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

Convolution Properties II01:17

Convolution Properties II

255
The important convolution properties include width, area, differentiation, and integration properties.
The width property indicates that if the durations of input signals are T1 and T2, then the width of the output response equals the sum of both durations, irrespective of the shapes of the two functions. For instance, convolving two rectangular pulses with durations of 2 seconds and 1 second results in a function with a width of 3 seconds.
The area property asserts that the area under the...
255
Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

130
A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
Zero-sequence current induces a voltage drop across the generator's neutral impedance and other...
130
Convolution Properties I01:20

Convolution Properties I

208
Convolution computations can be simplified by utilizing their inherent properties.
The commutative property reveals that the input and the impulse response of an LTI (Linear Time-Invariant) system can be interchanged without affecting the output:
208
Neural Circuits01:25

Neural Circuits

1.4K
Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...
1.4K
Masking and Demasking Agents01:19

Masking and Demasking Agents

2.5K
EDTA titrations may necessitate masking and demasking agents to temporarily protect a particular metal ion in a mixture from the EDTA reaction. These agents facilitate the sequential analysis of the metal ions by forming stable complexes with some—but not all—metal ions during certain steps.
There are many masking agents, such as cyanide, fluoride, triethanolamine, thiourea, and 2,3-bis(sulfanyl)propan-1-ol (formerly 2,3-dimercapto-1-propanol), with the masking agent chosen based on...
2.5K
Propagation of Action Potentials01:23

Propagation of Action Potentials

6.2K
The propagation of an action potential refers to the process by which a nerve impulse, or "action potential," travels along a neuron.
Neurons (nerve cells) have a resting membrane potential, with a slightly negative charge inside compared to outside. This is maintained by ion channels, such as sodium (Na+) and potassium (K+) channels, which control the flow of ions. When a stimulus, like a touch or a signal from another neuron, triggers the neuron, sodium channels open, allowing sodium ions to...
6.2K

You might also read

Related Articles

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

Sort by
Same author

An Anti-Jamming Method against Interrupted Sampling Repeater Jamming Based on Compressed Sensing.

Sensors (Basel, Switzerland)·2022
Same author

An Anti-Jamming Method against Two-Dimensional Deception Jamming by Spatial Location Feature Recognition.

Sensors (Basel, Switzerland)·2021
Same author

Potential correlation between volatiles and microbiome of Xiang xi sausages from four different regions.

Food research international (Ottawa, Ont.)·2021
Same author

The Relationship Between Behavioral Inhibition and Behavioral Activation Systems, Impulsiveness, and Internet Gaming Disorder Among Students of Different Ages.

Frontiers in psychiatry·2021
Same author

Top-down control of foundation species recovery during coastal wetland restoration.

The Science of the total environment·2021
Same author

Characterization of the complete chloroplast genome of <i>Camellia granthamiana</i> (Theaceae), a Vulnerable species endemic to China.

Mitochondrial DNA. Part B, Resources·2021

Related Experiment Video

Updated: Aug 10, 2025

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
08:27

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Published on: January 5, 2024

1.2K

Recognition of Micro-Motion Jamming Based on Complex-Valued Convolutional Neural Network.

Chongwei Shi1, Qun Zhang1, Tao Lin1

  • 1Information and Navigation School, Air Force Engineering University, Xi'an 710077, China.

Sensors (Basel, Switzerland)
|February 11, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a novel method for recognizing micro-motion jamming patterns in inverse synthetic aperture radar (ISAR) using complex-valued convolutional neural networks (CV-CNNs). The proposed CV-CNN approach demonstrates superior accuracy compared to traditional methods, enhancing ISAR anti-jamming capabilities.

Keywords:
complex-valued convolutional neural network (CV-CNN)inverse synthetic aperture radar (ISAR)micro-motion jammingrecognition

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

9.4K
Author Spotlight: Assessment of Visual Acuity in Central Vision Loss Through Motion-Based Peripheral Vision Testing
06:25

Author Spotlight: Assessment of Visual Acuity in Central Vision Loss Through Motion-Based Peripheral Vision Testing

Published on: February 23, 2024

669

Related Experiment Videos

Last Updated: Aug 10, 2025

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
08:27

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Published on: January 5, 2024

1.2K
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

9.4K
Author Spotlight: Assessment of Visual Acuity in Central Vision Loss Through Motion-Based Peripheral Vision Testing
06:25

Author Spotlight: Assessment of Visual Acuity in Central Vision Loss Through Motion-Based Peripheral Vision Testing

Published on: February 23, 2024

669

Area of Science:

  • Radar Systems Engineering
  • Artificial Intelligence
  • Signal Processing

Background:

  • Micro-motion jamming poses a significant and evolving threat to Inverse Synthetic Aperture Radar (ISAR) systems.
  • Effective anti-jamming strategies necessitate accurate recognition of micro-motion jamming patterns.
  • Existing jamming methods offer flexibility and control, increasing their potential impact on ISAR operations.

Purpose of the Study:

  • To propose and evaluate a novel method for recognizing micro-motion jamming patterns in ISAR.
  • To leverage complex-valued convolutional neural networks (CV-CNNs) for enhanced jamming pattern identification.
  • To compare the performance of the proposed CV-CNN method against real-valued convolutional neural networks (RV-CNNs).

Main Methods:

  • Serialization of micro-motion jamming echo signals for network input.
  • Development and application of a complex-valued convolutional neural network (CV-CNN) model.
  • Comparative analysis with real-valued convolutional neural networks (RV-CNNs) under varying signal-to-noise ratios (SNRs) and training sample sizes.

Main Results:

  • The proposed CV-CNN method achieved higher recognition accuracy rates for micro-motion jamming compared to RV-CNN.
  • Performance analysis indicated the method's effectiveness across different signal-to-noise ratios (SNRs).
  • The impact of the number of training samples on recognition accuracy was systematically evaluated.

Conclusions:

  • Complex-valued convolutional neural networks (CV-CNNs) offer a promising and effective approach for micro-motion jamming pattern recognition in ISAR.
  • The proposed method provides a robust solution for identifying jamming threats, crucial for developing advanced anti-jamming measures.
  • Simulation results validate the practical effectiveness and superior performance of the CV-CNN based recognition technique.