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Sensors, Volume 21, Issue 5 (March-1 2021) – 372 articles

Cover Story (view full-size image): Surgical gesture detection can provide targeted surgical skill assessment and feedback during surgical training for robot-assisted surgery (RAS). We extracted features from electroencephalogram (EEG) data, utilizing network neuroscience algorithms, and used them in machine learning algorithms to classify robot-assisted surgical gestures. EEG was collected from 5 RAS surgeons while performing 34 robot-assisted radical prostatectomies over the course of 3 years. Eight dominant and 6 non-dominant hand gesture types were extracted and synchronized with associated EEG data. Our proposed method was used to classify 8 gesture types performed by the dominant hand with accuracy: 90%, precision: 90%, sensitivity: 88%, and also 6 gesture types performed by the non-dominant hand with accuracy: 93%, precision: 94%, sensitivity: 94%. View this paper.
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Open AccessArticle
Supernovae Detection with Fully Convolutional One-Stage Framework
Sensors 2021, 21(5), 1926; https://doi.org/10.3390/s21051926 - 09 Mar 2021
Viewed by 579
Abstract
A series of sky surveys were launched in search of supernovae and generated a tremendous amount of data, which pushed astronomy into a new era of big data. However, it can be a disastrous burden to manually identify and report supernovae, because such [...] Read more.
A series of sky surveys were launched in search of supernovae and generated a tremendous amount of data, which pushed astronomy into a new era of big data. However, it can be a disastrous burden to manually identify and report supernovae, because such data have huge quantity and sparse positives. While the traditional machine learning methods can be used to deal with such data, deep learning methods such as Convolutional Neural Networks demonstrate more powerful adaptability in this area. However, most data in the existing works are either simulated or without generality. How do the state-of-the-art object detection algorithms work on real supernova data is largely unknown, which greatly hinders the development of this field. Furthermore, the existing works of supernovae classification usually assume the input images are properly cropped with a single candidate located in the center, which is not true for our dataset. Besides, the performance of existing detection algorithms can still be improved for the supernovae detection task. To address these problems, we collected and organized all the known objectives of the Panoramic Survey Telescope and Rapid Response System (Pan-STARRS) and the Popular Supernova Project (PSP), resulting in two datasets, and then compared several detection algorithms on them. After that, the selected Fully Convolutional One-Stage (FCOS) method is used as the baseline and further improved with data augmentation, attention mechanism, and small object detection technique. Extensive experiments demonstrate the great performance enhancement of our detection algorithm with the new datasets. Full article
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Open AccessArticle
Frequency Domain Analysis of Partial-Tensor Rotating Accelerometer Gravity Gradiometer
Sensors 2021, 21(5), 1925; https://doi.org/10.3390/s21051925 - 09 Mar 2021
Viewed by 460
Abstract
The output model of a rotating accelerometer gravity gradiometer (RAGG) established by the inertial dynamics method cannot reflect the change of signal frequency, and calibration sensitivity and self-gradient compensation effect for the RAGG is a very important stage in the development process that [...] Read more.
The output model of a rotating accelerometer gravity gradiometer (RAGG) established by the inertial dynamics method cannot reflect the change of signal frequency, and calibration sensitivity and self-gradient compensation effect for the RAGG is a very important stage in the development process that cannot be omitted. In this study, a model based on the outputs of accelerometers on the disc of RGAA is established to calculate the gravity gradient corresponding to the distance, through the study of the RAGG output influenced by a surrounding mass in the frequency domain. Taking particle, sphere, and cuboid as examples, the input-output models of gravity gradiometer are established based on the center gradient and four accelerometers, respectively. Simulation results show that, if the scale factors of the four accelerometers on the disk are the same, the output signal of the RAGG only contains (4k+2)ω (ω is the spin frequency of disc for RAGG) harmonic components, and its amplitude is related to the orientation of the surrounding mass. Based on the results of numerical simulation of the three models, if the surrounding mass is close to the RAGG, the input-output models of gravity gradiometer are more accurate based on the four accelerometers. Finally, some advantages and disadvantages of cuboid and sphere are compared and some suggestions related to calibration and self-gradient compensation are given. Full article
(This article belongs to the Section Physical Sensors)
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Open AccessArticle
Real-Time Compression for Tactile Internet Data Streams
Sensors 2021, 21(5), 1924; https://doi.org/10.3390/s21051924 - 09 Mar 2021
Viewed by 477
Abstract
The Tactile Internet will require ultra-low latencies for combining machines and humans in systems where humans are in the control loop. Real-time and perceptual coding in these systems commonly require content-specific approaches. We present a generic approach based on deliberately reduced number accuracy [...] Read more.
The Tactile Internet will require ultra-low latencies for combining machines and humans in systems where humans are in the control loop. Real-time and perceptual coding in these systems commonly require content-specific approaches. We present a generic approach based on deliberately reduced number accuracy and evaluate the trade-off between savings achieved and errors introduced with real-world data for kinesthetic movement and tele-surgery. Our combination of bitplane-level accuracy adaptability with perceptual threshold-based limits allows for great flexibility in broad application scenarios. Combining the attainable savings with the relatively small introduced errors enables the optimal selection of a working point for the method in actual implementations. Full article
(This article belongs to the Section Internet of Things)
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Open AccessCommunication
Characterization of Changes in P-Wave VCG Loops Following Pulmonary-Vein Isolation
Sensors 2021, 21(5), 1923; https://doi.org/10.3390/s21051923 - 09 Mar 2021
Viewed by 442
Abstract
Atrial fibrillation is the most common type of cardiac arrhythmia in clinical practice. Currently, catheter ablation for pulmonary-vein isolation is a well-established treatment for maintaining sinus rhythm when antiarrhythmic drugs do not succeed. Unfortunately, arrhythmia recurrence after catheter ablation remains common, with estimated [...] Read more.
