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Sensors, Volume 22, Issue 5 (March-1 2022) – 382 articles

Cover Story (view full-size image): The MoBiMet (Mobile Biometeorology System) is a low-cost device for thermal comfort monitoring. It measures air temperature, humidity, globe temperature, brightness temperature, light intensity, and wind and is capable of calculating thermal indices on site. It visualizes its data on an integrated display and sends them to a server, where web-based visualizations are available in real time. Data from many MoBiMets deployed in real occupational settings were used to demonstrate their suitability for large-scale and continued monitoring thermal comfort. This article describes the design and performance of the MoBiMet. Alternative methods to determine mean radiant temperature were tested. Networked MoBiMets can detect differences of thermal comfort at workplaces within the same building, and between different companies in the same city. View this paper
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18 pages, 3261 KiB  
Article
Sensing Control Parameters of Flute from Microphone Sound Based on Machine Learning from Robotic Performer
by Jin Kuroda and Gou Koutaki
Sensors 2022, 22(5), 2074; https://doi.org/10.3390/s22052074 - 7 Mar 2022
Cited by 2 | Viewed by 3091
Abstract
When learning to play a musical instrument, it is important to improve the quality of self-practice. Many systems have been developed to assist practice. Some practice assistance systems use special sensors (pressure, flow, and motion sensors) to acquire the control parameters of the [...] Read more.
When learning to play a musical instrument, it is important to improve the quality of self-practice. Many systems have been developed to assist practice. Some practice assistance systems use special sensors (pressure, flow, and motion sensors) to acquire the control parameters of the musical instrument, and provide specific guidance. However, it is difficult to acquire the control parameters of wind instruments (e.g., saxophone or flute) such as flow and angle between the player and the musical instrument, since it is not possible to place sensors into the mouth. In this paper, we propose a sensorless control parameter estimation system based on the recorded sound of a wind instrument using only machine learning. In the machine learning framework, many training samples that have both sound and correct labels are required. Therefore, we generated training samples using a robotic performer. This has two advantages: (1) it is easy to obtain many training samples with exhaustive control parameters, and (2) we can use the correct labels as the given control parameters of the robot. In addition to the samples generated by the robot, some human performance data were also used for training to construct an estimation model that enhanced the feature differences between robot and human performance. Finally, a flute control parameter estimation system was developed, and its estimation accuracy for eight novice flute players was evaluated using the Spearman’s rank correlation coefficient. The experimental results showed that the proposed system was able to estimate human control parameters with high accuracy. Full article
(This article belongs to the Section Intelligent Sensors)
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19 pages, 4188 KiB  
Article
A Custom-Made Electronic Dynamometer for Evaluation of Peak Ankle Torque after COVID-19
by Iulia Iovanca Dragoi, Florina Georgeta Popescu, Teodor Petrita, Florin Alexa, Romulus Fabian Tatu, Cosmina Ioana Bondor, Carmen Tatu, Frank L. Bowling, Neil D. Reeves and Mihai Ionac
Sensors 2022, 22(5), 2073; https://doi.org/10.3390/s22052073 - 7 Mar 2022
Cited by 2 | Viewed by 2486
Abstract
The negative effects of SARS-CoV-2 infection on the musculoskeletal system include symptoms of fatigue and sarcopenia. The aim of this study is to assess the impact of COVID-19 on foot muscle strength and evaluate the reproducibility of peak ankle torque measurements in time [...] Read more.
The negative effects of SARS-CoV-2 infection on the musculoskeletal system include symptoms of fatigue and sarcopenia. The aim of this study is to assess the impact of COVID-19 on foot muscle strength and evaluate the reproducibility of peak ankle torque measurements in time by using a custom-made electronic dynamometer. In this observational cohort study, we compare two groups of four participants, one exposed to COVID-19 throughout measurements and one unexposed. Peak ankle torque was measured using a portable custom-made electronic dynamometer. Ankle plantar flexor and dorsiflexor muscle strength was captured for both feet at different ankle angles prior and post COVID-19. Average peak torque demonstrated no significant statistical differences between initial and final moment for both groups (p = 0.945). An increase of 4.8%, p = 0.746 was obtained in the group with COVID-19 and a decrease of 1.3%, p = 0.953 was obtained in the group without COVID-19. Multivariate analysis demonstrated no significant differences between the two groups (p = 0.797). There was a very good test–retest reproducibility between the measurements in initial and final moments (ICC = 0.78, p < 0.001). In conclusion, peak torque variability is similar in both COVID-19 and non-COVID-19 groups and the custom-made electronic dynamometer is a reproducible method for repetitive ankle peak torque measurements. Full article
(This article belongs to the Special Issue Sensors and Technologies in Skeletal Muscle Disorder)
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19 pages, 8081 KiB  
Article
Naked-Eye Detection of Morphine by Au@Ag Nanoparticles-Based Colorimetric Chemosensors
by Tahereh Rohani Bastami, Mansour Bayat and Roberto Paolesse
Sensors 2022, 22(5), 2072; https://doi.org/10.3390/s22052072 - 7 Mar 2022
Cited by 15 | Viewed by 2972
Abstract
In this study, we report a novel and facile colorimetric assay based on silver citrate-coated Au@Ag nanoparticles (Au@AgNPs) as a chemosensor for the naked-eye detection of morphine (MOR). The developed optical sensing approach relied on the aggregation of Au@Ag NPs upon exposure to [...] Read more.
In this study, we report a novel and facile colorimetric assay based on silver citrate-coated Au@Ag nanoparticles (Au@AgNPs) as a chemosensor for the naked-eye detection of morphine (MOR). The developed optical sensing approach relied on the aggregation of Au@Ag NPs upon exposure to morphine, which led to an evident color variation from light-yellow to brown. Au@Ag NPs have been prepared by two different protocols, using high- and low-power ultrasonic irradiation. The sonochemical method was essential for the sensing properties of the resulting nanoparticles. This facile sensing method has several advantages including excellent stability, selectivity, prompt detection, and cost-effectiveness. Full article
(This article belongs to the Collection Optical Chemical Sensors: Design and Applications)
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16 pages, 1115 KiB  
Article
Design Guidelines for Sensors Based on Spiral Resonators
by Mahmoud Elgeziry, Filippo Costa and Simone Genovesi
Sensors 2022, 22(5), 2071; https://doi.org/10.3390/s22052071 - 7 Mar 2022
Cited by 8 | Viewed by 2288
Abstract
Wireless microwave sensors provide a practical alternative where traditional contact-based measurement techniques are not possible to implement or suffer from performance deterioration. Resonating elements are commonly used in these sensors as the sensing concept relies on the resonance properties of the employed structure. [...] Read more.
