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Sensors, Volume 19, Issue 18 (September-2 2019)

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Open AccessArticle
A Machine Learning Approach to Bridge-Damage Detection Using Responses Measured on a Passing Vehicle
Sensors 2019, 19(18), 4035; https://doi.org/10.3390/s19184035 (registering DOI) - 19 Sep 2019
Abstract
This paper proposes a new two-stage machine learning approach for bridge damage detection using the responses measured on a passing vehicle. In the first stage, an artificial neural network (ANN) is trained using the vehicle responses measured from multiple passes (training data set) [...] Read more.
This paper proposes a new two-stage machine learning approach for bridge damage detection using the responses measured on a passing vehicle. In the first stage, an artificial neural network (ANN) is trained using the vehicle responses measured from multiple passes (training data set) over a healthy bridge. The vehicle acceleration or Discrete Fourier Transform (DFT) spectrum of the acceleration is used. The vehicle response is predicted from its speed for multiple passes (monitoring data set) over the bridge. Root-mean-square error is used to calculate the prediction error, which indicates the differences between the predicted and measured responses for each passage. In the second stage of the proposed method, a damage indicator is defined using a Gaussian process that detects the changes in the distribution of the prediction errors. It is suggested that if the bridge condition is healthy, the distribution of the prediction errors will remain low. A recognizable change in the distribution might indicate a damage in the bridge. The performance of the proposed approach was evaluated using numerical case studies of vehicle–bridge interaction. It was demonstrated that the approach could successfully detect the damage in the presence of road roughness profile and measurement noise, even for low damage levels. Full article
(This article belongs to the Special Issue Bridge Damage Detection with Sensing Technology)
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Open AccessEditorial
Ubiquitous Computing and Ambient Intelligence—UCAmI
Sensors 2019, 19(18), 4034; https://doi.org/10.3390/s19184034 (registering DOI) - 19 Sep 2019
Abstract
The Ubiquitous Computing (UC) idea envisioned by Weiser in 1991 [...] Full article
Open AccessArticle
A Multiple Target Positioning and Tracking System Behind Brick-Concrete Walls Using Multiple Monostatic IR-UWB Radars
Sensors 2019, 19(18), 4033; https://doi.org/10.3390/s19184033 (registering DOI) - 18 Sep 2019
Abstract
Recognizing and tracking the targets located behind walls through impulse radio ultra-wideband (IR-UWB) radar provides a significant advantage, as the characteristics of the IR-UWB radar signal enable it to penetrate obstacles. In this study, we design a through-wall radar system to estimate and [...] Read more.
Recognizing and tracking the targets located behind walls through impulse radio ultra-wideband (IR-UWB) radar provides a significant advantage, as the characteristics of the IR-UWB radar signal enable it to penetrate obstacles. In this study, we design a through-wall radar system to estimate and track multiple targets behind a wall. The radar signal received through the wall experiences distortion, such as attenuation and delay, and the characteristics of the wall are estimated to compensate the distance error. In addition, unlike general cases, it is difficult to maintain a high detection rate and low false alarm rate in this through-wall radar application due to the attenuation and distortion caused by the wall. In particular, the generally used delay-and-sum algorithm is significantly affected by the motion of targets and distortion caused by the wall, rendering it difficult to obtain a good performance. Thus, we propose a novel method, which calculates the likelihood that a target exists in a certain location through a detection process. Unlike the delay-and-sum algorithm, this method does not use the radar signal directly. Simulations and experiments are conducted in different cases to show the validity of our through-wall radar system. The results obtained by using the proposed algorithm as well as delay-and-sum and trilateration are compared in terms of the detection rate, false alarm rate, and positioning error. Full article
(This article belongs to the Section Remote Sensors, Control, and Telemetry)
Open AccessArticle
Completion Time Minimization for Multi-UAV Information Collection via Trajectory Planning
Sensors 2019, 19(18), 4032; https://doi.org/10.3390/s19184032 (registering DOI) - 18 Sep 2019
Abstract
Unmanned Aerial Vehicles (UAVs) are widely used as mobile information collectors for sensors to prolong the network time in Wireless Sensor Networks (WSNs) due to their flexible deployment, high mobility, and low cost. This paper focuses on the scenario where rotary-wing UAVs complete [...] Read more.
Unmanned Aerial Vehicles (UAVs) are widely used as mobile information collectors for sensors to prolong the network time in Wireless Sensor Networks (WSNs) due to their flexible deployment, high mobility, and low cost. This paper focuses on the scenario where rotary-wing UAVs complete information collection mission cooperatively. For the first time, we study the problem of minimizing the mission completion time for a multi-UAV system in a monitoring scenario when considering the information collection quality. The mission completion time includes flying time and hovering time. By optimizing the trajectories of all UAVs, we minimize the mission completion time while ensuring that the information of each sensor is collected. This problem can be formulated as a mixed-integer non-convex one which has been proved to be NP-hard. To solve the formulated problem, we first propose a hovering point selection algorithm to select appropriate hovering points where the UAVs can sequentially collect the information from multiple sensors. We model this problem as a BS coverage problem with the information collection quality in consideration. Then, we use a min-max cycle cover algorithm to assign these hovering points and get the trajectory of each UAV. Finally, with the obtained UAVs trajectories, we further consider the UAVs can also collect information when flying and optimize the time allocations. The performance of our algorithm is verified by simulations, which show that the mission completion time is minimum compared with state-of-the-art algorithms. Full article
(This article belongs to the Special Issue Optimization and Communication in UAV Networks)
Open AccessArticle
DOA Tracking Based on Unscented Transform Multi-Bernoulli Filter in Impulse Noise Environment
Sensors 2019, 19(18), 4031; https://doi.org/10.3390/s19184031 (registering DOI) - 18 Sep 2019
Abstract
Aiming at the problem of multiple-source direction of arrival (DOA) tracking in impulse noise, this paper models the impulse noise by using the symmetric α stable (SαS) distribution, and proposes a DOA tracking algorithm based on the Unscented Transform Multi-target Multi-Bernoulli [...] Read more.
