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Sensors, Volume 21, Issue 20 (October-2 2021) – 248 articles

Cover Story (view full-size image): Accurate indoor material mapping in buildings and industrial halls with indoor self-localization systems has received receiving increasing attention in recent years, with the objective of providing enhanced information to emergency dispatchers in the case of disasters, such as indoor fires. These systems require fixed reference points (landmarks) in the rooms, comparable to lighthouses on the coast, for navigation and reliable results. Particularly in harsh environments, the employment of chipless landmarks is promising because of their robustness and predictability. To enhance their detection and identification, we propose an iterative algorithm based on the low-rank plus sparse recovery approach to separate strong environmental clutter from the frequency signature of two chipless RFID landmarks with complementary operation at W-band. View this paper
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Article
Ion Current Sensor for Gas Turbine Condition Dynamical Monitoring: Modeling and Characterization
Sensors 2021, 21(20), 6944; https://doi.org/10.3390/s21206944 - 19 Oct 2021
Cited by 1 | Viewed by 1144
Abstract
This paper aims to thoroughly investigate the potential of ion current measurements in the context of combustion process monitoring in gas turbines. The study is targeted at characterizing the dynamic behavior of a typical ion-current measurement system based on a spark-plug. Starting from [...] Read more.
This paper aims to thoroughly investigate the potential of ion current measurements in the context of combustion process monitoring in gas turbines. The study is targeted at characterizing the dynamic behavior of a typical ion-current measurement system based on a spark-plug. Starting from the preliminary study published in a previous work, the authors propose a refined model of the electrode (spark plug), based on the Langmuir probe theory, that incorporates the physical surface effects and proposes an optimized design of the conditioning electronics, which exploits a low frequency AC square wave biasing of the electrodes and allows for compensating some relevant parasitic effects. The authors present experimental results obtained in the laboratory, which allow for the evaluation of the validity of the model and the interpreting of the characteristics of the measurement signal. Finally, measurements carried out in the field on an industrial combustor are presented. The results confirm that the charged chemical species density sensed by the proposed measurement system and related to the mean value of the output signal is an indicator of the ‘average’ combustion process conditions in terms e.g., of air/fuel ratio, whereas the high frequency spectral component of the measured signal can give information related to the turbulent regime and to the presence of pressure pulsations. Results obtained with a prototype system demonstrated an achievable resolution of about 5 Pa on the estimated amplitude, even under small biasing voltage (22.5 V) and an estimated bandwidth of 10 kHz. Full article
(This article belongs to the Special Issue Sensors and Sensing Systems for Condition Monitoring)
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Review
Effect of Blue Light on Acne Vulgaris: A Systematic Review
Sensors 2021, 21(20), 6943; https://doi.org/10.3390/s21206943 - 19 Oct 2021
Cited by 7 | Viewed by 3298
Abstract
Acne is a dermatosis that affects almost 90% of the adolescent population worldwide and its treatment is performed with retinoids, antimicrobials, acids, and topical or systemic antibiotics. Side effects such as skin irritation in addition to microbial resistance to antibiotics are the main [...] Read more.
Acne is a dermatosis that affects almost 90% of the adolescent population worldwide and its treatment is performed with retinoids, antimicrobials, acids, and topical or systemic antibiotics. Side effects such as skin irritation in addition to microbial resistance to antibiotics are the main side effects found. Phototherapy with blue light is being used as an alternative treatment. Our objective was to analyze the use of blue light to treat inflammatory acne. We conducted a systematic literature review, following the recommendation PRISMA (Preferred Reporting Items for Systematic Reviews and MetaAnalyses), including in the sample randomized clinical trial studies that compared blue light with another intervention as control. The research was carried out in the PUBMED and WEB of SCIENCE databases and the methodological quality of the studies evaluated were made by the Cochrane Collaboration Bias Risk Scale. After the exclusion of duplicates, the titles and abstracts of 81 articles were evaluated, and 50 articles were selected for full reading, including in the review at the end 8 articles. Studies have shown significant improvements in the overall picture of acne. It is concluded that despite the great potential in its use in the treatment of acne, there is a need for more detailed trials on the effect of blue light on the treatment of inflammatory acne. Full article
(This article belongs to the Special Issue Advanced Laser Phototherapy: Sensing and Applications)
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Article
Mobile Computing with a Smart Cricket Ball: Discovery of Novel Performance Parameters and Their Practical Application to Performance Analysis, Advanced Profiling, Talent Identification and Training Interventions of Spin Bowlers
Sensors 2021, 21(20), 6942; https://doi.org/10.3390/s21206942 - 19 Oct 2021
Cited by 1 | Viewed by 1334
Abstract
Introduction: Profiling of cricket bowlers is performed with motion analyses systems that require the placement of markers on the bowler’s body and on the ball. Conventional smart balls such as cricket and baseballs provide only one speed and one spin rate datum at [...] Read more.
Introduction: Profiling of cricket bowlers is performed with motion analyses systems that require the placement of markers on the bowler’s body and on the ball. Conventional smart balls such as cricket and baseballs provide only one speed and one spin rate datum at the release point, which is insufficient for biomechanical profiling. Method: In this study, we used an advanced smart cricket ball that measures the angular velocity at 815 Hz and calculates four further physical performance parameters (resultant torque, spin torque, power and angular acceleration) and five new skill parameters (precession, normalised precession, precession torque, efficiency and ratio of angular acceleration to spin rate), which we used for profiling and talent identification of spin bowlers. Results: The results showed that the spin rate is a function of physical (torque) and skill proficiency, namely how efficiently the torque is converted to angular velocity rather than being wasted for precession. The kind of delivery also influences the efficiency, as finger-spin deliveries were less efficient than wrist-spin ones by 6.8% on average; and topspin deliveries were generally more efficient than backspin ones by 15% on average. We tested three bowlers in terms of physical and skill performance during a 10-over spell, revealing that some parameters can improve or decline. When profiling a topspinner, we detected from the performance parameters a lower skill performance than expected, because there was an initial arm motion for backspin delivery before releasing the ball with a topspin. After training intervention, the skill parameters improved significantly (the efficiency increased from 39% to 59%). Conclusions: The advanced smart cricket ball is a classic example of mobile computing for sport performance analysis that can conducted indoors as well as outdoors, generating instant data from 10 performance parameters that provide critical feedback to the coach and bowler. Full article
(This article belongs to the Special Issue Mobile Computing and Sensing for Sport Performance Analysis)
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Article
A Crack Size Quantification Method Using High-Resolution Lamb Waves
Sensors 2021, 21(20), 6941; https://doi.org/10.3390/s21206941 - 19 Oct 2021
Cited by 1 | Viewed by 934
Abstract
Traditional tone burst excitation cannot attain a high output resolution, due to the time duration. The received signal is much longer than that of excitation during the propagation, which can increase the difficulty of signal processing, and reduce the resolution. Therefore, it is [...] Read more.