Wireless microwave sensors provide a practical alternative where traditional contact-based measurement techniques are not possible to implement or suffer from performance deterioration. Resonating elements are commonly used in these sensors as the sensing concept relies on the resonance properties of the employed structure. This work presents some simple guidelines for designing displacement sensors based on spiral resonator (SR) tags. The working principle of this sensor is based on the variation of the coupling strength between the SR tag and a probing microstrip loop with the distance between them. The performance of the sensor depends on the main design parameters, such as tag dimensions, filling factor, number of turns, and the size of probing loop. The guidelines provided herein can be used for the initial phase of the design process by helping to select a preliminary set of parameters according to the desired application requirements. The provided conclusions are supported using electromagnetic simulations and analytical expressions. Finally, a corrected equivalent circuit model that takes into account the phenomenon of the resonant frequency shift at small distances is provided. The findings are compared against experimental measurements to verify their validity. Full article
(This article belongs to the Special Issue RFID and Zero-Power Backscatter Sensors)
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16 pages, 554 KiB  
Article
On-Axis Optical Bench for Laser Ranging Instruments in Future Gravity Missions
by Yichao Yang, Kohei Yamamoto, Miguel Dovale Álvarez, Daikang Wei, Juan José Esteban Delgado, Vitali Müller, Jianjun Jia and Gerhard Heinzel
Sensors 2022, 22(5), 2070; https://doi.org/10.3390/s22052070 - 7 Mar 2022
Cited by 4 | Viewed by 3183
Abstract
The laser ranging interferometer onboard the Gravity Recovery and Climate Experiment Follow-On mission proved the feasibility of an interferometric sensor for inter-satellite length tracking with sub-nanometer precision, establishing an important milestone for space laser interferometry and the general expectation that future gravity missions [...] Read more.
The laser ranging interferometer onboard the Gravity Recovery and Climate Experiment Follow-On mission proved the feasibility of an interferometric sensor for inter-satellite length tracking with sub-nanometer precision, establishing an important milestone for space laser interferometry and the general expectation that future gravity missions will employ heterodyne laser interferometry for satellite-to-satellite ranging. In this paper, we present the design of an on-axis optical bench for next-generation laser ranging which enhances the received optical power and the transmit beam divergence, enabling longer interferometer arms and relaxing the optical power requirement of the laser assembly. All design functionalities and requirements are verified by means of computer simulations. A thermal analysis is carried out to investigate the robustness of the proposed optical bench to the temperature fluctuations found in orbit. Full article
(This article belongs to the Section Optical Sensors)
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41 pages, 1540 KiB  
Review
A Review of Recent Developments in Driver Drowsiness Detection Systems
by Yaman Albadawi, Maen Takruri and Mohammed Awad
Sensors 2022, 22(5), 2069; https://doi.org/10.3390/s22052069 - 7 Mar 2022
Cited by 57 | Viewed by 17755
Abstract
Continuous advancements in computing technology and artificial intelligence in the past decade have led to improvements in driver monitoring systems. Numerous experimental studies have collected real driver drowsiness data and applied various artificial intelligence algorithms and feature combinations with the goal of significantly [...] Read more.
Continuous advancements in computing technology and artificial intelligence in the past decade have led to improvements in driver monitoring systems. Numerous experimental studies have collected real driver drowsiness data and applied various artificial intelligence algorithms and feature combinations with the goal of significantly enhancing the performance of these systems in real-time. This paper presents an up-to-date review of the driver drowsiness detection systems implemented over the last decade. The paper illustrates and reviews recent systems using different measures to track and detect drowsiness. Each system falls under one of four possible categories, based on the information used. Each system presented in this paper is associated with a detailed description of the features, classification algorithms, and used datasets. In addition, an evaluation of these systems is presented, in terms of the final classification accuracy, sensitivity, and precision. Furthermore, the paper highlights the recent challenges in the area of driver drowsiness detection, discusses the practicality and reliability of each of the four system types, and presents some of the future trends in the field. Full article
(This article belongs to the Section Physical Sensors)
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17 pages, 1846 KiB  
Article
Real-Time Object Detection and Classification by UAV Equipped With SAR
by Krzysztof Gromada, Barbara Siemiątkowska, Wojciech Stecz, Krystian Płochocki and Karol Woźniak
Sensors 2022, 22(5), 2068; https://doi.org/10.3390/s22052068 - 7 Mar 2022
Cited by 12 | Viewed by 4730
Abstract
The article presents real-time object detection and classification methods by unmanned aerial vehicles (UAVs) equipped with a synthetic aperture radar (SAR). Two algorithms have been extensively tested: classic image analysis and convolutional neural networks (YOLOv5). The research resulted in a new method that [...] Read more.
The article presents real-time object detection and classification methods by unmanned aerial vehicles (UAVs) equipped with a synthetic aperture radar (SAR). Two algorithms have been extensively tested: classic image analysis and convolutional neural networks (YOLOv5). The research resulted in a new method that combines YOLOv5 with post-processing using classic image analysis. It is shown that the new system improves both the classification accuracy and the location of the identified object. The algorithms were implemented and tested on a mobile platform installed on a military-class UAV as the primary unit for online image analysis. The usage of objective low-computational complexity detection algorithms on SAR scans can reduce the size of the scans sent to the ground control station. Full article
(This article belongs to the Special Issue State-of-the-Art Sensors Technology in Poland 2021-2022)
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28 pages, 12652 KiB  
Article
Single-Shot Intrinsic Calibration for Autonomous Driving Applications
by Abraham Monrroy Cano, Jacob Lambert, Masato Edahiro and Shinpei Kato
Sensors 2022, 22(5), 2067; https://doi.org/10.3390/s22052067 - 7 Mar 2022
Cited by 3 | Viewed by 3099
Abstract
In this paper, we present a first-of-its-kind method to determine clear and repeatable guidelines for single-shot camera intrinsic calibration using multiple checkerboards. With the help of a simulator, we found the position and rotation intervals that allow optimal corner detector performance. With these [...] Read more.
