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Sensors, Volume 21, Issue 2 (January-2 2021) – 276 articles

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Open AccessArticle
Revisiting the CompCars Dataset for Hierarchical Car Classification: New Annotations, Experiments, and Results
Sensors 2021, 21(2), 596; https://doi.org/10.3390/s21020596 (registering DOI) - 15 Jan 2021
Abstract
We address the task of classifying car images at multiple levels of detail, ranging from the top-level car type, down to the specific car make, model, and year. We analyze existing datasets for car classification, and identify the CompCars as an excellent starting [...] Read more.
We address the task of classifying car images at multiple levels of detail, ranging from the top-level car type, down to the specific car make, model, and year. We analyze existing datasets for car classification, and identify the CompCars as an excellent starting point for our task. We show that convolutional neural networks achieve an accuracy above 90% on the finest-level classification task. This high performance, however, is scarcely representative of real-world situations, as it is evaluated on a biased training/test split. In this work, we revisit the CompCars dataset by first defining a new training/test split, which better represents real-world scenarios by setting a more realistic baseline at 61% accuracy on the new test set. We also propagate the existing (but limited) type-level annotation to the entire dataset, and we finally provide a car-tight bounding box for each image, automatically defined through an ad hoc car detector. To evaluate this revisited dataset, we design and implement three different approaches to car classification, two of which exploit the hierarchical nature of car annotations. Our experiments show that higher-level classification in terms of car type positively impacts classification at a finer grain, now reaching 70% accuracy. The achieved performance constitutes a baseline benchmark for future research, and our enriched set of annotations is made available for public download. Full article
(This article belongs to the Special Issue Intelligent Sensors and Computer Vision)
Open AccessArticle
A Hybrid Planning Approach Based on MPC and Parametric Curves for Overtaking Maneuvers
Sensors 2021, 21(2), 595; https://doi.org/10.3390/s21020595 (registering DOI) - 15 Jan 2021
Abstract
Automated Driving Systems (ADS) have received a considerable amount of attention in the last few decades, as part of the Intelligent Transportation Systems (ITS) field. However, this technology still lacks total automation capacities while keeping driving comfort and safety under risky scenarios, for [...] Read more.
Automated Driving Systems (ADS) have received a considerable amount of attention in the last few decades, as part of the Intelligent Transportation Systems (ITS) field. However, this technology still lacks total automation capacities while keeping driving comfort and safety under risky scenarios, for example, overtaking, obstacle avoidance, or lane changing. Consequently, this work presents a novel method to resolve the obstacle avoidance and overtaking problems named Hybrid Planning. This solution combines the passenger’s comfort associated with the smoothness of Bézier curves and the reliable capacities of Model Predictive Control (MPC) to react against unexpected conditions, such as obstacles on the lane, overtaking and lane-change based maneuvers. A decoupled linear-model was used for the MPC formulation to ensure short computation times. The obstacles and other vehicles’ information are obtained via V2X (vehicle communications). The tests were performed in an automated Renault Twizy vehicle and they have shown good performance under complex scenarios involving static and moving obstacles at a maximum speed of 60 kph. Full article
(This article belongs to the Special Issue Smooth Motion Planning for Autonomous Vehicles)
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Open AccessArticle
Epidemic Analysis of Wireless Rechargeable Sensor Networks Based on an Attack–Defense Game Model
Sensors 2021, 21(2), 594; https://doi.org/10.3390/s21020594 (registering DOI) - 15 Jan 2021
Abstract
Energy constraint hinders the popularization and development of wireless sensor networks (WSNs). As an emerging technology equipped with rechargeable batteries, wireless rechargeable sensor networks (WRSNs) are being widely accepted and recognized. In this paper, we research the security issues in WRSNs which need [...] Read more.
Energy constraint hinders the popularization and development of wireless sensor networks (WSNs). As an emerging technology equipped with rechargeable batteries, wireless rechargeable sensor networks (WRSNs) are being widely accepted and recognized. In this paper, we research the security issues in WRSNs which need to be addressed urgently. After considering the charging process, the activating anti-malware program process, and the launching malicious attack process in the modeling, the susceptible–infected–anti-malware–low-energy–susceptible (SIALS) model is proposed. Through the method of epidemic dynamics, this paper analyzes the local and global stabilities of the SIALS model. Besides, this paper introduces a five-tuple attack–defense game model to further study the dynamic relationship between malware and WRSNs. By introducing a cost function and constructing a Hamiltonian function, the optimal strategies for malware and WRSNs are obtained based on the Pontryagin Maximum Principle. Furthermore, the simulation results show the validation of the proposed theories and reveal the influence of parameters on the infection. In detail, the Forward–Backward Sweep method is applied to solve the issues of convergence of co-state variables at terminal moment. Full article
(This article belongs to the Special Issue Security and Privacy in the Internet of Things (IoT))
Open AccessArticle
Wearable Vibration Sensor for Measuring the Wing Flapping of Insects
Sensors 2021, 21(2), 593; https://doi.org/10.3390/s21020593 (registering DOI) - 15 Jan 2021
Abstract
In this study, we fabricated a novel wearable vibration sensor for insects and measured their wing flapping. An analysis of insect wing deformation in relation to changes in the environment plays an important role in understanding the underlying mechanism enabling insects to dynamically [...] Read more.