Traditional tone burst excitation cannot attain a high output resolution, due to the time duration. The received signal is much longer than that of excitation during the propagation, which can increase the difficulty of signal processing, and reduce the resolution. Therefore, it is of significant interest to develop a general methodology for crack quantification through the optimal design of the excitation waveform and signal-processing methods. This paper presents a new crack size quantification method based on high-resolution Lamb waves. The linear chirp (L-Chirp) signal and Golay complementary code (GCC) signal are used as Lamb wave excitation signals. After dispersion removal, these excitation waveforms, based on pulse compression, can effectively improve the inspection resolution in plate-like structures. A series of simulations of both healthy plates and plates with different crack sizes are performed by Abaqus CAE, using different excitation waveforms. The first wave package of the S0 mode after pulse compression is chosen to extract the damage features. A multivariate regression model is proposed to correlate the damage features to the crack size. The effectiveness of the proposed crack size quantification method is verified by a comparison with tone burst excitation, and the accuracy of the crack size quantification method is verified by validation experiments. Full article
(This article belongs to the Special Issue Smart Materials for Structural Health Monitoring and Damage Detection)
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Article
Resolving Cross-Sensitivity Effect in Fluorescence Quenching for Simultaneously Sensing Oxygen and Ammonia Concentrations by an Optical Dual Gas Sensor
Sensors 2021, 21(20), 6940; https://doi.org/10.3390/s21206940 - 19 Oct 2021
Cited by 8 | Viewed by 1570
Abstract
Simultaneous sensing of multiple gases by a single fluorescent-based gas sensor is of utmost importance for practical applications. Such sensing is strongly hindered by cross-sensitivity effects. In this study, we propose a novel analysis method to ameliorate such hindrance. The trial sensor used [...] Read more.
Simultaneous sensing of multiple gases by a single fluorescent-based gas sensor is of utmost importance for practical applications. Such sensing is strongly hindered by cross-sensitivity effects. In this study, we propose a novel analysis method to ameliorate such hindrance. The trial sensor used here was fabricated by coating platinum(II) meso-tetrakis(pentafluorophenyl)porphyrin (PtTFPP) and eosin-Y dye molecules on both sides of a filter paper for sensing O2 and NH3 gases simultaneously. The fluorescent peak intensities of the dyes can be quenched by the analytes and this phenomenon is used to identify the gas concentrations. Ideally, each dye is only sensitive to one gas species. However, the fluorescent peak related to O2 sensing is also quenched by NH3 and vice versa. Such cross-sensitivity strongly hinders gas concentration detection. Therefore, we have studied this cross-sensitivity effect systematically and thus proposed a new analysis method for accurate estimation of gas concentration. Comparing with a traditional method (neglecting cross-sensitivity), this analysis improves O2-detection error from −11.4% ± 34.3% to 2.0% ± 10.2% in a mixed background of NH3 and N2. Full article
(This article belongs to the Collection Recent Advances in Fluorescent Sensors)
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Article
Synthesis of Planar Circular Arrays with Quantized Amplitude Weights
Sensors 2021, 21(20), 6939; https://doi.org/10.3390/s21206939 - 19 Oct 2021
Cited by 1 | Viewed by 833
Abstract
A new stepwise and radially processed method for synthesizing uniformly distributed circular planar arrays with quantized weights is proposed in this paper. This method is based on a generalized analytical equation describing that for high directivity focusing arrays, minimizing the weighted mean square [...] Read more.
A new stepwise and radially processed method for synthesizing uniformly distributed circular planar arrays with quantized weights is proposed in this paper. This method is based on a generalized analytical equation describing that for high directivity focusing arrays, minimizing the weighted mean square error between the reference pattern and the synthesized pattern is equivalent to minimizing the mean square error between the radial cumulative distributions of the reference distribution and the synthesized distribution. This principle has been successfully performed for designing large concentric ring arrays, and in this paper, we extend its use for synthesizing uniformly distributed planar circular arrays with quantized weights. Various numerical examples and comparisons with several reported statistical methods in terms of the lowest Maximum SideLobe Level (MSLL) demonstrate the effectiveness of the proposed method. Full article
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Article
Impact of Structural Health Monitoring on Aircraft Operating Costs by Multidisciplinary Analysis
Sensors 2021, 21(20), 6938; https://doi.org/10.3390/s21206938 - 19 Oct 2021
Cited by 10 | Viewed by 3568
Abstract
Structural health monitoring is recognized as a viable solution to increase aviation safety and decrease operating costs enabling a novel maintenance approach based on the actual condition of the airframe, mitigating operating costs induced by scheduled inspections. However, the net benefit is hardly [...] Read more.
Structural health monitoring is recognized as a viable solution to increase aviation safety and decrease operating costs enabling a novel maintenance approach based on the actual condition of the airframe, mitigating operating costs induced by scheduled inspections. However, the net benefit is hardly demonstrated, and it is still unclear how the implementation of such an autonomic system can affect performance at aircraft level. To close this gap, this paper presents a systematic analysis where the impact of cost and weight of integrating permanently attached sensors—used for diagnostics- affect the main performance of the aircraft. Through a multidisciplinary aircraft analysis framework, the increment of aircraft operating empty weight is compared with the possible benefits in terms of direct operating costs to identify a breakeven point. Furthermore, the analysis allows to establish a design guideline for structural health monitoring systems returning a safer aircraft without any economic penalties. The results show that the operating costs are lower than those of the reference aircraft up to 4% increase in maximum take-off weight. Paper findings suggest to considering a condition monitoring strategy from the conceptual design stage, since it could maximize the impact of such innovative technology. However, it involves in a design of a brand-new aircraft instead of a modification of an existing one. Full article
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Article
An Enhancement for IEEE 802.11p to Provision Quality of Service with Context Aware Channel Access for the Forward Collision Avoidance Application in Vehicular Ad Hoc Network
Sensors 2021, 21(20), 6937; https://doi.org/10.3390/s21206937 - 19 Oct 2021
Viewed by 934
Abstract
Key application of an intelligent transportation system is traffic safety, and it provides driver assistance. Safety messages are of two types, beacon messages and event messages. The nodes broadcast these messages in the vehicular networks. The system must rely on a robust medium [...] Read more.
