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Sensors, Volume 21, Issue 1 (January-1 2021) – 320 articles

  • Cover Story (view full-size image): Monitoring the interaction between people and places is critical during the COVID-19 pandemic. There is a lot of ongoing effort to combat COVID-19 using the internet of low-cost sensors. These ad hoc solutions use heterogeneous data models and protocols without interoperability to interconnect and exchange data on a cross-organizational scale. This research aims at designing an interoperable Internet of COVID-19 Things (IoCT) architecture using Open Geospatial Consortium Standards to aggregate various data sources. IoCT examined Bluetooth beacons and smart cameras for contact tracing, social distancing, and risky behavior detection. IoCT is combined with spatiotemporal information (e.g., indoor topology graphs and disinfecting schedules) to understand and mitigate the real-time COVID-19 spreading risk in workplace reopening scenarios. View this paper
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Open AccessArticle
Mask R-CNN and OBIA Fusion Improves the Segmentation of Scattered Vegetation in Very High-Resolution Optical Sensors
Sensors 2021, 21(1), 320; https://doi.org/10.3390/s21010320 - 05 Jan 2021
Viewed by 475
Abstract
Vegetation generally appears scattered in drylands. Its structure, composition and spatial patterns are key controls of biotic interactions, water, and nutrient cycles. Applying segmentation methods to very high-resolution images for monitoring changes in vegetation cover can provide relevant information for dryland conservation ecology. [...] Read more.
Vegetation generally appears scattered in drylands. Its structure, composition and spatial patterns are key controls of biotic interactions, water, and nutrient cycles. Applying segmentation methods to very high-resolution images for monitoring changes in vegetation cover can provide relevant information for dryland conservation ecology. For this reason, improving segmentation methods and understanding the effect of spatial resolution on segmentation results is key to improve dryland vegetation monitoring. We explored and analyzed the accuracy of Object-Based Image Analysis (OBIA) and Mask Region-based Convolutional Neural Networks (Mask R-CNN) and the fusion of both methods in the segmentation of scattered vegetation in a dryland ecosystem. As a case study, we mapped Ziziphus lotus, the dominant shrub of a habitat of conservation priority in one of the driest areas of Europe. Our results show for the first time that the fusion of the results from OBIA and Mask R-CNN increases the accuracy of the segmentation of scattered shrubs up to 25% compared to both methods separately. Hence, by fusing OBIA and Mask R-CNNs on very high-resolution images, the improved segmentation accuracy of vegetation mapping would lead to more precise and sensitive monitoring of changes in biodiversity and ecosystem services in drylands. Full article
(This article belongs to the Special Issue Deep Learning Methods for Remote Sensing)
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Open AccessArticle
A Contactless Laser Doppler Strain Sensor for Fatigue Testing with Resonance-Testing Machine
Sensors 2021, 21(1), 319; https://doi.org/10.3390/s21010319 - 05 Jan 2021
Viewed by 341
Abstract
In this article, a non-contact laser Doppler strain sensor designed for fatigue testing with the resonance-testing machine is presented. The compact sensor measures in-plane displacements simultaneously from two adjacent points using the principle of in-plane, laser-Doppler vibrometry. The strain is computed from the [...] Read more.
In this article, a non-contact laser Doppler strain sensor designed for fatigue testing with the resonance-testing machine is presented. The compact sensor measures in-plane displacements simultaneously from two adjacent points using the principle of in-plane, laser-Doppler vibrometry. The strain is computed from the relative displacements divided by the distance between these two points. The optical design, the mathematical model for estimating noise-limited resolution, the simulation results of this model, and the first measurement results are presented. The comparison of the measurement results of our sensor with the results of a conventional strain gauge shows that our design meets the measurement requirements. The maximum strain deviation compared to conventional strain gauges of the laser-Doppler extensometer is below 4×105 in all performed experiments. Full article
(This article belongs to the Special Issue Laser Doppler Sensors)
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Open AccessArticle
Micro-Fabricated RTD Based Sensor for Breathing Analysis and Monitoring
Sensors 2021, 21(1), 318; https://doi.org/10.3390/s21010318 - 05 Jan 2021
Viewed by 407
Abstract
The design, micro-fabrication, and characterization of a resistance temperature detector (RTD) based micro sensor for minimally invasive breathing analysis and monitoring is presented. Experimental results demonstrate that the change in air temperature while inhaling and exhaling can be transduced into a time varying [...] Read more.
The design, micro-fabrication, and characterization of a resistance temperature detector (RTD) based micro sensor for minimally invasive breathing analysis and monitoring is presented. Experimental results demonstrate that the change in air temperature while inhaling and exhaling can be transduced into a time varying electrical signal, which is subsequently used to determine the breathing frequency (respiratory rate). The RTD is placed into a Wheatstone bridge to simultaneously reduce the sensor’s output noise and improve overall system accuracy. The proposed design could potentially aid health care providers in the determination of respiratory rates, which is of critical importance during the current COVID-19 pandemic. Full article
(This article belongs to the Section Biomedical Sensors)
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Open AccessArticle
Imagery Network Fine Registration by Reference Point Cloud Data Based on the Tie Points and Planes
Sensors 2021, 21(1), 317; https://doi.org/10.3390/s21010317 - 05 Jan 2021
Viewed by 317
Abstract
Cameras and laser scanners are complementary tools for a 2D/3D information generation. Systematic and random errors cause the misalignment of the multi-sensor imagery and point cloud data. In this paper, a novel feature-based approach is proposed for imagery and point cloud fine registration. [...] Read more.
Cameras and laser scanners are complementary tools for a 2D/3D information generation. Systematic and random errors cause the misalignment of the multi-sensor imagery and point cloud data. In this paper, a novel feature-based approach is proposed for imagery and point cloud fine registration. The tie points and its two neighbor pixels are matched in the overlap images, which are intersected in the object space to create the differential tie plane. A preprocessing is applied to the corresponding tie points and non-robust ones are removed. Initial coarse Exterior Orientation Parameters (EOPs), Interior Orientation Parameters (IOPs), and Additional Parameters (APs) are used to transform tie plane points to the object space. Then, the nearest points of the point cloud data to the transformed tie plane points are estimated. These estimated points are used to calculate Directional Vectors (DV) of the differential planes. As a constraint equation along with the collinearity equation, each object space tie point is forced to be located on the point cloud differential plane. Two different indoor and outdoor experimental data are used to assess the proposed approach. Achieved results show about 2.5 pixels errors on checkpoints. Such results demonstrated the robustness and practicality of the proposed approach. Full article
(This article belongs to the Section Sensing and Imaging)
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Open AccessReview
Switched-Biasing Techniques for CMOS Voltage-Controlled Oscillator
Sensors 2021, 21(1), 316; https://doi.org/10.3390/s21010316 - 05 Jan 2021
Viewed by 305
Abstract
A voltage-controlled oscillator (VCO) is a key component to generate high-speed clock of mixed-mode circuits and local oscillation signals of the frequency conversion in wired and wireless application systems. In particular, the recent evolution of new high-speed wireless systems in the millimeter-wave frequency [...] Read more.