Atrial fibrillation is the most common type of cardiac arrhythmia in clinical practice. Currently, catheter ablation for pulmonary-vein isolation is a well-established treatment for maintaining sinus rhythm when antiarrhythmic drugs do not succeed. Unfortunately, arrhythmia recurrence after catheter ablation remains common, with estimated rates of up to 45%. A better understanding of factors leading to atrial-fibrillation recurrence is needed. Hence, the aim of this study is to characterize changes in the atrial propagation pattern following pulmonary-vein isolation, and investigate the relation between such characteristics and atrial-fibrillation recurrence. Fifty patients with paroxysmal atrial fibrillation who had undergone catheter ablation were included in this study. Time-segment and vectorcardiogram-loop-morphology analyses were applied to characterize P waves extracted from 1 min long 12-lead electrocardiogram segments before and after the procedure, respectively. Results showed that P-wave vectorcardiogram loops were significantly less round and more planar, P waves and PR intervals were significantly shorter, and heart rate was significantly higher after the procedure. Differences were larger for patients who did not have arrhythmia recurrences at 2 years of follow-up; for these patients, the pre- and postprocedure P waves could be identified with 84% accuracy. Full article
(This article belongs to the Special Issue Biomedical Signal Processing for Disease Diagnosis)
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Open AccessCommunication
Metal Oxide Nanorods-Based Sensor Array for Selective Detection of Biomarker Gases
Sensors 2021, 21(5), 1922; https://doi.org/10.3390/s21051922 - 09 Mar 2021
Viewed by 514
Abstract
The breath gas analysis through gas phase chemical analysis draws attention in terms of non-invasive and real time monitoring. The array-type sensors are one of the diagnostic methods with high sensitivity and selectivity towards the target gases. Herein, we presented a 2 × [...] Read more.
The breath gas analysis through gas phase chemical analysis draws attention in terms of non-invasive and real time monitoring. The array-type sensors are one of the diagnostic methods with high sensitivity and selectivity towards the target gases. Herein, we presented a 2 × 4 sensor array with a micro-heater and ceramic chip. The device is designed in a small size for portability, including the internal eight-channel sensor array. In2O3 NRs and WO3 NRs manufactured through the E-beam evaporator’s glancing angle method were used as sensing materials. Pt, Pd, and Au metal catalysts were decorated for each channel to enhance functionality. The sensor array was measured for the exhaled gas biomarkers CH3COCH3, NO2, and H2S to confirm the respiratory diagnostic performance. Through this operation, the theoretical detection limit was calculated as 1.48 ppb for CH3COCH3, 1.9 ppt for NO2, and 2.47 ppb for H2S. This excellent detection performance indicates that our sensor array detected the CH3COCH3, NO2, and H2S as biomarkers, applying to the breath gas analysis. Our results showed the high potential of the gas sensor array as a non-invasive diagnostic tool that enables real-time monitoring. Full article
(This article belongs to the Special Issue Gas Sensors for Internet of Things Era)
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Open AccessArticle
Deep Learning Driven Noise Reduction for Reduced Flux Computed Tomography
Sensors 2021, 21(5), 1921; https://doi.org/10.3390/s21051921 - 09 Mar 2021
Viewed by 624
Abstract
Deep neural networks have received considerable attention in clinical imaging, particularly with respect to the reduction of radiation risk. Lowering the radiation dose by reducing the photon flux inevitably results in the degradation of the scanned image quality. Thus, researchers have sought to [...] Read more.
Deep neural networks have received considerable attention in clinical imaging, particularly with respect to the reduction of radiation risk. Lowering the radiation dose by reducing the photon flux inevitably results in the degradation of the scanned image quality. Thus, researchers have sought to exploit deep convolutional neural networks (DCNNs) to map low-quality, low-dose images to higher-dose, higher-quality images, thereby minimizing the associated radiation hazard. Conversely, computed tomography (CT) measurements of geomaterials are not limited by the radiation dose. In contrast to the human body, however, geomaterials may be comprised of high-density constituents causing increased attenuation of the X-rays. Consequently, higher-dose images are required to obtain an acceptable scan quality. The problem of prolonged acquisition times is particularly severe for micro-CT based scanning technologies. Depending on the sample size and exposure time settings, a single scan may require several hours to complete. This is of particular concern if phenomena with an exponential temperature dependency are to be elucidated. A process may happen too fast to be adequately captured by CT scanning. To address the aforementioned issues, we apply DCNNs to improve the quality of rock CT images and reduce exposure times by more than 60%, simultaneously. We highlight current results based on micro-CT derived datasets and apply transfer learning to improve DCNN results without increasing training time. The approach is applicable to any computed tomography technology. Furthermore, we contrast the performance of the DCNN trained by minimizing different loss functions such as mean squared error and structural similarity index. Full article
(This article belongs to the Special Issue Image Sensing and Processing with Convolutional Neural Networks)
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Open AccessArticle
Vision-Based Tactile Sensor Mechanism for the Estimation of Contact Position and Force Distribution Using Deep Learning
Sensors 2021, 21(5), 1920; https://doi.org/10.3390/s21051920 - 09 Mar 2021
Viewed by 472
Abstract
This work describes the development of a vision-based tactile sensor system that utilizes the image-based information of the tactile sensor in conjunction with input loads at various motions to train the neural network for the estimation of tactile contact position, area, and force [...] Read more.
This work describes the development of a vision-based tactile sensor system that utilizes the image-based information of the tactile sensor in conjunction with input loads at various motions to train the neural network for the estimation of tactile contact position, area, and force distribution. The current study also addresses pragmatic aspects, such as choice of the thickness and materials for the tactile fingertips and surface tendency, etc. The overall vision-based tactile sensor equipment interacts with an actuating motion controller, force gauge, and control PC (personal computer) with a LabVIEW software on it. The image acquisition was carried out using a compact stereo camera setup mounted inside the elastic body to observe and measure the amount of deformation by the motion and input load. The vision-based tactile sensor test bench was employed to collect the output contact position, angle, and force distribution caused by various randomly considered input loads for motion in X, Y, Z directions and RxRy rotational motion. The retrieved image information, contact position, area, and force distribution from different input loads with specified 3D position and angle are utilized for deep learning. A convolutional neural network VGG-16 classification modelhas been modified to a regression network model and transfer learning was applied to suit the regression task of estimating contact position and force distribution. Several experiments were carried out using thick and thin sized tactile sensors with various shapes, such as circle, square, hexagon, for better validation of the predicted contact position, contact area, and force distribution. Full article
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Open AccessArticle
A Robot Object Recognition Method Based on Scene Text Reading in Home Environments
Sensors 2021, 21(5), 1919; https://doi.org/10.3390/s21051919 - 09 Mar 2021
Viewed by 420
Abstract
With the aim to solve issues of robot object recognition in complex scenes, this paper proposes an object recognition method based on scene text reading. The proposed method simulates human-like behavior and accurately identifies objects with texts through careful reading. First, deep learning [...] Read more.
