Content area

Abstract

The development of reliable gas sensors is very important in many fields such as safety, environment, and agriculture, and is especially essential for industrial waste and air pollution monitoring. As the performance of mobile platforms equipped with sensors such as smartphones and drones and the technologies supporting them (wireless communication, battery performance, data processing technology, etc.) are spreading and improving, a lot of efforts are being made to perform these tasks by using portable systems such as smartphones or installing them on unmanned wireless platforms such as drones. For example, research is continuously being conducted on chemical sensors for field monitoring using smartphones and rapid monitoring of air pollution using unmanned aerial vehicles (UAVs). In this paper, we review the measurement results of various chemical sensors available on mobile platforms including drones and smartphones, and the analysis of detection results using machine learning. This topic covers a wide range of specialized fields such as materials engineering, aerospace engineering, physics, chemistry, environmental engineering, electrical engineering, and machine learning, and it is difficult for experts in one field to grasp the entire content. Therefore, we have explained various concepts with relatively simple pictures so that experts in various fields can comprehensively understand the overall topics.

Details

1009240
Business indexing term
Title
Chemical Detection Using Mobile Platforms and AI-Based Data Processing Technologies
Author
Noh, Daegwon 1 ; Oh, Eunsoon 1   VIAFID ORCID Logo 

 Department of Physics, Chungnam National University, 99 Daehakro, Yuseong-gu, Daejeon 34134, Republic of Korea; [email protected]; Institute of Quantum Systems (IQS), Chungnam National University, 99 Daehakro, Yuseong-gu, Daejeon 34134, Republic of Korea 
Volume
14
Issue
1
First page
6
Publication year
2025
Publication date
2025
Publisher
MDPI AG
Place of publication
Basel
Country of publication
Switzerland
e-ISSN
22242708
Source type
Scholarly Journal
Language of publication
English
Document type
Journal Article
Publication history
 
 
Online publication date
2025-01-13
Milestone dates
2024-10-31 (Received); 2025-01-07 (Accepted)
Publication history
 
 
   First posting date
13 Jan 2025
ProQuest document ID
3171090122
Document URL
https://www.proquest.com/scholarly-journals/chemical-detection-using-mobile-platforms-ai/docview/3171090122/se-2?accountid=208611
Copyright
© 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
Last updated
2026-01-20
Database
2 databases
  • Coronavirus Research Database
  • ProQuest One Academic