A voltage-controlled oscillator (VCO) is a key component to generate high-speed clock of mixed-mode circuits and local oscillation signals of the frequency conversion in wired and wireless application systems. In particular, the recent evolution of new high-speed wireless systems in the millimeter-wave frequency band calls for the implementation of the VCO with high oscillation frequency and low close-in phase noise. The effect of the flicker noise on the phase noise of the VCO should be minimized because the flicker noise dramatically increases as the deep-submicron complementary metal-oxide-semiconductor (CMOS) process is scaled down, and the flicker corner frequency also increases, up to several MHz, in the up-to-date CMOS process. The flicker noise induced by the current source is a major factor affecting the phase noise of the VCO. Switched-biasing techniques have been proposed to minimize the effect of the flicker noise at the output of the VCO with biasing AC-coupled signals at the current source of the VCO. Reviewing the advantages and disadvantages reported in the previous studies, it is analyzed which topology to implement the switched-biasing technique is advantageous for improving the performance of the CMOS VCOs. Full article
(This article belongs to the Special Issue Advanced CMOS Integrated Circuit Design and Application)
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Open AccessLetter
Temporal Stability of Vegetation Cover across the Loess Plateau Based on GIMMS during 1982–2013
Sensors 2021, 21(1), 315; https://doi.org/10.3390/s21010315 - 05 Jan 2021
Viewed by 285
Abstract
The Loess Plateau, covering approximately 640,000 km2, has experienced the most severe soil erosion in the world. A greening tendency has been noticed since implementing the Grain to Green Program (GTGP), which may prevent further soil erosion. Therefore, understanding the underpinning [...] Read more.
The Loess Plateau, covering approximately 640,000 km2, has experienced the most severe soil erosion in the world. A greening tendency has been noticed since implementing the Grain to Green Program (GTGP), which may prevent further soil erosion. Therefore, understanding the underpinning basis of greening stability and persistence is important for sustainable improvement. Global Inventory Modeling and Mapping Studies (GIMMS) normalized difference vegetation index (NDVI) datasets for 1982–2013 were used to investigate the temporal stability and persistent time (PT) of vegetation over the Loess Plateau, utilizing the coefficient of variation (CV) and the estimation of tendencies of vegetation greening starting from the selected reference conditions. Two periods from 1982 to 1999 (as the reference period) and 2000 to 2013 were selected by considering the GTGP since 1999. The results indicate that: (1) A significant increase in vegetation cover occurred in the low NDVI area (NDVI < 0.3), with a high fluctuation from 2000 to 2013 compared with the reference period. Moreover, the fluctuation in vegetation is more related to precipitation variation since 1999. (2) Most areas recovered in the greening trend of the first period starting in 2009, occurring in 28.7% (2628 of 9148) of the total area. (3) The revegetated areas have a low PT and a high CVvi, that is, the revegetated areas need a long time to recover from disturbances. Therefore, we identify the sensitive areas with PT = 4; further management needs to be implemented for sustainable development in these areas. These results provide a method to quantify the stability and persistence of the complex interactions between vegetation greenness and environmental changes, particularly in fragile areas. Full article
(This article belongs to the Section Optical Sensors)
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Open AccessReview
Sensors for Structural Health Monitoring of Agricultural Structures
Sensors 2021, 21(1), 314; https://doi.org/10.3390/s21010314 - 05 Jan 2021
Viewed by 403
Abstract
The health diagnosis of agricultural structures is critical to detecting damages such as cracks in concrete, corrosion, spalling, and delamination. Agricultural structures are susceptible to environmental degradation due to frequent exposure to water, organic effluent, farm chemicals, structural loading, and unloading. Various sensors [...] Read more.
The health diagnosis of agricultural structures is critical to detecting damages such as cracks in concrete, corrosion, spalling, and delamination. Agricultural structures are susceptible to environmental degradation due to frequent exposure to water, organic effluent, farm chemicals, structural loading, and unloading. Various sensors have been employed for accurate and real-time monitoring of agricultural building structures, including electrochemical, ultrasonic, fiber-optic, piezoelectric, wireless, fiber Bragg grating sensors, and self-sensing concrete. The cost–benefits of each type of sensor and utility in a farm environment are explored in the review. Current literature suggests that the functionality of sensors has improved with progress in technology. Notable improvements made with the progress in technology include better accuracy of the measurements, reduction of signal-to-noise ratio, and transmission speed, and the deployment of machine learning, deep learning, and artificial intelligence in smart IoT-based agriculture. Key challenges include inconsistent installation of sensors in farm structures, technical constraints, and lack of support infrastructure, awareness, and preference for traditional inspection methods. Full article
(This article belongs to the Special Issue Smart Sensors for Automation in Agriculture 4.0)
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Open AccessArticle
Underwater Object Detection and Reconstruction Based on Active Single-Pixel Imaging and Super-Resolution Convolutional Neural Network
Sensors 2021, 21(1), 313; https://doi.org/10.3390/s21010313 - 05 Jan 2021
Viewed by 338
Abstract
Due to medium scattering, absorption, and complex light interactions, capturing objects from the underwater environment has always been a difficult task. Single-pixel imaging (SPI) is an efficient imaging approach that can obtain spatial object information under low-light conditions. In this paper, we propose [...] Read more.
