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Sensors, Volume 19, Issue 12 (June-2 2019)

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Open AccessArticle
Azimuth Sidelobes Suppression Using Multi-Azimuth Angle Synthetic Aperture Radar Images
Sensors 2019, 19(12), 2764; https://doi.org/10.3390/s19122764 (registering DOI)
Received: 20 March 2019 / Revised: 13 June 2019 / Accepted: 17 June 2019 / Published: 19 June 2019
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Abstract
A novel method is proposed for azimuth sidelobes suppression using multi-pass squinted (MPS) synthetic aperture radar (SAR) data. For MPS SAR, the radar observes the scene with different squint angles and heights on each pass. The MPS SAR mode acquisition geometry is given [...] Read more.
A novel method is proposed for azimuth sidelobes suppression using multi-pass squinted (MPS) synthetic aperture radar (SAR) data. For MPS SAR, the radar observes the scene with different squint angles and heights on each pass. The MPS SAR mode acquisition geometry is given first. Then, 2D signals are focused and the images are registered to the master image. Based on the new signal model, elevation processing and incoherent addition are introduced in detail, which are the main parts for azimuth sidelobes suppression. Moreover, parameter design criteria in incoherent addition are derived for the best performance. With the proposed parameter optimization step, the new method has a prominent azimuth sidelobes suppression effect with a slightly better azimuth resolution, as verified by experimental results on both simulated point targets and TerraSAR-X data. Full article
(This article belongs to the Special Issue Synthetic Aperture Radar (SAR) Techniques and Applications)
Open AccessReview
Recent Progress in Self-Powered Skin Sensors
Sensors 2019, 19(12), 2763; https://doi.org/10.3390/s19122763 (registering DOI)
Received: 14 April 2019 / Revised: 13 June 2019 / Accepted: 13 June 2019 / Published: 19 June 2019
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Abstract
Self-powered skin sensors have attracted significant attention in recent years due to their great potential in medical care, robotics, prosthetics, and sports. More importantly, self-powered skin sensors do not need any energy-supply components like batteries, which allows them to work sustainably and saves [...] Read more.
Self-powered skin sensors have attracted significant attention in recent years due to their great potential in medical care, robotics, prosthetics, and sports. More importantly, self-powered skin sensors do not need any energy-supply components like batteries, which allows them to work sustainably and saves them the trouble of replacement of batteries. The self-powered skin sensors are mainly based on energy harvesters, with the device itself generating electrical signals when triggered by the detected stimulus or analyte, such as body motion, touch/pressure, acoustic sound, and chemicals in sweat. Herein, the recent research achievements of self-powered skin sensors are comprehensively and systematically reviewed. According to the different monitoring signals, the self-powered skin sensors are summarized and discussed with a focus on the working mechanism, device structure, and the sensing principle. Based on the recent progress, the key challenges that exist and the opportunities that lie ahead are also discussed. Full article
(This article belongs to the Section State-of-the-Art Sensors Technologies)
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Open AccessArticle
Improving Short Term Clock Prediction for BDS-2 Real-Time Precise Point Positioning
Sensors 2019, 19(12), 2762; https://doi.org/10.3390/s19122762 (registering DOI)
Received: 4 May 2019 / Revised: 15 June 2019 / Accepted: 17 June 2019 / Published: 19 June 2019
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Abstract
Although there are already several real-time precise positioning service providers, unfortunately, not all users can use the correction information due to either cost of the service and limitation of their equipment or out of the service coverage. An alternative way is to enhance [...] Read more.
Although there are already several real-time precise positioning service providers, unfortunately, not all users can use the correction information due to either cost of the service and limitation of their equipment or out of the service coverage. An alternative way is to enhance the accuracy of the predicted satellite clocks for precise real-time positioning. Based on the study of existing prediction models, an improved model combing the spectrum analysis (SA) and the generalized regression neural network (GRNN) model is proposed especially for BeiDou satellite navigation system (BDS)-2 satellites. The periodic terms and GRNN-related parameters including length and interval of sample data, as well as a smooth factor, are optimized satellite by satellite to consider satellite-specific characteristics for all the fourteen BDS-2 satellites. The improved model is validated by comparing the predicted clocks of existing models and the improved model with precisely estimated ones. The bias of the predicted clock is within ±0.5 ns over three hours and better than that of the other models and can be used for several real-time precise applications. The clock prediction is further evaluated by applying clock corrections to precise point positioning (PPP) in both static and kinematic mode for eight IGS (International GNSS Service) MGEX (Multi-GNSS Experiment) stations in the Asia-Pacific region. In the static PPP, the improved model is validated to be effective, and position accuracies of some IGS MGEX stations achieve more than 30.0% improvements on average for each component, which enables us to obtain sub-decimeter positioning. In the kinematic PPP, the improved model performs much better than the others in terms of both the convergence time and the position accuracy. The convergence time can be shortened from 1–2 h to 0.5–1 h, while the position accuracy is enhanced by 15.4%, 21.6% and 19.3% on average in east, north and up component, respectively. Full article
(This article belongs to the Section Remote Sensors, Control, and Telemetry)
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Open AccessArticle
Handheld Microflow Cytometer Based on a Motorized Smart Pipette, a Microfluidic Cell Concentrator, and a Miniaturized Fluorescence Microscope
Sensors 2019, 19(12), 2761; https://doi.org/10.3390/s19122761 (registering DOI)
Received: 7 May 2019 / Revised: 10 June 2019 / Accepted: 17 June 2019 / Published: 19 June 2019
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Abstract
Miniaturizing flow cytometry requires a comprehensive approach to redesigning the conventional fluidic and optical systems to have a small footprint and simple usage and to enable rapid cell analysis. Microfluidic methods have addressed some challenges in limiting the realization of microflow cytometry, but [...] Read more.
