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Sensors, Volume 18, Issue 10 (October 2018)

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Cover Story (view full-size image) In this paper, the development of a wireless acquisition system (hardware and software) [...] Read more.
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Open AccessArticle FAST Pre-Filtering-Based Real Time Road Sign Detection for Low-Cost Vehicle Localization
Sensors 2018, 18(10), 3590; https://doi.org/10.3390/s18103590 (registering DOI)
Received: 11 September 2018 / Revised: 8 October 2018 / Accepted: 16 October 2018 / Published: 22 October 2018
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Abstract
In order to overcome the limitations of GNSS/INS and to keep the cost affordable for mass-produced vehicles, a precise localization system fusing the estimated vehicle positions from low-cost GNSS/INS and low-cost perception sensors is being developed. For vehicle position estimation, a perception sensor
[...] Read more.
In order to overcome the limitations of GNSS/INS and to keep the cost affordable for mass-produced vehicles, a precise localization system fusing the estimated vehicle positions from low-cost GNSS/INS and low-cost perception sensors is being developed. For vehicle position estimation, a perception sensor detects a road facility and uses it as a landmark. For this localization system, this paper proposes a method to detect a road sign as a landmark using a monocular camera whose cost is relatively low compared to other perception sensors. Since the inside pattern and aspect ratio of a road sign are various, the proposed method is based on the part-based approach that detects corners and combines them to detect a road sign. While the recall, precision, and processing time of the state of the art detector based on a convolutional neural network are 99.63%, 98.16%, and 4802 ms respectively, the recall, precision, and processing time of the proposed method are 97.48%, 98.78%, and 66.7 ms, respectively. The detection performance of the proposed method is as good as that of the state of the art detector and its processing time is drastically reduced to be applicable for an embedded system. Full article
(This article belongs to the Special Issue Sensors Applications in Intelligent Vehicle)
Open AccessArticle A New Disaster Information Sensing Mode: Using Multi-Robot System with Air Dispersal Mode
Sensors 2018, 18(10), 3589; https://doi.org/10.3390/s18103589 (registering DOI)
Received: 30 August 2018 / Revised: 5 October 2018 / Accepted: 18 October 2018 / Published: 22 October 2018
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Abstract
This paper presents a novel sensing mode for using mobile robots to collect disaster ground information when the ground traffic from the rescue center to disaster site is disrupted. Traditional sensing modes which use aerial robots or ground robots independently either have limited
[...] Read more.
This paper presents a novel sensing mode for using mobile robots to collect disaster ground information when the ground traffic from the rescue center to disaster site is disrupted. Traditional sensing modes which use aerial robots or ground robots independently either have limited ability to access disaster site or are only able to provide a bird’s eye view of the disaster site. To illustrate the proposed sensing mode, the authors have developed a Multi-robot System with Air Dispersal Mode (MSADM) by combining the unimpeded path of aerial robots with the detailed view of ground robots. In the MSADM, an airplane carries some minimal reconnaissance ground robots to overcome the paralyzed traffic problem and deploys them on the ground to collect detailed scene information using parachutes and separation device modules. In addition, the airplane cruises in the sky and relays the control and reported information between the ground robots and the human operator. This means that the proposed sensing mode is able to provide more reliable communication performance when there are obstacles between the human operators and the ground robots. Additionally, the proposed sensing mode can easily make use of different kinds of ground robots, as long as they have a compatible interface with the separation device. Finally, an experimental demonstration of the MSADM is presented to show the effectiveness of the proposed sensing mode. Full article
(This article belongs to the Section Physical Sensors)
Open AccessArticle Cooperative Sensing Data Collection and Distribution with Packet Collision Avoidance in Mobile Long-Thin Networks
Sensors 2018, 18(10), 3588; https://doi.org/10.3390/s18103588 (registering DOI)
Received: 14 September 2018 / Revised: 15 October 2018 / Accepted: 18 October 2018 / Published: 22 October 2018
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Abstract
Mobile ad hoc networks (MANETs) have gained a lot of interests in research communities for the infrastructure-less self-organizing nature. A MANET with fleet cyclists using smartphones forms a two-tier mobile long-thin network (MLTN) along a common cycling route, where the high-tier network is
[...] Read more.
Mobile ad hoc networks (MANETs) have gained a lot of interests in research communities for the infrastructure-less self-organizing nature. A MANET with fleet cyclists using smartphones forms a two-tier mobile long-thin network (MLTN) along a common cycling route, where the high-tier network is composed of 3G/LTE interfaces and the low-tier network is composed of IEEE 802.11 interfaces. The low-tier network may consist of several path-like networks. This work investigates cooperative sensing data collection and distribution with packet collision avoidance in a two-tier MLTN. As numbers of cyclists upload their sensing data and download global fleet information frequently, serious bandwidth and latency problems may result if all members rely on their high-tier interfaces. We designed and analyzed a cooperative framework consisting of a distributed grouping mechanism, a group merging and splitting method, and a sensing data aggregation scheme. Through cooperation between the two tiers, the proposed framework outperforms existing works by significantly reducing the 3G/LTE data transmission and the number of 3G/LTE connections. Full article
(This article belongs to the Special Issue Mobile Computing and Ubiquitous Networking)
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Open AccessArticle Hyperspectral Remote Sensing Image Classification Based on Maximum Overlap Pooling Convolutional Neural Network
Sensors 2018, 18(10), 3587; https://doi.org/10.3390/s18103587 (registering DOI)
Received: 27 September 2018 / Revised: 15 October 2018 / Accepted: 20 October 2018 / Published: 22 October 2018
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Abstract
In a traditional convolutional neural network structure, pooling layers generally use an average pooling method: a non-overlapping pooling. However, this condition results in similarities in the extracted image features, especially for the hyperspectral images of a continuous spectrum, which makes it more difficult
[...] Read more.
