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Recent Research on Intelligent Sensors

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Electrical, Electronics and Communications Engineering".

Deadline for manuscript submissions: 20 August 2025 | Viewed by 740

Special Issue Editors

National Institute for Research and Development in Microtechnologies, 126A, Erou Iancu Nicolae Street, 077190 Bucharest, Romania
Interests: engineering; instruments and instrumentation; biomedical; additive manufacturing; microtechnology

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Guest Editor
National Institute for Research and Development in Microtechnologies, 126A, Erou Iancu Nicolae Street, 077190 Bucharest, Romania
Interests: finite element modeling; CFD simulation; comsol multiphysics; coventorware; microelectromechanical systems (MEMS)

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Guest Editor
National Institute for Research and Development in Microtechnologies, 126A, Erou Iancu Nicolae Street, 077190 Bucharest, Romania
Interests: electrochemistry; medical sensors; analytical chemistry

Special Issue Information

Dear Colleagues,

In recent years, intelligent sensors have experienced rapid advancements and breakthroughs, revolutionizing a range of fields such as healthcare, manufacturing, environmental monitoring, and smart cities. These cutting-edge sensors combine the power of advanced technologies such as artificial intelligence, machine learning, connectivity, and miniaturization to enable accurate, real-time data acquisition, analysis, and decision-making.

The articles featured in this Special Issue delve into various aspects of intelligent sensors, encompassing both theoretical studies and practical implementations. Topics of interest include, but are not limited to, the following:

  • Sensor technology advancements and innovations;
  • Signal processing techniques for intelligent sensors;
  • Data fusion and integration methodologies;
  • Machine learning and artificial intelligence for sensor data analysis;
  • Internet of Things (IoT) and sensor networks;
  • Applications of intelligent sensors in healthcare, transportation, energy, agriculture, and more.

Dr. Marian Ion
Dr. Bogdan Firtat
Dr. Carmen Mihailescu
Guest Editors

Manuscript Submission Information

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Published Papers (1 paper)

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Research

23 pages, 59897 KiB  
Article
Method to Use Transport Microsimulation Models to Create Synthetic Distributed Acoustic Sensing Datasets
by Ignacio Robles-Urquijo, Juan Benavente, Javier Blanco García, Pelayo Diego Gonzalez, Alayn Loayssa, Mikel Sagues, Luis Rodriguez-Cobo and Adolfo Cobo
Appl. Sci. 2025, 15(9), 5203; https://doi.org/10.3390/app15095203 - 7 May 2025
Viewed by 200
Abstract
This research introduces a new method for creating synthetic Distributed Acoustic Sensing (DAS) datasets from transport microsimulation models. The process involves modeling detailed vehicle interactions, trajectories, and characteristics from the PTV VISSIM transport microsimulation tool. It then applies the Flamant–Boussinesq approximation to simulate [...] Read more.
This research introduces a new method for creating synthetic Distributed Acoustic Sensing (DAS) datasets from transport microsimulation models. The process involves modeling detailed vehicle interactions, trajectories, and characteristics from the PTV VISSIM transport microsimulation tool. It then applies the Flamant–Boussinesq approximation to simulate the resulting ground deformation detected by virtual fiber-optic cables. These synthetic DAS signals serve as large-scale, scenario-controlled, labeled datasets on training machine learning models for various transport applications. We demonstrate this by training several U-Net convolutional neural networks to enhance spatial resolution (reducing it to half the original gauge length), filtering traffic signals by vehicle direction, and simulating the effects of alternative cable layouts. The methodology is tested using simulations of real road scenarios, featuring a fiber-optic cable buried along the westbound shoulder with sections deviating from the roadside. The U-Net models, trained solely on synthetic data, showed promising performance (e.g., validation MSE down to 0.0015 for directional filtering) and improved the detectability of faint signals, like bicycles among heavy vehicles, when applied to real DAS measurements from the test site. This framework uniquely integrates detailed traffic modeling with DAS physics, providing a novel tool to develop and evaluate DAS signal processing techniques, optimize cable layout deployments, and advance DAS applications in complex transportation monitoring scenarios. Creating such a procedure offers significant potential for advancing the application of DAS in transportation monitoring and smart city initiatives. Full article
(This article belongs to the Special Issue Recent Research on Intelligent Sensors)
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