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State-of-the-Art Sensors Technologies in Belgium 2024-2025
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State-of-the-Art Sensors Technologies in Belgium 2024-2025

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

Deadline for manuscript submissions: 20 September 2025 | Viewed by 8055

Special Issue Editor

Prof. Dr. Abdellah Touhafi

E-Mail Website
Guest Editor
Department of Engineering Sciences and Technology (INDI), Vrije Universiteit Brussel, 1050 Brussels, Belgium
Interests: sensor arrays; acoustics; environmental monitoring; embedded energy harvesting; reconfigurable computing systems; embedded hardware design
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Sensors provide the primary environmental information necessary for the control of many processes. In the automation processes employed in Industry 4.0, many observations rely on information gained by sensors. Many sensor principles are well known, but their applicability depends on factors such as ease of use, robustness, accuracy, repeatability, wireless applicability, energy supply and consumption, and last but not least, cost.

This Special Issue focuses on state-of-the-art sensor principles which address those tasks. The topic is open; potential topics include, but are not limited to, the following research areas:

  • Sensor network;
  • Biomedical sensors;
  • Biosensors;
  • Wearable sensors;
  • Chemical sensors;
  • Physical sensors;
  • Lab-on-a-chip;
  • Electrochemical sensors;
  • Optoelectronic sensors;
  • Optical sensors;
  • Thermal sensors;
  • Magnetic sensors;
  • Piezoelectric sensors;
  • Gas sensors;
  • Affinity sensors;
  • Electronic nose and tongue;
  • Impedance sensors;
  • Conductometric sensors.

Prof. Dr. Abdellah Touhafi
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 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.

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Published Papers (6 papers)

