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Sensors, Volume 17, Issue 10 (October 2017)

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Cover Story Mirica and co-workers report a simple method for drawing chemiresistive sensors on paper using [...] Read more.
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Open AccessArticle ROSA: Resource-Oriented Service Management Schemes for Web of Things in a Smart Home
Sensors 2017, 17(10), 2159; doi:10.3390/s17102159
Received: 18 July 2017 / Revised: 4 September 2017 / Accepted: 18 September 2017 / Published: 21 September 2017
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Abstract
A Pervasive-computing-enriched smart home environment, which contains many embedded and tiny intelligent devices and sensors coordinated by service management mechanisms, is capable of anticipating intentions of occupants and providing appropriate services accordingly. Although there are a wealth of research achievements in recent years,
[...] Read more.
A Pervasive-computing-enriched smart home environment, which contains many embedded and tiny intelligent devices and sensors coordinated by service management mechanisms, is capable of anticipating intentions of occupants and providing appropriate services accordingly. Although there are a wealth of research achievements in recent years, the degree of market acceptance is still low. The main reason is that most of the devices and services in such environments depend on particular platform or technology, making it hard to develop an application by composing the devices or services. Meanwhile, the concept of Web of Things (WoT) is becoming popular recently. Based on WoT, the developers can build applications based on popular web tools or technologies. Consequently, the objective of this paper is to propose a set of novel WoT-driven plug-and-play service management schemes for a smart home called Resource-Oriented Service Administration (ROSA). We have implemented an application prototype, and experiments are performed to show the effectiveness of the proposed approach. The results of this research can be a foundation for realizing the vision of “end user programmable smart environments”. Full article
(This article belongs to the Special Issue Advances in Sensors for Sustainable Smart Cities and Smart Buildings)
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Open AccessArticle Spatial Copula Model for Imputing Traffic Flow Data from Remote Microwave Sensors
Sensors 2017, 17(10), 2160; doi:10.3390/s17102160
Received: 24 July 2017 / Revised: 11 September 2017 / Accepted: 16 September 2017 / Published: 21 September 2017
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Abstract
Issues of missing data have become increasingly serious with the rapid increase in usage of traffic sensors. Analyses of the Beijing ring expressway have showed that up to 50% of microwave sensors pose missing values. The imputation of missing traffic data must be
[...] Read more.
Issues of missing data have become increasingly serious with the rapid increase in usage of traffic sensors. Analyses of the Beijing ring expressway have showed that up to 50% of microwave sensors pose missing values. The imputation of missing traffic data must be urgently solved although a precise solution that cannot be easily achieved due to the significant number of missing portions. In this study, copula-based models are proposed for the spatial interpolation of traffic flow from remote traffic microwave sensors. Most existing interpolation methods only rely on covariance functions to depict spatial correlation and are unsuitable for coping with anomalies due to Gaussian consumption. Copula theory overcomes this issue and provides a connection between the correlation function and the marginal distribution function of traffic flow. To validate copula-based models, a comparison with three kriging methods is conducted. Results indicate that copula-based models outperform kriging methods, especially on roads with irregular traffic patterns. Copula-based models demonstrate significant potential to impute missing data in large-scale transportation networks. Full article
(This article belongs to the Special Issue Sensor Networks for Smart Roads)
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Open AccessArticle An Architecture for On-Line Measurement of the Tip Clearance and Time of Arrival of a Bladed Disk of an Aircraft Engine
Sensors 2017, 17(10), 2162; doi:10.3390/s17102162
Received: 21 July 2017 / Revised: 12 September 2017 / Accepted: 19 September 2017 / Published: 21 September 2017
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Abstract
Safety and performance of the turbo-engine in an aircraft is directly affected by the health of its blades. In recent years, several improvements to the sensors have taken place to monitor the blades in a non-intrusive way. The parameters that are usually measured
[...] Read more.
Safety and performance of the turbo-engine in an aircraft is directly affected by the health of its blades. In recent years, several improvements to the sensors have taken place to monitor the blades in a non-intrusive way. The parameters that are usually measured are the distance between the blade tip and the casing, and the passing time at a given point. Simultaneously, several techniques have been developed that allow for the inference—from those parameters and under certain conditions—of the amplitude and frequency of the blade vibration. These measurements are carried out on engines set on a rig, before being installed in an airplane. In order to incorporate these methods during the regular operation of the engine, signal processing that allows for the monitoring of those parameters at all times should be developed. This article introduces an architecture, based on a trifurcated optic sensor and a hardware processor, that fulfills this need. The proposed architecture is scalable and allows several sensors to be simultaneously monitored at different points around a bladed disk. Furthermore, the results obtained by the electronic system will be compared with the results obtained by the validation of the optic sensor. Full article
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Open AccessArticle Microwave Deposition of Palladium Catalysts on Graphite Spheres and Reduced Graphene Oxide Sheets for Electrochemical Glucose Sensing
Sensors 2017, 17(10), 2163; doi:10.3390/s17102163
Received: 1 July 2017 / Revised: 13 September 2017 / Accepted: 14 September 2017 / Published: 21 September 2017
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Abstract
This work outlines a synthetic strategy inducing the microwave-assisted synthesis of palladium (Pd) nanocrystals on a graphite sphere (GS) and reduced graphene oxide (rGO) supports, forming the Pd catalysts for non-enzymatic glucose oxidation reaction (GOR). The pulse microwave approach takes a short period
[...] Read more.
This work outlines a synthetic strategy inducing the microwave-assisted synthesis of palladium (Pd) nanocrystals on a graphite sphere (GS) and reduced graphene oxide (rGO) supports, forming the Pd catalysts for non-enzymatic glucose oxidation reaction (GOR). The pulse microwave approach takes a short period (i.e., 10 min) to fast synthesize Pd nanocrystals onto a carbon support at 150 °C. The selection of carbon support plays a crucial role in affecting Pd particle size and dispersion uniformity. The robust design of Pd-rGO catalyst electrode displays an enhanced electrocatalytic activity and sensitivity toward GOR. The enhanced performance is mainly attributed to the synergetic effect that combines small crystalline size and two-dimensional conductive support, imparting high accessibility to non-enzymatic GOR. The rGO sheets serve as a conductive scaffold, capable of fast conducting electron. The linear plot of current response versus glucose concentration exhibits good correlations within the range of 1–12 mM. The sensitivity of the Pd-rGO catalyst is significantly enhanced by 3.7 times, as compared to the Pd-GS catalyst. Accordingly, the Pd-rGO catalyst electrode can be considered as a potential candidate for non-enzymatic glucose biosensor. Full article
(This article belongs to the Special Issue Advanced Sensors Based on Carbon Electrodes)
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Open AccessArticle Pose Estimation of a Mobile Robot Based on Fusion of IMU Data and Vision Data Using an Extended Kalman Filter
Sensors 2017, 17(10), 2164; doi:10.3390/s17102164
Received: 7 August 2017 / Revised: 1 September 2017 / Accepted: 5 September 2017 / Published: 21 September 2017
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Abstract
Using a single sensor to determine the pose estimation of a device cannot give accurate results. This paper presents a fusion of an inertial sensor of six degrees of freedom (6-DoF) which comprises the 3-axis of an accelerometer and the 3-axis of a
[...] Read more.
Using a single sensor to determine the pose estimation of a device cannot give accurate results. This paper presents a fusion of an inertial sensor of six degrees of freedom (6-DoF) which comprises the 3-axis of an accelerometer and the 3-axis of a gyroscope, and a vision to determine a low-cost and accurate position for an autonomous mobile robot. For vision, a monocular vision-based object detection algorithm speeded-up robust feature (SURF) and random sample consensus (RANSAC) algorithms were integrated and used to recognize a sample object in several images taken. As against the conventional method that depend on point-tracking, RANSAC uses an iterative method to estimate the parameters of a mathematical model from a set of captured data which contains outliers. With SURF and RANSAC, improved accuracy is certain; this is because of their ability to find interest points (features) under different viewing conditions using a Hessain matrix. This approach is proposed because of its simple implementation, low cost, and improved accuracy. With an extended Kalman filter (EKF), data from inertial sensors and a camera were fused to estimate the position and orientation of the mobile robot. All these sensors were mounted on the mobile robot to obtain an accurate localization. An indoor experiment was carried out to validate and evaluate the performance. Experimental results show that the proposed method is fast in computation, reliable and robust, and can be considered for practical applications. The performance of the experiments was verified by the ground truth data and root mean square errors (RMSEs). Full article
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Open AccessArticle ZY3-02 Laser Altimeter Footprint Geolocation Prediction
Sensors 2017, 17(10), 2165; doi:10.3390/s17102165
Received: 17 July 2017 / Revised: 19 September 2017 / Accepted: 19 September 2017 / Published: 21 September 2017
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Abstract
Successfully launched on 30 May 2016, ZY3-02 is the first Chinese surveying and mapping satellite equipped with a lightweight laser altimeter. Calibration is necessary before the laser altimeter becomes operational. Laser footprint location prediction is the first step in calibration that is based
[...] Read more.
Successfully launched on 30 May 2016, ZY3-02 is the first Chinese surveying and mapping satellite equipped with a lightweight laser altimeter. Calibration is necessary before the laser altimeter becomes operational. Laser footprint location prediction is the first step in calibration that is based on ground infrared detectors, and it is difficult because the sample frequency of the ZY3-02 laser altimeter is 2 Hz, and the distance between two adjacent laser footprints is about 3.5 km. In this paper, we build an on-orbit rigorous geometric prediction model referenced to the rigorous geometric model of optical remote sensing satellites. The model includes three kinds of data that must be predicted: pointing angle, orbit parameters, and attitude angles. The proposed method is verified by a ZY3-02 laser altimeter on-orbit geometric calibration test. Five laser footprint prediction experiments are conducted based on the model, and the laser footprint prediction accuracy is better than 150 m on the ground. The effectiveness and accuracy of the on-orbit rigorous geometric prediction model are confirmed by the test results. The geolocation is predicted precisely by the proposed method, and this will give a reference to the geolocation prediction of future land laser detectors in other laser altimeter calibration test. Full article
(This article belongs to the Section Physical Sensors)
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Open AccessArticle Design and Analysis of an Efficient Energy Algorithm in Wireless Social Sensor Networks
Sensors 2017, 17(10), 2166; doi:10.3390/s17102166
Received: 18 July 2017 / Revised: 31 August 2017 / Accepted: 2 September 2017 / Published: 21 September 2017
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Abstract
Because mobile ad hoc networks have characteristics such as lack of center nodes, multi-hop routing and changeable topology, the existing checkpoint technologies for normal mobile networks cannot be applied well to mobile ad hoc networks. Considering the multi-frequency hierarchy structure of ad hoc
[...] Read more.
Because mobile ad hoc networks have characteristics such as lack of center nodes, multi-hop routing and changeable topology, the existing checkpoint technologies for normal mobile networks cannot be applied well to mobile ad hoc networks. Considering the multi-frequency hierarchy structure of ad hoc networks, this paper proposes a hybrid checkpointing strategy which combines the techniques of synchronous checkpointing with asynchronous checkpointing, namely the checkpoints of mobile terminals in the same cluster remain synchronous, and the checkpoints in different clusters remain asynchronous. This strategy could not only avoid cascading rollback among the processes in the same cluster, but also avoid too many message transmissions among the processes in different clusters. What is more, it can reduce the communication delay. In order to assure the consistency of the global states, this paper discusses the correctness criteria of hybrid checkpointing, which includes the criteria of checkpoint taking, rollback recovery and indelibility. Based on the designed Intra-Cluster Checkpoint Dependence Graph and Inter-Cluster Checkpoint Dependence Graph, the elimination rules for different kinds of checkpoints are discussed, and the algorithms for the same cluster checkpoints, different cluster checkpoints, and rollback recovery are also given. Experimental results demonstrate the proposed hybrid checkpointing strategy is a preferable trade-off method, which not only synthetically takes all kinds of resource constraints of Ad hoc networks into account, but also outperforms the existing schemes in terms of the dependence to cluster heads, the recovery time compared to the pure synchronous, and the pure asynchronous checkpoint advantage. Full article
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Open AccessArticle Mobile-Based Analysis of Malaria-Infected Thin Blood Smears: Automated Species and Life Cycle Stage Determination
Sensors 2017, 17(10), 2167; doi:10.3390/s17102167
Received: 17 August 2017 / Revised: 14 September 2017 / Accepted: 18 September 2017 / Published: 21 September 2017
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Abstract
Microscopy examination has been the pillar of malaria diagnosis, being the recommended procedure when its quality can be maintained. However, the need for trained personnel and adequate equipment limits its availability and accessibility in malaria-endemic areas. Rapid, accurate, accessible diagnostic tools are increasingly
[...] Read more.
Microscopy examination has been the pillar of malaria diagnosis, being the recommended procedure when its quality can be maintained. However, the need for trained personnel and adequate equipment limits its availability and accessibility in malaria-endemic areas. Rapid, accurate, accessible diagnostic tools are increasingly required, as malaria control programs extend parasite-based diagnosis and the prevalence decreases. This paper presents an image processing and analysis methodology using supervised classification to assess the presence of malaria parasites and determine the species and life cycle stage in Giemsa-stained thin blood smears. The main differentiation factor is the usage of microscopic images exclusively acquired with low cost and accessible tools such as smartphones, a dataset of 566 images manually annotated by an experienced parasilogist being used. Eight different species-stage combinations were considered in this work, with an automatic detection performance ranging from 73.9% to 96.2% in terms of sensitivity and from 92.6% to 99.3% in terms of specificity. These promising results attest to the potential of using this approach as a valid alternative to conventional microscopy examination, with comparable detection performances and acceptable computational times. Full article
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Open AccessArticle A Multi-Objective Partition Method for Marine Sensor Networks Based on Degree of Event Correlation
Sensors 2017, 17(10), 2168; doi:10.3390/s17102168
Received: 12 July 2017 / Revised: 25 August 2017 / Accepted: 15 September 2017 / Published: 21 September 2017
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Abstract
Existing marine sensor networks acquire data from sea areas that are geographically divided, and store the data independently in their affiliated sea area data centers. In the case of marine events across multiple sea areas, the current network structure needs to retrieve data
[...] Read more.
