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10th Anniversary of Optics and Lasers Section—Recent Advances in Optical Applications

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Optics and Lasers".

Deadline for manuscript submissions: closed (31 December 2025) | Viewed by 1096

Special Issue Editor


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Guest Editor

Special Issue Information

Dear Colleagues,

This year marks the 10th year since the inception of the Optics and Lasers Section. To celebrate such an achievement with all of you, in this Special Issue, we are seeking high-quality submissions that highlight emerging technologies and applications addressing recent advances and breakthroughs in the field of Optical Applications. The topics of interest include, but are not limited to, non-linear optics such as high-order harmonics, Raman, wave-mixing, multiphoton ionization, as well as other basic phenomena in this field.

Prof. Dr. Saulius Juodkazis
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 250 words) can be sent to the Editorial Office for assessment.

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. Applied Sciences 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 2400 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

  • imaging and holography
  • lasers
  • laser applications
  • non-linear optics
  • optical materials, devices, and systems
  • fiber optics and integrated optics

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Published Papers (1 paper)

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Research

13 pages, 2161 KB  
Article
A Novel Laser Mode Identification Method Based on Wavemeter Interference Fringe and Machine Learning
by Fan Yang, Yong Lin, Qi’an Wang, Wei Tan, Weiming Xu, Pengpeng Yan, Hongbo Zheng, Luning Li and Buhua Tu
Appl. Sci. 2026, 16(1), 502; https://doi.org/10.3390/app16010502 - 4 Jan 2026
Viewed by 544
Abstract
In many laser application scenarios, concentrated optical energy, high coherence, and narrow spectral linewidth are critical optical characteristics that ensure the excellent performance of lasers. These characteristics can be achieved when a laser operates in single longitudinal mode (SLM) rather than multiple longitudinal [...] Read more.
In many laser application scenarios, concentrated optical energy, high coherence, and narrow spectral linewidth are critical optical characteristics that ensure the excellent performance of lasers. These characteristics can be achieved when a laser operates in single longitudinal mode (SLM) rather than multiple longitudinal mode (MLM). Therefore, it is important to identify whether the laser operates in SLM or MLM accurately and efficiently, especially in scenarios with high real-time requirements such as high-precision time measurement. This study proposes a novel machine learning-based method for laser longitudinal mode identification, which has been effectively utilized in the development of an optical clock. Two machine learning classification models are designed, based on a support vector machine (SVM) and a convolutional neural network (CNN), respectively, with the datasets being the interference fringe data measured by a Fizeau wavemeter integrated in the optical clock. Using a dataset that includes 589 interference fringe samples from two different laser wavelengths, it is demonstrated that the machine learning models can achieve 96% to 100% classification accuracy in distinguishing between SLM and MLM. The methodology in this work offers valuable insights for future space missions that require high-precision measurements and lightweight payloads. Full article
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