In this study, we fabricated a novel wearable vibration sensor for insects and measured their wing flapping. An analysis of insect wing deformation in relation to changes in the environment plays an important role in understanding the underlying mechanism enabling insects to dynamically interact with their surrounding environment. It is common to use a high-speed camera to measure the wing flapping; however, it is difficult to analyze the feedback mechanism caused by the environmental changes caused by the flapping because this method applies an indirect measurement. Therefore, we propose the fabrication of a novel film sensor that is capable of measuring the changes in the wingbeat frequency of an insect. This novel sensor is composed of flat silver particles admixed with a silicone polymer, which changes the value of the resistor when a bending deformation occurs. As a result of attaching this sensor to the wings of a moth and a dragonfly and measuring the flapping of the wings, we were able to measure the frequency of the flapping with high accuracy. In addition, as a result of simultaneously measuring the relationship between the behavior of a moth during its search for an odor source and its wing flapping, it became clear that the frequency of the flapping changed depending on the frequency of the odor reception. From this result, a wearable film sensor for an insect that can measure the displacement of the body during a particular behavior was fabricated. Full article
(This article belongs to the Special Issue Printed Electrode Sensors and Biosensors)
Open AccessArticle
Development of a Human-Display Interface with Vibrotactile Feedback for Real-World Assistive Applications
Sensors 2021, 21(2), 592; https://doi.org/10.3390/s21020592 (registering DOI) - 15 Jan 2021
Abstract
It is important to operate devices with control panels and touch screens assisted by haptic feedback in mobile environments such as driving automobiles and electric power wheelchairs. A lot of consideration is needed to give accurate haptic feedback, especially, presenting clear touch feedback [...] Read more.
It is important to operate devices with control panels and touch screens assisted by haptic feedback in mobile environments such as driving automobiles and electric power wheelchairs. A lot of consideration is needed to give accurate haptic feedback, especially, presenting clear touch feedback to the elderly and people with reduced sensation is a very critical issue from healthcare and safety perspectives. In this study, we aimed to identify the perceptual characteristics for the frequency and direction of haptic vibration on the touch screen with vehicle-driving vibration and to propose an efficient haptic system based on these characteristics. As a result, we demonstrated that the detection threshold shift decreased at frequencies above 210 Hz due to the contact pressure during active touch, but the detection threshold shift increased at below 210 Hz. We found that the detection thresholds were 0.30–0.45 gpeak with similar sensitivity in the 80–270 Hz range. The haptic system implemented by reflecting the experimental results achieved characteristics suitable for use scenarios in automobiles. Ultimately, it could provide practical guidelines for the development of touch screens to give accurate touch feedback in the real-world environment. Full article
(This article belongs to the Special Issue Tactile Sensing and Rendering for Healthcare Applications)
Open AccessArticle
Ant Colony-Based Hyperparameter Optimisation in Total Variation Reconstruction in X-ray Computed Tomography
Sensors 2021, 21(2), 591; https://doi.org/10.3390/s21020591 (registering DOI) - 15 Jan 2021
Abstract
In this paper, a computer-aided training method for hyperparameter selection of limited data X-ray computed tomography (XCT) reconstruction was proposed. The proposed method employed the ant colony optimisation (ACO) approach to assist in hyperparameter selection for the adaptive-weighted projection-controlled steepest descent (AwPCSD) algorithm, [...] Read more.
In this paper, a computer-aided training method for hyperparameter selection of limited data X-ray computed tomography (XCT) reconstruction was proposed. The proposed method employed the ant colony optimisation (ACO) approach to assist in hyperparameter selection for the adaptive-weighted projection-controlled steepest descent (AwPCSD) algorithm, which is a total-variation (TV) based regularisation algorithm. During the implementation, there was a colony of artificial ants that swarm through the AwPCSD algorithm. Each ant chose a set of hyperparameters required for its iterative CT reconstruction and the correlation coefficient (CC) score was given for reconstructed images compared to the reference image. A colony of ants in one generation left a pheromone through its chosen path representing a choice of hyperparameters. Higher score means stronger pheromones/probabilities to attract more ants in the next generations. At the end of the implementation, the hyperparameter configuration with the highest score was chosen as an optimal set of hyperparameters. In the experimental results section, the reconstruction using hyperparameters from the proposed method was compared with results from three other cases: the conjugate gradient least square (CGLS), the AwPCSD algorithm using the set of arbitrary hyperparameters and the cross-validation method.The experiments showed that the results from the proposed method were superior to those of the CGLS algorithm and the AwPCSD algorithm using the set of arbitrary hyperparameters. Although the results of the ACO algorithm were slightly inferior to those of the cross-validation method as measured by the quantitative metrics, the ACO algorithm was over 10 times faster than cross—Validation. The optimal set of hyperparameters from the proposed method was also robust against an increase of noise in the data and can be applicable to different imaging samples with similar context. The ACO approach in the proposed method was able to identify optimal values of hyperparameters for a dataset and, as a result, produced a good quality reconstructed image from limited number of projection data. The proposed method in this work successfully solves a problem of hyperparameters selection, which is a major challenge in an implementation of TV based reconstruction algorithms. Full article
(This article belongs to the Special Issue Tomography Sensing Technologies)
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Open AccessArticle
Fast and Robust Time Synchronization with Median Kalman Filtering for Mobile Ad-Hoc Networks
Sensors 2021, 21(2), 590; https://doi.org/10.3390/s21020590 (registering DOI) - 15 Jan 2021
Abstract
Time synchronization is an important issue in ad-hoc networks for reliable information exchange. The algorithms for time synchronization in ad-hoc networks are largely categorized into two types. One is based on a selection of a reference node, and the other is based on [...] Read more.
Time synchronization is an important issue in ad-hoc networks for reliable information exchange. The algorithms for time synchronization in ad-hoc networks are largely categorized into two types. One is based on a selection of a reference node, and the other is based on a consensus among neighbor nodes. These two types of methods are targeting static environments. However, synchronization errors among nodes increase sharply when nodes move or when incorrect synchronization information is exchanged due to the failure of some nodes. In this paper, we propose a synchronization technique for mobile ad-hoc networks, which considers both the mobility of nodes and the abnormal behaviors of malicious or failed nodes. Specifically, synchronization information extracted from a median of the time information of the neighbor nodes is quickly disseminated. This information effectively excludes the outliers, which adversely affect the synchronization of the networks. In addition, Kalman filtering is applied to reduce the synchronization error occurring in the transmission and reception of time information. The simulation results confirm that the proposed scheme has a fast synchronization convergence speed and low synchronization error compared to conventional algorithms. Full article
(This article belongs to the Section Internet of Things)
Open AccessArticle
Action Unit Detection by Learning the Deformation Coefficients of a 3D Morphable Model
Sensors 2021, 21(2), 589; https://doi.org/10.3390/s21020589 (registering DOI) - 15 Jan 2021
Abstract
Facial Action Units (AUs) correspond to the deformation/contraction of individual facial muscles or their combinations. As such, each AU affects just a small portion of the face, with deformations that are asymmetric in many cases. Generating and analyzing AUs in 3D is particularly [...] Read more.