Key application of an intelligent transportation system is traffic safety, and it provides driver assistance. Safety messages are of two types, beacon messages and event messages. The nodes broadcast these messages in the vehicular networks. The system must rely on a robust medium access control (MAC) protocol to support delivery of safety messages. The standard medium access scheme that is used in vehicular networks to provide service differentiation to support various applications is IEEE 802.11p. The emergency event messages should reach the drivers immediately to take necessary steps to avoid casualties on the road. In IEEE 802.11p, both of these messages are considered with the same priority so that no separate differentiation is created. The proposed work focuses on improving the quality of service for forward collision warning applications in intelligent transportation systems. The scheme proposes a priority-based cooperative MAC (PCMAC) for channel access that works on the context of information. Simulation and analytical results validate improved performance of PCMAC in terms of packet delivery ratio, throughput, and average packet delivery delay, as compared with other eminent MAC protocols. The simulation results show that it has a 9% higher improvement in throughput than IEEE 802.11p and has better performance in the increasing number of emergency messages. Full article
(This article belongs to the Special Issue Sensor Networks for Vehicular Communications)
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Article
An Automatic Detection and Classification System of Five Stages for Hypertensive Retinopathy Using Semantic and Instance Segmentation in DenseNet Architecture
Sensors 2021, 21(20), 6936; https://doi.org/10.3390/s21206936 - 19 Oct 2021
Cited by 6 | Viewed by 3672
Abstract
The stage and duration of hypertension are connected to the occurrence of Hypertensive Retinopathy (HR) of eye disease. Currently, a few computerized systems have been developed to recognize HR by using only two stages. It is difficult to define specialized features to recognize [...] Read more.
The stage and duration of hypertension are connected to the occurrence of Hypertensive Retinopathy (HR) of eye disease. Currently, a few computerized systems have been developed to recognize HR by using only two stages. It is difficult to define specialized features to recognize five grades of HR. In addition, deep features have been used in the past, but the classification accuracy is not up-to-the-mark. In this research, a new hypertensive retinopathy (HYPER-RETINO) framework is developed to grade the HR based on five grades. The HYPER-RETINO system is implemented based on pre-trained HR-related lesions. To develop this HYPER-RETINO system, several steps are implemented such as a preprocessing, the detection of HR-related lesions by semantic and instance-based segmentation and a DenseNet architecture to classify the stages of HR. Overall, the HYPER-RETINO system determined the local regions within input retinal fundus images to recognize five grades of HR. On average, a 10-fold cross-validation test obtained sensitivity (SE) of 90.5%, specificity (SP) of 91.5%, accuracy (ACC) of 92.6%, precision (PR) of 91.7%, Matthews correlation coefficient (MCC) of 61%, F1-score of 92% and area-under-the-curve (AUC) of 0.915 on 1400 HR images. Thus, the applicability of the HYPER-RETINO method to reliably diagnose stages of HR is verified by experimental findings. Full article
(This article belongs to the Special Issue Recent Advances in Medical Image Processing Technologies)
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Article
Application of Machine Learning to Predict Dielectric Properties of In Vivo Biological Tissue
Sensors 2021, 21(20), 6935; https://doi.org/10.3390/s21206935 - 19 Oct 2021
Cited by 1 | Viewed by 1050
Abstract
In this paper we revisited a database with measurements of the dielectric properties of rat muscles. Measurements were performed both in vivo and ex vivo; the latter were performed in tissues with varying levels of hydration. Dielectric property measurements were performed with an [...] Read more.
In this paper we revisited a database with measurements of the dielectric properties of rat muscles. Measurements were performed both in vivo and ex vivo; the latter were performed in tissues with varying levels of hydration. Dielectric property measurements were performed with an open-ended coaxial probe between the frequencies of 500 MHz and 50 GHz at a room temperature of 25 °C. In vivo dielectric properties are more valuable for creating realistic electromagnetic models of biological tissue, but these are more difficult to measure and scarcer in the literature. In this paper, we used machine learning models to predict the in vivo dielectric properties of rat muscle from ex vivo dielectric property measurements for varying levels of hydration. We observed promising results that suggest that our model can make a fair estimation of in vivo properties from ex vivo properties. Full article
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Article
Prediction of In Vivo Laser-Induced Thermal Damage with Hyperspectral Imaging Using Deep Learning
Sensors 2021, 21(20), 6934; https://doi.org/10.3390/s21206934 - 19 Oct 2021
Cited by 7 | Viewed by 1727
Abstract
Thermal ablation is an acceptable alternative treatment for primary liver cancer, of which laser ablation (LA) is one of the least invasive approaches, especially for tumors in high-risk locations. Precise control of the LA effect is required to safely destroy the tumor. Although [...] Read more.
Thermal ablation is an acceptable alternative treatment for primary liver cancer, of which laser ablation (LA) is one of the least invasive approaches, especially for tumors in high-risk locations. Precise control of the LA effect is required to safely destroy the tumor. Although temperature imaging techniques provide an indirect measurement of the thermal damage, a degree of uncertainty remains about the treatment effect. Optical techniques are currently emerging as tools to directly assess tissue thermal damage. Among them, hyperspectral imaging (HSI) has shown promising results in image-guided surgery and in the thermal ablation field. The highly informative data provided by HSI, associated with deep learning, enable the implementation of non-invasive prediction models to be used intraoperatively. Here we show a novel paradigm “peak temperature prediction model” (PTPM), convolutional neural network (CNN)-based, trained with HSI and infrared imaging to predict LA-induced damage in the liver. The PTPM demonstrated an optimal agreement with tissue damage classification providing a consistent threshold (50.6 ± 1.5 °C) for the damage margins with high accuracy (~0.90). The high correlation with the histology score (r = 0.9085) and the comparison with the measured peak temperature confirmed that PTPM preserves temperature information accordingly with the histopathological assessment. Full article
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Article
Severity Grading and Early Retinopathy Lesion Detection through Hybrid Inception-ResNet Architecture
Sensors 2021, 21(20), 6933; https://doi.org/10.3390/s21206933 - 19 Oct 2021
Cited by 5 | Viewed by 1489
Abstract
Diabetic retinopathy (DR) is a diabetes disorder that disturbs human vision. It starts due to the damage in the light-sensitive tissues of blood vessels at the retina. In the beginning, DR may show no symptoms or only slight vision issues, but in the [...] Read more.