Existing marine sensor networks acquire data from sea areas that are geographically divided, and store the data independently in their affiliated sea area data centers. In the case of marine events across multiple sea areas, the current network structure needs to retrieve data from multiple data centers, and thus severely affects real-time decision making. In this study, in order to provide a fast data retrieval service for a marine sensor network, we use all the marine sensors as the vertices, establish the edge based on marine events, and abstract the marine sensor network as a graph. Then, we construct a multi-objective balanced partition method to partition the abstract graph into multiple regions and store them in the cloud computing platform. This method effectively increases the correlation of the sensors and decreases the retrieval cost. On this basis, an incremental optimization strategy is designed to dynamically optimize existing partitions when new sensors are added into the network. Experimental results show that the proposed method can achieve the optimal layout for distributed storage in the process of disaster data retrieval in the China Sea area, and effectively optimize the result of partitions when new buoys are deployed, which eventually will provide efficient data access service for marine events. Full article
(This article belongs to the Special Issue Marine Sensing)
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Open AccessArticle Exploiting the Capture Effect to Enhance RACH Performance in Cellular-Based M2M Communications
Sensors 2017, 17(10), 2169; doi:10.3390/s17102169
Received: 2 September 2017 / Revised: 17 September 2017 / Accepted: 19 September 2017 / Published: 21 September 2017
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Abstract
Cellular-based machine-to-machine (M2M) communication is expected to facilitate services for the Internet of Things (IoT). However, because cellular networks are designed for human users, they have some limitations. Random access channel (RACH) congestion caused by massive access from M2M devices is one of
[...] Read more.
Cellular-based machine-to-machine (M2M) communication is expected to facilitate services for the Internet of Things (IoT). However, because cellular networks are designed for human users, they have some limitations. Random access channel (RACH) congestion caused by massive access from M2M devices is one of the biggest factors hindering cellular-based M2M services because the RACH congestion causes random access (RA) throughput degradation and connection failures to the devices. In this paper, we show the possibility exploiting the capture effects, which have been known to have a positive impact on the wireless network system, on RA procedure for improving the RA performance of M2M devices. For this purpose, we analyze an RA procedure using a capture model. Through this analysis, we examine the effects of capture on RA performance and propose an Msg3 power-ramping (Msg3 PR) scheme to increase the capture probability (thereby increasing the RA success probability) even when severe RACH congestion problem occurs. The proposed analysis models are validated using simulations. The results show that the proposed scheme, with proper parameters, further improves the RA throughput and reduces the connection failure probability, by slightly increasing the energy consumption. Finally, we demonstrate the effects of coexistence with other RA-related schemes through simulation results. Full article
(This article belongs to the Special Issue Green Wireless Networks in 5G-inspired Applications)
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Open AccessArticle An Authentication and Key Management Mechanism for Resource Constrained Devices in IEEE 802.11-based IoT Access Networks
Sensors 2017, 17(10), 2170; doi:10.3390/s17102170
Received: 10 August 2017 / Revised: 18 September 2017 / Accepted: 20 September 2017 / Published: 21 September 2017
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Abstract
Many Internet of Things (IoT) services utilize an IoT access network to connect small devices with remote servers. They can share an access network with standard communication technology, such as IEEE 802.11ah. However, an authentication and key management (AKM) mechanism for resource constrained
[...] Read more.
Many Internet of Things (IoT) services utilize an IoT access network to connect small devices with remote servers. They can share an access network with standard communication technology, such as IEEE 802.11ah. However, an authentication and key management (AKM) mechanism for resource constrained IoT devices using IEEE 802.11ah has not been proposed as yet. We therefore propose a new AKM mechanism for an IoT access network, which is based on IEEE 802.11 key management with the IEEE 802.1X authentication mechanism. The proposed AKM mechanism does not require any pre-configured security information between the access network domain and the IoT service domain. It considers the resource constraints of IoT devices, allowing IoT devices to delegate the burden of AKM processes to a powerful agent. The agent has sufficient power to support various authentication methods for the access point, and it performs cryptographic functions for the IoT devices. Performance analysis shows that the proposed mechanism greatly reduces computation costs, network costs, and memory usage of the resource-constrained IoT device as compared to the existing IEEE 802.11 Key Management with the IEEE 802.1X authentication mechanism. Full article
(This article belongs to the Special Issue Next Generation Wireless Technologies for Internet of Things)
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Open AccessArticle Complete Systematic Error Model of SSR for Sensor Registration in ATC Surveillance Networks
Sensors 2017, 17(10), 2171; doi:10.3390/s17102171
Received: 14 July 2017 / Revised: 29 August 2017 / Accepted: 19 September 2017 / Published: 21 September 2017
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Abstract
In this paper, a complete and rigorous mathematical model for secondary surveillance radar systematic errors (biases) is developed. The model takes into account the physical effects systematically affecting the measurement processes. The azimuth biases are calculated from the physical error of the antenna
[...] Read more.
In this paper, a complete and rigorous mathematical model for secondary surveillance radar systematic errors (biases) is developed. The model takes into account the physical effects systematically affecting the measurement processes. The azimuth biases are calculated from the physical error of the antenna calibration and the errors of the angle determination dispositive. Distance bias is calculated from the delay of the signal produced by the refractivity index of the atmosphere, and from clock errors, while the altitude bias is calculated taking into account the atmosphere conditions (pressure and temperature). It will be shown, using simulated and real data, that adapting a classical bias estimation process to use the complete parametrized model results in improved accuracy in the bias estimation. Full article
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Open AccessArticle A Parameter Communication Optimization Strategy for Distributed Machine Learning in Sensors
Sensors 2017, 17(10), 2172; doi:10.3390/s17102172
Received: 19 August 2017 / Revised: 20 September 2017 / Accepted: 20 September 2017 / Published: 21 September 2017
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Abstract
In order to utilize the distributed characteristic of sensors, distributed machine learning has become the mainstream approach, but the different computing capability of sensors and network delays greatly influence the accuracy and the convergence rate of the machine learning model. Our paper describes
[...] Read more.
In order to utilize the distributed characteristic of sensors, distributed machine learning has become the mainstream approach, but the different computing capability of sensors and network delays greatly influence the accuracy and the convergence rate of the machine learning model. Our paper describes a reasonable parameter communication optimization strategy to balance the training overhead and the communication overhead. We extend the fault tolerance of iterative-convergent machine learning algorithms and propose the Dynamic Finite Fault Tolerance (DFFT). Based on the DFFT, we implement a parameter communication optimization strategy for distributed machine learning, named Dynamic Synchronous Parallel Strategy (DSP), which uses the performance monitoring model to dynamically adjust the parameter synchronization strategy between worker nodes and the Parameter Server (PS). This strategy makes full use of the computing power of each sensor, ensures the accuracy of the machine learning model, and avoids the situation that the model training is disturbed by any tasks unrelated to the sensors. Full article
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Open AccessArticle Uncooled Thermal Camera Calibration and Optimization of the Photogrammetry Process for UAV Applications in Agriculture
Sensors 2017, 17(10), 2173; doi:10.3390/s17102173
Received: 2 August 2017 / Revised: 15 September 2017 / Accepted: 19 September 2017 / Published: 23 September 2017
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Abstract
The acquisition, processing, and interpretation of thermal images from unmanned aerial vehicles (UAVs) is becoming a useful source of information for agronomic applications because of the higher temporal and spatial resolution of these products compared with those obtained from satellites. However, due to
[...] Read more.
The acquisition, processing, and interpretation of thermal images from unmanned aerial vehicles (UAVs) is becoming a useful source of information for agronomic applications because of the higher temporal and spatial resolution of these products compared with those obtained from satellites. However, due to the low load capacity of the UAV they need to mount light, uncooled thermal cameras, where the microbolometer is not stabilized to a constant temperature. This makes the camera precision low for many applications. Additionally, the low contrast of the thermal images makes the photogrammetry process inaccurate, which result in large errors in the generation of orthoimages. In this research, we propose the use of new calibration algorithms, based on neural networks, which consider the sensor temperature and the digital response of the microbolometer as input data. In addition, we evaluate the use of the Wallis filter for improving the quality of the photogrammetry process using structure from motion software. With the proposed calibration algorithm, the measurement accuracy increased from 3.55 °C with the original camera configuration to 1.37 °C. The implementation of the Wallis filter increases the number of tie-point from 58,000 to 110,000 and decreases the total positing error from 7.1 m to 1.3 m. Full article
(This article belongs to the Special Issue UAV or Drones for Remote Sensing Applications)
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Open AccessArticle A Compact Multiple Notched Ultra-Wide Band Antenna with an Analysis of the CSRR-TO-CSRR Coupling for Portable UWB Applications
Sensors 2017, 17(10), 2174; doi:10.3390/s17102174
Received: 22 August 2017 / Revised: 17 September 2017 / Accepted: 17 September 2017 / Published: 25 September 2017
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Abstract
We present a compact ultra-wideband (UWB) antenna integrated with sharp notches with a detailed analysis of the mutual coupling of the multiple notch resonators. By utilizing complementary split ring resonators (CSRR) on the radiating semi-circular patch, we achieve the sharp notch-filtering of various
[...] Read more.
We present a compact ultra-wideband (UWB) antenna integrated with sharp notches with a detailed analysis of the mutual coupling of the multiple notch resonators. By utilizing complementary split ring resonators (CSRR) on the radiating semi-circular patch, we achieve the sharp notch-filtering of various bands within the UWB band without increasing the antenna size. The notched frequency bands include WiMAX, INSAT, and lower and upper WLAN. In order to estimate the frequency shifts of the notch due to the coupling of the nearby CSRRs, an analysis of the coupling among the multiple notch resonators is carried out and we construct the lumped-circuit equivalent model. The time domain analysis of the proposed antenna is performed to show its validity on the UWB application. The measured frequency response of the input port corresponds quite well with the calculations and simulations. The radiation pattern of the implemented quad-notched UWB antenna is nearly omnidirectional in the passband. Full article
(This article belongs to the Section Remote Sensors)
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Open AccessArticle Robust Dehaze Algorithm for Degraded Image of CMOS Image Sensors
Sensors 2017, 17(10), 2175; doi:10.3390/s17102175
Received: 29 August 2017 / Accepted: 18 September 2017 / Published: 22 September 2017
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Abstract
The CMOS (Complementary Metal-Oxide-Semiconductor) is a new type of solid image sensor device widely used in object tracking, object recognition, intelligent navigation fields, and so on. However, images captured by outdoor CMOS sensor devices are usually affected by suspended atmospheric particles (such as
[...] Read more.
The CMOS (Complementary Metal-Oxide-Semiconductor) is a new type of solid image sensor device widely used in object tracking, object recognition, intelligent navigation fields, and so on. However, images captured by outdoor CMOS sensor devices are usually affected by suspended atmospheric particles (such as haze), causing a reduction in image contrast, color distortion problems, and so on. In view of this, we propose a novel dehazing approach based on a local consistent Markov random field (MRF) framework. The neighboring clique in traditional MRF is extended to the non-neighboring clique, which is defined on local consistent blocks based on two clues, where both the atmospheric light and transmission map satisfy the character of local consistency. In this framework, our model can strengthen the restriction of the whole image while incorporating more sophisticated statistical priors, resulting in more expressive power of modeling, thus, solving inadequate detail recovery effectively and alleviating color distortion. Moreover, the local consistent MRF framework can obtain details while maintaining better results for dehazing, which effectively improves the image quality captured by the CMOS image sensor. Experimental results verified that the method proposed has the combined advantages of detail recovery and color preservation. Full article
(This article belongs to the Section Physical Sensors)
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Open AccessArticle American Sign Language Alphabet Recognition Using a Neuromorphic Sensor and an Artificial Neural Network
Sensors 2017, 17(10), 2176; doi:10.3390/s17102176
Received: 24 July 2017 / Revised: 2 September 2017 / Accepted: 13 September 2017 / Published: 22 September 2017
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Abstract
This paper reports the design and analysis of an American Sign Language (ASL) alphabet translation system implemented in hardware using a Field-Programmable Gate Array. The system process consists of three stages, the first being the communication with the neuromorphic camera (also called Dynamic
[...] Read more.
This paper reports the design and analysis of an American Sign Language (ASL) alphabet translation system implemented in hardware using a Field-Programmable Gate Array. The system process consists of three stages, the first being the communication with the neuromorphic camera (also called Dynamic Vision Sensor, DVS) sensor using the Universal Serial Bus protocol. The feature extraction of the events generated by the DVS is the second part of the process, consisting of a presentation of the digital image processing algorithms developed in software, which aim to reduce redundant information and prepare the data for the third stage. The last stage of the system process is the classification of the ASL alphabet, achieved with a single artificial neural network implemented in digital hardware for higher speed. The overall result is the development of a classification system using the ASL signs contour, fully implemented in a reconfigurable device. The experimental results consist of a comparative analysis of the recognition rate among the alphabet signs using the neuromorphic camera in order to prove the proper operation of the digital image processing algorithms. In the experiments performed with 720 samples of 24 signs, a recognition accuracy of 79.58% was obtained. Full article
(This article belongs to the Special Issue Video Analysis and Tracking Using State-of-the-Art Sensors)
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Open AccessArticle A Methodology for the Design of Application-Specific Cyber-Physical Social Sensing Co-Simulators
Sensors 2017, 17(10), 2177; doi:10.3390/s17102177
Received: 25 July 2017 / Revised: 13 September 2017 / Accepted: 19 September 2017 / Published: 22 September 2017
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Abstract
Cyber-Physical Social Sensing (CPSS) is a new trend in the context of pervasive sensing. In these new systems, various domains coexist in time, evolve together and influence each other. Thus, application-specific tools are necessary for specifying and validating designs and simulating systems. However,
[...] Read more.
