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Volume 21
Issue 11
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Sensors, Volume 21, Issue 11 (June-1 2021) – 370 articles

Cover Story (view full-size image): The development of marine autonomous mobile robots during the last few decades has been largely inspired by Nature (biomimicking), as evolution has developed, in more than three billion years, the adaptations that maximize the performance. This paper, through a bibliographic term-map analysis based on 6980 publications since 1950, summarizes the main trends and thematic clusters in marine biomimicking. The main emerging clusters we found are: a) energy provision by microbial fuel cells; b) biomaterials for soft robotics; c) design and control with focus on locomotion. Marine research on biomimicking has grown exponentially in the last two decades, driven by the need for innovative military and industrial applications in the US, Europe and Asia in an attempt to optimize energy gain and consumption, aided by the development of new sensors and AI. View this paper
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19 pages, 3550 KiB  
Article
An Ensemble Learning Method for Robot Electronic Nose with Active Perception
by Shengming Li, Lin Feng, Yunfei Ge, Li Zhu and Liang Zhao
Sensors 2021, 21(11), 3941; https://doi.org/10.3390/s21113941 - 7 Jun 2021
Cited by 6 | Viewed by 3297
Abstract
The electronic nose is the olfactory organ of the robot, which is composed of a large number of sensors to perceive the smell of objects through free diffusion. Traditionally, it is difficult to realize the active perception function, and it is difficult to [...] Read more.
The electronic nose is the olfactory organ of the robot, which is composed of a large number of sensors to perceive the smell of objects through free diffusion. Traditionally, it is difficult to realize the active perception function, and it is difficult to meet the requirements of small size, low cost, and quick response that robots require. In order to address these issues, a novel electronic nose with active perception was designed and an ensemble learning method was proposed to distinguish the smell of different objects. An array of three MQ303 semiconductor gas sensors and an electrochemical sensor DART-2-Fe5 were used to construct the novel electronic nose, and the proposed ensemble learning method with four algorithms realized the active odor perception function. The experiment results verified that the accuracy of the active odor perception can reach more than 90%, even though it used 30% training data. The novel electronic nose with active perception based on the ensemble learning method can improve the efficiency and accuracy of odor data collection and olfactory perception. Full article
(This article belongs to the Section Sensors and Robotics)
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19 pages, 2584 KiB  
Article
Orientation-Invariant Spatio-Temporal Gait Analysis Using Foot-Worn Inertial Sensors
by Vânia Guimarães, Inês Sousa and Miguel Velhote Correia
Sensors 2021, 21(11), 3940; https://doi.org/10.3390/s21113940 - 7 Jun 2021
Cited by 10 | Viewed by 4330
Abstract
Inertial sensors can potentially assist clinical decision making in gait-related disorders. Methods for objective spatio-temporal gait analysis usually assume the careful alignment of the sensors on the body, so that sensor data can be evaluated using the body coordinate system. Some studies infer [...] Read more.
Inertial sensors can potentially assist clinical decision making in gait-related disorders. Methods for objective spatio-temporal gait analysis usually assume the careful alignment of the sensors on the body, so that sensor data can be evaluated using the body coordinate system. Some studies infer sensor orientation by exploring the cyclic characteristics of walking. In addition to being unrealistic to assume that the sensor can be aligned perfectly with the body, the robustness of gait analysis with respect to differences in sensor orientation has not yet been investigated—potentially hindering use in clinical settings. To address this gap in the literature, we introduce an orientation-invariant gait analysis approach and propose a method to quantitatively assess robustness to changes in sensor orientation. We validate our results in a group of young adults, using an optical motion capture system as reference. Overall, good agreement between systems is achieved considering an extensive set of gait metrics. Gait speed is evaluated with a relative error of −3.1±9.2 cm/s, but precision improves when turning strides are excluded from the analysis, resulting in a relative error of −3.4±6.9 cm/s. We demonstrate the invariance of our approach by simulating rotations of the sensor on the foot. Full article
(This article belongs to the Special Issue Wearable Sensors & Gait)
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21 pages, 21383 KiB  
Article
Parallel Structure from Motion for Sparse Point Cloud Generation in Large-Scale Scenes
by Yongtang Bao, Pengfei Lin, Yao Li, Yue Qi, Zhihui Wang, Wenxiang Du and Qing Fan
Sensors 2021, 21(11), 3939; https://doi.org/10.3390/s21113939 - 7 Jun 2021
Cited by 8 | Viewed by 4205
Abstract
Scene reconstruction uses images or videos as input to reconstruct a 3D model of a real scene and has important applications in smart cities, surveying and mapping, military, and other fields. Structure from motion (SFM) is a key step in scene reconstruction, which [...] Read more.
Scene reconstruction uses images or videos as input to reconstruct a 3D model of a real scene and has important applications in smart cities, surveying and mapping, military, and other fields. Structure from motion (SFM) is a key step in scene reconstruction, which recovers sparse point clouds from image sequences. However, large-scale scenes cannot be reconstructed using a single compute node. Image matching and geometric filtering take up a lot of time in the traditional SFM problem. In this paper, we propose a novel divide-and-conquer framework to solve the distributed SFM problem. First, we use the global navigation satellite system (GNSS) information from images to calculate the GNSS neighborhood. The number of images matched is greatly reduced by matching each image to only valid GNSS neighbors. This way, a robust matching relationship can be obtained. Second, the calculated matching relationship is used as the initial camera graph, which is divided into multiple subgraphs by the clustering algorithm. The local SFM is executed on several computing nodes to register the local cameras. Finally, all of the local camera poses are integrated and optimized to complete the global camera registration. Experiments show that our system can accurately and efficiently solve the structure from motion problem in large-scale scenes. Full article
(This article belongs to the Special Issue 3D Object and Scene Detection, Reconstruction, Segmentation Based on Advanced Sensing Technology)
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37 pages, 9764 KiB  
Article
ReS2tAC—UAV-Borne Real-Time SGM Stereo Optimized for Embedded ARM and CUDA Devices
by Boitumelo Ruf, Jonas Mohrs, Martin Weinmann, Stefan Hinz and Jürgen Beyerer
Sensors 2021, 21(11), 3938; https://doi.org/10.3390/s21113938 - 7 Jun 2021
Cited by 10 | Viewed by 4563
Abstract
With the emergence of low-cost robotic systems, such as unmanned aerial vehicle, the importance of embedded high-performance image processing has increased. For a long time, FPGAs were the only processing hardware that were capable of high-performance computing, while at the same time preserving [...] Read more.