Miniaturizing flow cytometry requires a comprehensive approach to redesigning the conventional fluidic and optical systems to have a small footprint and simple usage and to enable rapid cell analysis. Microfluidic methods have addressed some challenges in limiting the realization of microflow cytometry, but most microfluidics-based flow cytometry techniques still rely on bulky equipment (e.g., high-precision syringe pumps and bench-top microscopes). Here, we describe a comprehensive approach that achieves high-throughput white blood cell (WBC) counting in a portable and handheld manner, thereby allowing the complete miniaturization of flow cytometry. Our approach integrates three major components: a motorized smart pipette for accurate volume metering and controllable liquid pumping, a microfluidic cell concentrator for target cell enrichment, and a miniaturized fluorescence microscope for portable flow cytometric analysis. We first validated the capability of each component by precisely metering various fluid samples and controlling flow rates in a range from 219.5 to 840.5 μL/min, achieving high sample-volume reduction via on-chip WBC enrichment, and successfully counting single WBCs flowing through a region of interrogation. We synergistically combined the three major components to create a handheld, integrated microflow cytometer and operated it with a simple protocol of drawing up a blood sample via pipetting and injecting the sample into the microfluidic concentrator by powering the motorized smart pipette. We then demonstrated the utility of the microflow cytometer as a quality control means for leukoreduced blood products, quantitatively analyzing residual WBCs (rWBCs) in blood samples present at concentrations as low as 0.1 rWBCs/μL. These portable, controllable, high-throughput, and quantitative microflow cytometric technologies provide promising ways of miniaturizing flow cytometry. Full article
(This article belongs to the Special Issue Portable Biosensing Systems for Point-of-Care Diagnostic Applications)
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Open AccessArticle
Self-Calibration of Nonlinear Signal Model for Angular Position Sensors by Model-Based Automatic Search Algorithm
Sensors 2019, 19(12), 2760; https://doi.org/10.3390/s19122760 (registering DOI)
Received: 24 May 2019 / Revised: 13 June 2019 / Accepted: 18 June 2019 / Published: 19 June 2019
PDF Full-text (2299 KB)
Abstract
This study proposes a novel model-based automatic search algorithm to realize the self-calibration of nonlinear signal model for angular position sensors. In some high-precision angular position sensors, nonlinearity of the signal model is the main source of errors and cannot be handled effectively. [...] Read more.
This study proposes a novel model-based automatic search algorithm to realize the self-calibration of nonlinear signal model for angular position sensors. In some high-precision angular position sensors, nonlinearity of the signal model is the main source of errors and cannot be handled effectively. By constructing a signal flow network framework and by embedding a modeling search network, the parameters of the nonlinear signal model can be searched, and the calibration signal can be obtained. The convergence of the network search process was analyzed. The relationship between the optimization threshold and the convergence accuracy was also studied in simulations. Compared with the maximum angular error reduction to 47.42% after the calibration with simplified model that ignores signal nonlinearities, the proposed scheme was able to reduce this error to 0.0025% in simulations. By implementing the technique in a capacitive angular position sensor, the experimental results showed that the maximum angular error was reduced to 1.63% compared to a reduction of 86.02% achieved with the simplified model calibration. The effects of the search network order and layer number on the calibration accuracy were also analyzed, and the optimal parameters under experimental conditions were obtained. Correspondingly, the proposed scheme is able to handle calibration of nonlinear signal model and further improve sensor accuracy. Full article
(This article belongs to the Section Physical Sensors)
Open AccessArticle
Socially Compliant Path Planning for Robotic Autonomous Luggage Trolley Collection at Airports
Sensors 2019, 19(12), 2759; https://doi.org/10.3390/s19122759 (registering DOI)
Received: 30 May 2019 / Revised: 13 June 2019 / Accepted: 13 June 2019 / Published: 19 June 2019
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Abstract
This paper describes a socially compliant path planning scheme for robotic autonomous luggage trolley collection at airports. The robot is required to efficiently collect all assigned luggage trolleys in a designated area, while avoiding obstacles and not offending the pedestrians. This path planning [...] Read more.
This paper describes a socially compliant path planning scheme for robotic autonomous luggage trolley collection at airports. The robot is required to efficiently collect all assigned luggage trolleys in a designated area, while avoiding obstacles and not offending the pedestrians. This path planning problem is formulated in this paper as a Traveling Salesman Problem (TSP). Different from the conventional solutions to the TSP, in which the Euclidean distance between two sites is used as the metric, a high-dimensional metric including the factor of pedestrians’ feelings is applied in this work. To obtain the new metric, a novel potential function is firstly proposed to model the relationship between the robot, luggage trolleys, obstacles, and pedestrians. The Social Force Model (SFM) is utilized so that the pedestrians can bring extra influence on the potential field, different from ordinary obstacles. Directed by the attractive and repulsive force generated from the potential field, a number of paths connecting the robot and the luggage trolley, or two luggage trolleys, can be obtained. The length of the generated path is considered as the new metric. The Self-Organizing Map (SOM) satisfies the job of finding a final path to connect all luggage trolleys and the robot located in the potential field, as it can find the intrinsic connection in the high dimensional space. Therefore, while incorporating the new metric, the SOM is used to find the optimal path in which the robot can collect the assigned luggage trolleys in sequence. As a demonstration, the proposed path planning method is implemented in simulation experiments, showing an increase of efficiency and efficacy. Full article
(This article belongs to the Special Issue Mobile Robot Navigation)
Open AccessArticle
Non-Contact Monitoring of Breathing Pattern and Respiratory Rate via RGB Signal Measurement
Sensors 2019, 19(12), 2758; https://doi.org/10.3390/s19122758 (registering DOI)
Received: 12 April 2019 / Revised: 10 June 2019 / Accepted: 18 June 2019 / Published: 19 June 2019
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Abstract
Among all the vital signs, respiratory rate remains the least measured in several scenarios, mainly due to the intrusiveness of the sensors usually adopted. For this reason, all contactless monitoring systems are gaining increasing attention in this field. In this paper, we present [...] Read more.
Among all the vital signs, respiratory rate remains the least measured in several scenarios, mainly due to the intrusiveness of the sensors usually adopted. For this reason, all contactless monitoring systems are gaining increasing attention in this field. In this paper, we present a measuring system for contactless measurement of the respiratory pattern and the extraction of breath-by-breath respiratory rate. The system consists of a laptop’s built-in RGB camera and an algorithm for post-processing of acquired video data. From the recording of the chest movements of a subject, the analysis of the pixel intensity changes yields a waveform indicating respiratory pattern. The proposed system has been tested on 12 volunteers, both males and females seated in front of the webcam, wearing both slim-fit and loose-fit t-shirts. The pressure-drop signal recorded at the level of nostrils with a head-mounted wearable device was used as reference respiratory pattern. The two methods have been compared in terms of mean of absolute error, standard error, and percentage error. Additionally, a Bland–Altman plot was used to investigate the bias between methods. Results show the ability of the system to record accurate values of respiratory rate, with both slim-fit and loose-fit clothing. The measuring system shows better performance on females. Bland–Altman analysis showed a bias of −0.01 breaths · min 1 , with respiratory rate values between 10 and 43 breaths · min 1 . Promising performance has been found in the preliminary tests simulating tachypnea. Full article
(This article belongs to the Special Issue Smart Sensors for Healthcare and Medical Applications)
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Open AccessArticle
Identification and Statistical Analysis of Impulse-Like Patterns of Carbon Monoxide Variation in Deep Underground Mines Associated with the Blasting Procedure
Sensors 2019, 19(12), 2757; https://doi.org/10.3390/s19122757 (registering DOI)
Received: 29 March 2019 / Revised: 10 June 2019 / Accepted: 17 June 2019 / Published: 19 June 2019
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Abstract
The quality of the air in underground mines is a challenging issue due to many factors, such as technological processes related to the work of miners (blasting, air conditioning, and ventilation), gas release by the rock mass and geometry of mine corridors. However, [...] Read more.
