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

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

8.1K
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...
8.1K
Effects of EDTA on End-Point Detection Methods01:18

Effects of EDTA on End-Point Detection Methods

628
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...
628
Types of Errors: Detection and Minimization01:12

Types of Errors: Detection and Minimization

10.8K
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...
10.8K
Precipitation Titration: Endpoint Detection Methods01:19

Precipitation Titration: Endpoint Detection Methods

5.8K
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...
5.8K

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
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

Combination of Adhesive-tape-based Sampling and Fluorescence in situ Hybridization for Rapid Detection of Salmonella on Fresh Produce
09:10

Combination of Adhesive-tape-based Sampling and Fluorescence in situ Hybridization for Rapid Detection of Salmonella on Fresh Produce

Published on: October 18, 2010

13.5K

An Efficient Extended Targets Detection Framework Based on Sampling and Spatio-Temporal Detection.

Bo Yan1, Na Xu2, Wenbo Zhao1

  • 1School of Aerospace Science and Technology, XIDIAN University, 266 Xinglong Section of Xifeng Road, Xi'an 710126, China.

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

This study introduces a novel marine radar target detection framework. It achieves excellent performance with real-time processing and low memory usage by using sampling and spatiotemporal detection.

Keywords:
clutter suppressionextended targetmarine radar systemtarget detection

More Related Videos

Western Blotting: Sample Preparation to Detection
07:45

Western Blotting: Sample Preparation to Detection

Published on: October 14, 2010

145.4K
A Label-free Technique for the Spatio-temporal Imaging of Single Cell Secretions
09:09

A Label-free Technique for the Spatio-temporal Imaging of Single Cell Secretions

Published on: November 23, 2015

9.0K

Related Experiment Videos

Last Updated: Jan 22, 2026

Combination of Adhesive-tape-based Sampling and Fluorescence in situ Hybridization for Rapid Detection of Salmonella on Fresh Produce
09:10

Combination of Adhesive-tape-based Sampling and Fluorescence in situ Hybridization for Rapid Detection of Salmonella on Fresh Produce

Published on: October 18, 2010

13.5K
Western Blotting: Sample Preparation to Detection
07:45

Western Blotting: Sample Preparation to Detection

Published on: October 14, 2010

145.4K
A Label-free Technique for the Spatio-temporal Imaging of Single Cell Secretions
09:09

A Label-free Technique for the Spatio-temporal Imaging of Single Cell Secretions

Published on: November 23, 2015

9.0K

Area of Science:

  • Marine radar systems
  • Target detection algorithms
  • Signal processing

Background:

  • Current marine radar target detection methods struggle to balance performance, real-time processing, and low memory requirements, especially in complex scenes.
  • Achieving excellent detection ability alongside real-time capabilities and minimal memory footprint remains a significant challenge.

Purpose of the Study:

  • To propose a novel detection framework for high-resolution marine radar systems.
  • To address the coordination difficulties between real-time processing, low memory needs, and remarkable detection ability.
  • To enhance target detection performance in marine radar applications.

Main Methods:

  • A two-stage framework: coarse detection (sampling-based) and fine detection.
  • Coarse detection utilizes sampling and multi-scan video data for efficient area localization.
  • Fine detection categorizes candidate areas (single target, dense targets, sea clutter) for tailored processing.

Main Results:

  • The proposed framework demonstrates superior performance compared to state-of-the-art baselines.
  • Theoretical analysis confirmed the framework's low memory requirements.
  • Real-time processing capability was validated using data from two real-world marine scenarios.
  • Synthetic data testing showed improved tracking performance.

Conclusions:

  • The novel framework effectively coordinates excellent performance, real-time processing, and low memory usage in marine radar target detection.
  • The two-stage approach with category-specific processing enhances detection accuracy and efficiency.
  • This framework offers a significant advancement for high-resolution marine radar systems.