Cyber-Physical Social Sensing (CPSS) is a new trend in the context of pervasive sensing. In these new systems, various domains coexist in time, evolve together and influence each other. Thus, application-specific tools are necessary for specifying and validating designs and simulating systems. However, nowadays, different tools are employed to simulate each domain independently. Mainly, the cause of the lack of co-simulation instruments to simulate all domains together is the extreme difficulty of combining and synchronizing various tools. In order to reduce that difficulty, an adequate architecture for the final co-simulator must be selected. Therefore, in this paper the authors investigate and propose a methodology for the design of CPSS co-simulation tools. The paper describes the four steps that software architects should follow in order to design the most adequate co-simulator for a certain application, considering the final users’ needs and requirements and various additional factors such as the development team’s experience. Moreover, the first practical use case of the proposed methodology is provided. An experimental validation is also included in order to evaluate the performing of the proposed co-simulator and to determine the correctness of the proposal. Full article
(This article belongs to the Section Sensor Networks)
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Open AccessArticle Bioelectrochemical Detection of Mycobacterium tuberculosis ESAT-6 in an Antibody-Based Biomicrosystem
Sensors 2017, 17(10), 2178; doi:10.3390/s17102178
Received: 5 August 2017 / Revised: 2 September 2017 / Accepted: 7 September 2017 / Published: 22 September 2017
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Abstract
Bioelectrochemical sensing of Mycobacterium tuberculosis through electro-immunosensors is a promising technique to detect relevant analytes. In general, immunosensors require the formation of organic assemblies by the adsorption of molecular constituents. Moreover, they depend on the correct immobilization of the bio-recognition element in the
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Bioelectrochemical sensing of Mycobacterium tuberculosis through electro-immunosensors is a promising technique to detect relevant analytes. In general, immunosensors require the formation of organic assemblies by the adsorption of molecular constituents. Moreover, they depend on the correct immobilization of the bio-recognition element in the biosensor. These procedures cannot be easily monitored without the use of invasive methods. In this work, an impedance analysis technique was used, as a non-invasive method, to measure and differentiate the manufacturing stages of the sensors. Biomicrosystems were fabricated through physical vapor deposition (PVD) of 80 nm Au nanolayers on 35 µm copper surfaces. Later, the surface was modified through thiolation methods generating a self-assembled-monolayer (SAM) with 20 mM 4-aminothiophenol (4-ATP) on which a polyclonal antibody (pAb) was covalently attached. Using impedance analysis, every step of the electro-immunosensor fabrication protocol was characterized using 40 independent replicas. Results showed that, compared to the negative controls, distilled water, and 0.5 µg/mL HSA, a maximum variation of 171% between each replica was achieved when compared to samples containing 0.5 µg/mL of ESAT-6 M. tuberculosis immunodominant protein. Therefore, this development validates a non-invasive method to electrically monitor the assembly process of electro-immunosensors and a tool for its further measure for detection of relevant antigens. Full article
(This article belongs to the Special Issue Sensors for Toxic and Pathogen Detection)
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Open AccessArticle A Low Power Consumption Algorithm for Efficient Energy Consumption in ZigBee Motes
Sensors 2017, 17(10), 2179; doi:10.3390/s17102179
Received: 19 August 2017 / Revised: 18 September 2017 / Accepted: 20 September 2017 / Published: 22 September 2017
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Abstract
Wireless Sensor Networks (WSNs) are becoming increasingly popular since they can gather information from different locations without wires. This advantage is exploited in applications such as robotic systems, telecare, domotic or smart cities, among others. To gain independence from the electricity grid, WSNs
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Wireless Sensor Networks (WSNs) are becoming increasingly popular since they can gather information from different locations without wires. This advantage is exploited in applications such as robotic systems, telecare, domotic or smart cities, among others. To gain independence from the electricity grid, WSNs devices are equipped with batteries, therefore their operational time is determined by the time that the batteries can power on the device. As a consequence, engineers must consider low energy consumption as a critical objective to design WSNs. Several approaches can be taken to make efficient use of energy in WSNs, for instance low-duty-cycling sensor networks (LDC-WSN). Based on the LDC-WSNs, we present LOKA, a LOw power Konsumption Algorithm to minimize WSNs energy consumption using different power modes in a sensor mote. The contribution of the work is a novel algorithm called LOKA that implements two duty-cycling mechanisms using the end-device of the ZigBee protocol (of the Application Support Sublayer) and an external microcontroller (Cortex M0+) in order to minimize the energy consumption of a delay tolerant networking. Experiments show that using LOKA, the energy required by the sensor device is reduced to half with respect to the same sensor device without using LOKA. Full article
(This article belongs to the Special Issue Energy Harvesting Sensors for Long Term Applications in the IoT Era)
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Open AccessArticle Logistic Model to Support Service Modularity for the Promotion of Reusability in a Web Objects-Enabled IoT Environment
Sensors 2017, 17(10), 2180; doi:10.3390/s17102180
Received: 10 July 2017 / Revised: 13 September 2017 / Accepted: 18 September 2017 / Published: 22 September 2017
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Abstract
Due to a very large number of connected virtual objects in the surrounding environment, intelligent service features in the Internet of Things requires the reuse of existing virtual objects and composite virtual objects. If a new virtual object is created for each new
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Due to a very large number of connected virtual objects in the surrounding environment, intelligent service features in the Internet of Things requires the reuse of existing virtual objects and composite virtual objects. If a new virtual object is created for each new service request, then the number of virtual object would increase exponentially. The Web of Objects applies the principle of service modularity in terms of virtual objects and composite virtual objects. Service modularity is a key concept in the Web Objects-Enabled Internet of Things (IoT) environment which allows for the reuse of existing virtual objects and composite virtual objects in heterogeneous ontologies. In the case of similar service requests occurring at the same, or different locations, the already-instantiated virtual objects and their composites that exist in the same, or different ontologies can be reused. In this case, similar types of virtual objects and composite virtual objects are searched and matched. Their reuse avoids duplication under similar circumstances, and reduces the time it takes to search and instantiate them from their repositories, where similar functionalities are provided by similar types of virtual objects and their composites. Controlling and maintaining a virtual object means controlling and maintaining a real-world object in the real world. Even though the functional costs of virtual objects are just a fraction of those for deploying and maintaining real-world objects, this article focuses on reusing virtual objects and composite virtual objects, as well as discusses similarity matching of virtual objects and composite virtual objects. This article proposes a logistic model that supports service modularity for the promotion of reusability in the Web Objects-enabled IoT environment. Necessary functional components and a flowchart of an algorithm for reusing composite virtual objects are discussed. Also, to realize the service modularity, a use case scenario is studied and implemented. Full article
(This article belongs to the Section Sensor Networks)
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Open AccessArticle A New Proxy Measurement Algorithm with Application to the Estimation of Vertical Ground Reaction Forces Using Wearable Sensors
Sensors 2017, 17(10), 2181; doi:10.3390/s17102181
Received: 3 July 2017 / Revised: 18 September 2017 / Accepted: 18 September 2017 / Published: 22 September 2017
PDF Full-text (1001 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Measurement of the ground reaction forces (GRF) during walking is typically limited to laboratory settings, and only short observations using wearable pressure insoles have been reported so far. In this study, a new proxy measurement method is proposed to estimate the vertical component
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Measurement of the ground reaction forces (GRF) during walking is typically limited to laboratory settings, and only short observations using wearable pressure insoles have been reported so far. In this study, a new proxy measurement method is proposed to estimate the vertical component of the GRF (vGRF) from wearable accelerometer signals. The accelerations are used as the proxy variable. An orthogonal forward regression algorithm (OFR) is employed to identify the dynamic relationships between the proxy variables and the measured vGRF using pressure-sensing insoles. The obtained model, which represents the connection between the proxy variable and the vGRF, is then used to predict the latter. The results have been validated using pressure insoles data collected from nine healthy individuals under two outdoor walking tasks in non-laboratory settings. The results show that the vGRFs can be reconstructed with high accuracy (with an average prediction error of less than 5.0%) using only one wearable sensor mounted at the waist (L5, fifth lumbar vertebra). Proxy measures with different sensor positions are also discussed. Results show that the waist acceleration-based proxy measurement is more stable with less inter-task and inter-subject variability than the proxy measures based on forehead level accelerations. The proposed proxy measure provides a promising low-cost method for monitoring ground reaction forces in real-life settings and introduces a novel generic approach for replacing the direct determination of difficult to measure variables in many applications. Full article
(This article belongs to the Special Issue State-of-the-Art Sensors Technology in UK)
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Open AccessArticle Tip Pressure on Semicircular Specimens in Tapping Mode Atomic Force Microscopy in Viscous Fluid Environments
Sensors 2017, 17(10), 2182; doi:10.3390/s17102182
Received: 29 August 2017 / Revised: 21 September 2017 / Accepted: 21 September 2017 / Published: 22 September 2017
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Abstract
Tapping mode (TM) atomic force microscopy (AFM) in a liquid environment is widely used to measure the contours of biological specimens. The TM triggers the AFM probe approximately at the resonant frequencies and controls the tip such that it periodically touches the specimen
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Tapping mode (TM) atomic force microscopy (AFM) in a liquid environment is widely used to measure the contours of biological specimens. The TM triggers the AFM probe approximately at the resonant frequencies and controls the tip such that it periodically touches the specimen along the scanning path. The AFM probe and its tip produce a hydrodynamic pressure on the probe itself and press the specimen. The tip to specimen size ratio is known to affect the measurement accuracy of AFM, however, few studies have focused on the hydrodynamic pressure caused by the effects of specimen size. Such pressure affects the contour distortion of the biological specimen. In this study, a semi-analytical method is employed for a semicircular specimen to analyze the vorticity and pressure distributions for specimens of various sizes and at various tip locations. Changes in pressure distribution, fluid spin motion, and specimen deformation are identified as the tip approaches the specimen. The results indicate the following: the specimen surface experiences the highest pressure when the specimen diameter equals the tip width; the vorticity between tip and specimen is complex when the tip is close to the specimen center line; and the specimen inflates when the tip is aligned with the specimen center line. Full article
(This article belongs to the Section Physical Sensors)
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Open AccessArticle SFOL Pulse: A High Accuracy DME Pulse for Alternative Aircraft Position and Navigation
Sensors 2017, 17(10), 2183; doi:10.3390/s17102183
Received: 31 July 2017 / Revised: 17 September 2017 / Accepted: 20 September 2017 / Published: 22 September 2017
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Abstract
In the Federal Aviation Administration’s (FAA) performance based navigation strategy announced in 2016, the FAA stated that it would retain and expand the Distance Measuring Equipment (DME) infrastructure to ensure resilient aircraft navigation capability during the event of a Global Navigation Satellite System
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In the Federal Aviation Administration’s (FAA) performance based navigation strategy announced in 2016, the FAA stated that it would retain and expand the Distance Measuring Equipment (DME) infrastructure to ensure resilient aircraft navigation capability during the event of a Global Navigation Satellite System (GNSS) outage. However, the main drawback of the DME as a GNSS back up system is that it requires a significant expansion of the current DME ground infrastructure due to its poor distance measuring accuracy over 100 m. The paper introduces a method to improve DME distance measuring accuracy by using a new DME pulse shape. The proposed pulse shape was developed by using Genetic Algorithms and is less susceptible to multipath effects so that the ranging error reduces by 36.0–77.3% when compared to the Gaussian and Smoothed Concave Polygon DME pulses, depending on noise environment. Full article
(This article belongs to the Special Issue Sensors for Transportation)
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Open AccessArticle Tracking of Maneuvering Complex Extended Object with Coupled Motion Kinematics and Extension Dynamics Using Range Extent Measurements
Sensors 2017, 17(10), 2184; doi:10.3390/s17102184
Received: 1 August 2017 / Revised: 17 September 2017 / Accepted: 19 September 2017 / Published: 22 September 2017
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Abstract
The key to successful maneuvering complex extended object tracking (MCEOT) using range extent measurements provided by high resolution sensors lies in accurate and effective modeling of both the extension dynamics and the centroid kinematics. During object maneuvers, the extension dynamics of an object
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The key to successful maneuvering complex extended object tracking (MCEOT) using range extent measurements provided by high resolution sensors lies in accurate and effective modeling of both the extension dynamics and the centroid kinematics. During object maneuvers, the extension dynamics of an object with a complex shape is highly coupled with the centroid kinematics. However, this difficult but important problem is rarely considered and solved explicitly. In view of this, this paper proposes a general approach to modeling a maneuvering complex extended object based on Minkowski sum, so that the coupled turn maneuvers in both the centroid states and extensions can be described accurately. The new model has a concise and unified form, in which the complex extension dynamics can be simply and jointly characterized by multiple simple sub-objects’ extension dynamics based on Minkowski sum. The proposed maneuvering model fits range extent measurements very well due to its favorable properties. Based on this model, an MCEOT algorithm dealing with motion and extension maneuvers is also derived. Two different cases of the turn maneuvers with known/unknown turn rates are specifically considered. The proposed algorithm which jointly estimates the kinematic state and the object extension can also be easily implemented. Simulation results demonstrate the effectiveness of the proposed modeling and tracking approaches. Full article
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Open AccessArticle Design and Analysis of a Low Latency Deterministic Network MAC for Wireless Sensor Networks
Sensors 2017, 17(10), 2185; doi:10.3390/s17102185
Received: 2 August 2017 / Revised: 15 September 2017 / Accepted: 16 September 2017 / Published: 22 September 2017
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Abstract
The IEEE 802.15.4e standard has four different superframe structures for different applications. Use of a low latency deterministic network (LLDN) superframe for the wireless sensor network is one of them, which can operate in a star topology. In this paper, a new channel
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The IEEE 802.15.4e standard has four different superframe structures for different applications. Use of a low latency deterministic network (LLDN) superframe for the wireless sensor network is one of them, which can operate in a star topology. In this paper, a new channel access mechanism for IEEE 802.15.4e-based LLDN shared slots is proposed, and analytical models are designed based on this channel access mechanism. A prediction model is designed to estimate the possible number of retransmission slots based on the number of failed transmissions. Performance analysis in terms of data transmission reliability, delay, throughput and energy consumption are provided based on our proposed designs. Our designs are validated for simulation and analytical results, and it is observed that the simulation results well match with the analytical ones. Besides, our designs are compared with the IEEE 802.15.4 MAC mechanism, and it is shown that ours outperforms in terms of throughput, energy consumption, delay and reliability. Full article
(This article belongs to the Section Sensor Networks)
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Open AccessArticle Measurement of Non-Stationary Characteristics of a Landfall Typhoon at the Jiangyin Bridge Site
Sensors 2017, 17(10), 2186; doi:10.3390/s17102186
Received: 21 August 2017 / Revised: 12 September 2017 / Accepted: 20 September 2017 / Published: 22 September 2017
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Abstract
The wind-sensitive long-span suspension bridge is a vital element in land transportation. Understanding the wind characteristics at the bridge site is thus of great significance to the wind- resistant analysis of such a flexible structure. In this study, a strong wind event from