With the aim to solve issues of robot object recognition in complex scenes, this paper proposes an object recognition method based on scene text reading. The proposed method simulates human-like behavior and accurately identifies objects with texts through careful reading. First, deep learning models with high accuracy are adopted to detect and recognize text in multi-view. Second, datasets including 102,000 Chinese and English scene text images and their inverse are generated. The F-measure of text detection is improved by 0.4% and the recognition accuracy is improved by 1.26% because the model is trained by these two datasets. Finally, a robot object recognition method is proposed based on the scene text reading. The robot detects and recognizes texts in the image and then stores the recognition results in a text file. When the user gives the robot a fetching instruction, the robot searches for corresponding keywords from the text files and achieves the confidence of multiple objects in the scene image. Then, the object with the maximum confidence is selected as the target. The results show that the robot can accurately distinguish objects with arbitrary shape and category, and it can effectively solve the problem of object recognition in home environments. Full article
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Open AccessArticle
Visualizing and Evaluating Finger Movement Using Combined Acceleration and Contact-Force Sensors: A Proof-of-Concept Study
Sensors 2021, 21(5), 1918; https://doi.org/10.3390/s21051918 - 09 Mar 2021
Viewed by 438
Abstract
The 10-s grip and release is a method to evaluate hand dexterity. Current evaluations only visually determine the presence or absence of a disability, but experienced physicians may also make other diagnoses. In this study, we investigated a method for evaluating hand movement [...] Read more.
The 10-s grip and release is a method to evaluate hand dexterity. Current evaluations only visually determine the presence or absence of a disability, but experienced physicians may also make other diagnoses. In this study, we investigated a method for evaluating hand movement function by acquiring and analyzing fingertip data during a 10-s grip and release using a wearable sensor that can measure triaxial acceleration and strain. The subjects were two healthy females. The analysis was performed on the x-, y-, and z-axis data, and absolute acceleration and contact force of all fingertips. We calculated the variability of the data, the number of grip and release, the frequency response, and each finger’s correlation. Experiments with some grip-and-release patterns have resulted in different characteristics for each. It was suggested that this could be expressed in radar charts to intuitively know the state of grip and release. Contact-force data of each finger were found to be useful for understanding the characteristics of grip and release and improving the accuracy of calculating the number of times to grip and release. Frequency analysis suggests that knowing the periodicity of grip and release can detect unnatural grip and release and tremor states. The correlations between the fingers allow us to consider the finger’s grip-and-release characteristics, considering the hand’s anatomy. By taking these factors into account, it is thought that the 10-s grip-and-release test could give us a new value by objectively assessing the motor functions of the hands other than the number of times of grip and release. Full article
(This article belongs to the Special Issue Body Worn Sensors and Related Applications)
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Open AccessArticle
Automatic Identification of Tool Wear Based on Thermography and a Convolutional Neural Network during the Turning Process
Sensors 2021, 21(5), 1917; https://doi.org/10.3390/s21051917 - 09 Mar 2021
Viewed by 457
Abstract
This article presents a control system for a cutting tool condition supervision, which recognises tool wear automatically during turning. We used an infrared camera for process control, which—unlike common cameras—captures the thermographic state, in addition to the visual state of the process. Despite [...] Read more.
This article presents a control system for a cutting tool condition supervision, which recognises tool wear automatically during turning. We used an infrared camera for process control, which—unlike common cameras—captures the thermographic state, in addition to the visual state of the process. Despite challenging environmental conditions (e.g., hot chips) we protected the camera and placed it right up to the cutting knife, so that machining could be observed closely. During the experiment constant cutting conditions were set for the dry machining of workpiece (low alloy carbon steel 1.7225 or 42CrMo4). To build a dataset of over 9000 images, we machined on a lathe with tool inserts of different wear levels. Using a convolutional neural network (CNN), we developed a model for tool wear and tool damage prediction. It determines the state of a cutting tool automatically (none, low, medium, high wear level), based on thermographic process data. The accuracy of classification was 99.55%, which affirms the adequacy of the proposed method. Such a system enables immediate action in the case of cutting tool wear or breakage, regardless of the operator’s knowledge and competence. Full article
(This article belongs to the Special Issue Industry 4.0 and Smart Manufacturing)
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Open AccessArticle
Evaluation of Thawing and Stress Restoration Method for Artificial Frozen Sandy Soils Using Sensors
Sensors 2021, 21(5), 1916; https://doi.org/10.3390/s21051916 - 09 Mar 2021
Viewed by 480
Abstract
Undisturbed frozen samples can be efficiently obtained using the artificial ground freezing method. Thereafter, the restoration of in situ conditions, such as stress and density after thawing, is critical for laboratory testing. This study aims to experimentally explore the effects of thawing and [...] Read more.
Undisturbed frozen samples can be efficiently obtained using the artificial ground freezing method. Thereafter, the restoration of in situ conditions, such as stress and density after thawing, is critical for laboratory testing. This study aims to experimentally explore the effects of thawing and the in situ stress restoration process on the geomechanical properties of sandy soils. Specimens were prepared at a relative density of 60% and frozen at −20 °C under the vertical stress of 100 kPa. After freezing, the specimens placed in the triaxial cell underwent thawing and consolidation phases with various drainage and confining stress conditions, followed by the shear phase. The elastic wave signals and axial deformation were measured during the entire protocol; the shear strength was evaluated from the triaxial compression test. Monotonic and cyclic simple shear tests were conducted to determine the packing density effect on liquefaction resistance. The results show that axial deformation, stiffness, and strength are minimized for a specimen undergoing drained thawing, restoring the initial stress during the consolidation phase, and that denser specimens are less susceptible to liquefaction. Results highlight that the thawing and stress restoration process should be considered to prevent the overestimation of stiffness, strength, and liquefaction resistance of sandy soils. Full article
(This article belongs to the Special Issue Emerging Characterization of Geomaterials Using Advanced Geo-Sensors)
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Open AccessArticle
Tactile Sensors for Parallel Grippers: Design and Characterization
Sensors 2021, 21(5), 1915; https://doi.org/10.3390/s21051915 - 09 Mar 2021
Viewed by 521
Abstract
Tactile data perception is of paramount importance in today’s robotics applications. This paper describes the latest design of the tactile sensor developed in our laboratory. Both the hardware and firmware concepts are reported in detail in order to allow the research community the [...] Read more.