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Research

26 pages, 8898 KiB  
Article
Development and In-Field Validation of an Autonomous Soil Mechanical Resistance Sensor
by Valentijn De Cauwer, Simon Cool, Axel Willekens, Sébastien Temmerman, David Nuyttens, Tommy D’ Hose, Jan Pieters and Sam Leroux
Sensors 2025, 25(6), 1919; https://doi.org/10.3390/s25061919 - 19 Mar 2025
Viewed by 480
Abstract
Soil compaction is a widespread problem, leading to soil degradation, yield losses, and adverse environmental impacts. Nowadays, various measurement methods exist to assess and map soil compaction, with vertical cone penetration resistance measurements being one of the most commonly used. This method is [...] Read more.
Soil compaction is a widespread problem, leading to soil degradation, yield losses, and adverse environmental impacts. Nowadays, various measurement methods exist to assess and map soil compaction, with vertical cone penetration resistance measurements being one of the most commonly used. This method is easy, rapid, inexpensive, and generally accepted. However, manual penetration resistance measurements are time-consuming, labor-intensive, and often less accurate due to inconsistent penetration speed. To address these limitations, an automated penetrometer was developed and integrated on an autonomous robot platform, paving the way for high-resolution compaction mapping as a starting point for precision subsoiling to remediate soil compaction. The performance of this setup was validated in controlled and field conditions against a hand-held penetrometer. Therefore, experiments were conducted in soil-filled cylinders and on plots of a long-term field experiment, including measurements across spraying tracks. The automated penetrometer demonstrated high correlations with the hand-held device under controlled conditions, though the correlation was somewhat lower in the field due to the soil’s heterogeneity. Deviations between the two measurement devices were likely caused by the inconsistent insertion speed of the hand-held penetrometer, particularly in soils with high penetration resistance. Both penetrometers successfully identified the plow pan at a depth of 30–40 cm but were unable to clearly show the effect of the long-term presence of spraying tracks. Full article
(This article belongs to the Special Issue State-of-the-Art Sensors Technologies in Belgium 2024-2025)
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15 pages, 3344 KiB  
Article
Enhanced Vision-Based Quality Inspection: A Multiview Artificial Intelligence Framework for Defect Detection
by Geethika Bhavanasi, Davy Neven, Manuel Arteaga, Sina Ditzel, Sam Dehaeck and Abdellatif Bey-Temsamani
Sensors 2025, 25(6), 1703; https://doi.org/10.3390/s25061703 - 10 Mar 2025
Viewed by 766
Abstract
Automated defect detection is a critical component of modern industrial quality control. However, it is particularly difficult to identify subtle defects such as scratches on metallic surfaces. Therefore, this paper investigates the effectiveness of multiview deep learning approaches for improved defect detection by [...] Read more.
Automated defect detection is a critical component of modern industrial quality control. However, it is particularly difficult to identify subtle defects such as scratches on metallic surfaces. Therefore, this paper investigates the effectiveness of multiview deep learning approaches for improved defect detection by implementing and comparing early and late fusion methodologies. We propose MV-UNet, a novel early fusion architecture that aligns and aggregates multiview features using a transformation block to enhance detection accuracy. To evaluate performance, we conduct our experiments on a recorded metallic plates dataset, comparing the traditional single-view inspection to both late and early fusion methods. Our results demonstrate that both the early and late fusion methods improve detection accuracy over the mono-view baseline, with our MV-UNet achieving the hightest F1-score (0.942). Additionally, we introduce adapted precision–recall metrics designed for segmentation-based defect detection, addressing the limitations of traditional IoU-based evaluations. These tailored metrics more accurately reflect defect localization performance, particularly for thin, elongated scratches. Our findings highlight the advantages of early fusion for industrial defect detection, providing a robust and scalable approach to multiview analysis. Full article
(This article belongs to the Special Issue State-of-the-Art Sensors Technologies in Belgium 2024-2025)
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25 pages, 10469 KiB  
Article
Noise Analysis for Correlation-Assisted Direct Time-of-Flight
by Ayman Morsy, Jonathan Vrijsen, Jan Coosemans, Tuur Bruneel and Maarten Kuijk
Sensors 2025, 25(3), 771; https://doi.org/10.3390/s25030771 - 27 Jan 2025
Viewed by 739
Abstract
The development of a correlation-assisted direct time-of-flight (CA-dToF) pixel provides a novel solution for time-of-flight applications that combines low power consumption, robust ambient shot noise suppression, and a compact design. However, the pixel’s implementation introduces systematic errors, affecting its performance. We investigate the [...] Read more.
The development of a correlation-assisted direct time-of-flight (CA-dToF) pixel provides a novel solution for time-of-flight applications that combines low power consumption, robust ambient shot noise suppression, and a compact design. However, the pixel’s implementation introduces systematic errors, affecting its performance. We investigate the pixel’s robustness against various noise sources, including timing jitter, kTC noise, switching noise, and photon shot noise. Additionally, we address limitations such as the SPAD deadtime, and source follower gain mismatch and offset, identifying their impact on performance. The paper also proposes solutions to enhance the pixel’s overall reliability and to improve the pixel’s implementation. Full article
(This article belongs to the Special Issue State-of-the-Art Sensors Technologies in Belgium 2024-2025)
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25 pages, 13880 KiB  
Article
On the Measurement of Laser Lines in 3D Space with Uncertainty Estimation
by Ivan De Boi, Nasser Ghaderi, Steve Vanlanduit and Rudi Penne
Sensors 2025, 25(2), 298; https://doi.org/10.3390/s25020298 - 7 Jan 2025
Viewed by 627
Abstract