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The wind-sensitive long-span suspension bridge is a vital element in land transportation. Understanding the wind characteristics at the bridge site is thus of great significance to the wind- resistant analysis of such a flexible structure. In this study, a strong wind event from a landfall typhoon called Soudelor recorded at the Jiangyin Bridge site with the anemometer is taken as the research object. As inherent time-varying trends are frequently captured in typhoon events, the wind characteristics of Soudelor are analyzed in a non-stationary perspective. The time-varying mean is first extracted with the wavelet-based self-adaptive method. Then, the non-stationary turbulent wind characteristics, e.g.; turbulence intensity, gust factor, turbulence integral scale, and power spectral density, are investigated and compared with the results from the stationary analysis. The comparison highlights the importance of non-stationary considerations of typhoon events, and a transition from stationarity to non-stationarity for the analysis of wind effects. The analytical results could help enrich the database of non-stationary wind characteristics, and are expected to provide references for the wind-resistant analysis of engineering structures in similar areas. Full article
(This article belongs to the Special Issue Sensors for Transportation)
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Open AccessArticle SAW-Based Phononic Crystal Microfluidic Sensor—Microscale Realization of Velocimetry Approaches for Integrated Analytical Platform Applications
Sensors 2017, 17(10), 2187; doi:10.3390/s17102187
Received: 31 July 2017 / Revised: 1 September 2017 / Accepted: 12 September 2017 / Published: 23 September 2017
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Abstract
The current work demonstrates a novel surface acoustic wave (SAW) based phononic crystal sensor approach that allows the integration of a velocimetry-based sensor concept into single chip integrated solutions, such as Lab-on-a-Chip devices. The introduced sensor platform merges advantages of ultrasonic velocimetry analytic
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The current work demonstrates a novel surface acoustic wave (SAW) based phononic crystal sensor approach that allows the integration of a velocimetry-based sensor concept into single chip integrated solutions, such as Lab-on-a-Chip devices. The introduced sensor platform merges advantages of ultrasonic velocimetry analytic systems and a microacoustic sensor approach. It is based on the analysis of structural resonances in a periodic composite arrangement of microfluidic channels confined within a liquid analyte. Completed theoretical and experimental investigations show the ability to utilize periodic structure localized modes for the detection of volumetric properties of liquids and prove the efficacy of the proposed sensor concept. Full article
(This article belongs to the Special Issue Surface Acoustic Wave and Bulk Acoustic Wave Sensors)
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Open AccessArticle Raman Hyperspectral Imaging for Detection of Watermelon Seeds Infected with Acidovorax citrulli
Sensors 2017, 17(10), 2188; doi:10.3390/s17102188
Received: 19 July 2017 / Revised: 22 August 2017 / Accepted: 18 September 2017 / Published: 23 September 2017
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Abstract
The bacterial infection of seeds is one of the most important quality factors affecting yield. Conventional detection methods for bacteria-infected seeds, such as biological, serological, and molecular tests, are not feasible since they require expensive equipment, and furthermore, the testing processes are also
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The bacterial infection of seeds is one of the most important quality factors affecting yield. Conventional detection methods for bacteria-infected seeds, such as biological, serological, and molecular tests, are not feasible since they require expensive equipment, and furthermore, the testing processes are also time-consuming. In this study, we use the Raman hyperspectral imaging technique to distinguish bacteria-infected seeds from healthy seeds as a rapid, accurate, and non-destructive detection tool. We utilize Raman hyperspectral imaging data in the spectral range of 400–1800 cm−1 to determine the optimal band-ratio for the discrimination of watermelon seeds infected by the bacteria Acidovorax citrulli using ANOVA. Two bands at 1076.8 cm−1 and 437 cm−1 are selected as the optimal Raman peaks for the detection of bacteria-infected seeds. The results demonstrate that the Raman hyperspectral imaging technique has a good potential for the detection of bacteria-infected watermelon seeds and that it could form a suitable alternative to conventional methods. Full article
(This article belongs to the Section Biosensors)
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Open AccessArticle Monaural Sound Localization Based on Reflective Structure and Homomorphic Deconvolution
Sensors 2017, 17(10), 2189; doi:10.3390/s17102189
Received: 16 August 2017 / Revised: 16 September 2017 / Accepted: 19 September 2017 / Published: 23 September 2017
PDF Full-text (8257 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
The asymmetric structure around the receiver provides a particular time delay for the specific incoming propagation. This paper designs a monaural sound localization system based on the reflective structure around the microphone. The reflective plates are placed to present the direction-wise time delay,
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The asymmetric structure around the receiver provides a particular time delay for the specific incoming propagation. This paper designs a monaural sound localization system based on the reflective structure around the microphone. The reflective plates are placed to present the direction-wise time delay, which is naturally processed by convolutional operation with a sound source. The received signal is separated for estimating the dominant time delay by using homomorphic deconvolution, which utilizes the real cepstrum and inverse cepstrum sequentially to derive the propagation response’s autocorrelation. Once the localization system accurately estimates the information, the time delay model computes the corresponding reflection for localization. Because of the structure limitation, two stages of the localization process perform the estimation procedure as range and angle. The software toolchain from propagation physics and algorithm simulation realizes the optimal 3D-printed structure. The acoustic experiments in the anechoic chamber denote that 79.0% of the study range data from the isotropic signal is properly detected by the response value, and 87.5% of the specific direction data from the study range signal is properly estimated by the response time. The product of both rates shows the overall hit rate to be 69.1%. Full article
(This article belongs to the Section Physical Sensors)
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Open AccessCommunication Autonomous Microsystems for Downhole Applications: Design Challenges, Current State, and Initial Test Results
Sensors 2017, 17(10), 2190; doi:10.3390/s17102190
Received: 13 August 2017 / Revised: 20 September 2017 / Accepted: 21 September 2017 / Published: 23 September 2017
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Abstract
This paper describes two platforms for autonomous sensing microsystems that are intended for deployment in chemically corrosive environments at elevated temperatures and pressures. Following the deployment period, the microsystems are retrieved, recharged, and interrogated wirelessly at close proximity. The first platform is the
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This paper describes two platforms for autonomous sensing microsystems that are intended for deployment in chemically corrosive environments at elevated temperatures and pressures. Following the deployment period, the microsystems are retrieved, recharged, and interrogated wirelessly at close proximity. The first platform is the Michigan Micro Mote for High Temperature (M3HT), a chip stack 2.9 × 1.1 × 1.5 mm3 in size. It uses RF communications to support pre-deployment and post-retrieval functions, and it uses customized electronics to achieve ultralow power consumption, permitting the use of a chip-scale battery. The second platform is the Environmental Logging Microsystem (ELM). This system, which is 6.5 × 6.3 × 4.5 mm3 in size, uses the smallest suitable off-the-shelf electronic and battery components that are compatible with assembly on a flexible printed circuit board. Data are stored in non-volatile memory, permitting retrieval even after total power loss. Pre-deployment and post-retrieval functions are supported by optical communication. Two types of encapsulation methods are used to withstand high pressure and corrosive environments: an epoxy filled volume is used for the M3HT, and a hollow stainless-steel shell with a sapphire lid is used for both the M3HT and ELM. The encapsulated systems were successfully tested at temperature and pressure reaching 150 °C and 10,000 psi, in environments of concentrated brine, oil, and cement slurry. At elevated temperatures, the limited lifetimes of available batteries constrain the active deployment period to several hours. Full article
(This article belongs to the Special Issue Sensors and Materials for Harsh Environments)
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Open AccessArticle Secure and Lightweight Cloud-Assisted Video Reporting Protocol over 5G-Enabled Vehicular Networks
Sensors 2017, 17(10), 2191; doi:10.3390/s17102191
Received: 7 August 2017 / Revised: 17 September 2017 / Accepted: 18 September 2017 / Published: 23 September 2017
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Abstract
In the vehicular networks, the real-time video reporting service is used to send the recorded videos in the vehicle to the cloud. However, when facilitating the real-time video reporting service in the vehicular networks, the usage of the fourth generation (4G) long term
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In the vehicular networks, the real-time video reporting service is used to send the recorded videos in the vehicle to the cloud. However, when facilitating the real-time video reporting service in the vehicular networks, the usage of the fourth generation (4G) long term evolution (LTE) was proved to suffer from latency while the IEEE 802.11p standard does not offer sufficient scalability for a such congested environment. To overcome those drawbacks, the fifth-generation (5G)-enabled vehicular network is considered as a promising technology for empowering the real-time video reporting service. In this paper, we note that security and privacy related issues should also be carefully addressed to boost the early adoption of 5G-enabled vehicular networks. There exist a few research works for secure video reporting service in 5G-enabled vehicular networks. However, their usage is limited because of public key certificates and expensive pairing operations. Thus, we propose a secure and lightweight protocol for cloud-assisted video reporting service in 5G-enabled vehicular networks. Compared to the conventional public key certificates, the proposed protocol achieves entities’ authorization through anonymous credential. Also, by using lightweight security primitives instead of expensive bilinear pairing operations, the proposed protocol minimizes the computational overhead. From the evaluation results, we show that the proposed protocol takes the smaller computation and communication time for the cryptographic primitives than that of the well-known Eiza-Ni-Shi protocol. Full article
(This article belongs to the Section Sensor Networks)
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Open AccessArticle Drawing Sensors with Ball-Milled Blends of Metal-Organic Frameworks and Graphite
Sensors 2017, 17(10), 2192; doi:10.3390/s17102192
Received: 30 August 2017 / Revised: 15 September 2017 / Accepted: 17 September 2017 / Published: 23 September 2017
PDF Full-text (1943 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
The synthetically tunable properties and intrinsic porosity of conductive metal-organic frameworks (MOFs) make them promising materials for transducing selective interactions with gaseous analytes in an electrically addressable platform. Consequently, conductive MOFs are valuable functional materials with high potential utility in chemical detection. The
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The synthetically tunable properties and intrinsic porosity of conductive metal-organic frameworks (MOFs) make them promising materials for transducing selective interactions with gaseous analytes in an electrically addressable platform. Consequently, conductive MOFs are valuable functional materials with high potential utility in chemical detection. The implementation of these materials, however, is limited by the available methods for device incorporation due to their poor solubility and moderate electrical conductivity. This manuscript describes a straightforward method for the integration of moderately conductive MOFs into chemiresistive sensors by mechanical abrasion. To improve electrical contacts, blends of MOFs with graphite were generated using a solvent-free ball-milling procedure. While most bulk powders of pure conductive MOFs were difficult to integrate into devices directly via mechanical abrasion, the compressed solid-state MOF/graphite blends were easily abraded onto the surface of paper substrates equipped with gold electrodes to generate functional sensors. This method was used to prepare an array of chemiresistors, from four conductive MOFs, capable of detecting and differentiating NH3, H2S and NO at parts-per-million concentrations. Full article
(This article belongs to the Special Issue Chemiresistive Sensors: Status and the Future)
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Open AccessArticle On-Board Detection of Pedestrian Intentions
Sensors 2017, 17(10), 2193; doi:10.3390/s17102193
Received: 4 August 2017 / Revised: 19 September 2017 / Accepted: 20 September 2017 / Published: 23 September 2017
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Abstract
Avoiding vehicle-to-pedestrian crashes is a critical requirement for nowadays advanced driver assistant systems (ADAS) and future self-driving vehicles. Accordingly, detecting pedestrians from raw sensor data has a history of more than 15 years of research, with vision playing a central role. During the
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Avoiding vehicle-to-pedestrian crashes is a critical requirement for nowadays advanced driver assistant systems (ADAS) and future self-driving vehicles. Accordingly, detecting pedestrians from raw sensor data has a history of more than 15 years of research, with vision playing a central role. During the last years, deep learning has boosted the accuracy of image-based pedestrian detectors. However, detection is just the first step towards answering the core question, namely is the vehicle going to crash with a pedestrian provided preventive actions are not taken? Therefore, knowing as soon as possible if a detected pedestrian has the intention of crossing the road ahead of the vehicle is essential for performing safe and comfortable maneuvers that prevent a crash. However, compared to pedestrian detection, there is relatively little literature on detecting pedestrian intentions. This paper aims to contribute along this line by presenting a new vision-based approach which analyzes the pose of a pedestrian along several frames to determine if he or she is going to enter the road or not. We present experiments showing 750 ms of anticipation for pedestrians crossing the road, which at a typical urban driving speed of 50 km/h can provide 15 additional meters (compared to a pure pedestrian detector) for vehicle automatic reactions or to warn the driver. Moreover, in contrast with state-of-the-art methods, our approach is monocular, neither requiring stereo nor optical flow information. Full article
(This article belongs to the Section Physical Sensors)
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Open AccessCommunication A Multi-Fluorescent DNA/Graphene Oxide Conjugate Sensor for Signature-Based Protein Discrimination
Sensors 2017, 17(10), 2194; doi:10.3390/s17102194
Received: 25 July 2017 / Revised: 15 September 2017 / Accepted: 19 September 2017 / Published: 23 September 2017
PDF Full-text (2539 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Signature-based protein sensing has recently emerged as a promising prospective alternative to conventional lock-and-key methods. However, most of the current examples require the measurement of optical signals from spatially-separated materials for the generation of signatures. Herein, we present a new approach for the
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Signature-based protein sensing has recently emerged as a promising prospective alternative to conventional lock-and-key methods. However, most of the current examples require the measurement of optical signals from spatially-separated materials for the generation of signatures. Herein, we present a new approach for the construction of multi-fluorescent sensing systems with high accessibility and tunability, which allows generating protein fluorescent signatures from a single microplate well. This approach is based on conjugates between nano-graphene oxide (nGO) and three single-stranded DNAs (ssDNAs) that exhibit different sequences and fluorophores. Initially, the three fluorophore-modified ssDNAs were quenched simultaneously by binding to nGO. Subsequent addition of analyte proteins caused a partial recovery in fluorescent intensity of the individual ssDNAs. Based on this scheme, we have succeeded in acquiring fluorescence signatures unique to (i) ten proteins that differ with respect to pI and molecular weight and (ii) biochemical marker proteins in the presence of interferent human serum. Pattern-recognition methods demonstrated high levels of discrimination for this system. The high discriminatory power and simple format of this sensor system should enable an easy and fast evaluation of proteins and protein mixtures. Full article
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Open AccessArticle Towards Robot-Assisted Retinal Vein Cannulation: A Motorized Force-Sensing Microneedle Integrated with a Handheld Micromanipulator
Sensors 2017, 17(10), 2195; doi:10.3390/s17102195
Received: 8 August 2017 / Revised: 13 September 2017 / Accepted: 19 September 2017 / Published: 23 September 2017
PDF Full-text (7976 KB) | HTML Full-text | XML Full-text
Abstract
Retinal vein cannulation is a technically demanding surgical procedure where therapeutic agents are injected into the retinal veins to treat occlusions. The clinical feasibility of this approach has been largely limited by the technical challenges associated with performing the procedure. Among the challenges
