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

Standing Waves in a Cavity01:28

Standing Waves in a Cavity

1.0K
A household microwave and lasers are examples of standing electromagnetic waves in a cavity. When two conducting metal plates are placed parallel at the nodal planes, it creates a cavity where standing waves are formed. The cavity between the two planes is analogous to a stretched string held at the points x = 0 and x = L. Here, the distance 'L' between the two planes must be an integer multiple of half of the wavelength. The wavelengths that satisfy this condition are given by:
1.0K
Mass Analyzers: Common Types01:19

Mass Analyzers: Common Types

700
The quadrupole mass analyzer consists of four cylindrical metal rods arranged in a diamond carrying a DC voltage and a radio-frequency AC voltage. The motion of ions through the quadrupole depends on the field strength, causing only ions of a certain m/z to resonate successfully and strike the detector at a given field strength. Though the transmission rate for these analyzers is high, the exact elemental composition of the sample is not determined because of low resolution; however, they are...
700

You might also read

Related Articles

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

Sort by
Same author

AI-Assisted Ultra-High-Sensitivity/Resolution Active-Coupled CSRR-Based Sensor with Embedded Selectivity.

Sensors (Basel, Switzerland)·2023
Same author

A High-Resolution Reflective Microwave Planar Sensor for Sensing of Vanadium Electrolyte.

Sensors (Basel, Switzerland)·2021
Same author

Energy Harvesting Sources, Storage Devices and System Topologies for Environmental Wireless Sensor Networks: A Review.

Sensors (Basel, Switzerland)·2018
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 3, 2025

Fabrication and Characterization of Superconducting Resonators
10:26

Fabrication and Characterization of Superconducting Resonators

Published on: May 21, 2016

11.5K

Selective Microwave Zeroth-Order Resonator Sensor Aided by Machine Learning.

Nazli Kazemi1, Nastaran Gholizadeh1, Petr Musilek1,2

  • 1Electrical and Computer Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada.

Sensors (Basel, Switzerland)
|July 27, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a compact microwave sensor and a convolutional neural network (CNN) for accurately identifying water concentrations in mixtures. The system achieves high accuracy, enabling selective material detection.

Keywords:
generative adversarial networkmachine learningmicrowave sensorresonatorsselectivity

More Related Videos

Design and Characterization Methodology for Efficient Wide Range Tunable MEMS Filters
15:25

Design and Characterization Methodology for Efficient Wide Range Tunable MEMS Filters

Published on: February 4, 2018

6.2K
Recombination Dynamics in Thin-film Photovoltaic Materials via Time-resolved Microwave Conductivity
11:30

Recombination Dynamics in Thin-film Photovoltaic Materials via Time-resolved Microwave Conductivity

Published on: March 6, 2017

11.8K

Related Experiment Videos

Last Updated: Sep 3, 2025

Fabrication and Characterization of Superconducting Resonators
10:26

Fabrication and Characterization of Superconducting Resonators

Published on: May 21, 2016

11.5K
Design and Characterization Methodology for Efficient Wide Range Tunable MEMS Filters
15:25

Design and Characterization Methodology for Efficient Wide Range Tunable MEMS Filters

Published on: February 4, 2018

6.2K
Recombination Dynamics in Thin-film Photovoltaic Materials via Time-resolved Microwave Conductivity
11:30

Recombination Dynamics in Thin-film Photovoltaic Materials via Time-resolved Microwave Conductivity

Published on: March 6, 2017

11.8K

Area of Science:

  • Microwave Engineering
  • Material Science
  • Artificial Intelligence

Background:

  • Microwave sensors lack selectivity due to sensitivity to effective permittivity.
  • Characterizing liquid mixtures requires distinguishing specific components, like water, from interfering substances.
  • Existing methods struggle with selective material identification in complex mixtures.

Purpose of the Study:

  • To design a compact microwave planar sensor for selective water concentration detection.
  • To develop a robust method for identifying water in mixtures with methanol, ethanol, or acetone.
  • To achieve high accuracy in discriminating water concentrations using AI.

Main Methods:

  • Designed a compact zeroth-order resonance microwave sensor operating at 3.5, 4.3, and 5 GHz.
  • Utilized a convolutional neural network (CNN) for classifying water concentrations.
  • Employed Style-GAN to generate reliable sensor responses for training the CNN.

Main Results:

  • Achieved a sensor size of λg-min/8 per resonator.
  • Demonstrated selective recognition of water concentrations regardless of the host medium.
  • Attained a classification accuracy of 90.7% for water concentration discrimination.

Conclusions:

  • The developed microwave sensor and CNN approach effectively achieves selective water detection in liquid mixtures.
  • The integration of Style-GAN enhances the accuracy and reliability of the classification model.
  • This method offers a promising solution for precise material characterization in complex samples.