Sensors

14 pages, 4774 KiB

Open AccessArticle

ZnO Doped Silica Nanoparticles (ZnO@SiO₂) for Enhanced Electrochemical Detection of Cd²⁺ Ions in Real Samples

by Afef Dhaffouli, Michael Holzinger, Soledad Carinelli, Houcine Barhoumi and Pedro A. Salazar-Carballo

Sensors 2024, 24(13), 4179; https://doi.org/10.3390/s24134179 (registering DOI) - 27 Jun 2024

Abstract

Pollution by heavy metal ions has a serious impact on human health and the environment, which is why the monitoring of heavy metal ions is of great practical importance. In this work, we describe the development of an electrochemical sensor for the detection [...] Read more.

Pollution by heavy metal ions has a serious impact on human health and the environment, which is why the monitoring of heavy metal ions is of great practical importance. In this work, we describe the development of an electrochemical sensor for the detection of cadmium (Cd²⁺) involving the doping of porous SiO₂ spheres with ZnO nanoparticles. Zinc oxide is chosen as the central dopant in the composite material to increase the conductivity and thus improve the electrochemical detection of Cd²⁺ ions with the SiO₂ spheres. The resulting composite is characterized by electrochemical spectroscopic XRD and microscopic methods. As a result, the developed sensor shows good selectivity towards the targeted Cd²⁺ ions compared to other divalent ions. After optimization of the experimental conditions, the electrochemical sensor shows two different linear ranges between 2.5 × 10⁻¹¹ molL⁻¹ to 1.75 × 10⁻¹⁰ molL⁻¹ and 2 × 10⁻⁹ molL⁻¹ to 1.75 × 10⁻⁹ molL⁻¹, indicating a change from diffusion-controlled to surface-controlled oxidation of Cd²⁺. A detection limit was reached at 4.4 × 10⁻¹¹ molL⁻¹. In addition, it offers good repeatability and recovery, and can detect accurate trace amounts of Cd²⁺ ions in real samples such as tap water or seawater by spiking these samples with known Cd²⁺ concentrations. This setup also provides satisfactory recovery rates in the range of 89–102%. Full article

(This article belongs to the Special Issue Electrochemical Sensors for Food, Pharmaceutical and Biomedical Analysis)

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23 pages, 18261 KiB

Open AccessArticle

A Robust Monocular and Binocular Visual Ranging Fusion Method Based on an Adaptive UKF

by Jiake Wang, Yong Guan, Zhenjia Kang and Pengzhan Chen

Sensors 2024, 24(13), 4178; https://doi.org/10.3390/s24134178 (registering DOI) - 27 Jun 2024

Abstract

Visual ranging technology holds great promise in various fields such as unmanned driving and robot navigation. However, complex dynamic environments pose significant challenges to its accuracy and robustness. Existing monocular visual ranging methods are susceptible to scale uncertainty, while binocular visual ranging is [...] Read more.

Visual ranging technology holds great promise in various fields such as unmanned driving and robot navigation. However, complex dynamic environments pose significant challenges to its accuracy and robustness. Existing monocular visual ranging methods are susceptible to scale uncertainty, while binocular visual ranging is sensitive to changes in lighting and texture. To overcome the limitations of single visual ranging, this paper proposes a fusion method for monocular and binocular visual ranging based on an adaptive Unscented Kalman Filter (AUKF). The proposed method first utilizes a monocular camera to estimate the initial distance based on the pixel size, and then employs the triangulation principle with a binocular camera to obtain accurate depth. Building upon this foundation, a probabilistic fusion framework is constructed to dynamically fuse monocular and binocular ranging using the AUKF. The AUKF employs nonlinear recursive filtering to estimate the optimal distance and its uncertainty, and introduces an adaptive noise-adjustment mechanism to dynamically update the observation noise based on fusion residuals, thus suppressing outlier interference. Additionally, an adaptive fusion strategy based on depth hypothesis propagation is designed to autonomously adjust the noise prior of the AUKF by combining current environmental features and historical measurement information, further enhancing the algorithm’s adaptability to complex scenes. To validate the effectiveness of the proposed method, comprehensive evaluations were conducted on large-scale public datasets such as KITTI and complex scene data collected in real-world scenarios. The quantitative results demonstrate that the fusion method significantly improves the overall accuracy and stability of visual ranging, reducing the average relative error within an 8 m range by 43.1% and 40.9% compared to monocular and binocular ranging, respectively. Compared to traditional methods, the proposed method significantly enhances ranging accuracy and exhibits stronger robustness against factors such as lighting changes and dynamic targets. The sensitivity analysis further confirmed the effectiveness of the AUKF framework and adaptive noise strategy. In summary, the proposed fusion method effectively combines the advantages of monocular and binocular vision, significantly expanding the application range of visual ranging technology in intelligent driving, robotics, and other fields while ensuring accuracy, robustness, and real-time performance. Full article