With the emergence of low-cost robotic systems, such as unmanned aerial vehicle, the importance of embedded high-performance image processing has increased. For a long time, FPGAs were the only processing hardware that were capable of high-performance computing, while at the same time preserving a low power consumption, essential for embedded systems. However, the recently increasing availability of embedded GPU-based systems, such as the NVIDIA Jetson series, comprised of an ARM CPU and a NVIDIA Tegra GPU, allows for massively parallel embedded computing on graphics hardware. With this in mind, we propose an approach for real-time embedded stereo processing on ARM and CUDA-enabled devices, which is based on the popular and widely used Semi-Global Matching algorithm. In this, we propose an optimization of the algorithm for embedded CUDA GPUs, by using massively parallel computing, as well as using the NEON intrinsics to optimize the algorithm for vectorized SIMD processing on embedded ARM CPUs. We have evaluated our approach with different configurations on two public stereo benchmark datasets to demonstrate that they can reach an error rate as low as 3.3%. Furthermore, our experiments show that the fastest configuration of our approach reaches up to 46 FPS on VGA image resolution. Finally, in a use-case specific qualitative evaluation, we have evaluated the power consumption of our approach and deployed it on the DJI Manifold 2-G attached to a DJI Matrix 210v2 RTK unmanned aerial vehicle (UAV), demonstrating its suitability for real-time stereo processing onboard a UAV. Full article
(This article belongs to the Special Issue Onboard Sensemaking for Guidance, Control, and Remote Sensing Application in Image Processing)
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20 pages, 6963 KiB  
Article
Foot Gesture Recognition Using High-Compression Radar Signature Image and Deep Learning
by Seungeon Song, Bongseok Kim, Sangdong Kim and Jonghun Lee
Sensors 2021, 21(11), 3937; https://doi.org/10.3390/s21113937 - 7 Jun 2021
Cited by 5 | Viewed by 3964
Abstract
Recently, Doppler radar-based foot gesture recognition has attracted attention as a hands-free tool. Doppler radar-based recognition for various foot gestures is still very challenging. So far, no studies have yet dealt deeply with recognition of various foot gestures based on Doppler radar and [...] Read more.
Recently, Doppler radar-based foot gesture recognition has attracted attention as a hands-free tool. Doppler radar-based recognition for various foot gestures is still very challenging. So far, no studies have yet dealt deeply with recognition of various foot gestures based on Doppler radar and a deep learning model. In this paper, we propose a method of foot gesture recognition using a new high-compression radar signature image and deep learning. By means of a deep learning AlexNet model, a new high-compression radar signature is created by extracting dominant features via Singular Value Decomposition (SVD) processing; four different foot gestures including kicking, swinging, sliding, and tapping are recognized. Instead of using an original radar signature, the proposed method improves the memory efficiency required for deep learning training by using a high-compression radar signature. Original and reconstructed radar images with high compression values of 90%, 95%, and 99% were applied for the deep learning AlexNet model. As experimental results, movements of all four different foot gestures and of a rolling baseball were recognized with an accuracy of approximately 98.64%. In the future, due to the radar’s inherent robustness to the surrounding environment, this foot gesture recognition sensor using Doppler radar and deep learning will be widely useful in future automotive and smart home industry fields. Full article
(This article belongs to the Section Radar Sensors)
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16 pages, 1404 KiB  
Article
Towards 6G IoT: Tracing Mobile Sensor Nodes with Deep Learning Clustering in UAV Networks
by Yannis Spyridis, Thomas Lagkas, Panagiotis Sarigiannidis, Vasileios Argyriou, Antonios Sarigiannidis, George Eleftherakis and Jie Zhang
Sensors 2021, 21(11), 3936; https://doi.org/10.3390/s21113936 - 7 Jun 2021
Cited by 28 | Viewed by 4853
Abstract
Unmanned aerial vehicles (UAVs) in the role of flying anchor nodes have been proposed to assist the localisation of terrestrial Internet of Things (IoT) sensors and provide relay services in the context of the upcoming 6G networks. This paper considered the objective of [...] Read more.
Unmanned aerial vehicles (UAVs) in the role of flying anchor nodes have been proposed to assist the localisation of terrestrial Internet of Things (IoT) sensors and provide relay services in the context of the upcoming 6G networks. This paper considered the objective of tracing a mobile IoT device of unknown location, using a group of UAVs that were equipped with received signal strength indicator (RSSI) sensors. The UAVs employed measurements of the target’s radio frequency (RF) signal power to approach the target as quickly as possible. A deep learning model performed clustering in the UAV network at regular intervals, based on a graph convolutional network (GCN) architecture, which utilised information about the RSSI and the UAV positions. The number of clusters was determined dynamically at each instant using a heuristic method, and the partitions were determined by optimising an RSSI loss function. The proposed algorithm retained the clusters that approached the RF source more effectively, removing the rest of the UAVs, which returned to the base. Simulation experiments demonstrated the improvement of this method compared to a previous deterministic approach, in terms of the time required to reach the target and the total distance covered by the UAVs. Full article
(This article belongs to the Special Issue 6G Wireless Communication Systems)
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16 pages, 10028 KiB  
Article
Unified pH Measurements of Ethanol, Methanol, and Acetonitrile, and Their Mixtures with Water
by Lisa Deleebeeck, Alan Snedden, Dániel Nagy, Zsófia Szilágyi Nagyné, Matilda Roziková, Martina Vičarová, Agnes Heering, Frank Bastkowski, Ivo Leito, Raquel Quendera, Vítor Cabral and Daniela Stoica
Sensors 2021, 21(11), 3935; https://doi.org/10.3390/s21113935 - 7 Jun 2021
Cited by 16 | Viewed by 7059
Abstract
Measurement of pH in aqueous-organic mixtures with different compositions is of high importance in science and technology, but it is, at the same time, challenging both from a conceptual and practical standpoint. A big part of the difficulty comes from the fundamental incomparability [...] Read more.
Measurement of pH in aqueous-organic mixtures with different compositions is of high importance in science and technology, but it is, at the same time, challenging both from a conceptual and practical standpoint. A big part of the difficulty comes from the fundamental incomparability of conventional pH values between solvents (spH, solvent-specific scales). The recent introduction of the unified pH (pHabs) concept opens up the possibility of measuring pH, expressed as pHabsH2O, in a way that is comparable between solvent, and, thereby, removing the conceptual problem. However, practical issues remain. This work presents the experience of the authors with measuring pHabsH2O values in mixtures of methanol, ethanol, and acetonitrile, with water, but without the presence of buffers or other additives. The aim was to assigned pHabsH2O values to solvent–water mixtures using differential potentiometry and the ‘pHabs-ladder’ method. Measurements were made of the potential difference between glass electrodes immersed in different solutions, separated by an ionic liquid salt bridge. Data were acquired for a series of solutions of varying solvent content. This work includes experiences related to: a selection of commercial electrodes, purity of starting material, and comparability between laboratories. Ranges of pHabsH2O values for selected compositions of solvent–water mixtures are presented. Full article
(This article belongs to the Section Chemical Sensors)
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23 pages, 3726 KiB  
Article
Research and Development of Delay-Sensitive Routing Tensor Model in IoT Core Networks
by Oleksandr Lemeshko, Jozef Papan, Oleksandra Yeremenko, Maryna Yevdokymenko and Pavel Segec
Sensors 2021, 21(11), 3934; https://doi.org/10.3390/s21113934 - 7 Jun 2021
Cited by 12 | Viewed by 2707
Abstract
In the article, we present the research and development of an improved delay-sensitive routing tensor model for the core of the IoT network. The flow-based tensor model is considered within the coordinate system of interpolar paths and internal node pairs. The advantage of [...] Read more.
