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

Structural Classification of Joints01:20

Structural Classification of Joints

7.6K
Joints, also known as articulations, are classified based on their structural characteristics, i.e., based on whether the articulating surfaces of the adjacent bones are directly connected by fibrous connective tissue or cartilage, or whether the articulating surfaces contact each other within a fluid-filled joint cavity. These differences serve to divide the joints of the body into three structural classifications.
A fibrous joint is where the adjacent bones are united by fibrous connective...
7.6K
Functional Classification of Joints01:09

Functional Classification of Joints

7.2K
Functional Classification of Joints
The functional classification of joints is determined by the amount of mobility between the adjacent bones. Joints are functionally classified as a synarthrosis or immobile joint, an amphiarthrosis or slightly moveable joint, or as a diarthrosis, a freely moveable joint. Fibrous and cartilaginous joints can be functionally classified as either synarthroses  or amphiarthroses, whereas all synovial joints are classified as diarthroses.
Synarthrosis
An...
7.2K
Root Mean Square00:57

Root Mean Square

3.9K
If in an experiment, data values have a probability of being both positive and negative, neither the arithmetic mean, the geometric mean, nor the harmonic mean can be used to calculate the central tendency of the data set. In particular, if the positive and negative values are equally likely, the arithmetic mean is close to zero.
For example, consider the velocity of gas molecules in a container. The gas molecules are moving in different directions, which might impart positive and negative...
3.9K
One-Degree-of-Freedom System01:24

One-Degree-of-Freedom System

871
In mechanical engineering, one-degree-of-freedom systems form the basis of a wide range of electrical and mechanical components. Using these models, engineers can predict the behavior of various parts in a larger system, which gives them insight into how different forces interact with each other.
A one-degree-of-freedom system is defined by an independent variable that determines its state and behavior. One example of a one-degree-of-freedom system is a simple harmonic oscillator, such as a...
871
Method of Joints: Problem Solving II01:30

Method of Joints: Problem Solving II

1.0K
Consider a truss structure with frictionless joints fixed to a wall and roller support. If a force of 150 N is applied to joint A, the forces in each member of the truss can be determined using the method of joints.
1.0K
Radius of Gyration of an Area01:12

Radius of Gyration of an Area

2.8K
The second moment of area, also known as the moment of inertia of area, is a crucial factor in understanding an object's resistance against bending deformation, or stiffness. To accurately estimate the second moment of area along any axis, one needs to concentrate all areas associated with that object into a thin strip, which should be placed parallel to that particular axis.
2.8K

You might also read

Related Articles

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

Sort by
Same author

Adaptive Interacting Multiple Model Algorithm Based on Information-Weighted Consensus for Maneuvering Target Tracking.

Sensors (Basel, Switzerland)·2018
Same author

Calcium/calmodulin-dependent protein kinase II links ER stress with Fas and mitochondrial apoptosis pathways.

The Journal of clinical investigation·2009
Same author

Cripto-1 overexpression is involved in the tumorigenesis of nasopharyngeal carcinoma.

BMC cancer·2009
Same author

Range of motion and orientation of the lumbar facet joints in vivo.

Spine·2009
Same author

[Silencing of COX-2 in nasopharyngeal carcinoma cells with a shRNAmir lentivirus vector].

Nan fang yi ke da xue xue bao = Journal of Southern Medical University·2009
Same author

The risk of melamine-induced nephrolithiasis in young children starts at a lower intake level than recommended by the WHO.

Pediatric nephrology (Berlin, Germany)·2009
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: Feb 19, 2026

Sit-to-stand-and-walk from 120% Knee Height: A Novel Approach to Assess Dynamic Postural Control Independent of Lead-limb
08:24

Sit-to-stand-and-walk from 120% Knee Height: A Novel Approach to Assess Dynamic Postural Control Independent of Lead-limb

Published on: August 30, 2016

10.8K

Centralized Multi-Sensor Square Root Cubature Joint Probabilistic Data Association.

Yu Liu1,2, Jun Liu3, Gang Li4

  • 1School of Electronic and Information Engineering, Beihang University, Beijing 100191, China. liuyu77360132@126.com.

Sensors (Basel, Switzerland)
|November 9, 2017
PubMed
Summary
This summary is machine-generated.

A new algorithm improves multi-target tracking in cluttered environments. The centralized multi-sensor square root cubature joint probabilistic data association (CMSCJPDA) algorithm enhances accuracy and stability for multiple sensors.

Keywords:
centralized filteringcubature Kalman filterdata associationmulti-sensor trackingstate estimation

More Related Videos

An Inertial Measurement Unit Based Method to Estimate Hip and Knee Joint Kinematics in Team Sport Athletes on the Field
06:52

An Inertial Measurement Unit Based Method to Estimate Hip and Knee Joint Kinematics in Team Sport Athletes on the Field

Published on: May 26, 2020

8.7K

Related Experiment Videos

Last Updated: Feb 19, 2026

Sit-to-stand-and-walk from 120% Knee Height: A Novel Approach to Assess Dynamic Postural Control Independent of Lead-limb
08:24

Sit-to-stand-and-walk from 120% Knee Height: A Novel Approach to Assess Dynamic Postural Control Independent of Lead-limb

Published on: August 30, 2016

10.8K
An Inertial Measurement Unit Based Method to Estimate Hip and Knee Joint Kinematics in Team Sport Athletes on the Field
06:52

An Inertial Measurement Unit Based Method to Estimate Hip and Knee Joint Kinematics in Team Sport Athletes on the Field

Published on: May 26, 2020

8.7K

Area of Science:

  • Robotics and Automation
  • Signal Processing
  • Estimation Theory

Background:

  • Multi-target tracking in nonlinear, cluttered environments presents significant challenges.
  • Existing methods often struggle with Jacobian matrix computation and parameter adjustments, impacting accuracy and stability.
  • Accurate tracking is crucial for applications like autonomous systems and surveillance.

Purpose of the Study:

  • To propose a novel centralized multi-sensor square root cubature joint probabilistic data association (CMSCJPDA) algorithm.
  • To enhance numerical stability and tracking accuracy in nonlinear, cluttered environments.
  • To reduce computational cost compared to existing state-of-the-art algorithms.

Main Methods:

  • Decomposition of the multi-sensor tracking problem into sequential single-sensor multi-target tracking problems.
  • Application of joint probabilistic data association (JPDA) for measurement-to-track assignment within each sensor.
  • Utilization of a weighted probability fusion method with the square root cubature Kalman filter (SRCKF) for state estimation.

Main Results:

  • The proposed CMSCJPDA algorithm achieves superior tracking accuracy.
  • Demonstrated significant improvements in numerical stability.
  • Showcased a reduction in computational cost compared to existing methods.

Conclusions:

  • CMSCJPDA offers a robust and efficient solution for centralized multi-sensor nonlinear tracking.
  • The algorithm provides a novel approach to overcoming limitations of current tracking techniques.
  • Experimental results validate the superiority of CMSCJPDA in complex tracking scenarios.