Aiming at the problem of multiple-source direction of arrival (DOA) tracking in impulse noise, this paper models the impulse noise by using the symmetric α stable (SαS) distribution, and proposes a DOA tracking algorithm based on the Unscented Transform Multi-target Multi-Bernoulli (UT-MeMBer) filter framework. In order to overcome the problem of particle decay in particle filtering, UT is adopted to select a group of sigma points with different weights to make them close to the posterior probability density of the state. Since the α stable distribution does not have finite covariance, the Fractional Lower Order Moment (FLOM) matrix of the received array data is employed to replace the covariance matrix to formulate a MUSIC spatial spectra in the MeMBer filter. Further exponential weighting is used to enhance the weight of particles at high likelihood area and obtain a better resampling. Compared with the PASTD algorithm and the MeMBer DOA filter algorithm, the simulation results show that the proposed algorithm can more effectively solve the issue that the DOA and number of target are time-varying. In addition, we present the Sequential Monte Carlo (SMC) implementation of the UT-MeMBer algorithm. Full article
Open AccessArticle
Addressing Challenges in Fabricating Reflection-Based Fiber Optic Interferometers
Sensors 2019, 19(18), 4030; https://doi.org/10.3390/s19184030 (registering DOI) - 18 Sep 2019
Abstract
Fabrication of multimode fiber optic interferometers requires accurate control of certain parameters to obtain reproducible results. This paper evaluates the consequences of practical challenges in fabricating reflection-based, fiber optic interferometers by the use of theory and experiments. A guided-mode propagation approach is used [...] Read more.
Fabrication of multimode fiber optic interferometers requires accurate control of certain parameters to obtain reproducible results. This paper evaluates the consequences of practical challenges in fabricating reflection-based, fiber optic interferometers by the use of theory and experiments. A guided-mode propagation approach is used to investigate the effect of the end-face cleave angle and the accuracy of the splice in core-mismatched fiber optic sensors. Cleave angles from high-end fiber cleavers give differences in optical path lengths approaching the wavelength close to the circumference of the fiber, and the core-mismatched splice decides the ensemble of cladding modes excited. This investigation shows that the cleave angle may significantly alter the spectrum, whereas the splice is more robust. It is found that the interferometric visibility can be decreased by up to 70% for cleave angles typically obtained. An offset splice may reduce the visibility, but for offsets experienced experimentally the effect is negligible. An angled splice is found not to affect the visibility but causes a lower overall intensity in the spectrum. The sensitivity to the interferometer length is estimated to 60 nm/mm, which means that a 17 µm difference in length will shift the spectrum 1 nm. Comparisons to experimental results indicate that the spliced region also plays a significant role in the resulting spectrum. Full article
(This article belongs to the Section Optical Sensors)
Open AccessArticle
Fast Measurements with MOX Sensors: A Least-Squares Approach to Blind Deconvolution
Sensors 2019, 19(18), 4029; https://doi.org/10.3390/s19184029 (registering DOI) - 18 Sep 2019
Abstract
Metal oxide (MOX) sensors are widely used for chemical sensing due to their low cost, miniaturization, low power consumption and durability. Yet, getting instantaneous measurements of fluctuating gas concentration in turbulent plumes is not possible due to their slow response time. In this [...] Read more.
Metal oxide (MOX) sensors are widely used for chemical sensing due to their low cost, miniaturization, low power consumption and durability. Yet, getting instantaneous measurements of fluctuating gas concentration in turbulent plumes is not possible due to their slow response time. In this paper, we show that the slow response of MOX sensors can be compensated by deconvolution, provided that an invertible, parametrized, sensor model is available. We consider a nonlinear, first-order dynamic model that is mathematically tractable for MOX identification and deconvolution. By transforming the sensor signal in the log-domain, the system becomes linear in the parameters and these can be estimated by the least-squares techniques. Moreover, we use the MOX diversity in a sensor array to avoid training with a supervised signal. The information provided by two (or more) sensors, exposed to the same flow but responding with different dynamics, is exploited to recover the ground truth signal (gas input). This approach is known as blind deconvolution. We demonstrate its efficiency on MOX sensors recorded in turbulent plumes. The reconstructed signal is similar to the one obtained with a fast photo-ionization detector (PID). The technique is thus relevant to track a fast-changing gas concentration with MOX sensors, resulting in a compensated response time comparable to that of a PID. Full article
(This article belongs to the Section Chemical Sensors)
Open AccessArticle
A Lane Detection Method Based on a Ridge Detector and Regional G-RANSAC
Sensors 2019, 19(18), 4028; https://doi.org/10.3390/s19184028 (registering DOI) - 18 Sep 2019
Abstract
Lane detection plays an important role in improving autopilot’s safety. In this paper, a novel lane-division-lines detection method is proposed, which exhibits good performances in abnormal illumination and lane occlusion. It includes three major components: First, the captured image is converted to aerial [...] Read more.
Lane detection plays an important role in improving autopilot’s safety. In this paper, a novel lane-division-lines detection method is proposed, which exhibits good performances in abnormal illumination and lane occlusion. It includes three major components: First, the captured image is converted to aerial view to make full use of parallel lanes’ characteristics. Second, a ridge detector is proposed to extract each lane’s feature points and remove noise points with an adaptable neural network (ANN). Last, the lane-division-lines are accurately fitted by an improved random sample consensus (RANSAC), termed the (regional) gaussian distribution random sample consensus (G-RANSAC). To test the performances of this novel lane detection method, we proposed a new index named the lane departure index (LDI) describing the departure degree between true lane and predicted lane. Experimental results verified the superior performances of the proposed method over others in different testing scenarios, respectively achieving 99.02%, 96.92%, 96.65% and 91.61% true-positive rates (TPR); and 66.16, 54.85, 55.98 and 52.61 LDIs in four different types of testing scenarios. Full article
(This article belongs to the Section Intelligent Sensors)
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Open AccessArticle
Optimal Message Bundling with Delay and Synchronization Constraints in Wireless Sensor Networks
Sensors 2019, 19(18), 4027; https://doi.org/10.3390/s19184027 (registering DOI) - 18 Sep 2019
Abstract
Energy efficiency and end-to-end delay are two of the major requirements for the monitoring and detection applications based on resource-constrained wireless sensor networks (WSNs). As new advanced technologies for accurate monitoring and detection—such as device-free wireless sensing schemes for human activity and gesture [...] Read more.
