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Sensors, Volume 19, Issue 21 (November-1 2019)

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Cover Story (view full-size image) Tactile sensing is the current challenge in robotics and object manipulation by machines. Robots’ [...] Read more.
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Open AccessArticle
An Opportunistic Cooperative Packet Transmission Scheme in Wireless Multi-Hop Networks
Sensors 2019, 19(21), 4821; https://doi.org/10.3390/s19214821 - 05 Nov 2019
Abstract
Cooperative routing, combining cooperative communication in the physical layer and routing technology in the network layer, is one of the most widely used technologies for improving end-to-end transmission reliability and delay in the wireless multi-hop networks. However, the existing cooperative routing schemes are [...] Read more.
Cooperative routing, combining cooperative communication in the physical layer and routing technology in the network layer, is one of the most widely used technologies for improving end-to-end transmission reliability and delay in the wireless multi-hop networks. However, the existing cooperative routing schemes are designed based on an optimal fixed-path routing so that the end-to-end performance is greatly restricted by the low spatial efficiency. To address this problem, in this paper an opportunistic cooperative packet transmission (OCPT) scheme is explored by combining cooperative communication and opportunistic routing. The proposed scheme divides the multi-hop route into multiple virtual multiple-input-multiple-output (MIMO) transmissions. Before each transmission, based on the idea of opportunistic routing, a cluster head (CH) is introduced to determine the multiple transmitters and multiple receivers to form a cluster. Then, the single-hop transmission distance is defined as the metric of forward progress to the destination. Each intra-cluster cooperative packet transmission is formulated as a transmit beamforming optimization problem, and an iterative optimal beamforming policy is proposed to solve the problem and maximize the single-hop transmission distance. CH organizes multiple transmitters to cooperatively transmit packets to multiple receivers with the optimized transmit beamforming vector. Finally, according to the transmission results, the cluster is updated and the new cooperative transmission is started. Iteratively, the transmission lasts until the destination has successfully received the packet. We comprehensively evaluate the OCPT scheme by comparing it with conventional routing schemes. The simulation results demonstrate that the proposed OCPT scheme is effective on shortening the end-to-end transmission delay, increasing the number of successful packet transmissions and improving the packet arrival ratio and transmission efficiency. Full article
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Open AccessArticle
A Single-Shot Non-Line-of-Sight Range-Finder
Sensors 2019, 19(21), 4820; https://doi.org/10.3390/s19214820 - 05 Nov 2019
Abstract
The ability to locate a target around a corner is crucial in situations where it is impractical or unsafe to physically move around the obstruction. However, current techniques are limited to long acquisition times as they rely on single-photon counting for precise arrival [...] Read more.
The ability to locate a target around a corner is crucial in situations where it is impractical or unsafe to physically move around the obstruction. However, current techniques are limited to long acquisition times as they rely on single-photon counting for precise arrival time measurements. Here, we demonstrate a single-shot non-line-of-sight range-finding method operating at 10 Hz and capable of detecting a moving human target up to distances of 3 m around a corner. Due to the potential data acquisition speeds, this technique will find applications in search and rescue and autonomous vehicles. Full article
(This article belongs to the Section Physical Sensors)
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Open AccessArticle
Heartbeat Sound Signal Classification Using Deep Learning
Sensors 2019, 19(21), 4819; https://doi.org/10.3390/s19214819 - 05 Nov 2019
Abstract
Presently, most deaths are caused by heart disease. To overcome this situation, heartbeat sound analysis is a convenient way to diagnose heart disease. Heartbeat sound classification is still a challenging problem in heart sound segmentation and feature extraction. Dataset-B applied in this study [...] Read more.
Presently, most deaths are caused by heart disease. To overcome this situation, heartbeat sound analysis is a convenient way to diagnose heart disease. Heartbeat sound classification is still a challenging problem in heart sound segmentation and feature extraction. Dataset-B applied in this study that contains three categories Normal, Murmur and Extra-systole heartbeat sound. In the purposed framework, we remove the noise from the heartbeat sound signal by applying the band filter, After that we fixed the size of the sampling rate of each sound signal. Then we applied down-sampling techniques to get more discriminant features and reduce the dimension of the frame rate. However, it does not affect the results and also decreases the computational power and time. Then we applied a purposed model Recurrent Neural Network (RNN) that is based on Long Short-Term Memory (LSTM), Dropout, Dense and Softmax layer. As a result, the purposed method is more competitive compared to other methods. Full article
(This article belongs to the Special Issue Biomedical Signal Processing)
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Open AccessArticle
Asymmetric Encoder-Decoder Structured FCN Based LiDAR to Color Image Generation
Sensors 2019, 19(21), 4818; https://doi.org/10.3390/s19214818 - 05 Nov 2019
Abstract
In this paper, we propose a method of generating a color image from light detection and ranging (LiDAR) 3D reflection intensity. The proposed method is composed of two steps: projection of LiDAR 3D reflection intensity into 2D intensity, and color image generation from [...] Read more.