In the article, we present the research and development of an improved delay-sensitive routing tensor model for the core of the IoT network. The flow-based tensor model is considered within the coordinate system of interpolar paths and internal node pairs. The advantage of the presented model is the application for IoT architectures to ensure the Quality of Service under the parameters of bandwidth, average end-to-end delay, and the probability of packet loss. Hence, the technical task of delay-sensitive routing is formulated as the optimization problem together with constraints and conditions imposed on the corresponding routing variables. The system of optimality criteria is chosen for an investigation. Each selected criterion concerning the specifics of the demanded routing problem solution aims at the optimal use of available network resources and the improvement of QoS indicators, namely, average end-to-end delay. The analysis of the obtained routing solutions under different criteria is performed. Numerical research of the improved delay-sensitive routing tensor model allowed us to discover its features and proved the adequacy of the results for the multipath order of routing. Full article
(This article belongs to the Section Internet of Things)
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17 pages, 3747 KiB  
Technical Note
The Concept of the Constructional Solution of the Working Section of a Robot for Harvesting Strawberries
by Sławomir Kurpaska, Andrzej Bielecki, Zygmunt Sobol, Marzena Bielecka, Magdalena Habrat and Piotr Śmigielski
Sensors 2021, 21(11), 3933; https://doi.org/10.3390/s21113933 - 7 Jun 2021
Cited by 7 | Viewed by 3003
Abstract
Strawberry fruits are products of high commercial and consumption value, and, at the same time, they are difficult to harvest due to their very low mechanical strength and difficulties in identifying them within the bush. Therefore, robots collecting strawberries should be equipped with [...] Read more.
Strawberry fruits are products of high commercial and consumption value, and, at the same time, they are difficult to harvest due to their very low mechanical strength and difficulties in identifying them within the bush. Therefore, robots collecting strawberries should be equipped with four subsystems: a video object detection system, a collecting arm, a unit for the reception and possible packaging of the fruit, and a traction system unit. This paper presents a concept for the design and operation of the working section of a harvester for strawberry fruit crops grown in rows or beds, in open fields, and/or under cover. In principle, the working section of the combine should meet parameters comparable with those of manually harvested strawberries (efficiency, quality of harvested fruit) and minimise contamination in the harvested product. In order to meet these requirements, in the presented design concept, it was assumed that these activities would be performed during harvesting with the natural distribution of fruits within the strawberry bush, and the operation of the working head arm maneuvering in the vicinity of the picked fruit, the fruit receiving unit, and other obstacles was developed on the basis of image analysis, initially general, and in detail in the final phase. The paper also discusses the idea of a vision system in which the algorithm used has been positively tested to identify the shapes of objects, and due to the similarity of space, it can be successfully used for the correct location of strawberry fruit. Full article
(This article belongs to the Section Sensors and Robotics)
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25 pages, 9871 KiB  
Article
A Labeled GM-PHD Filter for Explicitly Tracking Multiple Targets
by Yiyue Gao, Defu Jiang, Chao Zhang and Su Guo
Sensors 2021, 21(11), 3932; https://doi.org/10.3390/s21113932 - 7 Jun 2021
Cited by 9 | Viewed by 2937
Abstract
In this study, an explicit track continuity algorithm is proposed for multitarget tracking (MTT) based on the Gaussian mixture (GM) implementation of the probability hypothesis density (PHD) filter. Trajectory maintenance and multitarget state extraction in the GM-PHD filter have not been effectively integrated [...] Read more.
In this study, an explicit track continuity algorithm is proposed for multitarget tracking (MTT) based on the Gaussian mixture (GM) implementation of the probability hypothesis density (PHD) filter. Trajectory maintenance and multitarget state extraction in the GM-PHD filter have not been effectively integrated to date. To address this problem, we propose an improved GM-PHD filter. In this approach, the Gaussian components are classified and labeled, and multitarget state extraction is converted into multiple single-state extractions. This provides the identity label of the individual target and can shield against the negative effects of clutter in the prior density region on the estimates, thus realizing the integration of trajectory maintenance with state extraction in the GM-PHD filter. As no additional associated procedures are required, the overall real-time performance of the proposed filter is similar to or slightly lower than that of the basic GM-PHD filter. The results of numerical experiments demonstrate that the proposed approach can achieve explicit track continuity. Full article
(This article belongs to the Section Physical Sensors)
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23 pages, 6228 KiB  
Article
Unified Chassis Control of Electric Vehicles Considering Wheel Vertical Vibrations
by Xinbo Chen, Mingyang Wang and Wei Wang
Sensors 2021, 21(11), 3931; https://doi.org/10.3390/s21113931 - 7 Jun 2021
Cited by 8 | Viewed by 3989
Abstract
In the process of vehicle chassis electrification, different active actuators and systems have been developed and commercialized for improved vehicle dynamic performances. For a vehicle system with actuation redundancy, the integration of individual chassis control systems can provide additional benefits compared to a [...] Read more.
In the process of vehicle chassis electrification, different active actuators and systems have been developed and commercialized for improved vehicle dynamic performances. For a vehicle system with actuation redundancy, the integration of individual chassis control systems can provide additional benefits compared to a single ABS/ESC system. This paper describes a Unified Chassis Control (UCC) strategy for enhancing vehicle stability and ride comfort by the coordination of four In-Wheel Drive (IWD), 4-Wheel Independent Steering (4WIS), and Active Suspension Systems (ASS). Desired chassis motion is determined by generalized forces/moment calculated through a high-level sliding mode controller. Based on tire force constraints subject to allocated normal forces, the generalized forces/moment are distributed to the slip and slip angle of each tire by a fixed-point control allocation algorithm. Regarding the uneven road, H∞ robust controllers are proposed based on a modified quarter-car model. Evaluation of the overall system was accomplished by simulation testing with a full-vehicle CarSim model under different scenarios. The conclusion shows that the vertical vibration of the four wheels plays a detrimental role in vehicle stability, and the proposed method can effectively realize the tire force distribution to control the vehicle body attitude and driving stability even in high-demanding scenarios. Full article
(This article belongs to the Special Issue Advanced Sensing and Control for Connected and Automated Vehicles)
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32 pages, 10596 KiB  
Article
A Self-Contained 3D Biomechanical Analysis Lab for Complete Automatic Spine and Full Skeleton Assessment of Posture, Gait and Run
by Moreno D’Amico, Edyta Kinel, Gabriele D’Amico and Piero Roncoletta
Sensors 2021, 21(11), 3930; https://doi.org/10.3390/s21113930 - 7 Jun 2021
Cited by 5 | Viewed by 5639
Abstract
Quantitative functional assessment of Posture and Motion Analysis of the entire skeleton and spine is highly desirable. Nonetheless, in most studies focused on posture and movement biomechanics, the spine is only grossly depicted because of its required level of complexity. Approaches integrating pressure [...] Read more.
