Journal Description
Sensors
Sensors
is an international, peer-reviewed, open access journal on the science and technology of sensors. Sensors is published semimonthly online by MDPI. The Polish Society of Applied Electromagnetics (PTZE), Japan Society of Photogrammetry and Remote Sensing (JSPRS), Spanish Society of Biomedical Engineering (SEIB) and International Society for the Measurement of Physical Behaviour (ISMPB) are affiliated with Sensors and their members receive a discount on the article processing charges.
- Open Access — free for readers, with article processing charges (APC) paid by authors or their institutions.
- High Visibility: indexed within Scopus, SCIE (Web of Science), PubMed, MEDLINE, PMC, Ei Compendex, Inspec, Astrophysics Data System, and other databases.
- Journal Rank: JCR - Q2 (Instruments & Instrumentation) / CiteScore - Q1 (Instrumentation)
- Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 17 days after submission; acceptance to publication is undertaken in 2.8 days (median values for papers published in this journal in the second half of 2023).
- Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
- Testimonials: See what our editors and authors say about Sensors.
- Companion journals for Sensors include: Chips, Automation, JCP and Targets.
Impact Factor:
3.9 (2022);
5-Year Impact Factor:
4.1 (2022)
Latest Articles
Laboratory Investigations of the Leica RTC360 Laser Scanner—Distance Measuring Performance
Sensors 2024, 24(12), 3742; https://doi.org/10.3390/s24123742 (registering DOI) - 8 Jun 2024
Abstract
A Leica RTC360 laser scanner was investigated using a linear horizontal comparator system with four targets of different reflectance. Several thousand panorama scans were conducted along the 30 m long comparator, basically in 40 mm steps. For a selected target, more detailed investigations
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A Leica RTC360 laser scanner was investigated using a linear horizontal comparator system with four targets of different reflectance. Several thousand panorama scans were conducted along the 30 m long comparator, basically in 40 mm steps. For a selected target, more detailed investigations were carried out with a 2 mm step width for a 2 m wide section. The absolute offset between the scanner and the relative interferometer measurements was determined with a calibrated total station. The investigations revealed several systematic effects like an offset in the distance measurement of about 1.3 mm. Furthermore, sections with stochastic behavior as well as sections with pseudo-cyclic parts were observed, depending on the reflectance of the target. The deterministic sections showed curved and striped patterns with some discontinuities of about 2 mm at 20 m, resulting in a saw-tooth like pattern along the distances. Within all the experiments, the distance deviations were below the manufacturer specifications of the 3D point accuracy. However, it was demonstrated that the distance measurements had clear systematic components. In using these new findings, the specification of the measurement “noise” in the data sheet has to be seen as critical.
Full article
(This article belongs to the Section Remote Sensors)
Open AccessArticle
Development of a Simple Setup to Measure Shielding Effectiveness at Microwave Frequencies
by
Emanuele Cardillo, Fabrizio Lorenzo Carcione, Luigi Ferro, Elpida Piperopoulos, Emanuela Mastronardo, Graziella Scandurra and Carmine Ciofi
Sensors 2024, 24(12), 3741; https://doi.org/10.3390/s24123741 (registering DOI) - 8 Jun 2024
Abstract
Testing the shielding effectiveness of materials is a key step for many applications, from the industrial to the biomedical field. This task is very relevant for high-sensitivity sensors, whose performance can be greatly affected by electromagnetic fields. However, the available testing procedures often
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Testing the shielding effectiveness of materials is a key step for many applications, from the industrial to the biomedical field. This task is very relevant for high-sensitivity sensors, whose performance can be greatly affected by electromagnetic fields. However, the available testing procedures often require expensive, bulky, and heavy measurement chambers. In this paper, a cost-effective and reliable measurement procedure for testing the shielding effectiveness of materials is proposed. It exploits a lab-scale anechoic shielded chamber, which is lightweight, compact, and cost-effective if compared to the available commercial solutions. The measurement procedure employs a vector network analyzer to allow an accurate and fast characterization setup. The chamber realization phases and the measurement procedure are described. The shielding capability of the chamber is measured up to 26 GHz, whereas the performance of commercial shielding coatings is tested to demonstrate the measurement’s effectiveness.
