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Sensors, Volume 19, Issue 4 (February-2 2019)

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Cover Story (view full-size image) The worldwide consumption of coffee exceeds 11 billion tons/year, and coffee grounds end up as [...] Read more.
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Open AccessArticle Estimating Crop Nutritional Status Using Smart Apps to Support Nitrogen Fertilization. A Case Study on Paddy Rice
Sensors 2019, 19(4), 981; https://doi.org/10.3390/s19040981
Received: 17 January 2019 / Revised: 5 February 2019 / Accepted: 20 February 2019 / Published: 25 February 2019
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Abstract
Accurate nitrogen (N) management is crucial for the economic and environmental sustainability of cropping systems. Different methods have been developed to increase the efficiency of N fertilizations. However, their costs and/or low usability have often prevented their adoption in operational contexts. We developed [...] Read more.
Accurate nitrogen (N) management is crucial for the economic and environmental sustainability of cropping systems. Different methods have been developed to increase the efficiency of N fertilizations. However, their costs and/or low usability have often prevented their adoption in operational contexts. We developed a diagnostic system to support topdressing N fertilization based on the use of smart apps to derive a N nutritional index (NNI; actual/critical plant N content). The system was tested on paddy rice via dedicated field experiments, where the smart apps PocketLAI and PocketN were used to estimate, respectively, critical (from leaf area index) and actual plant N content. Results highlighted the system’s capability to correctly detect the conditions of N stress (NNI < 1) and N surplus (NNI > 1), thereby effectively supporting topdressing fertilizations. A resource-efficient methodology to derive PocketN calibration curves for different varieties—needed to extend the system to new contexts—was also developed and successfully evaluated on 43 widely grown European varieties. The widespread availability of smartphones and the possibility to integrate NNI and remote sensing technologies to derive variable rate fertilization maps generate new opportunities for supporting N management under real farming conditions. Full article
(This article belongs to the Special Issue Sensors and Systems for Smart Agriculture)
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Open AccessArticle Multi-Objective Optimization Based Multi-Bernoulli Sensor Selection for Multi-Target Tracking
Sensors 2019, 19(4), 980; https://doi.org/10.3390/s19040980
Received: 2 February 2019 / Revised: 20 February 2019 / Accepted: 21 February 2019 / Published: 25 February 2019
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Abstract
This paper presents a novel multi-objective optimization based sensor selection method for multi-target tracking in sensor networks. The multi-target states are modelled as multi-Bernoulli random finite sets and the multi-Bernoulli filter is used to propagate the multi-target posterior density. The proposed method is [...] Read more.
This paper presents a novel multi-objective optimization based sensor selection method for multi-target tracking in sensor networks. The multi-target states are modelled as multi-Bernoulli random finite sets and the multi-Bernoulli filter is used to propagate the multi-target posterior density. The proposed method is designed to select the sensor that provides the most reliable cardinality estimate, since more accurate cardinality estimate indicates more accurate target states. In the multi-Bernoulli filter, the updated multi-target density is a multi-Bernoulli random finite set formed by a union of legacy tracks and measurement-updated tracks. The legacy track and the measurement-updated track have different theoretical and physical meanings, and hence these two kinds of tracks are considered separately in the sensor management problem. Specifically, two objectives are considered: (1) maximizing the mean cardinality of the measurement-updated tracks, (2) minimizing the cardinality variance of the legacy tracks. Considering the conflicting objectives simultaneously is a multi-objective optimization problem. Tradeoff solutions between two conflicting objectives will be derived. Theoretical analysis and examples show that the proposed approach is effective and direct. The performance of the proposed method is demonstrated using two scenarios with different levels of observability of targets in the passive sensor network. Full article
(This article belongs to the Special Issue Multiple Object Tracking: Making Sense of the Sensors)
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Open AccessArticle Adaptive Estimation and Cooperative Guidance for Active Aircraft Defense in Stochastic Scenario
Sensors 2019, 19(4), 979; https://doi.org/10.3390/s19040979
Received: 24 November 2018 / Revised: 1 February 2019 / Accepted: 20 February 2019 / Published: 25 February 2019
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Abstract
The active aircraft defense problem is investigated for the stochastic scenario wherein a defending missile (or a defender) is employed to protect a target aircraft from an attacking missile whose pursuit guidance strategy is unknown. For the purpose of identifying the guidance strategy, [...] Read more.
The active aircraft defense problem is investigated for the stochastic scenario wherein a defending missile (or a defender) is employed to protect a target aircraft from an attacking missile whose pursuit guidance strategy is unknown. For the purpose of identifying the guidance strategy, the static multiple model estimator (sMME) based on the square-root cubature Kalman filter is proposed, and each model represents a potential attacking missile guidance strategy. Furthermore, an estimation enhancement approach is provided by using pseudo-measurement. For each model in the sMME, the model-matched cooperative guidance laws for the target and defender are derived by formulating the active defense problem as a constrained linear quadratic problem, where an accurate defensive interception and the minimum evasion miss distance are both considered. The proposed adaptive cooperative guidance laws are the result of mixing the model-matched optimal cooperative guidance laws in the criterion of maximum a posteriori probability in the framework of the sMME. By adopting the adaptive cooperative guidance laws, the target can facilitate the defender’s interception with the attacking missile with less control effort. Also, simulation results show that the proposed guidance laws increase the probability of successful target protection in the stochastic scenario compared with other defensive guidance laws. Full article
(This article belongs to the Special Issue Aerospace Sensors and Multisensor Systems)
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Open AccessArticle DOA Estimation and Self-Calibration under Unknown Mutual Coupling
Sensors 2019, 19(4), 978; https://doi.org/10.3390/s19040978
Received: 22 January 2019 / Revised: 18 February 2019 / Accepted: 21 February 2019 / Published: 25 February 2019
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Abstract
In practical applications, the assumption of omnidirectional elements is not effective in general, which leads to the direction-dependent mutual coupling (MC). Under this condition, the performance of traditional calibration algorithms suffers. This paper proposes a new self-calibration method based on the time-frequency distributions [...] Read more.