Diabetic retinopathy (DR) is a diabetes disorder that disturbs human vision. It starts due to the damage in the light-sensitive tissues of blood vessels at the retina. In the beginning, DR may show no symptoms or only slight vision issues, but in the long run, it could be a permanent source of impaired vision, simply known as blindness in the advanced as well as in developing nations. This could be prevented if DR is identified early enough, but it can be challenging as we know the disease frequently shows rare signs until it is too late to deliver an effective cure. In our work, we recommend a framework for severity grading and early DR detection through hybrid deep learning Inception-ResNet architecture with smart data preprocessing. Our proposed method is composed of three steps. Firstly, the retinal images are preprocessed with the help of augmentation and intensity normalization. Secondly, the preprocessed images are given to the hybrid Inception-ResNet architecture to extract the vector image features for the categorization of different stages. Lastly, to identify DR and decide its stage (e.g., mild DR, moderate DR, severe DR, or proliferative DR), a classification step is used. The studies and trials have to reveal suitable outcomes when equated with some other previously deployed approaches. However, there are specific constraints in our study that are also discussed and we suggest methods to enhance further research in this field. Full article
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Article
EEG Signal Multichannel Frequency-Domain Ratio Indices for Drowsiness Detection Based on Multicriteria Optimization
Sensors 2021, 21(20), 6932; https://doi.org/10.3390/s21206932 - 19 Oct 2021
Cited by 5 | Viewed by 1285
Abstract
Drowsiness is a risk to human lives in many occupations and activities where full awareness is essential for the safe operation of systems and vehicles, such as driving a car or flying an airplane. Although it is one of the main causes of [...] Read more.
Drowsiness is a risk to human lives in many occupations and activities where full awareness is essential for the safe operation of systems and vehicles, such as driving a car or flying an airplane. Although it is one of the main causes of many road accidents, there is still no reliable definition of drowsiness or a system to reliably detect it. Many researchers have observed correlations between frequency-domain features of the EEG signal and drowsiness, such as an increase in the spectral power of the theta band or a decrease in the spectral power of the beta band. In addition, features calculated as ratio indices between these frequency-domain features show further improvements in detecting drowsiness compared to frequency-domain features alone. This work aims to develop novel multichannel ratio indices that take advantage of the diversity of frequency-domain features from different brain regions. In contrast to the state-of-the-art, we use an evolutionary metaheuristic algorithm to find the nearly optimal set of features and channels from which the indices are calculated. Our results show that drowsiness is best described by the powers in delta and alpha bands. Compared to seven existing single-channel ratio indices, our two novel six-channel indices show improvements in (1) statistically significant differences observed between wakefulness and drowsiness segments, (2) precision of drowsiness detection and classification accuracy of the XGBoost algorithm and (3) model performance by saving time and memory during classification. Our work suggests that a more precise definition of drowsiness is needed, and that accurate early detection of drowsiness should be based on multichannel frequency-domain features. Full article
(This article belongs to the Special Issue Advanced Signal Processing in Wearable Sensors for Health Monitoring)
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Article
Determination of the Kinematic Excitation Originating from the Irregular Envelope of an Omnidirectional Wheel
Sensors 2021, 21(20), 6931; https://doi.org/10.3390/s21206931 - 19 Oct 2021
Cited by 2 | Viewed by 889
Abstract
This paper describes a test stand for determining the kinematic excitation originating from the contact between a vehicle’s wheel and the ground, thus acting on the single suspension upright of the vehicle. This excitation is unique to the movement of omnidirectional wheels and [...] Read more.
This paper describes a test stand for determining the kinematic excitation originating from the contact between a vehicle’s wheel and the ground, thus acting on the single suspension upright of the vehicle. This excitation is unique to the movement of omnidirectional wheels and originates from the irregular envelope of the wheel. The presented attitude enables the vertical displacement of the wheel’s axis rolling on a horizontal surface to be determined. This work includes experimental results considering different wheel orientations against the direction of movement. Full article
(This article belongs to the Special Issue Intelligent Mechatronic Systems—Materials, Sensors and Interfaces)
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Article
Using Multi-Antenna Trajectory Constraint to Analyze BeiDou Carrier-Phase Observation Error of Dynamic Receivers
Sensors 2021, 21(20), 6930; https://doi.org/10.3390/s21206930 - 19 Oct 2021
Viewed by 735
Abstract
Appropriate cycle-slip and measurement-error models are essential for BeiDou carrier-phase-based integrity risk calculation. To establish the receiver’s measurement-error model, an accurate position reference of the GNSS antenna is fundamental for calculating the measurement error. However, it is still a challenge to acquire position [...] Read more.
Appropriate cycle-slip and measurement-error models are essential for BeiDou carrier-phase-based integrity risk calculation. To establish the receiver’s measurement-error model, an accurate position reference of the GNSS antenna is fundamental for calculating the measurement error. However, it is still a challenge to acquire position references for dynamic BeiDou receivers, resulting in improper GNSS measurement-error models and unreliable integrity monitoring. This paper proposes an improved precise relative positioning scheme by adopting multi-antenna trajectory constraints for dynamic BeiDou receivers. The dynamic experiments show an obvious decline of 78.7%, at most, in the positioning failure rate of the proposed method, as compared with the traditional method. The position solutions obtained from the proposed approach are used as the reference to analyze the cycle-slip and measurement-error characteristics of the dynamic receiver. The field test results indicate that the cycle-slip rate decreases with the increase of signal-to-noise ratio (SNR), and cycle slipping obeys a positively skewed distribution that could be fitted by the Gaussian mixture model (GMM). On the other hand, the standard deviation of the carrier-phase measurement error is inversely proportional to SNR, and its distribution is characteristically fat-tailed, which could be fitted by the bi-normal model. Full article
(This article belongs to the Section Navigation and Positioning)
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Article
Scenario-Mining for Level 4 Automated Vehicle Safety Assessment from Real Accident Situations in Urban Areas Using a Natural Language Process
Sensors 2021, 21(20), 6929; https://doi.org/10.3390/s21206929 - 19 Oct 2021
Cited by 4 | Viewed by 1730
Abstract
As the research and development activities of automated vehicles have been active in recent years, developing test scenarios and methods has become necessary to evaluate and ensure their safety. Based on the current context, this study developed an automated vehicle test scenario derivation [...] Read more.