Full article
(This article belongs to the Special Issue Electromagnetic Sensing and Its Applications)
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Open AccessArticle
3D Printed Hydrogel Sensor for Rapid Colorimetric Detection of Salivary pH
by
Magdalena B. Łabowska, Agnieszka Krakos and Wojciech Kubicki
Sensors 2024, 24(12), 3740; https://doi.org/10.3390/s24123740 (registering DOI) - 8 Jun 2024
Abstract
Salivary pH is one of the crucial biomarkers used for non-invasive diagnosis of intraoral diseases, as well as general health conditions. However, standard pH sensors are usually too bulky, expensive, and impractical for routine use outside laboratory settings. Herein, a miniature hydrogel sensor,
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Salivary pH is one of the crucial biomarkers used for non-invasive diagnosis of intraoral diseases, as well as general health conditions. However, standard pH sensors are usually too bulky, expensive, and impractical for routine use outside laboratory settings. Herein, a miniature hydrogel sensor, which enables quick and simple colorimetric detection of pH level, is shown. The sensor structure was manufactured from non-toxic hydrogel ink and patterned in the form of a matrix with 5 mm × 5 mm × 1 mm individual sensing pads using a 3D printing technique (bioplotting). The authors’ ink composition, which contains sodium alginate, polyvinylpyrrolidone, and bromothymol blue indicator, enables repeatable and stable color response to different pH levels. The developed analysis software with an easy-to-use graphical user interface extracts the R(ed), G(reen), and B(lue) components of the color image of the hydrogel pads, and evaluates the pH value in a second. A calibration curve used for the analysis was obtained in a pH range of 3.5 to 9.0 using a laboratory pH meter as a reference. Validation of the sensor was performed on samples of artificial saliva for medical use and its mixtures with beverages of different pH values (lemon juice, coffee, black and green tea, bottled and tap water), and correct responses to acidic and alkaline solutions were observed. The matrix of square sensing pads used in this study provided multiple parallel responses for parametric tests, but the applied 3D printing method and ink composition enable easy adjustment of the shape of the sensing layer to other desired patterns and sizes. Additional mechanical tests of the hydrogel layers confirmed the relatively high quality and durability of the sensor structure. The solution presented here, comprising 3D printed hydrogel sensor pads, simple colorimetric detection, and graphical software for signal processing, opens the way to development of miniature and biocompatible diagnostic devices in the form of flexible, wearable, or intraoral sensors for prospective application in personalized medicine and point-of-care diagnosis.
Full article
(This article belongs to the Special Issue Eurosensors 2023 Selected Papers)
Open AccessArticle
Occupancy Estimation from Blurred Video: A Multifaceted Approach with Privacy Consideration
by
Md Sakib Galib Sourav, Ehsan Yavari, Xiaomeng Gao, James Maskrey, Yao Zheng, Victor M. Lubecke and Olga Boric-Lubecke
Sensors 2024, 24(12), 3739; https://doi.org/10.3390/s24123739 (registering DOI) - 8 Jun 2024
Abstract
Building occupancy information is significant for a variety of reasons, from allocation of resources in smart buildings to responding during emergency situations. As most people spend more than 90% of their time indoors, a comfortable indoor environment is crucial. To ensure comfort, traditional
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Building occupancy information is significant for a variety of reasons, from allocation of resources in smart buildings to responding during emergency situations. As most people spend more than 90% of their time indoors, a comfortable indoor environment is crucial. To ensure comfort, traditional HVAC systems condition rooms assuming maximum occupancy, accounting for more than 50% of buildings’ energy budgets in the US. Occupancy level is a key factor in ensuring energy efficiency, as occupancy-controlled HVAC systems can reduce energy waste by conditioning rooms based on actual usage. Numerous studies have focused on developing occupancy estimation models leveraging existing sensors, with camera-based methods gaining popularity due to their high precision and widespread availability. However, the main concern with using cameras for occupancy estimation is the potential violation of occupants’ privacy. Unlike previous video-/image-based occupancy estimation methods, we addressed the issue of occupants’ privacy in this work by proposing and investigating both motion-based and motion-independent occupancy counting methods on intentionally blurred video frames. Our proposed approach included the development of a motion-based technique that inherently preserves privacy, as well as motion-independent techniques such as detection-based and density-estimation-based methods. To improve the accuracy of the motion-independent approaches, we utilized deblurring methods: an iterative statistical technique and a deep-learning-based method. Furthermore, we conducted an analysis of the privacy implications of our motion-independent occupancy counting system by comparing the original, blurred, and deblurred frames using different image quality assessment metrics. This analysis provided insights into the trade-off between occupancy estimation accuracy and the preservation of occupants’ visual privacy. The combination of iterative statistical deblurring and density estimation achieved a 16.29% counting error, outperforming our other proposed approaches while preserving occupants’ visual privacy to a certain extent. Our multifaceted approach aims to contribute to the field of occupancy estimation by proposing a solution that seeks to balance the trade-off between accuracy and privacy. While further research is needed to fully address this complex issue, our work provides insights and a step towards a more privacy-aware occupancy estimation system.