The quality of the air in underground mines is a challenging issue due to many factors, such as technological processes related to the work of miners (blasting, air conditioning, and ventilation), gas release by the rock mass and geometry of mine corridors. However, to allow miners to start their work, it is crucial to determine the quality of the air. One of the most critical parameters of the air quality is the carbon monoxide (CO) concentration. Thus, in this paper, we analyze the time series describing CO concentration. Firstly, the signal segmentation is proposed, then segmented data (daily patterns) is visualized and statistically analyzed. The method for blasting moment localization, with no prior knowledge, has been presented. It has been found that daily patterns differ and CO concentration values reach a safe level at a different time after blasting. The waiting time to achieve the safe level after blasting moment (with a certain probability) has been calculated based on the historical data. The knowledge about the nature of the CO variability and sources of a high CO concentration can be helpful in creating forecasting models, as well as while planning mining activities. Full article
(This article belongs to the Special Issue Sensor Technologies for Smart Industry and Smart Infrastructure)
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Open AccessReview
Review on V2X, I2X, and P2X Communications and Their Applications: A Comprehensive Analysis over Time
Sensors 2019, 19(12), 2756; https://doi.org/10.3390/s19122756 (registering DOI)
Received: 14 May 2019 / Revised: 3 June 2019 / Accepted: 16 June 2019 / Published: 19 June 2019
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Abstract
Smart cities are ecosystems where novel ideas and emerging technologies meet to improve economy, environment, governance, living, and mobility. One of the pillars of smart cities is transport, with the improvement of mobility and the reduction of traffic accidents being some of the [...] Read more.
Smart cities are ecosystems where novel ideas and emerging technologies meet to improve economy, environment, governance, living, and mobility. One of the pillars of smart cities is transport, with the improvement of mobility and the reduction of traffic accidents being some of the current key challenges. With this purpose, this manuscript reviews the state-of-the-art of communications and applications in which different actors of the road are involved. Thus, the objectives of this survey are intended to determine who, when, and about what is being researched around smart cities. Particularly, the goal is to situate the focus of scientific and industrial progress on V2X, I2X, and P2X communication to establish a taxonomy that reduces ambiguous acronyms around the communication between vehicles, infrastructure, and pedestrians, as well as to determine what the trends and future technologies are that will lead to more powerful applications. To this end, this literature review article presents a comprehensive study including a representative collection of the 100 most cited papers and patents published in the literature together with a statistical bibliometric analysis of 14,364 keywords over 3422 contributions between 1997 and 2018. As a result, this work provides a technological profile considering different dimensions along the paper, such as the type of communication, use case, country, organization, terminology, and year. Full article
(This article belongs to the Special Issue Internet of Vehicles)
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Open AccessArticle
A Highly Robust Silicon Ultraviolet Selective Radiation Sensor Using Differential Spectral Response Method
Sensors 2019, 19(12), 2755; https://doi.org/10.3390/s19122755 (registering DOI)
Received: 29 April 2019 / Revised: 9 June 2019 / Accepted: 14 June 2019 / Published: 19 June 2019
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Abstract
This paper presents a silicon ultraviolet radiation sensor with over 90% UV internal quantum efficiency (QE) and high selectivity to the UV waveband without using optical filters. The sensor was developed for applications that require UV measurement under strong background visible and near-infrared [...] Read more.
This paper presents a silicon ultraviolet radiation sensor with over 90% UV internal quantum efficiency (QE) and high selectivity to the UV waveband without using optical filters. The sensor was developed for applications that require UV measurement under strong background visible and near-infrared (NIR) lights, such as solar UV measurement, UV-C monitoring in greenhouses or automated factories, and so on. The developed sensor is composed of monolithically formed silicon photodiodes with different spectral sensitivities: a highly UV responsive photodiode with internal quantum efficiency (QE) of nearly 100% for UV light, and a lowly UV responsive photodiode with UV internal QE lower than 10%. The photodiodes were optimized to match their visible and NIR light responsivity, and the UV signal is extracted from the background radiation by using the differential spectral response method. With this approach, an internal QE of over 90% for UV light was obtained, with a residual internal QE to non-UV light lower than 20% for 400 nm, 5% for 500 nm, 2% for 600 nm and 0.6% to NIR light. The developed sensor showed no responsivity degradation after exposure towards strong UV light. It was confirmed by the simulation results that the residual responsivity is further suppressed by employing an on-chip band-rejection optical layer consisting of several layers of silicon oxide and silicon nitride films. Full article
(This article belongs to the Section Physical Sensors)
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Open AccessArticle
Application of Logging While Drilling Tool in Formation Boundary Detection and Geo-steering
Sensors 2019, 19(12), 2754; https://doi.org/10.3390/s19122754 (registering DOI)
Received: 8 May 2019 / Revised: 14 June 2019 / Accepted: 16 June 2019 / Published: 19 June 2019
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Abstract
Logging while drilling (LWD) plays a crucial role in geo-steering, which can determine the formation boundary and resistivity in real time. In this study, an efficient inversion, which can accurately invert formation information in real time on the basis of fast-forward modeling, is [...] Read more.
Logging while drilling (LWD) plays a crucial role in geo-steering, which can determine the formation boundary and resistivity in real time. In this study, an efficient inversion, which can accurately invert formation information in real time on the basis of fast-forward modeling, is presented. In forward modeling, the Gauss–Legendre quadrature combined with the continued fraction method is used to calculate the response of the LWD instrument in a layered formation. In inversion modeling, the Levenberg–Marquardt (LM) algorithm, combined with the line search method of the Armijo criterion, are used to minimize the cost function, and a constraint algorithm is added to ensure the stability of the inversion. A positive and negative sign is added to the distance parameter to determine whether the LWD instrument is located above or below the formation boundary. We have carried out a series of experiments to verify the accuracy of the inversion. The experimental results suggest that the forward algorithm can make the infinite integral of the Bessel function rapidly converge, and accurately obtain the response of the LWD instrument in a layered formation. The inversion can accurately determine the formation resistivity and boundary in real time. This is significant for geological exploration. Full article
(This article belongs to the Special Issue Sensing in Oil and Gas Applications)
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Open AccessArticle
Impact of Thermal Control Measures on the Imaging Quality of an Aerial Optoelectronic Sensor
Sensors 2019, 19(12), 2753; https://doi.org/10.3390/s19122753 (registering DOI)
Received: 23 April 2019 / Revised: 7 June 2019 / Accepted: 14 June 2019 / Published: 19 June 2019
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Abstract
The image resolution is the most important performance parameter for an aerial optoelectronic sensor. Existing thermal control methods cannot eliminate the sensor’s temperature gradient, making the image resolution difficult to further improve. This article analyzes the different impacts of temperature changes on the [...] Read more.
