Machine Learning and Spectral Analysis for Smart Sensing

A special issue of Chemosensors (ISSN 2227-9040). This special issue belongs to the section "Applied Chemical Sensors".

Deadline for manuscript submissions: closed (20 August 2021) | Viewed by 2097

Special Issue Editors

School of Computing, Ulster University, Jordanstown, UK
Interests: machine learning; chemometrics; spectroscopy; sensors; food authentication; virus detection
Special Issues, Collections and Topics in MDPI journals
School of Engineering, Ulster University, Jordanstown, UK
Interests: plasma emission spectroscopy; IR spectroscopy; trace gas sensing; aerosol sensing; autonomous environmental sensors; ocean sensing
School of Computing, Ulster University, Jordanstown, UK
Interests: chemometrics; sensing; machine learning
Dr. Weiran Song
E-Mail Website
Guest Editor
Department Of Energy And Power Engineering, Tsinghua University, Beijing, China
Interests: chemometrics; hyperspectral imaging; spectroscopy; machine learning; sensors

Special Issue Information

Dear Colleagues,

Recent advances in artificial intelligence, machine and deep learning and signal processing have the potential to transform the field of spectral data analysis with significant impact in sensing applications such as food authentication, virus detection and quality monitoring. Spectral data analysis is fast and often accurate, so it has allowed developments of sensing technologies that have broadened the range of applications, lowered deployment costs, improved performance and resolution, and increased portability and miniaturization for applications beyond the laboratory. These technologies enable the provision of solutions to many real-world challenges where non-destructive, in situ, fast-processing and cost-effective analyses are sought. 

This Special Issue puts a particular emphasis on machine and deep learning and signal processing in its broadest sense applied to spectral data analysis for sensing applications including food authentication, virus detection and quality monitoring. This issue will cover, but is not limited to, the following topics:  

  1. Signal processing, normalization, calibration and filtering; 
  2. Machine and deep learning and modelling; 
  3. Data-driven models and source separation; 
  4. Pattern recognition and classification; 
  5. Image processing and hyperspectral and multispectral imaging applications; 
  6. UV–Vis–NIR spectroscopy;
  7. Surface-enhanced Raman scattering (SERS) spectroscopy; 
  8. Bio-inspired sensors and systems;
  9. Portable and miniature spectrometers.

Prof. Dr. Hui Wang
Prof. Dr. Paul Maguire
Dr. Omar Nibouche
Dr. Weiran Song
Guest Editors

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. Chemosensors 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 2700 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

  • Machine learning
  • Deep learning
  • Signal processing
  • Spectroscopy and chemometrics
  • Smart sensors
  • Food authentication and quality monitoring, virus detection, and other applications

Published Papers (1 paper)

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Research

32 pages, 4578 KiB  
Article
Substance Detection and Identification Using Frequency Doubling of the THz Broadband Pulse
Chemosensors 2022, 10(7), 275; https://doi.org/10.3390/chemosensors10070275 - 13 Jul 2022
Cited by 1 | Viewed by 1271
Abstract
We propose and discuss an effective tool for substance detection and identification using a broadband THz pulse that is based on frequency conversion near the substance absorption frequencies. With this aim, we analyze the evolution of spectral intensities at the doubled absorption frequencies [...] Read more.
We propose and discuss an effective tool for substance detection and identification using a broadband THz pulse that is based on frequency conversion near the substance absorption frequencies. With this aim, we analyze the evolution of spectral intensities at the doubled absorption frequencies in order to prove their similarity to those at which the absorption of THz pulse energy occurs. This analysis is provided for both artificial THz signals and the real signals reflected from the substances under consideration. We demonstrate the feasibility of the proposed approach in the detection and identification of substances with an inhomogeneous surface, which is the most difficult case for practice, by using the method of spectral dynamic analysis and integral correlation criteria. Full article
(This article belongs to the Special Issue Machine Learning and Spectral Analysis for Smart Sensing)
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