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

You might also read

Related Articles

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

Sort by
Same author

Integrating comammox into anammox-dominated one-stage mainstream partial nitrification/anammox: nitrifier niche regulation and nitrite partitioning.

Water research·2026
Same author

ConsDreamer: Advancing Multi-View Consistency for Zero-Shot Text-to-3D Generation.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same author

WaterRAG: A Multiagent Retrieval-Augmented Generation Framework to Support Water Industry Transitions to Net-Zero.

Environmental science & technology·2026
Same author

eIF3 musketeers: loyal in health, rogue in disease, and redeemed by therapeutic targeting.

The EMBO journal·2026
Same author

Micro- and nanoplastics facilitate the propagation of antimicrobial resistance in mixed microbial consortia.

Cell reports·2026
Same author

Rethinking Multi-Focus Image Fusion: An Input Space Optimization View.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026

Related Experiment Video

Updated: Jun 15, 2025

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

2.7K

A Semantic-Consistent Few-Shot Modulation Recognition Framework for IoT Applications.

Jie Su, Peng Sun, Yuting Jiang

    IEEE Transactions on Neural Networks and Learning Systems
    |August 23, 2024
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a new method to improve automatic modulation classification in Internet of Things (IoT) networks. The semantic-consistent signal pretransformation (ScSP) enhances existing few-shot learning models for better performance with limited data.

    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

    9.2K
    The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy
    11:53

    The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

    Published on: October 14, 2017

    11.6K

    Related Experiment Videos

    Last Updated: Jun 15, 2025

    Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
    06:37

    Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

    Published on: December 15, 2023

    2.7K
    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

    9.2K
    The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy
    11:53

    The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

    Published on: October 14, 2017

    11.6K

    Area of Science:

    • Wireless Communications
    • Machine Learning
    • Cybersecurity

    Background:

    • The Internet of Things (IoT) relies on robust signal identification for security.
    • Automatic modulation classification (AMC) is crucial for detecting threats in noisy IoT environments.
    • Scarcity of labeled data hinders traditional machine learning approaches in IoT security.

    Purpose of the Study:

    • To address the challenge of limited labeled data in IoT signal analysis.
    • To adapt existing few-shot learning (FSL) models for wireless signal recognition.
    • To enhance the performance of state-of-the-art (SOTA) FSL models for signal modulation recognition.

    Main Methods:

    • Introduced semantic-consistent signal pretransformation (ScSP), a novel parameterized transformation architecture.
    • Ensured that signals with identical semantics are represented similarly.
    • Integrated ScSP with existing SOTA FSL models and deep learning backbones.

    Main Results:

    • ScSP significantly boosted the performance of various SOTA FSL models for signal modulation recognition.
    • The proposed method demonstrated flexibility and compatibility with existing architectures.
    • Maintained high performance even with limited labeled data, crucial for IoT applications.

    Conclusions:

    • ScSP offers an effective solution for enhancing FSL models in the wireless signal domain.
    • This approach overcomes the limitations of traditional FSL methods not designed for signal processing.
    • The technique provides a scalable and adaptable method for improving IoT security and anomaly detection.