In this paper, we propose a method of generating a color image from light detection and ranging (LiDAR) 3D reflection intensity. The proposed method is composed of two steps: projection of LiDAR 3D reflection intensity into 2D intensity, and color image generation from the projected intensity by using a fully convolutional network (FCN). The color image should be generated from a very sparse projected intensity image. For this reason, the FCN is designed to have an asymmetric network structure, i.e., the layer depth of the decoder in the FCN is deeper than that of the encoder. The well-known KITTI dataset for various scenarios is used for the proposed FCN training and performance evaluation. Performance of the asymmetric network structures are empirically analyzed for various depth combinations for the encoder and decoder. Through simulations, it is shown that the proposed method generates fairly good visual quality of images while maintaining almost the same color as the ground truth image. Moreover, the proposed FCN has much higher performance than conventional interpolation methods and generative adversarial network based Pix2Pix. One interesting result is that the proposed FCN produces shadow-free and daylight color images. This result is caused by the fact that the LiDAR sensor data is produced by the light reflection and is, therefore, not affected by sunlight and shadow. Full article
(This article belongs to the Special Issue LiDAR-Based Creation of Virtual Cities)
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Open AccessArticle
Geographic Object-Based Analysis of Airborne Multispectral Images for Health Assessment of Capsicum annuum L. Crops
Sensors 2019, 19(21), 4817; https://doi.org/10.3390/s19214817 - 05 Nov 2019
Abstract
Vegetation health assessment by using airborne multispectral images throughout crop production cycles, among other precision agriculture technologies, is an important tool for modern agriculture practices. However, to really take advantage of crop fields imagery, specialized analysis techniques are needed. In this paper we [...] Read more.
Vegetation health assessment by using airborne multispectral images throughout crop production cycles, among other precision agriculture technologies, is an important tool for modern agriculture practices. However, to really take advantage of crop fields imagery, specialized analysis techniques are needed. In this paper we present a geographic object-based image analysis (GEOBIA) approach to examine a set of very high resolution (VHR) multispectral images obtained by the use of small unmanned aerial vehicles (UAVs), to evaluate plant health states and to generate cropland maps for Capsicum annuum L. The scheme described here integrates machine learning methods with semi-automated training and validation, which allowed us to develop an algorithmic sequence for the evaluation of plant health conditions at individual sowing point clusters over an entire parcel. The features selected at the classification stages are based on phenotypic traits of plants with different health levels. Determination of areas without data dependencies for the algorithms employed allowed us to execute some of the calculations as parallel processes. Comparison with the standard normalized difference vegetation index (NDVI) and biological analyses were also performed. The classification obtained showed a precision level of about 95 % in discerning between vegetation and non-vegetation objects, and clustering efficiency ranging from 79 % to 89 % for the evaluation of different vegetation health categories, which makes our approach suitable for being incorporated at C. annuum crop’s production systems, as well as to other similar crops. This methodology can be reproduced and adjusted as an on-the-go solution to get a georeferenced plant health estimation. Full article
(This article belongs to the Section Remote Sensors, Control, and Telemetry)
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Open AccessArticle
ACK-MSCKF: Tightly-Coupled Ackermann Multi-State Constraint Kalman Filter for Autonomous Vehicle Localization
Sensors 2019, 19(21), 4816; https://doi.org/10.3390/s19214816 - 05 Nov 2019
Abstract
Visual-Inertial Odometry (VIO) is subjected to additional unobservable directions under the special motions of ground vehicles, resulting in larger pose estimation errors. To address this problem, a tightly-coupled Ackermann visual-inertial odometry (ACK-MSCKF) is proposed to fuse Ackermann error state measurements and the Stereo [...] Read more.
Visual-Inertial Odometry (VIO) is subjected to additional unobservable directions under the special motions of ground vehicles, resulting in larger pose estimation errors. To address this problem, a tightly-coupled Ackermann visual-inertial odometry (ACK-MSCKF) is proposed to fuse Ackermann error state measurements and the Stereo Multi-State Constraint Kalman Filter (S-MSCKF) with a tightly-coupled filter-based mechanism. In contrast with S-MSCKF, in which the inertial measurement unit (IMU) propagates the vehicle motion and then the propagation is corrected by stereo visual measurements, we successively update the propagation with Ackermann error state measurements and visual measurements after the process model and state augmentation. This way, additional constraints from the Ackermann measurements are exploited to improve the pose estimation accuracy. Both qualitative and quantitative experimental results evaluated under real-world datasets from an Ackermann steering vehicle lead to the following demonstration: ACK-MSCKF can significantly improve the pose estimation accuracy of S-MSCKF under the special motions of autonomous vehicles, and keep accurate and robust pose estimation available under different vehicle driving cycles and environmental conditions. This paper accompanies the source code for the robotics community. Full article
(This article belongs to the collection Positioning and Navigation)
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Open AccessFeature PaperReview
Differential Temperature Sensors: Review of Applications in the Test and Characterization of Circuits, Usage and Design Methodology
Sensors 2019, 19(21), 4815; https://doi.org/10.3390/s19214815 - 05 Nov 2019
Abstract
Differential temperature sensors can be placed in integrated circuits to extract a signature of the power dissipated by the adjacent circuit blocks built in the same silicon die. This review paper first discusses the singularity that differential temperature sensors provide with respect to [...] Read more.
Differential temperature sensors can be placed in integrated circuits to extract a signature of the power dissipated by the adjacent circuit blocks built in the same silicon die. This review paper first discusses the singularity that differential temperature sensors provide with respect to other sensor topologies, with circuit monitoring being their main application. The paper focuses on the monitoring of radio-frequency analog circuits. The strategies to extract the power signature of the monitored circuit are reviewed, and a list of application examples in the domain of test and characterization is provided. As a practical example, we elaborate the design methodology to conceive, step by step, a differential temperature sensor to monitor the aging degradation in a class-A linear power amplifier working in the 2.4 GHz Industrial Scientific Medical—ISM—band. It is discussed how, for this particular application, a sensor with a temperature resolution of 0.02 K and a high dynamic range is required. A circuit solution for this objective is proposed, as well as recommendations for the dimensions and location of the devices that form the temperature sensor. The paper concludes with a description of a simple procedure to monitor time variability. Full article
(This article belongs to the Section Physical Sensors)
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Open AccessArticle
Phenomic and Physiological Analysis of Salinity Effects on Lettuce
Sensors 2019, 19(21), 4814; https://doi.org/10.3390/s19214814 - 05 Nov 2019
Abstract
Salinity is a rising concern in many lettuce-growing regions. Lettuce (Lactuca sativa L.) is sensitive to salinity, which reduces plant biomass, and causes leaf burn and early senescence. We sought to identify physiological traits important in salt tolerance that allows lettuce adaptation [...] Read more.