(This article belongs to the Section Navigation and Positioning)

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31 pages, 13426 KiB

Open AccessArticle

Ant3D—A Fisheye Multi-Camera System to Survey Narrow Spaces

by Luca Perfetti, Francesco Fassi and Giorgio Vassena

Sensors 2024, 24(13), 4177; https://doi.org/10.3390/s24134177 (registering DOI) - 27 Jun 2024

Abstract

Although the field of geomatics has seen multiple technological advances in recent years which enabled new applications and simplified the consolidated ones, some tasks remain challenging, inefficient, and time- and cost-consuming. This is the case of accurate tridimensional surveys of narrow spaces. Static [...] Read more.

Although the field of geomatics has seen multiple technological advances in recent years which enabled new applications and simplified the consolidated ones, some tasks remain challenging, inefficient, and time- and cost-consuming. This is the case of accurate tridimensional surveys of narrow spaces. Static laser scanning is an accurate and reliable approach but impractical for extensive tunnel environments; on the other hand, portable laser scanning is time-effective and efficient but not very reliable without ground control constraints. This paper describes the development process of a novel image-based multi-camera system meant to solve this specific problem: delivering accurate, reliable, and efficient results. The development is illustrated from the system conceptualization and initial investigations to the design choices and requirements for accuracy. The resulting working prototype has been put to the test to verify the effectiveness of the proposed approach. Full article

(This article belongs to the Special Issue Innovative Approaches in 3D Sensing and Imaging Technologies for Cultural Heritage)

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15 pages, 2850 KiB

Open AccessArticle

Researching the CNN Collaborative Inference Mechanism for Heterogeneous Edge Devices

by Jian Wang, Chong Chen, Shiwei Li, Chaoyong Wang, Xianzhi Cao and Liusong Yang

Sensors 2024, 24(13), 4176; https://doi.org/10.3390/s24134176 (registering DOI) - 27 Jun 2024

Abstract

Convolutional Neural Networks (CNNs) have been widely applied in various edge computing devices based on intelligent sensors. However, due to the high computational demands of CNN tasks, the limited computing resources of edge intelligent terminal devices, and significant architectural differences among these devices, [...] Read more.

Convolutional Neural Networks (CNNs) have been widely applied in various edge computing devices based on intelligent sensors. However, due to the high computational demands of CNN tasks, the limited computing resources of edge intelligent terminal devices, and significant architectural differences among these devices, it is challenging for edge devices to independently execute inference tasks locally. Collaborative inference among edge terminal devices can effectively utilize idle computing and storage resources and optimize latency characteristics, thus significantly addressing the challenges posed by the computational intensity of CNNs. This paper targets efficient collaborative execution of CNN inference tasks among heterogeneous and resource-constrained edge terminal devices. We propose a pre-partitioning deployment method for CNNs based on critical operator layers, and optimize the system bottleneck latency during pipeline parallelism using data compression, queuing, and “micro-shifting” techniques. Experimental results demonstrate that our method achieves significant acceleration in CNN inference within heterogeneous environments, improving performance by 71.6% compared to existing popular frameworks. Full article

(This article belongs to the Topic Cloud and Edge Computing for Smart Devices)

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17 pages, 3007 KiB

Open AccessArticle

A Novel AI Approach for Assessing Stress Levels in Patients with Type 2 Diabetes Mellitus Based on the Acquisition of Physiological Parameters Acquired during Daily Life

by Gonçalo Ribeiro, João Monge, Octavian Postolache and José Miguel Dias Pereira

Sensors 2024, 24(13), 4175; https://doi.org/10.3390/s24134175 (registering DOI) - 27 Jun 2024

Abstract

Stress is the inherent sensation of being unable to handle demands and occurrences. If not properly managed, stress can develop into a chronic condition, leading to the onset of additional chronic health issues, such as cardiovascular illnesses and diabetes. Various stress meters have [...] Read more.