Full article
(This article belongs to the Special Issue Feature Papers in Sensing and Imaging 2024)
Open AccessReview
Internal and External Load Profile during Beach Invasion Sports Match-Play by Electronic Performance and Tracking Systems: A Systematic Review
by
Pau Vaccaro-Benet, Carlos D. Gómez-Carmona, Joaquín Martín Marzano-Felisatti and José Pino-Ortega
Sensors 2024, 24(12), 3738; https://doi.org/10.3390/s24123738 (registering DOI) - 8 Jun 2024
Abstract
Beach variants of popular sports like soccer and handball have grown in participation over the last decade. However, the characterization of the workload demands in beach sports remains limited compared to their indoor equivalents. This systematic review aimed to: (1) characterize internal and
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Beach variants of popular sports like soccer and handball have grown in participation over the last decade. However, the characterization of the workload demands in beach sports remains limited compared to their indoor equivalents. This systematic review aimed to: (1) characterize internal and external loads during beach invasion sports match-play; (2) identify technologies and metrics used for monitoring; (3) compare the demands of indoor sports; and (4) explore differences by competition level, age, sex, and beach sport. Fifteen studies ultimately met the inclusion criteria. The locomotive volumes averaged 929 ± 269 m (average) and 16.5 ± 3.3 km/h (peak) alongside 368 ± 103 accelerations and 8 ± 4 jumps per session. The impacts approached 700 per session. The heart rates reached 166–192 beats per minute (maximal) eliciting 60–95% intensity. The player load was 12.5 ± 2.9 to 125 ± 30 units. Males showed 10–15% higher external but equivalent internal loads versus females. Earlier studies relied solely on a time–motion analysis, while recent works integrate electronic performance and tracking systems, enabling a more holistic quantification. However, substantial metric intensity zone variability persists. Beach sports entail intermittent high-intensity activity with a lower-intensity recovery. Unstable surface likely explains the heightened internal strain despite moderately lower running volumes than indoor sports. The continued integration of technology together with the standardization of workload intensity zones is needed to inform a beach-specific training prescription.
Full article
(This article belongs to the Special Issue Wearable and Portable Devices in Sport Biomechanics and Training Science)
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Open AccessArticle
Test Platform for Developing New Optical Position Tracking Technology towards Improved Head Motion Correction in Magnetic Resonance Imaging
by
Marina Silic, Fred Tam and Simon J. Graham
Sensors 2024, 24(12), 3737; https://doi.org/10.3390/s24123737 (registering DOI) - 8 Jun 2024
Abstract
Optical tracking of head pose via fiducial markers has been proven to enable effective correction of motion artifacts in the brain during magnetic resonance imaging but remains difficult to implement in the clinic due to lengthy calibration and set up times. Advances in
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Optical tracking of head pose via fiducial markers has been proven to enable effective correction of motion artifacts in the brain during magnetic resonance imaging but remains difficult to implement in the clinic due to lengthy calibration and set up times. Advances in deep learning for markerless head pose estimation have yet to be applied to this problem because of the sub-millimetre spatial resolution required for motion correction. In the present work, two optical tracking systems are described for the development and training of a neural network: one marker-based system (a testing platform for measuring ground truth head pose) with high tracking fidelity to act as the training labels, and one markerless deep-learning-based system using images of the markerless head as input to the network. The markerless system has the potential to overcome issues of marker occlusion, insufficient rigid attachment of the marker, lengthy calibration times, and unequal performance across degrees of freedom (DOF), all of which hamper the adoption of marker-based solutions in the clinic. Detail is provided on the development of a custom moiré-enhanced fiducial marker for use as ground truth and on the calibration procedure for both optical tracking systems. Additionally, the development of a synthetic head pose dataset is described for the proof of concept and initial pre-training of a simple convolutional neural network. Results indicate that the ground truth system has been sufficiently calibrated and can track head pose with an error of <1 mm and <1°. Tracking data of a healthy, adult participant are shown. Pre-training results show that the average root-mean-squared error across the 6 DOF is 0.13 and 0.36 (mm or degrees) on a head model included and excluded from the training dataset, respectively. Overall, this work indicates excellent feasibility of the deep-learning-based approach and will enable future work in training and testing on a real dataset in the MRI environment.