As the research and development activities of automated vehicles have been active in recent years, developing test scenarios and methods has become necessary to evaluate and ensure their safety. Based on the current context, this study developed an automated vehicle test scenario derivation methodology using traffic accident data and a natural language processing technique. The natural language processing technique-based test scenario mining methodology generated 16 functional test scenarios for urban arterials and 38 scenarios for intersections in urban areas. The proposed methodology was validated by determining the number of traffic accident records that can be explained by the resulting test scenarios. That is, the resulting test scenarios are valid and represent a matching rate between the test scenarios and the increased number of traffic accident records. The resulting functional scenarios generated by the proposed methodology account for 43.69% and 27.63% of the actual traffic accidents for urban arterial and intersection scenarios, respectively. Full article
(This article belongs to the Topic Intelligent Transportation Systems)
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Article
Computational Three-Dimensional Imaging System via Diffraction Grating Imaging with Multiple Wavelengths
Sensors 2021, 21(20), 6928; https://doi.org/10.3390/s21206928 - 19 Oct 2021
Cited by 2 | Viewed by 1242
Abstract
This paper describes a computational 3-D imaging system based on diffraction grating imaging with laser sources of multiple wavelengths. It was proven that a diffraction grating imaging system works well as a 3-D imaging system in our previous studies. The diffraction grating imaging [...] Read more.
This paper describes a computational 3-D imaging system based on diffraction grating imaging with laser sources of multiple wavelengths. It was proven that a diffraction grating imaging system works well as a 3-D imaging system in our previous studies. The diffraction grating imaging system has advantages such as no spherical aberration and a low-cost system, compared with the well-known 3-D imaging systems based on a lens array or a camera array. However, a diffraction grating imaging system still suffers from noises, artifacts, and blurring due to the diffraction nature and illumination of single wavelength lasers. In this paper, we propose a diffraction grating imaging system with multiple wavelengths to overcome these problems. The proposed imaging system can produce multiple volumes through multiple laser illuminators with different wavelengths. Integration of these volumes can reduce noises, artifacts, and blurring in grating imaging since the original signals of 3-D objects inside these volumes are integrated by our computational reconstruction method. To apply the multiple wavelength system to a diffraction grating imaging system efficiently, we analyze the effects on the system parameters such as spatial periods and parallax angles for different wavelengths. A computational 3-D imaging system based on the analysis is proposed to enhance the image quality in diffraction grating imaging. Optical experiments with three-wavelength lasers are conducted to evaluate the proposed system. The results indicate that our diffraction grating imaging system is superior to the existing method. Full article
(This article belongs to the Section Sensing and Imaging)
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Article
HARNAS: Human Activity Recognition Based on Automatic Neural Architecture Search Using Evolutionary Algorithms
Sensors 2021, 21(20), 6927; https://doi.org/10.3390/s21206927 - 19 Oct 2021
Cited by 2 | Viewed by 1056
Abstract
Human activity recognition (HAR) based on wearable sensors is a promising research direction. The resources of handheld terminals and wearable devices limit the performance of recognition and require lightweight architectures. With the development of deep learning, the neural architecture search (NAS) has emerged [...] Read more.
Human activity recognition (HAR) based on wearable sensors is a promising research direction. The resources of handheld terminals and wearable devices limit the performance of recognition and require lightweight architectures. With the development of deep learning, the neural architecture search (NAS) has emerged in an attempt to minimize human intervention. We propose an approach for using NAS to search for models suitable for HAR tasks, namely, HARNAS. The multi-objective search algorithm NSGA-II is used as the search strategy of HARNAS. To make a trade-off between the performance and computation speed of a model, the F1 score and the number of floating-point operations (FLOPs) are selected, resulting in a bi-objective problem. However, the computation speed of a model not only depends on the complexity, but is also related to the memory access cost (MAC). Therefore, we expand the bi-objective search to a tri-objective strategy. We use the Opportunity dataset as the basis for most experiments and also evaluate the portability of the model on the UniMiB-SHAR dataset. The experimental results show that HARNAS designed without manual adjustments can achieve better performance than the best model tweaked by humans. HARNAS obtained an F1 score of 92.16% and parameters of 0.32 MB on the Opportunity dataset. Full article
(This article belongs to the Special Issue AI-Enabled Advanced Sensing for Human Action and Activity Recognition)
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Article
Acoustic Emission Monitoring of Carbon Fibre Reinforced Composites with Embedded Sensors for In-Situ Damage Identification
Sensors 2021, 21(20), 6926; https://doi.org/10.3390/s21206926 - 19 Oct 2021
Cited by 10 | Viewed by 1468
Abstract
Piezoelectric sensors can be embedded in carbon fibre-reinforced plastics (CFRP) for continuous measurement of acoustic emissions (AE) without the sensor being exposed or disrupting hydro- or aerodynamics. Insights into the sensitivity of the embedded sensor are essential for accurate identification of AE sources. [...] Read more.
Piezoelectric sensors can be embedded in carbon fibre-reinforced plastics (CFRP) for continuous measurement of acoustic emissions (AE) without the sensor being exposed or disrupting hydro- or aerodynamics. Insights into the sensitivity of the embedded sensor are essential for accurate identification of AE sources. Embedded sensors are considered to evoke additional modes of degradation into the composite laminate, accompanied by additional AE. Hence, to monitor CFRPs with embedded sensors, identification of this type of AE is of interest. This study (i) assesses experimentally the performance of embedded sensors for AE measurements, and (ii) investigates AE that emanates from embedded sensor-related degradation. CFRP specimens have been manufactured with and without embedded sensors and tested under four-point bending. AE signals have been recorded by the embedded sensor and two reference surface-bonded sensors. Sensitivity of the embedded sensor has been assessed by comparing centroid frequencies of AE measured using two sizes of embedded sensors. For identification of embedded sensor-induced AE, a hierarchical clustering approach has been implemented based on waveform similarity. It has been confirmed that both types of embedded sensors (7 mm and 20 mm diameter) can measure AE during specimen degradation and final failure. The 7 mm sensor showed higher sensitivity in the 350–450 kHz frequency range. The 20 mm sensor and the reference surface-bounded sensors predominately featured high sensitivity in ranges of 200–300 kHz and 150–350 kHz, respectively. The clustering procedure revealed a type of AE that seems unique to the region of the embedded sensor when under combined in-plane tension and out-of-plane shear stress. Full article
(This article belongs to the Special Issue Acoustic Emission Sensors for Structural Health Monitoring)
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Article
Noninvasive Methods for Fault Detection and Isolation in Internal Combustion Engines Based on Chaos Analysis
Sensors 2021, 21(20), 6925; https://doi.org/10.3390/s21206925 - 19 Oct 2021
Cited by 5 | Viewed by 1074
Abstract
The classic monitoring methods for detecting faults in automotive vehicles based on on-board diagnostics (OBD) are insufficient when diagnosing several mechanical failures. Other sensing techniques present drawbacks such as high invasiveness and limited physical range. The present work presents a fully noninvasive system [...] Read more.