Stress is the inherent sensation of being unable to handle demands and occurrences. If not properly managed, stress can develop into a chronic condition, leading to the onset of additional chronic health issues, such as cardiovascular illnesses and diabetes. Various stress meters have been suggested in the past, along with diverse approaches for its estimation. However, in the case of more serious health issues, such as hypertension and diabetes, the results can be significantly improved. This study presents the design and implementation of a distributed wearable-sensor computing platform with multiple channels. The platform aims to estimate the stress levels in diabetes patients by utilizing a fuzzy logic algorithm that is based on the assessment of several physiological indicators. Additionally, a mobile application was created to monitor the users’ stress levels and integrate data on their blood pressure and blood glucose levels. To obtain better performance metrics, validation experiments were carried out using a medical database containing data from 128 patients with chronic diabetes, and the initial results are presented in this study. Full article

(This article belongs to the Special Issue Non-contact Sensing Technologies for Motion Analysis and Health Monitoring)

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33 pages, 14510 KiB

Open AccessArticle

Development and Integration of a Digital Twin Model for a Real Hydroelectric Power Plant

by Mustafa Ersan and Erdal Irmak

Sensors 2024, 24(13), 4174; https://doi.org/10.3390/s24134174 (registering DOI) - 27 Jun 2024

Abstract

In this study, a digital twin model of a hydroelectric power plant has been created. Models of the entire power plant have been created and malfunction situations of a sensor located after the inlet valve of the plant have been analyzed using a [...] Read more.

In this study, a digital twin model of a hydroelectric power plant has been created. Models of the entire power plant have been created and malfunction situations of a sensor located after the inlet valve of the plant have been analyzed using a programmable logic controller (PLC). As a feature of the digital twin (DT), the error prediction and prevention function has been studied specifically for the pressure sensor. The accuracy and reliability of the data obtained from the sensor are compared with the data obtained from the DT model. The comparison results are evaluated and erroneous data are identified. In this way, it is determined whether the malfunction occurring in the system is a real malfunction or a malfunction caused by measurement or connection errors. In the case of sensor failure or measurement-related malfunction, this situation is determined through the digital twin-based control mechanism. In the case of actual failure, the system is stopped, but in the case of measurement or connection errors, since the data are calculated by the DT model, the value in the specified region is known and thus there is no need to stop the system. This prevents production loss in the hydroelectric power plant by ensuring the continuity of the system in case of errors. Full article

(This article belongs to the Section Electronic Sensors)

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18 pages, 3555 KiB

Open AccessArticle

Comparison of Six Sensor Fusion Algorithms with Electrogoniometer Estimation of Wrist Angle in Simulated Work Tasks

by Arvin Razavi, Mikael Forsman and Farhad Abtahi

Sensors 2024, 24(13), 4173; https://doi.org/10.3390/s24134173 (registering DOI) - 27 Jun 2024

Abstract

Hand-intensive work is strongly associated with work-related musculoskeletal disorders (WMSDs) of the hand/wrist and other upper body regions across diverse occupations, including office work, manufacturing, services, and healthcare. Addressing the prevalence of WMSDs requires reliable and practical exposure measurements. Traditional methods like electrogoniometry [...] Read more.

Hand-intensive work is strongly associated with work-related musculoskeletal disorders (WMSDs) of the hand/wrist and other upper body regions across diverse occupations, including office work, manufacturing, services, and healthcare. Addressing the prevalence of WMSDs requires reliable and practical exposure measurements. Traditional methods like electrogoniometry and optical motion capture, while reliable, are expensive and impractical for field use. In contrast, small inertial measurement units (IMUs) may provide a cost-effective, time-efficient, and user-friendly alternative for measuring hand/wrist posture during real work. This study compared six orientation algorithms for estimating wrist angles with an electrogoniometer, the current gold standard in field settings. Six participants performed five simulated hand-intensive work tasks (involving considerable wrist velocity and/or hand force) and one standardised hand movement. Three multiplicative Kalman filter algorithms with different smoothers and constraints showed the highest agreement with the goniometer. These algorithms exhibited median correlation coefficients of 0.75–0.78 for flexion/extension and 0.64 for radial/ulnar deviation across the six subjects and five tasks. They also ranked in the top three for the lowest mean absolute differences from the goniometer at the 10th, 50th, and 90th percentiles of wrist flexion/extension (9.3°, 2.9°, and 7.4°, respectively). Although the results of this study are not fully acceptable for practical field use, especially for some work tasks, they indicate that IMU-based wrist angle estimation may be useful in occupational risk assessments after further improvements. Full article