Full article
(This article belongs to the Section Biomedical Sensors)
Open AccessArticle
Experimental Demonstration of a Tunable Energy-Selective Gamma-Ray Imaging System Based on Recoil Electrons
by
Changqing Zhang, Liang Sheng, Zhaohui Song, Tianxing Da, Haoqing Li, Baojun Duan, Yang Li, Dongwei Hei and Qunshu Wang
Sensors 2024, 24(12), 3736; https://doi.org/10.3390/s24123736 (registering DOI) - 8 Jun 2024
Abstract
The domain of gamma-ray imaging necessitates technological advancements to surmount the challenge of energy-selective imaging. Conventional systems are constrained in their dynamic focus on specific energy ranges, a capability imperative for differentiating gamma-ray emissions from diverse sources. This investigation introduces an innovative imaging
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The domain of gamma-ray imaging necessitates technological advancements to surmount the challenge of energy-selective imaging. Conventional systems are constrained in their dynamic focus on specific energy ranges, a capability imperative for differentiating gamma-ray emissions from diverse sources. This investigation introduces an innovative imaging system predicated on the detection of recoil electrons, addressing the demand for adjustable energy selectivity. Our methodology encompasses the design of a gamma-ray imaging system that leverages recoil electron detection to execute energy-selective imaging. The system’s efficacy was investigated experimentally, with emphasis on the adaptability of the energy selection window. The experimental outcomes underscore the system’s adeptness at modulating the energy selection window, adeptly discriminating gamma rays across a stipulated energy spectrum. The results corroborate the system’s adaptability, with an adjustable energy resolution that coincides with theoretical projections and satisfies the established criteria. This study affirms the viability and merits of utilizing recoil electrons for tunable energy-selective gamma-ray imaging. The system’s conceptualization and empirical validation represent a notable progress in gamma-ray imaging technology, with prospective applications extending from medical imaging to astrophysics. This research sets a solid foundation for subsequent inquiries and advancements in this domain.
Full article
(This article belongs to the Special Issue Optical Spectral Sensing and Imaging Technology: 2nd Edition)
Open AccessArticle
Detecting DoS Attacks through Synthetic User Behavior with Long Short-Term Memory Network
by
Patrycja Nędza and Jerzy Domżał
Sensors 2024, 24(12), 3735; https://doi.org/10.3390/s24123735 (registering DOI) - 8 Jun 2024
Abstract
With the escalation in the size and complexity of modern Denial of Service attacks, there is a need for research in the context of Machine Learning (ML) used in attack execution and defense against such attacks. This paper investigates the potential use of
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With the escalation in the size and complexity of modern Denial of Service attacks, there is a need for research in the context of Machine Learning (ML) used in attack execution and defense against such attacks. This paper investigates the potential use of ML in generating behavioral telemetry data using Long Short-Term Memory network and spoofing requests for the analyzed traffic to look legitimate. For this research, a custom testing environment was built that listens for mouse and keyboard events and analyzes them accordingly. While the economic feasibility of this attack currently limits its immediate threat, advancements in technology could make it more cost-effective for attackers in the future. Therefore, proactive development of countermeasures remains essential to mitigate potential risks and stay ahead of evolving attack methods.
Full article
(This article belongs to the Special Issue Cybersecurity and Reliability for 5G and Beyond and IoT Applications)
Open AccessArticle
Electromagnetic Short-Term to Imminent Forecast Indices for M ≥ 5.5 Earthquakes in the Gansu–Qinghai–Sichuan Region of China
by
Xia Li, Ye Zhu, Lili Feng, Yingfeng Ji and Weiling Zhu
Sensors 2024, 24(12), 3734; https://doi.org/10.3390/s24123734 (registering DOI) - 8 Jun 2024
Abstract
Electromagnetic indices play a potential role in the forecast of short-term to imminent M ≥ 5.5 earthquakes and have good application prospects. However, despite possible progress in earthquake forecasting, concerns remain because it is difficult to obtain accurate epicenter forecasts based on different
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Electromagnetic indices play a potential role in the forecast of short-term to imminent M ≥ 5.5 earthquakes and have good application prospects. However, despite possible progress in earthquake forecasting, concerns remain because it is difficult to obtain accurate epicenter forecasts based on different forecast indices, and the forecast time span is as large as months in areas with multiple earthquakes. In this study, based on the actual demand for short-term earthquake forecasts in the Gansu–Qinghai–Sichuan region of western China, we refined the construction of earthquake forecast indicators in view of the abundant electromagnetic anomalies before moderate and strong earthquakes. We revealed the advantageous forecast indicators of each method for the three primary earthquake elements (time, epicenter, magnitude) and the spatiotemporal evolution characteristics of the anomalies. The correlations between the magnitude, time, intensity, and electromagnetic anomalies of different M ≥ 5.5 earthquakes indicate that the combination of short-term electromagnetic indices is pivotal in earthquake forecasting.