The classic monitoring methods for detecting faults in automotive vehicles based on on-board diagnostics (OBD) are insufficient when diagnosing several mechanical failures. Other sensing techniques present drawbacks such as high invasiveness and limited physical range. The present work presents a fully noninvasive system for fault detection and isolation in internal combustion engines through sound signals processing. An acquisition system was developed, whose data are transmitted to a smartphone in which the signal is processed, and the user has access to the information. A study of the chaotic behavior of the vehicle was carried out, and the feasibility of using fractal dimensions as a tool to diagnose engine misfire and problems in the alternator belt was verified. An artificial neural network was used for fault classification using the fractal dimension data extracted from the sound of the engine. For comparison purposes, a strategy based on wavelet multiresolution analysis was also implemented. The proposed solution allows a diagnosis without having any contact with the vehicle, with low computational cost, without the need for installing sensors, and in real time. The system and method were validated through experimental tests, with a success rate of 99% for the faults under consideration. Full article
(This article belongs to the Section Fault Diagnosis & Sensors)
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Communication
Mineral Interpretation Discrepancies Identified between Infrared Reflectance Spectra and X-ray Diffractograms
Sensors 2021, 21(20), 6924; https://doi.org/10.3390/s21206924 - 19 Oct 2021
Cited by 3 | Viewed by 1034
Abstract
Mineral composition can be determined using different methods such as reflectance spectroscopy and X-ray diffraction (XRD). However, in some cases, the composition of mineral maps obtained from reflectance spectroscopy with XRD shows inconsistencies in the mineral composition interpretation and the estimation of (semi-)quantitative [...] Read more.
Mineral composition can be determined using different methods such as reflectance spectroscopy and X-ray diffraction (XRD). However, in some cases, the composition of mineral maps obtained from reflectance spectroscopy with XRD shows inconsistencies in the mineral composition interpretation and the estimation of (semi-)quantitative mineral abundances. We show why these discrepancies exist and how should they be interpreted. Part of the explanation is related to the sample choice and preparation; another part is related to the fact that clay minerals are active in the short-wave infrared, whereas other elements in the composition are not. Together, this might lead to distinctly different interpretations for the same material, depending on the methods used. The main conclusion is that both methods can be useful, but care should be given to the limitations of the interpretation process. For infrared reflectance spectroscopy, the lack of an actual threshold value for the H–OH absorption feature at 1900 nm and the poorly defined Al–OH absorption feature at 2443 nm, as well as for XRD, detection limit, powder homogenizing, and the small amount of montmorillonite below 1 wt.%, was the source of discrepancies. Full article
(This article belongs to the Section Optical Sensors)
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Article
Multi-Agent Systems in Fog–Cloud Computing for Critical Healthcare Task Management Model (CHTM) Used for ECG Monitoring
Sensors 2021, 21(20), 6923; https://doi.org/10.3390/s21206923 - 19 Oct 2021
Cited by 19 | Viewed by 1545
Abstract
In the last decade, the developments in healthcare technologies have been increasing progressively in practice. Healthcare applications such as ECG monitoring, heartbeat analysis, and blood pressure control connect with external servers in a manner called cloud computing. The emerging cloud paradigm offers different [...] Read more.
In the last decade, the developments in healthcare technologies have been increasing progressively in practice. Healthcare applications such as ECG monitoring, heartbeat analysis, and blood pressure control connect with external servers in a manner called cloud computing. The emerging cloud paradigm offers different models, such as fog computing and edge computing, to enhance the performances of healthcare applications with minimum end-to-end delay in the network. However, many research challenges exist in the fog-cloud enabled network for healthcare applications. Therefore, in this paper, a Critical Healthcare Task Management (CHTM) model is proposed and implemented using an ECG dataset. We design a resource scheduling model among fog nodes at the fog level. A multi-agent system is proposed to provide the complete management of the network from the edge to the cloud. The proposed model overcomes the limitations of providing interoperability, resource sharing, scheduling, and dynamic task allocation to manage critical tasks significantly. The simulation results show that our model, in comparison with the cloud, significantly reduces the network usage by 79%, the response time by 90%, the network delay by 65%, the energy consumption by 81%, and the instance cost by 80%. Full article
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Article
FBG-Based Sensor for the Assessment of Heat Transfer Rate of Liquids in a Forced Convective Environment
Sensors 2021, 21(20), 6922; https://doi.org/10.3390/s21206922 - 19 Oct 2021
Cited by 3 | Viewed by 1018
Abstract
The assessment of heat transfer is a complex task, especially for operations in the oil and gas industry, due to the harsh and flammable workspace. In light of the limitations of conventional sensors in harsh environments, this paper presents a fiber Bragg grating [...] Read more.
The assessment of heat transfer is a complex task, especially for operations in the oil and gas industry, due to the harsh and flammable workspace. In light of the limitations of conventional sensors in harsh environments, this paper presents a fiber Bragg grating (FBG)-based sensor for the assessment of the heat transfer rate (HTR) in different liquids. To better understand the phenomenon of heat distribution, a preliminary analysis is performed by constructing two similar scenarios: those with and without the thermal insulation of a styrofoam box. The results indicate the need for a minimum of thermal power to balance the generated heat with the thermal losses of the setup. In this minimum heat, the behavior of the thermal distribution changes from quadratic to linear. To assess such features, the estimation of the specific heat capacity and the thermal conductivity of water are performed from 3 W to 12 W, in 3 W steps, resulting in a specific heat of 1.144 cal/g °C and thermal conductivity of 0.5682 W/m °C. The calibration and validation of the HTR sensor is performed in a thermostatic bath. The method, based on the temperature slope relative to the time curve, allowed for the measurement of HTR in water and Kryo 51 oil, for different heat insertion configurations. For water, the HTR estimation was 308.782 W, which means an uncertainty of 2.8% with the reference value of the cooling power (300 W). In Kryo 51 oil, the estimated heat absorbed by the oil was 4.38 kW in heating and 718.14 kW in cooling. Full article
(This article belongs to the Special Issue Optical Sensors for Flow Diagnostics)
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Article
Characterization of Temperature Gradients According to Height in a Baroque Church by Means of Wireless Sensors
Sensors 2021, 21(20), 6921; https://doi.org/10.3390/s21206921 - 19 Oct 2021
Cited by 1 | Viewed by 1240
Abstract
The baroque church of Saint Thomas and Saint Philip Neri (Valencia, Spain), which was built between 1727 and 1736, contains valuable paintings by renowned Spanish artists. Due to the considerable height of the central nave, the church can experience vertical temperature gradients. In [...] Read more.