Energy efficiency and end-to-end delay are two of the major requirements for the monitoring and detection applications based on resource-constrained wireless sensor networks (WSNs). As new advanced technologies for accurate monitoring and detection—such as device-free wireless sensing schemes for human activity and gesture recognition—have been developed, time synchronization accuracy becomes an important requirement for those WSN applications too. Message bundling is considered one of the effective methods to reduce the energy consumption for message transmissions in WSNs, but bundling more messages increases the transmission interval of bundled messages and thereby their end-to-end delays; the end-to-end delays need to be maintained within a certain value for time-sensitive applications like factory monitoring and disaster prevention, while the message transmission interval affects time synchronization accuracy when the bundling includes synchronization messages as well. Taking as an example a novel WSN time synchronization scheme recently proposed for energy efficiency, we investigate an optimal approach for message bundling to reduce the number of message transmissions while maintaining the user-defined requirements on end-to-end delay and time synchronization accuracy. Formulating the optimal message bundling problem as integer linear programming, we compute a set of optimal bundling numbers for the sensor nodes to constrain their link-level delays, thereby achieving and maintaining the required end-to-end delay and synchronization accuracy. Extensive experimental results based on a real WSN testbed using TelosB sensor nodes demonstrate that the proposed optimal bundling could reduce the number of message transmissions about 70% while simultaneously maintaining the required end-to-end delay and time synchronization accuracy. Full article
(This article belongs to the Section Sensor Networks)
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Open AccessArticle
An Adaptive Real-Time Detection Algorithm for Dim and Small Photoelectric GSO Debris
Sensors 2019, 19(18), 4026; https://doi.org/10.3390/s19184026 (registering DOI) - 18 Sep 2019
Abstract
Geosynchronous orbit (GSO) is the ideal orbit for communication, navigation, meteorology and other satellites, but the space of GSO is limited, and there are still a large number of space debris threatening the safety of spacecraft. Therefore, real-time detection of GSO debris is [...] Read more.
Geosynchronous orbit (GSO) is the ideal orbit for communication, navigation, meteorology and other satellites, but the space of GSO is limited, and there are still a large number of space debris threatening the safety of spacecraft. Therefore, real-time detection of GSO debris is necessary to avoid collision accidents. Because radar is limited by transmitting power and operating distance, it is difficult to detect GSO debris, so photoelectric detection becomes the mainstream way to detect GSO debris. This paper presents an adaptive real-time detection algorithm for GSO debris in the charge coupled device (CCD) images. The main work is as follows: An image adaptive fast registration algorithm and an enhanced dilation difference algorithm are proposed. Combining with mathematical morphology, threshold segmentation and global nearest neighbor (GNN) multi-target tracking algorithm, the functions of image background suppression, registration, suspected target extraction and multi-target tracking are realized. The processing results of a large number of measured data show that the algorithm can detect dim geostationary earth orbit (GEO) and non-GEO debris in GSO belt stably and efficiently, and the processing speed meets the real-time requirements, with strong adaptive ability, and has high practical application value. Full article
(This article belongs to the Section Optical Sensors)
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Open AccessArticle
An Extensible Positioning System for Locating Mobile Robots in Unfamiliar Environments
Sensors 2019, 19(18), 4025; https://doi.org/10.3390/s19184025 (registering DOI) - 18 Sep 2019
Abstract
In this paper, an extensible positioning system for mobile robots is proposed. The system includes a stereo camera module, inertial measurement unit (IMU) and an ultra-wideband (UWB) network which includes five anchors, one of which is with the unknown position. The anchors in [...] Read more.
In this paper, an extensible positioning system for mobile robots is proposed. The system includes a stereo camera module, inertial measurement unit (IMU) and an ultra-wideband (UWB) network which includes five anchors, one of which is with the unknown position. The anchors in the positioning system are without requirements of communication between UWB anchors and without requirements of clock synchronization of the anchors. By locating the mobile robot using the original system, and then estimating the position of a new anchor using the ranging between the mobile robot and the new anchor, the system can be extended after adding the new anchor into the original system. In an unfamiliar environment (such as fire and other rescue sites), it is able to locate the mobile robot after extending itself. To add the new anchor into the positioning system, a recursive least squares (RLS) approach is used to estimate the position of the new anchor. A maximum correntropy Kalman filter (MCKF) which is based on the maximum correntropy criterion (MCC) is used to fuse data from the UWB network and IMU. The initial attitude of the mobile robot relative to the navigation frame is calculated though comparing position vectors given by a visual simultaneous localization and mapping (SLAM) system and the UWB system respectively. As shown in the experiment section, the root mean square error (RMSE) of the positioning result given by the proposed positioning system with all anchors is 0.130 m. In the unfamiliar environment, the RMSE is 0.131 m which is close to the RMSE (0.137 m) given by the original system with a difference of 0.006 m. Besides, the RMSE based on Euler distance of the new anchor is 0.061 m. Full article
(This article belongs to the Section Physical Sensors)
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Open AccessFeature PaperArticle
Robust and Fast Scene Recognition in Robotics Through the Automatic Identification of Meaningful Images
Sensors 2019, 19(18), 4024; https://doi.org/10.3390/s19184024 (registering DOI) - 18 Sep 2019
Abstract
Scene recognition is still a very important topic in many fields, and that is definitely the case in robotics. Nevertheless, this task is view-dependent, which implies the existence of preferable directions when recognizing a particular scene. Both in human and computer vision-based classification, [...] Read more.