Salinity is a rising concern in many lettuce-growing regions. Lettuce (Lactuca sativa L.) is sensitive to salinity, which reduces plant biomass, and causes leaf burn and early senescence. We sought to identify physiological traits important in salt tolerance that allows lettuce adaptation to high salinity while maintaining its productivity. Based on previous salinity tolerance studies, one sensitive and one tolerant genotype each was selected from crisphead, butterhead, and romaine, as well as leaf types of cultivated lettuce and its wild relative, L. serriola L. Physiological parameters were measured four weeks after transplanting two-day old seedlings into 350 mL volume pots filled with sand, hydrated with Hoagland nutrient solution and grown in a growth chamber. Salinity treatment consisted of gradually increasing concentrations of NaCl and CaCl2 from 0 mM/0 mM at the time of transplanting, to 30 mM/15 mM at the beginning of week three, and maintaining it until harvest. Across the 10 genotypes, leaf area and fresh weight decreased 0–64% and 16–67%, respectively, under salinity compared to the control. Salinity stress increased the chlorophyll index by 4–26% in the cultivated genotypes, while decreasing it by 5–14% in the two wild accessions. Tolerant lines less affected by elevated salinity were characterized by high values of the chlorophyll fluorescence parameters Fv/Fm and instantaneous photosystem II quantum yield (QY), and lower leaf transpiration. Full article
(This article belongs to the Special Issue Chlorophyll Fluorescence Sensing in Plant Phenotyping)
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Open AccessArticle
A Colorimetric pH Sensor Based on Clitoria sp and Brassica sp for Monitoring of Food Spoilage Using Chromametry
Sensors 2019, 19(21), 4813; https://doi.org/10.3390/s19214813 - 05 Nov 2019
Abstract
A developed colorimetric pH sensor film based on edible materials for real-time monitoring of food freshness is described. The mixed natural dyes from edible plants Clitoria sp and Brassica sp were extracted and incorporated into ι-carrageenan film as a colorimetric pH sensor film [...] Read more.
A developed colorimetric pH sensor film based on edible materials for real-time monitoring of food freshness is described. The mixed natural dyes from edible plants Clitoria sp and Brassica sp were extracted and incorporated into ι-carrageenan film as a colorimetric pH sensor film for monitoring food spoilage and its freshness. The color changes of the developed colorimetric sensor film were measured with chromametry and UV-vis spectroscopy, respectively. Experimental results show that colorimetric pH sensor film demonstrated statistically significant differences (p < 0.05) between CIE-L*a*b* coordinates color system indicated that the developed colorimetric sensor film was able to give a gradual change in color over a wide pH range. The color of the colorimetric sensor film also changes discretely and linearly with factors that contribute to food spoilage using shrimp and durian samples. Moreover, the developed colorimetric pH sensor film has the potential to be used as a safe, non-destructive testing and also a flexibly visual method for direct assessment of food freshness indicator during storage. Full article
(This article belongs to the Section Chemical Sensors)
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Open AccessArticle
Analyte Quantity Detection from Lateral Flow Assay Using a Smartphone
Sensors 2019, 19(21), 4812; https://doi.org/10.3390/s19214812 - 05 Nov 2019
Abstract
Lateral flow assay (LFA) technology has recently received interest in the biochemical field since it is simple, low-cost, and rapid, while conventional laboratory test procedures are complicated, expensive, and time-consuming. In this paper, we propose a robust smartphone-based analyte detection method that estimates [...] Read more.
Lateral flow assay (LFA) technology has recently received interest in the biochemical field since it is simple, low-cost, and rapid, while conventional laboratory test procedures are complicated, expensive, and time-consuming. In this paper, we propose a robust smartphone-based analyte detection method that estimates the amount of analyte on an LFA strip using a smartphone camera. The proposed method can maintain high estimation accuracy under various illumination conditions without additional devices, unlike conventional methods. The robustness and simplicity of the proposed method are enabled by novel image processing and machine learning techniques. For the performance analysis, we applied the proposed method to LFA strips where the target analyte is albumin protein of human serum. We use two sets of training LFA strips and one set of testing LFA strips. Here, each set consists of five strips having different quantities of albumin—10 femtograms, 100 femtograms, 1 picogram, 10 picograms, and 100 picograms. A linear regression analysis approximates the analyte quantity, and then machine learning classifier, support vector machine (SVM), which is trained by the regression results, classifies the analyte quantity on the LFA strip in an optimal way. Experimental results show that the proposed smartphone application can detect the quantity of albumin protein on a test LFA set with 98% accuracy, on average, in real time. Full article
(This article belongs to the Section Biosensors)
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Open AccessArticle
Parking Line Based SLAM Approach Using AVM/LiDAR Sensor Fusion for Rapid and Accurate Loop Closing and Parking Space Detection
Sensors 2019, 19(21), 4811; https://doi.org/10.3390/s19214811 - 05 Nov 2019
Abstract
Parking is a challenging task for autonomous vehicles and requires a centimeter level precision of distance measurement for safe parking at a destination to avoid collisions with nearby vehicles. In order to avoid collisions with parked vehicles while parking, real-time localization performance should [...] Read more.