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Retinal vein cannulation is a technically demanding surgical procedure where therapeutic agents are injected into the retinal veins to treat occlusions. The clinical feasibility of this approach has been largely limited by the technical challenges associated with performing the procedure. Among the challenges to successful vein cannulation are identifying the moment of venous puncture, achieving cannulation of the micro-vessel, and maintaining cannulation throughout drug delivery. Recent advances in medical robotics and sensing of tool-tissue interaction forces have the potential to address each of these challenges as well as to prevent tissue trauma, minimize complications, diminish surgeon effort, and ultimately promote successful retinal vein cannulation. In this paper, we develop an assistive system combining a handheld micromanipulator, called “Micron”, with a force-sensing microneedle. Using this system, we examine two distinct methods of precisely detecting the instant of venous puncture. This is based on measured tool-tissue interaction forces and also the tracked position of the needle tip. In addition to the existing tremor canceling function of Micron, a new control method is implemented to actively compensate unintended movements of the operator, and to keep the cannulation device securely inside the vein following cannulation. To demonstrate the capabilities and performance of our uniquely upgraded system, we present a multi-user artificial phantom study with subjects from three different surgical skill levels. Results show that our puncture detection algorithm, when combined with the active positive holding feature enables sustained cannulation which is most evident in smaller veins. Notable is that the active holding function significantly attenuates tool motion in the vein, thereby reduces the trauma during cannulation. Full article
(This article belongs to the Special Issue Force and Pressure Based Sensing Medical Application)
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Open AccessArticle Towards the Automatic Detection of Pre-Existing Termite Mounds through UAS and Hyperspectral Imagery
Sensors 2017, 17(10), 2196; doi:10.3390/s17102196
Received: 7 August 2017 / Revised: 14 September 2017 / Accepted: 21 September 2017 / Published: 24 September 2017
PDF Full-text (9466 KB) | HTML Full-text | XML Full-text
Abstract
The increased technological developments in Unmanned Aerial Vehicles (UAVs) combined with artificial intelligence and Machine Learning (ML) approaches have opened the possibility of remote sensing of extensive areas of arid lands. In this paper, a novel approach towards the detection of termite mounds
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The increased technological developments in Unmanned Aerial Vehicles (UAVs) combined with artificial intelligence and Machine Learning (ML) approaches have opened the possibility of remote sensing of extensive areas of arid lands. In this paper, a novel approach towards the detection of termite mounds with the use of a UAV, hyperspectral imagery, ML and digital image processing is intended. A new pipeline process is proposed to detect termite mounds automatically and to reduce, consequently, detection times. For the classification stage, several ML classification algorithms’ outcomes were studied, selecting support vector machines as the best approach for their role in image classification of pre-existing termite mounds. Various test conditions were applied to the proposed algorithm, obtaining an overall accuracy of 68%. Images with satisfactory mound detection proved that the method is “resolution-dependent”. These mounds were detected regardless of their rotation and position in the aerial image. However, image distortion reduced the number of detected mounds due to the inclusion of a shape analysis method in the object detection phase, and image resolution is still determinant to obtain accurate results. Hyperspectral imagery demonstrated better capabilities to classify a huge set of materials than implementing traditional segmentation methods on RGB images only. Full article
(This article belongs to the Special Issue UAV or Drones for Remote Sensing Applications)
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Open AccessArticle Hardware in the Loop Performance Assessment of LIDAR-Based Spacecraft Pose Determination
Sensors 2017, 17(10), 2197; doi:10.3390/s17102197
Received: 29 August 2017 / Revised: 15 September 2017 / Accepted: 21 September 2017 / Published: 24 September 2017
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Abstract
In this paper an original, easy to reproduce, semi-analytic calibration approach is developed for hardware-in-the-loop performance assessment of pose determination algorithms processing point cloud data, collected by imaging a non-cooperative target with LIDARs. The laboratory setup includes a scanning LIDAR, a monocular camera,
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In this paper an original, easy to reproduce, semi-analytic calibration approach is developed for hardware-in-the-loop performance assessment of pose determination algorithms processing point cloud data, collected by imaging a non-cooperative target with LIDARs. The laboratory setup includes a scanning LIDAR, a monocular camera, a scaled-replica of a satellite-like target, and a set of calibration tools. The point clouds are processed by uncooperative model-based algorithms to estimate the target relative position and attitude with respect to the LIDAR. Target images, acquired by a monocular camera operated simultaneously with the LIDAR, are processed applying standard solutions to the Perspective-n-Points problem to get high-accuracy pose estimates which can be used as a benchmark to evaluate the accuracy attained by the LIDAR-based techniques. To this aim, a precise knowledge of the extrinsic relative calibration between the camera and the LIDAR is essential, and it is obtained by implementing an original calibration approach which does not need ad-hoc homologous targets (e.g., retro-reflectors) easily recognizable by the two sensors. The pose determination techniques investigated by this work are of interest to space applications involving close-proximity maneuvers between non-cooperative platforms, e.g., on-orbit servicing and active debris removal. Full article
(This article belongs to the Section Physical Sensors)
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Open AccessArticle Copper Oxide Chitosan Nanocomposite: Characterization and Application in Non-Enzymatic Hydrogen Peroxide Sensing
Sensors 2017, 17(10), 2198; doi:10.3390/s17102198
Received: 8 August 2017 / Revised: 16 September 2017 / Accepted: 22 September 2017 / Published: 24 September 2017
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Abstract
Electrochemical dissolution of metallic copper into slightly acidic aqueous solutions of chitosan yields a clear and stable dispersion of Copper Oxide nanoparticles into the organic polymer host. The electrochemically synthesized chitosan:CuOx nanocomposite is characterized by means of spectrophotometry, frequency domain electrical measurements and
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Electrochemical dissolution of metallic copper into slightly acidic aqueous solutions of chitosan yields a clear and stable dispersion of Copper Oxide nanoparticles into the organic polymer host. The electrochemically synthesized chitosan:CuOx nanocomposite is characterized by means of spectrophotometry, frequency domain electrical measurements and morphological analysis. Solid state electrochemical cells having pure chitosan as the electrolyte and using chitosan:CuOx as the electrode, are developed and characterized by means of electrical measurements performed in the ±1 V voltage window. The current-voltage loops of the cells, measured in deionized water, are found to reversibly change in response to hydrogen peroxide added to the water in 0.2 μM subsequent steps. Such changes, clearly distinguishable from changes recorded in response to other analytes, can be exploited in order to develop a hydrogen peroxide sensor able to work without the need for any supporting electrolyte. Full article
(This article belongs to the Section Chemical Sensors)
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Open AccessArticle Improving Night Time Driving Safety Using Vision-Based Classification Techniques
Sensors 2017, 17(10), 2199; doi:10.3390/s17102199
Received: 17 August 2017 / Revised: 19 September 2017 / Accepted: 20 September 2017 / Published: 24 September 2017
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Abstract
The risks involved in nighttime driving include drowsy drivers and dangerous vehicles. Prominent among the more dangerous vehicles around at night are the larger vehicles which are usually moving faster at night on a highway. In addition, the risk level of driving around
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The risks involved in nighttime driving include drowsy drivers and dangerous vehicles. Prominent among the more dangerous vehicles around at night are the larger vehicles which are usually moving faster at night on a highway. In addition, the risk level of driving around larger vehicles rises significantly when the driver’s attention becomes distracted, even for a short period of time. For the purpose of alerting the driver and elevating his or her safety, in this paper we propose two components for any modern vision-based Advanced Drivers Assistance System (ADAS). These two components work separately for the single purpose of alerting the driver in dangerous situations. The purpose of the first component is to ascertain that the driver would be in a sufficiently wakeful state to receive and process warnings; this is the driver drowsiness detection component. The driver drowsiness detection component uses infrared images of the driver to analyze his eyes’ movements using a MSR plus a simple heuristic. This component issues alerts to the driver when the driver’s eyes show distraction and are closed for a longer than usual duration. Experimental results show that this component can detect closed eyes with an accuracy of 94.26% on average, which is comparable to previous results using more sophisticated methods. The purpose of the second component is to alert the driver when the driver’s vehicle is moving around larger vehicles at dusk or night time. The large vehicle detection component accepts images from a regular video driving recorder as input. A bi-level system of classifiers, which included a novel MSR-enhanced KAZE-base Bag-of-Features classifier, is proposed to avoid false negatives. In both components, we propose an improved version of the Multi-Scale Retinex (MSR) algorithm to augment the contrast of the input. Several experiments were performed to test the effects of the MSR and each classifier, and the results are presented in experimental results section of this paper. Full article
(This article belongs to the Special Issue Selected Papers from IEEE ICASI 2017)
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Open AccessArticle Secure Utilization of Beacons and UAVs in Emergency Response Systems for Building Fire Hazard
Sensors 2017, 17(10), 2200; doi:10.3390/s17102200
Received: 15 August 2017 / Revised: 12 September 2017 / Accepted: 19 September 2017 / Published: 25 September 2017
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Abstract
An intelligent emergency system for hazard monitoring and building evacuation is a very important application area in Internet of Things (IoT) technology. Through the use of smart sensors, such a system can provide more vital and reliable information to first-responders and also reduce
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An intelligent emergency system for hazard monitoring and building evacuation is a very important application area in Internet of Things (IoT) technology. Through the use of smart sensors, such a system can provide more vital and reliable information to first-responders and also reduce the incidents of false alarms. Several smart monitoring and warning systems do already exist, though they exhibit key weaknesses such as a limited monitoring coverage and security, which have not yet been sufficiently addressed. In this paper, we propose a monitoring and emergency response method for buildings by utilizing beacons and Unmanned Aerial Vehicles (UAVs) on an IoT security platform. In order to demonstrate the practicability of our method, we also implement a proof of concept prototype, which we call the UAV-EMOR (UAV-assisted Emergency Monitoring and Response) system. Our UAV-EMOR system provides the following novel features: (1) secure communications between UAVs, smart sensors, the control server and a smartphone app for security managers; (2) enhanced coordination between smart sensors and indoor/outdoor UAVs to expand real-time monitoring coverage; and (3) beacon-aided rescue and building evacuation. Full article
(This article belongs to the Special Issue UAV or Drones for Remote Sensing Applications)
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Open AccessArticle DISPAQ: Distributed Profitable-Area Query from Big Taxi Trip Data
Sensors 2017, 17(10), 2201; doi:10.3390/s17102201
Received: 2 August 2017 / Revised: 8 September 2017 / Accepted: 19 September 2017 / Published: 25 September 2017
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Abstract
One of the crucial problems for taxi drivers is to efficiently locate passengers in order to increase profits. The rapid advancement and ubiquitous penetration of Internet of Things (IoT) technology into transportation industries enables us to provide taxi drivers with locations that have
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One of the crucial problems for taxi drivers is to efficiently locate passengers in order to increase profits. The rapid advancement and ubiquitous penetration of Internet of Things (IoT) technology into transportation industries enables us to provide taxi drivers with locations that have more potential passengers (more profitable areas) by analyzing and querying taxi trip data. In this paper, we propose a query processing system, called Distributed Profitable-Area Query (DISPAQ) which efficiently identifies profitable areas by exploiting the Apache Software Foundation’s Spark framework and a MongoDB database. DISPAQ first maintains a profitable-area query index (PQ-index) by extracting area summaries and route summaries from raw taxi trip data. It then identifies candidate profitable areas by searching the PQ-index during query processing. Then, it exploits a Z-Skyline algorithm, which is an extension of skyline processing with a Z-order space filling curve, to quickly refine the candidate profitable areas. To improve the performance of distributed query processing, we also propose local Z-Skyline optimization, which reduces the number of dominant tests by distributing killer profitable areas to each cluster node. Through extensive evaluation with real datasets, we demonstrate that our DISPAQ system provides a scalable and efficient solution for processing profitable-area queries from huge amounts of big taxi trip data. Full article
(This article belongs to the Special Issue Sensors for Transportation)
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Open AccessArticle Avionic Air Data Sensors Fault Detection and Isolation by means of Singular Perturbation and Geometric Approach
Sensors 2017, 17(10), 2202; doi:10.3390/s17102202
Received: 29 August 2017 / Revised: 18 September 2017 / Accepted: 22 September 2017 / Published: 25 September 2017
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Abstract
Singular Perturbations represent an advantageous theory to deal with systems characterized by a two-time scale separation, such as the longitudinal dynamics of aircraft which are called phugoid and short period. In this work, the combination of the NonLinear Geometric Approach and the
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Singular Perturbations represent an advantageous theory to deal with systems characterized by a two-time scale separation, such as the longitudinal dynamics of aircraft which are called phugoid and short period. In this work, the combination of the NonLinear Geometric Approach and the Singular Perturbations leads to an innovative Fault Detection and Isolation system dedicated to the isolation of faults affecting the air data system of a general aviation aircraft. The isolation capabilities, obtained by means of the approach proposed in this work, allow for the solution of a fault isolation problem otherwise not solvable by means of standard geometric techniques. Extensive Monte-Carlo simulations, exploiting a high fidelity aircraft simulator, show the effectiveness of the proposed Fault Detection and Isolation system. Full article
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Open AccessArticle Comparison of Methods Study between a Photonic Crystal Biosensor and Certified ELISA to Measure Biomarkers of Iron Deficiency in Chronic Kidney Disease Patients
Sensors 2017, 17(10), 2203; doi:10.3390/s17102203
Received: 20 August 2017 / Revised: 21 September 2017 / Accepted: 22 September 2017 / Published: 25 September 2017
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Abstract
The total analytical error of a photonic crystal (PC) biosensor in the determination of ferritin and soluble transferrin receptor (sTfR) as biomarkers of iron deficiency anemia in chronic kidney disease (CKD) patients was evaluated against certified ELISAs. Antigens were extracted from sera of
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The total analytical error of a photonic crystal (PC) biosensor in the determination of ferritin and soluble transferrin receptor (sTfR) as biomarkers of iron deficiency anemia in chronic kidney disease (CKD) patients was evaluated against certified ELISAs. Antigens were extracted from sera of CKD patients using functionalized iron-oxide nanoparticles (fAb-IONs) followed by magnetic separation. Immuno-complexes were recognized by complementary detection Ab affixed to the PC biosensor surface, and their signals were followed using the BIND instrument. Quantification was conducted against actual protein standards. Total calculated error (TEcalc) was estimated based on systematic (SE) and random error (RE) and compared against total allowed error (TEa) based on established quality specifications. Both detection platforms showed adequate linearity, specificity, and sensitivity for biomarkers. Means, SD, and CV were similar between biomarkers for both detection platforms. Compared to ELISA, inherent imprecision was higher on the PC biosensor for ferritin, but not for sTfR. High SE or RE in the PC biosensor when measuring either biomarker resulted in TEcalc higher than the TEa. This did not influence the diagnostic ability of the PC biosensor to discriminate CKD patients with low iron stores. The performance of the PC biosensor is similar to certified ELISAs; however, optimization is required to reduce TEcalc. Full article
(This article belongs to the Special Issue Sensors for Health Monitoring and Disease Diagnosis)
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Open AccessArticle Application of CMOS Technology to Silicon Photomultiplier Sensors
Sensors 2017, 17(10), 2204; doi:10.3390/s17102204
Received: 7 September 2017 / Revised: 20 September 2017 / Accepted: 21 September 2017 / Published: 25 September 2017
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Abstract
We use the 180 nm GLOBALFOUNDRIES (GF) BCDLite CMOS process for the production of a silicon photomultiplier prototype. We study the main characteristics of the developed sensor in comparison with commercial SiPMs obtained in custom technologies and other SiPMs developed with CMOS-compatible processes.