Scene recognition is still a very important topic in many fields, and that is definitely the case in robotics. Nevertheless, this task is view-dependent, which implies the existence of preferable directions when recognizing a particular scene. Both in human and computer vision-based classification, this actually often turns out to be biased. In our case, instead of trying to improve the generalization capability for different view directions, we have opted for the development of a system capable of filtering out noisy or meaningless images while, on the contrary, retaining those views from which is likely feasible that the correct identification of the scene can be made. Our proposal works with a heuristic metric based on the detection of key points in 3D meshes (Harris 3D). This metric is later used to build a model that combines a Minimum Spanning Tree and a Support Vector Machine (SVM). We have performed an extensive number of experiments through which we have addressed (a) the search for efficient visual descriptors, (b) the analysis of the extent to which our heuristic metric resembles the human criteria for relevance and, finally, (c) the experimental validation of our complete proposal. In the experiments, we have used both a public image database and images collected at our research center. Full article
(This article belongs to the Special Issue Mobile Robot Navigation)
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Open AccessArticle
Adaptive Binocular Fringe Dynamic Projection Method for High Dynamic Range Measurement
Sensors 2019, 19(18), 4023; https://doi.org/10.3390/s19184023 (registering DOI) - 18 Sep 2019
Abstract
Three-dimensional measurement with fringe projection sensor has been commonly researched. However, the measurement accuracy and efficiency of most fringe projection sensors are still seriously affected by image saturation and the non-linear effects of the projector. In order to solve the challenge, in conjunction [...] Read more.
Three-dimensional measurement with fringe projection sensor has been commonly researched. However, the measurement accuracy and efficiency of most fringe projection sensors are still seriously affected by image saturation and the non-linear effects of the projector. In order to solve the challenge, in conjunction with the advantages of stereo vision technology and fringe projection technology, an adaptive binocular fringe dynamic projection method is proposed. The proposed method can avoid image saturation by adaptively adjusting the projection intensity. Firstly, the flowchart of the proposed method is explained. Then, an adaptive optimal projection intensity method based on multi-threshold segmentation is introduced to adjust the projection illumination. Finally, the mapping relationship of binocular saturation point and projection point is established by binocular transformation and left camera–projector mapping. Experiments demonstrate that the proposed method can achieve higher accuracy for high dynamic range measurement. Full article
(This article belongs to the Special Issue Sensors for Precision Dimensional Metrology)
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Open AccessArticle
Detection of Cu2+ Ions with GGH Peptide Realized with Si-Nanoribbon ISFET
Sensors 2019, 19(18), 4022; https://doi.org/10.3390/s19184022 (registering DOI) - 18 Sep 2019
Abstract
The presence of heavy metal ions such as copper in the human body at certain concentrations and specific conditions can lead to the development of different diseases. The currently available analytical detection methods remain expensive, time-consuming, and often require sample pre-treatment. The development [...] Read more.
The presence of heavy metal ions such as copper in the human body at certain concentrations and specific conditions can lead to the development of different diseases. The currently available analytical detection methods remain expensive, time-consuming, and often require sample pre-treatment. The development of specific and quantitative, easy-in-operation, and cost-effective devices, capable of monitoring the level of Cu2+ ions in environmental and physiological media, is necessary. We use silicon nanoribbon (SiNR) ion-sensitive field effect transistor (ISFET) devices modified with a Gly–Gly–His peptide for the detection of copper ions in a large concentration range. The specific binding of copper ions causes a conformational change of the ligand, and a deprotonation of secondary amine groups. By performing differential measurements, we gain a deeper insight into the details of the ion–ligand interaction. We highlight in particular the importance of considering non-specific interactions to explain the sensors’ response. Full article
(This article belongs to the Special Issue Potentiometric Bio/Chemical Sensing)
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Open AccessArticle
Improved Traffic Sign Detection and Recognition Algorithm for Intelligent Vehicles
Sensors 2019, 19(18), 4021; https://doi.org/10.3390/s19184021 - 18 Sep 2019
Abstract
Traffic sign detection and recognition are crucial in the development of intelligent vehicles. An improved traffic sign detection and recognition algorithm for intelligent vehicles is proposed to address problems such as how easily affected traditional traffic sign detection is by the environment, and [...] Read more.
Traffic sign detection and recognition are crucial in the development of intelligent vehicles. An improved traffic sign detection and recognition algorithm for intelligent vehicles is proposed to address problems such as how easily affected traditional traffic sign detection is by the environment, and poor real-time performance of deep learning-based methodologies for traffic sign recognition. Firstly, the HSV color space is used for spatial threshold segmentation, and traffic signs are effectively detected based on the shape features. Secondly, the model is considerably improved on the basis of the classical LeNet-5 convolutional neural network model by using Gabor kernel as the initial convolutional kernel, adding the batch normalization processing after the pooling layer and selecting Adam method as the optimizer algorithm. Finally, the traffic sign classification and recognition experiments are conducted based on the German Traffic Sign Recognition Benchmark. The favorable prediction and accurate recognition of traffic signs are achieved through the continuous training and testing of the network model. Experimental results show that the accurate recognition rate of traffic signs reaches 99.75%, and the average processing time per frame is 5.4 ms. Compared with other algorithms, the proposed algorithm has remarkable accuracy and real-time performance, strong generalization ability and high training efficiency. The accurate recognition rate and average processing time are markedly improved. This improvement is of considerable importance to reduce the accident rate and enhance the road traffic safety situation, providing a strong technical guarantee for the steady development of intelligent vehicle driving assistance. Full article
(This article belongs to the Section Intelligent Sensors)
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Open AccessArticle
Deceleration Planning Algorithm Based on Classified Multi-Layer Perceptron Models for Smart Regenerative Braking of EV in Diverse Deceleration Conditions
Sensors 2019, 19(18), 4020; https://doi.org/10.3390/s19184020 - 18 Sep 2019
Abstract
The smart regenerative braking system (SRS) is an autonomous version of one-pedal driving in electric vehicles. To implement SRS, a deceleration planning algorithm is necessary to generate the deceleration used in automatic regenerative control. To reduce the discomfort from the automatic regeneration, the [...] Read more.