In this paper, we present a first-of-its-kind method to determine clear and repeatable guidelines for single-shot camera intrinsic calibration using multiple checkerboards. With the help of a simulator, we found the position and rotation intervals that allow optimal corner detector performance. With these intervals defined, we generated thousands of multiple checkerboard poses and evaluated them using ground truth values, in order to obtain configurations that lead to accurate camera intrinsic parameters. We used these results to define guidelines to create multiple checkerboard setups. We tested and verified the robustness of the guidelines in the simulator, and additionally in the real world with cameras with different focal lengths and distortion profiles, which help generalize our findings. Finally, we used a 3D LiDAR (Light Detection and Ranging) to project and confirm the quality of the intrinsic parameters projection. We found it possible to obtain accurate intrinsic parameters for 3D applications, with at least seven checkerboard setups in a single image that follow our positioning guidelines. Full article
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18 pages, 4044 KiB  
Article
On-Orbit Absolute Radiometric Calibration and Validation of ZY3-02 Satellite Multispectral Sensor
by Hongzhao Tang, Junfeng Xie, Xinming Tang, Wei Chen and Qi Li
Sensors 2022, 22(5), 2066; https://doi.org/10.3390/s22052066 - 7 Mar 2022
Cited by 10 | Viewed by 2331
Abstract
This study described the on-orbit vicarious radiometric calibration of Chinese civilian high-resolution stereo mapping satellite ZY3-02 multispectral imager (MUX). The calibration was based on gray-scale permanent artificial targets, and multiple radiometric calibration tarpaulins (tarps) using a reflectance-based approach between July and September 2016 [...] Read more.
This study described the on-orbit vicarious radiometric calibration of Chinese civilian high-resolution stereo mapping satellite ZY3-02 multispectral imager (MUX). The calibration was based on gray-scale permanent artificial targets, and multiple radiometric calibration tarpaulins (tarps) using a reflectance-based approach between July and September 2016 at Baotou calibration site in China was described. The calibration results reveal a good linear relationship between DN and TOA radiances of ZY3-02 MUX. The uncertainty of this radiometric calibration was 4.33%, indicating that radiometric coefficients of ZY3-02 MUX are reliable. A detailed discussion on the validation analysis of the comparison results between the different radiometric calibration coefficients is presented in this paper. To further validate the reliability of the three coefficients, the calibrated ZY3-02 MUX was compared with Landsat-8 Operational Land Imager (OLI). The results also indicate that radiometric characteristics of ZY3-02 MUX imagery are reliable and highly accurate for quantitative applications. Full article
(This article belongs to the Collection Remote Sensing Image Processing)
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21 pages, 9230 KiB  
Article
Tree Trunk Recognition in Orchard Autonomous Operations under Different Light Conditions Using a Thermal Camera and Faster R-CNN
by Ailian Jiang, Ryozo Noguchi and Tofael Ahamed
Sensors 2022, 22(5), 2065; https://doi.org/10.3390/s22052065 - 7 Mar 2022
Cited by 18 | Viewed by 3863
Abstract
In an orchard automation process, a current challenge is to recognize natural landmarks and tree trunks to localize intelligent robots. To overcome low-light conditions and global navigation satellite system (GNSS) signal interruptions under a dense canopy, a thermal camera may be used to [...] Read more.
In an orchard automation process, a current challenge is to recognize natural landmarks and tree trunks to localize intelligent robots. To overcome low-light conditions and global navigation satellite system (GNSS) signal interruptions under a dense canopy, a thermal camera may be used to recognize tree trunks using a deep learning system. Therefore, the objective of this study was to use a thermal camera to detect tree trunks at different times of the day under low-light conditions using deep learning to allow robots to navigate. Thermal images were collected from the dense canopies of two types of orchards (conventional and joint training systems) under high-light (12–2 PM), low-light (5–6 PM), and no-light (7–8 PM) conditions in August and September 2021 (summertime) in Japan. The detection accuracy for a tree trunk was confirmed by the thermal camera, which observed an average error of 0.16 m for 5 m, 0.24 m for 15 m, and 0.3 m for 20 m distances under high-, low-, and no-light conditions, respectively, in different orientations of the thermal camera. Thermal imagery datasets were augmented to train, validate, and test using the Faster R-CNN deep learning model to detect tree trunks. A total of 12,876 images were used to train the model, 2318 images were used to validate the training process, and 1288 images were used to test the model. The mAP of the model was 0.8529 for validation and 0.8378 for the testing process. The average object detection time was 83 ms for images and 90 ms for videos with the thermal camera set at 11 FPS. The model was compared with the YOLO v3 with same number of datasets and training conditions. In the comparisons, Faster R-CNN achieved a higher accuracy than YOLO v3 in tree truck detection using the thermal camera. Therefore, the results showed that Faster R-CNN can be used to recognize objects using thermal images to enable robot navigation in orchards under different lighting conditions. Full article
(This article belongs to the Section Smart Agriculture)
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24 pages, 10865 KiB  
Article
Modular Single-Stage Three-Phase Flyback Differential Inverter for Medium/High-Power Grid Integrated Applications
by Ahmed Ismail M. Ali, Cao Anh Tuan, Takaharu Takeshita, Mahmoud A. Sayed and Zuhair Muhammed Alaas
Sensors 2022, 22(5), 2064; https://doi.org/10.3390/s22052064 - 7 Mar 2022
Cited by 7 | Viewed by 2125
Abstract
This paper proposes a single-stage three-phase modular flyback differential inverter (MFBDI) for medium/high power solar PV grid-integrated applications. The proposed inverter structure consists of parallel modules of flyback DC-DC converters based on the required power level. The MFBDI offers many features for renewable [...] Read more.
This paper proposes a single-stage three-phase modular flyback differential inverter (MFBDI) for medium/high power solar PV grid-integrated applications. The proposed inverter structure consists of parallel modules of flyback DC-DC converters based on the required power level. The MFBDI offers many features for renewable energy applications, such as reduced components, single-stage power processing, high-power density, voltage-boosting property, improved footprint, flexibility with modular extension capability, and galvanic isolation. The proposed inverter has been modelled, designed, and scaled up to the required application rating. A new mathematical model of the proposed MFBDI is presented and analyzed with a time-varying duty-cycle, wide-range of frequency variation, and power balancing in order to display its grid current harmonic orders for grid-tied applications. In addition, an LPF-based harmonic compensation strategy is used for second-order harmonic component (SOHC) compensation. With the help of the compensation technique, the grid current THD is reduced from 36% to 4.6% by diminishing the SOHC from 51% to 0.8%. Moreover, the SOHC compensation technique eliminates third-order harmonic components from the DC input current. In addition, a 15% parameters mismatch has been applied between the flyback parallel modules to confirm the modular operation of the proposed MFBDI under modules divergence. In addition, SiC MOSFETs are used for inverter switches implementation, which decrease the inverter switching losses at high-switching frequency. The proposed MFBDI is verified by using three flyback parallel modules/phase using PSIM/Simulink software, with a rating of 5 kW, 200 V, and 50 kHz switching frequency, as well as experimental environments. Full article
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22 pages, 1966 KiB  
Article
Comparison of Empirical Mode Decomposition and Singular Spectrum Analysis for Quick and Robust Detection of Aerodynamic Instabilities in Centrifugal Compressors
by Mateusz Stajuda, David García Cava and Grzegorz Liśkiewicz
Sensors 2022, 22(5), 2063; https://doi.org/10.3390/s22052063 - 7 Mar 2022
Cited by 4 | Viewed by 2293
Abstract
Aerodynamic instabilities in centrifugal compressors are dangerous phenomena affecting machine efficiency and in severe cases leading to failure of the compressing system. Quick and robust instability detection during compressor operation is a challenge of utmost importance from an economical and safety point of [...] Read more.