Tactile data perception is of paramount importance in today’s robotics applications. This paper describes the latest design of the tactile sensor developed in our laboratory. Both the hardware and firmware concepts are reported in detail in order to allow the research community the sensor reproduction, also according to their needs. The sensor is based on optoelectronic technology and the pad shape can be adapted to various robotics applications. A flat surface, as the one proposed in this paper, can be well exploited if the object sizes are smaller than the pad and/or the shape recognition is needed, while a domed pad can be used to manipulate bigger objects. Compared to the previous version, the novel tactile sensor has a larger sensing area and a more robust electronic, mechanical and software design that yields less noise and higher flexibility. The proposed design exploits standard PCB manufacturing processes and advanced but now commercial 3D printing processes for the realization of all components. A GitHub repository has been prepared with all files needed to allow the reproduction of the sensor for the interested reader. The whole sensor has been tested with a maximum load equal to 15N, by showing a sensitivity equal to 0.018V/N. Moreover, a complete and detailed characterization for the single taxel and the whole pad is reported to show the potentialities of the sensor also in terms of response time, repeatability, hysteresis and signal to noise ratio. Full article
(This article belongs to the Section Sensors and Robotics)
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Open AccessArticle
Relay Positioning for Load-Balancing and Throughput Enhancement in Dual-Hop Relay Networks
Sensors 2021, 21(5), 1914; https://doi.org/10.3390/s21051914 - 09 Mar 2021
Viewed by 393
Abstract
In a cellular communication system, deploying a relay station (RS) is an effective alternative to installing a new base station (BS). A dual-hop network enhances the throughput of mobile stations (MSs) located in shadow areas or at cell edges by installing RSs between [...] Read more.
In a cellular communication system, deploying a relay station (RS) is an effective alternative to installing a new base station (BS). A dual-hop network enhances the throughput of mobile stations (MSs) located in shadow areas or at cell edges by installing RSs between BSs and MSs. Because additional radio resources should be allocated to the wireless link between BS and RS, a frame to be transmitted from BS is divided into an access zone (AZ) and a relay zone (RZ). BS and RS communicate with each other through the RZ, and they communicate with their registered MSs through an AZ. However, if too many MSs are registered with a certain BS or RS, MS overloading may cause performance degradation. To prevent such performance degradation, it is very important to find the proper positions for RSs to be deployed. In this paper, we propose a method for finding the sub-optimal RS deployment location for the purpose of load-balancing and throughput enhancement. The advantage of the proposed method is the efficiency in find the sub-optimal location of RSs and its reliable tradeoff between load-balancing throughput enhancement. Since the proposed scheme finds the proper position by adjusting the distance and angle of RSs, its computational complexity lower than other global optimization approach or learning-based approach. In addition, the proposed scheme is constituted with the two stages of load-balancing and throughput enhancement. These procedures result in the appropriate tradeoff between load-balancing and throughput enhancement. The simulation results support these advancements of the proposed scheme. Full article
(This article belongs to the Section Communications)
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Open AccessArticle
Modular MA-XRF Scanner Development in the Multi-Analytical Characterisation of a 17th Century Azulejo from Portugal
Sensors 2021, 21(5), 1913; https://doi.org/10.3390/s21051913 - 09 Mar 2021
Viewed by 569
Abstract
A modular X-ray scanning system was developed, to fill in the gap between portable instruments (with a limited analytical area) and mobile instruments (with large analytical areas, and sometimes bulky and difficult to transport). The scanner has been compared to a commercial tabletop [...] Read more.
A modular X-ray scanning system was developed, to fill in the gap between portable instruments (with a limited analytical area) and mobile instruments (with large analytical areas, and sometimes bulky and difficult to transport). The scanner has been compared to a commercial tabletop instrument, by analysing a Portuguese tile (azulejo) from the 17th century. Complementary techniques were used to achieve a throughout characterisation of the sample in a complete non-destructive approach. The complexity of the acquired X-ray fluorescence (XRF) spectra, due to inherent sample stratigraphy, has been resolved using Monte Carlo simulations, and Raman spectroscopy, as the most suitable technique to complement the analysis of azulejos colours, yielding satisfactory results. The colouring agents were identified as cobalt blue and a Zn-modified Naples-yellow. The stratigraphy of the area under study was partially modelled with Monte Carlo simulations. The scanners performance has been compared by evaluating the images outputs and the global spectrum. Full article
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Open AccessArticle
Design and Performance Evaluation of a “Fixed-Point” Spar Buoy Equipped with a Piezoelectric Energy Harvesting Unit for Floating Near-Shore Applications
Sensors 2021, 21(5), 1912; https://doi.org/10.3390/s21051912 - 09 Mar 2021
Viewed by 472
Abstract
In the present work, a spar-buoy scaled model was designed and built through a “Lab-on-Sea” unit, equipped with an energy harvesting system. Such a system is based on deformable bands, which are loyal to the unit, to convert wave motion energy into electricity [...] Read more.