Facial Action Units (AUs) correspond to the deformation/contraction of individual facial muscles or their combinations. As such, each AU affects just a small portion of the face, with deformations that are asymmetric in many cases. Generating and analyzing AUs in 3D is particularly relevant for the potential applications it can enable. In this paper, we propose a solution for 3D AU detection and synthesis by developing on a newly defined 3D Morphable Model (3DMM) of the face. Differently from most of the 3DMMs existing in the literature, which mainly model global variations of the face and show limitations in adapting to local and asymmetric deformations, the proposed solution is specifically devised to cope with such difficult morphings. During a training phase, the deformation coefficients are learned that enable the 3DMM to deform to 3D target scans showing neutral and facial expression of the same individual, thus decoupling expression from identity deformations. Then, such deformation coefficients are used, on the one hand, to train an AU classifier, on the other, they can be applied to a 3D neutral scan to generate AU deformations in a subject-independent manner. The proposed approach for AU detection is validated on the Bosphorus dataset, reporting competitive results with respect to the state-of-the-art, even in a challenging cross-dataset setting. We further show the learned coefficients are general enough to synthesize realistic 3D face instances with AUs activation. Full article
(This article belongs to the Special Issue Recent Advances in Depth Sensors and Applications)
Open AccessArticle
Using an Optimization Algorithm to Detect Hidden Waveforms of Signals
Sensors 2021, 21(2), 588; https://doi.org/10.3390/s21020588 (registering DOI) - 15 Jan 2021
Abstract
Source signals often contain various hidden waveforms, which further provide precious information. Therefore, detecting and capturing these waveforms is very important. For signal decomposition (SD), discrete Fourier transform (DFT) and empirical mode decomposition (EMD) are two main tools. They both can easily decompose [...] Read more.
Source signals often contain various hidden waveforms, which further provide precious information. Therefore, detecting and capturing these waveforms is very important. For signal decomposition (SD), discrete Fourier transform (DFT) and empirical mode decomposition (EMD) are two main tools. They both can easily decompose any source signal into different components. DFT is based on Cosine functions; EMD is based on a collection of intrinsic mode functions (IMFs). With the help of Cosine functions and IMFs respectively, DFT and EMD can extract additional information from sensed signals. However, due to a considerably finite frequency resolution, EMD easily causes frequency mixing. Although DFT has a larger frequency resolution than EMD, its resolution is also finite. To effectively detect and capture hidden waveforms, we use an optimization algorithm, differential evolution (DE), to decompose. The technique is called SD by DE (SDDE). In contrast, SDDE has an infinite frequency resolution, and hence it has the opportunity to exactly decompose. Our proposed SDDE approach is the first tool of directly applying an optimization algorithm to signal decomposition in which the main components of source signals can be determined. For source signals from four combinations of three periodic waves, our experimental results in the absence of noise show that the proposed SDDE approach can exactly or almost exactly determine their corresponding separate components. Even in the presence of white noise, our proposed SDDE approach is still able to determine the main components. However, DFT usually generates spurious main components; EMD cannot decompose well and is easily affected by white noise. According to the superior experimental performance, our proposed SDDE approach can be widely used in the future to explore various signals for more valuable information. Full article
Open AccessArticle
Impact of Current Pulsation on BLDC Motor Parameters
Sensors 2021, 21(2), 587; https://doi.org/10.3390/s21020587 (registering DOI) - 15 Jan 2021
Abstract
BrushLess Direct-Current (BLDC) motors are characterized by high efficiency and reliability due to the fact that the BLDC motor does not require power to the rotor. The rotor of the BLDC motor consists of permanent magnets. When examining the waveform of the current [...] Read more.
BrushLess Direct-Current (BLDC) motors are characterized by high efficiency and reliability due to the fact that the BLDC motor does not require power to the rotor. The rotor of the BLDC motor consists of permanent magnets. When examining the waveform of the current supplied to the motor windings, significant current ripple was observed within one power cycle, where the optimum value would be the constant value of this current during one power cycle. The variability of this current in one motor supply cycle results from the variability of the electromotive force induced in the motor winding. The paper presents a diagram of the power supply system consisting of an electronic commutator and a DC/DC converter made by the authors, and a proposed modification of the power supply system reducing the current pulsation of the motor windings and thus the possibility of reducing energy losses in the motor windings. The paper presents numerous results of measurements which showed a significant reduction in energy losses in the case of low-load operation. Full article
(This article belongs to the Section Intelligent Sensors)
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Open AccessArticle
Simulations of Switchback, Fragmentationand Sunspot Pair in δ-Sunspots during Magnetic Flux Emergence
Sensors 2021, 21(2), 586; https://doi.org/10.3390/s21020586 (registering DOI) - 15 Jan 2021
Abstract
Strong flares and coronal mass ejections (CMEs), launched from δ-sunspots, are the most catastrophic energy-releasing events in the solar system. The formations of δ-sunspots and relevant polarity inversion lines (PILs) are crucial for the understanding of flare eruptions and CMEs. In [...] Read more.
Strong flares and coronal mass ejections (CMEs), launched from δ-sunspots, are the most catastrophic energy-releasing events in the solar system. The formations of δ-sunspots and relevant polarity inversion lines (PILs) are crucial for the understanding of flare eruptions and CMEs. In this work, the kink-stable, spot-spot-type δ-sunspots induced by flux emergence are simulated, under different subphotospheric initial conditions of magnetic field strength, radius, twist, and depth. The time evolution of various plasma variables of the δ-sunspots are simulated and compared with the observation data, including magnetic bipolar structures, relevant PILs, and temperature. The simulation results show that magnetic polarities display switchbacks at a certain stage and then split into numerous fragments. The simulated fragmentation phenomenon in some δ-sunspots may provide leads for future observations in the field. Full article
(This article belongs to the Special Issue Simulation Studies on Remote Sensing Scenarios)
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Open AccessLetter
Design of a Curved Shape Photonic Crystal Taper for Highly Efficient Mode Coupling
Sensors 2021, 21(2), 585; https://doi.org/10.3390/s21020585 (registering DOI) - 15 Jan 2021
Abstract
The design and modeling of a curved shape photonic crystal taper consisting of Si rods integrated with a photonic crystal waveguide are presented. The waveguide is composed of a hexagonal lattice of Si rods and optimized for CO2 sensing based on absorption [...] Read more.