Aerodynamic instabilities in centrifugal compressors are dangerous phenomena affecting machine efficiency and in severe cases leading to failure of the compressing system. Quick and robust instability detection during compressor operation is a challenge of utmost importance from an economical and safety point of view. Rapid indication of instabilities can be obtained using a pressure signal from the compressor. Detection of aerodynamic instabilities using pressure signal results in specific challenges, as the signal is often highly contaminated with noise, which can influence the performance of detection methods. The aim of this study is to investigate and compare the performance of two non-linear signal processing methods—Empirical Mode Decomposition (EMD) and Singular Spectrum Analysis (SSA)—for aerodynamic instability detection. Two instabilities of different character, local—inlet recirculation and global—surge, are considered. The comparison focuses on the robustness, sensitivity and pace of detection—crucial parameters for a successful detection method. It is shown that both EMD and SSA perform similarly for the analysed machine, despite different underlying principles of the methods. Both EMD and SSA have great potential for instabilities detection, but tuning of their parameters is important for robust detection. Full article
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10 pages, 1105 KiB  
Article
Deductive Reasoning and Working Memory Skills in Individuals with Blindness
by Eyal Heled, Noa Elul, Maurice Ptito and Daniel-Robert Chebat
Sensors 2022, 22(5), 2062; https://doi.org/10.3390/s22052062 - 7 Mar 2022
Cited by 4 | Viewed by 2976
Abstract
Deductive reasoning and working memory are integral parts of executive functioning and are important skills for blind people in everyday life. Despite the importance of these skills, the influence of visual experience on reasoning and working memory skills, as well as on the [...] Read more.
Deductive reasoning and working memory are integral parts of executive functioning and are important skills for blind people in everyday life. Despite the importance of these skills, the influence of visual experience on reasoning and working memory skills, as well as on the relationship between these, is unknown. In this study, fifteen participants with congenital blindness (CB), fifteen with late blindness (LB), fifteen sighted blindfolded controls (SbfC), and fifteen sighted participants performed two tasks of deductive reasoning and two of working memory. We found that while the CB and LB participants did not differ in their deductive reasoning abilities, the CB group performed worse than the sighted controls, and the LB group performed better than the SbfC group. Those with CB outperformed all the other groups in both of the working memory tests. Working memory is associated with deductive reasoning in all three visually impaired groups, but not in the sighted group. These findings suggest that deductive reasoning is not a uniform skill, and that it is associated with visual impairment onset, the level of reasoning difficulty, and the degree of working memory load. Full article
(This article belongs to the Special Issue Spatial Perception and Navigation in the Absence of Vision)
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21 pages, 2602 KiB  
Article
Data Fusion of Observability Signals for Assisting Orchestration of Distributed Applications
by Ioannis Tzanettis, Christina-Maria Androna, Anastasios Zafeiropoulos, Eleni Fotopoulou and Symeon Papavassiliou
Sensors 2022, 22(5), 2061; https://doi.org/10.3390/s22052061 - 7 Mar 2022
Cited by 6 | Viewed by 3006
Abstract
Nowadays, various frameworks are emerging for supporting distributed tracing techniques over microservices-based distributed applications. The objective is to improve observability and management of operational problems of distributed applications, considering bottlenecks in terms of high latencies in the interaction among the deployed microservices. However, [...] Read more.
Nowadays, various frameworks are emerging for supporting distributed tracing techniques over microservices-based distributed applications. The objective is to improve observability and management of operational problems of distributed applications, considering bottlenecks in terms of high latencies in the interaction among the deployed microservices. However, such frameworks provide information that is disjoint from the management information that is usually collected by cloud computing orchestration platforms. There is a need to improve observability by combining such information to easily produce insights related to performance issues and to realize root cause analyses to tackle them. In this paper, we provide a modern observability approach and pilot implementation for tackling data fusion aspects in edge and cloud computing orchestration platforms. We consider the integration of signals made available by various open-source monitoring and observability frameworks, including metrics, logs and distributed tracing mechanisms. The approach is validated in an experimental orchestration environment based on the deployment and stress testing of a proof-of-concept microservices-based application. Helpful results are produced regarding the identification of the main causes of latencies in the various application parts and the better understanding of the behavior of the application under different stressing conditions. Full article
(This article belongs to the Special Issue Edge/Fog Computing for Intelligent IoT Applications)
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24 pages, 15785 KiB  
Article
Hand Measurement System Based on Haptic and Vision Devices towards Post-Stroke Patients
by Katarzyna Koter, Martyna Samowicz, Justyna Redlicka and Igor Zubrycki
Sensors 2022, 22(5), 2060; https://doi.org/10.3390/s22052060 - 7 Mar 2022
Cited by 3 | Viewed by 2455
Abstract
Diagnostics of a hand requires measurements of kinematics and joint limits. The standard tools for this purpose are manual devices such as goniometers which allow measuring only one joint simultaneously, making the diagnostics time-consuming. The paper presents a system for automatic measurement and [...] Read more.
Diagnostics of a hand requires measurements of kinematics and joint limits. The standard tools for this purpose are manual devices such as goniometers which allow measuring only one joint simultaneously, making the diagnostics time-consuming. The paper presents a system for automatic measurement and computer presentation of essential parameters of a hand. Constructed software uses an integrated vision system, a haptic device for measurement, and has a web-based user interface. The system provides a simplified way to obtain hand parameters, such as hand size, wrist, and finger range of motions, using the homogeneous-matrix-based notation. The haptic device allows for active measurement of the wrist’s range of motion and additional force measurement. A study was conducted to determine the accuracy and repeatability of measurements compared to the gold standard. The system functionality was confirmed on five healthy participants, with results showing comparable results to manual measurements regarding fingers’ lengths. The study showed that the finger’s basic kinematic structure could be measured by a vision system with a mean difference to caliper measurement of 4.5 mm and repeatability with the Standard Deviations up to 0.7 mm. Joint angle limits measurement achieved poorer results with a mean difference to goniometer of 23.6º. Force measurements taken by the haptic device showed the repeatability with a Standard Deviation of 0.7 N. The presented system allows for a unified measurement and a collection of important parameters of a human hand with therapist interface visualization and control with potential use for post-stroke patients’ precise rehabilitation. Full article
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17 pages, 21831 KiB  
Article
Detection and Mosaicing Techniques for Low-Quality Retinal Videos
by José Camara, Bruno Silva, António Gouveia, Ivan Miguel Pires, Paulo Coelho and António Cunha
Sensors 2022, 22(5), 2059; https://doi.org/10.3390/s22052059 - 7 Mar 2022
Viewed by 2125
Abstract
Ideally, to carry out screening for eye diseases, it is expected to use specialized medical equipment to capture retinal fundus images. However, since this kind of equipment is generally expensive and has low portability, and with the development of technology and the emergence [...] Read more.
