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

Tight Junctions01:29

Tight Junctions

7.2K
Tight junctions are molecular seals between cells that prevent the leaking of fluids, ions, and other small solutes across cavities and compartments in multicellular organisms. They are mainly composed of claudin and occludin transmembrane proteins, and other proteins such as tricellulin and JAM (junctional adhesion molecule). All these proteins are 4-pass transmembrane proteins, except JAM, which is a single-pass transmembrane protein belonging to the immunoglobulin superfamily. The...
7.2K
Velocity and Position by Integral Method01:13

Velocity and Position by Integral Method

7.9K
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...
7.9K
G-protein Coupled Receptors01:21

G-protein Coupled Receptors

132.0K
G-protein coupled receptors are ligand binding receptors that indirectly affect changes in the cell. The actual receptor is a single polypeptide that transverses the cell membrane seven times creating intracellular and extracellular loops. The extracellular loops create a ligand specific pocket which binds to neurotransmitters or hormones. The intracellular loops holds onto the G-protein.
132.0K
Spin–Spin Coupling: Two-Bond Coupling (Geminal Coupling)01:20

Spin–Spin Coupling: Two-Bond Coupling (Geminal Coupling)

1.7K
Two NMR-active nuclei bonded to a central atom can be involved in geminal or two-bond coupling. Geminal coupling is commonly seen between diastereotopic protons in chiral molecules and unsymmetrical alkenes, among others.
The central atom need not be NMR-active because its electrons are affected by the electron polarization of the spin-active atoms. However, spin information is transmitted less effectively than in one-bond coupling, and 2J values are usually weaker than 1J values. The energy of...
1.7K
Spin–Spin Coupling: Three-Bond Coupling (Vicinal Coupling)01:22

Spin–Spin Coupling: Three-Bond Coupling (Vicinal Coupling)

1.5K
Vicinal or three-bond coupling is commonly observed between protons attached to adjacent carbons. Here, nuclear spin information is primarily transferred via electron spin interactions between adjacent C‑H bond orbitals. This generally favors the antiparallel arrangement of spins, so 3J values are usually positive.
The extent of coupling depends on the C‑C bond length, the two H‑C‑C angles, any electron-withdrawing substituents, and the dihedral angle between the involved orbitals. The...
1.5K
Spin–Spin Coupling: One-Bond Coupling01:17

Spin–Spin Coupling: One-Bond Coupling

1.5K
Coupling interactions are strongest between NMR-active nuclei bonded to each other, where spin information can be transmitted directly through the pair of bonding electrons. While nuclei polarize their electrons to the opposite spins, the bonding electron pair has opposite spins. Configurations with antiparallel nuclear spins are expected to be lower in energy. When coupling makes antiparallel states more favorable, J is considered to have a positive value. The one-bond coupling constant, 1J,...
1.5K

You might also read

Related Articles

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

Sort by
Same author

Taxonomy, carcinogenic mechanisms, and advanced molecular diagnosis of Fusobacterium nucleatum in colorectal cancer: from bench to clinical practice.

Frontiers in immunology·2026
Same author

Identifying multiple drivers of stratified deformation in soft deltaic deposits using integrated geosensing.

Scientific reports·2026
Same author

A user-friendly strategy to engineer tailored intermediate strains for overcoming combined type I and type II restriction-modification barriers in <i>Staphylococcus aureus</i>.

Synthetic and systems biotechnology·2026
Same author

A synergistic dual-additive strategy inducing macromolecular disentanglement for highly stable zinc anodes.

Journal of colloid and interface science·2026
Same author

Potentially toxic elements in tailing-contaminated soils of Tongling, China: Pollution status, health risks and environmental capacity.

Environmental research·2026
Same author

Multidirectional strain-insensitive stretchable RF electronics.

Nature communications·2026

Related Experiment Video

Updated: Feb 2, 2026

Characterization of SiN Integrated Optical Phased Arrays on a Wafer-Scale Test Station
05:57

Characterization of SiN Integrated Optical Phased Arrays on a Wafer-Scale Test Station

Published on: April 1, 2020

8.6K

A Fuzzy Adaptive Tightly-Coupled Integration Method for Mobile Target Localization Using SINS/WSN.

