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

Ogive Graph01:07

Ogive Graph

6.7K
An ogive graph is sometimes called a cumulative frequency polygon. It is one type of frequency polygon that shows cumulative frequency. In other words, the cumulative percentages are added to the graph from left to right. An ogive graph plots cumulative frequency on the vertical y-axis and class boundaries along the horizontal x-axis. It’s very similar to a histogram; only instead of rectangles, an ogive displays a single point where the top right of the rectangle would be. Creating this...
6.7K
Graphing Antiderivatives01:30

Graphing Antiderivatives

51
The concept of an antiderivative is fundamental in calculus, describing how a function's values accumulate over time. This process is closely related to physical motion, such as the movement of a rolling ball. As the ball progresses, its position changes in response to variations in velocity, just as an antiderivative graph reflects the cumulative effect of the original function's values.Graphing an antiderivative requires interpreting how a function's values influence the shape of its...
51
Bar Graph01:07

Bar Graph

21.5K
A bar graph is also called a bar chart and consists of bars that are separated from each other. It either uses horizontal or vertical bars to show comparisons among categories. The bars can be rectangles, or they can be rectangular boxes (used in three-dimensional plots). One axis of the graph represents the specific categories being compared, and the other axis shows a discrete value. In this graph, the length of the bar for each category is proportional to the number or percent of individuals...
21.5K
Time-Series Graph00:54

Time-Series Graph

5.0K
A time-series graph is a line graph with repeated measurements taken at successive intervals of time. It is also called a time series chart. To construct a time-series graph, one must look at both pieces of a paired data set. The horizontal axis is used to plot the time increments, and the vertical axis is used to plot the values of the variable that one is measuring. By using the axes in this way, each point on the graph will correspond to time and a measured quantity. The points on the graph...
5.0K
Multiple Bar Graph01:07

Multiple Bar Graph

9.0K
As the name suggests, a multiple bar graph is the same as a bar graph but has multiple bars to depict relationships between different data values. One can include as many parameters as possible. However, each parameter must have the same unit of measurement.
Each bar or column in the multiple bar graph represents a data value. These graphs are used primarily in interrelating two or more sets of data. The categories of different kinds of data are listed along the horizontal or x-axis, whereas...
9.0K
First Derivatives and the Shape of a Graph01:22

First Derivatives and the Shape of a Graph

67
In calculus, the concept of the first derivative plays a crucial role in understanding the behavior of a function over its domain. The first derivative, denoted as f’(x), provides insight into how a function changes at any given point, much like a cyclist adjusting speed along a winding trail. By analyzing the first derivative, mathematicians can determine where a function is increasing, decreasing, or reaching critical points.The first derivative provides a precise method for classifying...
67

You might also read

Related Articles

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

Sort by
Same author

Correction: Tang, L. et al., Effect of Oxygen Variation on High Cycle Fatigue Behavior of Ti-6Al-4V Titanium Alloy. <i>Materials</i> 2020, <i>13</i>, 3858.

Materials (Basel, Switzerland)·2020
Same author

Increased early activation of CD56dimCD16dim/- natural killer cells in immunological non-responders correlates with CD4+ T-cell recovery.

Chinese medical journal·2020
Same author

In situ experimental measurement of mercury by combining PGNAA and characteristic X-ray fluorescence.

Applied radiation and isotopes : including data, instrumentation and methods for use in agriculture, industry and medicine·2020
Same author

Tris (1,3-dichloro-2-propyl) phosphate exposure disrupts the gut microbiome and its associated metabolites in mice.

Environment international·2020
Same author

Genome Resource of <i>Sphingomonas carotinifaciens</i> L9-754<sup>T</sup>, an Endophyte Isolated From Leaf Tissues of <i>Jatropha curcas</i>.

Plant disease·2020
Same author

Heterozygous <i>PGM3</i> Variants Are Associated With Idiopathic Focal Epilepsy With Incomplete Penetrance.

Frontiers in genetics·2020

Related Experiment Video

Updated: Jan 24, 2026

Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment
06:49

Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment

Published on: December 11, 2015

9.3K

Novel Unconventional-Active-Jamming Recognition Method for Wideband Radars Based on Visibility Graphs.

Congju Du1, Bin Tang2

  • 1School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China. ducongju@163.com.

Sensors (Basel, Switzerland)
|May 24, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces a novel method for recognizing unconventional active jamming in wideband radar systems. The approach achieves over 90% recognition probability, enhancing radar security against sophisticated threats.

Keywords:
feature extractionjamming recognitionradar unconventional active jammingrandom forestsvisibility graphs

More Related Videos

Wideband Optical Detector of Ultrasound for Medical Imaging Applications
08:21

Wideband Optical Detector of Ultrasound for Medical Imaging Applications

Published on: May 11, 2014

11.7K
Tracking Infiltration Front Depth Using Time-lapse Multi-offset Gathers Collected with Array Antenna Ground Penetrating Radar
07:14

Tracking Infiltration Front Depth Using Time-lapse Multi-offset Gathers Collected with Array Antenna Ground Penetrating Radar

Published on: May 1, 2018

8.2K

Related Experiment Videos

Last Updated: Jan 24, 2026

Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment
06:49

Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment

Published on: December 11, 2015

9.3K
Wideband Optical Detector of Ultrasound for Medical Imaging Applications
08:21

Wideband Optical Detector of Ultrasound for Medical Imaging Applications

Published on: May 11, 2014

11.7K
Tracking Infiltration Front Depth Using Time-lapse Multi-offset Gathers Collected with Array Antenna Ground Penetrating Radar
07:14

Tracking Infiltration Front Depth Using Time-lapse Multi-offset Gathers Collected with Array Antenna Ground Penetrating Radar

Published on: May 1, 2018

8.2K

Area of Science:

  • Electrical Engineering
  • Signal Processing
  • Network Science

Background:

  • Unconventional active jamming poses a significant threat to the operational integrity of wideband radar systems.
  • Existing recognition methods struggle to effectively counter these advanced jamming techniques, necessitating novel approaches.

Purpose of the Study:

  • To develop and validate an effective method for recognizing unconventional active jamming in wideband radar.
  • To leverage graph theory and machine learning for robust jamming identification.

Main Methods:

  • The proposed method utilizes the visibility graph algorithm to convert radar time series data into graph representations.
  • Key graph features, including average degree, clustering coefficient, assortativity, and network entropy, are extracted.
  • A random forests classifier is employed for the classification of jamming types.

Main Results:

  • The visibility graph of a linear-frequency-modulation (LFM) signal is theoretically proven to be a regular graph.
  • The extracted graph features provide a rational basis for distinguishing between different jamming scenarios.
  • Experimental results demonstrate a jamming recognition probability exceeding 90% at a jamming-to-noise ratio (JNR) above 0 dB.

Conclusions:

  • The proposed visibility graph-based method offers a promising solution for identifying unconventional active radar jamming.
  • The technique provides high accuracy and robustness, even under challenging signal conditions.
  • This research contributes to enhancing the resilience of wideband radar systems against electronic warfare threats.