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 Systems-I01:26

Classification of Systems-I

290
Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
290

You might also read

Related Articles

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

Sort by
Same author

A Holistic Approach Towards Evaluating Upper Limb Function in Children with Unilateral Cerebral Palsy: A Narrative Review of Clinical Tools and Promising Technologies for Comprehensive Assessment.

Journal of clinical medicine·2025
Same author

ECG synthesis for cardiac arrhythmias: Integrating self-supervised learning and generative adversarial networks.

Artificial intelligence in medicine·2025
Same author

Home-Based Intervention Tool for Cardiac Telerehabilitation: Protocol for a Controlled Trial.

JMIR research protocols·2025
Same author

Continual pre-training mitigates forgetting in language and vision.

Neural networks : the official journal of the International Neural Network Society·2024
Same author

Deep Learning for Dynamic Graphs: Models and Benchmarks.

IEEE transactions on neural networks and learning systems·2024
Same author

Assessment of Postural Control in Children with Movement Disorders by Means of a New Technological Tool: A Pilot Study.

Bioengineering (Basel, Switzerland)·2024
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: Sep 5, 2025

Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

574

A Systematic Review of Wi-Fi and Machine Learning Integration with Topic Modeling Techniques.

Daniele Atzeni1, Davide Bacciu1, Daniele Mazzei1

  • 1Department of Computer Science, University of Pisa, Largo B. Pontecorvo 3, 56127 Pisa, Italy.

Sensors (Basel, Switzerland)
|July 9, 2022
PubMed
Summary
This summary is machine-generated.

This review explores the synergy between Wi-Fi sensing and Machine Learning (ML). It details how Wi-Fi advancements impact ML-driven sensing applications and algorithm selection.

Keywords:
BERTopicWi-Fiartificial intelligencemachine learningtopic modeling

More Related Videos

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

395
Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

676

Related Experiment Videos

Last Updated: Sep 5, 2025

Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

574
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

395
Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

676

Area of Science:

  • Computer Science
  • Electrical Engineering
  • Data Science

Background:

  • Wireless networks, particularly Wi-Fi, are integral to modern life, supporting numerous devices.
  • The proliferation of mobile devices and the Internet of Things (IoT) generates vast amounts of data.
  • Machine Learning (ML) excels at analyzing high-velocity data streams.

Purpose of the Study:

  • To systematically review the interplay between Wi-Fi technology and Machine Learning.
  • To understand the evolution of this interaction over time.
  • To identify current applications and future trends.

Main Methods:

  • Systematic literature review using Scopus, Web of Science, and IEEE Xplore.
  • Application of BERTopic, a topic modeling technique, for analyzing retrieved abstracts.
  • Cluster inspection and statistical analysis for topic interpretation.

Main Results:

  • Identified diverse applications of Wi-Fi sensing.
  • Cataloged a variety of Machine Learning algorithms employed for Wi-Fi sensing.
  • Documented the influence of Wi-Fi technological progress on sensing capabilities and ML model choices.

Conclusions:

  • Wi-Fi sensing is a rapidly evolving field driven by technological advancements.
  • Machine Learning is crucial for unlocking the potential of Wi-Fi sensing data.
  • The choice of ML algorithms is closely tied to Wi-Fi infrastructure and application requirements.