In a traditional convolutional neural network structure, pooling layers generally use an average pooling method: a non-overlapping pooling. However, this condition results in similarities in the extracted image features, especially for the hyperspectral images of a continuous spectrum, which makes it more difficult to extract image features with differences, and image detail features are easily lost. This result seriously affects the accuracy of image classification. Thus, a new overlapping pooling method is proposed, where maximum pooling is used in an improved convolutional neural network to avoid the fuzziness of average pooling. The step size used is smaller than the size of the pooling kernel to achieve overlapping and coverage between the outputs of the pooling layer. The dataset selected for this experiment was the Indian Pines dataset, collected by the airborne visible/infrared imaging spectrometer (AVIRIS) sensor. Experimental results show that using the improved convolutional neural network for remote sensing image classification can effectively improve the details of the image and obtain a high classification accuracy. Full article
(This article belongs to the Special Issue Multi-Sensor Fusion and Data Analysis)
Open AccessArticle A Combined Ray Tracing Method for Improving the Precision of the USBL Positioning System in Smart Ocean
Sensors 2018, 18(10), 3586; https://doi.org/10.3390/s18103586 (registering DOI)
Received: 31 August 2018 / Revised: 19 October 2018 / Accepted: 19 October 2018 / Published: 22 October 2018
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Abstract
The ultra-short baseline positioning system (USBL) has the advantages of flexible application and easy installation, and it plays an extremely important role in the underwater positioning and communication. The error of the USBL in underwater positioning is mainly caused by a ranging error
[...] Read more.
The ultra-short baseline positioning system (USBL) has the advantages of flexible application and easy installation, and it plays an extremely important role in the underwater positioning and communication. The error of the USBL in underwater positioning is mainly caused by a ranging error due to ray tracing, a phase difference error of the USBL, and acoustic noise in the underwater communication. Most of these errors are related to the changes in the sound speed during its propagation through the ocean. Therefore, when using the USBL for underwater detection, it is necessary to correct the sound speed profile in the detection area and optimize the ray tracing. Taking into account the actual conditions, this paper aims at correcting the model of underwater sound speed propagation and improving the tracking method of sound lines when the marine environment in the shallow sea area changes. This paper proposes a combined ray tracing method that can adaptively determine whether to use the constant sound speed ray tracing method or the equal gradient ray tracing method. The theoretical analysis and simulation results show that the proposed method can effectively reduce the error of slant distance in USBL compared with the traditional acoustic tracking method and the constant sound speed ray tracing method. The proposed sound ray correction algorithm solves the contradiction between the number of iterations and the reduction of positioning error and has engineering application value. Full article
(This article belongs to the Special Issue Smart Ocean: Emerging Research Advances, Prospects and Challenges)
Open AccessArticle Energy-Balanced Multisensory Scheduling for Target Tracking in Wireless Sensor Networks
Sensors 2018, 18(10), 3585; https://doi.org/10.3390/s18103585 (registering DOI)
Received: 3 September 2018 / Revised: 3 October 2018 / Accepted: 19 October 2018 / Published: 22 October 2018
PDF Full-text (1068 KB)
Abstract
One important way to extend the lifetime of wireless sensor networks (WSNs) is to manage the sleep scheduling of sensor nodes after they are deployed. Most of the existing works on node scheduling mainly concentrate on nodes which have only one sensor, and
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One important way to extend the lifetime of wireless sensor networks (WSNs) is to manage the sleep scheduling of sensor nodes after they are deployed. Most of the existing works on node scheduling mainly concentrate on nodes which have only one sensor, and they regard a node and its sensor modules as a whole to manage sleep scheduling. Few works involve the sensed modules scheduling of the sensor nodes, which have multiple sensors. However, some of the sensed modules (such as video sensor) consume a lot of energy. Therefore, they have less energy efficiency for multisensory networks. In this paper, we propose a distributed and energy-balanced multisensory scheduling strategy (EBMS), which considers the scheduling of both the communication modules and the sensed modules for each node in target tracking WSNs. In EBMS, the network is organized as clustering structures. Each cluster head adaptively assigns a sleep time to its cluster members according to the position of the members. Energy-balanced multisensory scheduling strategy also proposes an energy balanced parameter to balance the energy consumption of each node in the network. In addition, multi-hop coordination scheme is proposed to find the optimal cooperation among clusters to maximize the energy conservation. Experimental results show that EBMS outperformed the state-of-the-art approaches. Full article
(This article belongs to the Section Intelligent Sensors)
Open AccessReview Survey on Prominent RFID Authentication Protocols for Passive Tags
Sensors 2018, 18(10), 3584; https://doi.org/10.3390/s18103584 (registering DOI)
Received: 1 September 2018 / Revised: 6 October 2018 / Accepted: 19 October 2018 / Published: 22 October 2018
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Abstract
Radio Frequency Identification (RFID) is one of the leading technologies in the Internet of Things (IoT) to create an efficient and reliable system to securely identify objects in many environments such as business, health, and manufacturing areas. Recent RFID authentication protocols have been
[...] Read more.
Radio Frequency Identification (RFID) is one of the leading technologies in the Internet of Things (IoT) to create an efficient and reliable system to securely identify objects in many environments such as business, health, and manufacturing areas. Recent RFID authentication protocols have been proposed to satisfy the security features of RFID communication. In this article, we identify and review some of the most recent and enhanced authentication protocols that mainly focus on the authentication between a reader and a tag. However, the scope of this survey includes only passive tags protocols, due to the large scale of the RFID framework. We examined some of the recent RFID protocols in term of security requirements, computation, and attack resistance. We conclude that only five protocols resist all of the major attacks, while only one protocol satisfies all of the security requirements of the RFID system. Full article
(This article belongs to the Section Internet of Things)
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Open AccessArticle A Low-Light Sensor Image Enhancement Algorithm Based on HSI Color Model
Sensors 2018, 18(10), 3583; https://doi.org/10.3390/s18103583 (registering DOI)
Received: 18 September 2018 / Revised: 15 October 2018 / Accepted: 20 October 2018 / Published: 22 October 2018
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Abstract
Images captured by sensors in unpleasant environment like low illumination condition are usually degraded, which means low visibility, low brightness, and low contrast. In order to improve this kind of images, in this paper, a low-light sensor image enhancement algorithm based on HSI
[...] Read more.