In the present work, a spar-buoy scaled model was designed and built through a “Lab-on-Sea” unit, equipped with an energy harvesting system. Such a system is based on deformable bands, which are loyal to the unit, to convert wave motion energy into electricity by means of piezo patch transducers. In a preliminary stage, the scaled model, suitable for tests in a controlled ripples-type wave motion channel, was tested in order to verify the “fixed-point” assumption in pitch and roll motions and, consequently, to optimize energy harvesting. A special type of structure was designed, numerically simulated, and experimentally verified. The proposed solution represents an advantageous compromise between the lightness of the used materials and the amount of recoverable energy. The energy, which was obtained from the piezo patch transducers during the simulations in the laboratory, was found to be enough to self-sustain the feasible on-board sensors and the remote data transmission system. Full article
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Open AccessCommunication
Rapid Fabrication of Renewable Carbon Fibres by Plasma Arc Discharge and Their Humidity Sensing Properties
Sensors 2021, 21(5), 1911; https://doi.org/10.3390/s21051911 - 09 Mar 2021
Viewed by 559
Abstract
Submicron-sized carbon fibres have been attracting research interest due to their outstanding mechanical and electrical properties. However, the non-renewable resources and their complex fabrication processes limit the scalability and pose difficulties for the utilisation of these materials. Here, we investigate the use of [...] Read more.
Submicron-sized carbon fibres have been attracting research interest due to their outstanding mechanical and electrical properties. However, the non-renewable resources and their complex fabrication processes limit the scalability and pose difficulties for the utilisation of these materials. Here, we investigate the use of plasma arc technology to convert renewable electrospun lignin fibres into a new kind of carbon fibre with a globular and porous microstructure. The influence of arc currents (up to 60 A) on the structural and morphological properties of as-prepared carbon fibres is discussed. Owing to the catalyst-free synthesis, high purity micro-structured carbon fibres with nanocrystalline graphitic domains are produced. Furthermore, the humidity sensing characteristics of the treated fibres at room temperature (23 °C) are demonstrated. Sensors produced from these carbon fibres exhibit good humidity response and repeatability in the range of 30% to 80% relative humidity (RH) and an excellent sensitivity (0.81/%RH) in the high RH regime (60–80%). These results demonstrate that the plasma arc technology has great potential for the development of sustainable, lignin-based carbon fibres for a broad range of application in electronics, sensors and energy storage. Full article
(This article belongs to the Special Issue Smart Composite and Sensors)
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Open AccessArticle
Kubernetes Cluster for Automating Software Production Environment
Sensors 2021, 21(5), 1910; https://doi.org/10.3390/s21051910 - 09 Mar 2021
Viewed by 545
Abstract
Microservices, Continuous Integration and Delivery, Docker, DevOps, Infrastructure as Code—these are the current trends and buzzwords in the technological world of 2020. A popular tool which can facilitate the deployment and maintenance of microservices is Kubernetes. Kubernetes is a platform for running containerized [...] Read more.
Microservices, Continuous Integration and Delivery, Docker, DevOps, Infrastructure as Code—these are the current trends and buzzwords in the technological world of 2020. A popular tool which can facilitate the deployment and maintenance of microservices is Kubernetes. Kubernetes is a platform for running containerized applications, for example microservices. There are two main questions which answer was important for us: how to deploy Kubernetes itself and how to ensure that the deployment fulfils the needs of a production environment. Our research concentrates on the analysis and evaluation of Kubernetes cluster as the software production environment. However, firstly it is necessary to determine and evaluate the requirements of production environment. The paper presents the determination and analysis of such requirements and their evaluation in the case of Kubernetes cluster. Next, the paper compares two methods of deploying a Kubernetes cluster: kops and eksctl. Both of the methods concern the AWS cloud, which was chosen mainly because of its wide popularity and the range of provided services. Besides the two chosen methods of deployment, there are many more, including the DIY method and deploying on-premises. Full article
(This article belongs to the Section Sensor Networks)
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Open AccessArticle
Automatic Ankle Angle Detection by Integrated RGB and Depth Camera System
Sensors 2021, 21(5), 1909; https://doi.org/10.3390/s21051909 - 09 Mar 2021
Viewed by 487
Abstract
Depth cameras are developing widely. One of their main virtues is that, based on their data and by applying machine learning algorithms and techniques, it is possible to perform body tracking and make an accurate three-dimensional representation of body movement. Specifically, this paper [...] Read more.
Depth cameras are developing widely. One of their main virtues is that, based on their data and by applying machine learning algorithms and techniques, it is possible to perform body tracking and make an accurate three-dimensional representation of body movement. Specifically, this paper will use the Kinect v2 device, which incorporates a random forest algorithm for 25 joints detection in the human body. However, although Kinect v2 is a powerful tool, there are circumstances in which the device’s design does not allow the extraction of such data or the accuracy of the data is low, as is usually the case with foot position. We propose a method of acquiring this data in circumstances where the Kinect v2 device does not recognize the body when only the lower limbs are visible, improving the ankle angle’s precision employing projection lines. Using a region-based convolutional neural network (Mask RCNN) for body recognition, raw data extraction for automatic ankle angle measurement has been achieved. All angles have been evaluated by inertial measurement units (IMUs) as gold standard. For the six tests carried out at different fixed distances between 0.5 and 4 m to the Kinect, we have obtained (mean ± SD) a Pearson’s coefficient, r = 0.89 ± 0.04, a Spearman’s coefficient, ρ = 0.83 ± 0.09, a root mean square error, RMSE = 10.7 ± 2.6 deg and a mean absolute error, MAE = 7.5 ± 1.8 deg. For the walking test, or variable distance test, we have obtained a Pearson’s coefficient, r = 0.74, a Spearman’s coefficient, ρ = 0.72, an RMSE = 6.4 deg and an MAE = 4.7 deg. Full article
(This article belongs to the Section Sensing and Imaging)
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Open AccessArticle
Lung Nodule Segmentation with a Region-Based Fast Marching Method
Sensors 2021, 21(5), 1908; https://doi.org/10.3390/s21051908 - 09 Mar 2021
Viewed by 491
Abstract
When dealing with computed tomography volume data, the accurate segmentation of lung nodules is of great importance to lung cancer analysis and diagnosis, being a vital part of computer-aided diagnosis systems. However, due to the variety of lung nodules and the similarity of [...] Read more.