The design and modeling of a curved shape photonic crystal taper consisting of Si rods integrated with a photonic crystal waveguide are presented. The waveguide is composed of a hexagonal lattice of Si rods and optimized for CO2 sensing based on absorption spectroscopy. We investigated two different approaches to design a taper for a photonic crystal waveguide in a hexagonal lattice of silicon rods. For the first approach (type 1), the taper consists of a square lattice taper followed by a lattice composed of a smooth transition from a square to a hexagonal lattice. In the second approach (type 2), the taper consists of a distorted hexagonal lattice. Different shapes, such as convex, concave, and linear, for the curvature of the taper were considered and investigated. The structure of the taper was improved to enhance the coupling efficiency up to 96% at a short taper length of 25 lattice periods. The finite-difference time-domain (FDTD) technique was used to study the transmission spectrum and the group index. The study proves the improvement of coupling using a curved shape taper. Controlling the group index along the taper could be further improved to enhance the coupling efficiency in a wider spectral range. Full article
(This article belongs to the Section Optical Sensors)
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Open AccessArticle
Non-Invasive Method to Detect Infection with Mycobacterium tuberculosis Complex in Wild Boar by Measurement of Volatile Organic Compounds Obtained from Feces with an Electronic Nose System
Sensors 2021, 21(2), 584; https://doi.org/10.3390/s21020584 (registering DOI) - 15 Jan 2021
Viewed by 46
Abstract
More effective methods to detect bovine tuberculosis, caused by Mycobacterium bovis, in wildlife, is of paramount importance for preventing disease spread to other wild animals, livestock, and human beings. In this study, we analyzed the volatile organic compounds emitted by fecal samples [...] Read more.
More effective methods to detect bovine tuberculosis, caused by Mycobacterium bovis, in wildlife, is of paramount importance for preventing disease spread to other wild animals, livestock, and human beings. In this study, we analyzed the volatile organic compounds emitted by fecal samples collected from free-ranging wild boar captured in Doñana National Park, Spain, with an electronic nose system based on organically-functionalized gold nanoparticles. The animals were separated by the age group for performing the analysis. Adult (>24 months) and sub-adult (12–24 months) animals were anesthetized before sample collection, whereas the juvenile (<12 months) animals were manually restrained while collecting the sample. Good accuracy was obtained for the adult and sub-adult classification models: 100% during the training phase and 88.9% during the testing phase for the adult animals, and 100% during both the training and testing phase for the sub-adult animals, respectively. The results obtained could be important for the further development of a non-invasive and less expensive detection method of bovine tuberculosis in wildlife populations. Full article
(This article belongs to the Special Issue Electronic Noses and Tongues for Environmental Monitoring)
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Open AccessArticle
Hyperspectral Imaging (HSI) Technology for the Non-Destructive Freshness Assessment of Pearl Gentian Grouper under Different Storage Conditions
Sensors 2021, 21(2), 583; https://doi.org/10.3390/s21020583 (registering DOI) - 15 Jan 2021
Viewed by 53
Abstract
This study used visible/near-infrared hyperspectral imaging (HSI) technology combined with chemometric methods to assess the freshness of pearl gentian grouper. The partial least square discrimination analysis (PLS-DA) and competitive adaptive reweighted sampling-PLS-DA (CARS-PLS-DA) models were used to classify fresh, refrigerated, and frozen–thawed fish. [...] Read more.
This study used visible/near-infrared hyperspectral imaging (HSI) technology combined with chemometric methods to assess the freshness of pearl gentian grouper. The partial least square discrimination analysis (PLS-DA) and competitive adaptive reweighted sampling-PLS-DA (CARS-PLS-DA) models were used to classify fresh, refrigerated, and frozen–thawed fish. The PLS-DA model achieved better classification of fresh, refrigerated, and frozen–thawed fish with the accuracy of 100%, 96.43%, and 96.43%, respectively. Further, the PLS regression (PLSR) and CARS-PLS regression (CARS-PLSR) models were used to predict the storage time of fish under different storage conditions, and the prediction accuracy was assessed using the prediction correlation coefficients (Rp2), root mean squared error of prediction (RMSEP), and residual predictive deviation (RPD). For the prediction of storage time, the CARS-PLS model presented the better result of room temperature (Rp2 = 0.948, RMSEP = 0.255, RPD = 4.380) and refrigeration (Rp2 = 0.9319, RMSEP = 1.188, RPD = 3.857), while the better prediction of freeze was by obtained by the PLSR model (Rp2 = 0.9250, RMSEP = 2.910, RPD = 3.469). Finally, the visualization of storage time based on the PLSR model under different storage conditions were realized. This study confirmed the potential of HSI as a rapid and non-invasive technique to identify fish freshness. Full article
(This article belongs to the Section Remote Sensors)
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Open AccessArticle
Estimating Functional Threshold Power in Endurance Running from Shorter Time Trials Using a 6-Axis Inertial Measurement Sensor
Sensors 2021, 21(2), 582; https://doi.org/10.3390/s21020582 (registering DOI) - 15 Jan 2021
Abstract
Wearable technology has allowed for the real-time assessment of mechanical work employed in several sporting activities. Through novel power metrics, Functional Threshold Power have shown a reliable indicator of training intensities. This study aims to determine the relationship between mean power output (MPO) [...] Read more.