Images captured by sensors in unpleasant environment like low illumination condition are usually degraded, which means low visibility, low brightness, and low contrast. In order to improve this kind of images, in this paper, a low-light sensor image enhancement algorithm based on HSI color model is proposed. At first, we propose a dataset generation method based on the Retinex model to overcome the shortage of sample data. Then, the original low-light image is transformed from RGB to HSI color space. The segmentation exponential method is used to process the saturation (S) and the specially designed Deep Convolutional Neural Network is applied to enhance the intensity component (I). At the end, we back into the original RGB space to get the final improved image. Experimental results show that the proposed algorithm not only enhances the image brightness and contrast significantly, but also avoids color distortion and over-enhancement in comparison with some other state-of-the-art research papers. So, it effectively improves the quality of sensor images. Full article
(This article belongs to the Special Issue Deep Learning-Based Image Sensors)
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Open AccessArticle Semantic-Enhanced Multi-Dimensional Markov Chains on Semantic Trajectories for Predicting Future Locations
Sensors 2018, 18(10), 3582; https://doi.org/10.3390/s18103582 (registering DOI)
Received: 1 September 2018 / Revised: 1 October 2018 / Accepted: 11 October 2018 / Published: 22 October 2018
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Abstract
In this work, we investigate the performance of Markov Chains with respect to modelling semantic trajectories and predicting future locations. In the first part, we examine whether and to what degree the semantic level of semantic trajectories affects the predictive performance of a
[...] Read more.
In this work, we investigate the performance of Markov Chains with respect to modelling semantic trajectories and predicting future locations. In the first part, we examine whether and to what degree the semantic level of semantic trajectories affects the predictive performance of a spatial Markov model. It can be shown that the choice of the semantic level when describing trajectories has a significant impact on the accuracy of the models. High-level descriptions lead to better results than low-level ones. The second part introduces a multi-dimensional Markov Chain construct that considers, besides locations, additional context information, such as time, day and the users’ activity. While the respective approach is able to outperform our baseline, we could also identify some limitations. These are mainly attributed to its sensitivity towards small-sized training datasets. We attempt to overcome this issue, among others, by adding a semantic similarity analysis component to our model that takes the varying role of locations due each time to the respective purpose of visiting the particular location explicitly into consideration. To capture the aforementioned dynamics, we define an entity, which we refer to as Purpose-of-Visit-Dependent Frame (PoVDF). In the third part of this work, we describe in detail the PoVDF-based approach and we evaluate it against the multi-dimensional Markov Chain model as well as with a semantic trajectory mining and prefix tree based model. Our evaluation shows that the PoVDF-based approach outperforms its competition and lays a solid foundation for further investigation. Full article
(This article belongs to the Special Issue Context and Activity Modelling and Recognition)
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Open AccessArticle Design of a Hybrid Indoor Location System Based on Multi-Sensor Fusion for Robot Navigation
Sensors 2018, 18(10), 3581; https://doi.org/10.3390/s18103581 (registering DOI)
Received: 2 September 2018 / Revised: 18 October 2018 / Accepted: 18 October 2018 / Published: 22 October 2018
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Abstract
In the case of a single scene feature, the positioning of an indoor service robot takes a long time, and localization errors are likely to occur. A new method for a hybrid indoor localization system according to multi-sensor fusion is proposed to solve
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In the case of a single scene feature, the positioning of an indoor service robot takes a long time, and localization errors are likely to occur. A new method for a hybrid indoor localization system according to multi-sensor fusion is proposed to solve these problems. The localization process is divided in two stages: rough positioning and precise positioning. By virtue of the K nearest neighbors based on possibility (KNNBP) algorithm first created in the present study, the rough position of a robot is determined according to the received signal strength indicator (RSSI) of Wi-Fi. Then, the hybrid particle filter localization (HPFL) algorithm improved on the basis of adaptive Monte Carlo localization (AMCL) is adopted to get the precise localization, which integrates various information, including the rough position and information from Lidar, a compass, an occupancy grid map, and encoders. The experiments indicated that the positioning error was 0.05 m; the success rate of localization was 96% with even 3000 particles, and the global positioning time was 1.9 s. However, under the same conditions, the success rate of AMCL was approximately 40%, the required time was approximately 25.6 s, and the positioning accuracy was the same. This indicates that the hybrid indoor location system is efficient and accurate. Full article
(This article belongs to the Special Issue Applications of Wireless Sensors in Localization and Tracking)
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Open AccessArticle A Novel Orthogonal Waveform Separation Scheme for Airborne MIMO-SAR Systems
Sensors 2018, 18(10), 3580; https://doi.org/10.3390/s18103580 (registering DOI)
Received: 25 September 2018 / Revised: 15 October 2018 / Accepted: 19 October 2018 / Published: 22 October 2018
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Abstract
In recent years, multi-input multi-output (MIMO) synthetic aperture radar (SAR) systems, which can promote the performance of 3D imaging, high-resolution wide-swath remote sensing, and multi-baseline interferometry, have received considerable attention. Several papers on MIMO-SAR have been published, but the research of such systems
[...] Read more.
In recent years, multi-input multi-output (MIMO) synthetic aperture radar (SAR) systems, which can promote the performance of 3D imaging, high-resolution wide-swath remote sensing, and multi-baseline interferometry, have received considerable attention. Several papers on MIMO-SAR have been published, but the research of such systems is seriously limited. This is mainly because the superposed echoes of the multiple transmitted orthogonal waveforms cannot be separated perfectly. The imperfect separation will introduce ambiguous energy and degrade SAR images dramatically. In this paper, a novel orthogonal waveform separation scheme based on echo-compression is proposed for airborne MIMO-SAR systems. Specifically, apart from the simultaneous transmissions, the transmitters are required to radiate several times alone in a synthetic aperture to sense their private inner-aperture channels. Since the channel responses at the neighboring azimuth positions are relevant, the energy of the solely radiated orthogonal waveforms in the superposed echoes will be concentrated. To this end, the echoes of the multiple transmitted orthogonal waveforms can be separated by cancelling the peaks. In addition, the cleaned echoes, along with original superposed one, can be used to reconstruct the unambiguous echoes. The proposed scheme is validated by simulations. Full article
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Open AccessArticle Voronoi Diagram and Crowdsourcing-Based Radio Map Interpolation for GRNN Fingerprinting Localization Using WLAN
Sensors 2018, 18(10), 3579; https://doi.org/10.3390/s18103579 (registering DOI)
Received: 19 September 2018 / Revised: 14 October 2018 / Accepted: 17 October 2018 / Published: 22 October 2018
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Abstract
In the last decade, fingerprinting localization using wireless local area network (WLAN) has been paid lots of attention. However, this method needs to establish a database called radio map in the off-line stage, which is a labor-intensive and time-consuming process. To save the
[...] Read more.