Parking is a challenging task for autonomous vehicles and requires a centimeter level precision of distance measurement for safe parking at a destination to avoid collisions with nearby vehicles. In order to avoid collisions with parked vehicles while parking, real-time localization performance should be maintained even when loop closing occurs. This study proposes a simultaneous localization and mapping (SLAM) method, using around view monitor (AVM)/light detection and ranging (LiDAR) sensor fusion, that provides rapid loop closing performance. We extract the parking line features by utilizing the sensor fusion data for sparse feature-based pose graph optimization that boosts the loop closing speed. Hence, the proposed method can perform the loop closing within a few milliseconds to compensate for the accumulative errors even in a large-scale outdoor environment, which is much faster than other LiDAR-based SLAM algorithms. Therefore, it easily satisfies real-time localization performance. Furthermore, thanks to the parking line features, the proposed method can detect a parking space by utilizing the accumulated parking lines in the map. The experiment was performed in three outdoor parking lots to validate the localization performance and parking space detection performance. All of the proposed methods can be operated in real-time in a single-CPU environment. Full article
(This article belongs to the Special Issue Mobile Robot Navigation)
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Open AccessArticle
Paradox Elimination in Dempster–Shafer Combination Rule with Novel Entropy Function: Application in Decision-Level Multi-Sensor Fusion
Sensors 2019, 19(21), 4810; https://doi.org/10.3390/s19214810 - 05 Nov 2019
Abstract
Multi-sensor data fusion technology in an important tool in building decision-making applications. Modified Dempster–Shafer (DS) evidence theory can handle conflicting sensor inputs and can be applied without any prior information. As a result, DS-based information fusion is very popular in decision-making applications, but [...] Read more.
Multi-sensor data fusion technology in an important tool in building decision-making applications. Modified Dempster–Shafer (DS) evidence theory can handle conflicting sensor inputs and can be applied without any prior information. As a result, DS-based information fusion is very popular in decision-making applications, but original DS theory produces counterintuitive results when combining highly conflicting evidences from multiple sensors. An effective algorithm offering fusion of highly conflicting information in spatial domain is not widely reported in the literature. In this paper, a successful fusion algorithm is proposed which addresses these limitations of the original Dempster–Shafer (DS) framework. A novel entropy function is proposed based on Shannon entropy, which is better at capturing uncertainties compared to Shannon and Deng entropy. An 8-step algorithm has been developed which can eliminate the inherent paradoxes of classical DS theory. Multiple examples are presented to show that the proposed method is effective in handling conflicting information in spatial domain. Simulation results showed that the proposed algorithm has competitive convergence rate and accuracy compared to other methods presented in the literature. Full article
(This article belongs to the collection Multi-Sensor Information Fusion)
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Open AccessArticle
Disposable Injection Molded Conductive Electrodes Modified with Antimony Film for the Electrochemical Determination of Trace Pb(II) and Cd(II)
Sensors 2019, 19(21), 4809; https://doi.org/10.3390/s19214809 - 05 Nov 2019
Abstract
This work describes a novel electrochemical sensor fabricated by an injection molding process. This device features a conductive polymer electrode encased in a plastic holder and electroplated in situ with a thin antimony film. The antimony film sensor was applied to the determination [...] Read more.
This work describes a novel electrochemical sensor fabricated by an injection molding process. This device features a conductive polymer electrode encased in a plastic holder and electroplated in situ with a thin antimony film. The antimony film sensor was applied to the determination of Pb(II) and Cd(II) by anodic stripping voltammetry (ASV). The deposition of Sb on the sensor was studied by cyclic voltammetry (CV) and microscopy. The experimental variables (concentration of the antimony plating solution, deposition potential and time, stripping waveform) were investigated, and the potential interferences were studied and addressed. The limits of detection were 0.95 μg L−1 for Pb(II) and 1.3 for Cd(II) (at 240 s of preconcentration) and the within-sensor percentage relative standard deviations were 4.2% and 4.9%, respectively, at the 25 μg L−1 level (n = 8). Finally, the sensor was applied to the determination of Pb(II) and Cd(II) in a phosphorite sample and a lake water sample. Full article
(This article belongs to the Special Issue Advanced Sensors for the Detection of Heavy Metals)
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Open AccessArticle
Origami-Inspired Frequency Selective Surface with Fixed Frequency Response under Folding
Sensors 2019, 19(21), 4808; https://doi.org/10.3390/s19214808 - 05 Nov 2019
Abstract
Filtering of electromagnetic signals is key for improved signal to noise ratios for a broad class of devices. However, maintaining filter performance in systems undergoing large changes in shape can be challenging, due to the interdependency between element geometry, orientation and lattice spacing. [...] Read more.
Filtering of electromagnetic signals is key for improved signal to noise ratios for a broad class of devices. However, maintaining filter performance in systems undergoing large changes in shape can be challenging, due to the interdependency between element geometry, orientation and lattice spacing. To address this challenge, an origami-based, reconfigurable spatial X-band filter with consistent frequency filtering is presented. Direct-write additive manufacturing is used to print metallic Archimedean spiral elements in a lattice on the substrate. Elements in the lattice couple to one another and this results in a frequency selective surface acting as a stop-band filter at a target frequency. The lattice is designed to maintain the filtered frequency through multiple fold angles. The combined design, modeling, fabrication, and experimental characterization results of this study provide a set of guidelines for future design of physically reconfigurable filters exhibiting sustained performance. Full article
(This article belongs to the Section Physical Sensors)
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Open AccessReview
Fog Computing Enabling Industrial Internet of Things: State-of-the-Art and Research Challenges
Sensors 2019, 19(21), 4807; https://doi.org/10.3390/s19214807 - 05 Nov 2019
Abstract
Industry is going through a transformation phase, enabling automation and data exchange in manufacturing technologies and processes, and this transformation is called Industry 4.0. Industrial Internet-of-Things (IIoT) applications require real-time processing, near-by storage, ultra-low latency, reliability and high data rate, all of which [...] Read more.