In practical applications, the assumption of omnidirectional elements is not effective in general, which leads to the direction-dependent mutual coupling (MC). Under this condition, the performance of traditional calibration algorithms suffers. This paper proposes a new self-calibration method based on the time-frequency distributions (TFDs) in the presence of direction-dependent MC. Firstly, the time-frequency (TF) transformation is used to calculate the space-time-frequency distributions (STFDs) matrix of received signals. After that, the estimated steering vector and corresponding noise subspace are estimated by the steps of noise removing, single-source TF points extracting and clustering. Then according to the transformation relationship between the MC coefficients, steering vector and MC matrix, we deduce a set of linear equations. Finally, with two-step alternating iteration, the equations are solved by least square method in order to estimate DOA and MC coefficients. Simulations results show that the proposed algorithm can achieve direction-dependent MC self-calibration and outperforms the existing algorithms. Full article
(This article belongs to the Special Issue Sensor Signal and Information Processing II)
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Open AccessArticle Monitoring Microwave Ablation Using Ultrasound Echo Decorrelation Imaging: An ex vivo Study
Sensors 2019, 19(4), 977; https://doi.org/10.3390/s19040977
Received: 21 January 2019 / Revised: 17 February 2019 / Accepted: 21 February 2019 / Published: 25 February 2019
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Abstract
In this study, a microwave-induced ablation zone (thermal lesion) monitoring method based on ultrasound echo decorrelation imaging was proposed. A total of 15 cases of ex vivo porcine liver microwave ablation (MWA) experiments were carried out. Ultrasound radiofrequency (RF) signals at different times [...] Read more.
In this study, a microwave-induced ablation zone (thermal lesion) monitoring method based on ultrasound echo decorrelation imaging was proposed. A total of 15 cases of ex vivo porcine liver microwave ablation (MWA) experiments were carried out. Ultrasound radiofrequency (RF) signals at different times during MWA were acquired using a commercial clinical ultrasound scanner with a 7.5-MHz linear-array transducer. Instantaneous and cumulative echo decorrelation images of two adjacent frames of RF data were calculated. Polynomial approximation images were obtained on the basis of the thresholded cumulative echo decorrelation images. Experimental results showed that the instantaneous echo decorrelation images outperformed conventional B-mode images in monitoring microwave-induced thermal lesions. Using gross pathology measurements as the reference standard, the estimation of thermal lesions using the polynomial approximation images yielded an average accuracy of 88.60%. We concluded that instantaneous ultrasound echo decorrelation imaging is capable of monitoring the change of thermal lesions during MWA, and cumulative ultrasound echo decorrelation imaging and polynomial approximation imaging are feasible for quantitatively depicting thermal lesions. Full article
(This article belongs to the Special Issue Ultrasound Transducers)
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Open AccessArticle Parametric Testing of Metasurface Stirrers for Metasurfaced Reverberation Chambers
Sensors 2019, 19(4), 976; https://doi.org/10.3390/s19040976
Received: 14 January 2019 / Revised: 7 February 2019 / Accepted: 19 February 2019 / Published: 25 February 2019
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Abstract
In this paper, the correlation coefficients and the total scattering cross sections (TSCSs) for different types of metasurfaced stirrers and the traditional metallic stirrer, and the effects on field uniformity when such stirrers are used in reverberation chambers, are analyzed. Three metasurfaced stirrers [...] Read more.
In this paper, the correlation coefficients and the total scattering cross sections (TSCSs) for different types of metasurfaced stirrers and the traditional metallic stirrer, and the effects on field uniformity when such stirrers are used in reverberation chambers, are analyzed. Three metasurfaced stirrers are considered: A stirrer with two unit cells arranged alternatively (#1), a stirrer with two unit cells arranged in a chessboard-like manner (#2), and a stirrer with two unit cells in random arrangement (#3). From the correlation coefficient and TSCS results obtained in simulations, it follows that metasurfaced stirrer #1 is the best option. Field uniformity analysis of the resulting metasurface reverberation chambers (MRC) equipped with the different stirrers also supports this conclusion. Full article
(This article belongs to the Section Physical Sensors)
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Open AccessArticle Remote Control System for Battery-Assisted Devices with 16 nW Standby Consumption
Sensors 2019, 19(4), 975; https://doi.org/10.3390/s19040975
Received: 17 January 2019 / Revised: 14 February 2019 / Accepted: 21 February 2019 / Published: 25 February 2019
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Abstract
One of the biggest impacts of the vision ‘Internet of Things’ is the massive number of connected devices, where billions of nodes will exchange data, information and commands. While wireless systems offer advantages such as increased flexibility, they also introduce one major challenge: [...] Read more.
One of the biggest impacts of the vision ‘Internet of Things’ is the massive number of connected devices, where billions of nodes will exchange data, information and commands. While wireless systems offer advantages such as increased flexibility, they also introduce one major challenge: how to power each individual node. In many cases, there is no way around the use of batteries. To minimize the environmental impact, increasing the battery’s longevity is the most important factor. This paper introduces a wireless battery-assisted node that has a drastically reduced energy consumption in the standby mode. The state (on/off) will be changed by harvesting a radiofrequency signal. A latching switch connects or disconnects the load—for example, a microcontroller—and the battery. The switch is connected to a charge pump which converts an AC (alternating current) signal into a usable DC (direct current) control signal. An antenna is mounted to the charge pump via a matching network. An electromagnetic wave is emitted by a remote control switch that switches the system on and off. The used frequency is 868 MHz and therefore in the UHF RFID (ultra high frequency radio frequency identification) band. The measurement results show that the wireless node consumes less than 16 nW in the standby mode. The remote controlling is possible from a distance of more than 12 m . The presented system can be integrated in further work on a UHF RFID tag. Thus, the existing protocol standard can be used to identify the object to be switched. By custom commands, the switching request can be transmitted from the remote control (UHF RFID reader) to the switching node. Full article
(This article belongs to the Special Issue RFID Sensor Tags: Hardware, Implementation, and Demonstrations)
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Open AccessArticle Adversarial Samples on Android Malware Detection Systems for IoT Systems
Sensors 2019, 19(4), 974; https://doi.org/10.3390/s19040974
Received: 22 December 2018 / Revised: 11 February 2019 / Accepted: 20 February 2019 / Published: 25 February 2019
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Abstract
Many IoT (Internet of Things) systems run Android systems or Android-like systems. With the continuous development of machine learning algorithms, the learning-based Android malware detection system for IoT devices has gradually increased. However, these learning-based detection models are often vulnerable to adversarial samples. [...] Read more.