Ideally, to carry out screening for eye diseases, it is expected to use specialized medical equipment to capture retinal fundus images. However, since this kind of equipment is generally expensive and has low portability, and with the development of technology and the emergence of smartphones, new portable and cheaper screening options have emerged, one of them being the D-Eye device. When compared to specialized equipment, this equipment and other similar devices associated with a smartphone present lower quality and less field-of-view in the retinal video captured, yet with sufficient quality to perform a medical pre-screening. Individuals can be referred for specialized screening to obtain a medical diagnosis if necessary. Two methods were proposed to extract the relevant regions from these lower-quality videos (the retinal zone). The first one is based on classical image processing approaches such as thresholds and Hough Circle transform. The other performs the extraction of the retinal location by applying a neural network, which is one of the methods reported in the literature with good performance for object detection, the YOLO v4, which was demonstrated to be the preferred method to apply. A mosaicing technique was implemented from the relevant retina regions to obtain a more informative single image with a higher field of view. It was divided into two stages: the GLAMpoints neural network was applied to extract relevant points in the first stage. Some homography transformations are carried out to have in the same referential the overlap of common regions of the images. In the second stage, a smoothing process was performed in the transition between images. Full article
(This article belongs to the Special Issue Computational Intelligence in Image Analysis)
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21 pages, 17934 KiB  
Article
Probabilistic Maritime Trajectory Prediction in Complex Scenarios Using Deep Learning
by Kristian Aalling Sørensen, Peder Heiselberg and Henning Heiselberg
Sensors 2022, 22(5), 2058; https://doi.org/10.3390/s22052058 - 7 Mar 2022
Cited by 25 | Viewed by 3633
Abstract
Maritime activity is expected to increase, and therefore also the need for maritime surveillance and safety. Most ships are obligated to identify themselves with a transponder system like the Automatic Identification System (AIS) and ships that do not, intentionally or unintentionally, are referred [...] Read more.
Maritime activity is expected to increase, and therefore also the need for maritime surveillance and safety. Most ships are obligated to identify themselves with a transponder system like the Automatic Identification System (AIS) and ships that do not, intentionally or unintentionally, are referred to as dark ships and must be observed by other means. Knowing the future location of ships can not only help with ship/ship collision avoidance, but also with determining the identity of these dark ships found in, e.g., satellite images. However, predicting the future location of ships is inherently probabilistic and the variety of possible routes is almost limitless. We therefore introduce a Bidirectional Long-Short-Term-Memory Mixture Density Network (BLSTM-MDN) deep learning model capable of characterising the underlying distribution of ship trajectories. It is consequently possible to predict a probabilistic future location as opposed to a deterministic location. AIS data from 3631 different cargo ships are acquired from a region west of Norway spanning 320,000 sqkm. Our implemented BLSTM-MDN model characterizes the conditional probability of the target, conditioned on an input trajectory using an 11-dimensional Gaussian distribution and by inferring a single target from the distribution, we can predict several probable trajectories from the same input trajectory with a test Negative Log Likelihood loss of 9.96 corresponding to a mean distance error of 2.53 km 50 min into the future. We compare our model to both a standard BLSTM and a state-of-the-art multi-headed self-attention BLSTM model and the BLSTM-MDN performs similarly to the two deterministic deep learning models on straight trajectories, but produced better results in complex scenarios. Full article
(This article belongs to the Special Issue Remote Sensing in Vessel Detection and Navigation: Edition Ⅱ)
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27 pages, 2913 KiB  
Article
Multi-Unit Serial Polynomial Multiplier to Accelerate NTRU-Based Cryptographic Schemes in IoT Embedded Systems
by Santiago Sánchez-Solano, Eros Camacho-Ruiz, Macarena C. Martínez-Rodríguez and Piedad Brox
Sensors 2022, 22(5), 2057; https://doi.org/10.3390/s22052057 - 7 Mar 2022
Cited by 5 | Viewed by 2816
Abstract
Concern for the security of embedded systems that implement IoT devices has become a crucial issue, as these devices today support an increasing number of applications and services that store and exchange information whose integrity, privacy, and authenticity must be adequately guaranteed. Modern [...] Read more.
Concern for the security of embedded systems that implement IoT devices has become a crucial issue, as these devices today support an increasing number of applications and services that store and exchange information whose integrity, privacy, and authenticity must be adequately guaranteed. Modern lattice-based cryptographic schemes have proven to be a good alternative, both to face the security threats that arise as a consequence of the development of quantum computing and to allow efficient implementations of cryptographic primitives in resource-limited embedded systems, such as those used in consumer and industrial applications of the IoT. This article describes the hardware implementation of parameterized multi-unit serial polynomial multipliers to speed up time-consuming operations in NTRU-based cryptographic schemes. The flexibility in selecting the design parameters and the interconnection protocol with a general-purpose processor allow them to be applied both to the standardized variants of NTRU and to the new proposals that are being considered in the post-quantum contest currently held by the National Institute of Standards and Technology, as well as to obtain an adequate cost/performance/security-level trade-off for a target application. The designs are provided as AXI4 bus-compliant intellectual property modules that can be easily incorporated into embedded systems developed with the Vivado design tools. The work provides an extensive set of implementation and characterization results in devices of the Xilinx Zynq-7000 and Zynq UltraScale+ families for the different sets of parameters defined in the NTRUEncrypt standard. It also includes details of their plug and play inclusion as hardware accelerators in the C implementation of this public-key encryption scheme codified in the LibNTRU library, showing that acceleration factors of up to 3.1 are achieved when compared to pure software implementations running on the processing systems included in the programmable devices. Full article
(This article belongs to the Special Issue Advances in Cybersecurity for the Internet of Things)
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14 pages, 4053 KiB  
Article
Highly Sensitive and Selective Detection of Hydrogen Using Pd-Coated SnO2 Nanorod Arrays for Breath-Analyzer Applications
by Hwaebong Jung, Junho Hwang, Yong-Sahm Choe, Hyun-Sook Lee and Wooyoung Lee
Sensors 2022, 22(5), 2056; https://doi.org/10.3390/s22052056 - 7 Mar 2022
Cited by 5 | Viewed by 2781
Abstract
We report a breath hydrogen analyzer based on Pd-coated SnO2 nanorods (Pd-SnO2 NRs) sensor integrated into a miniaturized gas chromatography (GC) column. The device can measure a wide range of hydrogen (1–100 ppm), within 100 s, using a small volume of [...] Read more.