In the last decade, fingerprinting localization using wireless local area network (WLAN) has been paid lots of attention. However, this method needs to establish a database called radio map in the off-line stage, which is a labor-intensive and time-consuming process. To save the radio map establishment cost and improve localization performance, in this paper, we first propose a Voronoi diagram and crowdsourcing-based radio map interpolation method. The interpolation method optimizes propagation model parameters for each Voronoi cell using the received signal strength (RSS) and location coordinates of crowdsourcing points and estimates the RSS samples of interpolation points with the optimized propagation model parameters to establish a new radio map. Then a general regression neural network (GRNN) is employed to fuse the new and original radio maps established through interpolation and manual operation, respectively, and also used as a fingerprinting localization algorithm to compute localization coordinates. The experimental results demonstrate that our proposed GRNN fingerprinting localization system with the fused radio map is able to considerably improve the localization performance. Full article
(This article belongs to the Section Sensor Networks)
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Open AccessArticle Assessment of Fringe Pattern Decomposition with a Cross-Correlation Index for Phase Retrieval in Fringe Projection 3D Measurements
Sensors 2018, 18(10), 3578; https://doi.org/10.3390/s18103578 (registering DOI)
Received: 8 September 2018 / Revised: 17 October 2018 / Accepted: 18 October 2018 / Published: 22 October 2018
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Abstract
Phase retrieval from single frame projection fringe patterns, a fundamental and challenging problem in fringe projection measurement, attracts wide attention and various new methods have emerged to address this challenge. Many phase retrieval methods are based on the decomposition of fringe patterns into
[...] Read more.
Phase retrieval from single frame projection fringe patterns, a fundamental and challenging problem in fringe projection measurement, attracts wide attention and various new methods have emerged to address this challenge. Many phase retrieval methods are based on the decomposition of fringe patterns into a background part and a fringe part, and then the phase is obtained from the decomposed fringe part. However, the decomposition results are subject to the selection of model parameters, which is usually performed manually by trial and error due to the lack of decomposition assessment rules under a no ground truth data situation. In this paper, we propose a cross-correlation index to assess the decomposition and phase retrieval results without the need of ground truth data. The feasibility of the proposed metric is verified by simulated and real fringe patterns with the well-known Fourier transform method and recently proposed Shearlet transform method. This work contributes to the automatic phase retrieval and three-dimensional (3D) measurement with less human intervention, and can be potentially employed in other fields such as phase retrieval in digital holography. Full article
(This article belongs to the Special Issue Depth Sensors and 3D Vision)
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Open AccessArticle Smart Contract-Based Review System for an IoT Data Marketplace
Sensors 2018, 18(10), 3577; https://doi.org/10.3390/s18103577 (registering DOI)
Received: 31 August 2018 / Revised: 10 October 2018 / Accepted: 16 October 2018 / Published: 22 October 2018
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Abstract
Internet of Things (IoT)-based devices, especially those used for home automation, consist of their own sensors and generate many logs during a process. Enterprises producing IoT devices convert these log data into more useful data through secondary processing; thus, they require data from
[...] Read more.
Internet of Things (IoT)-based devices, especially those used for home automation, consist of their own sensors and generate many logs during a process. Enterprises producing IoT devices convert these log data into more useful data through secondary processing; thus, they require data from the device users. Recently, a platform for data sharing has been developed because the demand for IoT data increases. Several IoT data marketplaces are based on peer-to-peer (P2P) networks, and in this type of marketplace, it is difficult for an enterprise to trust a data owner or the data they want to trade. Therefore, in this study, we propose a review system that can confirm the reputation of a data owner or the data traded in the P2P data marketplace. The traditional server-client review systems have many drawbacks, such as security vulnerability or server administrator’s malicious behavior. However, the review system developed in this study is based on Ethereum smart contracts; thus, this system is running on the P2P network and is more flexible for the network problem. Moreover, the integrity and immutability of the registered reviews are assured because of the blockchain public ledger. In addition, a certain amount of gas is essential for all functions to be processed by Ethereum transactions. Accordingly, we tested and analyzed the performance of our proposed model in terms of gas required. Full article
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Open AccessArticle Automatic Leaf Segmentation for Estimating Leaf Area and Leaf Inclination Angle in 3D Plant Images
Sensors 2018, 18(10), 3576; https://doi.org/10.3390/s18103576 (registering DOI)
Received: 7 September 2018 / Revised: 19 October 2018 / Accepted: 20 October 2018 / Published: 22 October 2018
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Abstract
Automatic and efficient plant monitoring offers accurate plant management. Construction of three-dimensional (3D) models of plants and acquisition of their spatial information is an effective method for obtaining plant structural parameters. Here, 3D images of leaves constructed with multiple scenes taken from different
[...] Read more.
Automatic and efficient plant monitoring offers accurate plant management. Construction of three-dimensional (3D) models of plants and acquisition of their spatial information is an effective method for obtaining plant structural parameters. Here, 3D images of leaves constructed with multiple scenes taken from different positions were segmented automatically for the automatic retrieval of leaf areas and inclination angles. First, for the initial segmentation, leave images were viewed from the top, then leaves in the top-view images were segmented using distance transform and the watershed algorithm. Next, the images of leaves after the initial segmentation were reduced by 90%, and the seed regions for each leaf were produced. The seed region was re-projected onto the 3D images, and each leaf was segmented by expanding the seed region with the 3D information. After leaf segmentation, the leaf area of each leaf and its inclination angle were estimated accurately via a voxel-based calculation. As a result, leaf area and leaf inclination angle were estimated accurately after automatic leaf segmentation. This method for automatic plant structure analysis allows accurate and efficient plant breeding and growth management. Full article
(This article belongs to the Section Remote Sensors)
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Open AccessArticle An End-to-End Deep Reinforcement Learning-Based Intelligent Agent Capable of Autonomous Exploration in Unknown Environments
Sensors 2018, 18(10), 3575; https://doi.org/10.3390/s18103575 (registering DOI)
Received: 14 September 2018 / Revised: 14 September 2018 / Accepted: 15 October 2018 / Published: 22 October 2018
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Abstract
In recent years, machine learning (and as a result artificial intelligence) has experienced considerable progress. As a result, robots in different shapes and with different purposes have found their ways into our everyday life. These robots, which have been developed with the goal
[...] Read more.