Quantitative functional assessment of Posture and Motion Analysis of the entire skeleton and spine is highly desirable. Nonetheless, in most studies focused on posture and movement biomechanics, the spine is only grossly depicted because of its required level of complexity. Approaches integrating pressure measurement devices with stereophotogrammetric systems have been presented in the literature, but spine biomechanics studies have rarely been linked to baropodometry. A new multi-sensor system called GOALS-E.G.G. (Global Opto-electronic Approach for Locomotion and Spine-Expert Gait Guru), integrating a fully genlock-synched baropodometric treadmill with a stereophotogrammetric device, is introduced to overcome the above-described limitations. The GOALS-EGG extends the features of a complete 3D parametric biomechanical skeleton model, developed in an original way for static 3D posture analysis, to kinematic and kinetic analysis of movement, gait and run. By integrating baropodometric data, the model allows the estimation of lower limb net-joint forces, torques and muscle power. Net forces and torques are also assessed at intervertebral levels. All the elaborations are completely automatised up to the mean behaviour extraction for both posture and cyclic-repetitive tasks, allowing the clinician/researcher to perform, per each patient, multiple postural/movement tests and compare them in a unified statistically reliable framework. Full article
(This article belongs to the Collection Sensors in Biomechanics)
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17 pages, 2862 KiB  
Article
Deep Learning Approach for Vibration Signals Applications
by Han-Yun Chen and Ching-Hung Lee
Sensors 2021, 21(11), 3929; https://doi.org/10.3390/s21113929 - 7 Jun 2021
Cited by 31 | Viewed by 7901
Abstract
This study discusses convolutional neural networks (CNNs) for vibration signals analysis, including applications in machining surface roughness estimation, bearing faults diagnosis, and tool wear detection. The one-dimensional CNNs (1DCNN) and two-dimensional CNNs (2DCNN) are applied for regression and classification applications using different types [...] Read more.
This study discusses convolutional neural networks (CNNs) for vibration signals analysis, including applications in machining surface roughness estimation, bearing faults diagnosis, and tool wear detection. The one-dimensional CNNs (1DCNN) and two-dimensional CNNs (2DCNN) are applied for regression and classification applications using different types of inputs, e.g., raw signals, and time-frequency spectra images by short time Fourier transform. In the application of regression and the estimation of machining surface roughness, the 1DCNN is utilized and the corresponding CNN structure (hyper parameters) optimization is proposed by using uniform experimental design (UED), neural network, multiple regression, and particle swarm optimization. It demonstrates the effectiveness of the proposed approach to obtain a structure with better performance. In applications of classification, bearing faults and tool wear classification are carried out by vibration signals analysis and CNN. Finally, the experimental results are shown to demonstrate the effectiveness and performance of our approach. Full article
(This article belongs to the Special Issue Sensors for Fault Detection and Diagnosis)
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12 pages, 4300 KiB  
Communication
Methodology for Addressing Infectious Aerosol Persistence in Real-Time Using Sensor Network
by Sepehr Makhsous, Joelle M. Segovia, Jiayang He, Daniel Chan, Larry Lee, Igor V. Novosselov and Alexander V. Mamishev
Sensors 2021, 21(11), 3928; https://doi.org/10.3390/s21113928 - 7 Jun 2021
Cited by 13 | Viewed by 5731
Abstract
Human exposure to infectious aerosols results in the transmission of diseases such as influenza, tuberculosis, and COVID-19. Most dental procedures generate a significant number of aerosolized particles, increasing transmission risk in dental settings. Since the generation of aerosols in dentistry is unavoidable, many [...] Read more.
Human exposure to infectious aerosols results in the transmission of diseases such as influenza, tuberculosis, and COVID-19. Most dental procedures generate a significant number of aerosolized particles, increasing transmission risk in dental settings. Since the generation of aerosols in dentistry is unavoidable, many clinics have started using intervention strategies such as area-filtration units and extraoral evacuation equipment, especially under the relatively recent constraints of the pandemic. However, the effectiveness of these devices in dental operatories has not been studied. Therefore, the ability of dental personnel to efficiently position and operate such instruments is also limited. To address these challenges, we utilized a real-time sensor network for assessment of aerosol dynamics during dental restoration and cleaning producers with and without intervention. The strategies tested during the procedures were (i) local area High-Efficiency Particle Air (HEPA) filters and (ii) Extra-Oral Suction Device (EOSD). The study was conducted at the University of Washington School of Dentistry using a network of 13 fixed sensors positioned within the operatory and one wearable sensor worn by the dental operator. The sensor network provides time and space-resolved particulate matter (PM) data. Three-dimensional (3D) visualization informed aerosol persistence in the operatory. It was found that area filters did not improve the overall aerosol concentration in dental offices in a significant way. A decrease in PM concentration by an average of 16% was observed when EOSD equipment was used during the procedures. The combination of real-time sensors and 3D visualization can provide dental personnel and facility managers with actionable feedback to effectively assess aerosol transmission in medical settings and develop evidence-based intervention strategies. Full article
(This article belongs to the Collection Robotic and Sensor Technologies in Environmental Exploration and Monitoring)
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21 pages, 3693 KiB  
Article
Application of Low-Cost MEMS Spectrometers for Forest Topsoil Properties Prediction
by Felix Thomas, Rainer Petzold, Carina Becker and Ulrike Werban
Sensors 2021, 21(11), 3927; https://doi.org/10.3390/s21113927 - 7 Jun 2021
Cited by 12 | Viewed by 3215
Abstract
Increasing temperatures and drought occurrences recently led to soil moisture depletion and increasing tree mortality. In the interest of sustainable forest management, the monitoring of forest soil properties will be of increasing importance in the future. Vis-NIR spectroscopy can be used as fast, [...] Read more.
Increasing temperatures and drought occurrences recently led to soil moisture depletion and increasing tree mortality. In the interest of sustainable forest management, the monitoring of forest soil properties will be of increasing importance in the future. Vis-NIR spectroscopy can be used as fast, non-destructive and cost-efficient method for soil parameter estimations. Microelectromechanical system devices (MEMS) have become available that are suitable for many application fields due to their low cost as well as their small size and weight. We investigated the performance of MEMS spectrometers in the visual and NIR range to estimate forest soil samples total C and N content of Ah and Oh horizons at the lab. The results were compared to a full-range device using PLSR and Cubist regression models at local (2.3 ha, n: Ah = 60, Oh = 50) and regional scale (State of Saxony, Germany, 184,000 km2, n: Ah = 186 and Oh = 176). For each sample, spectral reflectance was collected using MEMS spectrometer in the visual (Hamamatsu C12880MA) and NIR (NeoSpetrac SWS62231) range and using a conventional full range device (Veris Spectrophotometer). Both data sets were split into a calibration (70%) and a validation set (30%) to evaluate prediction power. Models were calibrated for Oh and Ah horizon separately for both data sets. Using the regional data, we also used a combination of both horizons. Our results show that MEMS devices are suitable for C and N prediction of forest topsoil on regional scale. On local scale, only models for the Ah horizon yielded sufficient results. We found moderate and good model results using MEMS devices for Ah horizons at local scale (R2≥ 0.71, RPIQ ≥ 2.41) using Cubist regression. At regional scale, we achieved moderate results for C and N content using data from MEMS devices in Oh (R2≥ 0.57, RPIQ ≥ 2.42) and Ah horizon (R2≥ 0.54, RPIQ ≥2.15). When combining Oh and Ah horizons, we achieved good prediction results using the MEMS sensors and Cubist (R2≥ 0.85, RPIQ ≥ 4.69). For the regional data, models using data derived by the Hamamatsu device in the visual range only were least precise. Combining visual and NIR data derived from MEMS spectrometers did in most cases improve the prediction accuracy. We directly compared our results to models based on data from a conventional full range device. Our results showed that the combination of both MEMS devices can compete with models based on full range spectrometers. MEMS approaches reached between 68% and 105% of the corresponding full ranges devices R2 values. Local models tended to be more accurate than regional approaches for the Ah horizon. Our results suggest that MEMS spectrometers are suitable for forest soil C and N content estimation. They can contribute to improved monitoring in the future as their small size and weight could make in situ measurements feasible. Full article
(This article belongs to the Special Issue New Sensors for Monitoring of Soil Parameters)
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22 pages, 6669 KiB  
Review
A Review of Remote Sensing Image Dehazing
by Juping Liu, Shiju Wang, Xin Wang, Mingye Ju and Dengyin Zhang
Sensors 2021, 21(11), 3926; https://doi.org/10.3390/s21113926 - 7 Jun 2021
Cited by 31 | Viewed by 5738
Abstract
Remote sensing (RS) is one of the data collection technologies that help explore more earth surface information. However, RS data captured by satellite are susceptible to particles suspended during the imaging process, especially for data with visible light band. To make up for [...] Read more.