Wearable technology has allowed for the real-time assessment of mechanical work employed in several sporting activities. Through novel power metrics, Functional Threshold Power have shown a reliable indicator of training intensities. This study aims to determine the relationship between mean power output (MPO) values obtained during three submaximal running time trials (i.e., 10 min, 20 min, and 30 min) and the functional threshold power (FTP). Twenty-two recreationally trained male endurance runners completed four submaximal running time trials of 10, 20, 30, and 60 min, trying to cover the longest possible distance on a motorized treadmill. Absolute MPO (W), normalized MPO (W/kg) and standard deviation (SD) were calculated for each time trial with a power meter device attached to the shoelaces. All simplified FTP trials analyzed (i.e., FTP10, FTP20, and FTP30) showed a significant association with the calculated FTP (p < 0.001) for both MPO and normalized MPO, whereas stronger correlations were found with longer time trials. Individual correction factors (ICF% = FTP60/FTPn) of ~90% for FTP10, ~94% for FTP20, and ~96% for FTP30 were obtained. The present study procures important practical applications for coaches and athletes as it provides a more accurate estimation of FTP in endurance running through less fatiguing, reproducible tests. Full article
(This article belongs to the Special Issue Wearable Sensors & Gait)
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Open AccessArticle
Fault Detection and Identification Method for Quadcopter Based on Airframe Vibration Signals
Sensors 2021, 21(2), 581; https://doi.org/10.3390/s21020581 (registering DOI) - 15 Jan 2021
Viewed by 79
Abstract
Quadcopters are widely used in a variety of military and civilian mission scenarios. Real-time online detection of the abnormal state of the quadcopter is vital to the safety of aircraft. Existing data-driven fault detection methods generally usually require numerous sensors to collect data. [...] Read more.
Quadcopters are widely used in a variety of military and civilian mission scenarios. Real-time online detection of the abnormal state of the quadcopter is vital to the safety of aircraft. Existing data-driven fault detection methods generally usually require numerous sensors to collect data. However, quadcopter airframe space is limited. A large number of sensors cannot be loaded, meaning that it is difficult to use additional sensors to capture fault signals for quadcopters. In this paper, without additional sensors, a Fault Detection and Identification (FDI) method for quadcopter blades based on airframe vibration signals is proposed using the airborne acceleration sensor. This method integrates multi-axis data information and effectively detects and identifies quadcopter blade faults through Long and Short-Term Memory (LSTM) network models. Through flight experiments, the quadcopter triaxial accelerometer data are collected for airframe vibration signals at first. Then, the wavelet packet decomposition method is employed to extract data features, and the standard deviations of the wavelet packet coefficients are employed to form the feature vector. Finally, the LSTM-based FDI model is constructed for quadcopter blade FDI. The results show that the method can effectively detect and identify quadcopter blade faults with a better FDI performance and a higher model accuracy compared with the Back Propagation (BP) neural network-based FDI model. Full article
(This article belongs to the collection Multi-Sensor Information Fusion) Printed Edition available
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Open AccessArticle
Finite Element Modelling of Bandgap Engineered Graphene FET with the Application in Sensing Methanethiol Biomarker
Sensors 2021, 21(2), 580; https://doi.org/10.3390/s21020580 (registering DOI) - 15 Jan 2021
Viewed by 77
Abstract
In this work, we have designed and simulated a graphene field effect transistor (GFET) with the purpose of developing a sensitive biosensor for methanethiol, a biomarker for bacterial infections. The surface of a graphene layer is functionalized by manipulation of its surface structure [...] Read more.
In this work, we have designed and simulated a graphene field effect transistor (GFET) with the purpose of developing a sensitive biosensor for methanethiol, a biomarker for bacterial infections. The surface of a graphene layer is functionalized by manipulation of its surface structure and is used as the channel of the GFET. Two methods, doping the crystal structure of graphene and decorating the surface by transition metals (TMs), are utilized to change the electrical properties of the graphene layers to make them suitable as a channel of the GFET. The techniques also change the surface chemistry of the graphene, enhancing its adsorption characteristics and making binding between graphene and biomarker possible. All the physical parameters are calculated for various variants of graphene in the absence and presence of the biomarker using counterpoise energy-corrected density functional theory (DFT). The device was modelled using COMSOL Multiphysics. Our studies show that the sensitivity of the device is affected by structural parameters of the device, the electrical properties of the graphene, and with adsorption of the biomarker. It was found that the devices made of graphene layers decorated with TM show higher sensitivities toward detecting the biomarker compared with those made by doped graphene layers. Full article
(This article belongs to the Special Issue Micro/Nanostructured Sensors for Biomedical Applications)
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Open AccessArticle
Ocular Phantom-Based Feasibility Study of an Early Diagnosis Device for Glaucoma
Sensors 2021, 21(2), 579; https://doi.org/10.3390/s21020579 (registering DOI) - 15 Jan 2021
Viewed by 74
Abstract
Glaucoma causes total or partial loss of vision in 10% of people over the age of 70, increasing their fragility and isolation. It is characterised by the destruction of the optic nerve fibres, which may result from excessively high intraocular pressure as well [...] Read more.
Glaucoma causes total or partial loss of vision in 10% of people over the age of 70, increasing their fragility and isolation. It is characterised by the destruction of the optic nerve fibres, which may result from excessively high intraocular pressure as well as other phenomena. Diagnosis is currently reached through a combination of several checks, mainly of the eyes’ fundus, tonometry and gonioscopy. Prior to validation for human subjects, the objective of this study is to validate whether ocular phantom-based models could be used to diagnose glaucoma using an onboard system, which could, even at home, prevent the early-stage development of the pathology. Eight phantoms modelling healthy eyes and eight phantoms modelling eyes with glaucoma due to excessive intraocular pressure were measured using an onboard system, including lens and electrophysiology electronics. We measured the actual average Zr (real part of impedance) impedance of 160.9 ± 24.3 ohms (glaucoma ocular phantom models) versus 211.9 ± 36.9 ohms (healthy ocular phantom models), and an average total water volume (Vt) of 3.02 ± 0.35 mL (glaucoma ocular phantom models) versus 2.45 ± 0.28 mL (healthy ocular Phantoms). On average, we obtained 51 ohms (−24.1%) less and 0.57 mL (22.9%) of total water volume more, respectively. Normality tests (Shapiro–Wilk) for Vt and Zr indicate p < 0.001 and p < 0.01, respectively. Since these variables do not respect normal laws, unmatched Mann–Whitney tests were performed indicating a significant difference between Vt and Zr in the healthy ocular phantom models and those modelling glaucoma. To conclude, this preliminary study indicates the possibility of discriminating between healthy eyes with those with glaucoma. However, further large-scale studies involving healthy eyes and those suffering from glaucoma are necessary to generate viable models. Full article
(This article belongs to the Section Biomedical Sensors)
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Open AccessArticle
A Soft Exoskeleton Glove for Hand Bilateral Training via Surface EMG
Sensors 2021, 21(2), 578; https://doi.org/10.3390/s21020578 (registering DOI) - 15 Jan 2021
Viewed by 77
Abstract
Traditional rigid exoskeletons can be challenging to the comfort of wearers and can have large pressure, which can even alter natural hand motion patterns. In this paper, we propose a low-cost soft exoskeleton glove (SExoG) system driven by surface electromyography (sEMG) signals from [...] Read more.
