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

You might also read

Related Articles

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

Sort by
Same author

Advanced Beam Detection for Free-Space Optics Operating in the Mid-Infrared Spectra.

Sensors (Basel, Switzerland)·2025
Same author

Towards Optoelectronic Technology: From Basic Research to Applications.

Sensors (Basel, Switzerland)·2025
Same author

A Review of Thermal Detectors of THz Radiation Operated at Room Temperature.

Sensors (Basel, Switzerland)·2024
Same author

Accelerating the Diagnosis of Pandemic Infection Based on Rapid Sampling Algorithm for Fast-Response Breath Gas Analyzers.

Sensors (Basel, Switzerland)·2024
Same author

Ultraviolet Photodetectors: From Photocathodes to Low-Dimensional Solids.

Sensors (Basel, Switzerland)·2023
Same author

Air sampling unit for breath analyzers.

The Review of scientific instruments·2017
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: Jun 29, 2025

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

543

Time-Efficient SNR Optimization of WMS-Based Gas Sensor Using a Genetic Algorithm.

Filip Musiałek1, Dariusz Szabra1, Jacek Wojtas1

  • 1Institute of Optoelectronics, Military University of Technology, 2 Kaliskiego Str., 00-908 Warsaw, Poland.

Sensors (Basel, Switzerland)
|March 28, 2024
PubMed
Summary
This summary is machine-generated.

Artificial Intelligence (AI) optimized a Wavelength Modulation Spectroscopy (WMS) experiment, significantly boosting the signal-to-noise ratio (SNR). This AI-driven approach drastically reduces optimization time for gas sensors, achieving optimal detection limits.

Keywords:
LWIRSNR optimizationWMSartificial intelligencegenetic algorithmlaser absorption spectroscopymethane sensor

More Related Videos

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
11:53

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Published on: December 9, 2012

13.0K
Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles
11:54

Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles

Published on: March 13, 2017

9.3K

Related Experiment Videos

Last Updated: Jun 29, 2025

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

543
Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
11:53

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Published on: December 9, 2012

13.0K
Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles
11:54

Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles

Published on: March 13, 2017

9.3K

Area of Science:

  • Spectroscopy
  • Artificial Intelligence
  • Gas Sensing

Background:

  • Optimizing Wavelength Modulation Spectroscopy (WMS) gas sensors typically involves extensive simulations and experimental trials.
  • Current methods are time-consuming and may not identify the absolute best operating conditions.

Purpose of the Study:

  • To develop an AI-driven method for optimizing WMS gas sensor parameters.
  • To significantly improve the signal-to-noise ratio (SNR) and reduce the time required for optimization.

Main Methods:

  • Utilized a genetic algorithm (GA), a type of AI, coupled with custom electronics for laser control.
  • Implemented the GA within a WMS experiment using a quantum cascade laser (QCL) in the long-wavelength-infrared (LWIR) range and a Herriott multipass cell.

Main Results:

  • Achieved signal-to-noise ratio (SNR) improvements ranging from 1.6 to 6.5 times, depending on the harmonic.
  • The evolutionary approach tested ~1.39 × 10^32 parameter combinations in just 300 seconds.
  • Efficiently identified optimal WMS parameters for the most favorable limit of detection (LOD).

Conclusions:

  • AI, specifically a genetic algorithm, offers a highly efficient and precise method for optimizing WMS gas sensor parameters.
  • This approach dramatically reduces the time needed to find optimal settings and improve sensor performance.
  • The AI-driven optimization ensures the determination of the most favorable operating conditions for minimizing the limit of detection (LOD).