Full article
(This article belongs to the Collection Seismology and Earthquake Engineering)
Open AccessArticle
A Virtual Testing Framework for Real-Time Validation of Automotive Software Systems Based on Hardware in the Loop and Fault Injection
by
Mohammad Abboush, Christoph Knieke and Andreas Rausch
Sensors 2024, 24(12), 3733; https://doi.org/10.3390/s24123733 (registering DOI) - 8 Jun 2024
Abstract
To validate safety-related automotive software systems, experimental tests are conducted at different stages of the V-model, which are referred as “X-in-the-loop (XIL) methods”. However, these methods have significant drawbacks in terms of cost, time, effort and effectiveness. In this study, based on hardware-in-the-loop
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To validate safety-related automotive software systems, experimental tests are conducted at different stages of the V-model, which are referred as “X-in-the-loop (XIL) methods”. However, these methods have significant drawbacks in terms of cost, time, effort and effectiveness. In this study, based on hardware-in-the-loop (HIL) simulation and real-time fault injection (FI), a novel testing framework has been developed to validate system performance under critical abnormal situations during the development process. The developed framework provides an approach for the real-time analysis of system behavior under single and simultaneous sensor/actuator-related faults during virtual test drives without modeling effort for fault mode simulations. Unlike traditional methods, the faults are injected programmatically and the system architecture is ensured without modification to meet the real-time constraints. Moreover, a virtual environment is modeled with various environmental conditions, such as weather, traffic and roads. The validation results demonstrate the effectiveness of the proposed framework in a variety of driving scenarios. The evaluation results demonstrate that the system behavior via HIL simulation has a high accuracy compared to the non-real-time simulation method with an average relative error of 2.52. The comparative study with the state-of-the-art methods indicates that the proposed approach exhibits superior accuracy and capability. This, in turn, provides a safe, reliable and realistic environment for the real-time validation of complex automotive systems at a low cost, with minimal time and effort.
Full article
(This article belongs to the Section Vehicular Sensing)
Open AccessArticle
Semi-Supervised Informer for the Compound Fault Diagnosis of Industrial Robots
by
Chuanhua Deng, Junjie Song, Chong Chen, Tao Wang and Lianglun Cheng
Sensors 2024, 24(12), 3732; https://doi.org/10.3390/s24123732 (registering DOI) - 8 Jun 2024
Abstract
The increasing deployment of industrial robots in manufacturing requires accurate fault diagnosis. Online monitoring data typically consist of a large volume of unlabeled data and a small quantity of labeled data. Conventional intelligent diagnosis methods heavily rely on supervised learning with abundant labeled
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The increasing deployment of industrial robots in manufacturing requires accurate fault diagnosis. Online monitoring data typically consist of a large volume of unlabeled data and a small quantity of labeled data. Conventional intelligent diagnosis methods heavily rely on supervised learning with abundant labeled data. To address this issue, this paper presents a semi-supervised Informer algorithm for fault diagnosis modeling, leveraging the Informer model’s long- and short-term memory capabilities and the benefits of semi-supervised learning to handle the diagnosis of a small amount of labeled data alongside a substantial amount of unlabeled data. An experimental study is conducted using real-world industrial robot monitoring data to assess the proposed algorithm’s effectiveness, demonstrating its ability to deliver accurate fault diagnosis despite limited labeled samples.
Full article
(This article belongs to the Section Industrial Sensors)
Open AccessArticle
Spatial Variation of Airborne Pollen Concentrations Locally around Brussels City, Belgium, during a Field Campaign in 2022–2023, Using the Automatic Sensor Beenose
by
Jean-Baptiste Renard, Houssam El Azari, Johann Lauthier and Jérémy Surcin
Sensors 2024, 24(12), 3731; https://doi.org/10.3390/s24123731 (registering DOI) - 8 Jun 2024
Abstract
As a growing part of the world population is suffering from pollen-induced allergies, increasing the number of pollen monitoring stations and developing new dedicated measurement networks has become a necessity. To this purpose, Beenose, a new automatic and relatively low-cost sensor, was developed
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As a growing part of the world population is suffering from pollen-induced allergies, increasing the number of pollen monitoring stations and developing new dedicated measurement networks has become a necessity. To this purpose, Beenose, a new automatic and relatively low-cost sensor, was developed to characterize and quantify the pollinic content of the air using multiangle light scattering. A field campaign was conducted at four locations around Brussels, Belgium, during summer 2022 and winter–spring 2023. First, the consistency was assessed between the automatic sensor and a collocated reference Hirst-type trap deployed at Ixelles, south-east of Brussels. Daily average total pollen concentrations provided by the two instruments showed a mean error of about 15%. Daily average pollen concentrations were also checked for a selection of pollen species and revealed Pearson and Spearman correlation coefficients ranging from 0.71 to 0.93. Subsequently, a study on the spatial variability of the pollen content around Brussels was conducted with Beenose sensors. The temporal evolution of daily average total pollen concentrations recorded at four sites were compared and showed strong variations from one location to another, up to a factor 10 over no more than a few kilometers apart. This variation is a consequence of multiple factors such as the local vegetation, the wind directions, the altitude of the measurement station, and the topology of the city. It is therefore highly necessary to multiply the number of measurement stations per city for a better evaluation of human exposure to pollen allergens and for more enhanced pollen allergy management.