Many IoT (Internet of Things) systems run Android systems or Android-like systems. With the continuous development of machine learning algorithms, the learning-based Android malware detection system for IoT devices has gradually increased. However, these learning-based detection models are often vulnerable to adversarial samples. An automated testing framework is needed to help these learning-based malware detection systems for IoT devices perform security analysis. The current methods of generating adversarial samples mostly require training parameters of models and most of the methods are aimed at image data. To solve this problem, we propose a testing framework for learning-based Android malware detection systems (TLAMD) for IoT Devices. The key challenge is how to construct a suitable fitness function to generate an effective adversarial sample without affecting the features of the application. By introducing genetic algorithms and some technical improvements, our test framework can generate adversarial samples for the IoT Android application with a success rate of nearly 100% and can perform black-box testing on the system. Full article
(This article belongs to the Special Issue Green, Energy-Efficient and Sustainable Networks)
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Open AccessArticle EffFeu Project: Towards Mission-Guided Application of Drones in Safety and Security Environments
Sensors 2019, 19(4), 973; https://doi.org/10.3390/s19040973
Received: 18 January 2019 / Revised: 19 February 2019 / Accepted: 20 February 2019 / Published: 25 February 2019
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The number of unmanned aerial system (UAS) applications for supporting rescue forces is growing in recent years. Nevertheless, the analysis of sensed information and control of unmanned aerial vehicle (UAV) creates an enormous psychological and emotional load for the involved humans especially in [...] Read more.
The number of unmanned aerial system (UAS) applications for supporting rescue forces is growing in recent years. Nevertheless, the analysis of sensed information and control of unmanned aerial vehicle (UAV) creates an enormous psychological and emotional load for the involved humans especially in critical and hectic situations. The introduced research project EffFeu (Efficient Operation of Unmanned Aerial Vehicle for Industrial Firefighters) especially focuses on a holistic integration of UAS in the daily work of industrial firefighters. This is done by enabling autonomous mission-guided control on top of the presented overall system architecture, goal-oriented high-level task control, comprehensive localisation process combining several approaches to enable the transition from and to GNSS-supported and GNSS-denied environments, as well as a deep-learning based object recognition of relevant entities. This work describes the concepts, current stage, and first evaluation results of the research project. Full article
(This article belongs to the Special Issue Unmanned Aerial Vehicle Networks, Systems and Applications)
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Open AccessArticle Fault Diagnosis of Rotating Machinery under Noisy Environment Conditions Based on a 1-D Convolutional Autoencoder and 1-D Convolutional Neural Network
Sensors 2019, 19(4), 972; https://doi.org/10.3390/s19040972
Received: 3 January 2019 / Revised: 15 February 2019 / Accepted: 19 February 2019 / Published: 25 February 2019
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Abstract
Deep learning methods have been widely used in the field of intelligent fault diagnosis due to their powerful feature learning and classification capabilities. However, it is easy to overfit depth models because of the large number of parameters brought by the multilayer-structure. As [...] Read more.
Deep learning methods have been widely used in the field of intelligent fault diagnosis due to their powerful feature learning and classification capabilities. However, it is easy to overfit depth models because of the large number of parameters brought by the multilayer-structure. As a result, the methods with excellent performance under experimental conditions may severely degrade under noisy environment conditions, which are ubiquitous in practical industrial applications. In this paper, a novel method combining a one-dimensional (1-D) denoising convolutional autoencoder (DCAE) and a 1-D convolutional neural network (CNN) is proposed to address this problem, whereby the former is used for noise reduction of raw vibration signals and the latter for fault diagnosis using the de-noised signals. The DCAE model is trained with noisy input for denoising learning. In the CNN model, a global average pooling layer, instead of fully-connected layers, is applied as a classifier to reduce the number of parameters and the risk of overfitting. In addition, randomly corrupted signals are adopted as training samples to improve the anti-noise diagnosis ability. The proposed method is validated by bearing and gearbox datasets mixed with Gaussian noise. The experimental result shows that the proposed DCAE model is effective in denoising and almost causes no loss of input information, while the using of global average pooling and input-corrupt training improves the anti-noise ability of the CNN model. As a result, the method combined the DCAE model and the CNN model can realize high-accuracy diagnosis even under noisy environment. Full article
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Open AccessArticle Piezoresistive Hydrogel-Based Sensors for the Detection of Ammonia
Sensors 2019, 19(4), 971; https://doi.org/10.3390/s19040971
Received: 28 November 2018 / Revised: 13 February 2019 / Accepted: 20 February 2019 / Published: 25 February 2019
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Abstract
Ammonia is an essential key compound in the chemical industry. However, excessively high ammonia concentrations can be harmful to the environment. Sensors for the detection of ammonia are therefore particularly important for environmental analysis. In this article, a novel hydrogel-based piezoresistive ammonia sensor [...] Read more.
Ammonia is an essential key compound in the chemical industry. However, excessively high ammonia concentrations can be harmful to the environment. Sensors for the detection of ammonia are therefore particularly important for environmental analysis. In this article, a novel hydrogel-based piezoresistive ammonia sensor is presented. In aqueous solution, ammonia reacts as a base. This alkaline pH change can be detected with stimuli-sensitive hydrogels. For such an application, highly sensitive hydrogels in the alkaline range with sufficient mechanical stability for the sensor application has to be developed. These conditions are fulfilled by the presented hydrogel system based on acrylic acid (AAc) and 2-(dimethylamino)ethyl methacrylate (DMAEMA). The hydrogel composition has a significant influence on the swelling behavior of the gel. Furthermore, the hydrogel swelling in ammonia solutions was tested and a detection limit in the range of 1 mmol/L ammonia depending on the buffer solution was determined. Ammonia-sensitive hydrogels can be used multiple times due to the repeatable swelling of the gel over several swelling cycles. To generate a measurable output voltage, the swelling pressure of ammonia-sensitive hydrogels were detected by using piezoresistive pressure sensors. All results of the free hydrogel swelling were verified in the sensor application. This low-cost ammonia sensor with a high sensitivity could be interesting for industrial chemical and biotechnological applications. Full article
(This article belongs to the Special Issue Eurosensors 2018 Selected Papers)
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Open AccessArticle A Trusted Routing Scheme Using Blockchain and Reinforcement Learning for Wireless Sensor Networks
Sensors 2019, 19(4), 970; https://doi.org/10.3390/s19040970
Received: 24 January 2019 / Revised: 21 February 2019 / Accepted: 22 February 2019 / Published: 25 February 2019
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Abstract
A trusted routing scheme is very important to ensure the routing security and efficiency of wireless sensor networks (WSNs). There are a lot of studies on improving the trustworthiness between routing nodes, using cryptographic systems, trust management, or centralized routing decisions, etc. However, [...] Read more.