Remote sensing (RS) is one of the data collection technologies that help explore more earth surface information. However, RS data captured by satellite are susceptible to particles suspended during the imaging process, especially for data with visible light band. To make up for such deficiency, numerous dehazing work and efforts have been made recently, whose strategy is to directly restore single hazy data without the need for using any extra information. In this paper, we first classify the current available algorithm into three categories, i.e., image enhancement, physical dehazing, and data-driven. The advantages and disadvantages of each type of algorithm are then summarized in detail. Finally, the evaluation indicators used to rank the recovery performance and the application scenario of the RS data haze removal technique are discussed, respectively. In addition, some common deficiencies of current available methods and future research focus are elaborated. Full article
(This article belongs to the Special Issue Communications and Sensing Technologies for the Future)
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34 pages, 10819 KiB  
Article
Medical Images Encryption Based on Adaptive-Robust Multi-Mode Synchronization of Chen Hyper-Chaotic Systems
by Ali Akbar Kekha Javan, Mahboobeh Jafari, Afshin Shoeibi, Assef Zare, Marjane Khodatars, Navid Ghassemi, Roohallah Alizadehsani and Juan Manuel Gorriz
Sensors 2021, 21(11), 3925; https://doi.org/10.3390/s21113925 - 7 Jun 2021
Cited by 30 | Viewed by 3488
Abstract
In this paper, a novel medical image encryption method based on multi-mode synchronization of hyper-chaotic systems is presented. The synchronization of hyper-chaotic systems is of great significance in secure communication tasks such as encryption of images. Multi-mode synchronization is a novel and highly [...] Read more.
In this paper, a novel medical image encryption method based on multi-mode synchronization of hyper-chaotic systems is presented. The synchronization of hyper-chaotic systems is of great significance in secure communication tasks such as encryption of images. Multi-mode synchronization is a novel and highly complex issue, especially if there is uncertainty and disturbance. In this work, an adaptive-robust controller is designed for multimode synchronized chaotic systems with variable and unknown parameters, despite the bounded disturbance and uncertainty with a known function in two modes. In the first case, it is a main system with some response systems, and in the second case, it is a circular synchronization. Using theorems it is proved that the two synchronization methods are equivalent. Our results show that, we are able to obtain the convergence of synchronization error and parameter estimation error to zero using Lyapunov’s method. The new laws to update time-varying parameters, estimating disturbance and uncertainty bounds are proposed such that stability of system is guaranteed. To assess the performance of the proposed synchronization method, various statistical analyzes were carried out on the encrypted medical images and standard benchmark images. The results show effective performance of the proposed synchronization technique in the medical images encryption for telemedicine application. Full article
(This article belongs to the Special Issue Explainable AI in Medical Sensors)
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17 pages, 1546 KiB  
Article
Weak Calibration of a Visible Light Positioning System Based on a Position-Sensitive Detector: Positioning Error Assessment
by Álvaro De-La-Llana-Calvo, José-Luis Lázaro-Galilea, Alfredo Gardel-Vicente, David Salido-Monzú, Ignacio Bravo-Muñoz, Andreea Iamnitchi and Rubén Gil-Vera
Sensors 2021, 21(11), 3924; https://doi.org/10.3390/s21113924 - 7 Jun 2021
Cited by 12 | Viewed by 3131
Abstract
Reduced deployment and calibration requirements are key for scalable and cost-effective indoor positioning systems. In this work, we propose a low-complexity, weak calibration procedure for an indoor positioning system based on infrastructure lighting and a positioning-sensitive detector. The proposed calibration relies on genetic [...] Read more.
Reduced deployment and calibration requirements are key for scalable and cost-effective indoor positioning systems. In this work, we propose a low-complexity, weak calibration procedure for an indoor positioning system based on infrastructure lighting and a positioning-sensitive detector. The proposed calibration relies on genetic algorithms to obtain the relevant system parameters in the real positioning environment without a priori information, and requires a low number of simple measurements. The achievable performance of the proposal was assessed by direct comparison with a formal offline calibration method requiring complex dedicated infrastructure and instruments. The comparative error assessment showed that the maximum accuracy reduction compared to the significantly more costly formal calibration was below 25 mm, and the overall absolute positioning error was smaller than 35 mm with orientation errors of around 0.25°. The performance achieved with the proposed weak calibration procedure is sufficient for many indoor positioning applications and largely reduces the cost and complexity of setting up the positioning system in real environments. Full article
(This article belongs to the Special Issue Sensors in Indoor Positioning Systems)
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13 pages, 2792 KiB  
Article
Design and Validation of a Portable Machine Learning-Based Electronic Nose
by Yixu Huang, Iyll-Joon Doh and Euiwon Bae
Sensors 2021, 21(11), 3923; https://doi.org/10.3390/s21113923 - 7 Jun 2021
Cited by 27 | Viewed by 5000
Abstract
Volatile organic compounds (VOCs) are chemicals emitted by various groups, such as foods, bacteria, and plants. While there are specific pathways and biological features significantly related to such VOCs, detection of these is achieved mostly by human odor testing or high-end methods such [...] Read more.
Volatile organic compounds (VOCs) are chemicals emitted by various groups, such as foods, bacteria, and plants. While there are specific pathways and biological features significantly related to such VOCs, detection of these is achieved mostly by human odor testing or high-end methods such as gas chromatography–mass spectrometry that can analyze the gaseous component. However, odor characterization can be quite helpful in the rapid classification of some samples in sufficient concentrations. Lower-cost metal-oxide gas sensors have the potential to allow the same type of detection with less training required. Here, we report a portable, battery-powered electronic nose system that utilizes multiple metal-oxide gas sensors and machine learning algorithms to detect and classify VOCs. An in-house circuit was designed with ten metal-oxide sensors and voltage dividers; an STM32 microcontroller was used for data acquisition with 12-bit analog-to-digital conversion. For classification of target samples, a supervised machine learning algorithm such as support vector machine (SVM) was applied to classify the VOCs based on the measurement results. The coefficient of variation (standard deviation divided by mean) of 8 of the 10 sensors stayed below 10%, indicating the excellent repeatability of these sensors. As a proof of concept, four different types of wine samples and three different oil samples were classified, and the training model reported 100% and 98% accuracy based on the confusion matrix analysis, respectively. When the trained model was challenged against new sets of data, sensitivity and specificity of 98.5% and 98.6% were achieved for the wine test and 96.3% and 93.3% for the oil test, respectively, when the SVM classifier was used. These results suggest that the metal-oxide sensors are suitable for usage in food authentication applications. Full article
(This article belongs to the Collection Instrument and Measurement)
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21 pages, 51271 KiB  
Article
Adversarial Attack and Defence through Adversarial Training and Feature Fusion for Diabetic Retinopathy Recognition
by Sheeba Lal, Saeed Ur Rehman, Jamal Hussain Shah, Talha Meraj, Hafiz Tayyab Rauf, Robertas Damaševičius, Mazin Abed Mohammed and Karrar Hameed Abdulkareem
Sensors 2021, 21(11), 3922; https://doi.org/10.3390/s21113922 - 7 Jun 2021
Cited by 84 | Viewed by 7096
Abstract
Due to the rapid growth in artificial intelligence (AI) and deep learning (DL) approaches, the security and robustness of the deployed algorithms need to be guaranteed. The security susceptibility of the DL algorithms to adversarial examples has been widely acknowledged. The artificially created [...] Read more.