Full article
(This article belongs to the Section Environmental Sensing)
Open AccessArticle
Study of the Bias of the Initial Phase Estimation of a Sinewave of Known Frequency in the Presence of Phase Noise
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Francisco A. C. Alegria, Lian Xie and Dário Pasadas
Sensors 2024, 24(12), 3730; https://doi.org/10.3390/s24123730 (registering DOI) - 8 Jun 2024
Abstract
The estimation of the parameters of a sinusoidal signal is of paramount importance in various applications in the fields of sensors, signal processing, parameter estimation, and device characterization, among others. The presence, in the measurement system, of non-ideal phenomena such as additive noise
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The estimation of the parameters of a sinusoidal signal is of paramount importance in various applications in the fields of sensors, signal processing, parameter estimation, and device characterization, among others. The presence, in the measurement system, of non-ideal phenomena such as additive noise in the signals, phase noise in the stimulus generation, jitter in the sampling system, frequency error in the experimental setup, among others, leads to increased uncertainty and bias in the estimated quantities obtained by least squares methods and those derived from them. Therefore, from a metrological point of view, it is important to be able to theoretically predict and quantify those uncertainties in order to properly design the measurement system and its parameters, such as the number of samples to acquire or the stimulus signal amplitude to use to minimize the uncertainty in the estimated values. Previous works have shown that the presence of these non-ideal phenomena leads to increased uncertainty and bias in the estimation of the sinewave amplitude. The present work complements this knowledge by focusing specifically on the effect of phase noise and sampling jitter in the bias of the initial phase estimation of a sinusoidal signal of known frequency (three‑parameter sine fitting procedure). A theoretical derivation of the bias of initial phase estimation that takes into consideration the presence of phase noise in the sinewave is presented. Since a Taylor series approximation was used where only the first term was retained, it was necessary to validate the analytical derivations with numerical simulations using a Monte Carlo type of procedure. This process was applied to different conditions regarding the phase noise standard deviation, initial phase value, and number of samples. It is concluded that, in most scenarios, initial phase estimation using sine fitting is unbiased in the presence of phase noise or jitter. It is shown, however, that in cases of extremely high phase noise standard deviation and a very low number of samples, a bias occurs.
Full article
(This article belongs to the Collection Machine Learning and Signal Processing in Sensing and Sensor Applications)
Open AccessArticle
Integration of Hollow Microneedle Arrays with Jellyfish-Shaped Electrochemical Sensor for the Detection of Biomarkers in Interstitial Fluid
by
Fangfang Luo, Zhanhong Li, Yiping Shi, Wen Sun, Yuwei Wang, Jianchao Sun, Zheyuan Fan, Yanyi Chang, Zifeng Wang, Yutong Han, Zhigang Zhu and Jean-Louis Marty
Sensors 2024, 24(12), 3729; https://doi.org/10.3390/s24123729 (registering DOI) - 8 Jun 2024
Abstract
This study integrates hollow microneedle arrays (HMNA) with a novel jellyfish-shaped electrochemical sensor for the detection of key biomarkers, including uric acid (UA), glucose, and pH, in artificial interstitial fluid. The jellyfish-shaped sensor displayed linear responses in detecting UA and glucose via differential
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This study integrates hollow microneedle arrays (HMNA) with a novel jellyfish-shaped electrochemical sensor for the detection of key biomarkers, including uric acid (UA), glucose, and pH, in artificial interstitial fluid. The jellyfish-shaped sensor displayed linear responses in detecting UA and glucose via differential pulse voltammetry (DPV) and chronoamperometry, respectively. Notably, the open circuit potential (OCP) of the system showed a linear variation with pH changes, validating its pH-sensing capability. The sensor system demonstrates exceptional electrochemical responsiveness within the physiological concentration ranges of these biomarkers in simulated epidermis sensing applications. The detection linear ranges of UA, glucose, and pH were 0~0.8 mM, 0~7 mM, and 4.0~8.0, respectively. These findings highlight the potential of the HMNA-integrated jellyfish-shaped sensors in real-world epidermal applications for comprehensive disease diagnosis and health monitoring.