The image resolution is the most important performance parameter for an aerial optoelectronic sensor. Existing thermal control methods cannot eliminate the sensor’s temperature gradient, making the image resolution difficult to further improve. This article analyzes the different impacts of temperature changes on the imaging resolution and proposes modifications. Firstly, the sensor was subjected to thermo-optical simulation by means of finite element analysis, and the different impacts of temperature changes on the imaging quality were analyzed. According to the simulation results, an active thermal control method is suggested to enhance the temperature uniformity of the sensor. Considering the impacts of active and passive thermal control measures, thermal optical analysis was carried out to predict the performance of the sensor. The results of the analysis show that the imaging quality of the sensor has been significantly improved. The experimental results show that the image resolution of the optoelectronic sensor improved from 47 to 59 lp/mm, which demonstrates that the sensor can produce a high image quality in a low-temperature environment. Full article
(This article belongs to the Special Issue Sensors In Target Detection)
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Open AccessArticle
An Energy-Efficient Clustering Routing Protocol Based on a High-QoS Node Deployment with an Inter-Cluster Routing Mechanism in WSNs
Sensors 2019, 19(12), 2752; https://doi.org/10.3390/s19122752 (registering DOI)
Received: 29 April 2019 / Revised: 15 June 2019 / Accepted: 16 June 2019 / Published: 19 June 2019
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Abstract
Currently, wireless sensor network (WSN) protocols are mainly used to achieve low power consumption of the network, but there are few studies on the quality of services (QoS) of these networks. Coverage can be used as a measure of the WSN’s QoS, which [...] Read more.
Currently, wireless sensor network (WSN) protocols are mainly used to achieve low power consumption of the network, but there are few studies on the quality of services (QoS) of these networks. Coverage can be used as a measure of the WSN’s QoS, which can further reflect the quality of data information. Additionally, the coverage requirements of regional monitoring target points are different in real applications. On this basis, this paper proposes an energy-efficient clustering routing protocol based on a high-QoS node deployment with an inter-cluster routing mechanism (EECRP-HQSND-ICRM) in WSNs. First, this paper proposes formula definitions for information integrity, validity, and redundancy from the coverage rate and introduces a node deployment strategy based on twofold coverage. Then, in order to satisfy the uniformity of the distribution of cluster heads (CHs), the monitoring area is divided into four small areas centered on the base station (BS), and the CHs are selected in the respective cells. Finally, combined with the practical application of the WSN, this paper optimizes the Dijkstra algorithm, including: (1) nonessential paths neglecting considerations, and (2) a simultaneous introduction of end-to-end weights and path weights, achieving the selection of optimal information transmission paths between the CHs. The simulation results show that, compared with the general node deployment strategies, the deployment strategy of the proposed protocol has higher information integrity and validity, as well as lower redundancy. Meanwhile, compared with some classic protocols, this protocol can greatly reduce and balance network energy consumption and extend the network lifetime. Full article
(This article belongs to the Section Sensor Networks)
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Open AccessArticle
Immunoassays Based on Hot Electron-Induced Electrochemiluminescence at Disposable Cell Chips with Printed Electrodes
Sensors 2019, 19(12), 2751; https://doi.org/10.3390/s19122751 (registering DOI)
Received: 16 May 2019 / Revised: 10 June 2019 / Accepted: 12 June 2019 / Published: 19 June 2019
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Abstract
Novel hot electron-emitting working electrodes and conventional counter electrodes were created by screen printing. Thus, low-cost disposable electrode chips for bioaffinity assays were produced to replace our older expensive electrode chips manufactured by manufacturing techniques of electronics from silicon or on glass chips. [...] Read more.
Novel hot electron-emitting working electrodes and conventional counter electrodes were created by screen printing. Thus, low-cost disposable electrode chips for bioaffinity assays were produced to replace our older expensive electrode chips manufactured by manufacturing techniques of electronics from silicon or on glass chips. The present chips were created by printing as follows: (i) silver lines provided the electronic contacts, counter electrode and the bottom of the working electrode and counter electrode, (ii) the composite layer was printed on appropriate parts of the silver layer, and (iii) finally a hydrophobic ring was added to produce the electrochemical cell boundaries. The applicability of these electrode chips in bioaffinity assays was demonstrated by an immunoassay of human C-reactive protein (i) using Tb(III) chelate label displaying long-lived hot electron-induced electrochemiluminescence (HECL) and (ii) now for the first time fluorescein isothiocyanate (FITC) was utilized as an a low-cost organic label displaying a short-lived HECL in a real-world bioaffinity assay. Full article
(This article belongs to the Special Issue Electrochemiluminescence Biosensor (ECL Biosensors))
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Open AccessArticle
Sensor Data-Driven Bearing Fault Diagnosis Based on Deep Convolutional Neural Networks and S-Transform
Sensors 2019, 19(12), 2750; https://doi.org/10.3390/s19122750 (registering DOI)
Received: 18 May 2019 / Revised: 13 June 2019 / Accepted: 17 June 2019 / Published: 19 June 2019
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Abstract
Accurate and timely bearing fault diagnosis is crucial to decrease the probability of unexpected failures of rotating machinery and improve the efficiency of its scheduled maintenance. Since convolutional neural networks (CNN) have poor feature extraction capability for sensor data with 1D format, CNN [...] Read more.
Accurate and timely bearing fault diagnosis is crucial to decrease the probability of unexpected failures of rotating machinery and improve the efficiency of its scheduled maintenance. Since convolutional neural networks (CNN) have poor feature extraction capability for sensor data with 1D format, CNN combined with signal processing algorithm is often adopted for fault diagnosis. This increases manual conversion work and expertise dependence while reducing the feasibility and robustness of the corresponding fault diagnosis method. In this paper, a novel sensor data-driven fault diagnosis method is proposed by fusing S-transform (ST) algorithm and CNN, namely ST-CNN. First of all, a ST layer is designed based on S-transform algorithm. In the ST layer, sensor data is automatically converted into 2D time-frequency matrix without manual conversion work. Then, a new ST-CNN model is constructed, and the time-frequency coefficient matrixes are inputted into the constructed ST-CNN model. After the training process of the ST-CNN model is completed, the classification layer such as softmax performs the fault diagnosis. Finally, the diagnosis performance of the proposed method is evaluated by using two public available datasets of bearings. The experimental results show that the proposed method performs the higher and more robust diagnosis performance than other existing methods. Full article
(This article belongs to the Special Issue Sensors for Fault Diagnosis)
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Open AccessArticle
A Personalized QoS Prediction Method for Web Services via Blockchain-Based Matrix Factorization
Sensors 2019, 19(12), 2749; https://doi.org/10.3390/s19122749 (registering DOI)
Received: 5 May 2019 / Revised: 12 June 2019 / Accepted: 17 June 2019 / Published: 19 June 2019
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Abstract
Personalized quality of service (QoS) prediction plays an important role in helping users build high-quality service-oriented systems. To obtain accurate prediction results, many approaches have been investigated in recent years. However, these approaches do not fully address untrustworthy QoS values submitted by unreliable [...] Read more.