Traditional rigid exoskeletons can be challenging to the comfort of wearers and can have large pressure, which can even alter natural hand motion patterns. In this paper, we propose a low-cost soft exoskeleton glove (SExoG) system driven by surface electromyography (sEMG) signals from non-paretic hand for bilateral training. A customization method of geometrical parameters of soft actuators was presented, and their structure was redesigned. Then, the corresponding pressure values of air-pump to generate different angles of actuators were determined to support four hand motions (extension, rest, spherical grip, and fist). A two-step hybrid model combining the neural network and the state exclusion algorithm was proposed to recognize four hand motions via sEMG signals from the healthy limb. Four subjects were recruited to participate in the experiments. The experimental results show that the pressure values for the four hand motions were about −2, 0, 40, and 70 KPa, and the hybrid model can yield a mean accuracy of 98.7% across four hand motions. It can be concluded that the novel SExoG system can mirror the hand motions of non-paretic hand with good performance. Full article
(This article belongs to the Special Issue Electromyography (EMG) Sensor and System)
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Open AccessArticle
A Novel Method for Determining Angular Speed and Acceleration Using Sin-Cos Encoders
Sensors 2021, 21(2), 577; https://doi.org/10.3390/s21020577 (registering DOI) - 15 Jan 2021
Viewed by 76
Abstract
The performance of vehicle safety systems depends very much on the accuracy of the signals coming from vehicle sensors. Among them, the wheel speed is of vital importance. This paper describes a new method to obtain the wheel speed by using Sin-Cos encoders. [...] Read more.
The performance of vehicle safety systems depends very much on the accuracy of the signals coming from vehicle sensors. Among them, the wheel speed is of vital importance. This paper describes a new method to obtain the wheel speed by using Sin-Cos encoders. The methodology is based on the use of the Savitzky–Golay filters to optimally determine the coefficients of the polynomials that best fit the measured signals and their time derivatives. The whole process requires a low computational cost, which makes it suitable for real-time applications. This way it is possible to provide the safety system with an accurate measurement of both the angular speed and acceleration of the wheels. The proposed method has been compared to other conventional approaches. The results obtained in simulations and real tests show the superior performance of the proposed method, particularly for medium and low wheel angular speeds. Full article
(This article belongs to the Special Issue Sensors for Road Vehicles of the Future)
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Open AccessCommunication
Brain-Inspired Data Transmission in Dense Wireless Network
Sensors 2021, 21(2), 576; https://doi.org/10.3390/s21020576 (registering DOI) - 15 Jan 2021
Viewed by 81
Abstract
In this paper, the authors investigate the innovative concept of a dense wireless network supported by additional functionalities inspired by the human nervous system. The nervous system controls the entire human body due to reliable and energetically effective signal transmission. Among the structure [...] Read more.
In this paper, the authors investigate the innovative concept of a dense wireless network supported by additional functionalities inspired by the human nervous system. The nervous system controls the entire human body due to reliable and energetically effective signal transmission. Among the structure and modes of operation of such an ultra-dense network of neurons and glial cells, the authors selected the most worthwhile when planning a dense wireless network. These ideas were captured, modeled in the context of wireless data transmission. The performance of such an approach have been analyzed in two ways, first, the theoretic limits of such an approach has been derived based on the stochastic geometry, in particular—based on the percolation theory. Additionally, computer experiments have been carried out to verify the performance of the proposed transmission schemes in four simulation scenarios. Achieved results showed the prospective improvement of the reliability of the wireless networks while applying proposed bio-inspired solutions and keeping the transmission extremely simple. Full article
(This article belongs to the Special Issue Emerging Technologies in Communications and Networking: 5G and Beyond)
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Open AccessArticle
Theoretical Studies and Implementation on the Temporary Data Storage Method for Cone Penetration Test
Sensors 2021, 21(2), 575; https://doi.org/10.3390/s21020575 (registering DOI) - 15 Jan 2021
Viewed by 70
Abstract
The traditional cone penetration test system uses cable to transmit data; as the probe goes deeper into the ground, the length of the cable will become longer. This makes the installation of the test equipment more complicated, and excessively long cables cause signal [...] Read more.
The traditional cone penetration test system uses cable to transmit data; as the probe goes deeper into the ground, the length of the cable will become longer. This makes the installation of the test equipment more complicated, and excessively long cables cause signal distortion and seriously affect data accuracy. To simplify the experimental equipment and improve the accuracy of data acquisition, a cableless cone penetration test system is proposed. The improved system uses an SD card to store the experimental data, as opposed to using cables for communication which, often lead to the distortion of signals caused by long-distance communication and data loss caused by accidental cable breaks. Therefore, the accuracy of the collected data is higher, and the experimental device is simplified. To evaluate the applicability and efficiency of our design, we have carried out exploration experiments with the sensor system proposed in this paper. The test results show that the experimental data collected by the new system are basically consistent with the data collected by traditional cable CPT equipment, and the accuracy of the collected data is higher. It is more reliable and accurate to analyze the comprehensive mechanical properties of the soil layers with the data collected by the new system. Full article
(This article belongs to the Section Electronic Sensors)
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Open AccessArticle
Performance Analysis in Olympic Sailors of the Formula Kite Class Using GPS
Sensors 2021, 21(2), 574; https://doi.org/10.3390/s21020574 (registering DOI) - 15 Jan 2021
Viewed by 89
Abstract
Formula Kite is an Olympic sport that mainly differs from other kitesurfing modalities for the use of a hydrofoil. It is considered an extreme sport due to the great technical ability required. Regarding performance, the variables that determine performance in a real competition [...] Read more.