Industry is going through a transformation phase, enabling automation and data exchange in manufacturing technologies and processes, and this transformation is called Industry 4.0. Industrial Internet-of-Things (IIoT) applications require real-time processing, near-by storage, ultra-low latency, reliability and high data rate, all of which can be satisfied by fog computing architecture. With smart devices expected to grow exponentially, the need for an optimized fog computing architecture and protocols is crucial. Therein, efficient, intelligent and decentralized solutions are required to ensure real-time connectivity, reliability and green communication. In this paper, we provide a comprehensive review of methods and techniques in fog computing. Our focus is on fog infrastructure and protocols in the context of IIoT applications. This article has two main research areas: In the first half, we discuss the history of industrial revolution, application areas of IIoT followed by key enabling technologies that act as building blocks for industrial transformation. In the second half, we focus on fog computing, providing solutions to critical challenges and as an enabler for IIoT application domains. Finally, open research challenges are discussed to enlighten fog computing aspects in different fields and technologies. Full article
(This article belongs to the Special Issue Internet of Things and Sensors Networks in 5G Wireless Communications)
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Open AccessArticle
Fault Diagnosis of a Rotor and Ball-Bearing System Using DWT Integrated with SVM, GRNN, and Visual Dot Patterns
Sensors 2019, 19(21), 4806; https://doi.org/10.3390/s19214806 - 05 Nov 2019
Abstract
In this study, a set of methods for the inspection of a working motor in real time was proposed. The aim was to determine if ball-bearing operation is normal or abnormal and to conduct an inspection in real time. The system consists of [...] Read more.
In this study, a set of methods for the inspection of a working motor in real time was proposed. The aim was to determine if ball-bearing operation is normal or abnormal and to conduct an inspection in real time. The system consists of motor control and measurement systems. The motor control system provides a set fixed speed, and the measurement system uses an accelerometer to measure the vibration, and the collected signal data are sent to a PC for analysis. This paper gives the details of the decomposition of vibration signals, using discrete wavelet transform (DWT) and computation of the features. It includes the classification of the features after analysis. Two major methods are used for the diagnosis of malfunction, the support vector machines (SVM) and general regression neural networks (GRNN). For visualization and to input the signals for visualization, they were input into a convolutional neural network (CNN) for further classification, as well as for the comparison of performance and results. Unique experimental processes were established with a particular hardware combination, and a comparison with commonly used methods was made. The results can be used for the design of a real-time motor that bears a diagnostic and malfunction warning system. This research establishes its own experimental process, according to the hardware combination and comparison of commonly used methods in research; a design for a real-time diagnosis of motor malfunction, as well as an early warning system, can be built thereupon. Full article
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Open AccessArticle
An Evaluation Framework for Spectral Filter Array Cameras to Optimize Skin Diagnosis
Sensors 2019, 19(21), 4805; https://doi.org/10.3390/s19214805 - 05 Nov 2019
Abstract
Comparing and selecting an adequate spectral filter array (SFA) camera is application-specific and usually requires extensive prior measurements. An evaluation framework for SFA cameras is proposed and three cameras are tested in the context of skin analysis. The proposed framework does not require [...] Read more.
Comparing and selecting an adequate spectral filter array (SFA) camera is application-specific and usually requires extensive prior measurements. An evaluation framework for SFA cameras is proposed and three cameras are tested in the context of skin analysis. The proposed framework does not require application-specific measurements and spectral sensitivities together with the number of bands are the main focus. An optical model of skin is used to generate a specialized training set to improve spectral reconstruction. The quantitative comparison of the cameras is based on reconstruction of measured skin spectra, colorimetric accuracy, and oxygenation level estimation differences. Specific spectral sensitivity shapes influence the results directly and a 9-channel camera performed best regarding the spectral reconstruction metrics. Sensitivities at key wavelengths influence the performance of oxygenation level estimation the strongest. The proposed framework allows to compare spectral filter array cameras and can guide their application-specific development. Full article
(This article belongs to the Section Biomedical Sensors)
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Open AccessArticle
Feasibility of a Sensor-Based Gait Event Detection Algorithm for Triggering Functional Electrical Stimulation during Robot-Assisted Gait Training
Sensors 2019, 19(21), 4804; https://doi.org/10.3390/s19214804 - 05 Nov 2019
Abstract
Technologies such as robot-assisted gait trainers or functional electrical stimulation can improve the rehabilitation process of people affected with gait disorders due to stroke or other neurological defects. By combining both technologies, the potential disadvantages of each technology could be compensated and simultaneously, [...] Read more.