Wei Li1, Hai Yang2, Mengbao Fan3

  • 1School of Mechatronic Engineering, China University of Mining and Technology, Xuzhou 221116, Jiangsu, China. weilicmee@cumt.edu.cn.

Micromachines
|November 9, 2018
PubMed
Summary

This study introduces a fuzzy adaptive tightly-coupled integration (FATCI) method for mobile target localization using strapdown inertial navigation systems (SINS) and wireless sensor networks (WSN). The FATCI method enhances positioning accuracy and stability, especially during WSN signal disruptions.

Keywords:
Kalman filterfuzzy adaptiveintegrated positioningmobile targetstrapdown inertial navigation system (SINS)tightly-coupled integrationwireless sensor network (WSN)

More Related Videos

A Practical Guide on Coupling a Scanning Mobility Sizer and Inductively Coupled Plasma Mass Spectrometer SMPS-ICPMS
11:18

A Practical Guide on Coupling a Scanning Mobility Sizer and Inductively Coupled Plasma Mass Spectrometer SMPS-ICPMS

Published on: July 11, 2017

11.2K
Characterization of G Protein-coupled Receptors by a Fluorescence-based Calcium Mobilization Assay
11:49

Characterization of G Protein-coupled Receptors by a Fluorescence-based Calcium Mobilization Assay

Published on: July 28, 2014

41.6K

Related Experiment Videos

Last Updated: Feb 2, 2026

Characterization of SiN Integrated Optical Phased Arrays on a Wafer-Scale Test Station
05:57

Characterization of SiN Integrated Optical Phased Arrays on a Wafer-Scale Test Station

Published on: April 1, 2020

8.6K
A Practical Guide on Coupling a Scanning Mobility Sizer and Inductively Coupled Plasma Mass Spectrometer SMPS-ICPMS
11:18

A Practical Guide on Coupling a Scanning Mobility Sizer and Inductively Coupled Plasma Mass Spectrometer SMPS-ICPMS

Published on: July 11, 2017

11.2K
Characterization of G Protein-coupled Receptors by a Fluorescence-based Calcium Mobilization Assay
11:49

Characterization of G Protein-coupled Receptors by a Fluorescence-based Calcium Mobilization Assay

Published on: July 28, 2014

41.6K

Area of Science:

  • Robotics and Automation
  • Sensor Networks
  • Navigation Systems

Background:

  • Mobile target localization in enclosed environments faces challenges from wireless signal outages and multipath propagation in Wireless Sensor Networks (WSN).
  • Strapdown Inertial Navigation Systems (SINS) are prone to time-drifted errors, impacting localization accuracy.
  • Existing loosely-coupled integration methods and traditional tightly-coupled systems struggle with WSN inaccuracies and SINS drift.

Purpose of the Study:

  • To propose a novel fuzzy adaptive tightly-coupled integration (FATCI) method for enhanced mobile target localization.
  • To improve the accuracy and stability of integrated SINS/WSN positioning systems.
  • To address the limitations of WSN signal degradation and SINS drift in enclosed environments.

Main Methods:

  • Developed a tightly-coupled integrated positioning system for SINS/WSN, improving upon the loosely-coupled approach.
  • Applied a least squares regression (LSR) algorithm to correct measured distances from WSN, reducing systematic errors.
  • Utilized a fuzzy inference system (FIS) to adaptively adjust the Kalman filter's observation covariance matrix based on WSN distance measurement characteristics.

Main Results:

  • The proposed FATCI system demonstrated superior accuracy and stability compared to traditional loosely-coupled and tightly-coupled integration models.
  • Experimental results validated the effectiveness of the FATCI method in real-world scenarios.
  • The system showed improved performance even during short-term WSN failures or normal operating conditions.

Conclusions:

  • The FATCI method offers a robust solution for mobile target localization in challenging enclosed environments.
  • Adaptive adjustment of measurement confidence levels significantly enhances positioning system resilience.
  • This integrated SINS/WSN approach provides a reliable and accurate alternative for indoor navigation and tracking applications.