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

Estimation of the Physical Quantities01:05

Estimation of the Physical Quantities

4.6K
On many occasions, physicists, other scientists, and engineers need to make estimates of a particular quantity. These are sometimes referred to as guesstimates, order-of-magnitude approximations, back-of-the-envelope calculations, or Fermi calculations. The physicist Enrico Fermi was famous for his ability to estimate various kinds of data with surprising precision. Estimating does not mean guessing a number or a formula at random. Instead, estimation means using prior experience and sound...
4.6K
Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

121
Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear....
121
Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

113
Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length,...
113
Determination of Expected Frequency01:08

Determination of Expected Frequency

2.2K
Suppose one wants to test independence between the two variables of a contingency table. The values in the table constitute the observed frequencies of the dataset. But how does one determine the expected frequency of the dataset? One of the important assumptions is that the two variables are independent, which means the variables do not influence each other. For independent variables, the statistical probability of any event involving both variables is calculated by multiplying the individual...
2.2K
Receiver Operating Characteristic Plot01:15

Receiver Operating Characteristic Plot

294
A ROC (Receiver Operating Characteristic) plot is a graphical tool used to assess the performance of a binary classification model by illustrating the trade-off between sensitivity (true positive rate) and specificity (false positive rate). By plotting sensitivity against 1 - specificity across various threshold settings, the ROC curve shows how well the model distinguishes between classes, with a curve closer to the top-left corner indicating a more accurate model. The area under the ROC curve...
294
Classification of Signals01:30

Classification of Signals

612
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...
612

You might also read

Related Articles

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

Sort by
Same author

Intelligent Device for Harvesting the Vibration Energy of the Automobile Exhaust with a Piezoelectric Generator.

Micromachines·2023
Same author

S-MAT: Semantic-Driven Masked Attention Transformer for Multi-Label Aerial Image Classification.

Sensors (Basel, Switzerland)·2022
Same author

Imides modified benzopicenes: synthesis, solid structure and optoelectronic properties.

Organic & biomolecular chemistry·2014
Same author

An efficient identity-based key management scheme for wireless sensor networks using the Bloom filter.

Sensors (Basel, Switzerland)·2014
Same author

A rare variant in APOC3 is associated with plasma triglyceride and VLDL levels in Europeans.

Nature communications·2014
Same author

A new nodavirus is associated with covert mortality disease of shrimp.

The Journal of general virology·2014
Same journal

Big Data-Driven Video Anomaly Detection Using VideoMAE for Visual Analytics in CCTV Surveillance.

Big data·2026
Same journal

Agentic Artificial Intelligence-Driven Explainable Deep Learning for Deciphering Noncoding Pathogenic Mechanisms of Delirium Through Genomic Big Data Integration.

Big data·2026
Same journal

Personalized Driven Instruction Through Explainable Agentic AI in Multicultural Higher Education Environments.

Big data·2026
Same journal

Big Data-Driven Explainable Agentic AI Decision Frameworks for Enterprise Innovation in FinTech Ecosystems.

Big data·2026
Same journal

An Edge-Enabled Low-Latency Cross-Lingual Speech-to-Text Framework for Efficient Human-Robot Interaction.

Big data·2026
Same journal

DS<sup>2</sup>PT: A Deep Two-Stage Patent Text Segmentation Framework Informed by Low-Latency Neural Network Characteristics.

Big data·2026
See all related articles

Related Experiment Video

Updated: Aug 8, 2025

Continuous-Wave Propagation Channel-Sounding Measurement System - Testing, Verification, and Measurements
09:36

Continuous-Wave Propagation Channel-Sounding Measurement System - Testing, Verification, and Measurements

Published on: June 25, 2021

3.2K

An Intelligent Channel Estimation Algorithm Based on Extended Model for 5G-V2X.

Jie Huang1, Cheng Xu2, Zhaohua Ji1

  • 1Beijing Information Technology College, Beijing, China.

Big Data
|February 27, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a deep learning channel estimation algorithm for 5G-V2X car networking, improving reliability and reducing errors in high-speed scenarios. The new method enhances communication performance for vehicle-to-everything systems.

Keywords:
5G-V2XInternet of Vehiclesbase extension modelchannel estimationdeep learning

More Related Videos

Integration of 5G Experimentation Infrastructures into a Multi-Site NFV Ecosystem
10:15

Integration of 5G Experimentation Infrastructures into a Multi-Site NFV Ecosystem

Published on: February 3, 2021

3.8K
Calibration of Vector Network Analyzer for Measurements in Radio Frequency Propagation Channels
10:00

Calibration of Vector Network Analyzer for Measurements in Radio Frequency Propagation Channels

Published on: June 2, 2020

21.2K

Related Experiment Videos

Last Updated: Aug 8, 2025

Continuous-Wave Propagation Channel-Sounding Measurement System - Testing, Verification, and Measurements
09:36

Continuous-Wave Propagation Channel-Sounding Measurement System - Testing, Verification, and Measurements

Published on: June 25, 2021

3.2K
Integration of 5G Experimentation Infrastructures into a Multi-Site NFV Ecosystem
10:15

Integration of 5G Experimentation Infrastructures into a Multi-Site NFV Ecosystem

Published on: February 3, 2021

3.8K
Calibration of Vector Network Analyzer for Measurements in Radio Frequency Propagation Channels
10:00

Calibration of Vector Network Analyzer for Measurements in Radio Frequency Propagation Channels

Published on: June 2, 2020

21.2K

Area of Science:

  • Telecommunications Engineering
  • Deep Learning Applications
  • Wireless Communication Systems

Background:

  • 5G-V2X (vehicle-to-everything) systems demand high reliability and low-latency for enhanced performance.
  • Accurate channel state information is crucial for optimizing V2X communication in dynamic environments.

Purpose of the Study:

  • To develop an advanced channel estimation algorithm for 5G-V2X systems operating in high-speed mobile scenarios.
  • To improve the accuracy and reliability of channel estimation in vehicular networks.

Main Methods:

  • An extended channel model was developed for high-speed mobile V2X scenarios, leveraging channel impulse response sparsity.
  • A deep learning approach was proposed, utilizing a multilayer convolutional neural network for frequency domain interpolation.
  • A bidirectional gated recurrent unit was employed for time-domain state prediction, incorporating speed and multipath parameters.

Main Results:

  • The proposed deep learning algorithm accurately estimates channel characteristics under varying speeds.
  • Simulations demonstrated superior performance compared to traditional car networking channel estimation algorithms.
  • Significant improvements in channel estimation accuracy and a reduction in bit error rate were observed.

Conclusions:

  • The developed deep learning algorithm effectively addresses the challenges of channel estimation in high-speed 5G-V2X environments.
  • This method offers a promising solution for enhancing the reliability and performance of vehicular communication systems.