When dealing with computed tomography volume data, the accurate segmentation of lung nodules is of great importance to lung cancer analysis and diagnosis, being a vital part of computer-aided diagnosis systems. However, due to the variety of lung nodules and the similarity of visual characteristics for nodules and their surroundings, robust segmentation of nodules becomes a challenging problem. A segmentation algorithm based on the fast marching method is proposed that separates the image into regions with similar features, which are then merged by combining regions growing with k-means. An evaluation was performed with two distinct methods (objective and subjective) that were applied on two different datasets, containing simulation data generated for this study and real patient data, respectively. The objective experimental results show that the proposed technique can accurately segment nodules, especially in solid cases, given the mean Dice scores of 0.933 and 0.901 for round and irregular nodules. For non-solid and cavitary nodules the performance dropped—0.799 and 0.614 mean Dice scores, respectively. The proposed method was compared to active contour models and to two modern deep learning networks. It reached better overall accuracy than active contour models, having comparable results to DBResNet but lesser accuracy than 3D-UNet. The results show promise for the proposed method in computer-aided diagnosis applications. Full article
(This article belongs to the Special Issue Image and Signal Processing for Biomedical Applications)
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Open AccessFeature PaperReview
Review on Carbon Nanomaterials-Based Nano-Mass and Nano-Force Sensors by Theoretical Analysis of Vibration Behavior
Sensors 2021, 21(5), 1907; https://doi.org/10.3390/s21051907 - 09 Mar 2021
Viewed by 416
Abstract
Carbon nanomaterials, such as carbon nanotubes (CNTs), graphene sheets (GSs), and carbyne, are an important new class of technological materials, and have been proposed as nano-mechanical sensors because of their extremely superior mechanical, thermal, and electrical performance. The present work reviews the recent [...] Read more.
Carbon nanomaterials, such as carbon nanotubes (CNTs), graphene sheets (GSs), and carbyne, are an important new class of technological materials, and have been proposed as nano-mechanical sensors because of their extremely superior mechanical, thermal, and electrical performance. The present work reviews the recent studies of carbon nanomaterials-based nano-force and nano-mass sensors using mechanical analysis of vibration behavior. The mechanism of the two kinds of frequency-based nano sensors is firstly introduced with mathematical models and expressions. Afterward, the modeling perspective of carbon nanomaterials using continuum mechanical approaches as well as the determination of their material properties matching with their continuum models are concluded. Moreover, we summarize the representative works of CNTs/GSs/carbyne-based nano-mass and nano-force sensors and overview the technology for future challenges. It is hoped that the present review can provide an insight into the application of carbon nanomaterials-based nano-mechanical sensors. Showing remarkable results, carbon nanomaterials-based nano-mass and nano-force sensors perform with a much higher sensitivity than using other traditional materials as resonators, such as silicon and ZnO. Thus, more intensive investigations of carbon nanomaterials-based nano sensors are preferred and expected. Full article
(This article belongs to the Special Issue Micro and Nanosensors for Biomedical)
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Open AccessArticle
Detection of Myocardial Infarction Using ECG and Multi-Scale Feature Concatenate
Sensors 2021, 21(5), 1906; https://doi.org/10.3390/s21051906 - 09 Mar 2021
Viewed by 384
Abstract
Diverse computer-aided diagnosis systems based on convolutional neural networks were applied to automate the detection of myocardial infarction (MI) found in electrocardiogram (ECG) for early diagnosis and prevention. However, issues, particularly overfitting and underfitting, were not being taken into account. In other words, [...] Read more.
Diverse computer-aided diagnosis systems based on convolutional neural networks were applied to automate the detection of myocardial infarction (MI) found in electrocardiogram (ECG) for early diagnosis and prevention. However, issues, particularly overfitting and underfitting, were not being taken into account. In other words, it is unclear whether the network structure is too simple or complex. Toward this end, the proposed models were developed by starting with the simplest structure: a multi-lead features-concatenate narrow network (N-Net) in which only two convolutional layers were included in each lead branch. Additionally, multi-scale features-concatenate networks (MSN-Net) were also implemented where larger features were being extracted through pooling the signals. The best structure was obtained via tuning both the number of filters in the convolutional layers and the number of inputting signal scales. As a result, the N-Net reached a 95.76% accuracy in the MI detection task, whereas the MSN-Net reached an accuracy of 61.82% in the MI locating task. Both networks give a higher average accuracy and a significant difference of p < 0.001 evaluated by the U test compared with the state-of-the-art. The models are also smaller in size thus are suitable to fit in wearable devices for offline monitoring. In conclusion, testing throughout the simple and complex network structure is indispensable. However, the way of dealing with the class imbalance problem and the quality of the extracted features are yet to be discussed. Full article
(This article belongs to the Special Issue Intelligent Biosignal Analysis Methods)
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Open AccessArticle
Advanced Network Sampling with Heterogeneous Multiple Chains
Sensors 2021, 21(5), 1905; https://doi.org/10.3390/s21051905 - 09 Mar 2021
Viewed by 419
Abstract
Recently, researchers have paid attention to many types of huge networks such as the Internet of Things, sensor networks, social networks, and traffic networks because of their untapped potential for theoretical and practical outcomes. A major obstacle in studying large-scale networks is that [...] Read more.
Recently, researchers have paid attention to many types of huge networks such as the Internet of Things, sensor networks, social networks, and traffic networks because of their untapped potential for theoretical and practical outcomes. A major obstacle in studying large-scale networks is that their size tends to increase exponentially. In addition, access to large network databases is limited for security or physical connection reasons. In this paper, we propose a novel sampling method that works effectively for large-scale networks. The proposed approach makes multiple heterogeneous Markov chains by adjusting random-walk traits on the given network to explore the target space efficiently. This approach provides better unbiased sampling results with reduced asymptotic variance within reasonable execution time than previous random-walk-based sampling approaches. We perform various experiments on large networks databases obtained from synthesis to real–world applications. The results demonstrate that the proposed method outperforms existing network sampling methods. Full article
(This article belongs to the Section Sensor Networks)
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Open AccessArticle
Intermuscular Coordination in the Power Clean Exercise: Comparison between Olympic Weightlifters and Untrained Individuals—A Preliminary Study
Sensors 2021, 21(5), 1904; https://doi.org/10.3390/s21051904 - 09 Mar 2021
Viewed by 456
Abstract
Muscle coordination in human movement has been assessed through muscle synergy analysis. In sports science, this procedure has been mainly applied to the comparison between highly trained and unexperienced participants. However, the lack of knowledge regarding strength training exercises led us to study [...] Read more.