(This article belongs to the Special Issue Innovative Sensors and IoT for AI-Enabled Smart Healthcare)

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14 pages, 3600 KiB

Open AccessArticle

Inertial Measurement Unit and Heart Rate Monitoring to Assess Cardiovascular Fitness of Manual Wheelchair Users during the Six-Minute Push Test

by Grace Fasipe, Maja Goršič, Erika V. Zabre and Jacob R. Rammer

Sensors 2024, 24(13), 4172; https://doi.org/10.3390/s24134172 (registering DOI) - 27 Jun 2024

Abstract

Manual wheelchair users (MWUs) are prone to a sedentary life that can negatively affect their physical and cardiovascular health, making regular assessment important to identify appropriate interventions and lifestyle modifications. One mean of assessing MWUs’ physical health is the 6 min push test [...] Read more.

Manual wheelchair users (MWUs) are prone to a sedentary life that can negatively affect their physical and cardiovascular health, making regular assessment important to identify appropriate interventions and lifestyle modifications. One mean of assessing MWUs’ physical health is the 6 min push test (6MPT), where the user propels themselves as far as they can in six minutes. However, reliance on observer input introduces subjectivity, while limited quantitative data inhibit comprehensive assessment. Incorporating sensors into the 6MPT can address these limitations. Here, ten MWUs performed the 6MPT with additional sensors: two inertial measurement units (IMUs)—one on the wheelchair and one on the wrist together with a heart rate wristwatch. The conventional measurements of distance and laps were recorded by the observer, and the IMU data were used to calculate laps, distance, speed, and cadence. The results demonstrated that the IMU can provide the metrics of the traditional 6MPT with strong significant correlations between calculated laps and observer lap counts (r = 0.947, p < 0.001) and distances (r = 0.970, p < 0.001). Moreover, heart rate during the final minute was significantly correlated with calculated distance (r = 0.762, p = 0.017). Enhanced 6MPT assessment can provide objective, quantitative, and comprehensive data for clinicians to effectively inform interventions in rehabilitation. Full article

(This article belongs to the Special Issue Assistive and Rehabilitation Technologies Based on Intelligent Sensors)

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18 pages, 703 KiB

Open AccessArticle

Higher-Order Spectral Analysis Combined with a Convolution Neural Network for Atrial Fibrillation Detection-Preliminary Study

by Barbara Mika and Dariusz Komorowski

Sensors 2024, 24(13), 4171; https://doi.org/10.3390/s24134171 (registering DOI) - 27 Jun 2024

Abstract

The global burden of atrial fibrillation (AFIB) is constantly increasing, and its early detection is still a challenge for public health and motivates researchers to improve methods for automatic AFIB prediction and management. This work proposes higher-order spectra analysis, especially the bispectrum of [...] Read more.

The global burden of atrial fibrillation (AFIB) is constantly increasing, and its early detection is still a challenge for public health and motivates researchers to improve methods for automatic AFIB prediction and management. This work proposes higher-order spectra analysis, especially the bispectrum of electrocardiogram (ECG) signals combined with the convolution neural network (CNN) for AFIB detection. Like other biomedical signals, ECG is non-stationary, non-linear, and non-Gaussian in nature, so the spectra of higher-order cumulants, in this case, bispectra, preserve valuable features. The two-dimensional (2D) bispectrum images were applied as input for the two CNN architectures with the output AFIB vs. no-AFIB: the pre-trained modified GoogLeNet and the proposed CNN called AFIB-NET. The MIT-BIH Atrial Fibrillation Database (AFDB) was used to evaluate the performance of the proposed methodology. AFIB-NET detected atrial fibrillation with a sensitivity of 95.3%, a specificity of 93.7%, and an area under the receiver operating characteristic (ROC) of 98.3%, while for GoogLeNet results for sensitivity and specificity were equal to 96.7%, 82%, respectively, and the area under ROC was equal to 96.7%. According to preliminary studies, bispectrum images as input to 2D CNN can be successfully used for AFIB rhythm detection. Full article