Personalized quality of service (QoS) prediction plays an important role in helping users build high-quality service-oriented systems. To obtain accurate prediction results, many approaches have been investigated in recent years. However, these approaches do not fully address untrustworthy QoS values submitted by unreliable users, leading to inaccurate predictions. To address this issue, inspired by blockchain with distributed ledger technology, distributed consensus mechanisms, encryption algorithms, etc., we propose a personalized QoS prediction method for web services that we call blockchain-based matrix factorization (BMF). We develop a user verification approach based on homomorphic hash, and use the Byzantine agreement to remove unreliable users. Then, matrix factorization is employed to improve the accuracy of predictions and we evaluate the proposed BMF on a real-world web services dataset. Experimental results show that the proposed method significantly outperforms existing approaches, making it much more effective than traditional techniques. Full article
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Open AccessArticle
A Binder Jet Printed, Stainless Steel Preconcentrator as an In-Line Injector of Volatile Organic Compounds
Sensors 2019, 19(12), 2748; https://doi.org/10.3390/s19122748 (registering DOI)
Received: 5 May 2019 / Revised: 11 June 2019 / Accepted: 17 June 2019 / Published: 19 June 2019
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Abstract
A conventional approach to making miniature or microscale gas chromatography (GC) components relies on silicon as a base material and MEMS fabrication as manufacturing processes. However, these devices often fail in medium-to-high temperature applications due to a lack of robust fluidic interconnects and [...] Read more.
A conventional approach to making miniature or microscale gas chromatography (GC) components relies on silicon as a base material and MEMS fabrication as manufacturing processes. However, these devices often fail in medium-to-high temperature applications due to a lack of robust fluidic interconnects and a high-yield bonding process. This paper explores the feasibility of using metal additive manufacturing (AM), which is also known as metal 3D printing, as an alternative platform to produce small-scale microfluidic devices that can operate at a temperature higher than that which polymers can withstand. Binder jet printing (BJP), one of the metal AM processes, was utilized to make stainless steel (SS) preconcentrators (PCs) with submillimeter internal features. PCs can increase the concentration of gaseous analytes or serve as an inline injector for GC or gas sensor applications. Normally, parts printed by BJP are highly porous and thus often infiltrated with low melting point metal. By adding to SS316 powder sintering additives such as boron nitride (BN), which reduces the liquidus line temperature, we produce near full-density SS PCs at sintering temperatures much lower than the SS melting temperature, and importantly without any measurable shape distortion. Conversely, the SS PC without BN remains porous after the sintering process and unsuitable for fluidic applications. Since the SS parts, unlike Si, are compatible with machining, they can be modified to work with commercial compression fitting. The PC structures as well as the connection with the fitting are leak-free with relatively high operating pressures. A flexible membrane heater along with a resistance-temperature detector is integrated with the SS PCs for thermal desorption. The proof-of-concept experiment demonstrates that the SS PC can preconcentrate and inject 0.6% headspace toluene to enhance the detector’s response. Full article
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Open AccessArticle
SmarTEG: An Autonomous Wireless Sensor Node for High Accuracy Accelerometer-Based Monitoring
Sensors 2019, 19(12), 2747; https://doi.org/10.3390/s19122747 (registering DOI)
Received: 30 April 2019 / Revised: 12 June 2019 / Accepted: 12 June 2019 / Published: 19 June 2019
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Abstract
We report on a self-sustainable, wireless accelerometer-based system for wear detection in a band saw blade. Due to the combination of low power hardware design, thermal energy harvesting with a small thermoelectric generator (TEG), an ultra-low power wake-up radio, power management and the [...] Read more.
We report on a self-sustainable, wireless accelerometer-based system for wear detection in a band saw blade. Due to the combination of low power hardware design, thermal energy harvesting with a small thermoelectric generator (TEG), an ultra-low power wake-up radio, power management and the low complexity algorithm implemented, our solution works perpetually while also achieving high accuracy. The onboard algorithm processes sensor data, extracts features, performs the classification needed for the blade’s wear detection, and sends the report wirelessly. Experimental results in a real-world deployment scenario demonstrate that its accuracy is comparable to state-of-the-art algorithms executed on a PC and show the energy-neutrality of the solution using a small thermoelectric generator to harvest energy. The impact of various low-power techniques implemented on the node is analyzed, highlighting the benefits of onboard processing, the nano-power wake-up radio, and the combination of harvesting and low power design. Finally, accurate in-field energy intake measurements, coupled with simulations, demonstrate that the proposed approach is energy autonomous and can work perpetually. Full article
(This article belongs to the Special Issue Energy Harvesting Sensor Systems)
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Open AccessArticle
A Novel Relative Position Estimation Method for Capsule Robot Moving in Gastrointestinal Tract
Sensors 2019, 19(12), 2746; https://doi.org/10.3390/s19122746 (registering DOI)
Received: 16 May 2019 / Revised: 14 June 2019 / Accepted: 16 June 2019 / Published: 19 June 2019
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Abstract
Recently, a variety of positioning and tracking methods have been proposed for capsule robots moving in the gastrointestinal (GI) tract to provide real-time unobstructed spatial pose results. However, the current absolute position-based result cannot match the GI structure due to its unstructured environment. [...] Read more.
Recently, a variety of positioning and tracking methods have been proposed for capsule robots moving in the gastrointestinal (GI) tract to provide real-time unobstructed spatial pose results. However, the current absolute position-based result cannot match the GI structure due to its unstructured environment. To overcome this disadvantage and provide a proper position description method to match the GI tract, we here present a relative position estimation method for tracking the capsule robot, which uses the moving distance of the robot along the GI tract to indicate the position result. The procedure of the proposed method is as follows: firstly, the absolute position results of the capsule robot are obtained with the magnetic tracking method; then, the moving status of the robot along the GI tract is determined according to the moving direction; and finally, the movement trajectory of the capsule robot is fitted with the Bézier curve, where the moving distance can then be evaluated using the integral method. Compared to state-of-the-art capsule tracking methods, the proposed method can directly help to guide medical instruments by providing physicians the insertion distance in patients’ bodies, which cannot be done based on absolute position results. Moreover, as relative distance information was used, no reference tracking objects needed to be mounted onto the human body. The experimental results prove that the proposed method achieves a good distance estimation of the capsule robot moving in the simulation platform. Full article
(This article belongs to the Special Issue Integrated Magnetic Sensors)
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Open AccessArticle
Slice Management for Quality of Service Differentiation in Wireless Network Slicing
Sensors 2019, 19(12), 2745; https://doi.org/10.3390/s19122745 (registering DOI)
Received: 19 April 2019 / Revised: 12 June 2019 / Accepted: 17 June 2019 / Published: 19 June 2019
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Abstract
Network slicing is a technology that virtualizes a single infrastructure into multiple logical networks (called slices) where resources or virtualized functions can be flexibly configured by demands of applications to satisfy their quality of service (QoS) requirements. Generally, to provide the guaranteed QoS [...] Read more.