Muscle coordination in human movement has been assessed through muscle synergy analysis. In sports science, this procedure has been mainly applied to the comparison between highly trained and unexperienced participants. However, the lack of knowledge regarding strength training exercises led us to study the differences in neural strategies to perform the power clean between weightlifters and untrained individuals. Synergies were extracted from electromyograms of 16 muscles of ten unexperienced participants and seven weightlifters. To evaluate differences, we determined the pairwise correlations for the synergy components and electromyographic profiles. While the shape of activation patterns presented strong correlations across participants of each group, the weightings of each muscle were more variable. The three extracted synergies were shifted in time with the unexperienced group anticipating synergy #1 (−2.46 ± 18.7%; p < 0.001) and #2 (−4.60 ± 5.71%; p < 0.001) and delaying synergy #3 (1.86 ± 17.39%; p = 0.01). Moreover, muscle vectors presented more inter-group variability, changing the composition of synergy #1 and #3. These results may indicate an adaptation in intermuscular coordination with training, and athletes in an initial phase of training should attempt to delay the hip extension (synergy #1), as well as the upper-limb flexion (synergy #2). Full article
(This article belongs to the Special Issue Sensors and Technologies in Skeletal Muscle Disorder)
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Open AccessArticle
A New Approach to Enhanced Swarm Intelligence Applied to Video Target Tracking
Sensors 2021, 21(5), 1903; https://doi.org/10.3390/s21051903 - 09 Mar 2021
Viewed by 299
Abstract
This work proposes a new approach to improve swarm intelligence algorithms for dynamic optimization problems by promoting a balance between the transfer of knowledge and the diversity of particles. The proposed method was designed to be applied to the problem of video tracking [...] Read more.
This work proposes a new approach to improve swarm intelligence algorithms for dynamic optimization problems by promoting a balance between the transfer of knowledge and the diversity of particles. The proposed method was designed to be applied to the problem of video tracking targets in environments with almost constant lighting. This approach also delimits the solution space for a more efficient search. A robust version to outliers of the double exponential smoothing (DES) model is used to predict the target position in the frame delimiting the solution space in a more promising region for target tracking. To assess the quality of the proposed approach, an appropriate tracker for a discrete solution space was implemented using the meta-heuristic Shuffled Frog Leaping Algorithm (SFLA) adapted to dynamic optimization problems, named the Dynamic Shuffled Frog Leaping Algorithm (DSFLA). The DSFLA was compared with other classic and current trackers whose algorithms are based on swarm intelligence. The trackers were compared in terms of the average processing time per frame and the area under curve of the success rate per Pascal metric. For the experiment, we used a random sample of videos obtained from the public Hanyang visual tracker benchmark. The experimental results suggest that the DSFLA has an efficient processing time and higher quality of tracking compared with the other competing trackers analyzed in this work. The success rate of the DSFLA tracker is about 7.2 to 76.6% higher on average when comparing the success rate of its competitors. The average processing time per frame is about at least 10% faster than competing trackers, except one that was about 26% faster than the DSFLA tracker. The results also show that the predictions of the robust DES model are quite accurate. Full article
(This article belongs to the Section Remote Sensors)
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Open AccessArticle
Smartwatch-Based Eating Detection: Data Selection for Machine Learning from Imbalanced Data with Imperfect Labels
Sensors 2021, 21(5), 1902; https://doi.org/10.3390/s21051902 - 09 Mar 2021
Viewed by 608
Abstract
Understanding people’s eating habits plays a crucial role in interventions promoting a healthy lifestyle. This requires objective measurement of the time at which a meal takes place, the duration of the meal, and what the individual eats. Smartwatches and similar wrist-worn devices are [...] Read more.
Understanding people’s eating habits plays a crucial role in interventions promoting a healthy lifestyle. This requires objective measurement of the time at which a meal takes place, the duration of the meal, and what the individual eats. Smartwatches and similar wrist-worn devices are an emerging technology that offers the possibility of practical and real-time eating monitoring in an unobtrusive, accessible, and affordable way. To this end, we present a novel approach for the detection of eating segments with a wrist-worn device and fusion of deep and classical machine learning. It integrates a novel data selection method to create the training dataset, and a method that incorporates knowledge from raw and virtual sensor modalities for training with highly imbalanced datasets. The proposed method was evaluated using data from 12 subjects recorded in the wild, without any restriction about the type of meals that could be consumed, the cutlery used for the meal, or the location where the meal took place. The recordings consist of data from accelerometer and gyroscope sensors. The experiments show that our method for detection of eating segments achieves precision of 0.85, recall of 0.81, and F1-score of 0.82 in a person-independent manner. The results obtained in this study indicate that reliable eating detection using in the wild recorded data is possible with the use of wearable sensors on the wrist. Full article
(This article belongs to the Special Issue New Frontiers in Sensor-Based Activity Recognition)
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Open AccessCommunication
Angle-of-Arrival Estimation Using Difference Beams in Localized Hybrid Arrays
Sensors 2021, 21(5), 1901; https://doi.org/10.3390/s21051901 - 09 Mar 2021
Viewed by 493
Abstract
Angle-of-arrival (AoA) estimation in localized hybrid arrays suffers from phase ambiguity owing to its localized structure and vulnerability to noise. In this letter, we propose a novel phase shift design, allowing each subarray to exploit difference beam steering in two potential AoA directions. [...] Read more.