In recent years, machine learning (and as a result artificial intelligence) has experienced considerable progress. As a result, robots in different shapes and with different purposes have found their ways into our everyday life. These robots, which have been developed with the goal of human companionship, are here to help us in our everyday and routine life. These robots are different to the previous family of robots that were used in factories and static environments. These new robots are social robots that need to be able to adapt to our environment by themselves and to learn from their own experiences. In this paper, we contribute to the creation of robots with a high degree of autonomy, which is a must for social robots. We try to create an algorithm capable of autonomous exploration in and adaptation to unknown environments and implement it in a simulated robot. We go further than a simulation and implement our algorithm in a real robot, in which our sensor fusion method is able to overcome real-world noise and perform robust exploration. Full article
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Open AccessArticle Electrostatic Sensor Application for On-Line Monitoring of Wind Turbine Gearboxes
Sensors 2018, 18(10), 3574; https://doi.org/10.3390/s18103574 (registering DOI)
Received: 3 September 2018 / Revised: 19 October 2018 / Accepted: 19 October 2018 / Published: 22 October 2018
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Abstract
The oil-line electrostatic sensor (OLES) is a new online monitoring technology for wear debris based on the principle of electrostatic induction that has achieved good measurement results under laboratory conditions. However, for practical applications, the utility of the sensor is still unclear. The
[...] Read more.
The oil-line electrostatic sensor (OLES) is a new online monitoring technology for wear debris based on the principle of electrostatic induction that has achieved good measurement results under laboratory conditions. However, for practical applications, the utility of the sensor is still unclear. The aim of this work was to investigate in detail the application potential of the electrostatic sensor for wind turbine gearboxes. Firstly, a wear debris recognition method based on the electrostatic sensor with two-probes is proposed. Further, with the wind turbine gearbox bench test, the performance of the electrostatic sensor and the effectiveness of the debris recognition method are comprehensively evaluated. The test demonstrates that the electrostatic sensor is capable of monitoring the debris and indicating the abnormality of the gearbox effectively using the proposed method. Moreover, the test also reveals that the background signal of the electrostatic sensor is related to the oil temperature and oil flow rate, but has no relationship to the working conditions of the gearbox. This research brings the electrostatic sensor closer to practical applications. Full article
(This article belongs to the Section Physical Sensors)
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Open AccessArticle Fast Adaptive RNN Encoder–Decoder for Anomaly Detection in SMD Assembly Machine
Sensors 2018, 18(10), 3573; https://doi.org/10.3390/s18103573 (registering DOI)
Received: 4 September 2018 / Revised: 16 October 2018 / Accepted: 18 October 2018 / Published: 22 October 2018
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Abstract
Surface Mounted Device (SMD) assembly machine manufactures various products on a flexible manufacturing line. An anomaly detection model that can adapt to the various manufacturing environments very fast is required. In this paper, we proposed a fast adaptive anomaly detection model based on
[...] Read more.
Surface Mounted Device (SMD) assembly machine manufactures various products on a flexible manufacturing line. An anomaly detection model that can adapt to the various manufacturing environments very fast is required. In this paper, we proposed a fast adaptive anomaly detection model based on a Recurrent Neural Network (RNN) Encoder–Decoder with operating machine sounds. RNN Encoder–Decoder has a structure very similar to Auto-Encoder (AE), but the former has significantly reduced parameters compared to the latter because of its rolled structure. Thus, the RNN Encoder–Decoder only requires a short training process for fast adaptation. The anomaly detection model decides abnormality based on Euclidean distance between generated sequences and observed sequence from machine sounds. Experimental evaluation was conducted on a set of dataset from the SMD assembly machine. Results showed cutting-edge performance with fast adaptation. Full article
(This article belongs to the Section Intelligent Sensors)
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Open AccessArticle Efficient Hybrid Emergency Aware MAC Protocol for Wireless Body Sensor Networks
Sensors 2018, 18(10), 3572; https://doi.org/10.3390/s18103572 (registering DOI)
Received: 10 September 2018 / Revised: 13 October 2018 / Accepted: 18 October 2018 / Published: 22 October 2018
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Abstract
In Body Sensor Networks (BSNs), two types of events should be addressed: periodic and emergency events. Traffic rate is usually low during periodic observation, and becomes very high upon emergency. One of the main and challenging requirements of BSNs is to design Medium
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In Body Sensor Networks (BSNs), two types of events should be addressed: periodic and emergency events. Traffic rate is usually low during periodic observation, and becomes very high upon emergency. One of the main and challenging requirements of BSNs is to design Medium Access Control (MAC) protocols that guarantee immediate and reliable transmission of data in emergency situations, while maintaining high energy efficiency in non-emergency conditions. In this paper, we propose a new emergency aware hybrid DTDMA/DS-CDMA protocol that can accommodate BSN traffic variations by addressing emergency and periodic traffic requirements. It takes advantage of the high delay efficiency of DS-CDMA in traffic burst, and the high energy efficiency of DTDMA in periodic traffic. The proposed scheme is evaluated in terms of delay, packet drop percentage, and energy consumption. Different OPNET simulations are performed for various number of nodes carrying emergency data, and for various payload sizes. The protocol performance is compared to other existing hybrid protocols. Results show that the proposed scheme outperforms the others in terms of delay and packet drop percentage for different number of nodes carrying emergency data, as well as for different payload sizes. It also offers the highest energy efficiency during periodic observation, while adjusting the energy consumption during emergency by assigning spreading codes only to nodes holding emergency data. Full article
(This article belongs to the Section Sensor Networks)
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Open AccessArticle Flying Ad Hoc Networks: A New Domain for Network Communications
Sensors 2018, 18(10), 3571; https://doi.org/10.3390/s18103571 (registering DOI)
Received: 28 September 2018 / Revised: 15 October 2018 / Accepted: 16 October 2018 / Published: 21 October 2018
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Abstract
The advent of flying ad hoc networks (FANETs) has opened an opportunity to create new added-value services. Even though it is clear that these networks share common features with its predecessors, e.g., with mobile ad hoc networks and with vehicular ad hoc networks,
