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Cutting-Edge Developments in Optical Communications, Perception and Computing

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

Deadline for manuscript submissions: 30 April 2025 | Viewed by 295

Special Issue Editors


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Guest Editor
Drilling & Production Technology Research Institute, Chuanqing Drilling Engineering Company Limited, Guanghan 618300, China
Interests: optical communications; optical information processing; object detection; optical perception; machine learning

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Guest Editor
Department of Electrical Engineering, University of Southern California, Los Angeles, CA 90089, USA
Interests: lightwave technology

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Guest Editor
Shphotonics, LLC, Tianjin 300450, China
Interests: metasurface; AI; optical computing

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Guest Editor
School of Information and Communications Engineering, Xi’an Jiaotong University, Xi’an 710049, China
Interests: intelligent photonics; optical communications; optical perception; optical chip
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the rapid evolution and widespread adoption of emerging information technologies such as 5G, cloud computing, big data, and artificial intelligence, global data traffic and computing power demands have surged dramatically. Traditional data transmission speeds, information capacities, and computing performance can no longer adequately support the processing requirements of big data. Recent advancements in optical technologies present viable solutions to address these challenges. In particular, optical communications, optical perception, and computing are recognized as promising research directions for the future.

Currently, optical communication is focused on resolving critical technical challenges, including increasing bandwidth, reducing latency, and minimizing packet loss. These improvements are essential for ensuring that high-quality networks can support the demands of new industries, such as 4K/8K video streaming, and virtual and augmented reality. Optical perception technologies offer advantages in terms of ease of data acquisition, cost-effectiveness, and the ability to handle vast amounts of information. Ongoing innovations in foundational theories and analytical techniques are driving the development of sophisticated perception systems, constructing multi-dimensional image knowledge frameworks, and advancing image theory and applications to new depths. In the realm of optical computing, the integration of optical technologies with advanced computational methods enables high-speed processing and improved efficiency. This field addresses significant challenges related to data processing times and energy consumption, facilitating faster and more efficient computation across a variety of applications.

This Special Issue aims to showcase the latest advances in optical technologies across communication, perception, and computing. Topics of interest include, but are not limited to, digital, electrical, and optical signal processing theories; artificial intelligence applications; device and subsystem designs; and forward-looking perspectives. Further topics of interest include, but are not limited to, the following areas:

  • Active and passive optical devices;
  • Digital signal processing for sensing and communication;
  • Optical AI: optical neuromorphic neural networking;
  • Optical communications and networking;
  • Optical 5G/6G;
  • Optical imaging and display;
  • Optical sensing and perception;
  • Optical signal processing;
  • Signal processing for 3D/AR/VR;
  • Optical information processing;
  • Optical computing;
  • Computational imaging.

Dr. Yiwen Zhang
Dr. Hao Song
Dr. Lei Sun
Prof. Dr. Yang Yue
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

  • optical communications
  • optical computing
  • optical sensing
  • optical perception
  • artificial intelligence

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Published Papers (1 paper)

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Review

29 pages, 1042 KiB  
Review
A Review on Face Mask Recognition
by Jiaonan Zhang, Dong An, Yiwen Zhang, Xiaoyan Wang, Xinyue Wang, Qiang Wang, Zhongqi Pan and Yang Yue
Sensors 2025, 25(2), 387; https://doi.org/10.3390/s25020387 - 10 Jan 2025
Viewed by 132
Abstract
This review offers a comprehensive and in-depth analysis of face mask detection and recognition technologies, emphasizing their critical role in both public health and technological advancements. Existing detection methods are systematically categorized into three primary classes: feaRture-extraction-and-classification-based approaches, object-detection-models-based methods and multi-sensor-fusion-based methods. [...] Read more.
This review offers a comprehensive and in-depth analysis of face mask detection and recognition technologies, emphasizing their critical role in both public health and technological advancements. Existing detection methods are systematically categorized into three primary classes: feaRture-extraction-and-classification-based approaches, object-detection-models-based methods and multi-sensor-fusion-based methods. Through a detailed comparison, their respective workflows, strengths, limitations, and applicability across different contexts are examined. The review underscores the paramount importance of accurate face mask detection, especially in response to global public health challenges such as pandemics. A central focus is placed on the role of datasets in driving algorithmic performance, addressing key factors, including dataset diversity, scale, annotation granularity, and modality. The integration of depth and infrared data is explored as a promising avenue for improving robustness in real-world conditions, highlighting the advantages of multimodal datasets in enhancing detection capabilities. Furthermore, the review discusses the synergistic use of real-world and synthetic datasets in overcoming challenges such as dataset bias, scalability, and resource scarcity. Emerging solutions, such as lightweight model optimization, domain adaptation, and privacy-preserving techniques, are also examined as means to improve both algorithmic efficiency and dataset quality. By synthesizing the current state of the field, identifying prevailing challenges, and outlining potential future research directions, this paper aims to contribute to the development of more effective, scalable, and robust face mask detection systems for diverse real-world applications. Full article
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