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 Experiment Video

Updated: May 24, 2026

Using Insect Electroantennogram Sensors on Autonomous Robots for Olfactory Searches
07:23

Using Insect Electroantennogram Sensors on Autonomous Robots for Olfactory Searches

Published on: August 4, 2014

A real-time de-noising algorithm for e-noses in a wireless sensor network.

Jianfeng Qu1, Yi Chai, Simon X Yang

  • 1College of Automation, Chongqing University, Chongqing, P.R. China 400030; E-Mails: sxbjq@163.com ; chaiyi@cqu.edu.cn.

Sensors (Basel, Switzerland)
|March 9, 2012
PubMed
Summary
This summary is machine-generated.

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

Single-Agent Selinexor Versus Physician's Choice in Previously Treated Myelofibrosis: Results From the Phase 2 XPORT-035 Study.

EJHaem·2026
Same author

Selinexor Plus Ruxolitinib in Janus Kinase Inhibitor-Naïve Myelofibrosis: Phase III SENTRY Trial.

Journal of clinical oncology : official journal of the American Society of Clinical Oncology·2026
Same author

CBL2/4-CIPK31-mediated phosphorylation of raffinose synthase enhances cold tolerance in Campeiostachys nutans and rice.

Plant physiology·2026
Same author

Edible Bird's Nest Attenuates Neuroinflammation and Cognitive Impairment in a Model of Chronic Fatigue Syndrome.

Food science & nutrition·2026
Same author

Comparative Cardiovascular Effectiveness of Glucagon-Like Peptide 1 Receptor Agonists and Sodium-Glucose Cotransporter 2 Inhibitors in Diabetes Mellitus.

Journal of the American College of Cardiology·2026
Same author

Phenotypic prediction of missense variants via deep contrastive learning.

Nature biomedical engineering·2026
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

A wireless electronic-nose (e-nose) network system effectively monitors livestock farm odors. A modified Kalman filtering technique significantly reduces sensor noise, enabling accurate remote odor strength estimation.

Area of Science:

  • Environmental Science
  • Sensor Technology
  • Agricultural Engineering

Background:

  • Livestock farms generate significant odorant gases requiring effective monitoring.
  • Existing odor monitoring systems may lack remote, multi-location accuracy.
  • Metal-oxide semiconductor (MOS) gas sensors are susceptible to noise, affecting data reliability.

Purpose of the Study:

  • To develop a wireless e-nose network system for real-time odorant gas monitoring and odor strength estimation in livestock farm environments.
  • To enhance data accuracy by implementing a noise reduction technique for MOS gas sensors.

Main Methods:

  • A wireless e-nose network system comprising multiple nodes, each with four MOS gas sensors, was deployed.
  • A modified Kalman filtering technique was applied for raw data collection and noise reduction.
Keywords:
Kalman filterMOS gas sensordata analysisnoise reduction

Related Experiment Videos

Last Updated: May 24, 2026

Using Insect Electroantennogram Sensors on Autonomous Robots for Olfactory Searches
07:23

Using Insect Electroantennogram Sensors on Autonomous Robots for Olfactory Searches

Published on: August 4, 2014

  • Real-time measurement noise variance was determined using a slip windows average method, and optimal system noise variance was found using experimental data.

    • Main Results:

    • The proposed modified Kalman filtering technique effectively reduced noise from MOS gas sensors.
    • Simulation results confirmed the method's ability to adjust Kalman filter parameters for optimal performance.
    • The system demonstrated capability for simultaneous, remote acquisition of accurate odor strength values.

    Conclusions:

    • The developed wireless e-nose network system with modified Kalman filtering provides a robust solution for livestock farm odor monitoring.
    • The noise reduction technique significantly improves the reliability and accuracy of gas sensor data for odor estimation.