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The advent of flying ad hoc networks (FANETs) has opened an opportunity to create new added-value services. Even though it is clear that these networks share common features with its predecessors, e.g., with mobile ad hoc networks and with vehicular ad hoc networks, there are several unique characteristics that make FANETs different. These distinctive features impose a series of guidelines to be considered for its successful deployment. Particularly, the use of FANETs for telecommunication services presents demanding challenges in terms of quality of service, energy efficiency, scalability, and adaptability. The proper use of models in research activities will undoubtedly assist to solve those challenges. Therefore, in this paper, we review mobility, positioning, and propagation models proposed for FANETs in the related scientific literature. A common limitation that affects these three topics is the lack of studies evaluating the influence that the unmanned aerial vehicles (UAV) may have in the on-board/embedded communication devices, usually just assuming isotropic or omnidirectional radiation patterns. For this reason, we also investigate in this work the radiation pattern of an 802.11 n/ac (WiFi) device embedded in a UAV working on both the 2.4 and 5 GHz bands. Our findings show that the impact of the UAV is not negligible, representing up to a 10 dB drop for some angles of the communication links. Full article
(This article belongs to the Special Issue Unmanned Aerial Vehicle Networks, Systems and Applications)
Open AccessArticle Underwater Target Detection and 3D Reconstruction System Based on Binocular Vision
Sensors 2018, 18(10), 3570; https://doi.org/10.3390/s18103570 (registering DOI)
Received: 4 September 2018 / Revised: 16 October 2018 / Accepted: 18 October 2018 / Published: 21 October 2018
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Abstract
To better solve the problem of target detection in marine environment and to deal with the difficulty of 3D reconstruction of underwater target, a binocular vision-based underwater target detection and 3D reconstruction system is proposed in this paper. Two optical sensors are used
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To better solve the problem of target detection in marine environment and to deal with the difficulty of 3D reconstruction of underwater target, a binocular vision-based underwater target detection and 3D reconstruction system is proposed in this paper. Two optical sensors are used as the vision of the system. Firstly, denoising and color restoration are performed on the image sequence acquired by the vision of the system and the underwater target is segmented and extracted according to the image saliency using the super-pixel segmentation method. Secondly, aiming to reduce mismatch, we improve the semi-global stereo matching method by strictly constraining the matching in the valid target area and then optimizing the basic disparity map within each super-pixel area using the least squares fitting interpolation method. Finally, based on the optimized disparity map, triangulation principle is used to calculate the three-dimensional data of the target and the 3D structure and color information of the target can be given by MeshLab. The experimental results show that for a specific size underwater target, the system can achieve higher measurement accuracy and better 3D reconstruction effect within a suitable distance. Full article
(This article belongs to the Special Issue Multi-Sensor Fusion and Data Analysis)
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Open AccessArticle Efficient and Secure Key Distribution Protocol for Wireless Sensor Networks
Sensors 2018, 18(10), 3569; https://doi.org/10.3390/s18103569 (registering DOI)
Received: 1 September 2018 / Revised: 28 September 2018 / Accepted: 16 October 2018 / Published: 21 October 2018
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Abstract
Modern wireless sensor networks have adopted the IEEE 802.15.4 standard. This standard defines the first two layers, the physical and medium access control layers; determines the radio wave used for communication; and defines the 128-bit advanced encryption standard (AES-128) for encrypting and validating
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Modern wireless sensor networks have adopted the IEEE 802.15.4 standard. This standard defines the first two layers, the physical and medium access control layers; determines the radio wave used for communication; and defines the 128-bit advanced encryption standard (AES-128) for encrypting and validating the transmitted data. However, the standard does not specify how to manage, store, or distribute the encryption keys. Many solutions have been proposed to address this problem, but the majority are impractical in resource-constrained devices such as wireless sensor nodes or cause degradation of other metrics. Therefore, we propose an efficient and secure key distribution protocol that is simple, practical, and feasible to implement on resource-constrained wireless sensor nodes. We conduct simulations and hardware implementations to analyze our work and compare it to existing solutions based on different metrics such as energy consumption, storage overhead, key connectivity, replay attack, man-in-the-middle attack, and resiliency to node capture attack. Our findings show that the proposed protocol is secure and more efficient than other solutions. Full article
(This article belongs to the Section Sensor Networks)
Open AccessArticle Industrial IoT Monitoring: Technologies and Architecture Proposal
Sensors 2018, 18(10), 3568; https://doi.org/10.3390/s18103568 (registering DOI)
Received: 12 September 2018 / Revised: 9 October 2018 / Accepted: 16 October 2018 / Published: 21 October 2018
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Abstract
Dependability and standardization are essential to the adoption of Wireless Sensor Networks (WSN) in industrial applications. Standards such as ZigBee, WirelessHART, ISA100.11a and WIA-PA are, nowadays, at the basis of the main process-automation technologies. However, despite the success of these standards, management of
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Dependability and standardization are essential to the adoption of Wireless Sensor Networks (WSN) in industrial applications. Standards such as ZigBee, WirelessHART, ISA100.11a and WIA-PA are, nowadays, at the basis of the main process-automation technologies. However, despite the success of these standards, management of WSNs is still an open topic, which clearly is an obstacle to dependability. Existing diagnostic tools are mostly application- or problem-specific, and do not support standard-based multi-network monitoring. This paper proposes a WSN monitoring architecture for process-automation technologies that addresses the mentioned limitations. Specifically, the architecture has low impact on sensor node resources, uses network metrics already available in industrial standards, and takes advantage of widely used management standards to share the monitoring information. The proposed architecture was validated through prototyping, and the obtained performance results are presented and discussed in the final part of the paper. In addition to proposing a monitoring architecture, the paper provides an in-depth insight into metrics, techniques, management protocols, and standards applicable to industrial WSNs. Full article