We report a breath hydrogen analyzer based on Pd-coated SnO2 nanorods (Pd-SnO2 NRs) sensor integrated into a miniaturized gas chromatography (GC) column. The device can measure a wide range of hydrogen (1–100 ppm), within 100 s, using a small volume of human breath (1 mL) without pre-concentration. Especially, the mini-GC integrated with Pd-SnO2 NRs can detect 1 ppm of H2, as a lower detection limit, at a low operating temperature of 152 °C. Furthermore, when the breath hydrogen analyzer was exposed to a mixture of interfering gases, such as carbon dioxide, nitrogen, methane, and acetone, it was found to be capable of selectively detecting only H2. We found that the Pd-SnO2 NRs were superior to other semiconducting metal oxides that lack selectivity in H2 detection. Our study reveals that the Pd-SnO2 NRs integrated into the mini-GC device can be utilized in breath hydrogen analyzers to rapidly and accurately detect hydrogen due to its high selectivity and sensitivity. Full article
(This article belongs to the Section Biosensors)
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13 pages, 3523 KiB  
Article
Investigation of Red Blood Cells by Atomic Force Microscopy
by Viktoria Sergunova, Stanislav Leesment, Aleksandr Kozlov, Vladimir Inozemtsev, Polina Platitsina, Snezhanna Lyapunova, Alexander Onufrievich, Vyacheslav Polyakov and Ekaterina Sherstyukova
Sensors 2022, 22(5), 2055; https://doi.org/10.3390/s22052055 - 7 Mar 2022
Cited by 16 | Viewed by 4079
Abstract
Currently, much research is devoted to the study of biological objects using atomic force microscopy (AFM). This method’s resolution is superior to the other non-scanning techniques. Our study aims to further emphasize some of the advantages of using AFM as a clinical screening [...] Read more.
Currently, much research is devoted to the study of biological objects using atomic force microscopy (AFM). This method’s resolution is superior to the other non-scanning techniques. Our study aims to further emphasize some of the advantages of using AFM as a clinical screening tool. The study focused on red blood cells exposed to various physical and chemical factors, namely hemin, zinc ions, and long-term storage. AFM was used to investigate the morphological, nanostructural, cytoskeletal, and mechanical properties of red blood cells (RBCs). Based on experimental data, a set of important biomarkers determining the status of blood cells have been identified. Full article
(This article belongs to the Special Issue Medical and Biomedical Sensing and Imaging)
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17 pages, 2497 KiB  
Article
Influence of Engine Electronic Management Fault Simulation on Vehicle Operation
by Branislav Šarkan, Michal Loman, František Synák, Michal Richtář and Mirosław Gidlewski
Sensors 2022, 22(5), 2054; https://doi.org/10.3390/s22052054 - 7 Mar 2022
Cited by 4 | Viewed by 2627
Abstract
The preparation of the fuel mixture of a conventional internal combustion engine is currently controlled exclusively electronically. In order for the electrical management of an internal combustion engine to function properly, it is necessary that all its electronic components work flawlessly and fulfill [...] Read more.
The preparation of the fuel mixture of a conventional internal combustion engine is currently controlled exclusively electronically. In order for the electrical management of an internal combustion engine to function properly, it is necessary that all its electronic components work flawlessly and fulfill their role. Failure of these electronic components can cause incorrect fuel mixture preparation and also affect driving safety. Due to the effect of individual failures, it has a negative impact on road safety and also negatively affects other participants. The task of the research is to investigate the effect of the failure of electronic engine components on the selected operating characteristics of a vehicle. The purpose of this article is to specify the extent to which a failure of an electronic engine component may affect the operation of a road vehicle. Eight failures of electronic systems (sensors and actuators) were simulated on a specific vehicle, with a petrol internal combustion engine. Measurements were performed in laboratory conditions, the purpose of which was to quantify the change in the operating characteristics of the vehicle between the faulty and fault-free state. The vehicle performance parameters and the production of selected exhaust emission components were determined for selected vehicle operating characteristics. The results show that in the normal operation of vehicles, there are situations where a failure in the electronic system of the engine has a significant impact on its operating characteristics and, at the same time, some of these failures are not identifiable by the vehicle operator. The findings of the publication can be used in the drafting of legislation, in the field of production and operation of road vehicles, and also in the mathematical modeling of the production of gaseous emissions by road transport. Full article
(This article belongs to the Special Issue Sensors and Systems for Automotive and Road Safety)
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13 pages, 6302 KiB  
Article
High-Accuracy Event Classification of Distributed Optical Fiber Vibration Sensing Based on Time–Space Analysis
by Zhao Ge, Hao Wu, Can Zhao and Ming Tang
Sensors 2022, 22(5), 2053; https://doi.org/10.3390/s22052053 - 7 Mar 2022
Cited by 10 | Viewed by 2413
Abstract
Distributed optical fiber vibration sensing (DVS) can measure vibration information along with an optical fiber. Accurate classification of vibration events is a key issue in practical applications of DVS. In this paper, we propose a convolutional neural network (CNN) to analyze DVS data [...] Read more.
Distributed optical fiber vibration sensing (DVS) can measure vibration information along with an optical fiber. Accurate classification of vibration events is a key issue in practical applications of DVS. In this paper, we propose a convolutional neural network (CNN) to analyze DVS data and achieve high-accuracy event recognition fully. We conducted experiments outdoors and collected more than 10,000 sets of vibration data. Through training, the CNN acquired the features of the raw DVS data and achieved the accurate classification of multiple vibration events. The recognition accuracy reached 99.9% based on the time–space data, a higher than used time-domain, frequency–domain, and time–frequency domain data. Moreover, considering that the performance of the DVS and the testing environment would change over time, we experimented again after one week to verify the method’s generalization performance. The classification accuracy using the previously trained CNN is 99.2%, which is of great value in practical applications. Full article
(This article belongs to the Special Issue Distributed Optical Fiber Sensors: Applications and Technology)
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18 pages, 2244 KiB  
Article
FT-NIR Spectroscopy for the Non-Invasive Study of Binders and Multi-Layered Structures in Ancient Paintings: Artworks of the Lombard Renaissance as Case Studies
by Margherita Longoni, Beatrice Genova, Alessia Marzanni, Daniela Melfi, Carlotta Beccaria and Silvia Bruni
Sensors 2022, 22(5), 2052; https://doi.org/10.3390/s22052052 - 6 Mar 2022
Cited by 7 | Viewed by 2509
Abstract
This work deals with the identification of natural binders and the study of the complex stratigraphy in paintings using reflection FT-IR spectroscopy, a common diagnostic tool for cultural heritage materials thanks to its non-invasiveness. In particular, the potential of the near-infrared (NIR) spectral [...] Read more.