A trusted routing scheme is very important to ensure the routing security and efficiency of wireless sensor networks (WSNs). There are a lot of studies on improving the trustworthiness between routing nodes, using cryptographic systems, trust management, or centralized routing decisions, etc. However, most of the routing schemes are difficult to achieve in actual situations as it is difficult to dynamically identify the untrusted behaviors of routing nodes. Meanwhile, there is still no effective way to prevent malicious node attacks. In view of these problems, this paper proposes a trusted routing scheme using blockchain and reinforcement learning to improve the routing security and efficiency for WSNs. The feasible routing scheme is given for obtaining routing information of routing nodes on the blockchain, which makes the routing information traceable and impossible to tamper with. The reinforcement learning model is used to help routing nodes dynamically select more trusted and efficient routing links. From the experimental results, we can find that even in the routing environment with 50% malicious nodes, our routing scheme still has a good delay performance compared with other routing algorithms. The performance indicators such as energy consumption and throughput also show that our scheme is feasible and effective. Full article
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Open AccessArticle Adaptive Image Rendering Using a Nonlinear Mapping-Function-Based Retinex Model
Sensors 2019, 19(4), 969; https://doi.org/10.3390/s19040969
Received: 12 February 2019 / Accepted: 21 February 2019 / Published: 25 February 2019
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This paper introduces an adaptive image rendering using a parametric nonlinear mapping-function-based on the retinex model in a low-light source. For this study, only a luminance channel was used to estimate the reflectance component of an observed low-light image, therefore halo artifacts coming [...] Read more.
This paper introduces an adaptive image rendering using a parametric nonlinear mapping-function-based on the retinex model in a low-light source. For this study, only a luminance channel was used to estimate the reflectance component of an observed low-light image, therefore halo artifacts coming from the use of the multiple center/surround Gaussian filters were reduced. A new nonlinear mapping function that incorporates the statistics of the luminance and the estimated reflectance in the reconstruction process is proposed. In addition, a new method to determine the gain and offset of the mapping function is addressed to adaptively control the contrast ratio. Finally, the relationship between the estimated luminance and the reconstructed luminance is used to reconstruct the chrominance channels. The experimental results demonstrate that the proposed method leads to the promised subjective and objective improvements over state-of-the-art, scale-based retinex methods. Full article
(This article belongs to the Special Issue Computational Intelligence-Based Sensors)
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Open AccessArticle IKULDAS: An Improved kNN-Based UHF RFID Indoor Localization Algorithm for Directional Radiation Scenario
Sensors 2019, 19(4), 968; https://doi.org/10.3390/s19040968
Received: 29 January 2019 / Revised: 16 February 2019 / Accepted: 18 February 2019 / Published: 25 February 2019
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Abstract
Ultra high frequency radio frequency identification (UHF RFID)-based indoor localization technology has been a competitive candidate for context-awareness services. Previous works mainly utilize a simplified Friis transmission equation for simulating/rectifying received signal strength indicator (RSSI) values, in which the directional radiation of tag [...] Read more.
Ultra high frequency radio frequency identification (UHF RFID)-based indoor localization technology has been a competitive candidate for context-awareness services. Previous works mainly utilize a simplified Friis transmission equation for simulating/rectifying received signal strength indicator (RSSI) values, in which the directional radiation of tag antenna and reader antenna was not fully considered, leading to unfavorable performance degradation. Moreover, a k-nearest neighbor (kNN) algorithm is widely used in existing systems, whereas the selection of an appropriate k value remains a critical issue. To solve such problems, this paper presents an improved kNN-based indoor localization algorithm for a directional radiation scenario, IKULDAS. Based on the gain features of dipole antenna and patch antenna, a novel RSSI estimation model is first established. By introducing the inclination angle and rotation angle to characterize the antenna postures, the gains of tag antenna and reader antenna referring to direct path and reflection paths are re-expressed. Then, three strategies are proposed and embedded into typical kNN for improving the localization performance. In IKULDAS, the optimal single fixed rotation angle is introduced for filtering a superior measurement and an NJW-based algorithm is advised for extracting nearest-neighbor reference tags. Furthermore, a dynamic mapping mechanism is proposed to accelerate the tracking process. Simulation results show that IKULDAS achieves a higher positioning accuracy and lower time consumption compared to other typical algorithms. Full article
(This article belongs to the Special Issue Augmented RFID Technologies for the Internet of Things and Beyond)
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Open AccessArticle A Method to Construct an Indoor Air Pollution Monitoring System Based on a Wireless Sensor Network
Sensors 2019, 19(4), 967; https://doi.org/10.3390/s19040967
Received: 14 January 2019 / Revised: 17 February 2019 / Accepted: 17 February 2019 / Published: 25 February 2019
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Abstract
Wireless Sensors Networks (WSNs) are currently receiving much research interest due to their wide-ranging use is a number of different fields. In the current study, a system based on a WSN is proposed that can monitor indoor air pollution in several public spaces, [...] Read more.
Wireless Sensors Networks (WSNs) are currently receiving much research interest due to their wide-ranging use is a number of different fields. In the current study, a system based on a WSN is proposed that can monitor indoor air pollution in several public spaces, such as subway stations, offices, schools, and hospitals. The proposed system uses integrated sensors in mobile phones, moving from a stationary nodes model to a mobile nodes model. The main objective of building this system is to provide full coverage of the target area. To achieve this goal, the system is simulated by MATLAB and the following algorithms are applied: Particle Swarm Optimization (PSO) to maximize the coverage in the region of interest (RoI), Voronoi Diagram (VD) to detect holes in the coverage, and finally the Point in Polygon (PiP) algorithm to heal the holes in the coverage. The application of the algorithms mentioned above has been very effective as PSO has increased the coverage rate of the monitoring area to 100%. The VD allowed us to define the exact location of coverage holes whilew the Point in Polygon algorithm allowed us to heal the holes and find the remaining sensors in order to improve network coverage. This enabled us to achieve full coverage of the monitoring area. Full article
(This article belongs to the Section Intelligent Sensors)
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Open AccessArticle Design and Calibration of a Force/Tactile Sensor for Dexterous Manipulation
Sensors 2019, 19(4), 966; https://doi.org/10.3390/s19040966
Received: 24 January 2019 / Revised: 19 February 2019 / Accepted: 20 February 2019 / Published: 25 February 2019
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This paper presents the design and calibration of a new force/tactile sensor for robotic applications. The sensor is suitably designed to provide the robotic grasping device with a sensory system mimicking the human sense of touch, namely, a device sensitive to contact forces, [...] Read more.
