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

Attenuated Total Reflectance (ATR) Infrared Spectroscopy: Overview01:13

Attenuated Total Reflectance (ATR) Infrared Spectroscopy: Overview

1.1K
Attenuated total reflectance (ATR) infrared spectroscopy is a powerful analytical technique used to study the composition of materials. It is widely employed in chemistry, materials science, forensic science, and other fields where sample characterization is required. ATR has several advantages over traditional transmission IR spectroscopy, including the requirement of little to no sample preparation and the ability to analyze a wide range of samples.
The ATR process begins by directing a beam...
1.1K
Association Areas of the Cortex01:21

Association Areas of the Cortex

8.7K
Association areas are regions of the cerebral cortex that do not have a specific sensory or motor function. Instead, they integrate and interpret information from various sources to enable higher cognitive processes such as memory, learning, and decision-making. Some key association areas include the following:
Prefrontal Association Area: This area is located in the frontal lobe and is involved in planning, decision-making, and moderating social behavior. It connects with primary motor areas,...
8.7K
Deconvolution01:20

Deconvolution

520
Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...
520

You might also read

Related Articles

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

Sort by
Same author

Comparison of minimally invasive single-position left transthoracic and esophageal hiatal approach versus laparoscopic transesophageal hiatus approach for Siewert type II adenocarcinoma of the esophagogastric junction.

Surgical endoscopy·2026
Same author

Key influencing factors and spatiotemporal patterns of annual net ecosystem CO<sub>2</sub> exchange in China.

Journal of environmental management·2026
Same author

Atmospheric systems drive spatiotemporal divergence of dust and moisture changes across Asia over the past 130,000 years.

Science advances·2026
Same author

A coupled spatial reduction-reconstruction and LSTM framework (SRR-LSTM) for groundwater level prediction in large irrigation districts.

Scientific reports·2026
Same author

Multi-Omics and Functional Analyses Identify let-7b-3p as a Negative Regulator of EMT in Lung Adenocarcinoma.

Journal of biochemical and molecular toxicology·2026
Same author

Exploring the Role of LINC00115 in Esophageal Squamous Cell Carcinoma: Insights Into JAK1/STAT3 Pathway Activation and Metastatic Potential.

JCO precision oncology·2025

Related Experiment Video

Updated: Jan 7, 2026

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

721

RAFF-AMACNet: Adaptive Multi-Rate Atrous Convolution Network with Residual Attentional Feature Fusion for Satellite

Leyan Chen1, Bo Zang1, Yi Zhang1

  • 1School of Electronic Engineering, Xidian University, Xi'an 710071, China.

Sensors (Basel, Switzerland)
|December 31, 2025
PubMed
Summary

This study introduces a novel network for automatic modulation recognition (AMR) in satellite communications. The RAFF-AMACNet achieves high accuracy in complex environments, improving spectrum management.

Keywords:
atrous convolutionautomatic modulation recognitiondual-attention collaborative mechanismmulti-scale featuresatellite signal

More Related Videos

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

990

Related Experiment Videos

Last Updated: Jan 7, 2026

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

721
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

990

Area of Science:

  • Electrical Engineering
  • Computer Science
  • Signal Processing

Background:

  • Automatic modulation recognition (AMR) is vital for satellite communication networks and spectrum management.
  • Existing AMR models face challenges in complex satellite environments with significant Doppler effects and nonlinear influences.

Purpose of the Study:

  • To propose an advanced AMR model capable of handling complex satellite communication scenarios.
  • To enhance feature extraction and fusion for improved modulation recognition accuracy.

Main Methods:

  • Developed an adaptive multi-rate atrous convolution network with residual attentional feature fusion (RAFF-AMACNet).
  • Utilized an adaptive multi-rate atrous convolution (AMAC) module for multi-scale feature extraction.
  • Employed a pyramid backbone with stacked residual attentional feature fusion (RAFF) modules and a dual-attention mechanism.

Main Results:

  • The RAFF-AMACNet demonstrated robust feature map generation and enhanced time-series context awareness.
  • The dual-attention mechanism effectively mitigated feature map shifts and increased class separation under adverse conditions.
  • Achieved 92.99% modulation recognition accuracy at a 0 dB signal-to-noise ratio on the RML24 dataset.

Conclusions:

  • The proposed RAFF-AMACNet significantly improves AMR performance in challenging satellite communication environments.
  • This model offers a robust solution for satellite signal recognition and spectrum management.
  • The RML24 dataset provides a valuable resource for evaluating AMR techniques in cognitive radio systems.