Due to the rapid growth in artificial intelligence (AI) and deep learning (DL) approaches, the security and robustness of the deployed algorithms need to be guaranteed. The security susceptibility of the DL algorithms to adversarial examples has been widely acknowledged. The artificially created examples will lead to different instances negatively identified by the DL models that are humanly considered benign. Practical application in actual physical scenarios with adversarial threats shows their features. Thus, adversarial attacks and defense, including machine learning and its reliability, have drawn growing interest and, in recent years, has been a hot topic of research. We introduce a framework that provides a defensive model against the adversarial speckle-noise attack, the adversarial training, and a feature fusion strategy, which preserves the classification with correct labelling. We evaluate and analyze the adversarial attacks and defenses on the retinal fundus images for the Diabetic Retinopathy recognition problem, which is considered a state-of-the-art endeavor. Results obtained on the retinal fundus images, which are prone to adversarial attacks, are 99% accurate and prove that the proposed defensive model is robust. Full article
(This article belongs to the Special Issue Deep Learning Image Recognition Systems)
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11 pages, 7423 KiB  
Communication
Construction and Application of Graphene Oxide-Bovine Serum Albumin Modified Extended Gate Field Effect Transistor Chiral Sensor
by Le Li, Xiaofei Ma, Yin Xiao and Yong Wang
Sensors 2021, 21(11), 3921; https://doi.org/10.3390/s21113921 - 7 Jun 2021
Cited by 11 | Viewed by 2932
Abstract
Chirality is an essential natural attribute of organisms. Chiral molecules exhibit differences in biochemical processes, pharmacodynamics, and toxicological properties, and their enantioselective recognition plays an important role in explaining life science processes and guiding drug design. Herein, we developed an ultra-sensitive enantiomer recognition [...] Read more.
Chirality is an essential natural attribute of organisms. Chiral molecules exhibit differences in biochemical processes, pharmacodynamics, and toxicological properties, and their enantioselective recognition plays an important role in explaining life science processes and guiding drug design. Herein, we developed an ultra-sensitive enantiomer recognition platform based on an extended-gate metal-oxide semiconductor field-effect-transistor (Nafion–GO@BSA–EG-MOSFET) that achieved effective chiral resolution of ultra-sensitive Lysine (Lys) and α-Methylbenzylamine (α-Met) enantiodiscrimination at the femtomole level. Bovine serum albumin (BSA) was immobilized on the surface of graphene oxide (GO) through amide bond coupling to prepare the GO@BSA complex. GO@BSA was drop-cast on deposited Au surfaces with a Nafion solution to afford the extended-gate sensing unit. Effective recognition of chiral enantiomers of mandelic acid (MA), tartaric acid (TA), tryptophan (Trp), Lys and α-Met was realized. Moreover, the introduction of GO reduced non-specific adsorption, and the chiral resolution concentration of α-Met reached the level of picomole in a 5-fold diluted fetal bovine serum (FBS). Finally, the chiral recognition mechanism of the as-fabricated sensor was proposed. Full article
(This article belongs to the Section Sensor Materials)
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13 pages, 712 KiB  
Article
Physical Unclonable Function Based on the Internal State Transitions of a Fibonacci Ring Oscillator
by Łukasz Matuszewski, Jakub Nikonowicz, Paweł Kubczak and Wiktor Woźniak
Sensors 2021, 21(11), 3920; https://doi.org/10.3390/s21113920 - 7 Jun 2021
Cited by 4 | Viewed by 2663
Abstract
This article introduces a new class of physical unclonable functions (PUFs) based on the Fibonacci ring oscillator (FIRO). The research conducted here proves that before reaching the desired randomness, the oscillator shows a certain degree of repeatability and uniqueness in the initial sequence [...] Read more.
This article introduces a new class of physical unclonable functions (PUFs) based on the Fibonacci ring oscillator (FIRO). The research conducted here proves that before reaching the desired randomness, the oscillator shows a certain degree of repeatability and uniqueness in the initial sequence of internal state transitions. The use of an FIRO in conjunction with the restart method makes it possible to obtain a set of short boot sequences, which are processed with an innovative feature extraction algorithm that enables reliable device identification. This approach ensures the reuse of the existing random number generator (RNG), rather than multiplying ring oscillators in a dedicated structure. Moreover, the algorithm for the recovery of the device key from the boot set can be successfully implemented in the authorizing center, thus significantly releasing the resources of authorized low-complexity devices. The proposed methodology provides an easily obtainable key with identifiability, which was proven experimentally on FPGAs from different manufacturers. Full article
(This article belongs to the Special Issue Cyber Security in IoT Era)
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17 pages, 7774 KiB  
Article
Soil Nutrient Estimation and Mapping in Farmland Based on UAV Imaging Spectrometry
by Xiaoyu Yang, Nisha Bao, Wenwen Li, Shanjun Liu, Yanhua Fu and Yachun Mao
Sensors 2021, 21(11), 3919; https://doi.org/10.3390/s21113919 - 6 Jun 2021
Cited by 16 | Viewed by 5580
Abstract
Soil nutrient is one of the most important properties for improving farmland quality and product. Imaging spectrometry has the potential for rapid acquisition and real-time monitoring of soil characteristics. This study aims to explore the preprocessing and modeling methods of hyperspectral images obtained [...] Read more.