Technologies such as robot-assisted gait trainers or functional electrical stimulation can improve the rehabilitation process of people affected with gait disorders due to stroke or other neurological defects. By combining both technologies, the potential disadvantages of each technology could be compensated and simultaneously, therapy effects could be improved. Thus, an algorithm was designed that aims to detect the gait cycle of a robot-assisted gait trainer. Based on movement data recorded with inertial measurement units, gait events can be detected. These events can further be used to trigger functional electrical stimulation. This novel setup offers the possibility of equipping a broad range of potential robot-assisted gait trainers with functional electrical stimulation. The aim of this paper in particular was to test the feasibility of a system using inertial measurement units for gait event detection during robot-assisted gait training. Thus, a 39-year-old healthy male adult executed a total of six training sessions with two robot-assisted gait trainers (Lokomat and Lyra). The measured data from the sensors were analyzed by a custom-made gait event detection algorithm. An overall detection rate of 98.1% ± 5.2% for the Lokomat and 94.1% ± 6.8% for the Lyra was achieved. The mean type-1 error was 0.3% ± 1.2% for the Lokomat and 1.9% ± 4.3% for the Lyra. As a result, the setup provides promising results for further research and a technique that can enhance robot-assisted gait trainers by adding functional electrical stimulation to the rehabilitation process. Full article
(This article belongs to the Special Issue Inertial Sensors)
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Open AccessArticle
A High-Sensitivity Methane Sensor with Localized Surface Plasmon Resonance Behavior in an Improved Hexagonal Gold Nanoring Array
Sensors 2019, 19(21), 4803; https://doi.org/10.3390/s19214803 - 05 Nov 2019
Abstract
This paper proposes a methane sensor based on localized surface plasmon resonance (LSPR) of a hexagonal periodic gold nanoring array. The effects of structural parameters on the extinction spectrum and refractive index (RI) sensitivity are analyzed to obtain optimal parameters. In particular, the [...] Read more.
This paper proposes a methane sensor based on localized surface plasmon resonance (LSPR) of a hexagonal periodic gold nanoring array. The effects of structural parameters on the extinction spectrum and refractive index (RI) sensitivity are analyzed to obtain optimal parameters. In particular, the RI sensitivity can reach 550.08 nm/RIU through improvement of the sensor structure, which is an increase of 17.4% over the original value. After coating a methane-sensitive membrane on the inner and outer surfaces of the gold rings, the methane concentration can be accurately measured with a gas sensitivity of −1.02 nm/%. The proposed method is also applicable to quantitative analyses of components concentration and qualitative analyses of gas composition. Full article
(This article belongs to the Section Chemical Sensors)
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Open AccessArticle
Localization and Tracking of Discrete Mobile Scatterers in Vehicular Environments Using Delay Estimates
Sensors 2019, 19(21), 4802; https://doi.org/10.3390/s19214802 - 05 Nov 2019
Abstract
This paper describes an approach to detect, localize, and track moving, non-cooperative objects by exploiting multipath propagation. In a network of spatially distributed transmitting and receiving nodes, moving objects appear as discrete mobile scatterers. Therefore, the localization of mobile scatterers is formulated as [...] Read more.
This paper describes an approach to detect, localize, and track moving, non-cooperative objects by exploiting multipath propagation. In a network of spatially distributed transmitting and receiving nodes, moving objects appear as discrete mobile scatterers. Therefore, the localization of mobile scatterers is formulated as a nonlinear optimization problem. An iterative nonlinear least squares algorithm following Levenberg and Marquardt is used for solving the optimization problem initially, and an extended Kalman filter is used for estimating the scatterer location recursively over time. The corresponding performance bounds are derived for both the snapshot based position estimation and the nonlinear sequential Bayesian estimation with the classic and the posterior Cramér–Rao lower bound. Thereby, a comparison of simulation results to the posterior Cramér–Rao lower bound confirms the applicability of the extended Kalman filter. The proposed approach is applied to estimate the position of a walking pedestrian sequentially based on wideband measurement data in an outdoor scenario. The evaluation shows that the pedestrian can be localized throughout the scenario with an accuracy of 0 . 8 m at 90% confidence. Full article
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Open AccessArticle
A Role-Based Software Architecture to Support Mobile Service Computing in IoT Scenarios
Sensors 2019, 19(21), 4801; https://doi.org/10.3390/s19214801 - 05 Nov 2019
Abstract
The interaction among components of an IoT-based system usually requires using low latency or real time for message delivery, depending on the application needs and the quality of the communication links among the components. Moreover, in some cases, this interaction should consider the [...] Read more.
The interaction among components of an IoT-based system usually requires using low latency or real time for message delivery, depending on the application needs and the quality of the communication links among the components. Moreover, in some cases, this interaction should consider the use of communication links with poor or uncertain Quality of Service (QoS). Research efforts in communication support for IoT scenarios have overlooked the challenge of providing real-time interaction support in unstable links, making these systems use dedicated networks that are expensive and usually limited in terms of physical coverage and robustness. This paper presents an alternative to address such a communication challenge, through the use of a model that allows soft real-time interaction among components of an IoT-based system. The behavior of the proposed model was validated using state machine theory, opening an opportunity to explore a whole new branch of smart distributed solutions and to extend the state-of-the-art and the-state-of-the-practice in this particular IoT study scenario. Full article
(This article belongs to the Special Issue Pub/Sub Solutions for IoT)
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Open AccessArticle
A Survey of Practical Design Considerations of Optical Imaging Stabilization Systems for Small Unmanned Aerial Systems
Sensors 2019, 19(21), 4800; https://doi.org/10.3390/s19214800 - 04 Nov 2019
Abstract
Optical imaging systems are one of the most common sensors used for collecting data with small Unmanned Aerial Systems (sUAS). Plenty of research exists which present custom-made optical imaging systems for specific missions. However, the research commonly leaves out the explanation of design [...] Read more.
Optical imaging systems are one of the most common sensors used for collecting data with small Unmanned Aerial Systems (sUAS). Plenty of research exists which present custom-made optical imaging systems for specific missions. However, the research commonly leaves out the explanation of design parameters and considerations taken during the design of the optical imaging system, especially the image stabilization strategy used, which is a significant issue in sUAS imaging missions. This paper surveys useful methodologies for designing a stabilized optical imaging system by presenting an overview of the important aspects that must be addressed in the designing phase and which tools and techniques are available and should be chosen according to the design requirements. Full article
(This article belongs to the Special Issue Sensors for Unmanned Aircraft Systems and Related Technologies)
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Open AccessArticle
A Joint Power and Channel Scheduling Scheme for Underlay D2D Communications in the Cellular Network
Sensors 2019, 19(21), 4799; https://doi.org/10.3390/s19214799 - 04 Nov 2019
Abstract
Device-to-device (D2D) communication, as one of the promising candidates for the fifth generation mobile network, can afford effective service of new mobile applications and business models. In this paper, we study the resource management strategies for D2D communication underlying the cellular networks. To [...] Read more.
