Evidence map›Paper›PMID 31480359›Full record

ReviewSensors (Basel, Switzerland)2019

Review on Smart Gas Sensing Technology.

Shaobin Feng, Fadi Farha, Qingjuan Li, Yueliang Wan, Yang Xu, Tao Zhang, Huansheng Ning

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Sensors (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 71 papers.

0numbers the graph read from it
0cells of the map it votes in
71citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

71 citing papers in PubMed.

  1. Review
  2. Review
  3. Article
  4. Article
  5. Review
  6. Review
  7. Article
  8. Highly Sensitive T-Shaped Quartz Tuning Fork Based CHSensors (Basel, Switzerland) · 2024
    Article
  9. Review
  10. Review
  11. Article
  12. Article
  13. Article
  14. Article
  15. Review
  16. Article
  17. Review
  18. Review
  19. Article
  20. Article

11 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Shaobin FengSchool of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China.
Fadi FarhaSchool of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China.
Qingjuan LiSchool of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China.
Yueliang WanBeijing Engineering Research Center for Cyberspace Data Analysis and Applications, Beijing 100083, China.
Yang XuSchool of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China.
Tao ZhangKey Lab of Information Network Security of Ministry of Public Security (The Third Research Institute of Ministry of Public Security), Shanghai 201204, China. zhangtao@stars.org.cn.
Huansheng NingSchool of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China. ninghuansheng@ustb.edu.cn.ORCID 0000-0001-6413-193X

Funding

Fundamental Research Funds for the Central Universities under Grant FRF-BD-18-016ANational Natural Science Foundation of China 61872038, 42161811530335, 61774014
6 · The paper itself

Abstract

With the development of the Internet-of-Things (IoT) technology, the applications of gas sensors in the fields of smart homes, wearable devices, and smart mobile terminals have developed by leaps and bounds. In such complex sensing scenarios, the gas sensor shows the defects of cross sensitivity and low selectivity. Therefore, smart gas sensing methods have been proposed to address these issues by adding sensor arrays, signal processing, and machine learning techniques to traditional gas sensing technologies. This review introduces the reader to the overall framework of smart gas sensing technology, including three key points; gas sensor arrays made of different materials, signal processing for drift compensation and feature extraction, and gas pattern recognition including Support Vector Machine (SVM), Artificial Neural Network (ANN), and other techniques. The implementation, evaluation, and comparison of the proposed solutions in each step have been summarized covering most of the relevant recently published studies. This review also highlights the challenges facing smart gas sensing technology represented by repeatability and reusability, circuit integration and miniaturization, and real-time sensing. Besides, the proposed solutions, which show the future directions of smart gas sensing, are explored. Finally, the recommendations for smart gas sensing based on brain-like sensing are provided in this paper.

Indexed as

gas sensormachine learningselectivitysensitivesensor arrayssmart gas sensing

Identifiers

PMID31480359
PMCPMC6749323

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.