(This article belongs to the Special Issue Biomedical Signals, Images and Healthcare Data Analysis)

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18 pages, 11695 KiB

Open AccessArticle

A Compact Dual-Polarized Vivaldi Antenna with High Gain for Tree Radar Applications

by Kaixuan Cheng, Yee Hui Lee, Jiwei Qian, Daryl Lee, Mohamed Lokman Mohd Yusof and Abdulkadir C. Yucel

Sensors 2024, 24(13), 4170; https://doi.org/10.3390/s24134170 (registering DOI) - 27 Jun 2024

Abstract

A dual-polarized compact Vivaldi antenna with high gain performance is proposed for tree radar applications. The proposed design introduces an array configuration consisting of two pairs of two Vivaldi elements to optimize the operating bandwidth and gain while providing dual-polarization capability. To enhance [...] Read more.

A dual-polarized compact Vivaldi antenna with high gain performance is proposed for tree radar applications. The proposed design introduces an array configuration consisting of two pairs of two Vivaldi elements to optimize the operating bandwidth and gain while providing dual-polarization capability. To enhance the gain of the proposed antenna over a certain frequency range of interest, directors and edge slots are incorporated into each Vivaldi element. To further enhance the overall antenna gain, a metal back reflector is used. The measurement results of the fabricated antenna show that the proposed antenna achieves a high gain of 5.5 to 14.8 dBi over broadband from 0.5 GHz to 3 GHz. Moreover, it achieves cross-polarization discrimination larger than 20 dB, ensuring high polarization purity. The fabricated antenna is used to detect and image the defects inside tree trunks. The results show that the proposed antenna yields a better-migrated image with a clear defect region compared to that obtained by a commercial Horn antenna. Full article

(This article belongs to the Special Issue Novel Antennas for Wireless Communication and Intelligent Sensing)

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9 pages, 6106 KiB

Open AccessArticle

Performance Studies of Aluminium-Based Gas Electron Multiplier Detector

by Bartosz Mindur, Tomasz Fiutowski and Piotr Wiącek

Sensors 2024, 24(13), 4169; https://doi.org/10.3390/s24134169 (registering DOI) - 27 Jun 2024

Abstract

In this paper, we report on a systematic study of a soft X-ray Gas Electron Multiplier (GEM) detector built with aluminium-clad kapton GEM foils. The primary objective of this research is to comprehend the performance of this type of detector when irradiated with [...] Read more.

In this paper, we report on a systematic study of a soft X-ray Gas Electron Multiplier (GEM) detector built with aluminium-clad kapton GEM foils. The primary objective of this research is to comprehend the performance of this type of detector when irradiated with soft energy photons. The results are analysed and discussed with a particular focus on the long-term detector stability, as well as its gas gain and energy resolution uniformity across the detector area. Presented results lead us to the conclusion that the aluminium based GEM detector is a promising device to suppress the X-ray Fluorescence (XRF) background, simultaneously providing very good stability during long-term measurement campaigns. Full article

(This article belongs to the Section Physical Sensors)

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11 pages, 2167 KiB

Open AccessArticle

Oxygen Saturation Curve Analysis in 2298 Hypoxia Awareness Training Tests of Military Aircrew Members in a Hypobaric Chamber

by Manuel Alvear-Catalán, Claudio Montiglio, Danilo Aravena-Nazif, Ginés Viscor and Oscar F. Araneda

Sensors 2024, 24(13), 4168; https://doi.org/10.3390/s24134168 (registering DOI) - 27 Jun 2024

Abstract

We aim to provide reference values for military aircrews participating in hypoxia awareness training (HAT). We describe several parameters with potential biomedical interest based on selected segments and slopes of the changes in oxygen saturation (SatO₂) during a standard HAT. A [...] Read more.