Due to medium scattering, absorption, and complex light interactions, capturing objects from the underwater environment has always been a difficult task. Single-pixel imaging (SPI) is an efficient imaging approach that can obtain spatial object information under low-light conditions. In this paper, we propose a single-pixel object inspection system for the underwater environment based on compressive sensing super-resolution convolutional neural network (CS-SRCNN). With the CS-SRCNN algorithm, image reconstruction can be achieved with 30% of the total pixels in the image. We also investigate the impact of compression ratios on underwater object SPI reconstruction performance. In addition, we analyzed the effect of peak signal to noise ratio (PSNR) and structural similarity index (SSIM) to determine the image quality of the reconstructed image. Our work is compared to the SPI system and SRCNN method to demonstrate its efficiency in capturing object results from an underwater environment. The PSNR and SSIM of the proposed method have increased to 35.44% and 73.07%, respectively. This work provides new insight into SPI applications and creates a better alternative for underwater optical object imaging to achieve good quality. Full article
(This article belongs to the Special Issue Marine Imaging and Recognition)
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Open AccessArticle
An Energy-Efficient and Secure Data Inference Framework for Internet of Health Things: A Pilot Study
Sensors 2021, 21(1), 312; https://doi.org/10.3390/s21010312 - 05 Jan 2021
Viewed by 351
Abstract
Privacy protection in electronic healthcare applications is an important consideration, due to the sensitive nature of personal health data. Internet of Health Things (IoHT) networks that are used within a healthcare setting have unique challenges and security requirements (integrity, authentication, privacy, and availability) [...] Read more.
Privacy protection in electronic healthcare applications is an important consideration, due to the sensitive nature of personal health data. Internet of Health Things (IoHT) networks that are used within a healthcare setting have unique challenges and security requirements (integrity, authentication, privacy, and availability) that must also be balanced with the need to maintain efficiency in order to conserve battery power, which can be a significant limitation in IoHT devices and networks. Data are usually transferred without undergoing filtering or optimization, and this traffic can overload sensors and cause rapid battery consumption when interacting with IoHT networks. This poses certain restrictions on the practical implementation of these devices. In order to address these issues, this paper proposes a privacy-preserving two-tier data inference framework solution that conserves battery consumption by inferring the sensed data and reducing data size for transmission, while also protecting sensitive data from leakage to adversaries. The results from experimental evaluations on efficiency and privacy show the validity of the proposed scheme, as well as significant data savings without compromising data transmission accuracy, which contributes to energy efficiency of IoHT sensor devices. Full article
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Open AccessReview
The Role of 8-Amidoquinoline Derivatives as Fluorescent Probes for Zinc Ion Determination
Sensors 2021, 21(1), 311; https://doi.org/10.3390/s21010311 - 05 Jan 2021
Viewed by 234
Abstract
Mass-spectrometry-based and X-ray fluorescence-based techniques have allowed the study of the distribution of Zn2+ ions at extracellular and intracellular levels over the past few years. However, there are some issues during purification steps, sample preparation, suitability for quantification, and the instruments’ availability. [...] Read more.
Mass-spectrometry-based and X-ray fluorescence-based techniques have allowed the study of the distribution of Zn2+ ions at extracellular and intracellular levels over the past few years. However, there are some issues during purification steps, sample preparation, suitability for quantification, and the instruments’ availability. Therefore, work on fluorescent sensors based on 8-aminoquinoline as tools to detect Zn2+ ions in environmental and biological applications has been popular. Introducing various carboxamide groups into an 8-aminoquinoline molecule to create 8-amidoquinoline derivatives to improve water solubility and cell membrane permeability is also a recent trend. This review aims to present a general overview of the fluorophore 8-aminoquinoline and its derivatives as Zn2+ receptors for zinc sensor probes. Various fluorescent chemosensor designs based on 8-amidoquinoline and their effectiveness and potential as a recognition probe for zinc analysis were discussed. Based on this review, it can be concluded that derivatives of 8-amidoquinoline have vast potential as functional receptors for zinc ions primarily because of their fast reactivity, good selectivity, and bio-compatibility, especially for biological applications. To better understand the Zn2+ ion fluorophores’ function, diversity of the coordination complex and geometries need further studies. This review provides information in elucidating, designing, and exploring new 8-amidoquinoline derivatives for future studies for the improvement of chemosensors that are selective and sensitive to Zn2+. Full article
(This article belongs to the Section Chemical Sensors)
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Open AccessArticle
Towards a Robust Visual Place Recognition in Large-Scale vSLAM Scenarios Based on a Deep Distance Learning
Sensors 2021, 21(1), 310; https://doi.org/10.3390/s21010310 - 05 Jan 2021
Viewed by 232
Abstract
The application of deep learning is blooming in the field of visual place recognition, which plays a critical role in visual Simultaneous Localization and Mapping (vSLAM) applications. The use of convolutional neural networks (CNNs) achieve better performance than handcrafted feature descriptors. However, visual [...] Read more.
The application of deep learning is blooming in the field of visual place recognition, which plays a critical role in visual Simultaneous Localization and Mapping (vSLAM) applications. The use of convolutional neural networks (CNNs) achieve better performance than handcrafted feature descriptors. However, visual place recognition is still a challenging task due to two major problems, i.e., perceptual aliasing and perceptual variability. Therefore, designing a customized distance learning method to express the intrinsic distance constraints in the large-scale vSLAM scenarios is of great importance. Traditional deep distance learning methods usually use the triplet loss which requires the mining of anchor images. This may, however, result in very tedious inefficient training and anomalous distance relationships. In this paper, a novel deep distance learning framework for visual place recognition is proposed. Through in-depth analysis of the multiple constraints of the distance relationship in the visual place recognition problem, the multi-constraint loss function is proposed to optimize the distance constraint relationships in the Euclidean space. The new framework can support any kind of CNN such as AlexNet, VGGNet and other user-defined networks to extract more distinguishing features. We have compared the results with the traditional deep distance learning method, and the results show that the proposed method can improve the performance by 19–28%. Additionally, compared to some contemporary visual place recognition techniques, the proposed method can improve the performance by 40%/36% and 27%/24% in average on VGGNet/AlexNet using the New College and the TUM datasets, respectively. It’s verified the method is capable to handle appearance changes in complex environments. Full article
(This article belongs to the Section Intelligent Sensors)
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Open AccessArticle
Influence of the Bearing Thermal Deformation on Nonlinear Dynamic Characteristics of an Electric Drive Helical Gear System
Sensors 2021, 21(1), 309; https://doi.org/10.3390/s21010309 - 05 Jan 2021
Viewed by 276
Abstract
Based on the statics and quasi-statics analysis methods, the thermal deformation calculation model of a deep-groove ball bearing was constructed for the helical gear transmission system of a high speed electric drive, and the radial and axial bearing stiffness values of the bearing [...] Read more.