Device-to-device (D2D) communication, as one of the promising candidates for the fifth generation mobile network, can afford effective service of new mobile applications and business models. In this paper, we study the resource management strategies for D2D communication underlying the cellular networks. To cater for green communications, our design goal is to the maximize ergodic energy efficiency (EE) of all D2D links taking into account the fact that it may be tricky for the base station (BS) to receive all the real-time channel state information (CSI) while guaranteeing the stability and the power requirements for D2D links. We formulate the optimization problem which is difficult to resolve directly because of its non-convex nature. Then a novel maximum weighted ergodic energy efficiency (MWEEE) algorithm is proposed to solve the formulated optimization problem which consists of two sub-problems: the power control (PC) sub-problem which can be solved by employing convex optimization theory for both cellular user equipment (CUE) and D2D user equipment (DUE) and the channel allocation (CA) sub-problem which can be solved by obtaining the weighted allocation matrix. In particular, we shed light into the impact on EE metric of D2D communication by revealing the nonlinear power relationship between CUE and DUE and taking the QoS of CUEs into account. Furthermore, simulation results show that our proposed algorithm is superior to the existing algorithms. Full article
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Open AccessArticle
MicroServices Suite for Smart City Applications
Sensors 2019, 19(21), 4798; https://doi.org/10.3390/s19214798 - 04 Nov 2019
Abstract
Smart Cities are approaching the Internet of Things (IoT) World. Most of the first-generation Smart City solutions are based on Extract Transform Load (ETL); processes and languages that mainly support pull protocols for data gathering. IoT solutions are moving forward to event-driven processes [...] Read more.
Smart Cities are approaching the Internet of Things (IoT) World. Most of the first-generation Smart City solutions are based on Extract Transform Load (ETL); processes and languages that mainly support pull protocols for data gathering. IoT solutions are moving forward to event-driven processes using push protocols. Thus, the concept of IoT applications has turned out to be widespread; but it was initially “implemented” with ETL; rule-based solutions; and finally; with true data flows. In this paper, these aspects are reviewed, highlighting the requirements for smart city IoT applications and in particular, the ones that implement a set of specific MicroServices for IoT Applications in Smart City contexts. Moreover; our experience has allowed us to implement a suite of MicroServices for Node-RED; which has allowed for the creation of a wide range of new IoT applications for smart cities that includes dashboards, IoT Devices, data analytics, discovery, etc., as well as a corresponding Life Cycle. The proposed solution has been validated against a large number of IoT applications, as it can be verified by accessing the https://www.Snap4City.org portal; while only three of them have been described in the paper. In addition, the reported solution assessment has been carried out by a number of smart city experts. The work has been developed in the framework of the Select4Cities PCP (PreCommercial Procurement), funded by the European Commission as Snap4City platform. Full article
(This article belongs to the Special Issue Emerging IoT Technologies for Smart Environments)
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Open AccessArticle
Sensor to Monitor Localized Stresses on Steel Surfaces Using the Magnetostrictive Delay Line Technique
Sensors 2019, 19(21), 4797; https://doi.org/10.3390/s19214797 - 04 Nov 2019
Abstract
In this paper, a new type of force sensor is presented, able to monitor localized residual stresses on steel surfaces. The principle of operation of the proposed sensor is based on the monitoring of the force exerted between a permanent magnet and the [...] Read more.
In this paper, a new type of force sensor is presented, able to monitor localized residual stresses on steel surfaces. The principle of operation of the proposed sensor is based on the monitoring of the force exerted between a permanent magnet and the under-test steel which is dependent on the surface permeability of the steel providing a non-hysteretic response. The sensor’s response, calibration, and performance are described followed by a discussion concerning the applications for steel health monitoring. Full article
(This article belongs to the Special Issue Magnetic Sensors 2020)
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Open AccessArticle
Learning to Detect Cracks on Damaged Concrete Surfaces Using Two-Branched Convolutional Neural Network
Sensors 2019, 19(21), 4796; https://doi.org/10.3390/s19214796 - 04 Nov 2019
Abstract
Image sensors are widely used for detecting cracks on concrete surfaces to help proactive and timely management of concrete structures. However, it is a challenging task to reliably detect cracks on damaged surfaces in the real world due to noise and undesired artifacts. [...] Read more.
Image sensors are widely used for detecting cracks on concrete surfaces to help proactive and timely management of concrete structures. However, it is a challenging task to reliably detect cracks on damaged surfaces in the real world due to noise and undesired artifacts. In this paper, we propose an autonomous crack detection algorithm based on convolutional neural network (CNN) to solve the problem. To this aim, the proposed algorithm uses a two-branched CNN architecture, consisting of sub-networks named a crack-component-aware (CCA) network and a crack-region-aware (CRA) network. The CCA network is to learn gradient component regarding cracks, and the CRA network is to learn a region-of-interest by distinguishing critical cracks and noise such as scratches. Specifically, the two sub-networks are built on convolution-deconvolution CNN architectures, but also they are comprised of different functional components to achieve their own goals efficiently. The two sub-networks are trained in an end-to-end to jointly optimize parameters and produce the final output of localizing important cracks. Various crack image samples and learning methods are used for efficiently training the proposed network. In the experimental results, the proposed algorithm provides better performance in the crack detection than the conventional algorithms. Full article
(This article belongs to the Section Intelligent Sensors)
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Open AccessArticle
Gas Biosensor Arrays Based on Single-Stranded DNA-Functionalized Single-Walled Carbon Nanotubes for the Detection of Volatile Organic Compound Biomarkers Released by Huanglongbing Disease-Infected Citrus Trees
Sensors 2019, 19(21), 4795; https://doi.org/10.3390/s19214795 - 04 Nov 2019
Abstract
Volatile organic compounds (VOCs) released by plants are closely associated with plant metabolism and can serve as biomarkers for disease diagnosis. Huanglongbing (HLB), also known as citrus greening or yellow shoot disease, is a lethal threat to the multi-billion-dollar citrus industry. Early detection [...] Read more.
