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

Effects of EDTA on End-Point Detection Methods01:18

Effects of EDTA on End-Point Detection Methods

634
Different methods, such as visual observance of metal-ion indicators, spectroscopic techniques, and potentiometric methods, can determine the endpoint of an EDTA titration.
In the visual method, metal-ion indicators (metallochromic dyes), which have distinct colors in their free and complex forms, are added to the mixture to signal the titration's end point. They form stable complexes with metal ions, but these complexes are weaker than the corresponding metal–EDTA complexes. As a...
634
Precipitation Titration: Endpoint Detection Methods01:19

Precipitation Titration: Endpoint Detection Methods

5.9K
In argentometric precipitation titrations, endpoints can be detected visually by the Mohr, Volhard, and Fajans methods. In the Mohr method, adding a soluble chromate indicator gives an initial yellow color to the analyte solution. As the titrant is added, the first excess of silver ions forms a red silver chromate precipitate, marking the endpoint. The solution pH should be maintained at about 8 by adding solid CaCO3.
In the Volhard method, a standard excess of AgNO3 is first added to the...
5.9K
Detection of Gross Error: The Q Test01:00

Detection of Gross Error: The Q Test

6.9K
When one or more data points appear far from the rest of the data, there is a need to determine whether they are outliers and whether they should be eliminated from the data set to ensure an accurate representation of the measured value. In many cases, outliers arise from gross errors (or human errors) and do not accurately reflect the underlying phenomenon. In some cases, however, these apparent outliers reflect true phenomenological differences. In these cases, we can use statistical methods...
6.9K
Detection of Black Holes01:10

Detection of Black Holes

2.5K
Although black holes were theoretically postulated in the 1920s, they remained outside the domain of observational astronomy until the 1970s.
Their closest cousins are neutron stars, which are composed almost entirely of neutrons packed against each other, making them extremely dense. A neutron star has the same mass as the Sun but its diameter is only a few kilometers. Therefore, the escape velocity from their surface is close to the speed of light.
Not until the 1960s, when the first neutron...
2.5K
Difference from Background: Limit of Detection01:05

Difference from Background: Limit of Detection

8.2K
The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
The LOD indicates the presence or absence...
8.2K
Types of Errors: Detection and Minimization01:12

Types of Errors: Detection and Minimization

10.9K
Error is the deviation of the obtained result from the true, expected value or the estimated central value. Errors are expressed in absolute or relative terms.
Absolute error in a measurement is the numerical difference from the true or central value. Relative error is the ratio between absolute error and the true or central value, expressed as a percentage.
Errors can be classified by source, magnitude, and sign. There are three types of errors: systematic, random, and gross.
Systematic or...
10.9K

You might also read

Related Articles

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

Sort by
Same author

Evaluation of the properties of daughter bubbles generated by inertial cavitation of preformed microbubbles.

Ultrasonics sonochemistry·2020
Same author

Graph Adaptation Network with Domain-Specific Word Alignment for Cross-Domain Relation Extraction.

Sensors (Basel, Switzerland)·2020
Same author

Membrane Damage during Ferroptosis Is Caused by Oxidation of Phospholipids Catalyzed by the Oxidoreductases POR and CYB5R1.

Molecular cell·2020
Same author

Printing special surface components for THz 2D and 3D imaging.

Scientific reports·2020
Same author

The role of post-loss anxiety in the development of depressive symptoms and complicated grief symptoms: a longitudinal SEM study.

Journal of affective disorders·2020
Same author

Enhancement of PAHs biodegradation in biosurfactant/phenol system by increasing the bioavailability of PAHs.

Chemosphere·2020

Related Experiment Video

Updated: Jan 26, 2026

Optimized PCR-based Detection of Mycoplasma
06:01

Optimized PCR-based Detection of Mycoplasma

Published on: June 20, 2011

55.0K

A Grey Wolf Optimization-based Track-Before-Detect Method for Maneuvering Extended Target Detection and Tracking.

Bo Yan1, Xu Yang Zhao2, Na Xu3

  • 1School of Aerospace Science and Technology, Xidian University, 266 Xinglong Section of Xifeng Road, Xi'an 710126, China. boyan@xidian.edu.cn.

Sensors (Basel, Switzerland)
|April 4, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces a novel Grey Wolf Optimization-based Track-Before-Detect (GWO-TBD) method for tracking weak, maneuvering targets. The GWO-TBD method demonstrates superior performance in cluttered environments, requiring less prior information for effective extended target detection.

Keywords:
extended target trackinggrey wolf optimizationtrack-before-detect

More Related Videos

Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles
11:54

Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles

Published on: March 13, 2017

9.8K
Psychophysical Tracking Method to Assess Taste Detection Thresholds in Children, Adolescents, and Adults: The Taste Detection Threshold TDT Test
08:52

Psychophysical Tracking Method to Assess Taste Detection Thresholds in Children, Adolescents, and Adults: The Taste Detection Threshold TDT Test

Published on: April 21, 2021

5.4K

Related Experiment Videos

Last Updated: Jan 26, 2026

Optimized PCR-based Detection of Mycoplasma
06:01

Optimized PCR-based Detection of Mycoplasma

Published on: June 20, 2011

55.0K
Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles
11:54

Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles

Published on: March 13, 2017

9.8K
Psychophysical Tracking Method to Assess Taste Detection Thresholds in Children, Adolescents, and Adults: The Taste Detection Threshold TDT Test
08:52

Psychophysical Tracking Method to Assess Taste Detection Thresholds in Children, Adolescents, and Adults: The Taste Detection Threshold TDT Test

Published on: April 21, 2021

5.4K

Area of Science:

  • * Signal Processing
  • * Artificial Intelligence
  • * Radar Systems Engineering

Background:

  • * Extended targets in cluttered environments pose significant challenges for traditional detection and tracking algorithms.
  • * Weak and maneuvering targets are particularly difficult to discern from noise and interference in air surveillance radar data.

Purpose of the Study:

  • * To develop and evaluate a novel Grey Wolf Optimization-based Track-Before-Detect (GWO-TBD) method.
  • * To enhance the detection and tracking of weak and maneuvering extended targets using air surveillance radar measurements.
  • * To provide an engineering-friendly approach with reduced prior information requirements.

Main Methods:

  • * Development of a Grey Wolf Optimization-based Track-Before-Detect (GWO-TBD) algorithm.
  • * Initial clustering of radar measurements into sets, followed by tracklet association.
  • * Iterative refinement of tracklets using an improved GWO algorithm for solution optimization and convergence acceleration.

Main Results:

  • * The GWO-TBD method demonstrated superior performance in detecting and tracking maneuvering extended targets compared to existing algorithms.
  • * Both real and synthetic data validated the effectiveness of the proposed GWO-TBD approach.
  • * The method requires significantly less prior information, enhancing its practical applicability.

Conclusions:

  • * The GWO-TBD method offers a robust and effective solution for extended target detection and tracking in challenging environments.
  • * The algorithm's ability to handle weak and maneuvering targets, coupled with its reduced information dependency, makes it highly suitable for engineering applications.
  • * This approach advances the field of radar signal processing for air surveillance.