Formula Kite is an Olympic sport that mainly differs from other kitesurfing modalities for the use of a hydrofoil. It is considered an extreme sport due to the great technical ability required. Regarding performance, the variables that determine performance in a real competition situation have not been studied, and even less so with Olympic sailors. Therefore, the objective of this study was to determine the technical and tactical variables that differentiate elite sailors. The sample consisted of 42 Olympic sailors of the Formula Kite class, who were evaluated in three World Cups. Using a GPS device, the speed, distance traveled, maneuvers, and time spent on the courses of upwind, downwind, and beam reach were recorded. The highest-level sailors presented a higher speed in upwind/downwind/beam reach and a shorter time in upwind and beam reach. Performance seems to be more strongly influenced by technical variables, such as speed, than by tactical variables. Full article
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Open AccessArticle
Singular Value Decomposition for Removal of Cardiac Interference from Trunk Electromyogram
Sensors 2021, 21(2), 573; https://doi.org/10.3390/s21020573 (registering DOI) - 15 Jan 2021
Viewed by 102
Abstract
A new algorithm based on singular value decomposition (SVD) to remove cardiac contamination from trunk electromyography (EMG) is proposed. Its performance is compared to currently available algorithms at different signal-to-noise ratios (SNRs). The algorithm is applied on individual channels. An experimental calibration curve [...] Read more.
A new algorithm based on singular value decomposition (SVD) to remove cardiac contamination from trunk electromyography (EMG) is proposed. Its performance is compared to currently available algorithms at different signal-to-noise ratios (SNRs). The algorithm is applied on individual channels. An experimental calibration curve to adjust the number of SVD components to the SNR (0–20 dB) is proposed. A synthetic dataset is generated by the combination of electrocardiography (ECG) and EMG to establish a ground truth reference for validation. The performance is compared with state-of-the-art algorithms: gating, high-pass filtering, template subtraction (TS), and independent component analysis (ICA). Its applicability on real data is investigated in an illustrative diaphragm EMG of a patient with sleep apnea. The SVD-based algorithm outperforms existing methods in reconstructing trunk EMG. It is superior to the others in the time (relative mean squared error < 15%) and frequency (shift in mean frequency < 1 Hz) domains. Its feasibility is proven on diaphragm EMG, which shows a better agreement with the respiratory cycle (correlation coefficient = 0.81, p-value < 0.01) compared with TS and ICA. Its application on real data is promising to non-obtrusively estimate respiratory effort for sleep-related breathing disorders. The algorithm is not limited to the need for additional reference ECG, increasing its applicability in clinical practice. Full article
(This article belongs to the Special Issue Sensors and Biomedical Signal Processing for Patient Monitoring)
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Open AccessArticle
Induction of Neural Plasticity Using a Low-Cost Open Source Brain-Computer Interface and a 3D-Printed Wrist Exoskeleton
Sensors 2021, 21(2), 572; https://doi.org/10.3390/s21020572 (registering DOI) - 15 Jan 2021
Viewed by 115
Abstract
Brain–computer interfaces (BCIs) have been proven to be useful for stroke rehabilitation, but there are a number of factors that impede the use of this technology in rehabilitation clinics and in home-use, the major factors including the usability and costs of the BCI [...] Read more.
Brain–computer interfaces (BCIs) have been proven to be useful for stroke rehabilitation, but there are a number of factors that impede the use of this technology in rehabilitation clinics and in home-use, the major factors including the usability and costs of the BCI system. The aims of this study were to develop a cheap 3D-printed wrist exoskeleton that can be controlled by a cheap open source BCI (OpenViBE), and to determine if training with such a setup could induce neural plasticity. Eleven healthy volunteers imagined wrist extensions, which were detected from single-trial electroencephalography (EEG), and in response to this, the wrist exoskeleton replicated the intended movement. Motor-evoked potentials (MEPs) elicited using transcranial magnetic stimulation were measured before, immediately after, and 30 min after BCI training with the exoskeleton. The BCI system had a true positive rate of 86 ± 12% with 1.20 ± 0.57 false detections per minute. Compared to the measurement before the BCI training, the MEPs increased by 35 ± 60% immediately after and 67 ± 60% 30 min after the BCI training. There was no association between the BCI performance and the induction of plasticity. In conclusion, it is possible to detect imaginary movements using an open-source BCI setup and control a cheap 3D-printed exoskeleton that when combined with the BCI can induce neural plasticity. These findings may promote the availability of BCI technology for rehabilitation clinics and home-use. However, the usability must be improved, and further tests are needed with stroke patients. Full article
(This article belongs to the Special Issue EEG-Based Brain–Computer Interface for a Real-Life Appliance)
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Open AccessArticle
Hull and Aerial Holonomic Propulsion System Design for Optimal Underwater Sensor Positioning in Autonomous Surface Vessels
Sensors 2021, 21(2), 571; https://doi.org/10.3390/s21020571 (registering DOI) - 15 Jan 2021
Viewed by 95
Abstract
Acoustic Doppler Current Profiler (ADCP) sensors measure water inflows and are essential to evaluate the Flow Curve (FC) of rivers. The FC is used to calibrate hydrological models responsible for planning the electrical dispatch of all power plants in several countries. Therefore, errors [...] Read more.