The baroque church of Saint Thomas and Saint Philip Neri (Valencia, Spain), which was built between 1727 and 1736, contains valuable paintings by renowned Spanish artists. Due to the considerable height of the central nave, the church can experience vertical temperature gradients. In order to investigate this issue, temperatures were recorded between August 2017 and February 2018 from a wireless monitoring system composed of 21 sensor nodes, which were located at different heights in the church from 2 to 13 m from the floor level. For characterizing the temperature at high, medium and low altitude heights, a novel methodology is proposed based on sparse Partial Least Squares regression (sPLS), Linear Discriminant Analysis (LDA), and the Holt-Winters method, among others, which were applied to a time series of temperature. This approach is helpful to discriminate temperature profiles according to sensor height. Once the vertical thermal gradients for each month were characterized, it was found that temperature reached the maximum correlation with sensor height in the period between August 10th and September 9th. Furthermore, the most important features from the time series that explain this correlation are the mean temperature and the mean of moving range. In the period mentioned, the vertical thermal gradient was estimated to be about 0.043 C/m, which implies a difference of 0.47 C on average between sensor nodes at 2 m from the floor with respect to the upper ones located at 13 m from the floor level. The gradient was estimated as the slope from a linear regression model using height and hourly mean temperature as the predictor and response, respectively. This gradient is consistent with similar reported studies. The fact that such gradient was only found in one month suggests that the mechanisms of dust deposition on walls involved in vertical thermal gradients are not important in this case regarding the preventive conservation of artworks. Furthermore, the methodology proposed here was useful to discriminate the time series at high, medium and low altitude levels. This approach can be useful when a set of sensors is installed for microclimate monitoring in churches, cathedrals, and other historical buildings, at different levels and positions. Full article
(This article belongs to the Special Issue Sensors and Data Processing Techniques for Cultural Heritage)
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Article
Discovering Daily Activity Patterns from Sensor Data Sequences and Activity Sequences
Sensors 2021, 21(20), 6920; https://doi.org/10.3390/s21206920 - 19 Oct 2021
Cited by 4 | Viewed by 1229
Abstract
The necessity of caring for elderly people is increasing. Great efforts are being made to enable the elderly population to remain independent for as long as possible. Technologies are being developed to monitor the daily activities of a person to detect their state. [...] Read more.
The necessity of caring for elderly people is increasing. Great efforts are being made to enable the elderly population to remain independent for as long as possible. Technologies are being developed to monitor the daily activities of a person to detect their state. Approaches that recognize activities from simple environment sensors have been shown to perform well. It is also important to know the habits of a resident to distinguish between common and uncommon behavior. In this paper, we propose a novel approach to discover a person’s common daily routines. The approach consists of sequence comparison and a clustering method to obtain partitions of daily routines. Such partitions are the basis to detect unusual sequences of activities in a person’s day. Two types of partitions are examined. The first partition type is based on daily activity vectors, and the second type is based on sensor data. We show that daily activity vectors are needed to obtain reasonable results. We also show that partitions obtained with generalized Hamming distance for sequence comparison are better than partitions obtained with the Levenshtein distance. Experiments are performed with two publicly available datasets. Full article
(This article belongs to the Special Issue Advanced Sensors/Devices for Ambient Assisted Living)
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Article
Microwave Tomography Using Neural Networks for Its Application in an Industrial Microwave Drying System
Sensors 2021, 21(20), 6919; https://doi.org/10.3390/s21206919 - 19 Oct 2021
Cited by 7 | Viewed by 1715
Abstract
The article presents an application of microwave tomography (MWT) in an industrial drying system to develop tomographic-based process control. The imaging modality is applied to estimate moisture distribution in a polymer foam undergoing drying process. Our Leading challenges are fast data acquisition from [...] Read more.
The article presents an application of microwave tomography (MWT) in an industrial drying system to develop tomographic-based process control. The imaging modality is applied to estimate moisture distribution in a polymer foam undergoing drying process. Our Leading challenges are fast data acquisition from the MWT sensors and real-time image reconstruction of the process. Thus, a limited number of sensors are chosen for the MWT and are placed only on top of the polymer foam to enable fast data acquisition. For real-time estimation, we present a neural network-based reconstruction scheme to estimate moisture distribution in a polymer foam. Training data for the neural network is generated using a physics-based electromagnetic scattering model and a parametric model for moisture sample generation. Numerical data for different moisture scenarios are considered to validate and test the performance of the network. Further, the trained network performance is evaluated with data from our developed prototype of the MWT sensor array. The experimental results show that the network has good accuracy and generalization capabilities. Full article
(This article belongs to the Special Issue Tomographic Sensors for Industrial Process Control)
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Review
Detecting Fall Risk and Frailty in Elders with Inertial Motion Sensors: A Survey of Significant Gait Parameters
Sensors 2021, 21(20), 6918; https://doi.org/10.3390/s21206918 - 19 Oct 2021
Cited by 4 | Viewed by 2355
Abstract
In the elderly, geriatric problems such as the risk of fall or frailty are a challenge for society. Patients with frailty present difficulties in walking and higher fall risk. The use of sensors for gait analysis allows the detection of objective parameters related [...] Read more.