Based on the statics and quasi-statics analysis methods, the thermal deformation calculation model of a deep-groove ball bearing was constructed for the helical gear transmission system of a high speed electric drive, and the radial and axial bearing stiffness values of the bearing were calculated under the thermal deformation in this study. The obtained radial and axial stiffness values were introduced into the established dynamics model of helical gear system, and the influence of changed bearing stiffness, resulting from the thermal deformation, on the nonlinear dynamic characteristics of gear pair was analyzed using the Runge–Kutta method. The results show that the axial and radial deformations of bearing occur due to the increase of working speed and temperature, in which the axial stiffness of bearing is improved but the radial stiffness is reduced. The decreasing degree of axial stiffness and the increasing degree of radial stiffness decrease with the gradually increasing working rotational speed. When considering the influence of thermal deformation on the bearing stiffness, the helical gear system will have nonlinear behaviors, such as single periodic, double periodic, and chaotic motion with the change of working speed. Therefore, in order to improve the nonlinear dynamic characteristics of high speed electric drive gear systems, the influence of bearing stiffness change on the dynamic performance of a gear system should be considered in the industrial applications. Full article
(This article belongs to the Section Physical Sensors)
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Open AccessArticle
A Low-Resources TDC for Multi-Channel Direct ToF Readout Based on a 28-nm FPGA
Sensors 2021, 21(1), 308; https://doi.org/10.3390/s21010308 - 05 Jan 2021
Viewed by 352
Abstract
In this paper, we present a proposed field programmable gate array (FPGA)-based time-to-digital converter (TDC) architecture to achieve high performance with low usage of resources. This TDC can be employed for multi-channel direct Time-of-Flight (ToF) applications. The proposed architecture consists of a synchronizing [...] Read more.
In this paper, we present a proposed field programmable gate array (FPGA)-based time-to-digital converter (TDC) architecture to achieve high performance with low usage of resources. This TDC can be employed for multi-channel direct Time-of-Flight (ToF) applications. The proposed architecture consists of a synchronizing input stage, a tuned tapped delay line (TDL), a combinatory encoder of ones and zeros counters, and an online calibration stage. The experimental results of the TDC in an Artix-7 FPGA show a differential non-linearity (DNL) in the range of [−0.953, 1.185] LSB, and an integral non-linearity (INL) within [−2.750, 1.238] LSB. The measured LSB size and precision are 22.2 ps and 26.04 ps, respectively. Moreover, the proposed architecture requires low FPGA resources. Full article
(This article belongs to the Special Issue SPAD Image Sensors)
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Open AccessArticle
A Hybrid Approach to Industrial Augmented Reality Using Deep Learning-Based Facility Segmentation and Depth Prediction
Sensors 2021, 21(1), 307; https://doi.org/10.3390/s21010307 - 05 Jan 2021
Viewed by 225
Abstract
Typical AR methods have generic problems such as visual mismatching, incorrect occlusions, and limited augmentation due to the inability to estimate depth from AR images and attaching the AR markers onto physical objects, which prevents the industrial worker from conducting manufacturing tasks effectively. [...] Read more.
Typical AR methods have generic problems such as visual mismatching, incorrect occlusions, and limited augmentation due to the inability to estimate depth from AR images and attaching the AR markers onto physical objects, which prevents the industrial worker from conducting manufacturing tasks effectively. This paper proposes a hybrid approach to industrial AR for complementing existing AR methods using deep learning-based facility segmentation and depth prediction without AR markers and a depth camera. First, the outlines of physical objects are extracted by applying a deep learning-based instance segmentation method to the RGB image acquired from the AR camera. Simultaneously, a depth prediction method is applied to the AR image to estimate the depth map as a 3D point cloud for the detected object. Based on the segmented 3D point cloud data, 3D spatial relationships among the physical objects are calculated, which can assist in solving the visual mismatch and occlusion problems properly. In addition, it can deal with a dynamically operating or a moving facility, such as a robot—the conventional AR cannot do so. For these reasons, the proposed approach can be utilized as a hybrid or complementing function to existing AR methods, since it can be activated whenever the industrial worker requires handing of visual mismatches or occlusions. Quantitative and qualitative analyses verify the advantage of the proposed approach compared with existing AR methods. Some case studies also prove that the proposed method can be applied not only to manufacturing but also to other fields. These studies confirm the scalability, effectiveness, and originality of this proposed approach. Full article
(This article belongs to the Section Internet of Things)
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Open AccessArticle
Assessing the Security of Campus Networks: The Case of Seven Universities
Sensors 2021, 21(1), 306; https://doi.org/10.3390/s21010306 - 05 Jan 2021
Viewed by 257
Abstract
The network security situation of campus networks on CERNET (China Education and Research Network) has received great concern. However, most network managers have no complete picture of the network security because of its special management and the rapid growth of network assets. In [...] Read more.
The network security situation of campus networks on CERNET (China Education and Research Network) has received great concern. However, most network managers have no complete picture of the network security because of its special management and the rapid growth of network assets. In this investigation, the security of campus networks belonging to seven universities in Wuhan was investigated. A tool called “WebHunt” was designed for campus networks, and with its help, the network security risks were found. Differently from existing tools for network probing, WebHunt can adopt the network scale and special rules of the campus network. According to the characteristics of campus websites, a series of functions were integrated into WebHunt, including reverse resolution of domain names, active network detection and fingerprint identification for software assets. Besides, WebHunt builds its vulnerability intelligence database with a knowledge graph structure and locates the vulnerabilities through matching knowledge graph information. Security assessments of seven universities presents WebHunt’s applicability for campus networks. Besides, it also shows that many security risks are concealed in campus networks, such as non-compliance IP addresses and domain names, system vulnerabilities and so on. The security reports containing risks have been sent to the relevant universities, and positive feedback was received. Full article
(This article belongs to the Special Issue Cybersecurity and Privacy in Smart Cities)
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Open AccessArticle
Hybrid Blockchain for IoT—Energy Analysis and Reward Plan
Sensors 2021, 21(1), 305; https://doi.org/10.3390/s21010305 - 05 Jan 2021
Viewed by 262
Abstract
Blockchain technology has brought significant advantages for security and trustworthiness, in particular for Internet of Things (IoT) applications where there are multiple organisations that need to verify data and ensure security of shared smart contracts. Blockchain technology offers security features by means of [...] Read more.