The smart regenerative braking system (SRS) is an autonomous version of one-pedal driving in electric vehicles. To implement SRS, a deceleration planning algorithm is necessary to generate the deceleration used in automatic regenerative control. To reduce the discomfort from the automatic regeneration, the deceleration should be similar to human driving. In this paper, a deceleration planning algorithm based on multi-layer perceptron (MLP) is proposed. The MLP models can mimic the human driving behavior by learning the driving data. In addition, the proposed deceleration planning algorithm has a classified structure to improve the planning performance in each deceleration condition. Therefore, the individual MLP models were designed according to three different deceleration conditions: car-following, speed bump, and intersection. The proposed algorithm was validated through driving simulations. Then, time to collision and similarity to human driving were analyzed. The results show that the minimum time to collision was 1.443 s and the velocity root-mean-square error (RMSE) with human driving was 0.302 m/s. Through the driving simulation, it was validated that the vehicle moves safely with desirable velocity when SRS is in operation, based on the proposed algorithm. Furthermore, the classified structure has more advantages than the integrated structure in terms of planning performance. Full article
(This article belongs to the Special Issue Intelligent Vehicles)
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Open AccessReview
Fault Detection and Diagnosis in Multi-Robot Systems: A Survey
Sensors 2019, 19(18), 4019; https://doi.org/10.3390/s19184019 - 18 Sep 2019
Abstract
The use of robots has increased significantly in the recent years; rapidly expending to numerous applications. These sophisticated machines are susceptible to different types of faults that might endanger the robot or its surroundings. These faults must be detected and diagnosed in time [...] Read more.
The use of robots has increased significantly in the recent years; rapidly expending to numerous applications. These sophisticated machines are susceptible to different types of faults that might endanger the robot or its surroundings. These faults must be detected and diagnosed in time to allow continual operation. The field of Fault Detection and Diagnosis (FDD) has been studied for many years. This research has given birth to many approaches that are applicable to different types of physical machines. However, the domain of robotics poses unique requirements that challenge traditional FDD approaches. The study of FDD for robotics is relatively new; only few surveys were presented. These surveys have focused on the single robot scenario. To the best of our knowledge, there is no survey that focuses on FDD for Multi-Robot Systems (MRS). In this paper we set out to fill this gap. This paper provides detailed insights to the world of FDD for MRS. We first describe how different attributes of MRS pose different challenges for FDD. With respect to these challenges, we survey different FDD approaches applicable for MRS. We conclude with a description of research opportunities in this field. With these contributions it is the authors’ intention to provide detailed insights to the world of FDD for MRS. Full article
(This article belongs to the Section Physical Sensors)
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Open AccessArticle
Ultrathin Tunable Lens Based on Boundary Tension Effect
Sensors 2019, 19(18), 4018; https://doi.org/10.3390/s19184018 - 18 Sep 2019
Viewed by 33
Abstract
Solid and liquid lenses are commonly used in optical design. Such lenses have suitable thicknesses due to their working principle and processing mode. Thus, zoom optical systems comprising solid and liquid lenses are extremely large. This work presents a new ultrathin tunable lens [...] Read more.
Solid and liquid lenses are commonly used in optical design. Such lenses have suitable thicknesses due to their working principle and processing mode. Thus, zoom optical systems comprising solid and liquid lenses are extremely large. This work presents a new ultrathin tunable lens (UTL) comprising two liquid film lenses (LFLs) obtained through aspheric deformation and produced from the surface of a micro-liquid under gravity and boundary tension. The UTL can flexibly change focal lengths between positive and negative lenses when the device thickness is merely 2.15 mm. The proposed lens has the advantages of small volume, light weight, simple fabrication, and independence from external force during zooming. This research makes up for the drawback that traditional solid and liquid lenses cannot further reduce their thicknesses. The proposed UTL provides a new lens form and fabrication method, and can be used to replace solid and liquid lenses for designing miniature zoom optical systems. Full article
(This article belongs to the Special Issue Integrated Photonics for Novel Sensing and Measurement Applications)
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Open AccessArticle
Elemental: An Open-Source Wireless Hardware and Software Platform for Building Energy and Indoor Environmental Monitoring and Control
Sensors 2019, 19(18), 4017; https://doi.org/10.3390/s19184017 - 18 Sep 2019
Viewed by 47
Abstract
This work demonstrates an open-source hardware and software platform for monitoring the performance of buildings, called Elemental, that is designed to provide data on indoor environmental quality, energy usage, HVAC operation, and other factors to its users. It combines: (i) custom printed [...] Read more.
This work demonstrates an open-source hardware and software platform for monitoring the performance of buildings, called Elemental, that is designed to provide data on indoor environmental quality, energy usage, HVAC operation, and other factors to its users. It combines: (i) custom printed circuit boards (PCBs) with RFM69 frequency shift keying (FSK) radio frequency (RF) transceivers for wireless sensors, control nodes, and USB gateway, (ii) a Raspberry Pi 3B with custom firmware acting as either a centralized or distributed backhaul, and (iii) a custom dockerized application for the backend called Brood that serves as the director software managing message brokering via Message Queuing Telemetry Transport (MQTT) protocol using VerneMQ, database storage using InfluxDB, and data visualization using Grafana. The platform is built around the idea of a private, secure, and open technology for the built environment. Among its many applications, the platform allows occupants to investigate anomalies in energy usage, environmental quality, and thermal performance via a comprehensive dashboard with rich querying capabilities. It also includes multiple frontends to view and analyze building activity data, which can be used directly in building controls or to provide recommendations on how to increase operational efficiency or improve operating conditions. Here, we demonstrate three distinct applications of the Elemental platform, including: (1) deployment in a research lab for long-term data collection and automated analysis, (2) use as a full-home energy and environmental monitoring solution, and (3) fault and anomaly detection and diagnostics of individual building systems at the zone-level. Through these applications we demonstrate that the platform allows easy and virtually unlimited datalogging, monitoring, and analysis of real-time sensor data with low setup costs. Low-power sensor nodes placed in abundance in a building can also provide precise and immediate fault-detection, allowing for tuning equipment for more efficient operation and faster maintenance during the lifetime of the building. Full article
(This article belongs to the Section Intelligent Sensors)
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Open AccessArticle
Fluorescence Characteristics of Dissolved Organic Matter (DOM) in Percolation Water and Lateral Seepage Affected by Soil Solution (S-S) in a Lysimeter Test
Sensors 2019, 19(18), 4016; https://doi.org/10.3390/s19184016 - 17 Sep 2019
Viewed by 151
Abstract
: The composition and structure of dissolved organic matter (DOM) are sensitive indicators that guide the water infiltration process in soil. The DOM chemical composition in seepage affects river water quality and changes soil organic matter (SOM). In this lysimeter test study, fluorescence [...] Read more.