Soil nutrient is one of the most important properties for improving farmland quality and product. Imaging spectrometry has the potential for rapid acquisition and real-time monitoring of soil characteristics. This study aims to explore the preprocessing and modeling methods of hyperspectral images obtained from an unmanned aerial vehicle (UAV) platform for estimating the soil organic matter (SOM) and soil total nitrogen (STN) in farmland. The results showed that: (1) Multiplicative Scattering Correction (MSC) performed better in reducing image scattering noise than Standard Normal Variate (SNV) transformation or spectral derivatives, and it yielded a result with higher correlation and lower signal-to-noise ratio; (2) The proposed feature selection method combining Successive Projections Algorithm (SPA) and Competitive Adaptive Reweighted Sampling algorithm (CARS), could provide selective preference for hyperspectral bands. Exploiting this method, 24 and 22 feature bands were selected for SOM and STN estimation, respectively; (3) The particle swarm optimization (PSO) algorithm was employed to obtain optimized input weights and bias values of the extreme learning machine (ELM) model for more accurate prediction of SOM and STN. The improved PSO-ELM model based on the selected preference bands achieved higher prediction accuracy (R2 of 0.73 and RPD of 1.91 for SOM, R2 of 0.63, and RPD of 1.53 for STN) than support vector machine (SVM), partial least squares regression (PLSR), and the ELM model. This study provides an important guideline for monitoring soil nutrient for precision agriculture with imaging spectrometry. Full article
(This article belongs to the Special Issue Proximal and Remote Soil Sensing Technologies for Multiscale Soil Investigation)
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24 pages, 103626 KiB  
Article
Anatomical 3D Modeling Using IR Sensors and Radiometric Processing Based on Structure from Motion: Towards a Tool for the Diabetic Foot Diagnosis
by Rafael Bayareh Mancilla, Bình Phan Tấn, Christian Daul, Josefina Gutiérrez Martínez, Lorenzo Leija Salas, Didier Wolf and Arturo Vera Hernández
Sensors 2021, 21(11), 3918; https://doi.org/10.3390/s21113918 - 6 Jun 2021
Cited by 5 | Viewed by 3756
Abstract
Medical infrared thermography has proven to be a complementary procedure to physiological disorders, such as the diabetic foot. However, the technique remains essentially based on 2D images that display partial anatomy. In this context, a 3D thermal model provides improved visualization and faster [...] Read more.
Medical infrared thermography has proven to be a complementary procedure to physiological disorders, such as the diabetic foot. However, the technique remains essentially based on 2D images that display partial anatomy. In this context, a 3D thermal model provides improved visualization and faster inspection. This paper presents a 3D reconstruction method associated with temperature information. The proposed solution is based on a Structure from Motion and Multi-view Stereo approach, exploiting a set of multimodal merged images. The infrared images were obtained by automatically processing the radiometric data to remove thermal interferences, segment the RoI, enhance false-color contrast, and for multimodal co-registration under a controlled environment and a ∆T < 2.6% between the RoI and thermal interferences. The geometric verification accuracy was 77% ± 2%. Moreover, a normalized error was adjusted per sample based on a linear model to compensate for the curvature emissivity (error ≈ 10% near to 90°). The 3D models were displayed with temperature information and interaction controls to observe any point of view. The temperature sidebar values were assigned with information retrieved only from the RoI. The results have proven the feasibility of the 3D multimodal construction to be used as a promising tool in the diagnosis of diabetic foot. Full article
(This article belongs to the Special Issue Novel Optical Sensors for Biomedical Application)
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13 pages, 5590 KiB  
Communication
Quantitative Analysis of Fluorescence Detection Using a Smartphone Camera for a PCR Chip
by Jong-Dae Kim, Chan-Young Park, Yu-Seop Kim and Ji-Soo Hwang
Sensors 2021, 21(11), 3917; https://doi.org/10.3390/s21113917 - 6 Jun 2021
Cited by 7 | Viewed by 4296
Abstract
Most existing commercial real-time polymerase chain reaction (RT-PCR) instruments are bulky because they contain expensive fluorescent detection sensors or complex optical structures. In this paper, we propose an RT-PCR system using a camera module for smartphones that is an ultra small, high-performance and [...] Read more.
Most existing commercial real-time polymerase chain reaction (RT-PCR) instruments are bulky because they contain expensive fluorescent detection sensors or complex optical structures. In this paper, we propose an RT-PCR system using a camera module for smartphones that is an ultra small, high-performance and low-cost sensor for fluorescence detection. The proposed system provides stable DNA amplification. A quantitative analysis of fluorescence intensity changes shows the camera’s performance compared with that of commercial instruments. Changes in the performance between the experiments and the sets were also observed based on the threshold cycle values in a commercial RT-PCR system. The overall difference in the measured threshold cycles between the commercial system and the proposed camera was only 0.76 cycles, verifying the performance of the proposed system. The set calibration even reduced the difference to 0.41 cycles, which was less than the experimental variation in the commercial system, and there was no difference in performance. Full article
(This article belongs to the Special Issue Selected Papers from TIKI IEEE ICICE 2019＆ ICASI 2020)
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15 pages, 1775 KiB  
Communication
Development of an Onboard Robotic Platform for Embedded Programming Education
by Hyun-Jae Lee and Hak Yi
Sensors 2021, 21(11), 3916; https://doi.org/10.3390/s21113916 - 6 Jun 2021
Cited by 8 | Viewed by 3415
Abstract
Robotics has been used as an attractive tool in diverse educational fields. A variety of robotic platforms have contributed to teaching practical embedded programming to engineering students at universities. However, most platforms only support content with a low level of programming skills and [...] Read more.
Robotics has been used as an attractive tool in diverse educational fields. A variety of robotic platforms have contributed to teaching practical embedded programming to engineering students at universities. However, most platforms only support content with a low level of programming skills and are unlikely to support a high level of embedded programming. This low association negatively affects students, such as incomprehension, decreased participation, dissatisfaction with course quality, etc. Therefore, this paper proposed a new robotic platform with relevant curricula to improve their effectiveness. The developed platform provided practical content used in mechatronics classes and the capability to operate a robot with a high level of embedded programming. To verify the effectiveness of the proposed platform, participants (undergraduates) examined course evaluations for educational programs based on the developed platform compared with the previous year’s class evaluation. The results showed that the proposed platform positively affects students’ intellectual ability (performance) and satisfaction in programming education. Full article
(This article belongs to the Special Issue Mechatronics and Robotics in Future Engineering Education)
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19 pages, 1624 KiB  
Article
Integration of Radiometric Ground-Based Data and High-Resolution QuickBird Imagery with Multivariate Modeling to Estimate Maize Traits in the Nile Delta of Egypt
by Adel H. Elmetwalli, Andrew N. Tyler, Farahat S. Moghanm, Saad A.M. Alamri, Ebrahem M. Eid and Salah Elsayed
Sensors 2021, 21(11), 3915; https://doi.org/10.3390/s21113915 - 6 Jun 2021
Cited by 6 | Viewed by 2885
Abstract
In site-specific management, rapid and accurate identification of crop stress at a large scale is critical. Radiometric ground-based data and satellite imaging with advanced spatial and spectral resolution allow for a deeper understanding of crop stress and the level of stress in a [...] Read more.