This paper presents the design and calibration of a new force/tactile sensor for robotic applications. The sensor is suitably designed to provide the robotic grasping device with a sensory system mimicking the human sense of touch, namely, a device sensitive to contact forces, object slip and object geometry. This type of perception information is of paramount importance not only in dexterous manipulation but even in simple grasping tasks, especially when objects are fragile, such that only a minimum amount of grasping force can be applied to hold the object without damaging it. Moreover, sensing only forces and not moments can be very limiting to securely grasp an object when it is grasped far from its center of gravity. Therefore, the perception of torsional moments is a key requirement of the designed sensor. Furthermore, the sensor is also the mechanical interface between the gripper and the manipulated object, therefore its design should consider also the requirements for a correct holding of the object. The most relevant of such requirements is the necessity to hold a torsional moment, therefore a soft distributed contact is necessary. The presence of a soft contact poses a number of challenges in the calibration of the sensor, and that is another contribution of this work. Experimental validation is provided in real grasping tasks with two sensors mounted on an industrial gripper. Full article
(This article belongs to the Special Issue Tactile Sensors for Robotic Applications)
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Open AccessArticle Privacy-Preserving Vehicular Rogue Node Detection Scheme for Fog Computing
Sensors 2019, 19(4), 965; https://doi.org/10.3390/s19040965
Received: 29 December 2018 / Revised: 29 January 2019 / Accepted: 15 February 2019 / Published: 25 February 2019
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Abstract
In the last few decades, urban areas across the world have experienced rapid growth in transportation technology with a subsequent increase in transport-related challenges. These challenges have increased our need to employ technology for creating more intelligent solutions. One of the essential tools [...] Read more.
In the last few decades, urban areas across the world have experienced rapid growth in transportation technology with a subsequent increase in transport-related challenges. These challenges have increased our need to employ technology for creating more intelligent solutions. One of the essential tools used to address challenges in traffic is providing vehicles with information about traffic conditions in nearby areas. Vehicle ad-hoc networks (VANETs) allow vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication with the aim of providing safe and efficient transportation. Since drivers might make life-critical decisions based on information provided by other vehicles, dealing with rogue vehicles that send invalid data or breach users’ privacy is an essential security issue in VANETs. This paper proposes a novel privacy-preserving vehicular rogue node detection scheme using fog computing. The proposed scheme improves vehicle privacy, communication between vehicles, and computation efficiency by avoiding the exchange of traffic data between vehicles, allowing communication only through roadside units (RSUs). This scheme also proposes an RSU authentication mechanism, along with a mechanism that would allow RSUs to detect and eliminate vehicles providing false traffic data, which will improve the accuracy and efficiency of VANETs. The proposed scheme is analyzed and evaluated using simulation, which presents significant improvements for data processing, accurately detecting rogue vehicles, minimizing overhead, and immunizing the system against colluding vehicles. Full article
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Open AccessArticle Discrimination of Different Species of Dendrobium with an Electronic Nose Using Aggregated Conformal Predictor
Sensors 2019, 19(4), 964; https://doi.org/10.3390/s19040964
Received: 21 January 2019 / Revised: 14 February 2019 / Accepted: 19 February 2019 / Published: 25 February 2019
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A method using electronic nose to discriminate 10 different species of dendrobium, which is a kind of precious herb with medicinal application, was developed with high efficiency and low cost. A framework named aggregated conformal prediction was applied to make predictions with accuracy [...] Read more.
A method using electronic nose to discriminate 10 different species of dendrobium, which is a kind of precious herb with medicinal application, was developed with high efficiency and low cost. A framework named aggregated conformal prediction was applied to make predictions with accuracy and reliability for E-nose detection. This method achieved a classification accuracy close to 80% with an average improvement of 6.2% when compared with the results obtained by using traditional inductive conformal prediction. It also provided reliability assessment to show more comprehensive information for each prediction. Meanwhile, two main indicators of conformal predictor, validity and efficiency, were also compared and discussed in this work. The result shows that the approach integrating electronic nose with aggregated conformal prediction to classify the species of dendrobium with reliability and validity is promising. Full article
(This article belongs to the Special Issue Electronic Noses)
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Open AccessArticle Classification of Lifting Techniques for Application of A Robotic Hip Exoskeleton
Sensors 2019, 19(4), 963; https://doi.org/10.3390/s19040963
Received: 31 January 2019 / Revised: 18 February 2019 / Accepted: 21 February 2019 / Published: 25 February 2019
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Abstract
The number of exoskeletons providing load-lifting assistance has significantly increased over the last decade. In this field, to take full advantage of active exoskeletons and provide appropriate assistance to users, it is essential to develop control systems that are able to reliably recognize [...] Read more.
The number of exoskeletons providing load-lifting assistance has significantly increased over the last decade. In this field, to take full advantage of active exoskeletons and provide appropriate assistance to users, it is essential to develop control systems that are able to reliably recognize and classify the users’ movement when performing various lifting tasks. To this end, the movement-decoding algorithm should work robustly with different users and recognize different lifting techniques. Currently, there are no studies presenting methods to classify different lifting techniques in real time for applications with lumbar exoskeletons. We designed a real-time two-step algorithm for a portable hip exoskeleton that can detect the onset of the lifting movement and classify the technique used to accomplish the lift, using only the exoskeleton-embedded sensors. To evaluate the performance of the proposed algorithm, 15 healthy male subjects participated in two experimental sessions in which they were asked to perform lifting tasks using four different techniques (namely, squat lifting, stoop lifting, left-asymmetric lifting, and right-asymmetric lifting) while wearing an active hip exoskeleton. Five classes (the four lifting techniques plus the class “no lift”) were defined for the classification model, which is based on a set of rules (first step) and a pattern recognition algorithm (second step). Leave-one-subject-out cross-validation showed a recognition accuracy of 99.34 ± 0.85%, and the onset of the lift movement was detected within the first 121 to 166 ms of movement. Full article
(This article belongs to the Special Issue Sensors and Wearable Assistive Devices)
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Open AccessArticle A New Self-Powered Sensor Using the Radial Field Piezoelectric Diaphragm in d33 Mode for Detecting Underwater Disturbances
Sensors 2019, 19(4), 962; https://doi.org/10.3390/s19040962
Received: 17 January 2019 / Revised: 19 February 2019 / Accepted: 21 February 2019 / Published: 24 February 2019
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Abstract
This paper presents a new sensor based on a radial field bulk piezoelectric diaphragm to provide energy-efficient and high-performance situational sensing for autonomous underwater vehicles (AUVs). This sensor is self-powered, does not need an external power supply, and works efficiently in d33 [...] Read more.