This work deals with the identification of natural binders and the study of the complex stratigraphy in paintings using reflection FT-IR spectroscopy, a common diagnostic tool for cultural heritage materials thanks to its non-invasiveness. In particular, the potential of the near-infrared (NIR) spectral region, dominated by the absorption bands due to CH, CO, OH and NH functional groups, is successfully exploited to distinguish a lipid binder from a proteinaceous one, as well as the coexistence of the two media in laboratory-made model samples that simulate the complex multi-layered structure of a painting. The combination with multivariate analysis methods or with the calculation of indicative ratios between the intensity values of characteristic absorption bands is proposed to facilitate the interpretation of the spectral data. Furthermore, the greater penetration depth of NIR radiation is exploited to obtain information about the inner layers of the paintings, focusing in particular on the preparatory coatings of the supports. Finally, as proof of concept, FT-NIR analyses were also carried out on six paintings by artists working in Lombardy at the end of the 15th century, that exemplify different pictorial techniques. Full article
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16 pages, 6542 KiB  
Article
Control Method of the Dual-Winding Motor for Online High-Frequency Resistance Measurement in Fuel Cell Vehicle
by Cheng Chang, Yafu Zhou, Jing Lian and Jicai Liang
Sensors 2022, 22(5), 2051; https://doi.org/10.3390/s22052051 - 6 Mar 2022
Cited by 1 | Viewed by 2132
Abstract
The dual-winding motor drive has recently been proposed in the field of fuel cell vehicles due to its performance and high robust advantages. Efforts for this new topology have been made by many researchers. However, the high-frequency resistance measurement of a proton exchange [...] Read more.
The dual-winding motor drive has recently been proposed in the field of fuel cell vehicles due to its performance and high robust advantages. Efforts for this new topology have been made by many researchers. However, the high-frequency resistance measurement of a proton exchange membrane fuel cell based on dual-winding motor drive architecture, which is important for water management to optimize the lifespan of fuel cells, was not employed in earlier works. In this paper, a new control method of the dual-winding motor is proposed by introducing a dc input current control to realize high-frequency resistance measurement and normal drive control simultaneously, without using extra dc-dc converter. On the basis of the revealed energy exchange principles among electrical ports and mechanical port of the dual-winding motor, the load ripple caused by high-frequency current perturbation is optimized based on the q-axis current distribution between two winding sets. The decoupling control algorithm for the coupling effect within and across windings is also discussed to improve the dynamic response during high-frequency resistance measurement. Finally, simulation results verify the effectiveness and improvement of the proposed method. Fast Fourier transform results indicated that the total harmonic distortion of the dc input current was reduced from 22.53% to 4.47% of the fundamental, and the torque ripple was suppressed from about ±4.5 Nm to ±0.5 Nm at the given operation points. Full article
(This article belongs to the Section Vehicular Sensing)
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20 pages, 14311 KiB  
Article
An Observation Scheduling Approach Based on Task Clustering for High-Altitude Airship
by Jiawei Chen, Qizhang Luo and Guohua Wu
Sensors 2022, 22(5), 2050; https://doi.org/10.3390/s22052050 - 6 Mar 2022
Viewed by 2074
Abstract
Airship-based Earth observation is of great significance in many fields such as disaster rescue and environment monitoring. To facilitate efficient observation of high-altitude airships (HAA), a high-quality observation scheduling approach is crucial. This paper considers the scheduling of the imaging sensor and proposes [...] Read more.
Airship-based Earth observation is of great significance in many fields such as disaster rescue and environment monitoring. To facilitate efficient observation of high-altitude airships (HAA), a high-quality observation scheduling approach is crucial. This paper considers the scheduling of the imaging sensor and proposes a hierarchical observation scheduling approach based on task clustering (SA-TC). The original observation scheduling problem of HAA is transformed into three sub-problems (i.e., task clustering, sensor scheduling, and cruise path planning) and these sub-problems are respectively solved by three stages of the proposed SA-TC. Specifically, a novel heuristic algorithm integrating an improved ant colony optimization and the backtracking strategy is proposed to address the task clustering problem. The 2-opt local search is embedded into a heuristic algorithm to solve the sensor scheduling problem and the improved ant colony optimization is also implemented to solve the cruise path planning problem. Finally, extensive simulation experiments are conducted to verify the superiority of the proposed approach. Besides, the performance of the three algorithms for solving the three sub-problems are further analyzed on instances with different scales. Full article
(This article belongs to the Special Issue Parallel and Distributed Computing in Wireless Sensor Networks)
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18 pages, 26542 KiB  
Article
Design and Implementation of a UAV-Based Airborne Computing Platform for Computer Vision and Machine Learning Applications
by Athanasios Douklias, Lazaros Karagiannidis, Fay Misichroni and Angelos Amditis
Sensors 2022, 22(5), 2049; https://doi.org/10.3390/s22052049 - 6 Mar 2022
Cited by 14 | Viewed by 6811
Abstract
Visual sensing of the environment is crucial for flying an unmanned aerial vehicle (UAV) and is a centerpiece of many related applications. The ability to run computer vision and machine learning algorithms onboard an unmanned aerial system (UAS) is becoming more of a [...] Read more.
Visual sensing of the environment is crucial for flying an unmanned aerial vehicle (UAV) and is a centerpiece of many related applications. The ability to run computer vision and machine learning algorithms onboard an unmanned aerial system (UAS) is becoming more of a necessity in an effort to alleviate the communication burden of high-resolution video streaming, to provide flying aids, such as obstacle avoidance and automated landing, and to create autonomous machines. Thus, there is a growing interest on the part of many researchers in developing and validating solutions that are suitable for deployment on a UAV system by following the general trend of edge processing and airborne computing, which transforms UAVs from moving sensors into intelligent nodes that are capable of local processing. In this paper, we present, in a rigorous way, the design and implementation of a 12.85 kg UAV system equipped with the necessary computational power and sensors to serve as a testbed for image processing and machine learning applications, explain the rationale behind our decisions, highlight selected implementation details, and showcase the usefulness of our system by providing an example of how a sample computer vision application can be deployed on our platform. Full article
(This article belongs to the Special Issue UAV Imaging and Sensing)
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10 pages, 2075 KiB  
Communication
Improvement of Schottky Contacts of Gallium Oxide (Ga2O3) Nanowires for UV Applications
by Badriyah Alhalaili, Ahmad Al-Duweesh, Ileana Nicoleta Popescu, Ruxandra Vidu, Luige Vladareanu and M. Saif Islam
Sensors 2022, 22(5), 2048; https://doi.org/10.3390/s22052048 - 6 Mar 2022
Cited by 6 | Viewed by 2692
Abstract
Interest in the synthesis and fabrication of gallium oxide (Ga2O3) nanostructures as wide bandgap semiconductor-based ultraviolet (UV) photodetectors has recently increased due to their importance in cases of deep-UV photodetectors operating in high power/temperature conditions. Due to their unique [...] Read more.