(This article belongs to the Section Sensor Networks)
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Open AccessArticle Research on a Handheld 3D Laser Scanning System for Measuring Large-Sized Objects
Sensors 2018, 18(10), 3567; https://doi.org/10.3390/s18103567 (registering DOI)
Received: 3 September 2018 / Revised: 17 October 2018 / Accepted: 18 October 2018 / Published: 21 October 2018
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Abstract
A handheld 3D laser scanning system is proposed for measuring large-sized objects on site. This system is mainly composed of two CCD cameras and a line laser projector, in which the two CCD cameras constitute a binocular stereo vision system to locate the
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A handheld 3D laser scanning system is proposed for measuring large-sized objects on site. This system is mainly composed of two CCD cameras and a line laser projector, in which the two CCD cameras constitute a binocular stereo vision system to locate the scanner’s position in the fixed workpiece coordinate system online, meanwhile the left CCD camera and the laser line projector constitute a structured light system to get the laser lines modulated by the workpiece features. The marked points and laser line are both obtained in the coordinate system of the left camera in each moment. To get the workpiece outline, the handheld scanner’s position is evaluated online by matching up the marked points got by the binocular stereo vision system and those in the workpiece coordinate system measured by a TRITOP system beforehand; then the laser line with workpiece’s features got at this moment is transformed into the fixed workpiece coordinate system. Finally, the 3D information composed by the laser lines can be reconstructed in the workpiece coordinate system. A ball arm with two standard balls, which is placed on a glass plate with many marked points randomly stuck on, is measured to test the system accuracy. The distance errors between the two balls are within ±0.05 mm, the radius errors of the two balls are all within ±0.04 mm, the distance errors from the scatter points to the fitted sphere are distributed evenly, within ±0.25 mm, without accumulated errors. Measurement results of two typical workpieces show that the system can measure large-sized objects completely with acceptable accuracy and have the advantage of avoiding some deficiencies, such as sheltering and limited measuring range. Full article
(This article belongs to the Section Physical Sensors)
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Open AccessArticle Photo-Electrochemical Sensing of Dopamine by a Novel Porous TiO2 Array-Modified Screen-Printed Ti Electrode
Sensors 2018, 18(10), 3566; https://doi.org/10.3390/s18103566 (registering DOI)
Received: 24 September 2018 / Revised: 15 October 2018 / Accepted: 16 October 2018 / Published: 21 October 2018
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Abstract
In this paper, the development of a nanoporous TiO2 array-modified Ti electrode for photo-electrochemical (PEC) sensing of dopamine (DA) is reported. A porous TiO2 array-modified electrode was fabricated from the controlled anodic oxidation of a Ti working electrode of commercial screen-printed
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In this paper, the development of a nanoporous TiO2 array-modified Ti electrode for photo-electrochemical (PEC) sensing of dopamine (DA) is reported. A porous TiO2 array-modified electrode was fabricated from the controlled anodic oxidation of a Ti working electrode of commercial screen-printed electrodes (SPE). The anodization process and the related morphological and microstructural transformation of the bare Ti electrode into a TiO2/Ti electrode was followed by scanning electron microscopy (SEM) and UV-visible reflectance spectroscopy (DR-UV-Vis). The modified electrode was irradiated with a low-power (120 mW) UV-Vis LED lamp (λ = 400 nm) and showed good performance for the detection of DA with a large linear response range, a sensitivity of 462 nA mM−1 cm−2, and a limit of detection of 20 µM. Moreover, it showed higher photocurrents in the presence of DA in comparison to some foreign species such as ascorbic acid, uric acid, glucose, K+, Na+, and Cl. Thus, this proposed low-cost photo-electrochemical sensor, with the advantage of very simple fabrication, demonstrates potential applications for the determination of dopamine in real samples. Full article
(This article belongs to the Section Biosensors)
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Open AccessArticle Proof of Concept for an Intracochlear Acoustic Receiver for Use in Acute Large Animal Experiments
Sensors 2018, 18(10), 3565; https://doi.org/10.3390/s18103565 (registering DOI)
Received: 20 September 2018 / Revised: 17 October 2018 / Accepted: 18 October 2018 / Published: 21 October 2018
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Abstract
(1) Background: The measurement of intracochlear sound pressure (ICSP) is relevant to obtain better understanding of the biomechanics of hearing. The goal of this work was a proof of concept of a partially implantable intracochlear acoustic receiver (ICAR) fulfilling all requirements for acute
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(1) Background: The measurement of intracochlear sound pressure (ICSP) is relevant to obtain better understanding of the biomechanics of hearing. The goal of this work was a proof of concept of a partially implantable intracochlear acoustic receiver (ICAR) fulfilling all requirements for acute ICSP measurements in a large animal. The ICAR was designed not only to be used in chronic animal experiments but also as a microphone for totally implantable cochlear implants (TICI). (2) Methods: The ICAR concept was based on a commercial MEMS condenser microphone customized with a protective diaphragm that provided a seal and optimized geometry for accessing the cochlea. The ICAR was validated under laboratory conditions and using in-vivo experiments in sheep. (3) Results: For the first time acute ICSP measurements were successfully performed in a live specimen that is representative of the anatomy and physiology of the human. Data obtained are in agreement with published data from cadavers. The surgeons reported high levels of ease of use and satisfaction with the system design. (4) Conclusions: Our results confirm that the developed ICAR can be used to measure ICSP in acute experiments. The next generation of the ICAR will be used in chronic sheep experiments and in TICI. Full article
(This article belongs to the Special Issue Implantable Sensors 2018)
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Open AccessArticle Study on Improved Flight Coefficient Estimation and Trajectory Analysis of a Flying Disc through Onboard Magnetometer Measurements
Sensors 2018, 18(10), 3564; https://doi.org/10.3390/s18103564 (registering DOI)
Received: 31 August 2018 / Revised: 13 October 2018 / Accepted: 18 October 2018 / Published: 20 October 2018
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Abstract
This paper proposes a novel and accurate method for estimating the flight coefficient of a flying disc typically operating at a high rotation rate. In particular, the proposed method introduces a new algorithm that takes advantage of magnetic data measured by a miniaturized
