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Sensors and Applications in Deep Learning and Artificial Intelligence
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Sensors and Applications in Deep Learning and Artificial Intelligence

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

Deadline for manuscript submissions: 25 January 2027 | Viewed by 1312

Special Issue Editor

Prof. Dr. Shyan-Ming Yuan

E-Mail Website
Guest Editor
Department of Computer Science, National Yang Ming Chiao Tung University, Hsinchu 300, Taiwan
Interests: distributed systems; blockchain technologies and applications; artificial intelligence applications
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Artificial intelligence (AI) and deep learning (DL) have revolutionized the field of advanced robotics in recent years. AI and DL are also transforming healthcare and industrial applications, as well as more complex tasks and environments.

This Special Issue, ‘Sensors and Applications in Deep Learning and Artificial Intelligence’, welcomes submissions in the state-of-the-art current and emerging technologies and methodologies in AI (deep learning) and sensor technology.

Prospective authors are welcome to submit original research and technical papers in these fields. Using sensor(s) technologies, the expected topics include, but are not limited to, the following:

  • AI and DL for sensor applications.
  • Intelligent and learning-based sensor communication technology.
  • Applications and sensors for manufacturing, machinery, semiconductors, and related industries such as quality inspection, defect detection, predictive maintenance, and yield control, and related applications.
  • Smart applications and sensors for architecture, construction, buildings, e-learning, and recommendation systems.
  • Applications and sensors for autonomous vehicles, surveillance systems, traffic monitoring, suspicious tracking, and transportation.
  • Object recognition, image classification, object detection, speech processing, human behavior analysis, and related sensing applications.
  • Safety in nuclear power plants, drone-based delivery, medical systems, automation systems, security systems, smart farming, sensor performance optimization, and thermal imaging (infection detection).
  • Sensor group-based communication for collective task operations, i.e., vehicle platooning, AI drones, manufacturing synchronization, etc.
  • Autonomous sensor devices in edge networks performing AI-based applications.

Prof. Dr. Shyan-Ming Yuan
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. 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

  • artificial intelligence
  • deep learning
  • sensor networks
  • object detection
  • reinforcement learning

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (1 paper)

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Research

18 pages, 1819 KB  
Article
A Hybrid Deep Learning Approach for Performance Prediction in Optical Communication Systems Based on PON Scenarios
by Ali Muslim, Esra Gündoğan, Mehmet Kaya and Reda Alhajj
Sensors 2026, 26(8), 2377; https://doi.org/10.3390/s26082377 - 12 Apr 2026
Viewed by 559
Abstract
As optical access networks continue to evolve toward higher capacity, longer reach, and increased user density, accurately predicting transmission performance has become increasingly complex. Conventional physics-based models often struggle to capture the nonlinear and stochastic behavior of modern passive optical networks (PONs), particularly [...] Read more.
As optical access networks continue to evolve toward higher capacity, longer reach, and increased user density, accurately predicting transmission performance has become increasingly complex. Conventional physics-based models often struggle to capture the nonlinear and stochastic behavior of modern passive optical networks (PONs), particularly under diverse operating conditions. In this study, a hybrid deep learning (DL) framework is proposed for the prediction of key performance indicators, including Q-factor, receiver sensitivity, and bit error rate (BER), in asymmetric 160/80 Gbps TWDM-PON systems, which is the target capacity by ITU-T G.989.1 specifications. The proposed approach integrates Gradient Boosting Regression and Multi-Layer Perceptron models within an ensemble learning structure to enhance robustness and predictive accuracy. A synthetic dataset comprising 1000 samples was generated to emulate realistic transmission scenarios with variations in distance, power level, and noise conditions for both upstream and downstream channels. Experimental results demonstrate strong agreement between the proposed DL-based predictions and conventional optical simulation outcomes, while the proposed predictions achieve superior adaptability and reduced computational complexity. High coefficients of determination (R2 > 0.94) and low error metrics confirm the effectiveness of the framework, highlighting its potential as a fast and reliable alternative to traditional performance evaluation methods in next-generation optical access networks. Full article
(This article belongs to the Special Issue Sensors and Applications in Deep Learning and Artificial Intelligence)
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