Interest in the synthesis and fabrication of gallium oxide (Ga2O3) nanostructures as wide bandgap semiconductor-based ultraviolet (UV) photodetectors has recently increased due to their importance in cases of deep-UV photodetectors operating in high power/temperature conditions. Due to their unique properties, i.e., higher surface-to-volume ratio and quantum effects, these nanostructures can significantly enhance the sensitivity of detection. In this work, two Ga2O3 nanostructured films with different nanowire densities and sizes obtained by thermal oxidation of Ga on quartz, in the presence and absence of Ag catalyst, were investigated. The electrical properties influenced by the density of Ga2O3 nanowires (NWs) were analyzed to define the configuration of UV detection. The electrical measurements were performed on two different electric contacts and were located at distances of 1 and 3 mm. Factors affecting the detection performance of Ga2O3 NWs film, such as the distance between metal contacts (1 and 3 mm apart), voltages (5–20 V) and transient photocurrents were discussed in relation to the composition and nanostructure of the Ga2O3 NWs film. Full article
(This article belongs to the Section Chemical Sensors)
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13 pages, 4424 KiB  
Article
Long Range Raman-Amplified Distributed Acoustic Sensor Based on Spontaneous Brillouin Scattering for Large Strain Sensing
by Shahab Bakhtiari Gorajoobi, Ali Masoudi and Gilberto Brambilla
Sensors 2022, 22(5), 2047; https://doi.org/10.3390/s22052047 - 6 Mar 2022
Cited by 2 | Viewed by 3003
Abstract
A Brillouin distributed acoustic sensor (DAS) based on optical time-domain refractometry exhibiting a maximum detectible strain of 8.7 mε and a low signal fading is developed. Strain waves with frequencies of up to 120 Hz are measured with an accuracy of 12 [...] Read more.
A Brillouin distributed acoustic sensor (DAS) based on optical time-domain refractometry exhibiting a maximum detectible strain of 8.7 mε and a low signal fading is developed. Strain waves with frequencies of up to 120 Hz are measured with an accuracy of 12 με at a sampling rate of 1.2 kHz and a spatial resolution of 4 m over a sensing range of 8.5 km. The sensing range is further extended by using a modified inline Raman amplifier configuration. Using 80 ns Raman pump pulses, the signal-to-noise ratio is improved by 3.5 dB, while the accuracy of the measurement is enhanced by a factor of 2.5 to 62 με at the far-end of a 20 km fiber. Full article
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20 pages, 6719 KiB  
Article
Intelligent Diagnosis of Rolling Element Bearing Based on Refined Composite Multiscale Reverse Dispersion Entropy and Random Forest
by Aiqiang Liu, Zuye Yang, Hongkun Li, Chaoge Wang and Xuejun Liu
Sensors 2022, 22(5), 2046; https://doi.org/10.3390/s22052046 - 6 Mar 2022
Cited by 12 | Viewed by 2371
Abstract
Rolling bearings are the vital components of large electromechanical equipment, thus it is of great significance to develop intelligent fault diagnoses for them to improve equipment operation reliability. In this paper, a fault diagnosis method based on refined composite multiscale reverse dispersion entropy [...] Read more.
Rolling bearings are the vital components of large electromechanical equipment, thus it is of great significance to develop intelligent fault diagnoses for them to improve equipment operation reliability. In this paper, a fault diagnosis method based on refined composite multiscale reverse dispersion entropy (RCMRDE) and random forest is developed. Firstly, rolling bearing vibration signals are adaptively decomposed by variational mode decomposition (VMD), and then the RCMRDE values of 25 scales are calculated for original signal and each decomposed component as the initial feature set. Secondly, based on the joint mutual information maximization (JMIM) algorithm, the top 15 sensitive features are selected as a new feature set and feed into random forest model to identify bearing health status. Finally, to verify the effectiveness and superiority of the presented method, actual data acquisition and analysis are performed on the bearing fault diagnosis experimental platform. These results indicate that the presented method can precisely diagnose bearing fault types and damage degree, and the average identification accuracy rate is 97.33%. Compared with the refine composite multiscale dispersion entropy (RCMDE) and multiscale dispersion entropy (MDE), the fault diagnosis accuracy is improved by 2.67% and 8.67%, respectively. Furthermore, compared with the RCMRDE method without VMD decomposition, the fault diagnosis accuracy is improved by 3.67%. Research results prove that a better feature extraction technique is proposed, which can effectively overcome the deficiency of existing entropy and significantly enhance the ability of fault identification. Full article
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15 pages, 2077 KiB  
Article
Vehicle-Assisted UAV Delivery Scheme Considering Energy Consumption for Instant Delivery
by Xudong Deng, Mingke Guan, Yunfeng Ma, Xijie Yang and Ting Xiang
Sensors 2022, 22(5), 2045; https://doi.org/10.3390/s22052045 - 5 Mar 2022
Cited by 19 | Viewed by 2858
Abstract
Unmanned aerial vehicles (UAVs) are increasingly used in instant delivery scenarios. The combined delivery of vehicles and UAVs has many advantages compared to their respective separate delivery, which can greatly improve delivery efficiency. Although a few studies in the literature have explored the [...] Read more.
Unmanned aerial vehicles (UAVs) are increasingly used in instant delivery scenarios. The combined delivery of vehicles and UAVs has many advantages compared to their respective separate delivery, which can greatly improve delivery efficiency. Although a few studies in the literature have explored the issue of vehicle-assisted UAV delivery, we did not find any studies on the scenario of an UAV serving several customers. This study aims to design a new vehicle-assisted UAV delivery solution that allows UAVs to serve multiple customers in a single take-off and takes energy consumption into account. A multi-UAV task allocation model and a vehicle path planning model were established to determine the task allocation of the UAVs as well as the path of UAVs and the vehicle, respectively. The model also considered the impact of changing the payload of the UAV on energy consumption, bringing the results closer to reality. Finally, a hybrid heuristic algorithm based on an improved K-means algorithm and ant colony optimization (ACO) was proposed to solve the problem, and the effectiveness of the scheme was proven by multi-scale experimental instances and comparative experiments. Full article
(This article belongs to the Section Vehicular Sensing)
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