Network slicing is a technology that virtualizes a single infrastructure into multiple logical networks (called slices) where resources or virtualized functions can be flexibly configured by demands of applications to satisfy their quality of service (QoS) requirements. Generally, to provide the guaranteed QoS in applications, resources of slices are isolated. In wired networks, this resource isolation is enabled by allocating dedicated data bandwidths to slices. However, in wireless networks, resource isolation may be challenging because the interference between links affects the actual bandwidths of slices and degrades their QoS. In this paper, we propose a slice management scheme that mitigates the interference imposed on each slice according to their priorities by determining routes of flows with a different routing policy. Traffic flows in the slice with the highest priority are routed into shortest paths. In each lower-priority slice, the routing of traffic flows is conducted while minimizing a weighted summation of interference to other slices. Since higher-priority slices have higher interference weights, they receive lower interference from other slices. As a result, the QoS of slices is differentiated according to their priorities while the interference imposed on slices is reduced. We compared the proposed slice management scheme with a naïve slice management (NSM) method that differentiates QoS among slices by priority queuing. We conducted some simulations and the simulation results show that our proposed management scheme not only differentiates the QoS of slices according to their priorities but also enhances the average throughput and delay performance of slices remarkably compared to that of the NSM method. The simulations were conducted in grid network topologies with 16 and 100 nodes and a random network topology with 200 nodes. Simulation results indicate that the proposed slice management increased the average throughput of slices up to 6%, 13%, and 7% and reduced the average delay of slices up to 14%, 15%, and 11% in comparison with the NSM method. Full article
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Open AccessArticle
Modeling Flaw Pulse-Echo Signals in Cylindrical Components Using an Ultrasonic Line-Focused Transducer with Consideration of Wave Mode Conversion
Sensors 2019, 19(12), 2744; https://doi.org/10.3390/s19122744 (registering DOI)
Received: 17 May 2019 / Revised: 10 June 2019 / Accepted: 15 June 2019 / Published: 18 June 2019
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Abstract
Investigations on flaw responses can benefit the nondestructive testing of cylinders using line-focused transducers. In this work, the system function, the wave beam model, and a flaw scattering model are combined to develop an ultrasonic measurement model for line-focused transducers to predict flaw [...] Read more.
Investigations on flaw responses can benefit the nondestructive testing of cylinders using line-focused transducers. In this work, the system function, the wave beam model, and a flaw scattering model are combined to develop an ultrasonic measurement model for line-focused transducers to predict flaw responses in cylindrical components. The system function is characterized using reference signals by developing an acoustic transfer function for line-focused transducers, which works at different distances for both planar and curved surfaces. The wave beams in cylindrical components are modeled using a multi-Gaussian beam model, where the effects of wave mode conversion and curvatures of cylinders are considered. Simulation results of wave beams are provided to analyze their propagation behaviors. The proposed ultrasonic measurement model is certified from good agreement between the experimental and predicted signals of side-drilled holes. This work provides guidance for evaluating the detection ability of line-focused transducers in cylindrical component testing applications. Full article
(This article belongs to the Special Issue Sensors Based NDE and NDT)
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Open AccessArticle
Integration of Ground- Penetrating Radar and Gamma-Ray Detectors for Nonintrusive Characterisation of Buried Radioactive Objects
Sensors 2019, 19(12), 2743; https://doi.org/10.3390/s19122743 (registering DOI)
Received: 16 May 2019 / Revised: 6 June 2019 / Accepted: 16 June 2019 / Published: 18 June 2019
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Abstract
The characterisation of buried radioactive wastes is challenging because they are not readily accessible. Therefore, this study reports on the development of a method for integrating ground-penetrating radar (GPR) and gamma-ray detector measurements for nonintrusive characterisation of buried radioactive objects. The method makes [...] Read more.
The characterisation of buried radioactive wastes is challenging because they are not readily accessible. Therefore, this study reports on the development of a method for integrating ground-penetrating radar (GPR) and gamma-ray detector measurements for nonintrusive characterisation of buried radioactive objects. The method makes use of the density relationship between soil permittivity models and the flux measured by gamma ray detectors to estimate the soil density, depth and radius of a disk-shaped buried radioactive object simultaneously. The method was validated using numerical simulations with experimentally-validated gamma-ray detector and GPR antenna models. The results showed that the method can simultaneously retrieve the soil density, depth and radius of disk-shaped radioactive objects buried in soil of varying conditions with a relative error of less than 10%. This result will enable the development of an integrated GPR and gamma ray detector tool for rapid characterisation of buried radioactive objects encountered during monitoring and decontamination of nuclear sites and facilities. Full article
(This article belongs to the Special Issue Radiation Sensing: Design and Deployment of Sensors and Detectors)
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Open AccessArticle
Mango Fruit Load Estimation Using a Video Based MangoYOLO—Kalman Filter—Hungarian Algorithm Method
Sensors 2019, 19(12), 2742; https://doi.org/10.3390/s19122742
Received: 11 May 2019 / Revised: 14 June 2019 / Accepted: 14 June 2019 / Published: 18 June 2019
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Abstract
: Pre-harvest fruit yield estimation is useful to guide harvesting and marketing resourcing, but machine vision estimates based on a single view from each side of the tree (“dual-view”) underestimates the fruit yield as fruit can be hidden from view. A method is [...] Read more.