: The composition and structure of dissolved organic matter (DOM) are sensitive indicators that guide the water infiltration process in soil. The DOM chemical composition in seepage affects river water quality and changes soil organic matter (SOM). In this lysimeter test study, fluorescence spectra and optical indices were used to examine the interaction between the percolation water (P-W) and leachate water (L-W) DOMs affected by the soil solution (S-S). The L-W DOM had a higher aromaticity (SUVA254), average molecular weight (S275-295) and terrestrial source (fluorescence index (FI)), but fewer autochthonous sources (biological index (BIX)) than the P-W DOM. Organic carbon standardization (OCS) and protein- (PLF), fulvic- (FLF) and humic-like fluorescence (HLF) intensity showed that L-W DOM increased 44%, 55% and 81%, respectively, compared to the P-W DOM. The linear regression slopes between OCS FLF and PLF were 0.62, 1.74 and 1.79 for P-W, L-W and S-S, respectively. The slopes between OCS HLF and PLF were 0.15, 0.58 and 0.64 for P-W, L-W and S-S, respectively. The P-W DOM was in contact with the soil litter layer, where S-S labile lignin phenolic compounds released and dissolved into the L-W DOM. This increased its aromaticity, and extent of humification. Full article
(This article belongs to the Section Optical Sensors)
Open AccessArticle
Design of Cavity-Backed Bow-Tie Antenna with Matching Layer for Human Body Application
Sensors 2019, 19(18), 4015; https://doi.org/10.3390/s19184015 - 17 Sep 2019
Viewed by 128
Abstract
This paper presents the broadband antenna for the microwave radiometric sensing of internal body temperature. For broadband operation, the bow-tie antenna was designed and backed with a cylindrical cavity, which decreased environmental electromagnetic interference and also improved the directivity of the antenna. The [...] Read more.
This paper presents the broadband antenna for the microwave radiometric sensing of internal body temperature. For broadband operation, the bow-tie antenna was designed and backed with a cylindrical cavity, which decreased environmental electromagnetic interference and also improved the directivity of the antenna. The broadband impedance-transforming balun in microstrip form was also designed to feed the bow-tie antenna, and was located inside the cavity. An impedance-matching dielectric layer (IMDL) was introduced on top of the bow-tie antenna, for impedance match with the human body with high permittivity. The fabricated antenna was measured in free space with the IMDL removed, showing an input reflection coefficient lower than −10 dB from 2.64 to > 3.60 GHz with antenna gain over 6.0 dBi and radiation efficiency over 74.7% from 2.7 to 3.5 GHz. The IMDL was re-installed on the cavity-backed bow-tie antenna to measure the antenna performance for the human head with relative permittivity of about 40. The measured reflection coefficient was as low as −28.9 dB at 2.95 GHz and lower than −10 dB from 2.65 to > 3.5 GHz. It was also shown that the designed antenna recovered a good impedance match by adjusting the permittivity and thickness of the IMDL for the different parts of the human body with different permittivities. Full article
(This article belongs to the Section Biomedical Sensors)
Open AccessArticle
A Wearable In-Ear EEG Device for Emotion Monitoring
Sensors 2019, 19(18), 4014; https://doi.org/10.3390/s19184014 - 17 Sep 2019
Viewed by 156
Abstract
For future healthcare applications, which are increasingly moving towards out-of-hospital or home-based caring models, the ability to remotely and continuously monitor patients’ conditions effectively are imperative. Among others, emotional state is one of the conditions that could be of interest to doctors or [...] Read more.
For future healthcare applications, which are increasingly moving towards out-of-hospital or home-based caring models, the ability to remotely and continuously monitor patients’ conditions effectively are imperative. Among others, emotional state is one of the conditions that could be of interest to doctors or caregivers. This paper discusses a preliminary study to develop a wearable device that is a low cost, single channel, dry contact, in-ear EEG suitable for non-intrusive monitoring. All aspects of the designs, engineering, and experimenting by applying machine learning for emotion classification, are covered. Based on the valence and arousal emotion model, the device is able to classify basic emotion with 71.07% accuracy (valence), 72.89% accuracy (arousal), and 53.72% (all four emotions). The results are comparable to those measured from the more conventional EEG headsets at T7 and T8 scalp positions. These results, together with its earphone-like wearability, suggest its potential usage especially for future healthcare applications, such as home-based or tele-monitoring systems as intended. Full article
Open AccessArticle
Crop Water Content of Winter Wheat Revealed with Sentinel-1 and Sentinel-2 Imagery
Sensors 2019, 19(18), 4013; https://doi.org/10.3390/s19184013 - 17 Sep 2019
Viewed by 149
Abstract
This study aims to efficiently estimate the crop water content of winter wheat using high spatial and temporal resolution satellite-based imagery. Synthetic-aperture radar (SAR) data collected by the Sentinel-1 satellite and optical imagery from the Sentinel-2 satellite was used to create inversion models [...] Read more.