Blockchain technology has brought significant advantages for security and trustworthiness, in particular for Internet of Things (IoT) applications where there are multiple organisations that need to verify data and ensure security of shared smart contracts. Blockchain technology offers security features by means of consensus mechanisms; two key consensus mechanisms are, Proof of Work (PoW) and Practical Byzantine Fault Tolerance (PBFT). While the PoW based mechanism is computationally intensive, due to the puzzle solving, the PBFT consensus mechanism is communication intensive due to the all-to-all messages; thereby, both may result in high energy consumption and, hence, there is a trade-off between the computation and the communication energy costs. In this paper, we propose a hybrid-blockchain (H-chain) framework appropriate for scenarios where multiple organizations exist and where the framework enables private transaction verification and public transaction sharing and audit, according to application needs. In particular, we study the energy consumption of the hybrid consensus mechanisms in H-chain. Moreover, this paper proposes a reward plan to incentivize the blockchain agents so that they make contributions to the H-chain while also considering the energy consumption. While the work is generally applicable to IoT applications, the paper illustrates the framework in a scenario which secures an IoT application connected using a software defined network (SDN). The evaluation results first provide a method to balance the public and private parts of the H-chain deployment according to network conditions, computation capability, verification complexity, among other parameters. The simulation results demonstrate that the reward plan can incentivize the blockchain agents to contribute to the H-chain considering the energy consumption of the hybrid consensus mechanism, this enables the proposed H-chain to achieve optimal social welfare. Full article
(This article belongs to the Special Issue Selected Papers from the Global IoT Summit GIoTS 2020)
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Open AccessArticle
Trajectory Identification for Moving Loads by Multicriterial Optimization
Sensors 2021, 21(1), 304; https://doi.org/10.3390/s21010304 - 05 Jan 2021
Viewed by 219
Abstract
Moving load is a fundamental loading pattern for many civil engineering structures and machines. This paper proposes and experimentally verifies an approach for indirect identification of 2D trajectories of moving loads. In line with the “structure as a sensor” paradigm, the identification is [...] Read more.
Moving load is a fundamental loading pattern for many civil engineering structures and machines. This paper proposes and experimentally verifies an approach for indirect identification of 2D trajectories of moving loads. In line with the “structure as a sensor” paradigm, the identification is performed indirectly, based on the measured mechanical response of the structure. However, trivial solutions that directly fit the mechanical response tend to be erratic due to measurement and modeling errors. To achieve physically meaningful results, these solutions need to be numerically regularized with respect to expected geometric characteristics of trajectories. This paper proposes a respective multicriterial optimization framework based on two groups of criteria of a very different nature: mechanical (to fit the measured response of the structure) and geometric (to account for the geometric regularity of typical trajectories). The state-of-the-art multiobjective genetic algorithm NSGA-II is used to find the Pareto front. The proposed approach is verified experimentally using a lab setup consisting of a plate instrumented with strain gauges and a line-follower robot. Three trajectories are tested, and in each case the determined Pareto front is found to properly balance between the mechanical response fit and the geometric regularity of the trajectory. Full article
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Open AccessArticle
Identification of the Olfactory Profile of Rapeseed Oil as a Function of Heating Time and Ratio of Volume and Surface Area of Contact with Oxygen Using an Electronic Nose
Sensors 2021, 21(1), 303; https://doi.org/10.3390/s21010303 - 05 Jan 2021
Viewed by 244
Abstract
The process of deep fat frying is the most common technological procedure applied to rapeseed oil. During heat treatment, oil loses its nutritional properties and its original consumer quality is lowered, which is often impossible to determine by organoleptic assessment. Therefore, the aim [...] Read more.
The process of deep fat frying is the most common technological procedure applied to rapeseed oil. During heat treatment, oil loses its nutritional properties and its original consumer quality is lowered, which is often impossible to determine by organoleptic assessment. Therefore, the aim of the study was to correlate markers of the loss of the nutritional properties by rapeseed oil related to the frying time and the surface area of contact with oxygen with changes in the profile of volatile compounds. The investigations involved the process of 6-, 12-, and 18-h heating of oil with a surface-to-volume ratio (s/v ratio) of 0.378 cm−1, 0.189 cm−1, and 0.126 cm−1. Samples were analysed to determine changes in the content of polar compounds, colour, fatty acid composition, iodine value, and total chromanol content. The results were correlated with the emission of volatile compounds determined using gas chromatography and an electronic nose. The results clearly show a positive correlation between the qualitative degradation of the oil induced by prolonged heating and the response of the electronic nose to these changes. The three volumes, the maximum reaction of the metal oxide semiconductor chemoresistors, and the content of polar compounds increased along the extended frying time. Full article
(This article belongs to the Section Chemical Sensors)
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Open AccessArticle
Noninvasive Monitoring of Dynamical Processes in Bruised Human Skin Using Diffuse Reflectance Spectroscopy and Pulsed Photothermal Radiometry
Sensors 2021, 21(1), 302; https://doi.org/10.3390/s21010302 - 05 Jan 2021
Viewed by 237
Abstract
We have augmented a recently introduced method for noninvasive analysis of skin structure and composition and applied it to monitoring of dynamical processes in traumatic bruises. The approach combines diffuse reflectance spectroscopy in visible spectral range and pulsed photothermal radiometry. Data from both [...] Read more.
We have augmented a recently introduced method for noninvasive analysis of skin structure and composition and applied it to monitoring of dynamical processes in traumatic bruises. The approach combines diffuse reflectance spectroscopy in visible spectral range and pulsed photothermal radiometry. Data from both techniques are analyzed simultaneously using a numerical model of light and heat transport in a four-layer model of human skin. Compared to the earlier presented approach, the newly introduced elements include two additional chromophores (β-carotene and bilirubin), individually adjusted thickness of the papillary dermal layer, and analysis of the bruised site using baseline values assessed from intact skin in its vicinity. Analyses of traumatic bruises in three volunteers over a period of 16 days clearly indicate a gradual, yet substantial increase of the dermal blood content and reduction of its oxygenation level in the first days after injury. This is followed by the emergence of bilirubin and relaxation of all model parameters towards the values characteristic for healthy skin approximately two weeks after the injury. The assessed parameter values and time dependences are consistent with existing literature. Thus, the presented methodology offers a viable approach for objective characterization of the bruise healing process. Full article
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Open AccessArticle
Assessment of Stem Volume on Plots Using Terrestrial Laser Scanner: A Precision Forestry Application
Sensors 2021, 21(1), 301; https://doi.org/10.3390/s21010301 - 05 Jan 2021
Viewed by 283
Abstract
Timber volume is an important asset, not only as an ecological component, but also as a key source of present and future revenues, which requires precise estimates. We used the Trimble TX8 survey-grade terrestrial laser scanner (TLS) to create a detailed 3D point [...] Read more.