Laser-based systems, essential in diverse applications, demand accurate geometric calibration to ensure precise performance. The calibration process of the system requires establishing a reliable relationship between input parameters and the corresponding 3D description of the outgoing laser beams. The quality of the calibration [...] Read more.
Laser-based systems, essential in diverse applications, demand accurate geometric calibration to ensure precise performance. The calibration process of the system requires establishing a reliable relationship between input parameters and the corresponding 3D description of the outgoing laser beams. The quality of the calibration depends on the quality of the dataset of measured laser lines. To address this challenge, we present a stochastic method for measuring the coordinates of these lines, considering both the camera calibration uncertainties and measurement noise inherent in laser dot detection on a detection board. Our approach to composing an accurate dataset of lines utilises a standard webcam and a checkerboard, avoiding the need for specialised hardware. By modelling the uncertainties involved, we provide a probabilistic description of the fitted laser line, enabling quality assessment of the measurement and integration into subsequent algorithms. We also offer insights into the optimal number of board positions and the number of repeated laser dot measurements, which are both the main time-consuming factors in practice. In summary, our proposed method represents a significant advancement in the field of laser-based system calibration, offering a robust and efficient solution. Full article
(This article belongs to the Special Issue State-of-the-Art Sensors Technologies in Belgium 2024-2025)
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21 pages, 2921 KiB  
Article
Correlation-Assisted Pixel Array for Direct Time of Flight
by Ayman Morsy and Maarten Kuijk
Sensors 2024, 24(16), 5380; https://doi.org/10.3390/s24165380 - 20 Aug 2024
Cited by 1 | Viewed by 1517
Abstract
Time of flight is promising technology in machine vision and sensing, with an emerging need for low power consumption, a high image resolution, and reliable operation in high ambient light conditions. Therefore, we propose a novel direct time-of-flight pixel using the single-photon avalanche [...] Read more.
Time of flight is promising technology in machine vision and sensing, with an emerging need for low power consumption, a high image resolution, and reliable operation in high ambient light conditions. Therefore, we propose a novel direct time-of-flight pixel using the single-photon avalanche diode (SPAD) sensor, with an in-pixel averaging method to suppress ambient light and detect the laser pulse arrival time. The system utilizes two orthogonal sinusoidal signals applied to the pixel as inputs, which are synchronized with a pulsed laser source. The detected signal phase indicates the arrival time. To evaluate the proposed system’s potential, we developed analytical and statistical models for assessing the phase error and precision of the arrival time under varying ambient light levels. The pixel simulation showed that the phase precision is less than 1% of the detection range when the ambient-to-signal ratio is 120. A proof-of-concept pixel array prototype was fabricated and characterized to validate the system’s performance. The pixel consumed, on average, 40 μW of power in operation with ambient light. The results demonstrate that the system can operate effectively under varying ambient light conditions and its potential for customization based on specific application requirements. This paper concludes by discussing the system’s performance relative to the existing direct time-of-flight technologies, identifying their strengths and limitations. Full article
(This article belongs to the Special Issue State-of-the-Art Sensors Technologies in Belgium 2024-2025)
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15 pages, 4473 KiB  
Article
Integrating Wearable Textiles Sensors and IoT for Continuous sEMG Monitoring
by Bulcha Belay Etana, Benny Malengier, Janarthanan Krishnamoorthy and Lieva Van Langenhove
Sensors 2024, 24(6), 1834; https://doi.org/10.3390/s24061834 - 13 Mar 2024
Cited by 4 | Viewed by 2812
Abstract
Surface electromyography is a technique used to measure the electrical activity of muscles. sEMG can be used to assess muscle function in various settings, including clinical, academic/industrial research, and sports medicine. The aim of this study is to develop a wearable textile sensor [...] Read more.
Surface electromyography is a technique used to measure the electrical activity of muscles. sEMG can be used to assess muscle function in various settings, including clinical, academic/industrial research, and sports medicine. The aim of this study is to develop a wearable textile sensor for continuous sEMG monitoring. Here, we have developed an integrated biomedical monitoring system that records sEMG signals through a textile electrode embroidered within a smart sleeve bandage for telemetric assessment of muscle activities and fatigue. We have taken an “Internet of Things”-based approach to acquire the sEMG, using a Myoware sensor and transmit the signal wirelessly through a WiFi-enabled microcontroller unit (NodeMCU; ESP8266). Using a wireless router as an access point, the data transmitted from ESP8266 was received and routed to the webserver-cum-database (Xampp local server) installed on a mobile phone or PC for processing and visualization. The textile electrode integrated with IoT enabled us to measure sEMG, whose quality is similar to that of conventional methods. To verify the performance of our developed prototype, we compared the sEMG signal recorded from the biceps, triceps, and tibialis muscles, using both the smart textile electrode and the gelled electrode. The root mean square and average rectified values of the sEMG measured using our prototype for the three muscle types were within the range of 1.001 ± 0.091 mV to 1.025 ± 0.060 mV and 0.291 ± 0.00 mV to 0.65 ± 0.09 mV, respectively. Further, we also performed the principal component analysis for a total of 18 features (15 time domain and 3 frequency domain) for the same muscle position signals. On the basis on the hierarchical clustering analysis of the PCA’s score, as well as the one-way MANOVA of the 18 features, we conclude that the differences observed in the data for the different muscle types as well as the electrode types are statistically insignificant. Full article
(This article belongs to the Special Issue State-of-the-Art Sensors Technologies in Belgium 2024-2025)
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