In the elderly, geriatric problems such as the risk of fall or frailty are a challenge for society. Patients with frailty present difficulties in walking and higher fall risk. The use of sensors for gait analysis allows the detection of objective parameters related to these pathologies and to make an early diagnosis. Inertial Measurement Units (IMUs) are wearables that, due to their accuracy, portability, and low price, are an excellent option to analyze human gait parameters in health-monitoring applications. Many relevant gait parameters (e.g., step time, walking speed) are used to assess motor, or even cognitive, health problems in the elderly, but we perceived that there is not a full consensus on which parameters are the most significant to estimate the risk of fall and the frailty state. In this work, we analyzed the different IMU-based gait parameters proposed in the literature to assess frailty state (robust, prefrail, or frail) or fall risk. The aim was to collect the most significant gait parameters, measured from inertial sensors, able to discriminate between patient groups and to highlight those parameters that are not relevant or for which there is controversy among the examined works. For this purpose, a literature review of the studies published in recent years was carried out; apart from 10 previous relevant reviews using inertial and other sensing technologies, a total of 22 specific studies giving statistical significance values were analyzed. The results showed that the most significant parameters are double-support time, gait speed, stride time, step time, and the number of steps/day or walking percentage/day, for frailty diagnosis. In the case of fall risk detection, parameters related to trunk stability or movements are the most relevant. Although these results are important, the total number of works found was limited and most of them performed the significance statistics on subsets of all possible gait parameters; this fact highlights the need for new frailty studies using a more complete set of gait parameters. Full article
(This article belongs to the Collection Sensors for Human Movement Applications)
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Article
Correlates of Balance and Aerobic Indices in Lower-Limb Prostheses Users on Arm Crank Exercise
Sensors 2021, 21(20), 6917; https://doi.org/10.3390/s21206917 - 19 Oct 2021
Cited by 1 | Viewed by 1381
Abstract
Background: The HUMAC Balance System (HBS) offers valid measurement of balance, and the arm crank exercise test (ACE) is a valid measure of physiological capacity. Neither have been used to evaluate associations between balance and physiological capacity in lower-limb amputees. Methods: Thirty-five participants [...] Read more.
Background: The HUMAC Balance System (HBS) offers valid measurement of balance, and the arm crank exercise test (ACE) is a valid measure of physiological capacity. Neither have been used to evaluate associations between balance and physiological capacity in lower-limb amputees. Methods: Thirty-five participants with lower-limb amputations were recruited. Standing balance (center of pressure) was evaluated during eyes opened (EO) and eyes closed (EC) conditions using the HBS. Participants performed ACE graded exercise testing (GXT) to evaluate aerobic capacity. Spearman’s rho was used to identify relationships between variables. Cut-points for three groups were generated for time on ACE. Mann–Whitney U tests were used to explore significant differences in variables of balance and ACE between low and high performers. Results: Relationships between variables of eyes open displacement (EOD), eyes open velocity (EOV), eyes closed displacement (ECD), and eyes closed velocity (ECV) were significant (p < 0.05), and high performers with EO also performed best with EC. Longer exercise times were significantly associated with increased HRpeak, VO2peak, VEpeak, and RERpeak (p < 0.05). HRpeak (143.0 ± 30.6 b/min), VO2peak (22.7 ± 7.9 and 10.6 ± 4.7 mL/kg/min), VEpeak (80.2 ± 22.2 and 33.2 ± 12.7 L/min), and RERpeak (1.26 ± 0.08 and 1.13 ± 0.11) were significantly greater in high performers than low performers, respectively (p < 0.05). There was no significant association among VO2peak and any balance task variables; however, there were significant associations between some balance and physiological variables. Conclusions: Findings differentiated high and low performers; however, participants were still well below able-bodied norms of physical capacity. Training to mitigate deconditioning is suggested. Full article
(This article belongs to the Collection Sensors for Human Movement Applications)
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Article
The Experimental Verification of Direct-Write Silver Conductive Grid and ARIMA Time Series Analysis for Crack Propagation
Sensors 2021, 21(20), 6916; https://doi.org/10.3390/s21206916 - 19 Oct 2021
Cited by 1 | Viewed by 995
Abstract
The paper presents experimental verification of customized resistive crack propagation sensors as an alternative method for other common structural health monitoring (SHM) techniques. Most of these are sensitive to changes in the sensor network configuration and a baseline dataset must be collected for [...] Read more.
The paper presents experimental verification of customized resistive crack propagation sensors as an alternative method for other common structural health monitoring (SHM) techniques. Most of these are sensitive to changes in the sensor network configuration and a baseline dataset must be collected for the analysis of the structure condition. Sensors investigated within the paper are manufactured by the direct-write process with electrically conductive, silver-microparticle-filled paint to prepare a tailored measuring grid on an epoxy or polyurethane coating as a driving/insulating layer. This method is designed to enhance the functionality and usability compared to commercially available crack gauges. By using paint with conductive metal particles, the shape of the sensor measuring grid can be more easily adapted to the structure, while, in the previous approach, only a few grid-fixed sensors are available. A fatigue test on the compact tension (CT) specimen is presented and discussed to evaluate the ability of the developed sensors to detect and monitor fatigue cracks. Additionally, the ARIMA time series algorithm is developed both for monitoring and predicting crack growth, based on the acquired data. The proposed sensors’ verification reveal their good performance to detect and monitor fatigue fractures with a relatively low measurement error and ARIMA estimated crack length compared with the crack opening displacement (COD) gauge. Full article
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Article
Piezoelectric Sensor for the Monitoring of Arterial Pulse Wave: Detection of Arrhythmia Occurring in PAC/PVC Patients
Sensors 2021, 21(20), 6915; https://doi.org/10.3390/s21206915 - 19 Oct 2021
Cited by 3 | Viewed by 2776
Abstract
Previous studies have found that the non-invasive blood pressure measurement method based on the oscillometric method is inaccurate when an arrhythmia occurs. Therefore, we propose a high-sensitivity pulse sensor that can measure the hemodynamic characteristics of the pulse wave and then estimate the [...] Read more.
Previous studies have found that the non-invasive blood pressure measurement method based on the oscillometric method is inaccurate when an arrhythmia occurs. Therefore, we propose a high-sensitivity pulse sensor that can measure the hemodynamic characteristics of the pulse wave and then estimate the blood pressure. When an arrhythmia occurs, the hemodynamics of the pulse wave are abnormal and change the morphology of the pulse wave. Our proposed sensor can measure the occurrence of ectopic beats from the radial artery, and the detection algorithm can reduce the error of blood pressure estimation caused by the distortion of ectopic beats that occurs when the pulse wave is measured. In this study, we tested patients with premature atrial contraction (PAC) or premature ventricular contraction (PVC) and analyzed the morphology of the pulse waves when the sensor detected the ectopic beats. We discuss the advantages of using the Moens–Korteweg equation to estimate the blood pressure of patients with arrhythmia, which is different from the oscillometric method. Our research provides a possible arrhythmia detection method for wearable devices and can accurately estimate blood pressure in a non-invasive way during an arrhythmia. Full article
(This article belongs to the Section Physical Sensors)
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