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

Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

488
A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
Zero-sequence current induces a voltage drop across the generator's neutral impedance and other...
488
Dense Connective Tissue01:13

Dense Connective Tissue

11.8K
Dense connective tissue contains more collagen fibers than loose connective tissue. As a consequence, it displays greater resistance to stretching. There are two major categories of dense connective tissue— regular and irregular.
Dense Regular Connective Tissue
In dense regular connective tissue, fibers are arranged parallel to each other, enhancing its tensile strength and resistance to stretching in the direction of the fiber orientations. Ligaments and tendons are made of dense regular...
11.8K
Protein Networks02:26

Protein Networks

4.5K
An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
4.5K
Machines01:19

Machines

559
Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. One example of a machine is the cutting plier, which is used to cut wires by applying forces to its handles. When equal and opposite forces are exerted on the handles of the cutting plier, they cause the cutting edges to come together and apply equal and opposite reaction forces on the wire, which are greater than the applied forces.
A free-body diagram of the...
559
Trial and Error and Algorithm01:12

Trial and Error and Algorithm

398
A problem-solving strategy is a plan of action used to find a solution. Different strategies have distinct action plans. Trial and error involves trying different solutions until one works. For instance, to fix a broken printer, you might check ink levels, ensure the paper tray isn't jammed, and verify the printer's connection to your laptop. This method can be time-consuming but is commonly used. Thomas Edison, for example, used trial and error to find a suitable filament for the light...
398
Ion Channels01:19

Ion Channels

91.2K
The movement of ions like sodium, potassium, and calcium into and out of the cell is essential to maintain the electrochemical gradient in living cells. The ion channels—a class of membrane transport proteins—help maintain this ionic gradient for the smooth functioning of physiological activities such as maintaining cell size and volume, conducting nerve impulses, and gas and nutrient exchange.
Ion channels are specialized integral membrane proteins on the plasma membrane that allow...
91.2K

You might also read

Related Articles

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

Sort by
Same author

Altered Brain-Behavior Association During Resting State is a Potential Psychosis Risk Marker.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2025
Same author

Author Correction: A feasibility study of 5G positioning with current cellular network deployment.

Scientific reports·2023
Same author

A feasibility study of 5G positioning with current cellular network deployment.

Scientific reports·2023
Same author

Dual-View Single-Shot Multibox Detector at Urban Intersections: Settings and Performance Evaluation.

Sensors (Basel, Switzerland)·2023
Same author

A Self-Calibrating Localization Solution for Sport Applications with UWB Technology.

Sensors (Basel, Switzerland)·2022
Same author

The (a)typical burden of COVID-19 pandemic scenario in Autism Spectrum Disorder.

Scientific reports·2021

Related Experiment Video

Updated: Jan 21, 2026

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

7.4K

People Counting by Dense WiFi MIMO Networks: Channel Features and Machine Learning Algorithms.

Sanaz Kianoush1, Stefano Savazzi2, Vittorio Rampa2

  • 1National Research Council of Italy (CNR), Institute of Electronics, Computer and Telecommunication Engineering (IEIIT), Piazza Leonardo da Vinci 32, 20133 Milano, Italy. sanaz.kianoush@ieiit.cnr.it.

Sensors (Basel, Switzerland)
|August 10, 2019
PubMed
Summary

This study transforms WiFi infrastructure into a passive sensing system for subject counting. WiFi-based sensing achieves 99% average accuracy for detecting people in smart environments.

Keywords:
5GMIMO WiFicloud computingcrowd sensingmachine learning

More Related Videos

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

7.9K
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

2.3K

Related Experiment Videos

Last Updated: Jan 21, 2026

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

7.4K
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

7.9K
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

2.3K

Area of Science:

  • Ambient Intelligence
  • Wireless Sensing
  • Machine Learning

Background:

  • Subject counting systems are crucial for ambient intelligence applications like smart homes and retail.
  • Existing methods often require dedicated sensors, limiting flexibility.

Purpose of the Study:

  • To develop a passive subject counting system using unmodified WiFi infrastructure.
  • To explore machine learning techniques for accurate subject detection and counting.

Main Methods:

  • Utilized multi-dimensional channel features from WiFi signals to detect subject presence.
  • Compared Bayesian and neural network models for subject discrimination and counting.
  • Employed ensemble classification to combine diverse learning models and leverage space-frequency diversity.

Main Results:

  • Ensemble classification significantly improved counting accuracy by combining multiple models.
  • The system achieved 99% average accuracy in detecting up to five moving people in an indoor environment.
  • Considered real-time computing and cloud migration for practical deployment.

Conclusions:

  • Unmodified WiFi infrastructure can be repurposed as a flexible and accurate passive sensing system.
  • Machine learning, particularly ensemble methods, enhances subject counting performance.
  • The proposed WiFi-based sensing offers a cost-effective solution for ambient intelligence scenarios.