Full article
(This article belongs to the Special Issue Wearable and Implantable Electrochemical Sensors)
Open AccessArticle
Interpretability of Causal Discovery in Tracking Deterioration in a Highly Dynamic Process
by
Asha Choudhary, Matej Vuković, Belgin Mutlu, Michael Haslgrübler and Roman Kern
Sensors 2024, 24(12), 3728; https://doi.org/10.3390/s24123728 (registering DOI) - 8 Jun 2024
Abstract
In a dynamic production processes, mechanical degradation poses a significant challenge, impacting product quality and process efficiency. This paper explores a novel approach for monitoring degradation in the context of viscose fiber production, a highly dynamic manufacturing process. Using causal discovery techniques, our
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In a dynamic production processes, mechanical degradation poses a significant challenge, impacting product quality and process efficiency. This paper explores a novel approach for monitoring degradation in the context of viscose fiber production, a highly dynamic manufacturing process. Using causal discovery techniques, our method allows domain experts to incorporate background knowledge into the creation of causal graphs. Further, it enhances the interpretability and increases the ability to identify potential problems via changes in causal relations over time. The case study employs a comprehensive analysis of the viscose fiber production process within a prominent textile industry, emphasizing the advantages of causal discovery for monitoring degradation. The results are compared with state-of-the-art methods, which are not considered to be interpretable, specifically LSTM-based autoencoder, UnSupervised Anomaly Detection on Multivariate Time Series (USAD), and Deep Transformer Networks for Anomaly Detection in Multivariate Time Series Data (TranAD), showcasing the alignment and validation of our approach. This paper provides valuable information on degradation monitoring strategies, demonstrating the efficacy of causal discovery in dynamic manufacturing environments. The findings contribute to the evolving landscape of process optimization and quality control.
Full article
(This article belongs to the Special Issue Intelligent Sensors Technologies for Industry 5.0 and Smart Manufacturing)
Open AccessArticle
Mobile Sensor Path Planning for Kalman Filter Spatiotemporal Estimation
by
Jiazhong Mei, Steven L. Brunton and J. Nathan Kutz
Sensors 2024, 24(12), 3727; https://doi.org/10.3390/s24123727 (registering DOI) - 8 Jun 2024
Abstract
The estimation of spatiotemporal data from limited sensor measurements is a required task across many scientific disciplines. In this paper, we consider the use of mobile sensors for estimating spatiotemporal data via Kalman filtering. The sensor selection problem, which aims to optimize the
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The estimation of spatiotemporal data from limited sensor measurements is a required task across many scientific disciplines. In this paper, we consider the use of mobile sensors for estimating spatiotemporal data via Kalman filtering. The sensor selection problem, which aims to optimize the placement of sensors, leverages innovations in greedy algorithms and low-rank subspace projection to provide model-free, data-driven estimates. Alternatively, Kalman filter estimation balances model-based information and sparsely observed measurements to collectively make better estimation with limited sensors. It is especially important with mobile sensors to utilize historical measurements. We show that mobile sensing along dynamic trajectories can achieve the equivalent performance of a larger number of stationary sensors, with performance gains related to three distinct timescales: (i) the timescale of the spatiotemporal dynamics, (ii) the velocity of the sensors, and (iii) the rate of sampling. Taken together, these timescales strongly influence how well-conditioned the estimation task is. We draw connections between the Kalman filter performance and the observability of the state space model and propose a greedy path planning algorithm based on minimizing the condition number of the observability matrix. This approach has better scalability and computational efficiency compared to previous works. Through a series of examples of increasing complexity, we show that mobile sensing along our paths improves Kalman filter performance in terms of better limiting estimation and faster convergence. Moreover, it is particularly effective for spatiotemporal data that contain spatially localized structures, whose features are captured along dynamic trajectories.
Full article
(This article belongs to the Section Navigation and Positioning)
Open AccessArticle
Sky Image Classification Based on Transfer Learning Approaches
by
Ruymán Hernández-López, Carlos M. Travieso-González and Nabil I. Ajali-Hernández
Sensors 2024, 24(12), 3726; https://doi.org/10.3390/s24123726 (registering DOI) - 8 Jun 2024
Abstract
Cloudy conditions at a local scale pose a significant challenge for forecasting renewable energy generation through photovoltaic panels. Consequently, having real-time knowledge of sky conditions becomes highly valuable. This information could inform decision-making processes in system operations, such as determining whether conditions are
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Cloudy conditions at a local scale pose a significant challenge for forecasting renewable energy generation through photovoltaic panels. Consequently, having real-time knowledge of sky conditions becomes highly valuable. This information could inform decision-making processes in system operations, such as determining whether conditions are favorable for activating a standalone system requiring a minimum level of radiation or whether sky conditions might lead to higher energy consumption than generation during adverse cloudy conditions. This research leveraged convolutional neural networks (CNNs) and transfer learning (TL) classification techniques, testing various architectures from the EfficientNet family and two ResNet models for classifying sky images. Cross-validation methods were applied across different experiments, where the most favorable outcome was achieved with the EfficientNetV2-B1 and EfficientNetV2-B2 models boasting a mean Accuracy of 98.09%. This study underscores the efficacy of the architectures employed for sky image classification, while also highlighting the models yielding the best results.