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This paper proposes a novel and accurate method for estimating the flight coefficient of a flying disc typically operating at a high rotation rate. In particular, the proposed method introduces a new algorithm that takes advantage of magnetic data measured by a miniaturized sensor module onboard a conventional disc. Since the geomagnetic field measured by the magnetic sensor mounted on the rotating body yields a general sinusoidal waveform, a frequency domain analysis is employed in computing the rotational rate. Furthermore, on the basis of the estimated rate during a whole flight period, a yaw damping derivative coefficient is derived, which enables an accurate prediction of the disc’s flight trajectory. For performance verification, both a reference rotation table test and a real flight test are performed, for which a miniaturized embedded sensor module is designed and manufactured for an onboard flight test. A reference rotation test validates the performance of the proposed method. Subsequently, a flight test, in which a simulator-based trajectory is compared with the true reference trajectory, verifies that the proposed method better predicts the flight trajectory by incorporating the estimated coefficient. Full article
(This article belongs to the Section Physical Sensors)
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Open AccessArticle Three-Dimensional Imaging Method for Array ISAR Based on Sparse Bayesian Inference
Sensors 2018, 18(10), 3563; https://doi.org/10.3390/s18103563 (registering DOI)
Received: 28 August 2018 / Revised: 6 October 2018 / Accepted: 18 October 2018 / Published: 20 October 2018
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Abstract
The problem of synthesis scatterers in inverse synthetic aperture radar (ISAR) make it difficult to realize high-resolution three-dimensional (3D) imaging. Radar array provides an available solution to this problem, but the resolution is restricted by limited aperture size and number of antennas, leading
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The problem of synthesis scatterers in inverse synthetic aperture radar (ISAR) make it difficult to realize high-resolution three-dimensional (3D) imaging. Radar array provides an available solution to this problem, but the resolution is restricted by limited aperture size and number of antennas, leading to deterioration of the 3D imaging performance. To solve these problems, we propose a novel 3D imaging method with an array ISAR system based on sparse Bayesian inference. First, the 3D imaging model using a sparse linear array is introduced. Then the elastic net estimation and Bayesian information criterion are introduced to fulfill model order selection automatically. Finally, the sparse Bayesian inference is adopted to realize super-resolution imaging and to get the 3D image of target of interest. The proposed method is used to process real radar data of a Ku band array ISAR system. The results show that the proposed method can effectively solve the problem of synthesis scatterers and realize super-resolution 3D imaging, which verify the practicality of our proposed method. Full article
(This article belongs to the Section Remote Sensors)
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Open AccessArticle Detection and Characterization of Damage in Quasi-Static Loaded Composite Structures using Passive Thermography
Sensors 2018, 18(10), 3562; https://doi.org/10.3390/s18103562 (registering DOI)
Received: 22 August 2018 / Revised: 27 September 2018 / Accepted: 8 October 2018 / Published: 20 October 2018
Viewed by 133 | PDF Full-text (3742 KB)
Abstract
Real-time nondestructive evaluation is critical during composites load testing. Of particular importance is the real time measurement of damage onset, growth, and ultimate failure. When newly formed damage is detected, the loading is stopped for further detailed characterization using ultrasound inspections or X-ray
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Real-time nondestructive evaluation is critical during composites load testing. Of particular importance is the real time measurement of damage onset, growth, and ultimate failure. When newly formed damage is detected, the loading is stopped for further detailed characterization using ultrasound inspections or X-ray computed tomography. This detailed inspection data are used to document failure modes and ultimately validate damage prediction models. Passive thermography is used to monitor heating from damage formation in a hat-stiffened woven graphite epoxy composite panel during quasi-static seven-point load testing. Data processing techniques are presented that enable detection of the small transient thermographic signals resulting from damage formation in real time. It has been observed that the temperature rise due to damage formation at the surface is composed of two thermal responses. The first response is instantaneous and conforms to the shape of the damage. This heating is most likely due to irreversible thermoelastic, plastic deformation, and microstructural heating. The second response is a transient increase in temperature due to mechanical heating at the interface of failure. Two-dimensional multi-layered thermal simulations based on quadrupole method are used to investigate the thermal responses. In particular, the instantaneous response is used as the transient response start time to determine damage depth. The passive thermography measurement results are compared to ultrasonic measurements for validation. Full article
Open AccessArticle A Blockchain-Based Authorization System for Trustworthy Resource Monitoring and Trading in Smart Communities
Sensors 2018, 18(10), 3561; https://doi.org/10.3390/s18103561 (registering DOI)
Received: 30 August 2018 / Revised: 5 October 2018 / Accepted: 16 October 2018 / Published: 20 October 2018
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Abstract
Resource consumption in residential areas requires novel contributions in the field of consumer information management and collaborative mechanisms for the exchange of resources, in order to optimize the overall consumption of the community. We propose an authorization system to facilitate access to consumer
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Resource consumption in residential areas requires novel contributions in the field of consumer information management and collaborative mechanisms for the exchange of resources, in order to optimize the overall consumption of the community. We propose an authorization system to facilitate access to consumer information and resource trading, based on blockchain technology. Our proposal is oriented to the Smart communities, an evolution of Community Energy Management Systems, in which communities are involved in the monitoring and coordination of resource consumption. The proposed environment allows a more reliable management of monitoring and authorization functions, with secure data access and storage and delegation of controller functions among householders. We provide the definition of virtual assets for energy and water resource sharing as an auction, which encourages the optimization of global consumption and saves resources. The proposed solution is implemented and validated in application scenarios that demonstrate the suitability of the defined consensus mechanism, trustworthiness in the level of provided security for resource monitoring and delegation and reduction on resource consumption by the resource trading contribution. Full article
(This article belongs to the Special Issue Intelligent Sensor Systems for Environmental Monitoring)
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