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

Classification of Signals01:30

Classification of Signals

995
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...
995
Force Classification01:22

Force Classification

1.8K
Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
1.8K

You might also read

Related Articles

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

Sort by
Same author

Fiber-optic triggering of a two-stage high-current linear transformer driver with laser energy below 100 μJ.

The Review of scientific instruments·2026
Same author

Molecular epidemiological investigation of viruses in Amur tigers in Northeast China.

Archives of virology·2026
Same author

Tailoring current pulses to obtain ramp and quasi-square waveforms on a 4-MA linear-transformer-driver facility.

The Review of scientific instruments·2026
Same author

Breaking the immune barrier: construction of cartilaginous organoids using alpha-1,3-galactosyltransferase-deficient pig cartilage-derived particles.

Journal of translational medicine·2026
Same author

<i>Advenella alkanexedens</i>, a specific phosphate-solubilizing bacterium from rapeseed rhizosphere soil, highly activates insoluble phosphorus in calcareous soil.

Microbiology spectrum·2026
Same author

Modality-specific effects of structured exercise on immunometabolic biomarkers in postmenopausal obesity: a Bayesian network meta-analysis.

Frontiers in immunology·2026
Same journal

RETRACTED: Zhang et al. A Novel Framework for Reconstruction and Imaging of Target Scattering Centers via Wide-Angle Incidence in Radar Networks. <i>Sensors</i> 2025, <i>25</i>, 6802.

Sensors (Basel, Switzerland)·2026
Same journal

Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation.

Sensors (Basel, Switzerland)·2026
Same journal

Development of an Instrumented Glove for Palmar Pressure Assessment in Kayakers.

Sensors (Basel, Switzerland)·2026
Same journal

Development and Experimental Validation of an Autonomous IoT-Based Monitoring System for Real-Time Water Quality Assessment in the Amazon River.

Sensors (Basel, Switzerland)·2026
Same journal

Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection.

Sensors (Basel, Switzerland)·2026
Same journal

Smart Optimization Method for Safety Signs in Innovative Manufacturing Environments Integrating Industrial Field IoT Sensors and Knowledge Graphs.

Sensors (Basel, Switzerland)·2026
See all related articles

Related Experiment Video

Updated: Oct 8, 2025

Author Spotlight: Advancing Alzheimer's Research &#8211; Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.3K

Accuracy Analysis of Feature-Based Automatic Modulation Classification via Deep Neural Network.

Zhan Ge1, Hongyu Jiang1, Youwei Guo1

  • 1Institute of Electronic Engineering, China Academy of Engineering Physics, Mianyang 621000, China.

Sensors (Basel, Switzerland)
|December 28, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a deep learning model for feature-based automatic modulation classification (FB-AMC). The novel CCT classifier demonstrates superior performance across various channel conditions, outperforming existing methods for modulation identification.

Keywords:
automatic modulation classificationdeep learningflat-fadingfuzzy c-means clusteringhigher-order cumulantsnon-Gaussian channel

More Related Videos

Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology
09:44

Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology

Published on: March 8, 2024

5.2K
Classification of Neural Stem Cell Activation State In Vitro using Autofluorescence
06:56

Classification of Neural Stem Cell Activation State In Vitro using Autofluorescence

Published on: April 12, 2024

718

Related Experiment Videos

Last Updated: Oct 8, 2025

Author Spotlight: Advancing Alzheimer's Research &#8211; Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.3K
Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology
09:44

Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology

Published on: March 8, 2024

5.2K
Classification of Neural Stem Cell Activation State In Vitro using Autofluorescence
06:56

Classification of Neural Stem Cell Activation State In Vitro using Autofluorescence

Published on: April 12, 2024

718

Area of Science:

  • Electrical Engineering
  • Computer Science
  • Signal Processing

Background:

  • Feature-based automatic modulation classification (FB-AMC) is crucial for cognitive radio and electronic warfare.
  • Existing FB-AMC algorithms face challenges in performance and complexity, necessitating advanced solutions.
  • Evaluating diverse features like HOC, FCM, GCD, CDF, and raw IQ data under various channel impairments is essential.

Purpose of the Study:

  • To design and evaluate a novel deep learning model, the CCT classifier, for FB-AMC.
  • To compare the classification performance of various features under different channel conditions (Gaussian, non-Gaussian, fading, phase/frequency offset).
  • To assess the effectiveness of transfer learning in reducing training time for modulation classification.

Main Methods:

  • A deep learning-based CCT classifier was developed for end-to-end modulation classification.
  • Features (HOC, FCM, CDF, raw IQ) were converted to 2D representations and fed into the CCT classifier.
  • Experiments were conducted under various channel conditions, including Gaussian, non-Gaussian, flat-fading, phase offset, and frequency offset.
  • Transfer learning was employed to optimize training efficiency.

Main Results:

  • HOC, raw IQ data, and GCD outperformed CDF and FCM under Gaussian channels.
  • CDF and FCM showed less sensitivity to phase and frequency offsets.
  • CDF proved effective in non-Gaussian and flat-fading channels; raw IQ data demonstrated versatility across channel types.
  • The CCT classifier significantly improved MQAM classification accuracy (N=512) compared to CNN and K-S classifiers, achieving ~3.2% and ~2.1% gains under Gaussian channels, respectively.

Conclusions:

  • The proposed CCT classifier offers a robust and high-performance solution for FB-AMC.
  • Feature selection and performance vary significantly depending on channel conditions.
  • Raw IQ data presents a versatile feature option applicable to diverse channel environments.
  • Deep learning, particularly the CCT architecture, significantly advances automatic modulation classification capabilities.