Full article
(This article belongs to the Special Issue Digital Image Processing and Sensing Technologies)
Open AccessEditorial
Sensor Data Fusion Analysis for Broad Applications
by
Natividad Duro
Sensors 2024, 24(12), 3725; https://doi.org/10.3390/s24123725 - 7 Jun 2024
Abstract
Sensor data fusion analysis plays a pivotal role in a variety of fields by integrating data from multiple sensors to produce more accurate, reliable, and comprehensive information than that achieved by individual sensors alone [...]
Full article
(This article belongs to the Special Issue Sensor Data Fusion Analysis for Broad Applications)
Open AccessArticle
Full-Scale Aggregated MobileUNet: An Improved U-Net Architecture for SAR Oil Spill Detection
by
Yi-Ting Chen, Lena Chang and Jung-Hua Wang
Sensors 2024, 24(12), 3724; https://doi.org/10.3390/s24123724 - 7 Jun 2024
Abstract
Oil spills are a major threat to marine and coastal environments. Their unique radar backscatter intensity can be captured by synthetic aperture radar (SAR), resulting in dark regions in the images. However, many marine phenomena can lead to erroneous detections of oil spills.
[...] Read more.
Oil spills are a major threat to marine and coastal environments. Their unique radar backscatter intensity can be captured by synthetic aperture radar (SAR), resulting in dark regions in the images. However, many marine phenomena can lead to erroneous detections of oil spills. In addition, SAR images of the ocean include multiple targets, such as sea surface, land, ships, and oil spills and their look-alikes. The training of a multi-category classifier will encounter significant challenges due to the inherent class imbalance. Addressing this issue requires extracting target features more effectively. In this study, a lightweight U-Net-based model, Full-Scale Aggregated MobileUNet (FA-MobileUNet), was proposed to improve the detection performance for oil spills using SAR images. First, a lightweight MobileNetv3 model was used as the backbone of the U-Net encoder for feature extraction. Next, atrous spatial pyramid pooling (ASPP) and a convolutional block attention module (CBAM) were used to improve the capacity of the network to extract multi-scale features and to increase the speed of module calculation. Finally, full-scale features from the encoder were aggregated to enhance the network’s competence in extracting features. The proposed modified network enhanced the extraction and integration of features at different scales to improve the accuracy of detecting diverse marine targets. The experimental results showed that the mean intersection over union (mIoU) of the proposed model reached more than 80% for the detection of five types of marine targets including sea surface, land, ships, and oil spills and their look-alikes. In addition, the IoU of the proposed model reached 75.85 and 72.67% for oil spill and look-alike detection, which was 18.94% and 25.55% higher than that of the original U-Net model, respectively. Compared with other segmentation models, the proposed network can more accurately classify the black regions in SAR images into oil spills and their look-alikes. Furthermore, the detection performance and computational efficiency of the proposed model were also validated against other semantic segmentation models.
Full article
(This article belongs to the Special Issue Intelligent SAR Target Detection and Recognition)
Open AccessReview
Detecting and Predicting Pilot Mental Workload Using Heart Rate Variability: A Systematic Review
by
Peizheng Wang, Robert Houghton and Arnab Majumdar
Sensors 2024, 24(12), 3723; https://doi.org/10.3390/s24123723 - 7 Jun 2024
Abstract
Measuring pilot mental workload (MWL) is crucial for enhancing aviation safety. However, MWL is a multi-dimensional construct that could be affected by multiple factors. Particularly, in the context of a more automated cockpit setting, the traditional methods of assessing pilot MWL may face
[...] Read more.
Measuring pilot mental workload (MWL) is crucial for enhancing aviation safety. However, MWL is a multi-dimensional construct that could be affected by multiple factors. Particularly, in the context of a more automated cockpit setting, the traditional methods of assessing pilot MWL may face challenges. Heart rate variability (HRV) has emerged as a potential tool for detecting pilot MWL during real-flight operations. This review aims to investigate the relationship between HRV and pilot MWL and to assess the performance of machine-learning-based MWL detection systems using HRV parameters. A total of 29 relevant papers were extracted from three databases for review based on rigorous eligibility criteria. We observed significant variability across the reviewed studies, including study designs and measurement methods, as well as machine-learning techniques. Inconsistent results were observed regarding the differences in HRV measures between pilots under varying levels of MWL. Furthermore, for studies that developed HRV-based MWL detection systems, we examined the diverse model settings and discovered that several advanced techniques could be used to address specific challenges. This review serves as a practical guide for researchers and practitioners who are interested in employing HRV indicators for evaluating MWL and wish to incorporate cutting-edge techniques into their MWL measurement approaches.
Full article
(This article belongs to the Special Issue Wearable Sensors for Behavioral and Physiological Monitoring)
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