Angle-of-arrival (AoA) estimation in localized hybrid arrays suffers from phase ambiguity owing to its localized structure and vulnerability to noise. In this letter, we propose a novel phase shift design, allowing each subarray to exploit difference beam steering in two potential AoA directions. This enables the calibration of cross-correlations and an enhanced phase offset estimation between adjacent subarrays. We propose two unambiguous AoA estimation schemes based on the even and odd ratios of the number of antennas per subarray N to the number of different phase shifts per symbol K (i.e., N/K), respectively. The simulation results show that the proposed approach greatly improves the estimation accuracy as compared to the state of the art when the ratio N/K is even. Full article
(This article belongs to the Special Issue Communications and Sensing Technologies for the Future)
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Open AccessArticle
Analytical Approach to Sampling Estimation of Underwater Tunnels Using Mechanical Profiling Sonars
Sensors 2021, 21(5), 1900; https://doi.org/10.3390/s21051900 - 09 Mar 2021
Viewed by 575
Abstract
Hydroelectric power plants often make use of tunnels to redirect the flow of water to the plant power house. Such tunnels are often flooded and can span considerable distances. Periodical inspections of such tunnels are highly desirable since a tunnel collapse will be [...] Read more.
Hydroelectric power plants often make use of tunnels to redirect the flow of water to the plant power house. Such tunnels are often flooded and can span considerable distances. Periodical inspections of such tunnels are highly desirable since a tunnel collapse will be catastrophic, disrupting the power plant operation. In many cases, the use of Unmanned Underwater Vehicles (UUVs) equipped with mechanical profiling sonars is a suitable and affordable way to gather data to generate 3D mapping of flooded tunnels. In this paper, we study the resolution of 3D tunnel maps generated by one or more mechanical profiling sonars working in tandem, considering synchronization and occlusion problems. The article derives the analytical equations to estimate the sampling of the underwater tunnels using mechanical profiling sonars (scanning sonars). Experiments in a simulated environment using up to four sensors simultaneously are presented. We also report experimental results obtained by a UUV inside a large power plant tunnel, together with a first map of this environment using a single sonar sensor. Full article
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Open AccessArticle
Variable Admittance Control Based on Human–Robot Collaboration Observer Using Frequency Analysis for Sensitive and Safe Interaction
Sensors 2021, 21(5), 1899; https://doi.org/10.3390/s21051899 - 08 Mar 2021
Viewed by 430
Abstract
A collaborative robot should be sensitive to the user intention while maintaining safe interaction during tasks such as hand guiding. Observers based on the discrete Fourier transform have been studied to distinguish between the low-frequency motion elicited by the operator and high-frequency behavior [...] Read more.
A collaborative robot should be sensitive to the user intention while maintaining safe interaction during tasks such as hand guiding. Observers based on the discrete Fourier transform have been studied to distinguish between the low-frequency motion elicited by the operator and high-frequency behavior resulting from system instability and disturbances. However, the discrete Fourier transform requires an excessively long sampling time. We propose a human–robot collaboration observer based on an infinite impulse response filter to increase the intention recognition speed. By using this observer, we also propose a variable admittance controller to ensure safe collaboration. The recognition speed of the human–robot collaboration observer is 0.29 s, being 3.5 times faster than frequency analysis based on the discrete Fourier transform. The performance of the variable admittance controller and its improved recognition speed are experimentally verified on a two-degrees-of-freedom manipulator. We confirm that the improved recognition speed of the proposed human–robot collaboration observer allows us to timely recover from unsafe to safe collaboration. Full article
(This article belongs to the Special Issue Advances in Human-Robot Interaction: Sensing, Cognition and Control)
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Open AccessArticle
Occupational Noise on Floating Storage and Offloading Vessels (FSO)
Sensors 2021, 21(5), 1898; https://doi.org/10.3390/s21051898 - 08 Mar 2021
Viewed by 400
Abstract
The purpose and scope of this paper are to provide guidance of the potential impacts of being subjected to high level noise recorded on 1st generation (30 years old) floating storage and offloading vessels (FSO) in sector offshore. The international community recognizes that [...] Read more.
The purpose and scope of this paper are to provide guidance of the potential impacts of being subjected to high level noise recorded on 1st generation (30 years old) floating storage and offloading vessels (FSO) in sector offshore. The international community recognizes that vibroacoustic impacts from commercial ships may have negative consequences for both humans (worker’s) and marine life, especially marine mammals. As regards the effect of noise on human health, there are legal requirements imposing the noise exposure control on personnel working on ships. The acceptable noise exposure standards are established in European Union Directive 2003/10/EC (2003), the NOPSEMA Regulation (2006), the Maritime Labor Convention (MLC) guidelines (2006), and the recommendations of the International Maritime Organization IMO contained, e.g., IMO MEPC.1/Circ.833 (2014). These regulations inform employers and employees what they must do to effectively protect both the marine environment and the health and life safety of workers employed in the maritime industry offshore. This study also presents an analysis of the results of noise measurements carried out on exemplary 1st generation FSO units. Full article
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Open AccessArticle
Deploying an NFV-Based Experimentation Scenario for 5G Solutions in Underserved Areas
Sensors 2021, 21(5), 1897; https://doi.org/10.3390/s21051897 - 08 Mar 2021
Viewed by 394
Abstract
Presently, a significant part of the world population does not have Internet access. The fifth-generation cellular network technology evolution (5G) is focused on reducing latency, increasing the available bandwidth, and enhancing network performance. However, researchers and companies have not invested enough effort into [...] Read more.
Presently, a significant part of the world population does not have Internet access. The fifth-generation cellular network technology evolution (5G) is focused on reducing latency, increasing the available bandwidth, and enhancing network performance. However, researchers and companies have not invested enough effort into the deployment of the Internet in remote/rural/undeveloped areas for different techno-economic reasons. This article presents the result of a collaboration between Brazil and the European Union, introducing the steps designed to create a fully operational experimentation scenario with the main purpose of integrating the different achievements of the H2020 5G-RANGE project so that they can be trialed together into a 5G networking use case. The scenario encompasses (i) a novel radio access network that targets a bandwidth of 100 Mb/s in a cell radius of 50 km, and (ii) a network of Small Unmanned Aerial Vehicles (SUAV). This set of SUAVs is NFV-enabled, on top of which Virtual Network Functions (VNF) can be automatically deployed to support occasional network communications beyond the boundaries of the 5G-RANGE radio cells. The whole deployment implies the use of a virtual private overlay network enabling the preliminary validation of the scenario components from their respective remote locations, and simplifying their subsequent integration into a single local demonstrator, the configuration of the required GRE/IPSec tunnels, the integration of the new 5G-RANGE physical, MAC and network layer components and the overall validation with voice and data services. Full article
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