Timber volume is an important asset, not only as an ecological component, but also as a key source of present and future revenues, which requires precise estimates. We used the Trimble TX8 survey-grade terrestrial laser scanner (TLS) to create a detailed 3D point cloud for extracting total tree height and diameter at breast height (1.3 m; DBH). We compared two different methods to accurately estimate total tree heights: the first method was based on a modified version of the local maxima algorithm for treetop detection, “HTTD”, and for the second method we used the centers of stem cross-sections at stump height (30 cm), “HTSP”. DBH was estimated by a computationally robust algebraic circle-fitting algorithm through hierarchical cluster analysis (HCA). This study aimed to assess the accuracy of these descriptors for evaluating total stem volume by comparing the results with the reference tree measurements. The difference between the estimated total stem volume from HTTD and measured stems was 2.732 m3 for European oak and 2.971 m3 for Norway spruce; differences between the estimated volume from HTSP and measured stems was 1.228 m3 and 2.006 m3 for European oak and Norway spruce, respectively. The coefficient of determination indicated a strong relationship between the measured and estimated total stem volumes from both height estimation methods with an R2 = 0.89 for HTTD and R2 = 0.87 for HTSP for European oak, and R2 = 0.98 for both HTTD and HTSP for Norway spruce. Our study has demonstrated the feasibility of finer-resolution remote sensing data for semi-automatic stem volumetric modeling of small-scale studies with high accuracy as a potential advancement in precision forestry. Full article
(This article belongs to the Section Remote Sensors)
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Open AccessArticle
SynPo-Net—Accurate and Fast CNN-Based 6DoF Object Pose Estimation Using Synthetic Training
Sensors 2021, 21(1), 300; https://doi.org/10.3390/s21010300 - 05 Jan 2021
Viewed by 285
Abstract
Estimation and tracking of 6DoF poses of objects in images is a challenging problem of great importance for robotic interaction and augmented reality. Recent approaches applying deep neural networks for pose estimation have shown encouraging results. However, most of them rely on training [...] Read more.
Estimation and tracking of 6DoF poses of objects in images is a challenging problem of great importance for robotic interaction and augmented reality. Recent approaches applying deep neural networks for pose estimation have shown encouraging results. However, most of them rely on training with real images of objects with severe limitations concerning ground truth pose acquisition, full coverage of possible poses, and training dataset scaling and generalization capability. This paper presents a novel approach using a Convolutional Neural Network (CNN) trained exclusively on single-channel Synthetic images of objects to regress 6DoF object Poses directly (SynPo-Net). The proposed SynPo-Net is a network architecture specifically designed for pose regression and a proposed domain adaptation scheme transforming real and synthetic images into an intermediate domain that is better fit for establishing correspondences. The extensive evaluation shows that our approach significantly outperforms the state-of-the-art using synthetic training in terms of both accuracy and speed. Our system can be used to estimate the 6DoF pose from a single frame, or be integrated into a tracking system to provide the initial pose. Full article
(This article belongs to the Special Issue Object Tracking and Motion Analysis)
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Open AccessArticle
A Co-Nanoparticles Modified Electrode for On-Site and Rapid Phosphate Detection in Hydroponic Solutions
Sensors 2021, 21(1), 299; https://doi.org/10.3390/s21010299 - 05 Jan 2021
Viewed by 299
Abstract
Conventional strategies for determining phosphate concentration is limited in efficiency due to the cost, time, and labor that is required in laboratory analysis. Therefore, an on-site and rapid detection sensor for phosphate is urgently needed to characterize phosphate variability in a hydroponic system. [...] Read more.
Conventional strategies for determining phosphate concentration is limited in efficiency due to the cost, time, and labor that is required in laboratory analysis. Therefore, an on-site and rapid detection sensor for phosphate is urgently needed to characterize phosphate variability in a hydroponic system. Cobalt (Co) is a highly sensitive metal that has shown a selectivity towards phosphate to a certain extent. A disposable phosphate sensor based on the screen-printed electrode (SPE) was developed to exploit the advantages of Co-nanoparticles. A support vector machine regression model was established to predict the concentration of phosphate in the hydroponic solutions. The results showed that Co-nanoparticles improve the detection limit of the sensor in the initial state. Meanwhile, the corrosion of Co-nanoparticles leads to a serious time-drift and instability of the electrodes. On the other hand, the coefficient of variation of the disposable phosphate detection chip is 0.4992%, the sensitivity is 33 mV/decade, and the linear range is 10−1–10−4.56 mol/L. The R2 and mean square error of the buffer-free sensor in the hydroponic solution are 0.9792 and 0.4936, respectively. In summary, the SPE modified by the Co-nanoparticles is a promising low-cost sensor for on-site and rapid measurement of the phosphate concentration in hydroponic solutions. Full article
(This article belongs to the Special Issue Water Quality Sensors)
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Open AccessArticle
Microstrip Resonant Sensor for Differentiation of Components in Vapor Mixtures
Sensors 2021, 21(1), 298; https://doi.org/10.3390/s21010298 - 05 Jan 2021
Viewed by 278
Abstract
A novel microstrip resonant vapor sensor made from a conductive multiwalled carbon nanotubes/ethylene-octene copolymer composite, of which its sensing properties were distinctively altered by vapor polarity, was developed for the detection of organic vapors. The alteration resulted from the modified composite electronic impedance [...] Read more.
A novel microstrip resonant vapor sensor made from a conductive multiwalled carbon nanotubes/ethylene-octene copolymer composite, of which its sensing properties were distinctively altered by vapor polarity, was developed for the detection of organic vapors. The alteration resulted from the modified composite electronic impedance due to the penetration of the vapors into the copolymer matrix, which subsequently swelled, increased the distances between the carbon nanotubes, and disrupted the conducting paths. This in turn modified the reflection coefficient frequency spectra. Since both the spectra and magnitudes of the reflection coefficients at the resonant frequencies of tested vapors were distinct, a combination of these parameters was used to identify the occurrence of a particular vapor or to differentiate components of vapor mixtures. Thus, one multivariate MWCNT/copolymer microstrip resonant sensor superseded an array of selective sensors. Full article
(This article belongs to the Section State-of-the-Art Sensors Technologies)
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Open AccessArticle
Autonomous Navigation of a Team of Unmanned Surface Vehicles for Intercepting Intruders on a Region Boundary
Sensors 2021, 21(1), 297; https://doi.org/10.3390/s21010297 - 04 Jan 2021
Viewed by 387
Abstract
We study problems of intercepting single and multiple invasive intruders on a boundary of a planar region by employing a team of autonomous unmanned surface vehicles. First, the problem of intercepting a single intruder has been studied and then the proposed strategy has [...] Read more.