We aim to provide reference values for military aircrews participating in hypoxia awareness training (HAT). We describe several parameters with potential biomedical interest based on selected segments and slopes of the changes in oxygen saturation (SatO₂) during a standard HAT. A retrospective analysis of 2298 records of the SatO₂ curve was performed, including 1526 military men aged 30.48 ± 6.47 years during HAT in a hypobaric chamber. HAT consisted of pre-oxygenation at 100% and an ascent to 7620 m, followed by O₂ disconnection starting the phase of descent of SatO₂ until reaching the time of useful consciousness (TUC), and finally reconnection to 100% O₂ in the recovery phase. Using an ad hoc computational procedure, the time taken to reach several defined critical values was computed. These key parameters were the time until desaturation of 97% and 90% (hypoxia) after oxygen mask disconnection (D97/D90) and reconnection (R97/R90) phases, the time of desaturation (TUC-D97) and hypoxia (TUC-D90) during disconnection, the total time in desaturation (L97) or hypoxia (L90), and the slopes of SatO₂ drop (SDSAT97 and SDSAT90) and recovery (SRSAT97). The mean of the quartiles according to TUC were compared by ANOVA. The correlations between the different parameters were studied using Pearson’s test and the effect size was estimated with ω². Potentially useful parameters for the HAT study were those with statistical significance (p < 0.05) and a large effect size. D97, D90, R97, and R90 showed significant differences with small effect sizes, while TUC-D97, TUC-D90, L97, L90, and SDSAT97 showed significant differences and large effect sizes. SDSAT97 correlated with TUC (R = 0.79), TUC-D97 (R = 0.81), and TUC-D90 (R = 0.81). In conclusion, several parameters of the SatO₂ curve are useful for the study and monitoring of HAT. The SDSAT97 measured during the test can estimate the TUC and thus contribute to taking measures to characterize and protect the aircrew members. Full article

(This article belongs to the Special Issue Technologies Related to Oxygen Supply to Tissue: Measurements of Oxygen Saturation, Tissue Blood Flow and Arterial Blood Pressure)

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13 pages, 675 KiB

Open AccessArticle

The Effect of Stress on a Personal Identification System Based on Electroencephalographic Signals

by Eman A. Abdel-Ghaffar and May Salama

Sensors 2024, 24(13), 4167; https://doi.org/10.3390/s24134167 (registering DOI) - 27 Jun 2024

Abstract

Personal identification systems based on electroencephalographic (EEG) signals have their own strengths and limitations. The stability of EEG signals strongly affects such systems. The human emotional state is one of the important factors that affects EEG signals’ stability. Stress is a major emotional [...] Read more.

Personal identification systems based on electroencephalographic (EEG) signals have their own strengths and limitations. The stability of EEG signals strongly affects such systems. The human emotional state is one of the important factors that affects EEG signals’ stability. Stress is a major emotional state that affects individuals’ capability to perform day-to-day tasks. The main objective of this work is to study the effect of mental and emotional stress on such systems. Two experiments have been performed. In the first, we used hand-crafted features (time domain, frequency domain, and non-linear features), followed by a machine learning classifier. In the second, raw EEG signals were used as an input for the deep learning approaches. Different types of mental and emotional stress have been examined using two datasets, SAM 40 and DEAP. The proposed experiments proved that performing enrollment in a relaxed or calm state and identification in a stressed state have a negative effect on the identification system’s performance. The best achieved accuracy for the DEAP dataset was

99.67 %

in the calm state and

96.67 %

in the stressed state. For the SAM 40 dataset, the best accuracy was

99.67 %

,

93.33 %

,

92.5 %

, and

91.67 %

for the relaxed state and stress caused by identifying mirror images, the Stroop color-word test, and solving arithmetic operations, respectively. Full article

(This article belongs to the Collection EEG-Based Brain–Computer Interface for a Real-Life Appliance)

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4 pages, 174 KiB

Open AccessEditorial

Advancements in Sensors and Analyses for Emotion Sensing

by Wataru Sato

Sensors 2024, 24(13), 4166; https://doi.org/10.3390/s24134166 (registering DOI) - 27 Jun 2024

Abstract

Exploring the objective signals associated with subjective emotional states has practical significance [...] Full article

(This article belongs to the Special Issue Advanced-Sensors-Based Emotion Sensing and Recognition)

16 pages, 2507 KiB

Open AccessArticle

Design of a Cavity for the High-Power Radio-Frequency Quadrupole Coupler Test for the ANTHEM Project

by Andrea Passarelli, Maria Rosaria Masullo, Carlo Baltador, Francesco Grespan, Antonio Palmieri, Andrea Pisent, Paolo Mereu, Carlo Mingioni, Marco Nenni and Edoardo Nicoletti

Sensors 2024, 24(13), 4165; https://doi.org/10.3390/s24134165 (registering DOI) - 26 Jun 2024

Abstract

The ANTHEM (Advanced Technologies for Human-centered Medicine) Radio-Frequency Quadrupole (RFQ) will employ eight coaxial power couplers, which will be magnetically coupled to the device through a loop antenna. The coupler design can support up to 140 kW in continuous wave operation. This paper [...] Read more.