This paper presents a new sensor based on a radial field bulk piezoelectric diaphragm to provide energy-efficient and high-performance situational sensing for autonomous underwater vehicles (AUVs). This sensor is self-powered, does not need an external power supply, and works efficiently in d33 mode by using inter-circulating electrodes to release the radial in-plane poling. Finite element analysis was conducted to estimate the sensor behavior. Sensor prototypes were fabricated by microfabrication technology. The dynamic behaviors of the piezoelectric diaphragm were examined by the impedance spectrum. By imitating the underwater disturbance and generating the oscillatory flow velocities with a vibrating sphere, the performance of the sensor in detecting the oscillatory flow was tested. Experimental results show that the sensitivity of the sensor is up to 1.16 mV/(mm/s), and the detectable oscillatory flow velocity is as low as 4 mm/s. Further, this sensor can work well under a disturbance with low frequency. The present work provides a good application prospect for the underwater sensing of AUVs. Full article
(This article belongs to the Section Physical Sensors)
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Open AccessArticle An Insulated Flexible Sensor for Stable Electromyography Detection: Application to Prosthesis Control
Sensors 2019, 19(4), 961; https://doi.org/10.3390/s19040961
Received: 11 January 2019 / Revised: 5 February 2019 / Accepted: 16 February 2019 / Published: 24 February 2019
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Abstract
Electromyography (EMG), the measurement of electrical muscle activity, is used in a variety of applications, including myoelectric upper-limb prostheses, which help amputees to regain independence and a higher quality of life. The state-of-the-art sensors in prostheses have a conductive connection to the skin [...] Read more.
Electromyography (EMG), the measurement of electrical muscle activity, is used in a variety of applications, including myoelectric upper-limb prostheses, which help amputees to regain independence and a higher quality of life. The state-of-the-art sensors in prostheses have a conductive connection to the skin and are therefore sensitive to sweat and require preparation of the skin. They are applied with some pressure to ensure a conductive connection, which may result in pressure marks and can be problematic for patients with circulatory disorders, who constitute a major group of amputees. Due to their insulating layer between skin and sensor area, capacitive sensors are insensitive to the skin condition, they require neither conductive connection to the skin nor electrolytic paste or skin preparation. Here, we describe a highly stable, low-power capacitive EMG measurement set-up that is suitable for real-world application. Various flexible multi-layer sensor set-ups made of copper and insulating foils, flex print and textiles were compared. These flexible sensor set-ups adapt to the anatomy of the human forearm, therefore they provide high wearing comfort and ensure stability against motion artifacts. The influence of the materials used in the sensor set-up on the magnitude of the coupled signal was demonstrated based on both theoretical analysis and measurement.The amplifier circuit was optimized for high signal quality, low power consumption and mobile application. Different shielding and guarding concepts were compared, leading to high SNR. Full article
(This article belongs to the Special Issue EMG Sensors and Applications)
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Open AccessArticle Double Q-Learning for Radiation Source Detection
Sensors 2019, 19(4), 960; https://doi.org/10.3390/s19040960
Received: 17 January 2019 / Revised: 19 February 2019 / Accepted: 22 February 2019 / Published: 24 February 2019
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Abstract
Anomalous radiation source detection in urban environments is challenging due to the complex nature of background radiation. When a suspicious area is determined, a radiation survey is usually carried out to search for anomalous radiation sources. To locate the source with high accuracy [...] Read more.
Anomalous radiation source detection in urban environments is challenging due to the complex nature of background radiation. When a suspicious area is determined, a radiation survey is usually carried out to search for anomalous radiation sources. To locate the source with high accuracy and in a short time, different survey approaches have been studied such as scanning the area with fixed survey paths and data-driven approaches that update the survey path on the fly with newly acquired measurements. In this work, we propose reinforcement learning as a data-driven approach to conduct radiation detection tasks with no human intervention. A simulated radiation environment is constructed, and a convolutional neural network-based double Q-learning algorithm is built and tested for radiation source detection tasks. Simulation results show that the double Q-learning algorithm can reliably navigate the detector and reduce the searching time by at least 44% compared with traditional uniform search methods and gradient search methods. Full article
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Open AccessArticle Ultra-Low-Power Digital Filtering for Insulated EMG Sensing
Sensors 2019, 19(4), 959; https://doi.org/10.3390/s19040959
Received: 11 January 2019 / Revised: 14 February 2019 / Accepted: 23 February 2019 / Published: 24 February 2019
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Abstract
Myoelectric prostheses help amputees to regain independence and a higher quality of life. These prostheses are controlled by state-of-the-art electromyography sensors, which use a conductive connection to the skin and are therefore sensitive to sweat. They are applied with some pressure to ensure [...] Read more.
Myoelectric prostheses help amputees to regain independence and a higher quality of life. These prostheses are controlled by state-of-the-art electromyography sensors, which use a conductive connection to the skin and are therefore sensitive to sweat. They are applied with some pressure to ensure a conductive connection, which may result in pressure marks and can be problematic for patients with circulatory disorders, who constitute a major group of amputees. Here, we present ultra-low-power digital signal processing algorithms for an insulated EMG sensor which couples the EMG signal capacitively. These sensors require neither conductive connection to the skin nor electrolytic paste or skin preparation. Capacitive sensors allow straightforward application. However, they make a sophisticated signal amplification and noise suppression necessary. A low-cost sensor has been developed for real-time myoelectric prostheses control. The major hurdles in measuring the EMG are movement artifacts and external noise. We designed various digital filters to attenuate this noise. Optimal system setup and filter parameters for the trade-off between attenuation of this noise and sufficient EMG signal power for high signal quality were investigated. Additionally, an algorithm for movement artifact suppression, enabling robust application in real-world environments, is presented. The algorithms, which require minimal calculation resources and memory, are implemented on an ultra-low-power microcontroller. Full article
(This article belongs to the Special Issue EMG Sensors and Applications)
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Open AccessArticle IMLADS: Intelligent Maintenance and Lightweight Anomaly Detection System for Internet of Things
Sensors 2019, 19(4), 958; https://doi.org/10.3390/s19040958
Received: 19 December 2018 / Revised: 9 February 2019 / Accepted: 20 February 2019 / Published: 24 February 2019
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Abstract
System security monitoring has become more and more difficult with the ever-growing complexity and dynamicity of the Internet of Things (IoT). In this paper, we develop an Intelligent Maintenance and Lightweight Anomaly Detection System (IMLADS) for efficient security management of the IoT. Firstly, [...] Read more.
