic 3D scenarios.