The ANTHEM (Advanced Technologies for Human-centered Medicine) Radio-Frequency Quadrupole (RFQ) will employ eight coaxial power couplers, which will be magnetically coupled to the device through a loop antenna. The coupler design can support up to 140 kW in continuous wave operation. This paper presents the design of the cavity used for high-power testing, with the primary objectives of both optimizing the coupling between the couplers and ensuring operations at the designated operating frequency. Furthermore, the paper encompasses thermal and structural assessments conducted through numerical simulations. Full article

(This article belongs to the Section Physical Sensors)

33 pages, 20024 KiB

Open AccessReview

A Review of Subsidence Monitoring Techniques in Offshore Environments

by Frank Thomas, Franz A. Livio, Francesca Ferrario, Marco Pizza and Rick Chalaturnyk

Sensors 2024, 24(13), 4164; https://doi.org/10.3390/s24134164 - 26 Jun 2024

Abstract

In view of the ever-increasing global energy demands and the imperative for sustainability in extraction methods, this article surveys subsidence monitoring systems applied to oil and gas fields located in offshore areas. Subsidence is an issue that can harm infrastructure, whether onshore or [...] Read more.

In view of the ever-increasing global energy demands and the imperative for sustainability in extraction methods, this article surveys subsidence monitoring systems applied to oil and gas fields located in offshore areas. Subsidence is an issue that can harm infrastructure, whether onshore or especially offshore, so it must be carefully monitored to ensure safety and prevent potential environmental damage. A comprehensive review of major monitoring technologies used offshore is still lacking; here, we address this gap by evaluating several techniques, including InSAR, GNSSs, hydrostatic leveling, and fiber optic cables, among others. Their accuracy, applicability, and limitations within offshore operations have also been assessed. Based on an extensive literature review of more than 60 published papers and technical reports, we have found that no single method works best for all settings; instead, a combination of different monitoring approaches is more likely to provide a reliable subsidence assessment. We also present selected case histories to document the results achieved using integrated monitoring studies. With the emerging offshore energy industry, combining GNSSs, InSAR, and other subsidence monitoring technologies offers a pathway to achieving precision in the assessment of offshore infrastructural stability, thus underpinning the sustainability and safety of offshore oil and gas operations. Reliable and comprehensive subsidence monitoring systems are essential for safety, to protect the environment, and ensure the sustainable exploitation of hydrocarbon resources. Full article

(This article belongs to the Section Environmental Sensing)

18 pages, 749 KiB

Open AccessArticle

DTR-SHIELD: Mutual Synchronization for Protecting against DoS Attacks on the SHIELD Protocol with AES-CTR Mode

by Sang-su Lee, Jong-sik Moon, Yong-je Choi, Daewon Kim and Seungkwang Lee

Sensors 2024, 24(13), 4163; https://doi.org/10.3390/s24134163 - 26 Jun 2024

Abstract

To enhance security in the semiconductor industry’s globalized production, the Defense Advanced Research Projects Agency (DARPA) proposed an authentication protocol under the Supply Chain Hardware Integrity for Electronics Defense (SHIELD) program. This protocol integrates a secure hardware root-of-trust, known as a dielet, into [...] Read more.

To enhance security in the semiconductor industry’s globalized production, the Defense Advanced Research Projects Agency (DARPA) proposed an authentication protocol under the Supply Chain Hardware Integrity for Electronics Defense (SHIELD) program. This protocol integrates a secure hardware root-of-trust, known as a dielet, into integrated circuits (ICs). The SHIELD protocol, combined with the Advanced Encryption Standard (AES) in counter mode, named CTR-SHIELD, targets try-and-check attacks. However, CTR-SHIELD is vulnerable to desynchronization attacks on its counter blocks. To counteract this, we introduce the DTR-SHIELD protocol, where DTR stands for double counters. DTR-SHIELD addresses the desynchronization issue by altering the counter incrementation process, which previously solely relied on truncated serial IDs. Our protocol adds a new AES encryption step and requires the dielet to transmit an additional 100 bits, ensuring more robust security through active server involvement and message verification. Full article