System security monitoring has become more and more difficult with the ever-growing complexity and dynamicity of the Internet of Things (IoT). In this paper, we develop an Intelligent Maintenance and Lightweight Anomaly Detection System (IMLADS) for efficient security management of the IoT. Firstly, unlike the traditional system use static agents, we employ the mobile agent to perform data collection and analysis, which can automatically transfer to other nodes according to the pre-set monitoring task. The mobility is handled by the mobile agent running platform, which is irrelevant with the node or its operation system. Combined with this technology, we can greatly reduce the number of agents running in the system while increasing the system stability and scalability. Secondly, we design different methods for node level and system level security monitoring. For the node level security monitoring, we develop a lightweight data collection and analysis method which only occupy little local computing resources. For the system level security monitoring, we proposed a parameter calculation method based on sketch, whose computational complexity is constant and irrelevant with the system scale. Finally, we design agents to perform suitable response policies for system maintenance and abnormal behavior control based on the anomaly mining results. The experimental results based on the platform constructed show that the proposed method has lower computational complexity and higher detection accuracy. For the node level monitoring, the time complexity is reduced by 50% with high detection accuracy. For the system level monitoring, the time complexity is about 1 s for parameter calculation in a middle scale IoT network. Full article
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Open AccessArticle A Wavelet Transform-Based Neural Network Denoising Algorithm for Mobile Phonocardiography
Sensors 2019, 19(4), 957; https://doi.org/10.3390/s19040957
Received: 31 December 2018 / Revised: 13 February 2019 / Accepted: 20 February 2019 / Published: 24 February 2019
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Abstract
Cardiovascular pathologies cause 23.5% of human deaths, worldwide. An auto-diagnostic system monitoring heart activity, which can identify the early symptoms of cardiac illnesses, might reduce the death rate caused by these problems. Phonocardiography (PCG) is one of the possible techniques able to detect [...] Read more.
Cardiovascular pathologies cause 23.5% of human deaths, worldwide. An auto-diagnostic system monitoring heart activity, which can identify the early symptoms of cardiac illnesses, might reduce the death rate caused by these problems. Phonocardiography (PCG) is one of the possible techniques able to detect heart problems. Nevertheless, acoustic signal enhancement is required since it is exposed to various disturbances coming from different sources. The most common denoising enhancement is based on the Wavelet Transform (WT). However, the WT is highly susceptible to variations in the noise frequency distribution. This paper proposes a new adaptive denoising algorithm, which combines WT and Time Delay Neural Networks (TDNN). The acquired signal is decomposed by means of the WT using the coif five-wavelet basis at the tenth decomposition level and then provided as input to the TDNN. Besides the advantage of adaptive thresholding, the reason for using TDNNs is their capacity of estimating the Inverse Wavelet Transform (IWT). The best parameters of the TDNN were found for a NN consisting of 25 neurons in the first and 15 in the second layer and the delay block of 12 samples. The method was evaluated on several pathological heart sounds and on signals recorded in a noisy environment. The performance of the developed system with respect to other wavelet-based denoising approaches was validated by the online questionnaire. Full article
(This article belongs to the Special Issue Intelligent Signal Processing, Data Science and the IoT World)
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Open AccessArticle Indoor Visible Light Positioning: Overcoming the Practical Limitations of the Quadrant Angular Diversity Aperture Receiver (QADA) by Using the Two-Stage QADA-Plus Receiver
Sensors 2019, 19(4), 956; https://doi.org/10.3390/s19040956
Received: 20 December 2018 / Revised: 20 February 2019 / Accepted: 20 February 2019 / Published: 24 February 2019
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Abstract
Visible light positioning (VLP), using LED luminaires as beacons, is a promising solution to the growing demand for accurate indoor positioning. In this paper, we introduce a two-stage receiver that has been specifically designed for VLP. This receiver exploits the advantages of two [...] Read more.
Visible light positioning (VLP), using LED luminaires as beacons, is a promising solution to the growing demand for accurate indoor positioning. In this paper, we introduce a two-stage receiver that has been specifically designed for VLP. This receiver exploits the advantages of two different VLP receiver types: photodiodes and imaging sensors. In this new receiver design a quadrant angular diversity aperture (QADA) receiver is combined with an off-the-shelf camera to form a robust new receiver called QADA-plus. Results are presented for QADA that show the impact of noise and luminaire geometry on angle of arrival estimation accuracy and positioning accuracy. Detailed discussions highlight other potential sources of error for the QADA receiver and explain how the two-stage QADA-plus can overcome these issues. Full article
(This article belongs to the Special Issue Sensor Fusion and Novel Technologies in Positioning and Navigation)
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Open AccessArticle Ultrasound Radiation Force for the Assessment of Bone Fracture Healing in Children: An In Vivo Pilot Study
Sensors 2019, 19(4), 955; https://doi.org/10.3390/s19040955
Received: 25 January 2019 / Revised: 12 February 2019 / Accepted: 19 February 2019 / Published: 24 February 2019
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Abstract
Vibrational characteristics of bone are directly dependent on its physical properties. In this study, a vibrational method for bone evaluation is introduced. We propose a new type of quantitative vibro-acoustic method based on the acoustic radiation force of ultrasound for bone characterization in [...] Read more.