In site-specific management, rapid and accurate identification of crop stress at a large scale is critical. Radiometric ground-based data and satellite imaging with advanced spatial and spectral resolution allow for a deeper understanding of crop stress and the level of stress in a given area. This research aimed to assess the potential of radiometric ground-based data and high-resolution QuickBird satellite imagery to determine the leaf area index (LAI), biomass fresh weight (BFW) and chlorophyll meter (Chlm) of maize across well-irrigated, water stress and salinity stress areas in the Nile Delta of Egypt. Partial least squares regression (PLSR) and multiple linear regression (MLR) were evaluated to estimate the three measured traits based on vegetation spectral indices (vegetation-SRIs) derived from these methods and their combination. Maize field visits were conducted during the summer seasons from 28 to 30 July 2007 to collect ground reference data concurrent with the acquisition of radiometric ground-based measurements and QuickBird satellite imagery. The results showed that the majority of vegetation-SRIs extracted from radiometric ground-based data and high-resolution satellite images were more effective in estimating LAI, BFW, and Chlm. In general, the vegetation-SRIs of radiometric ground-based data showed higher R2 with measured traits compared to the vegetation-SRIs extracted from high-resolution satellite imagery. The coefficient of determination (R2) of the significant relationships between vegetation-SRIs of both methods and three measured traits varied from 0.64 to 0.89. For example, with QuickBird high-resolution satellite images, the relationships of the green normalized difference vegetation index (GNDVI) with LAI and BFW showed the highest R2 of 0.80 and 0.84, respectively. Overall, the ground-based vegetation-SRIs and the satellite-based indices were found to be in good agreement to assess the measured traits of maize. Both the calibration (Cal.) and validation (Val.) models of PLSR and MLR showed the highest performance in predicting the three measured traits based on the combination of vegetation-SRIs from radiometric ground-based data and high-resolution QuickBird satellite imagery. For example, validation (Val.) models of PLSR and MLR showed the highest performance in predicting the measured traits based on the combination of vegetation-SRIs from radiometric ground-based data and high-resolution QuickBird satellite imagery with R2 (0.91) of both methods for LAI, R2 (0.91–0.93) for BFW respectively, and R2 (0.82) of both methods for Chlm. The models of PLSR and MLR showed approximately the same performance in predicting the three measured traits and no clear difference was found between them and their combinations. In conclusion, the results obtained from this study showed that radiometric ground-based measurements and high spectral resolution remote-sensing imagery have the potential to offer necessary crop monitoring information across well-irrigated, water stress and salinity stress in regions suffering lack of freshwater resources. Full article
(This article belongs to the Section Remote Sensors)
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11 pages, 8303 KiB  
Communication
High-Gain Millimeter-Wave Patch Array Antenna for Unmanned Aerial Vehicle Application
by Kyei Anim, Jung-Nam Lee and Young-Bae Jung
Sensors 2021, 21(11), 3914; https://doi.org/10.3390/s21113914 - 6 Jun 2021
Cited by 9 | Viewed by 3941
Abstract
A high-gain millimeter-wave patch array antenna is presented for unmanned aerial vehicles (UAVs). For the large-scale patch array antenna, microstrip lines and higher-mode surface wave radiations contribute enormously to the antenna loss, especially at the millimeter-wave band. Here, the element of a large [...] Read more.
A high-gain millimeter-wave patch array antenna is presented for unmanned aerial vehicles (UAVs). For the large-scale patch array antenna, microstrip lines and higher-mode surface wave radiations contribute enormously to the antenna loss, especially at the millimeter-wave band. Here, the element of a large patch array antenna is implemented with a substrate integrated waveguide (SIW) cavity-backed patch fed by the aperture-coupled feeding (ACF) structure. However, in this case, a large coupling aperture is used to create strongly bound waves, which maximizes the coupling level between the patch and the feedline. This approach helps to improve antenna gain, but at the same time leads to a significant level of back radiation due to the microstrip feedline and unwanted surface-wave radiation, especially for the large patch arrays. Using the SIW cavity-backed patch and stripline feedline of the ACF in the element design, therefore, provides a solution to this problem. Thus, a full-corporate feed 32 × 32 array antenna achieves realized gain of 30.71–32.8 dBi with radiation efficiency above 52% within the operational band of 25.43–26.91 GHz. The fabricated antenna also retains being lightweight, which is desirable for UAVs, because it has no metal plate at the backside to support the antenna. Full article
(This article belongs to the Special Issue RF Sensors: Design, Optimization and Applications)
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20 pages, 7301 KiB  
Article
Adaptive and Robust Operation with Active Fuzzy Harvester under Nonstationary and Random Disturbance Conditions
by Yushin Hara, Keisuke Otsuka and Kanjuro Makihara
Sensors 2021, 21(11), 3913; https://doi.org/10.3390/s21113913 - 6 Jun 2021
Cited by 7 | Viewed by 2877
Abstract
The objective of this paper is to amplify the output voltage magnitude from a piezoelectric vibration energy harvester under nonstationary and broadband vibration conditions. Improving the transferred energy, which is converted from mechanical energy to electrical energy through a piezoelectric transducer, achieved a [...] Read more.
The objective of this paper is to amplify the output voltage magnitude from a piezoelectric vibration energy harvester under nonstationary and broadband vibration conditions. Improving the transferred energy, which is converted from mechanical energy to electrical energy through a piezoelectric transducer, achieved a high output voltage and effective harvesting. A threshold-based switching strategy is used to improve the total transferred energy with consideration of the signs and amplitudes of the electromechanical conditions of the harvester. A time-invariant threshold cannot accomplish effective harvesting under nonstationary vibration conditions because the assessment criterion for desirable control changes in accordance with the disturbance scale. To solve this problem, we developed a switching strategy for the active harvester, namely, adaptive switching considering vibration suppression-threshold strategy. The strategy adopts a tuning algorithm for the time-varying threshold and implements appropriate intermittent switching without pre-tuning by means of the fuzzy control theory. We evaluated the proposed strategy under three realistic vibration conditions: a frequency sweep, a change in the number of dominant frequencies, and wideband frequency vibration. Experimental comparisons were conducted with existing strategies, which consider only the signs of the harvester electromechanical conditions. The results confirm that the presented strategy achieves a greater output voltage than the existing strategies under all nonstationary vibration conditions. The average amplification rate of output voltage for the proposed strategy is 203% compared with the output voltage by noncontrolled harvesting. Full article
(This article belongs to the Special Issue Composite Materials for Sensor and Energy Harvesting Applications)
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17 pages, 744 KiB  
Communication
Using Smart Virtual-Sensor Nodes to Improve the Robustness of Indoor Localization Systems
by Guilherme Pedrollo, Andréa Aparecida Konzen, Wagner Ourique de Morais and Edison Pignaton de Freitas
Sensors 2021, 21(11), 3912; https://doi.org/10.3390/s21113912 - 6 Jun 2021
Cited by 10 | Viewed by 4199
Abstract
Young, older, frail, and disabled individuals can require some form of monitoring or assistance, mainly when critical situations occur, such as falling and wandering. Healthcare facilities are increasingly interested in e-health systems that can detect and respond to emergencies on time. Indoor localization [...] Read more.
Young, older, frail, and disabled individuals can require some form of monitoring or assistance, mainly when critical situations occur, such as falling and wandering. Healthcare facilities are increasingly interested in e-health systems that can detect and respond to emergencies on time. Indoor localization is an essential function in such e-health systems, and it typically relies on wireless sensor networks (WSN) composed of fixed and mobile nodes. Nodes in the network can become permanently or momentarily unavailable due to, for example, power failures, being out of range, and wrong placement. Consequently, unavailable sensors not providing data can compromise the system’s overall function. One approach to overcome the problem is to employ virtual sensors as replacements for unavailable sensors and generate synthetic but still realistic data. This paper investigated the viability of modelling and artificially reproducing the path of a monitored target tracked by a WSN with unavailable sensors. Particularly, the case with just a single sensor was explored. Based on the coordinates of the last measured positions by the unavailable node, a neural network was trained with 4 min of not very linear data to reproduce the behavior of a sensor that become unavailable for about 2 min. Such an approach provided reasonably successful results, especially for areas close to the room’s entrances and exits, which are critical for the security monitoring of patients in healthcare facilities. Full article
(This article belongs to the Section Intelligent Sensors)
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