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We use the 180 nm GLOBALFOUNDRIES (GF) BCDLite CMOS process for the production of a silicon photomultiplier prototype. We study the main characteristics of the developed sensor in comparison with commercial SiPMs obtained in custom technologies and other SiPMs developed with CMOS-compatible processes. We support our discussion with a transient modeling of the detection process of the silicon photomultiplier as well as with a series of static and dynamic experimental measurements in dark and illuminated environments. Full article
(This article belongs to the Special Issue Silicon Technologies for Photonic Sensors)
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Open AccessArticle Study of GNSS Loss of Lock Characteristics under Ionosphere Scintillation with GNSS Data at Weipa (Australia) During Solar Maximum Phase
Sensors 2017, 17(10), 2205; doi:10.3390/s17102205
Received: 22 July 2017 / Revised: 19 September 2017 / Accepted: 20 September 2017 / Published: 25 September 2017
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Abstract
One of the adverse impacts of scintillation on GNSS signals is the loss of lock status, which can lead to GNSS geometry and visibility reductions that compromise the accuracy and integrity of navigation performance. In this paper the loss of lock based on
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One of the adverse impacts of scintillation on GNSS signals is the loss of lock status, which can lead to GNSS geometry and visibility reductions that compromise the accuracy and integrity of navigation performance. In this paper the loss of lock based on ionosphere scintillation in this solar maximum phase has been well investigated with respect to both temporal and spatial behaviors, based on GNSS observatory data collected at Weipa (Australia; geographic: 12.45° S, 130.95° E; geomagnetic: 21.79° S, 214.41° E) from 2011 to 2015. Experiments demonstrate that the percentage of occurrence of loss of lock events under ionosphere scintillation is closely related with solar activity and seasonal shifts. Loss of lock behaviors under ionosphere scintillation related to elevation and azimuth angles are statistically analyzed, with some distinct characteristics found. The influences of daytime scintillation and geomagnetic storms on loss of lock have also been discussed in details. The proposed work is valuable for a deeper understanding of theoretical mechanisms of—loss of lock under ionosphere scintillation in global regions, and provides a reference for GNSS applications in certain regions at Australian low latitudes. Full article
(This article belongs to the Section Remote Sensors)
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Open AccessArticle Average Throughput Performance of Myopic Policy in Energy Harvesting Wireless Sensor Networks
Sensors 2017, 17(10), 2206; doi:10.3390/s17102206
Received: 1 August 2017 / Revised: 16 September 2017 / Accepted: 20 September 2017 / Published: 26 September 2017
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Abstract
This paper considers a single-hop wireless sensor network where a fusion center collects data from M energy harvesting wireless sensors. The harvested energy is stored losslessly in an infinite-capacity battery at each sensor. In each time slot, the fusion center schedules K sensors
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This paper considers a single-hop wireless sensor network where a fusion center collects data from M energy harvesting wireless sensors. The harvested energy is stored losslessly in an infinite-capacity battery at each sensor. In each time slot, the fusion center schedules K sensors for data transmission over K orthogonal channels. The fusion center does not have direct knowledge on the battery states of sensors, or the statistics of their energy harvesting processes. The fusion center only has information of the outcomes of previous transmission attempts. It is assumed that the sensors are data backlogged, there is no battery leakage and the communication is error-free. An energy harvesting sensor can transmit data to the fusion center whenever being scheduled only if it has enough energy for data transmission. We investigate average throughput of Round-Robin type myopic policy both analytically and numerically under an average reward (throughput) criterion. We show that Round-Robin type myopic policy achieves optimality for some class of energy harvesting processes although it is suboptimal for a broad class of energy harvesting processes. Full article
(This article belongs to the Special Issue Energy Harvesting Sensors for Long Term Applications in the IoT Era)
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Open AccessArticle ePave: A Self-Powered Wireless Sensor for Smart and Autonomous Pavement
Sensors 2017, 17(10), 2207; doi:10.3390/s17102207
Received: 8 August 2017 / Revised: 18 September 2017 / Accepted: 19 September 2017 / Published: 26 September 2017
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Abstract
“Smart Pavement” is an emerging infrastructure for various on-road applications in transportation and road engineering. However, existing road monitoring solutions demand a certain periodic maintenance effort due to battery life limits in the sensor systems. To this end, we present an end-to-end self-powered
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“Smart Pavement” is an emerging infrastructure for various on-road applications in transportation and road engineering. However, existing road monitoring solutions demand a certain periodic maintenance effort due to battery life limits in the sensor systems. To this end, we present an end-to-end self-powered wireless sensor—ePave—to facilitate smart and autonomous pavements. The ePave system includes a self-power module, an ultra-low-power sensor system, a wireless transmission module and a built-in power management module. First, we performed an empirical study to characterize the piezoelectric module in order to optimize energy-harvesting efficiency. Second, we developed an integrated sensor system with the optimized energy harvester. An adaptive power knob is designated to adjust the power consumption according to energy budgeting. Finally, we intensively evaluated the ePave system in real-world applications to examine the system’s performance and explore the trade-off. Full article
(This article belongs to the Special Issue Wireless Rechargeable Sensor Networks)
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Open AccessArticle Pulsed Eddy Current Sensing for Critical Pipe Condition Assessment
Sensors 2017, 17(10), 2208; doi:10.3390/s17102208
Received: 1 August 2017 / Revised: 5 September 2017 / Accepted: 20 September 2017 / Published: 26 September 2017
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Abstract
Pulsed Eddy Current (PEC) sensing is used for Non-Destructive Evaluation (NDE) of the structural integrity of metallic structures in the aircraft, railway, oil and gas sectors. Urban water utilities also have extensive large ferromagnetic structures in the form of critical pressure pipe systems
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Pulsed Eddy Current (PEC) sensing is used for Non-Destructive Evaluation (NDE) of the structural integrity of metallic structures in the aircraft, railway, oil and gas sectors. Urban water utilities also have extensive large ferromagnetic structures in the form of critical pressure pipe systems made of grey cast iron, ductile cast iron and mild steel. The associated material properties render NDE of these pipes by means of electromagnetic sensing a necessity. In recent years PEC sensing has established itself as a state-of-the-art NDE technique in the critical water pipe sector. This paper presents advancements to PEC inspection in view of the specific information demanded from water utilities along with the challenges encountered in this sector. Operating principles of the sensor architecture suitable for application on critical pipes are presented with the associated sensor design and calibration strategy. A Gaussian process-based approach is applied to model a functional relationship between a PEC signal feature and critical pipe wall thickness. A case study demonstrates the sensor’s behaviour on a grey cast iron pipe and discusses the implications of the observed results and challenges relating to this application. Full article
(This article belongs to the Special Issue Magnetic Sensors and Their Applications)
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Open AccessArticle A Fast Terahertz Imaging Method Using Sparse Rotating Array
Sensors 2017, 17(10), 2209; doi:10.3390/s17102209
Received: 19 July 2017 / Revised: 21 September 2017 / Accepted: 24 September 2017 / Published: 26 September 2017
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Abstract
For fast and standoff personal screening, a novel terahertz imaging scheme using a sparse rotating array is developed in this paper. A linearly sparse array is designed to move along a circular path with respect to an axis perpendicular to the imaging scenario.
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For fast and standoff personal screening, a novel terahertz imaging scheme using a sparse rotating array is developed in this paper. A linearly sparse array is designed to move along a circular path with respect to an axis perpendicular to the imaging scenario. For this new scheme, a modified imaging algorithm is proposed based on the frequency-domain reconstruction method in circular synthetic aperture radar. To achieve better imaging performance, an optimization method of the sparse array is also proposed, according to the distribution of the spectral support. Theoretical and numerical analysis of the point spread function (PSF) is provided to demonstrate the high-resolution imaging ability of the proposed scheme. Comprehensive simulations are carried out to validate the feasibility and effectiveness of the array optimization method. Finally, the imaging results of a human-scattering model are also obtained to further demonstrate the good performance of this new imaging scheme and the effectiveness of the array optimization approach. This work can facilitate the design and practice of terahertz imaging systems for security inspection. Full article
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Open AccessArticle Towards a Transferable UAV-Based Framework for River Hydromorphological Characterization
Sensors 2017, 17(10), 2210; doi:10.3390/s17102210
Received: 23 June 2017 / Revised: 18 September 2017 / Accepted: 21 September 2017 / Published: 26 September 2017
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Abstract
The multiple protocols that have been developed to characterize river hydromorphology, partly in response to legislative drivers such as the European Union Water Framework Directive (EU WFD), make the comparison of results obtained in different countries challenging. Recent studies have analyzed the comparability
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The multiple protocols that have been developed to characterize river hydromorphology, partly in response to legislative drivers such as the European Union Water Framework Directive (EU WFD), make the comparison of results obtained in different countries challenging. Recent studies have analyzed the comparability of existing methods, with remote sensing based approaches being proposed as a potential means of harmonizing hydromorphological characterization protocols. However, the resolution achieved by remote sensing products may not be sufficient to assess some of the key hydromorphological features that are required to allow an accurate characterization. Methodologies based on high resolution aerial photography taken from Unmanned Aerial Vehicles (UAVs) have been proposed by several authors as potential approaches to overcome these limitations. Here, we explore the applicability of an existing UAV based framework for hydromorphological characterization to three different fluvial settings representing some of the distinct ecoregions defined by the WFD geographical intercalibration groups (GIGs). The framework is based on the automated recognition of hydromorphological features via tested and validated Artificial Neural Networks (ANNs). Results show that the framework is transferable to the Central-Baltic and Mediterranean GIGs with accuracies in feature identification above 70%. Accuracies of 50% are achieved when the framework is implemented in the Very Large Rivers GIG. The framework successfully identified vegetation, deep water, shallow water, riffles, side bars and shadows for the majority of the reaches. However, further algorithm development is required to ensure a wider range of features (e.g., chutes, structures and erosion) are accurately identified. This study also highlights the need to develop an objective and fit for purpose hydromorphological characterization framework to be adopted within all EU member states to facilitate comparison of results. Full article
(This article belongs to the Special Issue UAV or Drones for Remote Sensing Applications)
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Open AccessArticle Energy Neutral Wireless Bolt for Safety Critical Fastening
Sensors 2017, 17(10), 2211; doi:10.3390/s17102211
Received: 31 July 2017 / Revised: 9 September 2017 / Accepted: 22 September 2017 / Published: 26 September 2017
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Abstract
Thermoelectric generators (TEGs) are now capable of powering the abundant low power electronics from very small (just a few degrees Celsius) temperature gradients. This factor along with the continuously lowering cost and size of TEGs, has contributed to the growing number of miniaturized
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Thermoelectric generators (TEGs) are now capable of powering the abundant low power electronics from very small (just a few degrees Celsius) temperature gradients. This factor along with the continuously lowering cost and size of TEGs, has contributed to the growing number of miniaturized battery-free sensor modules powered by TEGs. In this article, we present the design of an ambient-powered wireless bolt for high-end electro-mechanical systems. The bolt is equipped with a temperature sensor and a low power RF chip powered from a TEG. A DC-DC converter interfacing the TEG with the RF chip is used to step-up the low TEG voltage. The work includes the characterizations of different TEGs and DC-DC converters to determine the optimal design based on the amount of power that can be generated from a TEG under different loads and at temperature gradients typical of industrial environments. A prototype system was implemented and the power consumption of this system under different conditions was also measured. Results demonstrate that the power generated by the TEG at very low temperature gradients is sufficient to guarantee continuous wireless monitoring of the critical fasteners in critical systems such as avionics, motorsport and aerospace. Full article
(This article belongs to the Special Issue Energy Harvesting Sensors for Long Term Applications in the IoT Era)
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Open AccessArticle EDOVE: Energy and Depth Variance-Based Opportunistic Void Avoidance Scheme for Underwater Acoustic Sensor Networks
Sensors 2017, 17(10), 2212; doi:10.3390/s17102212
Received: 17 August 2017 / Revised: 13 September 2017 / Accepted: 22 September 2017 / Published: 26 September 2017
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Abstract
Underwater Acoustic Sensor Network (UASN) comes with intrinsic constraints because it is deployed in the aquatic environment and uses the acoustic signals to communicate. The examples of those constraints are long propagation delay, very limited bandwidth, high energy cost for transmission, very high
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Underwater Acoustic Sensor Network (UASN) comes with intrinsic constraints because it is deployed in the aquatic environment and uses the acoustic signals to communicate. The examples of those constraints are long propagation delay, very limited bandwidth, high energy cost for transmission, very high signal attenuation, costly deployment and battery replacement, and so forth. Therefore, the routing schemes for UASN must take into account those characteristics to achieve energy fairness, avoid energy holes, and improve the network lifetime. The depth based forwarding schemes in literature use node’s depth information to forward data towards the sink. They minimize the data packet duplication by employing the holding time strategy. However, to avoid void holes in the network, they use two hop node proximity information. In this paper, we propose the Energy and Depth variance-based Opportunistic Void avoidance (EDOVE) scheme to gain energy balancing and void avoidance in the network. EDOVE considers not only the depth parameter, but also the normalized residual energy of the one-hop nodes and the normalized depth variance of the second hop neighbors. Hence, it avoids the void regions as well as balances the network energy and increases the network lifetime. The simulation results show that the EDOVE gains more than 15 % packet delivery ratio, propagates 50 % less copies of data packet, consumes less energy, and has more lifetime than the state of the art forwarding schemes. Full article