(This article belongs to the Special Issue Security of Sensor Network Systems and Circuits from a Hardware Perspective)

15 pages, 5243 KiB

Open AccessArticle

A Deep Learning-Based Emergency Alert Wake-Up Signal Detection Method for the UHD Broadcasting System

by Jin-Hyuk Song, Myung-Sun Baek, Byungjun Bae and Hyoung-Kyu Song

Sensors 2024, 24(13), 4162; https://doi.org/10.3390/s24134162 - 26 Jun 2024

Abstract

With the increasing frequency and severity of disasters and accidents, there is a growing need for efficient emergency alert systems. The ultra-high definition (UHD) broadcasting service based on Advanced Television Systems Committee (ATSC) 3.0, a leading terrestrial digital broadcasting system, offers such capabilities, [...] Read more.

With the increasing frequency and severity of disasters and accidents, there is a growing need for efficient emergency alert systems. The ultra-high definition (UHD) broadcasting service based on Advanced Television Systems Committee (ATSC) 3.0, a leading terrestrial digital broadcasting system, offers such capabilities, including a wake-up function for minimizing damage through early alerts. In case of a disaster situation, the emergency alert wake-up signal is transmitted, allowing UHD TVs to be activated, enabling individuals to receive emergency alerts and access emergency broadcasting content. However, conventional methods for detecting the bootstrap signal, essential for this function, typically require an ATSC 3.0 demodulator. In this paper, we propose a novel deep learning-based method capable of detecting an emergency wake-up signal without the need for an ATSC 3.0. The proposed method leverages deep learning techniques, specifically a deep neural network (DNN) structure for bootstrap detection and a convolutional neural network (CNN) structure for wake-up signal demodulation and to detect the bootstrap and 2 bit emergency alert wake-up signal. Specifically, our method eliminates the need for Fast Fourier Transform (FFT), frequency synchronization, and interleaving processes typically required by a demodulator. By applying a deep learning in the time domain, we simplify the detection process, allowing for the detection of an emergency alert signal without the full suite of demodulator components required for ATSC 3.0. Furthermore, we have verified the performance of the deep learning-based method using ATSC 3.0-based RF signals and a commercial Software-Defined Radio (SDR) platform in a real environment. Full article

(This article belongs to the Special Issue Advances and Applications of Machine Learning for Wireless Communications and Networking)

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14 pages, 1880 KiB

Open AccessArticle

A Within-Subject Multimodal NIRS-EEG Classifier for Infant Data

by Jessica Gemignani and Judit Gervain

Sensors 2024, 24(13), 4161; https://doi.org/10.3390/s24134161 - 26 Jun 2024

Abstract

Functional Near Infrared Spectroscopy (fNIRS) and Electroencephalography (EEG) are commonly employed neuroimaging methods in developmental neuroscience. Since they offer complementary strengths and their simultaneous recording is relatively easy, combining them is highly desirable. However, to date, very few infant studies have been conducted [...] Read more.

Functional Near Infrared Spectroscopy (fNIRS) and Electroencephalography (EEG) are commonly employed neuroimaging methods in developmental neuroscience. Since they offer complementary strengths and their simultaneous recording is relatively easy, combining them is highly desirable. However, to date, very few infant studies have been conducted with NIRS-EEG, partly because analyzing and interpreting multimodal data is challenging. In this work, we propose a framework to carry out a multivariate pattern analysis that uses an NIRS-EEG feature matrix, obtained by selecting EEG trials presented within larger NIRS blocks, and combining the corresponding features. Importantly, this classifier is intended to be sensitive enough to apply to individual-level, and not group-level data. We tested the classifier on NIRS-EEG data acquired from five newborn infants who were listening to human speech and monkey vocalizations. We evaluated how accurately the model classified stimuli when applied to EEG data alone, NIRS data alone, or combined NIRS-EEG data. For three out of five infants, the classifier achieved high and statistically significant accuracy when using features from the NIRS data alone, but even higher accuracy when using combined EEG and NIRS data, particularly from both hemoglobin components. For the other two infants, accuracies were lower overall, but for one of them the highest accuracy was still achieved when using combined EEG and NIRS data with both hemoglobin components. We discuss how classification based on joint NIRS-EEG data could be modified to fit the needs of different experimental paradigms and needs. Full article

(This article belongs to the Special Issue Developmental EEG: Advances on Data Analysis Methods)

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