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Micromachines
Special Issues
Electronic Noses: Principles and Applications
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Electronic Noses: Principles and Applications

A special issue of Micromachines (ISSN 2072-666X). This special issue belongs to the section "E：Engineering and Technology".

Deadline for manuscript submissions: closed (20 June 2022) | Viewed by 1691

Special Issue Editor

Dr. Jia Yan

E-Mail Website
Guest Editor
College of Artificial Intelligence, Southwest University, Chongqing 400715, China
Interests: sensing system signal and information processing; machine olfaction; machine learning; deep learning; pattern recognition
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Over the past few decades, electronic noses have played an increasingly important role in daily life as the olfaction economy has grown. There is an increasing need for rapid, highly sensitive, and selective analytical methods to address emerging challenges in environmental monitoring, food safety, and public health. To meet this need, gas-sensing systems based on a variety of sensors have become promising tools, which are more affordable and require less complex specialized operations compared with expensive and complex traditional analytical instruments. Electronic noses have been widely used in qualitative/quantitative analysis and sensory evaluation of food, biological, environmental, and medical samples. Accordingly, this Special Issue aims to provide a timely and comprehensive introduction to the latest and emerging concepts, principles, technologies and applications in the field of electronic noses, including, but not limited to, hardware system construction and software algorithm analysis of electronic noses, as well as novel applications. Research papers and review articles will be considered.

Dr. Jia Yan
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Micromachines is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • principle of olfactory sensing
  • signal processing
  • pattern recognition
  • chemometrics
  • deep learning
  • quality control
  • food safety
  • medical diagnostics
  • environmental monitoring

Published Papers (1 paper)

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Research

15 pages, 2199 KiB  
Article
Cross-Domain Active Learning for Electronic Nose Drift Compensation
by Fangyu Sun, Ruihong Sun and Jia Yan
Micromachines 2022, 13(8), 1260; https://0-doi-org.brum.beds.ac.uk/10.3390/mi13081260 - 05 Aug 2022
Cited by 5 | Viewed by 1354
Abstract
The problem of drift in the electronic nose (E-nose) is an important factor in the distortion of data. The existing active learning methods do not take into account the misalignment of the data feature distribution between different domains due to drift when selecting [...] Read more.
The problem of drift in the electronic nose (E-nose) is an important factor in the distortion of data. The existing active learning methods do not take into account the misalignment of the data feature distribution between different domains due to drift when selecting samples. For this, we proposed a cross-domain active learning (CDAL) method based on the Hellinger distance (HD) and maximum mean difference (MMD). In this framework, we weighted the HD with the MMD as a criterion for sample selection, which can reflect as much drift information as possible with as few labeled samples as possible. Overall, the CDAL framework has the following advantages: (1) CDAL combines active learning and domain adaptation to better assess the interdomain distribution differences and the amount of information contained in the selected samples. (2) The introduction of a Gaussian kernel function mapping aligns the data distribution between domains as closely as possible. (3) The combination of active learning and domain adaptation can significantly suppress the effects of time drift caused by sensor ageing, thus improving the detection accuracy of the electronic nose system for data collected at different times. The results showed that the proposed CDAL method has a better drift compensation effect compared with several recent methodological frameworks. Full article
(This article belongs to the Special Issue Electronic Noses: Principles and Applications)
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