Volatile organic compounds (VOCs) released by plants are closely associated with plant metabolism and can serve as biomarkers for disease diagnosis. Huanglongbing (HLB), also known as citrus greening or yellow shoot disease, is a lethal threat to the multi-billion-dollar citrus industry. Early detection of HLB is vital for removal of susceptible citrus trees and containment of the disease. Gas sensors are applied to monitor the air quality or toxic gases owing to their low-cost fabrication, smooth operation, and possible miniaturization. Here, we report on the development, characterization, and application of electrical biosensor arrays based on single-walled carbon nanotubes (SWNTs) decorated with single-stranded DNA (ssDNA) for the detection of four VOCs—ethylhexanol, linalool, tetradecene, and phenylacetaldehyde—that serve as secondary biomarkers for detection of infected citrus trees during the asymptomatic stage. SWNTs were noncovalently functionalized with ssDNA using π–π interaction between the nucleotide and sidewall of SWNTs. The resulting ssDNA-SWNT hybrid structure and device properties were investigated using Raman spectroscopy, ultraviolet (UV) spectroscopy, and electrical measurements. To monitor changes in the four VOCs, gas biosensor arrays consisting of bare SWNTs before and after being decorated with different ssDNA were employed to determine the different concentrations of the four VOCs. The data was processed using principal component analysis (PCA) and neural net fitting (NNF). Full article
(This article belongs to the Section Biosensors)
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Open AccessArticle
Vision-Based Multirotor Following Using Synthetic Learning Techniques
Sensors 2019, 19(21), 4794; https://doi.org/10.3390/s19214794 - 04 Nov 2019
Abstract
Deep- and reinforcement-learning techniques have increasingly required large sets of real data to achieve stable convergence and generalization, in the context of image-recognition, object-detection or motion-control strategies. On this subject, the research community lacks robust approaches to overcome unavailable real-world extensive data by [...] Read more.
Deep- and reinforcement-learning techniques have increasingly required large sets of real data to achieve stable convergence and generalization, in the context of image-recognition, object-detection or motion-control strategies. On this subject, the research community lacks robust approaches to overcome unavailable real-world extensive data by means of realistic synthetic-information and domain-adaptation techniques. In this work, synthetic-learning strategies have been used for the vision-based autonomous following of a noncooperative multirotor. The complete maneuver was learned with synthetic images and high-dimensional low-level continuous robot states, with deep- and reinforcement-learning techniques for object detection and motion control, respectively. A novel motion-control strategy for object following is introduced where the camera gimbal movement is coupled with the multirotor motion during the multirotor following. Results confirm that our present framework can be used to deploy a vision-based task in real flight using synthetic data. It was extensively validated in both simulated and real-flight scenarios, providing proper results (following a multirotor up to 1.3 m/s in simulation and 0.3 m/s in real flights). Full article
(This article belongs to the Special Issue Mobile Robot Navigation)
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Open AccessArticle
Energy Efficiency of a Decode-and-Forward Multiple-Relay Network with Rate Adaptive LDPC Codes
Sensors 2019, 19(21), 4793; https://doi.org/10.3390/s19214793 - 04 Nov 2019
Abstract
This paper presents cooperative transmission (CT), where multiple relays are used to achieve array and diversity gains, as an enabling technology for Internet of Things (IoT) networks with hardware-limited devices. We investigate a channel coding aided decode-and-forward (DF) relaying network, considering a two-hop [...] Read more.
This paper presents cooperative transmission (CT), where multiple relays are used to achieve array and diversity gains, as an enabling technology for Internet of Things (IoT) networks with hardware-limited devices. We investigate a channel coding aided decode-and-forward (DF) relaying network, considering a two-hop multiple-relay network, where the data transmission between the source and the destination is realized with the help of DF relays. Low density parity check (LDPC) codes are adopted as forward error correction (FEC) codes to encode and decode the data both at the source and relays. We consider both fixed and variable code rates depending upon the quality-of-service (QoS) provisioning such as spectral efficiency and maximum energy efficiency. Furthermore, an optimal power allocation scheme is studied for the cooperative system under the energy efficiency constraint. We present the simulation results of our proposed scheme, compared with conventional methods, which show that if decoupled code rates are used on both hops then a trade-off has to be maintained between system complexity, transmission delay, and bit error rate (BER). Full article
(This article belongs to the Special Issue Recent Advances in Internet of Things and Sensor Networks)
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Open AccessCorrection
Correction: Kutafina, E.; Laukamp, D.; Bettermann, R.; Schroeder, U.; Jonas, S.M. Wearable Sensors for eLearning of Manual Tasks: Using Forearm EMG in Hand Hygiene Training. Sensors 2016, 16, 1221
Sensors 2019, 19(21), 4792; https://doi.org/10.3390/s19214792 - 04 Nov 2019
Abstract
The authors wish to make the following erratum to this paper [...] Full article
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