Acoustic Doppler Current Profiler (ADCP) sensors measure water inflows and are essential to evaluate the Flow Curve (FC) of rivers. The FC is used to calibrate hydrological models responsible for planning the electrical dispatch of all power plants in several countries. Therefore, errors in those measures propagate to the final energy cost evaluation. One problem regarding this sensor is its positioning on the vessel. If placed on the bow, it becomes exposed to flowing obstacles, and if it is installed on the stern, the redirected water from the boat and its propulsion system change the sensor readings. To improve the sensor readings, this paper proposes the design of a catamaran-like Autonomous Surface Vessel (ASV) with an optimized hull design, aerial propulsion, and optimal sensor placement to keep them protected and precise, allowing inspections in critical areas such as ultra-shallow waters and mangroves. Full article
(This article belongs to the Section Remote Sensors)
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Open AccessArticle
A Framework for Coverage Path Planning Optimization Based on Point Cloud for Structural Inspection
Sensors 2021, 21(2), 570; https://doi.org/10.3390/s21020570 (registering DOI) - 15 Jan 2021
Viewed by 99
Abstract
Different practical applications have emerged in the last few years, requiring periodic and detailed inspections to verify possible structural changes. Inspections using Unmanned Aerial Vehicles (UAVs) should minimize flight time due to battery time restrictions and identify the terrain’s topographic features. In this [...] Read more.
Different practical applications have emerged in the last few years, requiring periodic and detailed inspections to verify possible structural changes. Inspections using Unmanned Aerial Vehicles (UAVs) should minimize flight time due to battery time restrictions and identify the terrain’s topographic features. In this sense, Coverage Path Planning (CPP) aims at finding the best path to coverage of a determined area respecting the operation’s restrictions. Photometric information from the terrain is used to create routes or even refine paths already created. Therefore, this research’s main contribution is developing a methodology that uses a metaheuristic algorithm based on point cloud data to inspect slope and dams structures. The technique was applied in a simulated and real scenario to verify its effectiveness. The results showed an increasing 3D reconstructions’ quality observing optimizing photometric and mission time criteria. Full article
(This article belongs to the Section Intelligent Sensors)
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Open AccessArticle
Analysis of the Single Frequency Network Gain in Digital Audio Broadcasting Networks
Sensors 2021, 21(2), 569; https://doi.org/10.3390/s21020569 (registering DOI) - 14 Jan 2021
Viewed by 198
Abstract
The single frequency network (SFN) is a popular solution in modern digital audio and television system networks for extending effective coverage, compared to its traditional single-transmitter counterpart. As benefits of this configuration appear to be obvious, this paper focuses on the exact analysis [...] Read more.
The single frequency network (SFN) is a popular solution in modern digital audio and television system networks for extending effective coverage, compared to its traditional single-transmitter counterpart. As benefits of this configuration appear to be obvious, this paper focuses on the exact analysis of so-called SFN gain—a quantitative effect of advantage in terms of the received signal strength. The investigations cover a statistical analysis of SFN gain values, obtained by means of computer simulations, with respect to the factors influencing the coverage, i.e., the protection level, the reception mode (fixed, portable, mobile), and the receiver location (outdoor, indoor). The analyses conclude with an observation that the most noteworthy contribution of the SFN gain is observed on the far edges of the networks, and the least one close to the transmitters. It is also observed that the highest values of the SFN gain can be expected in the fixed mode, while the protection level has the lowest impact. Full article
(This article belongs to the Special Issue Emerging Technologies in Communications and Networking: 5G and Beyond)
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Open AccessReview
Internet of Everything (IoE) Taxonomies: A Survey and a Novel Knowledge-Based Taxonomy
Sensors 2021, 21(2), 568; https://doi.org/10.3390/s21020568 - 14 Jan 2021
Viewed by 160
Abstract
The paradigm of the Internet of everything (IoE) is advancing toward enriching people’s lives by adding value to the Internet of things (IoT), with connections among people, processes, data, and things. This paper provides a survey of the literature on IoE research, highlighting [...] Read more.
The paradigm of the Internet of everything (IoE) is advancing toward enriching people’s lives by adding value to the Internet of things (IoT), with connections among people, processes, data, and things. This paper provides a survey of the literature on IoE research, highlighting concerns in terms of intelligence services and knowledge creation. The significant contributions of this study are as follows: (1) a systematic literature review of IoE taxonomies (including IoT); (2) development of a taxonomy to guide the identification of critical knowledge in IoE applications, an in-depth classification of IoE enablers (sensors and actuators); (3) validation of the defined taxonomy with 50 IoE applications; and (4) identification of issues and challenges in existing IoE applications (using the defined taxonomy) with regard to insights about knowledge processes. To the best of our knowledge, and taking into consideration the 76 other taxonomies compared, this present work represents the most comprehensive taxonomy that provides the orchestration of intelligence in network connections concerning knowledge processes, type of IoE enablers, observation characteristics, and technological capabilities in IoE applications. Full article
(This article belongs to the Special Issue Internet of Things, Big Data and Smart Systems)
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Open AccessArticle
Development and Implementation of a Hybrid Wireless Sensor Network of Low Power and Long Range for Urban Environments
Sensors 2021, 21(2), 567; https://doi.org/10.3390/s21020567 - 14 Jan 2021
Viewed by 182
Abstract
The urban population, worldwide, is growing exponentially and with it the demand for information on pollution levels, vehicle traffic, or available parking, giving rise to citizens connected to their environment. This article presents an experimental long range (LoRa) and low power consumption network, [...] Read more.
The urban population, worldwide, is growing exponentially and with it the demand for information on pollution levels, vehicle traffic, or available parking, giving rise to citizens connected to their environment. This article presents an experimental long range (LoRa) and low power consumption network, with a combination of static and mobile wireless sensors (hybrid architecture) to tune and validate concentrator placement, to obtain a large coverage in an urban environment. A mobile node has been used, carrying a gateway and various sensors. The Activation By Personalization (ABP) mode has been used, justified for urban applications requiring multicasting. This allows to compare the coverage of each static gateway, being able to make practical decisions about its location. With this methodology, it has been possible to provide service to the city of Malaga, through a single concentrator node. The information acquired is synchronized in an external database, to monitor the data in real time, being able to geolocate the dataframes through web mapping services. This work presents the development and implementation of a hybrid wireless sensor network of long range and low power, configured and tuned to achieve efficient performance in a mid-size city, and tested in experiments in a real urban environment. Full article
(This article belongs to the Special Issue LoRa Sensor Network)
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