(This article belongs to the Special Issue Advances and Challenges in Underwater Sensor Networks)
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Open AccessArticle Variation of River Islands around a Large City along the Yangtze River from Satellite Remote Sensing Images
Sensors 2017, 17(10), 2213; doi:10.3390/s17102213
Received: 3 August 2017 / Revised: 5 September 2017 / Accepted: 21 September 2017 / Published: 27 September 2017
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Abstract
River islands are sandbars formed by scouring and silting. Their evolution is affected by several factors, among which are runoff and sediment discharge. The spatial-temporal evolution of seven river islands in the Nanjing Section of the Yangtze River of China was examined using
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River islands are sandbars formed by scouring and silting. Their evolution is affected by several factors, among which are runoff and sediment discharge. The spatial-temporal evolution of seven river islands in the Nanjing Section of the Yangtze River of China was examined using TM (Thematic Mapper) and ETM (Enhanced Thematic Mapper)+ images from 1985 to 2015 at five year intervals. The following approaches were applied in this study: the threshold value method, binarization model, image registration, image cropping, convolution and cluster analysis. Annual runoff and sediment discharge data as measured at the Datong hydrological station upstream of Nanjing section were also used to determine the roles and impacts of various factors. The results indicated that: (1) TM/ETM+ images met the criteria of information extraction of river islands; (2) generally, the total area of these islands in this section and their changing rate decreased over time; (3) sediment and river discharge were the most significant factors in island evolution. They directly affect river islands through silting or erosion. Additionally, anthropocentric influences could play increasingly important roles. Full article
(This article belongs to the Section Remote Sensors)
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Open AccessArticle Doping Ag in ZnO Nanorods to Improve the Performance of Related Enzymatic Glucose Sensors
Sensors 2017, 17(10), 2214; doi:10.3390/s17102214
Received: 15 August 2017 / Revised: 23 September 2017 / Accepted: 25 September 2017 / Published: 27 September 2017
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Abstract
In this paper, the performance of a zinc oxide (ZnO) nanorod-based enzymatic glucose sensor was enhanced with silver (Ag)-doped ZnO (ZnO-Ag) nanorods. The effect of the doped Ag on the surface morphologies, wettability, and electron transfer capability of the ZnO-Ag nanorods, as well
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In this paper, the performance of a zinc oxide (ZnO) nanorod-based enzymatic glucose sensor was enhanced with silver (Ag)-doped ZnO (ZnO-Ag) nanorods. The effect of the doped Ag on the surface morphologies, wettability, and electron transfer capability of the ZnO-Ag nanorods, as well as the catalytic character of glucose oxidase (GOx) and the performance of the glucose sensor was investigated. The results indicate that the doped Ag slightly weakens the surface roughness and hydrophilicity of the ZnO-Ag nanorods, but remarkably increases their electron transfer ability and enhances the catalytic character of GOx. Consequently, the combined effects of the above influencing factors lead to a notable improvement of the performance of the glucose sensor, that is, the sensitivity increases and the detection limit decreases. The optimal amount of the doped Ag is determined to be 2 mM, and the corresponding glucose sensor exhibits a sensitivity of 3.85 μA/(mM·cm2), detection limit of 1.5 μM, linear range of 1.5 × 10−3–6.5 mM, and Michaelis-Menten constant of 3.87 mM. Moreover, the glucose sensor shows excellent selectivity to urea, ascorbic acid, and uric acid, in addition to displaying good storage stability. These results demonstrate that ZnO-Ag nanorods are promising matrix materials for the construction of other enzymatic biosensors. Full article
(This article belongs to the Special Issue Semiconductor Materials on Biosensors Application)
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Open AccessArticle Cluster Cooperation in Wireless-Powered Sensor Networks: Modeling and Performance Analysis
Sensors 2017, 17(10), 2215; doi:10.3390/s17102215
Received: 9 August 2017 / Revised: 13 September 2017 / Accepted: 19 September 2017 / Published: 27 September 2017
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Abstract
A wireless-powered sensor network (WPSN) consisting of one hybrid access point (HAP), a near cluster and the corresponding far cluster is investigated in this paper. These sensors are wireless-powered and they transmit information by consuming the harvested energy from signal ejected by the
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A wireless-powered sensor network (WPSN) consisting of one hybrid access point (HAP), a near cluster and the corresponding far cluster is investigated in this paper. These sensors are wireless-powered and they transmit information by consuming the harvested energy from signal ejected by the HAP. Sensors are able to harvest energy as well as store the harvested energy. We propose that if sensors in near cluster do not have their own information to transmit, acting as relays, they can help the sensors in a far cluster to forward information to the HAP in an amplify-and-forward (AF) manner. We use a finite Markov chain to model the dynamic variation process of the relay battery, and give a general analyzing model for WPSN with cluster cooperation. Though the model, we deduce the closed-form expression for the outage probability as the metric of this network. Finally, simulation results validate the start point of designing this paper and correctness of theoretical analysis and show how parameters have an effect on system performance. Moreover, it is also known that the outage probability of sensors in far cluster can be drastically reduced without sacrificing the performance of sensors in near cluster if the transmit power of HAP is fairly high. Furthermore, in the aspect of outage performance of far cluster, the proposed scheme significantly outperforms the direct transmission scheme without cooperation. Full article
(This article belongs to the Special Issue Energy Harvesting Sensors for Long Term Applications in the IoT Era)
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Open AccessFeature PaperArticle Co3O4 as p-Type Material for CO Sensing in Humid Air
Sensors 2017, 17(10), 2216; doi:10.3390/s17102216
Received: 25 August 2017 / Revised: 20 September 2017 / Accepted: 25 September 2017 / Published: 27 September 2017
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Abstract
Nanocrystalline cobalt oxide Co3O4 has been prepared by precipitation and subsequent thermal decomposition of a carbonate precursor, and has been characterized in detail using XRD, transmission electron microscopy, and FTIR spectroscopy. The sensory characteristics of the material towards carbon monoxide
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Nanocrystalline cobalt oxide Co3O4 has been prepared by precipitation and subsequent thermal decomposition of a carbonate precursor, and has been characterized in detail using XRD, transmission electron microscopy, and FTIR spectroscopy. The sensory characteristics of the material towards carbon monoxide in the concentration range 6.7–20 ppm have been examined in both dry and humid air. A sensor signal is achieved in dry air at sufficiently low temperatures T = 80–120 °C, but the increase in relative humidity results in the disappearance of sensor signal in this temperature range. At temperatures above 200 °C the inversion of the sensor signal in dry air was observed. In the temperature interval 180–200 °C the sensor signal toward CO is nearly the same at 0, 20 and 60% r.h. The obtained results are discussed in relation with the specific features of the adsorption of CO, oxygen, and water molecules on the surface of Co3O4. The independence of the sensor signal from the air humidity combined with a sufficiently short response time at a moderate operating temperature makes Co3O4 a very promising material for CO detection in conditions of variable humidity. Full article
(This article belongs to the Special Issue Gas Sensors based on Semiconducting Metal Oxides)
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Open AccessArticle Smart Bandwidth Assignation in an Underlay Cellular Network for Internet of Vehicles
Sensors 2017, 17(10), 2217; doi:10.3390/s17102217
Received: 31 August 2017 / Revised: 18 September 2017 / Accepted: 25 September 2017 / Published: 27 September 2017
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Abstract
The evolution of the IoT (Internet of Things) paradigm applied to new scenarios as VANETs (Vehicular Ad Hoc Networks) has gained momentum in recent years. Both academia and industry have triggered advanced studies in the IoV (Internet of Vehicles), which is understood as
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The evolution of the IoT (Internet of Things) paradigm applied to new scenarios as VANETs (Vehicular Ad Hoc Networks) has gained momentum in recent years. Both academia and industry have triggered advanced studies in the IoV (Internet of Vehicles), which is understood as an ecosystem where different types of users (vehicles, elements of the infrastructure, pedestrians) are connected. How to efficiently share the available radio resources among the different types of eligible users is one of the important issues to be addressed. This paper briefly analyzes various concepts presented hitherto in the literature and it proposes an enhanced algorithm for ensuring a robust co-existence of the aforementioned system users. Therefore, this paper introduces an underlay RRM (Radio Resource Management) methodology which is capable of (1) improving cellular spectral efficiency while making a minimal impact on cellular communications and (2) ensuring the different QoS (Quality of Service) requirements of ITS (Intelligent Transportation Systems) applications. Simulation results, where we compare the proposed algorithm to the other two RRM, show the promising spectral efficiency performance of the proposed RRM methodology. Full article
(This article belongs to the Special Issue Next Generation Wireless Technologies for Internet of Things)
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Open AccessArticle Clustered Multi-Task Learning for Automatic Radar Target Recognition
Sensors 2017, 17(10), 2218; doi:10.3390/s17102218
Received: 4 August 2017 / Revised: 22 September 2017 / Accepted: 23 September 2017 / Published: 27 September 2017
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Abstract
Model training is a key technique for radar target recognition. Traditional model training algorithms in the framework of single task leaning ignore the relationships among multiple tasks, which degrades the recognition performance. In this paper, we propose a clustered multi-task learning, which can
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Model training is a key technique for radar target recognition. Traditional model training algorithms in the framework of single task leaning ignore the relationships among multiple tasks, which degrades the recognition performance. In this paper, we propose a clustered multi-task learning, which can reveal and share the multi-task relationships for radar target recognition. To further make full use of these relationships, the latent multi-task relationships in the projection space are taken into consideration. Specifically, a constraint term in the projection space is proposed, the main idea of which is that multiple tasks within a close cluster should be close to each other in the projection space. In the proposed method, the cluster structures and multi-task relationships can be autonomously learned and utilized in both of the original and projected space. In view of the nonlinear characteristics of radar targets, the proposed method is extended to a non-linear kernel version and the corresponding non-linear multi-task solving method is proposed. Comprehensive experimental studies on simulated high-resolution range profile dataset and MSTAR SAR public database verify the superiority of the proposed method to some related algorithms. Full article
(This article belongs to the Section Remote Sensors)
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Open AccessArticle Analyzing Sensor-Based Time Series Data to Track Changes in Physical Activity during Inpatient Rehabilitation
Sensors 2017, 17(10), 2219; doi:10.3390/s17102219
Received: 15 August 2017 / Revised: 21 September 2017 / Accepted: 22 September 2017 / Published: 27 September 2017
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Abstract
Time series data collected from sensors can be analyzed to monitor changes in physical activity as an individual makes a substantial lifestyle change, such as recovering from an injury or illness. In an inpatient rehabilitation setting, approaches to detect and explain changes in
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Time series data collected from sensors can be analyzed to monitor changes in physical activity as an individual makes a substantial lifestyle change, such as recovering from an injury or illness. In an inpatient rehabilitation setting, approaches to detect and explain changes in longitudinal physical activity data collected from wearable sensors can provide value as a monitoring, research, and motivating tool. We adapt and expand our Physical Activity Change Detection (PACD) approach to analyze changes in patient activity in such a setting. We use Fitbit Charge Heart Rate devices with two separate populations to continuously record data to evaluate PACD, nine participants in a hospitalized inpatient rehabilitation group and eight in a healthy control group. We apply PACD to minute-by-minute Fitbit data to quantify changes within and between the groups. The inpatient rehabilitation group exhibited greater variability in change throughout inpatient rehabilitation for both step count and heart rate, with the greatest change occurring at the end of the inpatient hospital stay, which exceeded day-to-day changes of the control group. Our additions to PACD support effective change analysis of wearable sensor data collected in an inpatient rehabilitation setting and provide insight to patients, clinicians, and researchers. Full article
(This article belongs to the Special Issue Wearable and Ambient Sensors for Healthcare and Wellness Applications)
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Open AccessArticle Ag-Modified In2O3 Nanoparticles for Highly Sensitive and Selective Ethanol Alarming
Sensors 2017, 17(10), 2220; doi:10.3390/s17102220
Received: 31 August 2017 / Revised: 22 September 2017 / Accepted: 26 September 2017 / Published: 27 September 2017
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Abstract
Pure In2O3 nanoparticles are prepared by a facile precipitation method and are further modified by Ag. The synthesized samples are characterized by scanning electron microscopy, transmission electron microscopy, energy dispersive X-ray spectroscopy, X-ray diffraction, Raman and UV-Vis spectra. The results
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Pure In2O3 nanoparticles are prepared by a facile precipitation method and are further modified by Ag. The synthesized samples are characterized by scanning electron microscopy, transmission electron microscopy, energy dispersive X-ray spectroscopy, X-ray diffraction, Raman and UV-Vis spectra. The results show the successful heterojunction formation between Ag and In2O3. Gas sensing property measurements show that the 5 mol % Ag-modified In2O3 sensor has the response of 67 to 50 ppm ethanol, and fast response and recovery time of 22.3 and 11.7 s. The response is over one magnitude higher than that of pure In2O3, which can be attributed to the enhanced catalytic activity of Ag-modified In2O3 as compared with the pure one. The mechanism of the gas sensor can be explained by the spillover effect of Ag, which enhances the oxygen adsorption onto the surface of In2O3 and thus give rise to the higher activity and larger surface barrier height. Full article
(This article belongs to the Special Issue Gas Sensors based on Semiconducting Metal Oxides)
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Open AccessArticle Development and Measurements of a Mid-Infrared Multi-Gas Sensor System for CO, CO2 and CH4 Detection
Sensors 2017, 17(10), 2221; doi:10.3390/s17102221