: Pre-harvest fruit yield estimation is useful to guide harvesting and marketing resourcing, but machine vision estimates based on a single view from each side of the tree (“dual-view”) underestimates the fruit yield as fruit can be hidden from view. A method is proposed involving deep learning, Kalman filter, and Hungarian algorithm for on-tree mango fruit detection, tracking, and counting from 10 frame-per-second videos captured of trees from a platform moving along the inter row at 5 km/h. The deep learning based mango fruit detection algorithm, MangoYOLO, was used to detect fruit in each frame. The Hungarian algorithm was used to correlate fruit between neighbouring frames, with the improvement of enabling multiple-to-one assignment. The Kalman filter was used to predict the position of fruit in following frames, to avoid multiple counts of a single fruit that is obscured or otherwise not detected with a frame series. A “borrow” concept was added to the Kalman filter to predict fruit position when its precise prediction model was absent, by borrowing the horizontal and vertical speed from neighbouring fruit. By comparison with human count for a video with 110 frames and 192 (human count) fruit, the method produced 9.9% double counts and 7.3% missing count errors, resulting in around 2.6% over count. In another test, a video (of 1162 frames, with 42 images centred on the tree trunk) was acquired of both sides of a row of 21 trees, for which the harvest fruit count was 3286 (i.e., average of 156 fruit/tree). The trees had thick canopies, such that the proportion of fruit hidden from view from any given perspective was high. The proposed method recorded 2050 fruit (62% of harvest) with a bias corrected Root Mean Square Error (RMSE) = 18.0 fruit/tree while the dual-view image method (also using MangoYOLO) recorded 1322 fruit (40%) with a bias corrected RMSE = 21.7 fruit/tree. The video tracking system is recommended over the dual-view imaging system for mango orchard fruit count. Full article
(This article belongs to the Section Remote Sensors, Control, and Telemetry)
Open AccessArticle
Action Graphs for Performing Goal Recognition Design on Human-Inhabited Environments
Sensors 2019, 19(12), 2741; https://doi.org/10.3390/s19122741
Received: 29 March 2019 / Revised: 4 June 2019 / Accepted: 14 June 2019 / Published: 18 June 2019
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Abstract
Goal recognition is an important component of many context-aware and smart environment services; however, a person’s goal often cannot be determined until their plan nears completion. Therefore, by modifying the state of the environment, our work aims to reduce the number of observations [...] Read more.
Goal recognition is an important component of many context-aware and smart environment services; however, a person’s goal often cannot be determined until their plan nears completion. Therefore, by modifying the state of the environment, our work aims to reduce the number of observations required to recognise a human’s goal. These modifications result in either: Actions in the available plans being replaced with more distinctive actions; or removing the possibility of performing some actions, so humans are forced to take an alternative (more distinctive) plan. In our solution, a symbolic representation of actions and the world state is transformed into an Action Graph, which is then traversed to discover the non-distinctive plan prefixes. These prefixes are processed to determine which actions should be replaced or removed. For action replacement, we developed an exhaustive approach and an approach that shrinks the plans then reduces the non-distinctive plan prefixes, namely Shrink–Reduce. Exhaustive is guaranteed to find the minimal distinctiveness but is more computationally expensive than Shrink–Reduce. These approaches are compared using a test domain with varying amounts of goals, variables and values, and a realistic kitchen domain. Our action removal method is shown to increase the distinctiveness of various grid-based navigation problems, with a width/height ranging from 4 to 16 and between 2 and 14 randomly selected goals, by an average of 3.27 actions in an average time of 4.69 s, whereas a state-of-the-art approach often breaches a 10 min time limit. Full article
(This article belongs to the Special Issue Context-Awareness in the Internet of Things)
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Open AccessArticle
A Fault Detection System for a Geothermal Heat Exchanger Sensor Based on Intelligent Techniques
Sensors 2019, 19(12), 2740; https://doi.org/10.3390/s19122740
Received: 10 May 2019 / Revised: 11 June 2019 / Accepted: 16 June 2019 / Published: 18 June 2019
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Abstract
This paper proposes a methodology for dealing with an issue of crucial practical importance in real engineering systems such as fault detection and recovery of a sensor. The main goal is to define a strategy to identify a malfunctioning sensor and to establish [...] Read more.
This paper proposes a methodology for dealing with an issue of crucial practical importance in real engineering systems such as fault detection and recovery of a sensor. The main goal is to define a strategy to identify a malfunctioning sensor and to establish the correct measurement value in those cases. As study case, we use the data collected from a geothermal heat exchanger installed as part of the heat pump installation in a bioclimatic house. The sensor behaviour is modeled by using six different machine learning techniques: Random decision forests, gradient boosting, extremely randomized trees, adaptive boosting, k-nearest neighbors, and shallow neural networks. The achieved results suggest that this methodology is a very satisfactory solution for this kind of systems. Full article
(This article belongs to the Special Issue Selected Papers from HAIS2018)
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Open AccessArticle
Proposition and Real-Time Implementation of an Energy-Aware Routing Protocol for a Software Defined Wireless Sensor Network
Sensors 2019, 19(12), 2739; https://doi.org/10.3390/s19122739
Received: 27 April 2019 / Revised: 1 June 2019 / Accepted: 12 June 2019 / Published: 18 June 2019
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Abstract
A wireless sensor network (WSN) has achieved significant importance in tracking different physical or environmental conditions using wireless sensor nodes. Such types of networks are used in various applications including smart cities, smart building, military target tracking and surveillance, natural disaster relief, and [...] Read more.
A wireless sensor network (WSN) has achieved significant importance in tracking different physical or environmental conditions using wireless sensor nodes. Such types of networks are used in various applications including smart cities, smart building, military target tracking and surveillance, natural disaster relief, and smart homes. However, the limited power capacity of sensor nodes is considered a major issue that hampers the performance of a WSN. A plethora of research has been conducted to reduce the energy consumption of sensor nodes in traditional WSN, however the limited functional capability of such networks is the main constraint in designing sophisticated and dynamic solutions. Given this, software defined networking (SDN) has revolutionized traditional networks by providing a programmable and flexible framework. Therefore, SDN concepts can be utilized in designing energy-efficient WSN solutions. In this paper, we exploit SDN capabilities to conserve energy consumption in a traditional WSN. To achieve this, an energy-aware multihop routing protocol (named EASDN) is proposed for software defined wireless sensor network (SDWSN). The proposed protocol is evaluated in a real environment. For this purpose, a test bed is developed using Raspberry Pi. The experimental results show that the proposed algorithm exhibits promising results in terms of network lifetime, average energy consumption, the packet delivery ratio, and average delay in comparison to an existing energy efficient routing protocol for SDWSN and a traditional source routing algorithm. Full article
(This article belongs to the Section Sensor Networks)
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Open AccessArticle
Classification for Human Balance Capacity Based on Visual Stimulation under a Virtual Reality Environment
Sensors 2019, 19(12), 2738; https://doi.org/10.3390/s19122738
Received: 15 May 2019 / Revised: 11 June 2019 / Accepted: 13 June 2019 / Published: 18 June 2019
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Abstract
The normal and disordered people balance ability classification is a key premise for rehabilitation training. This paper proposes a multi-barycentric area model (MBAM), which can be applied for accurate video analysis based classification. First, we have invited fifty-three subjects to wear an HTC [...] Read more.