We study problems of intercepting single and multiple invasive intruders on a boundary of a planar region by employing a team of autonomous unmanned surface vehicles. First, the problem of intercepting a single intruder has been studied and then the proposed strategy has been applied to intercepting multiple intruders on the region boundary. Based on the proposed decentralised motion control algorithm and decision making strategy, each autonomous vehicle intercepts any intruder, which tends to leave the region by detecting the most vulnerable point of the boundary. An efficient and simple mathematical rules based control algorithm for navigating the autonomous vehicles on the boundary of the see region is developed. The proposed algorithm is computationally simple and easily implementable in real life intruder interception applications. In this paper, we obtain necessary and sufficient conditions for the existence of a real-time solution to the considered problem of intruder interception. The effectiveness of the proposed method is confirmed by computer simulations with both single and multiple intruders. Full article
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Open AccessArticle
Rational Design of Molecularly Imprinted Polymers Using Quaternary Ammonium Cations for Glyphosate Detection
Sensors 2021, 21(1), 296; https://doi.org/10.3390/s21010296 - 04 Jan 2021
Viewed by 333
Abstract
Molecularly imprinted polymers have emerged as cost-effective and rugged artificial selective sorbents for combination with different sensors. In this study, quaternary ammonium cations, as functional monomers, were systematically evaluated to design imprinted polymers for glyphosate as an important model compound for electrically charged [...] Read more.
Molecularly imprinted polymers have emerged as cost-effective and rugged artificial selective sorbents for combination with different sensors. In this study, quaternary ammonium cations, as functional monomers, were systematically evaluated to design imprinted polymers for glyphosate as an important model compound for electrically charged and highly water-soluble chemical compounds. To this aim, a small pool of monomers were used including (3-acrylamidopropyl)trimethylammonium chloride, [2-(acryloyloxy)ethyl]trimethylammonium chloride, and diallyldimethylammonium chloride. The simultaneous interactions between three positively charged monomers and glyphosate were preliminary evaluated using statistical design of the experiment method. Afterwards, different polymers were synthesized at the gold surface of the quartz crystal microbalance sensor using optimized and not optimized glyphosate-monomers ratios. All synthesized polymers were characterized using atomic force microscopy, contact angle, Fourier-transform infrared, and X-ray photoelectron spectroscopy. Evaluated functional monomers showed promise as highly efficient functional monomers, when they are used together and at the optimized ratio, as predicted by the statistical method. Obtained results from the modified sensors were used to develop a simple model describing the binding characteristics at the surface of the different synthesized polymers. This model helps to develop new synthesis strategies for rational design of the highly selective imprinted polymers and to use as a sensing platform for water soluble and polar targets. Full article
(This article belongs to the Special Issue Molecularly Imprinted Polymer Sensing Platforms)
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Open AccessArticle
Urban Green Infrastructure Monitoring Using Remote Sensing from Integrated Visible and Thermal Infrared Cameras Mounted on a Moving Vehicle
Sensors 2021, 21(1), 295; https://doi.org/10.3390/s21010295 - 04 Jan 2021
Viewed by 283
Abstract
Climate change forecasts higher temperatures in urban environments worsening the urban heat island effect (UHI). Green infrastructure (GI) in cities could reduce the UHI by regulating and reducing ambient temperatures. Forest cities (i.e., Melbourne, Australia) aimed for large-scale planting of trees to adapt [...] Read more.
Climate change forecasts higher temperatures in urban environments worsening the urban heat island effect (UHI). Green infrastructure (GI) in cities could reduce the UHI by regulating and reducing ambient temperatures. Forest cities (i.e., Melbourne, Australia) aimed for large-scale planting of trees to adapt to climate change in the next decade. Therefore, monitoring cities’ green infrastructure requires close assessment of growth and water status at the tree-by-tree resolution for its proper maintenance and needs to be automated and efficient. This project proposed a novel monitoring system using an integrated visible and infrared thermal camera mounted on top of moving vehicles. Automated computer vision algorithms were used to analyze data gathered at an Elm trees avenue in the city of Melbourne, Australia (n = 172 trees) to obtain tree growth in the form of effective leaf area index (LAIe) and tree water stress index (TWSI), among other parameters. Results showed the tree-by-tree variation of trees monitored (5.04 km) between 2016–2017. The growth and water stress parameters obtained were mapped using customized codes and corresponded with weather trends and urban management. The proposed urban tree monitoring system could be a useful tool for city planning and GI monitoring, which can graphically show the diurnal, spatial, and temporal patterns of change of LAIe and TWSI to monitor the effects of climate change on the GI of cities. Full article
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Open AccessArticle
Temperature Hysteresis Mechanism and Compensation of Quartz Flexible Accelerometer in Aerial Inertial Navigation System
Sensors 2021, 21(1), 294; https://doi.org/10.3390/s21010294 - 04 Jan 2021
Viewed by 251
Abstract
Strap-down inertial navigation systems (INSs) with quartz flexible accelerometers (QFAs) are widely used in many conditions, particularly in aerial vehicles. Temperature is one of the significant issues impacting the performance of INS. The variation and the gradient of temperature are complex under aerial [...] Read more.
Strap-down inertial navigation systems (INSs) with quartz flexible accelerometers (QFAs) are widely used in many conditions, particularly in aerial vehicles. Temperature is one of the significant issues impacting the performance of INS. The variation and the gradient of temperature are complex under aerial conditions, which severely degrades the navigation performance of INS. Previous work has indicated that parts of navigation errors could be restrained by simple temperature compensation of QFA. However, the temperature hysteresis of the accelerometer is seldom considered in INS. In this paper, the temperature hysteresis mechanism of QFA and the compensation method would be analyzed. Based on the fundamental model, a comprehensive temperature hysteresis model is proposed and the parameters in this model were derived through a temperature cycling test. Furthermore, the comparative experiments in the laboratory were executed to refine the temperature hysteresis model and to verify the effectiveness of the new compensation method. Applying the temperature hysteresis compensation in flight condition, the result shows that the position error (CEP) is restrained from 1.54 nmile/h to 1.29 nmile/h. The proposed temperature hysteresis compensation method improves the performance of INS effectively and feasibly, which could be promoted to other applications of INS in similar temperature changing environment correspondingly. Full article
(This article belongs to the Special Issue Electronics for Sensors)
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Open AccessArticle
Assessing the Potential of LPWAN Communication Technologies for Near Real-Time Leak Detection in Water Distribution Systems
Sensors 2021, 21(1), 293; https://doi.org/10.3390/s21010293 - 04 Jan 2021
Viewed by 343
Abstract
While low-power wide-area network (LPWAN) technologies have been studied extensively for a broad spectrum of smart city applications, their potential for water distribution system monitoring in high temporal resolution has not been studied in detail. However, due to their low power demand, these [...] Read more.