Vibrational characteristics of bone are directly dependent on its physical properties. In this study, a vibrational method for bone evaluation is introduced. We propose a new type of quantitative vibro-acoustic method based on the acoustic radiation force of ultrasound for bone characterization in persons with fracture. Using this method, we excited the clavicle or ulna by an ultrasound radiation force pulse which induces vibrations in the bone, resulting in an acoustic wave that is measured by a hydrophone placed on the skin. The acoustic signals were used for wave velocity estimation based on a cross-correlation technique. To further separate different vibration characteristics, we adopted a variational mode decomposition technique to decompose the received signal into an ensemble of band-limited intrinsic mode functions, allowing analysis of the acoustic signals by their constitutive components. This prospective study included 15 patients: 12 with clavicle fractures and three with ulna fractures. Contralateral intact bones were used as controls. Statistical analysis demonstrated that fractured bones can be differentiated from intact ones with a detection probability of 80%. Additionally, we introduce a “healing factor” to quantify the bone healing progress which successfully tracked the progress of healing in 80% of the clavicle fractures in the study. Full article
(This article belongs to the Section Biosensors)
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Open AccessArticle Application of a Novel Long-Gauge Fiber Bragg Grating Sensor for Corrosion Detection via a Two-level Strategy
Sensors 2019, 19(4), 954; https://doi.org/10.3390/s19040954
Received: 17 January 2019 / Revised: 16 February 2019 / Accepted: 19 February 2019 / Published: 23 February 2019
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Abstract
Corrosion of main steel reinforcement is one of the most significant causes of structural
deterioration and durability reduction. This research proposes a two-level detection strategy to
locate and quantify corrosion damage via a new kind of long-gauge fiber Bragg grating (FBG) sensor.
Compared [...] Read more.
Corrosion of main steel reinforcement is one of the most significant causes of structural
deterioration and durability reduction. This research proposes a two-level detection strategy to
locate and quantify corrosion damage via a new kind of long-gauge fiber Bragg grating (FBG) sensor.
Compared with the traditional point strain gauges, this new sensor has been developed for both
local and global structural monitoring by measuring the averaged strain within a long gauge length.
Based on the dynamic macrostrain responses of FBG sensors, the strain flexibility of structures are
identified for corrosion locating (Level 1), and then the corrosion is quantified (Level 2) in terms of
reduction of sectional stiffness of reinforcement through the sensitivity analysis of strain flexibility.
The two-level strategy has the merit of reducing the number of unknown structural parameters
through corrosion damage location (Level 1), which guarantees that the corrosion quantification
(Level 2) can be performed efficiently in a reduced domain. Both numerical and experimental
examples have been studied to reveal the ability of distributed long-gauge FBG sensors for corrosion
localization and quantification. Full article
(This article belongs to the Special Issue Fiber Optic Sensors for Structural and Geotechnical Monitoring)
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Open AccessArticle Solving Monocular Visual Odometry Scale Factor with Adaptive Step Length Estimates for Pedestrians Using Handheld Devices
Sensors 2019, 19(4), 953; https://doi.org/10.3390/s19040953
Received: 29 December 2018 / Revised: 25 January 2019 / Accepted: 18 February 2019 / Published: 23 February 2019
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Abstract
The urban environments represent challenging areas for handheld device pose estimation (i.e., 3D position and 3D orientation) in large displacements. It is even more challenging with low-cost sensors and computational resources that are available in pedestrian mobile devices (i.e., monocular camera and Inertial [...] Read more.
The urban environments represent challenging areas for handheld device pose estimation (i.e., 3D position and 3D orientation) in large displacements. It is even more challenging with low-cost sensors and computational resources that are available in pedestrian mobile devices (i.e., monocular camera and Inertial Measurement Unit). To address these challenges, we propose a continuous pose estimation based on monocular Visual Odometry. To solve the scale ambiguity and suppress the scale drift, an adaptive pedestrian step lengths estimation is used for the displacements on the horizontal plane. To complete the estimation, a handheld equipment height model, with respect to the Digital Terrain Model contained in Geographical Information Systems, is used for the displacement on the vertical axis. In addition, an accurate pose estimation based on the recognition of known objects is punctually used to correct the pose estimate and reset the monocular Visual Odometry. To validate the benefit of our framework, experimental data have been collected on a 0.7 km pedestrian path in an urban environment for various people. Thus, the proposed solution allows to achieve a positioning error of 1.6–7.5% of the walked distance, and confirms the benefit of the use of an adaptive step length compared to the use of a fixed-step length. Full article
(This article belongs to the Special Issue Sensor Fusion and Novel Technologies in Positioning and Navigation)
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Open AccessArticle Early Visual Detection of Wheat Stripe Rust Using Visible/Near-Infrared Hyperspectral Imaging
Sensors 2019, 19(4), 952; https://doi.org/10.3390/s19040952
Received: 22 January 2019 / Revised: 12 February 2019 / Accepted: 20 February 2019 / Published: 23 February 2019
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Abstract
Wheat stripe rust is one of the most important and devastating diseases in wheat production. In order to detect wheat stripe rust at an early stage, a visual detection method based on hyperspectral imaging is proposed in this paper. Hyperspectral images of wheat [...] Read more.
Wheat stripe rust is one of the most important and devastating diseases in wheat production. In order to detect wheat stripe rust at an early stage, a visual detection method based on hyperspectral imaging is proposed in this paper. Hyperspectral images of wheat leaves infected by stripe rust for 15 consecutive days were collected, and their corresponding chlorophyll content (SPAD value) were measured using a handheld SPAD-502 chlorophyll meter. The spectral reflectance of the samples were then extracted from the hyperspectral images, using image segmentation based on a leaf mask. The effective wavebands were selected by the loadings of principal component analysis (PCA-loadings) and the successive projections algorithm (SPA). Next, the regression model of the SPAD values in wheat leaves was established, based on the back propagation neural network (BPNN), using the full spectra and the selected effective wavelengths as inputs, respectively. The results showed that the PCA-loadings–BPNN model had the best performance, which modeling accuracy (RC2) and validation accuracy (RP2) were 0.921 and 0.918, respectively, and the RPD was 3.363. The number of effective wavelengths extracted by this model accounted for only 3.12% of the total number of wavelengths, thus simplifying the models and improving the rate of operation greatly. Finally, the optimal models were used to estimate the SPAD of each pixel within the wheat leaf images, to generate spatial distribution maps of chlorophyll content. The visualized distribution map showed that wheat leaves infected by stripe rust could be identified six days after inoculation, and at least three days before the appearance of visible symptoms, which provides a new method for the early detection of wheat stripe rust. Full article
(This article belongs to the Section Remote Sensors)
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