The normal and disordered people balance ability classification is a key premise for rehabilitation training. This paper proposes a multi-barycentric area model (MBAM), which can be applied for accurate video analysis based classification. First, we have invited fifty-three subjects to wear an HTC (High Tech Computer Corporation) VIVE (Very Immersive Virtual Experience) helmet and to walk ten meters while seeing a virtual environment. The subjects’ motion behaviors are collected as our balance ability classification dataset. Secondly, we use background differential algorithm and bilateral filtering as the preprocessing to alleviate the video noise and motion blur. Inspired by the balance principle of a tumbler, we introduce a MBAM model to describe the body balancing condition by computing the gravity center of a triangle area, which is surrounded by the upper, middle and lower parts of the human body. Finally, we can obtain the projection coordinates according to the center of gravity of the triangle, and get the roadmap of the subjects by connecting those projection coordinates. In the experiments, we adopt four kinds of metrics (the MBAM, the area variance, the roadmap and the walking speed) innumerical analysis to verify the effect of the proposed method. Experimental results show that the proposed method can obtain a more accurate classification for human balance ability. The proposed research may provide potential theoretical support for the clinical diagnosis and treatment for balance dysfunction patients. Full article
(This article belongs to the Special Issue Smart Sensors for eHealth Applications)
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Open AccessArticle
Corrections of BDS Code Multipath Error in Geostationary Orbit Satellite and Their Application in Precise Data Processing
Sensors 2019, 19(12), 2737; https://doi.org/10.3390/s19122737
Received: 11 May 2019 / Revised: 13 June 2019 / Accepted: 15 June 2019 / Published: 18 June 2019
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Abstract
Multipath error is a main error source in Global Navigation Satellite System (GNSS) data processing, which cannot be removed by a differential technique because of the strong relationship with the environment around the station. The multipath effect of the code observables is more [...] Read more.
Multipath error is a main error source in Global Navigation Satellite System (GNSS) data processing, which cannot be removed by a differential technique because of the strong relationship with the environment around the station. The multipath effect of the code observables is more complex than that of the carrier-phase observables, especially for BeiDou Navigation Satellite System (BDS) geostationary orbit (GEO) satellites. In this contribution, we deeply analyzed the characteristic and effect on the precise data processing of GEO satellite multipath errors based on a large number of permanent GNSS stations. A linear combination of code and carrier-phase observables was used to analyze the characteristics of repeatability for BDS GEO’s multipath. Then, a correction method was proposed to eliminate the multipath error of the GEO code observables, based on wavelet transform. The experiment data were collected at 83 globally distributed stations, from multi-GNSS experiments and national BDS augmentation systems, from days 32 to 66 in 2017. The results show that the systematic multipath variation component of the GEO code observables can be obtained with wavelet transform, which can significantly contribute to correcting the multipath error of GEO satellites. The average root mean square error (RMSE) of the multipath series is decreased by approximately 19.5%, 20.2%, and 7.5% for B1, B2, and B3, respectively. In addition, some experiments, including ionospheric delay extraction and satellite clock estimation, were conducted in simulated real-time mode in order to validate the effect of the correction methods. For the ionospheric delay estimation, the average RMSE of the slant ionospheric delay is reduced by approximately 15.5%. Moreover, the multipath correction can contribute greatly to shortening the convergence time of the satellite clock estimation of the BDS GEO satellites. Full article
(This article belongs to the Section Remote Sensors, Control, and Telemetry)
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Open AccessArticle
Exploration of Chlorophyll a Fluorescence and Plant Gas Exchange Parameters as Indicators of Drought Tolerance in Perennial Ryegrass
Sensors 2019, 19(12), 2736; https://doi.org/10.3390/s19122736
Received: 20 April 2019 / Revised: 18 May 2019 / Accepted: 4 June 2019 / Published: 18 June 2019
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Abstract
Perennial ryegrass (Lolium perenne L.) belongs to the common cultivated grass species in Central and Western Europe. Despite being considered to be susceptible to drought, it is frequently used for forming the turf in urban green areas. In such areas, the water [...] Read more.
Perennial ryegrass (Lolium perenne L.) belongs to the common cultivated grass species in Central and Western Europe. Despite being considered to be susceptible to drought, it is frequently used for forming the turf in urban green areas. In such areas, the water deficit in soil is recognized as one of the most important environmental factors, which can limit plant growth. The basic aim of this work was to explore the mechanisms standing behind the changes in the photosynthetic apparatus performance of two perennial ryegrass turf varieties grown under drought stress using comprehensive in vivo chlorophyll fluorescence signal analyses and plant gas exchange measurements. Drought was applied after eight weeks of sowing by controlling the humidity of the roots ground medium at the levels of 30, 50, and 70% of the field water capacity. Measurements were carried out at four times: 0, 120, and 240 h after drought application and after recovery (refilling water to 70%). We found that the difference between the two tested varieties’ response resulted from a particular re-reduction of P700+ (reaction certer of PSI) that was caused by slower electron donation from P680. The difference in the rate of electron flow from Photosystem II (PSII) to PSI was also detected. The application of the combined tools (plants’ photosynthetic efficiency analysis and plant gas exchange measurements) allowed exploring and explaining the specific variety response to drought stress. Full article
(This article belongs to the Special Issue Chlorophyll Fluorescence Sensing in Plant Phenotyping)
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Open AccessArticle
A Virtual Force Algorithm-Lévy-Embedded Grey Wolf Optimization Algorithm for Wireless Sensor Network Coverage Optimization
Sensors 2019, 19(12), 2735; https://doi.org/10.3390/s19122735
Received: 10 May 2019 / Revised: 5 June 2019 / Accepted: 11 June 2019 / Published: 18 June 2019
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Abstract
The random placement of a large-scale sensor network in an outdoor environment often causes low coverage. In order to effectively improve the coverage of a wireless sensor network in the monitoring area, a coverage optimization algorithm for wireless sensor networks with a Virtual [...] Read more.
The random placement of a large-scale sensor network in an outdoor environment often causes low coverage. In order to effectively improve the coverage of a wireless sensor network in the monitoring area, a coverage optimization algorithm for wireless sensor networks with a Virtual Force-Lévy-embedded Grey Wolf Optimization (VFLGWO) algorithm is proposed. The simulation results show that the VFLGWO algorithm has a better optimization effect on the coverage rate, uniformity, and average moving distance of sensor nodes than a wireless sensor network coverage optimization algorithm using Lévy-embedded Grey Wolf Optimizer, Cuckoo Search algorithm, and Chaotic Particle Swarm Optimization. The VFLGWO algorithm has good adaptability with respect to changes of the number of sensor nodes and the size of the monitoring area. Full article
(This article belongs to the Section Sensor Networks)
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