Received: 19 August 2017 / Revised: 14 September 2017 / Accepted: 25 September 2017 / Published: 27 September 2017
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Abstract
A multi-gas sensor system was developed that uses a single broadband light source and multiple carbon monoxide (CO), carbon dioxide (CO2) and methane (CH4) pyroelectric detectors by use of the time division multiplexing (TDM) technique. A stepper motor-based rotating
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A multi-gas sensor system was developed that uses a single broadband light source and multiple carbon monoxide (CO), carbon dioxide (CO2) and methane (CH4) pyroelectric detectors by use of the time division multiplexing (TDM) technique. A stepper motor-based rotating system and a single-reflection spherical optical mirror were designed and adopted to realize and enhance multi-gas detection. Detailed measurements under static detection mode (without rotation) and dynamic mode (with rotation) were performed to study the performance of the sensor system for the three gas species. Effects of the motor rotating period on sensor performances were also investigated and a rotation speed of 0.4π rad/s was required to obtain a stable sensing performance, corresponding to a detection period of ~10 s to realize one round of detection. Based on an Allan deviation analysis, the 1σ detection limits under static operation are 2.96, 4.54 and 2.84 parts per million in volume (ppmv) for CO, CO2 and CH4, respectively and the 1σ detection limits under dynamic operations are 8.83, 8.69 and 10.29 ppmv for the three gas species, respectively. The reported sensor has potential applications in various fields requiring CO, CO2 and CH4 detection such as in coal mines. Full article
(This article belongs to the Section Physical Sensors)
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Open AccessArticle A Weighted Measurement Fusion Particle Filter for Nonlinear Multisensory Systems Based on Gauss–Hermite Approximation
Sensors 2017, 17(10), 2222; doi:10.3390/s17102222
Received: 8 August 2017 / Revised: 24 September 2017 / Accepted: 26 September 2017 / Published: 28 September 2017
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Abstract
We addressed the fusion estimation problem for nonlinear multisensory systems. Based on the Gauss–Hermite approximation and weighted least square criterion, an augmented high-dimension measurement from all sensors was compressed into a lower dimension. By combining the low-dimension measurement function with the particle filter
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We addressed the fusion estimation problem for nonlinear multisensory systems. Based on the Gauss–Hermite approximation and weighted least square criterion, an augmented high-dimension measurement from all sensors was compressed into a lower dimension. By combining the low-dimension measurement function with the particle filter (PF), a weighted measurement fusion PF (WMF-PF) is presented. The accuracy of WMF-PF appears good and has a lower computational cost when compared to centralized fusion PF (CF-PF). An example is given to show the effectiveness of the proposed algorithms. Full article
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Open AccessArticle Adaptation of Dubins Paths for UAV Ground Obstacle Avoidance When Using a Low Cost On-Board GNSS Sensor
Sensors 2017, 17(10), 2223; doi:10.3390/s17102223
Received: 12 July 2017 / Revised: 24 September 2017 / Accepted: 26 September 2017 / Published: 28 September 2017
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Abstract
Current research on Unmanned Aerial Vehicles (UAVs) shows a lot of interest in autonomous UAV navigation. This interest is mainly driven by the necessity to meet the rules and restrictions for small UAV flights that are issued by various international and national legal
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Current research on Unmanned Aerial Vehicles (UAVs) shows a lot of interest in autonomous UAV navigation. This interest is mainly driven by the necessity to meet the rules and restrictions for small UAV flights that are issued by various international and national legal organizations. In order to lower these restrictions, new levels of automation and flight safety must be reached. In this paper, a new method for ground obstacle avoidance derived by using UAV navigation based on the Dubins paths algorithm is presented. The accuracy of the proposed method has been tested, and research results have been obtained by using Software-in-the-Loop (SITL) simulation and real UAV flights, with the measurements done with a low cost Global Navigation Satellite System (GNSS) sensor. All tests were carried out in a three-dimensional space, but the height accuracy was not assessed. The GNSS navigation data for the ground obstacle avoidance algorithm is evaluated statistically. Full article
(This article belongs to the Special Issue UAV or Drones for Remote Sensing Applications)
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Open AccessArticle An Acoustic Signal Enhancement Method Based on Independent Vector Analysis for Moving Target Classification in the Wild
Sensors 2017, 17(10), 2224; doi:10.3390/s17102224
Received: 20 July 2017 / Revised: 17 September 2017 / Accepted: 25 September 2017 / Published: 28 September 2017
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Abstract
In this paper, we study how to improve the performance of moving target classification by using an acoustic signal enhancement method based on independent vector analysis (IVA) in the unattended ground sensor (UGS) system. Inspired by the IVA algorithm, we propose an improved
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In this paper, we study how to improve the performance of moving target classification by using an acoustic signal enhancement method based on independent vector analysis (IVA) in the unattended ground sensor (UGS) system. Inspired by the IVA algorithm, we propose an improved IVA method based on a microphone array for acoustic signal enhancement in the wild, which adopts a particular multivariate generalized Gaussian distribution as the source prior, an adaptive variable step strategy for the learning algorithm and discrete cosine transform (DCT) to convert the time domain observed signals to the frequency domain. We term the proposed method as DCT-G-IVA. Moreover, we design a target classification system using the improved IVA method for signal enhancement in the UGS system. Different experiments are conducted to evaluate the proposed method for acoustic signal enhancement by comparing with the baseline methods in our classification system under different wild environments. The experimental results validate the superiority of the DCT-G-IVA enhancement method in the classification system for moving targets in the presence of dynamic wind noise. Full article
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Open AccessArticle A Machine Learning Approach to Argo Data Analysis in a Thermocline
Sensors 2017, 17(10), 2225; doi:10.3390/s17102225
Received: 10 August 2017 / Revised: 17 September 2017 / Accepted: 21 September 2017 / Published: 28 September 2017
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Abstract
With the rapid development of sensor networks, big marine data arises. To efficiently use these data to predict thermoclines, we propose a machine learning approach. We firstly focus on analyzing how temperature, salinity, and geographic location features affect the formation of thermocline. Then,
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With the rapid development of sensor networks, big marine data arises. To efficiently use these data to predict thermoclines, we propose a machine learning approach. We firstly focus on analyzing how temperature, salinity, and geographic location features affect the formation of thermocline. Then, an improved model based on entropy value method for the thermocline selection is demonstrated. The experiments adopt BOA Argo data sets and the experimental results show that our novel model can predict thermoclines and related data effectively. Full article
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Open AccessArticle A Clustering-Oriented Closeness Measure Based on Neighborhood Chain and Its Application in the Clustering Ensemble Framework Based on the Fusion of Different Closeness Measures
Sensors 2017, 17(10), 2226; doi:10.3390/s17102226
Received: 21 July 2017 / Revised: 9 September 2017 / Accepted: 19 September 2017 / Published: 28 September 2017
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Abstract
Closeness measures are crucial to clustering methods. In most traditional clustering methods, the closeness between data points or clusters is measured by the geometric distance alone. These metrics quantify the closeness only based on the concerned data points’ positions in the feature space,
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Closeness measures are crucial to clustering methods. In most traditional clustering methods, the closeness between data points or clusters is measured by the geometric distance alone. These metrics quantify the closeness only based on the concerned data points’ positions in the feature space, and they might cause problems when dealing with clustering tasks having arbitrary clusters shapes and different clusters densities. In this paper, we first propose a novel Closeness Measure between data points based on the Neighborhood Chain (CMNC). Instead of using geometric distances alone, CMNC measures the closeness between data points by quantifying the difficulty for one data point to reach another through a chain of neighbors. Furthermore, based on CMNC, we also propose a clustering ensemble framework that combines CMNC and geometric-distance-based closeness measures together in order to utilize both of their advantages. In this framework, the “bad data points” that are hard to cluster correctly are identified; then different closeness measures are applied to different types of data points to get the unified clustering results. With the fusion of different closeness measures, the framework can get not only better clustering results in complicated clustering tasks, but also higher efficiency. Full article
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Open AccessArticle Analytical Modeling Tool for Design of Hydrocarbon Sensitive Optical Fibers
Sensors 2017, 17(10), 2227; doi:10.3390/s17102227
Received: 13 August 2017 / Revised: 13 September 2017 / Accepted: 20 September 2017 / Published: 28 September 2017
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Abstract
Pipelines are the main transportation means for oil and gas products across large distances. Due to the severe conditions they operate in, they are regularly inspected using conventional Pipeline Inspection Gages (PIGs) for corrosion damage. The motivation for researching a real-time distributed monitoring
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Pipelines are the main transportation means for oil and gas products across large distances. Due to the severe conditions they operate in, they are regularly inspected using conventional Pipeline Inspection Gages (PIGs) for corrosion damage. The motivation for researching a real-time distributed monitoring solution arose to mitigate costs and provide a proactive indication of potential failures. Fiber optic sensors with polymer claddings provide a means of detecting contact with hydrocarbons. By coating the fibers with a layer of metal similar in composition to that of the parent pipeline, corrosion of this coating may be detected when the polymer cladding underneath is exposed to the surrounding hydrocarbons contained within the pipeline. A Refractive Index (RI) change occurs in the polymer cladding causing a loss in intensity of a traveling light pulse due to a reduction in the fiber’s modal capacity. Intensity losses may be detected using Optical Time Domain Reflectometry (OTDR) while pinpointing the spatial location of the contact via time delay calculations of the back-scattered pulses. This work presents a theoretical model for the above sensing solution to provide a design tool for the fiber optic cable in the context of hydrocarbon sensing following corrosion of an external metal coating. Results are verified against the experimental data published in the literature. Full article
(This article belongs to the Special Issue Sensors for Oil Applications)
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Open AccessArticle Towards a Continuous Biometric System Based on ECG Signals Acquired on the Steering Wheel
Sensors 2017, 17(10), 2228; doi:10.3390/s17102228
Received: 23 August 2017 / Revised: 16 September 2017 / Accepted: 26 September 2017 / Published: 28 September 2017
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Abstract
Electrocardiogram signals acquired through a steering wheel could be the key to seamless, highly comfortable, and continuous human recognition in driving settings. This paper focuses on the enhancement of the unprecedented lesser quality of such signals, through the combination of Savitzky-Golay and moving
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Electrocardiogram signals acquired through a steering wheel could be the key to seamless, highly comfortable, and continuous human recognition in driving settings. This paper focuses on the enhancement of the unprecedented lesser quality of such signals, through the combination of Savitzky-Golay and moving average filters, followed by outlier detection and removal based on normalised cross-correlation and clustering, which was able to render ensemble heartbeats of significantly higher quality. Discrete Cosine Transform (DCT) and Haar transform features were extracted and fed to decision methods based on Support Vector Machines (SVM), k-Nearest Neighbours (kNN), Multilayer Perceptrons (MLP), and Gaussian Mixture Models - Universal Background Models (GMM-UBM) classifiers, for both identification and authentication tasks. Additional techniques of user-tuned authentication and past score weighting were also studied. The method’s performance was comparable to some of the best recent state-of-the-art methods (94.9% identification rate (IDR) and 2.66% authentication equal error rate (EER)), despite lesser results with scarce train data (70.9% IDR and 11.8% EER). It was concluded that the method was suitable for biometric recognition with driving electrocardiogram signals, and could, with future developments, be used on a continuous system in seamless and highly noisy settings. Full article
(This article belongs to the Section Biosensors)
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Open AccessArticle Eddy Current Testing with Giant Magnetoresistance (GMR) Sensors and a Pipe-Encircling Excitation for Evaluation of Corrosion under Insulation
Sensors 2017, 17(10), 2229; doi:10.3390/s17102229
Received: 18 August 2017 / Revised: 21 September 2017 / Accepted: 26 September 2017 / Published: 28 September 2017
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Abstract
This work investigates an eddy current-based non-destructive testing (NDT) method to characterize corrosion of pipes under thermal insulation, one of the leading failure mechanisms for insulated pipe infrastructure. Artificial defects were machined into the pipe surface to simulate the effect of corrosion wall
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This work investigates an eddy current-based non-destructive testing (NDT) method to characterize corrosion of pipes under thermal insulation, one of the leading failure mechanisms for insulated pipe infrastructure. Artificial defects were machined into the pipe surface to simulate the effect of corrosion wall loss. We show that by using a giant magnetoresistance (GMR) sensor array and a high current (300 A), single sinusoidal low frequency (5–200 Hz) pipe-encircling excitation scheme it is possible to quantify wall loss defects without removing the insulation or weather shield. An analysis of the magnetic field distribution and induced currents was undertaken using the finite element method (FEM) and analytical calculations. Simple algorithms to remove spurious measured field variations not associated with defects were developed and applied. The influence of an aluminium weather shield with discontinuities and dents was ascertained and found to be small for excitation frequency values below 40 Hz. The signal dependence on the defect dimensions was analysed in detail. The excitation frequency at which the maximum field amplitude change occurred increased linearly with the depth of the defect by about 3 Hz/mm defect depth. The change in magnetic field amplitude due to defects for sensors aligned in the azimuthal and radial directions were measured and found to be linearly dependent on the defect volume between 4400–30,800 mm3 with 1.2 × 10−3−1.6 × 10−3 µT/mm3. The results show that our approach is well suited for measuring wall loss defects similar to the defects from corrosion under insulation. Full article
(This article belongs to the Section Physical Sensors)
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