This study aims to efficiently estimate the crop water content of winter wheat using high spatial and temporal resolution satellite-based imagery. Synthetic-aperture radar (SAR) data collected by the Sentinel-1 satellite and optical imagery from the Sentinel-2 satellite was used to create inversion models for winter wheat crop water content, respectively. In the Sentinel-1 approach, several enhanced radar indices were constructed by Sentinel-1 backscatter coefficient of imagery, and selected the one that was most sensitive to soil water content as the input parameter of a water cloud model. Finally, a water content inversion model for winter wheat crop was established. In the Sentinel-2 approach, the gray relational analysis was used for several optical vegetation indices constructed by Sentinel-2 spectral feature of imagery, and three vegetation indices were selected for multiple linear regression modeling to retrieve the wheat crop water content. 58 ground samples were utilized in modeling and verification. The water content inversion model based on Sentinel-2 optical images exhibited higher verification accuracy (R = 0.632, RMSE = 0.021 and nRMSE = 19.65%) than the inversion model based on Sentinel-1 SAR (R = 0.433, RMSE = 0.026 and nRMSE = 21.24%). This study provides a reference for estimating the water content of wheat crops using data from the Sentinel series of satellites. Full article
(This article belongs to the Special Issue Advanced Sensors in Agriculture)
Open AccessReview
Radio Frequency Identification and Sensing Techniques and Their Applications—A Review of the State-of-the-Art
Sensors 2019, 19(18), 4012; https://doi.org/10.3390/s19184012 - 17 Sep 2019
Viewed by 179
Abstract
Radio Frequency Identification (RFID) sensors, integrating the features of Wireless Information and Power Transfer (WIPT), object identification and energy efficient sensing capabilities, have been considered a new paradigm of sensing and communication for the futuristic information systems. RFID sensor tags featuring contactless sensing, [...] Read more.
Radio Frequency Identification (RFID) sensors, integrating the features of Wireless Information and Power Transfer (WIPT), object identification and energy efficient sensing capabilities, have been considered a new paradigm of sensing and communication for the futuristic information systems. RFID sensor tags featuring contactless sensing, wireless information transfer, wireless powered, light weight, non-line-of-sight transmission, flexible and pasteable are a critical enabling technology for future Internet-of-Things (IoT) applications, such as manufacturing, logistics, healthcare, agriculture and food. They have attracted numerous research efforts due to their innovative potential in the various application fields. However, there has been a gap between the in-lab investigations and the practical IoT application scenarios, which has motivated this survey of this research to identify the promising enabling techniques and the underlying challenges. This study aims to provide an exhaustive review on the state-of-art RFID sensor technologies from the system implementation perspective by focusing on the fundamental RF energy harvesting theories, the recent technical progresses and commercial solutions, innovative applications and some RFID sensor based IoT solutions, identify the underlying technological challenges at the time being, and give the future research trends and promising application fields in the rich sensing applications of the forthcoming IoT era. Full article
(This article belongs to the Special Issue Augmented RFID Technologies for the Internet of Things and Beyond)
Open AccessArticle
An FPGA-Based Neuro-Fuzzy Sensor for Personalized Driving Assistance
Sensors 2019, 19(18), 4011; https://doi.org/10.3390/s19184011 - 17 Sep 2019
Viewed by 128
Abstract
Advanced driving-assistance systems (ADAS) are intended to automatize driver tasks, as well as improve driving and vehicle safety. This work proposes an intelligent neuro-fuzzy sensor for driving style (DS) recognition, suitable for ADAS enhancement. The development of the driving style intelligent sensor uses [...] Read more.
Advanced driving-assistance systems (ADAS) are intended to automatize driver tasks, as well as improve driving and vehicle safety. This work proposes an intelligent neuro-fuzzy sensor for driving style (DS) recognition, suitable for ADAS enhancement. The development of the driving style intelligent sensor uses naturalistic driving data from the SHRP2 study, which includes data from a CAN bus, inertial measurement unit, and front radar. The system has been successfully implemented using a field-programmable gate array (FPGA) device of the Xilinx Zynq programmable system-on-chip (PSoC). It can mimic the typical timing parameters of a group of drivers as well as tune these typical parameters to model individual DSs. The neuro-fuzzy intelligent sensor provides high-speed real-time active ADAS implementation and is able to personalize its behavior into safe margins without driver intervention. In particular, the personalization procedure of the time headway (THW) parameter for an ACC in steady car following was developed, achieving a performance of 0.53 microseconds. This performance fulfilled the requirements of cutting-edge active ADAS specifications. Full article
(This article belongs to the Special Issue Intelligent Sensor Signal in Machine Learning)
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Open AccessArticle
Height Variation Depending on the Source of Antenna Phase Centre Corrections: LEIAR25.R3 Case Study
Sensors 2019, 19(18), 4010; https://doi.org/10.3390/s19184010 - 17 Sep 2019
Viewed by 125
Abstract
In this study, we compared two sets of antenna phase center corrections for groups of the same type of antenna mounted at the continuously operating global navigation satellite system (GNSS) reference stations. The first set involved type mean models provided by the International [...] Read more.
In this study, we compared two sets of antenna phase center corrections for groups of the same type of antenna mounted at the continuously operating global navigation satellite system (GNSS) reference stations. The first set involved type mean models provided by the International GNSS Service (release igs08), while the second set involved individual models developed by Geo++. Our goal was to check which set gave better results in the case of height estimation. The paper presents the differences between models and their impact on resulting height. Analyses showed that, in terms of the stability of the determined height, as well as its variability caused by increasing the facade mask, both models gave very similar results. Finally, we present a method for how to estimate the impact of differences in phase center corrections on height changes. Full article
(This article belongs to the Special Issue GNSS Data Processing and Navigation)
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Open AccessArticle
Optimization of Modulation Methods for Solenoid Valves to Realize an Odor Generation System
Sensors 2019, 19(18), 4009; https://doi.org/10.3390/s19184009 - 17 Sep 2019
Viewed by 126
Abstract
An artificial olfactory system coupled with an odor generation system is herein reported. The artificial olfactory system is composed of four chemical sensors consisting of quartz crystal microbalances (QCMs) coated with room temperature ionic liquids (RTILs). The sensors are interrogated by four vector [...] Read more.
