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Applications of Sensors Based on Embedded Systems

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

Deadline for manuscript submissions: 31 January 2025 | Viewed by 3503

Special Issue Editors


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Guest Editor
Department of Electronic Engineering, University of Rome Tor Vergata, 00133 Rome, Italy
Interests: electronics and communication engineering; digital signal processing; machine learning; FPGA
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Special Issue Information

Dear Colleagues,

In the field of sensors applied to the edge, non-functional constraints such as execution time, memory capacity, and energy consumption are a significant challenge for designers of embedded systems. New applications are being proposed that integrate an increasing variety of functionality into everyday objects, imposing several additional requirements on embedded system designers, as follows:

  • Increased computing workloads, elaborating and fusing multiple sensor data, even by advanced machine learning techniques;
  • Reduced power consumption, allowing for smaller batteries and renewable power sources;
  • Faster interaction with the environment, necessitating a high performance in data processing that is often reached by hardware implementations.

As an example, the physical dimensions and power consumption of embedded sensors for the Internet of Things are frequently of interest. However, the need for small systems does not prevent higher demands for functionality and speed. Simultaneously, designers must respond to a growing need for more powerful edge systems capable of managing vast fleets of connected devices while running resource-intensive algorithms such as sensor fusion, feedback control, and machine learning. Developers must grasp the nature of embedded systems architectures and strategies for extracting their full performance potential in this environment, as well as embedded design in general.

This Special Issue invites researchers to contribute original research, case studies, and reviews that address topics related to designs and applications of sensors that are based on embedded systems.

Dr. Sergio Spanò
Dr. Luca Di Nunzio
Guest Editors

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Keywords

  • applications of sensors to low-power IoT systems
  • FPGA and SoC processing for sensors data
  • microcontrollers processing for sensors data
  • embedded GPU processing for sensors data
  • ASIC processing for sensors data
  • machine learning on the Edge
  • efficient sensors data-processing algorithms

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Published Papers (2 papers)

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13 pages, 748 KiB  
Article
Improving Monitoring of Indoor RF-EMF Exposure Using IoT-Embedded Sensors and Kriging Techniques
by Randa Jabeur and Alaa Alaerjan
Sensors 2024, 24(23), 7849; https://doi.org/10.3390/s24237849 - 8 Dec 2024
Viewed by 686
Abstract
Distributed wireless sensor networks (WSNs) are widely used to enhance the quality and safety of various applications. These networks consist of numerous sensor nodes, often deployed in challenging terrains where maintenance is difficult. Efficient monitoring approaches are essential to maximize the functionality and [...] Read more.
Distributed wireless sensor networks (WSNs) are widely used to enhance the quality and safety of various applications. These networks consist of numerous sensor nodes, often deployed in challenging terrains where maintenance is difficult. Efficient monitoring approaches are essential to maximize the functionality and lifespan of each sensor node, thereby improving the overall performance of the WSN. In this study, we propose a method to efficiently monitor radiofrequency electromagnetic fields (RF-EMF) exposure using WSNs. Our approach leverages sensor nodes to provide real-time measurements, ensuring accurate and timely data collection. With the increasing prevalence of wireless communication systems, assessing RF-EMF exposure has become crucial due to public health concerns. Since individuals spend over 70% of their time indoors, it is vital to evaluate indoor RF-EMF exposure. However, this task is complicated by the complex indoor environments, furniture arrangements, temporal variability of exposure, numerous obstructions with unknown dielectric properties, and uncontrolled factors such as people’s movements and the random positioning of furniture and doors. To address these challenges, we employ a sensor network to monitor RF-EMF exposure limits using embedded sensors. By integrating Internet of Things-embedded sensors with advanced modeling techniques, such as kriging, we characterize and model indoor RF-EMF downlink (DL) exposure effectively. Measurements taken in several buildings within a few hundred meters of base stations equipped with multiple cellular antennas (2G, 3G, 4G, and 5G) demonstrate that the kriging technique using the spherical model provides superior RF-EMF prediction compared with the exponential model. Using the spherical model, we constructed a high-resolution coverage map for the entire corridor, showcasing the effectiveness of our approach. Full article
(This article belongs to the Special Issue Applications of Sensors Based on Embedded Systems)
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27 pages, 22106 KiB  
Article
A Real-Time Embedded System for Driver Drowsiness Detection Based on Visual Analysis of the Eyes and Mouth Using Convolutional Neural Network and Mouth Aspect Ratio
by Ruben Florez, Facundo Palomino-Quispe, Ana Beatriz Alvarez, Roger Jesus Coaquira-Castillo and Julio Cesar Herrera-Levano
Sensors 2024, 24(19), 6261; https://doi.org/10.3390/s24196261 - 27 Sep 2024
Viewed by 2340
Abstract
Currently, the number of vehicles in circulation continues to increase steadily, leading to a parallel increase in vehicular accidents. Among the many causes of these accidents, human factors such as driver drowsiness play a fundamental role. In this context, one solution to address [...] Read more.
Currently, the number of vehicles in circulation continues to increase steadily, leading to a parallel increase in vehicular accidents. Among the many causes of these accidents, human factors such as driver drowsiness play a fundamental role. In this context, one solution to address the challenge of drowsiness detection is to anticipate drowsiness by alerting drivers in a timely and effective manner. Thus, this paper presents a Convolutional Neural Network (CNN)-based approach for drowsiness detection by analyzing the eye region and Mouth Aspect Ratio (MAR) for yawning detection. As part of this approach, endpoint delineation is optimized for extraction of the region of interest (ROI) around the eyes. An NVIDIA Jetson Nano-based device and near-infrared (NIR) camera are used for real-time applications. A Driver Drowsiness Artificial Intelligence (DD-AI) architecture is proposed for the eye state detection procedure. In a performance analysis, the results of the proposed approach were compared with architectures based on InceptionV3, VGG16, and ResNet50V2. Night-Time Yawning–Microsleep–Eyeblink–Driver Distraction (NITYMED) was used for training, validation, and testing of the architectures. The proposed DD-AI network achieved an accuracy of 99.88% with the NITYMED test data, proving superior to the other networks. In the hardware implementation, tests were conducted in a real environment, resulting in 96.55% and 14 fps on average for the DD-AI network, thereby confirming its superior performance. Full article
(This article belongs to the Special Issue Applications of Sensors Based on Embedded Systems)
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