Search research articles

Related Concept Videos

Instrument Calibration

Instrument Calibration

Instrument calibration is essential for ensuring that instruments produce accurate and consistent results. It is vital in manufacturing, healthcare, testing laboratories, and scientific research. Calibration processes are specific to each instrument and help enhance data accuracy. Each instrument has a unique calibration process tailored to its design and function to improve data accuracy.
Analytical Balance Calibration
An analytical balance measures mass and requires regular calibration to...

Calibration Curves: Linear Least Squares

Calibration Curves: Linear Least Squares

A calibration curve is a plot of the instrument's response against a series of known concentrations of a substance. This curve is used to set the instrument response levels, using the substance and its concentrations as standards. Alternatively, or additionally, an equation is fitted to the calibration curve plot and subsequently used to calculate the unknown concentrations of other samples reliably.
For data that follow a straight line, the standard method for fitting is the linear...

Calibration Curves: Correlation Coefficient

Calibration Curves: Correlation Coefficient

In a linear calibration curve, there is a value called the calibration coefficient, denoted by 'r,' which measures the strength and the direction of association between two variables. The correlation coefficient value ranges from −1 to +1. A value of +1 indicates a perfect positive linear correlation, −1 denotes a perfect negative correlation, and 0 implies no correlation between the two variables. A positive correlation value establishes that as one variable increases, the...

Multi-input and Multi-variable systems

Multi-input and Multi-variable systems

Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...

Measurement of Air Content in Concrete

Measurement of Air Content in Concrete

Air content measurement in concrete is critical for ensuring structural integrity and durability of concrete structures, especially in environments prone to severe weather conditions. Accurate air content analysis optimizes concrete's resistance to freeze-thaw cycles and enhances its workability and strength. Several methods are standardized under ASTM guidelines to measure the air content in fresh concrete, each suitable for different concrete types and conditions.
The pressure method,...

You might also read

Related Articles

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

Sort by

Same author

HERMEES: A Holistic Evaluation and Ranking Model for Energy-Efficient Systems Applied to Selecting Optimal Lightweight Cryptographic and Topology Construction Protocols in Wireless Sensor Networks.

Sensors (Basel, Switzerland)·2025

Same author

A Video Mosaicing-Based Sensing Method for Chicken Behavior Recognition on Edge Computing Devices.

Sensors (Basel, Switzerland)·2024

Same author

Accessible Dyslexia Detection with Real-Time Reading Feedback through Robust Interpretable Eye-Tracking Features.

Brain sciences·2023

Same author

Spatiotemporal Eye-Tracking Feature Set for Improved Recognition of Dyslexic Reading Patterns in Children.

Sensors (Basel, Switzerland)·2022

Same author

Building Low-Cost Sensing Infrastructure for Air Quality Monitoring in Urban Areas Based on Fog Computing.

Sensors (Basel, Switzerland)·2022

Same author

Developing Relative Humidity and Temperature Corrections for Low-Cost Sensors Using Machine Learning.

Sensors (Basel, Switzerland)·2021

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Search research articles

Related Experiment Video

Updated: Aug 7, 2025

Calibrated Passive Sampling - Multi-plot Field Measurements of NH3 Emissions with a Combination of Dynamic Tube Method and Passive Samplers

Calibrated Passive Sampling - Multi-plot Field Measurements of NH3 Emissions with a Combination of Dynamic Tube Method and Passive Samplers

Published on: March 21, 2016

Data-Driven Machine Learning Calibration Propagation in A Hybrid Sensor Network for Air Quality Monitoring.

Ivan Vajs^1,2, Dejan Drajic^1,2,3, Zoran Cica¹

¹School of Electrical Engineering, University of Belgrade, 11120 Belgrade, Serbia.

Sensors (Basel, Switzerland)

|March 11, 2023

Summary

This study introduces a machine learning method to calibrate low-cost air quality sensors using data-driven calibration propagation. This approach enhances the accuracy of hybrid sensor networks for more effective environmental monitoring.

Keywords:

air pollution monitoring air quality calibration propagation hybrid network low-cost sensors machine learning sensor calibration

More Related Videos

Continuous Instream Monitoring of Nutrients and Sediment in Agricultural Watersheds

Continuous Instream Monitoring of Nutrients and Sediment in Agricultural Watersheds

Published on: September 26, 2017

Additive Manufacturing-Enabled Low-Cost Particle Detector

Additive Manufacturing-Enabled Low-Cost Particle Detector

Published on: March 24, 2023

Related Experiment Videos

Last Updated: Aug 7, 2025

Calibrated Passive Sampling - Multi-plot Field Measurements of NH3 Emissions with a Combination of Dynamic Tube Method and Passive Samplers

Calibrated Passive Sampling - Multi-plot Field Measurements of NH3 Emissions with a Combination of Dynamic Tube Method and Passive Samplers

Published on: March 21, 2016

Continuous Instream Monitoring of Nutrients and Sediment in Agricultural Watersheds

Continuous Instream Monitoring of Nutrients and Sediment in Agricultural Watersheds

Published on: September 26, 2017

Additive Manufacturing-Enabled Low-Cost Particle Detector

Additive Manufacturing-Enabled Low-Cost Particle Detector

Published on: March 24, 2023

Area of Science:

Environmental Science
Sensor Technology
Data Science

Background:

Public air quality monitoring uses accurate but costly stations, limiting spatial resolution.
Low-cost sensors offer mobility and affordability, ideal for dense hybrid networks.
Sensor calibration is crucial due to weather influences and degradation in low-cost devices.

Purpose of the Study:

Investigate data-driven machine learning calibration propagation for hybrid air quality sensor networks.
Assess the effectiveness of using calibrated low-cost devices to calibrate uncalibrated ones.
Improve the accuracy and efficiency of low-cost sensor deployments in air quality monitoring.

Main Methods:

Implemented a hybrid sensor network with one public station and ten low-cost devices (NO2, PM10, RH, Temp).
Utilized a machine learning approach for calibration propagation within the network.
Employed a calibrated low-cost device to calibrate uncalibrated devices.

Main Results:

Achieved up to 0.35/0.14 improvement in Pearson correlation coefficient for NO2/PM10.
Reduced Root Mean Square Error (RMSE) by 6.82 µg/m³ for NO2 and 20.56 µg/m³ for PM10.
Demonstrated the promise of calibration propagation for accurate hybrid sensor networks.

Conclusions:

Data-driven machine learning calibration propagation is effective for hybrid air quality monitoring.
The proposed method offers an efficient and inexpensive solution for calibrating low-cost sensors.
This approach supports the development of high-spatial-resolution air quality measurement grids.