An artificial olfactory system coupled with an odor generation system is herein reported. The artificial olfactory system is composed of four chemical sensors consisting of quartz crystal microbalances (QCMs) coated with room temperature ionic liquids (RTILs). The sensors are interrogated by four vector network analyzers, which are used to measure the series resonant frequency and motional resistance. The odor generation system can generate eight different odors and mix them in any composition. Solenoid valves are used to switch the path and control the concentration of the different odors before blending. Two algorithms to control the solenoid valves, delta-sigma modulator, and simple pulse width modulation (PWM) are studied, optimized, and compared. Finally, the uncertainty of the odor generating system is calculated. Full article
(This article belongs to the Section Chemical Sensors)
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Open AccessArticle
A Container-Attachable Inertial Sensor for Real-Time Hydration Tracking
Sensors 2019, 19(18), 4008; https://doi.org/10.3390/s19184008 - 17 Sep 2019
Viewed by 119
Abstract
Various sensors have been proposed to address the negative health ramifications of inadequate fluid consumption. Amongst these solutions, motion-based sensors estimate fluid intake using the characteristics of drinking kinematics. This sensing approach is complicated due to the mutual influence of both the drink [...] Read more.
Various sensors have been proposed to address the negative health ramifications of inadequate fluid consumption. Amongst these solutions, motion-based sensors estimate fluid intake using the characteristics of drinking kinematics. This sensing approach is complicated due to the mutual influence of both the drink volume and the current fill level on the resulting motion pattern, along with differences in biomechanics across individuals. While motion-based strategies are a promising approach due to the proliferation of inertial sensors, previous studies have been characterized by limited accuracy and substantial variability in performance across subjects. This research seeks to address these limitations for a container-attachable triaxial accelerometer sensor. Drink volume is computed using support vector machine regression models with hand-engineered features describing the container’s estimated inclination. Results are presented for a large-scale data collection consisting of 1908 drinks consumed from a refillable bottle by 84 individuals. Per-drink mean absolute percentage error is reduced by 11.05% versus previous state-of-the-art results for a single wrist-wearable inertial measurement unit (IMU) sensor assessed using a similar experimental protocol. Estimates of aggregate consumption are also improved versus previously reported results for an attachable sensor architecture. An alternative tracking approach using the fill level from which a drink is consumed is also explored herein. Fill level regression models are shown to exhibit improved accuracy and reduced inter-subject variability versus volume estimators. A technique for segmenting the entire drink motion sequence into transport and sip phases is also assessed, along with a multi-target framework for addressing the known interdependence of volume and fill level on the resulting drink motion signature. Full article
(This article belongs to the Section Biomedical Sensors)
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Open AccessArticle
Method for Detecting Abnormal Activity in a Group of Mobile Robots
Sensors 2019, 19(18), 4007; https://doi.org/10.3390/s19184007 - 17 Sep 2019
Viewed by 131
Abstract
The range of attacks implemented on wireless networks is quite wide. To avoid or reduce the likelihood of an attack, it is necessary to use various defense mechanisms. Existing protection mechanisms are not always suitable for robotic systems and may not fully provide [...] Read more.
The range of attacks implemented on wireless networks is quite wide. To avoid or reduce the likelihood of an attack, it is necessary to use various defense mechanisms. Existing protection mechanisms are not always suitable for robotic systems and may not fully provide the necessary level of security. Thus, it is necessary to develop new ways of protection, which would be specific to groups of mobile robots. In this study, we propose an analysis of the following cyber parameters: the power consumption and the residual energy, as well as an in-depth traffic analysis to evaluate the effectiveness of the attack and identify abnormal network. We realized an analysis of the behavior of robotic systems under normal conditions and determined that, by their nature, robotic systems have a static and uniform behavior. We developed an experimental stand, and also conducted a theoretical analysis to confirm our assumptions. We found that some indicators of the components of the robotic system change statically; that is, there was a little deviation from the mean. Thus, we identified a set of metrics that allow us to determine how static the operation of the robotic system and its components is. Metrics allow us to evaluate parameters such as power consumption, and incoming/outgoing/redirected/dropped network packets. The results obtained are important for creating an integrated system for detecting anomalies in robotic systems. At the same time, the robotic node can analyze these parameters independently and make calculations that do not greatly affect performance. The main idea of this paper is to define a set of metrics characterizing the static behavior of a robotic system for the further development of an anomaly detection system. Full article
(This article belongs to the Section Biomedical Sensors)
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Open AccessArticle
Dual-Channel Spectral Domain Optical Coherence Tomography Based on a Single Spectrometer Using Compressive Sensing
Sensors 2019, 19(18), 4006; https://doi.org/10.3390/s19184006 - 16 Sep 2019
Viewed by 182
Abstract
Dual-channel spectral domain optical coherence tomography (SD-OCT) is one of the effective methods for improving imaging depth and imaging speed. In this paper, we design a dual-channel SD-OCT system based on a single spectrometer that can operate in two modes: (1) Increasing imaging [...] Read more.
Dual-channel spectral domain optical coherence tomography (SD-OCT) is one of the effective methods for improving imaging depth and imaging speed. In this paper, we design a dual-channel SD-OCT system based on a single spectrometer that can operate in two modes: (1) Increasing imaging speed and (2) expanding imaging depth. An optical path offset is preintroduced between the two channels to separate the two-channel data. However, this offset increases the requirement for the spectral resolution of the spectrometer in mode (1), so compressive sensing (CS) technology is used herein to overcome this problem. Consequently, in mode (1), when the spectral resolution of the spectrometer is the same as that used in the single-channel system, we use a dual-channel SD-OCT system combined with CS technology to double the imaging speed. In mode (2), when the spectral resolution of the spectrometer is only half of that used in a single-channel system, the imaging depth can be nearly doubled. We demonstrate the feasibility and effectiveness of the method proposed in this work by imaging a mirror, a fish fin, a fish eye, and an onion. Full article
(This article belongs to the Section Optical Sensors)
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