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

NMR Spectrometers: Radiofrequency Pulses and Pulse Sequences01:17

NMR Spectrometers: Radiofrequency Pulses and Pulse Sequences

1.1K
A pulse is a short burst of radio waves distributed over a range of frequencies that simultaneously excites all the nuclei in the sample. Upon passing a radio frequency pulse along the x-axis, the nuclei absorb energy corresponding to their Larmor frequencies and achieve resonance. This shifts the net magnetization vector from the z-axis toward the transverse plane. This angle of rotation of the magnetization vector, or the flip angle, is proportional to the duration and intensity of the pulse.
1.1K
Classification of Signals01:30

Classification of Signals

1.0K
In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
1.0K

You might also read

Related Articles

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

Sort by
Same author

Repurposing nifedipine as a dual-function adjuvant: potentiating gentamicin activity and disrupting biofilm formation in methicillin-resistant Staphylococcus aureus.

Biochimie·2026
Same author

A Radar Waveform Design Method Based on Multicarrier Phase Coding for Suppressing Autocorrelation Sidelobes.

Sensors (Basel, Switzerland)·2025
Same author

Development of a prognostic risk stratification model for HER2-positive breast cancer brain metastasis and its implications in guiding treatment decisions.

Scientific reports·2025
Same author

The tissue-specific ferroptosis in zebrafish (Danio rerio) exposed to dibutyl phthalate at environmental-relevant concentrations.

Comparative biochemistry and physiology. Toxicology & pharmacology : CBP·2025
Same author

Multi-omics insights into the roles of CCNB1, PLK1, and HPSE in breast cancer progression: implications for prognosis and immunotherapy.

Discover oncology·2025
Same author

A comparative study on contamination profiles of liquid crystal monomers (LCMs) between outdoor and indoor dusts, and the assessment of health risk of human exposure.

Chemosphere·2024

Related Experiment Video

Updated: Oct 31, 2025

Asthma Detection Research Based on Voice Signal Processing and Machine Learning
04:04

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Published on: July 22, 2025

593

A Multipulse Radar Signal Recognition Approach via HRF-Net Deep Learning Models.

Ji Li1, Huiqiang Zhang1, Jianping Ou2

  • 1School of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha 410114, China.

Computational Intelligence and Neuroscience
|June 30, 2021
PubMed
Summary

This study introduces HRF-Net, a novel deep learning model for radar signal recognition. HRF-Net achieves high accuracy in identifying complex multipulse radar signals, demonstrating robust performance even in noisy environments.

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

9.5K
Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

729

Related Experiment Videos

Last Updated: Oct 31, 2025

Asthma Detection Research Based on Voice Signal Processing and Machine Learning
04:04

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Published on: July 22, 2025

593
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

9.5K
Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

729

Area of Science:

  • Electronic Warfare and Signal Processing
  • Artificial Intelligence in Defense
  • Radar Systems Engineering

Background:

  • Accurate recognition of radar signals is critical for electronic countermeasures.
  • Existing methods for radar signal recognition face challenges with complex multipulse signals.
  • The need for robust and efficient automated recognition systems is paramount.

Purpose of the Study:

  • To develop a novel deep learning model for the accurate recognition of 10 classes of multipulse radar signals.
  • To design a distinguishing feature fusion extraction module (DFFE) tailored for radar signal time-frequency images (TFIs).
  • To evaluate the performance and robustness of the proposed HRF-Net model under various signal-to-noise ratio (SNR) conditions.

Main Methods:

  • Generation of 10 classes of multipulse radar signals using GNU Radio and Universal Software Radio Peripherals.
  • Time-frequency image (TFI) generation via Choi-Williams distribution (CWD) transformation.
  • Development and application of the HRF-Net deep learning model incorporating the DFFE module.

Main Results:

  • The HRF-Net model achieved a recognition accuracy of 99.583% at -6 dB SNR.
  • The model maintained a high recognition accuracy of 97.500% even at a low SNR of -14 dB.
  • Experimental results demonstrated superior generalization and robustness compared to other existing methods.

Conclusions:

  • The proposed HRF-Net model, featuring the DFFE module, offers an effective solution for multipulse radar signal recognition.
  • HRF-Net demonstrates excellent performance and robustness, making it suitable for practical electronic countermeasure applications.
  • The model's efficiency in terms of parameters and computation makes it a viable option for real-time systems.