While low-power wide-area network (LPWAN) technologies have been studied extensively for a broad spectrum of smart city applications, their potential for water distribution system monitoring in high temporal resolution has not been studied in detail. However, due to their low power demand, these technologies offer new possibilities for operating pressure-monitoring devices for near real-time leak detection in water distribution systems (WDS). By combining long-distance wireless communication with low power consumption, LPWAN technologies promise long periods of maintenance-free device operation without having to rely on an external power source. This is of particular importance for pressure-based leak detection where optimal sensor positions are often located in the periphery of WDS without a suitable power source. To assess the potential of these technologies for replacing widely-used wireless communication technologies for leak detection, GPRS is compared with the LPWAN standards Narrowband IoT, long-range wide area network (LoRaWAN) and Sigfox. Based on sampling and transmission rates commonly applied in leak detection, the ability of these three technologies to replace GPRS is analyzed based on a self-developed low-power pressure-monitoring device and a simplified, linear energy-consumption model. The results indicate that even though some of the analyzed LPWAN technologies may suffer from contractual and technical limitations, all of them offer viable alternatives, meeting the requirements of leak detection in WDS. In accordance with existing research on data transmission with these technologies, the findings of this work show that even while retaining a compact design, which entails a limited battery capacity, pressure-monitoring devices can exceed runtimes of 5 years, as required for installation at water meters in Austria. Thus, LPWAN technologies have the potential to advance the wide application of near real-time, pressure-based leak detection in WDS, while simultaneously reducing the cost of device operation significantly. Full article
(This article belongs to the Section Physical Sensors)
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Open AccessArticle
Improving the Performance of Time-Relative GNSS Precise Positioning in Remote Areas
Sensors 2021, 21(1), 292; https://doi.org/10.3390/s21010292 - 04 Jan 2021
Viewed by 326
Abstract
Global navigation satellite systems (GNSS) can attain centimeter level positioning accuracy, which is conventionally provided by real-time precise point positioning (PPP) and real-time kinematic (RTK) techniques. Corrections from the data center or the reference stations are required in these techniques to reduce various [...] Read more.
Global navigation satellite systems (GNSS) can attain centimeter level positioning accuracy, which is conventionally provided by real-time precise point positioning (PPP) and real-time kinematic (RTK) techniques. Corrections from the data center or the reference stations are required in these techniques to reduce various GNSS errors. The time-relative positioning approach differs from the traditional PPP and RTK in the sense that it does not require external real-time corrections. It computes the differences in positions of a single receiver at different epochs using phase observations. As the code observations are not used in this approach, its performance is not affected by the noise and multipath of code observations. High reliability is another advantage of time-relative precise positioning because the ambiguity resolution is not needed in this approach. Since the data link is not required in the method, this approach has been widely used in remote areas where wireless data link is not available. The main limitation of time-relative positioning is that its accuracy degrades over time between epochs because of the temporal variation of various errors. The application of the approach is usually limited to be within a time interval of less than 20 min. The purpose of this study was to increase the time interval of time-relative positioning and to extend the use of this method to applications with a longer time requirement, especially in remote areas without wireless communication. In this paper, the main error sources of the time-relative method are first analyzed in detail, and then the approach to improve the accumulated time relative positioning method is proposed. The performance of the proposed method is assessed using both static and dynamic observations with a duration as long as several hours. The experiments presented in this paper show that, among the four scenarios tested (i.e., GPS, GPS/Galileo, GPS/Galileo/BeiDou, and GPS/Galileo/BeiDou/GLONASS), GPS/Galileo/BeiDou performed best and GPS/Galileo/BeiDou/GLONASS performed worst. The maximum positioning errors were mostly within 0.5 m in the horizontal direction, even after three hours with GPS/Galileo/BeiDou. It is expected that the method could be used for positioning and navigation for as long as several hours with decimeter level horizontal accuracy in remote areas without wireless communication. Full article
(This article belongs to the Special Issue Multi-GNSS Precise Positioning and Applications)
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Open AccessArticle
Automatic Resting Tremor Assessment in Parkinson’s Disease Using Smartwatches and Multitask Convolutional Neural Networks
Sensors 2021, 21(1), 291; https://doi.org/10.3390/s21010291 - 04 Jan 2021
Viewed by 407
Abstract
Resting tremor in Parkinson’s disease (PD) is one of the most distinctive motor symptoms. Appropriate symptom monitoring can help to improve management and medical treatments and improve the patients’ quality of life. Currently, tremor is evaluated by physical examinations during clinical appointments; however, [...] Read more.
Resting tremor in Parkinson’s disease (PD) is one of the most distinctive motor symptoms. Appropriate symptom monitoring can help to improve management and medical treatments and improve the patients’ quality of life. Currently, tremor is evaluated by physical examinations during clinical appointments; however, this method could be subjective and does not represent the full spectrum of the symptom in the patients’ daily lives. In recent years, sensor-based systems have been used to obtain objective information about the disease. However, most of these systems require the use of multiple devices, which makes it difficult to use them in an ambulatory setting. This paper presents a novel approach to evaluate the amplitude and constancy of resting tremor using triaxial accelerometers from consumer smartwatches and multitask classification models. These approaches are used to develop a system for an automated and accurate symptom assessment without interfering with the patients’ daily lives. Results show a high agreement between the amplitude and constancy measurements obtained from the smartwatch in comparison with those obtained in a clinical assessment. This indicates that consumer smartwatches in combination with multitask convolutional neural networks are suitable for providing accurate and relevant information about tremor in patients in the early stages of the disease, which can contribute to the improvement of PD clinical evaluation, early detection of the disease, and continuous monitoring. Full article
(This article belongs to the Special Issue Sensors and Sensing Technology Applied in Parkinson Disease)
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