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Advanced Optical Sensors Based on Machine Learning: 2nd Edition
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Advanced Optical Sensors Based on Machine Learning: 2nd Edition

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Optical Sensors".

Deadline for manuscript submissions: 1 September 2025 | Viewed by 1617

Special Issue Editors

Dr. Jinhui Chen

E-Mail Website
Guest Editor
Institute of Electromagnetics and Acoustics, Xiamen University, Xiamen 361005, China
Interests: optical sensors; microcavity photonics; optoelectronics
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Daquan Yang

E-Mail Website
Guest Editor
School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China
Interests: photonic crystal sensors; microcavity photonics; micro-nano optical precision measurement
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Optical sensors have attracted broad scholarly interest due to their immunity to electromagnetic interference, high sensitivity, multiplexing, and remote sensing capabilities. Various optical structures, such as integrated waveguides, optical fibers and optical microcavities, have been developed for sensing applications over the past decades. Although conventional optical sensing platforms have displayed impressive performances, most sensing information relies on manual analysis, which is time-consuming and prone to human error. As a result, there are significant limitations in sensing accuracy, sensing range, and real-time detection. With the dramatic increase in the availability of computational resources and the rapid development of machine learning, new sensor design paradigms and signal processing methods have become available for advanced optical sensing technology. For example, deep learning algorithms can be used to automatically identify key features in sensing information and quickly identify changes in optical signals, thus further improving detection accuracy and response speed. We believe that optical sensors, taken in combination with machine learning, open up a new opportunity for next-generation intelligent optical sensors in the terms of hardware design and signal readout.

This Special Issue aims to attract original contributions. These should focus on a wide array of topics, related to both experiments on and the theory of advanced optical sensors and relying on machine learning.

Dr. Jinhui Chen
Prof. Dr. Daquan Yang
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. Sensors is an international peer-reviewed open access semimonthly 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

  • machine learning
  • intelligent sensor design
  • computational sensing
  • hyperspectral imaging and sensing
  • inverse design optics
  • wearable sensors
  • intelligent spectroscopy

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Published Papers (2 papers)

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Research

14 pages, 9188 KiB  
Article
Filament Type Recognition for Additive Manufacturing Using a Spectroscopy Sensor and Machine Learning
by Gorkem Anil Al and Uriel Martinez-Hernandez
Sensors 2025, 25(5), 1543; https://doi.org/10.3390/s25051543 - 2 Mar 2025
Viewed by 901
Abstract
This study presents a novel approach for filament recognition in fused filament fabrication (FFF) processes using a multi-spectral spectroscopy sensor module combined with machine learning techniques. The sensor module measures 18 wavelengths spanning the visible to near-infrared spectra, with a custom-designed shroud to [...] Read more.
This study presents a novel approach for filament recognition in fused filament fabrication (FFF) processes using a multi-spectral spectroscopy sensor module combined with machine learning techniques. The sensor module measures 18 wavelengths spanning the visible to near-infrared spectra, with a custom-designed shroud to ensure systematic data collection. Filament samples include polylactic acid (PLA), thermoplastic polyurethane (TPU), thermoplastic copolyester (TPC), carbon fibre, acrylonitrile butadiene styrene (ABS), and ABS blended with Carbon fibre. Data are collected using the Triad Spectroscopy module AS7265x (composed of AS72651, AS72652, AS72653 sensor units) positioned at three measurement distances (12 mm, 16 mm, 20 mm) to evaluate recognition performance under varying configurations. Machine learning models, including k-Nearest Neighbors (kNN), Logistic Regression, Support Vector Machine (SVM), and Multi-Layer Perceptron (MLP), are employed with hyperparameter tuning applied to optimise classification accuracy. Results show that the data collected on the AS72651 sensor, paired with the SVM model, achieves the highest accuracy of 98.95% at a 20 mm measurement distance. This work introduces a compact, high-accuracy filament recognition module that can enhance the autonomy of multi-material 3D printing by dynamically identifying and switching between different filaments, optimising printing parameters for each material, and expanding the versatility of additive manufacturing applications. Full article
(This article belongs to the Special Issue Advanced Optical Sensors Based on Machine Learning: 2nd Edition)
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11 pages, 6904 KiB  
Communication
The Application of Kernel Ridge Regression for the Improvement of a Sensing Interferometric System
by Ana Dinora Guzman-Chavez and Everardo Vargas-Rodriguez
Sensors 2025, 25(5), 1292; https://doi.org/10.3390/s25051292 - 20 Feb 2025
Viewed by 374
Abstract
Sensors based on interferometric systems have been studied due to their wide range of advantages, such as high sensitivity. For these types of sensors, traditional methods, which generally depend on the linear sensitivity of one variable, have been used to determine the measurand [...] Read more.
Sensors based on interferometric systems have been studied due to their wide range of advantages, such as high sensitivity. For these types of sensors, traditional methods, which generally depend on the linear sensitivity of one variable, have been used to determine the measurand parameter. Usually, these methods are only effective for short measurement ranges, which is one of the main limiting factors of these sensors. In this work, it is shown that Kernel Ridge Regression (KRR), which is a machine learning method, can be applied to improve the range of measurement of multilayer interferometric sensors. This method estimates the value of a response variable (temperature) based on a set of spectral features, which are transformed by means of kernel functions. Here, these features were the wavelength positions and maximum amplitudes of some peaks of the interference spectrum of the sensing system. To sustain the application of the method, four kernel functions were used to estimate the values of the response variable. Finally, the results show that by implementing KRR with a Gaussian kernel, the temperature could be estimated with a root-mean-square error of 0.094 °C for the measurement range from 4.5 to 50 °C, which indicates that it was widened by a factor of eight compared with traditional methods. Full article
(This article belongs to the Special Issue Advanced Optical Sensors Based on Machine Learning: 2nd Edition)
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