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Sensors, Volume 18, Issue 4 (April 2018)

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Editorial

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Open AccessEditorial Advances in Multi-Sensor Information Fusion: Theory and Applications 2017
Sensors 2018, 18(4), 1162; https://doi.org/10.3390/s18041162
Received: 8 April 2018 / Revised: 8 April 2018 / Accepted: 9 April 2018 / Published: 11 April 2018
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Abstract
The information fusion technique can integrate a large amount of data and knowledge representing the same real-world object and obtain a consistent, accurate, and useful representation of that object. The data may be independent or redundant, and can be obtained by different sensors
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The information fusion technique can integrate a large amount of data and knowledge representing the same real-world object and obtain a consistent, accurate, and useful representation of that object. The data may be independent or redundant, and can be obtained by different sensors at the same time or at different times. A suitable combination of investigative methods can substantially increase the profit of information in comparison with that from a single sensor. Multi-sensor information fusion has been a key issue in sensor research since the 1970s, and it has been applied in many fields. For example, manufacturing and process control industries can generate a lot of data, which have real, actionable business value. The fusion of these data can greatly improve productivity through digitization. The goal of this special issue is to report innovative ideas and solutions for multi-sensor information fusion in the emerging applications era, focusing on development, adoption, and applications. Full article

Research

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Open AccessArticle An Internet of Things System for Underground Mine Air Quality Pollutant Prediction Based on Azure Machine Learning
Sensors 2018, 18(4), 930; https://doi.org/10.3390/s18040930
Received: 20 February 2018 / Revised: 15 March 2018 / Accepted: 16 March 2018 / Published: 21 March 2018
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Abstract
The implementation of wireless sensor networks (WSNs) for monitoring the complex, dynamic, and harsh environment of underground coal mines (UCMs) is sought around the world to enhance safety. However, previously developed smart systems are limited to monitoring or, in a few cases, can
[...] Read more.
The implementation of wireless sensor networks (WSNs) for monitoring the complex, dynamic, and harsh environment of underground coal mines (UCMs) is sought around the world to enhance safety. However, previously developed smart systems are limited to monitoring or, in a few cases, can report events. Therefore, this study introduces a reliable, efficient, and cost-effective internet of things (IoT) system for air quality monitoring with newly added features of assessment and pollutant prediction. This system is comprised of sensor modules, communication protocols, and a base station, running Azure Machine Learning (AML) Studio over it. Arduino-based sensor modules with eight different parameters were installed at separate locations of an operational UCM. Based on the sensed data, the proposed system assesses mine air quality in terms of the mine environment index (MEI). Principal component analysis (PCA) identified CH4, CO, SO2, and H2S as the most influencing gases significantly affecting mine air quality. The results of PCA were fed into the ANN model in AML studio, which enabled the prediction of MEI. An optimum number of neurons were determined for both actual input and PCA-based input parameters. The results showed a better performance of the PCA-based ANN for MEI prediction, with R2 and RMSE values of 0.6654 and 0.2104, respectively. Therefore, the proposed Arduino and AML-based system enhances mine environmental safety by quickly assessing and predicting mine air quality. Full article
(This article belongs to the Special Issue Artificial Intelligence and Machine Learning in Sensors Networks)
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Open AccessArticle Comparison between Modelled and Measured Magnetic Field Scans of Different Planar Coil Topologies for Stress Sensor Applications
Sensors 2018, 18(4), 931; https://doi.org/10.3390/s18040931
Received: 20 February 2018 / Revised: 13 March 2018 / Accepted: 19 March 2018 / Published: 21 March 2018
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Abstract
The investigation of planar coils of differing topologies, when combined with a magnetostrictive amorphous ribbon to form a stress-sensitive self-inductor, is an active research area for applications as stress or pressure sensors. Four topologies of planar coil (Circular, Mesh, Meander, and Square) have
[...] Read more.
The investigation of planar coils of differing topologies, when combined with a magnetostrictive amorphous ribbon to form a stress-sensitive self-inductor, is an active research area for applications as stress or pressure sensors. Four topologies of planar coil (Circular, Mesh, Meander, and Square) have been constructed using copper track on 30 mm wide PCB substrate. The coils are energized to draw 0.4 A and the resulting magnetic field distribution is observed with a newly developed three-dimensional magnetic field scanner. The system is based on a variably angled Micromagnetics® STJ-020 tunneling magneto-resistance sensor with a spatial resolution of 5–10 µm and sensitivity to fields of less than 10 A/m. These experimental results are compared with the fields computed by ANSYS Maxwell® finite element modelling of the same topologies. Measured field shape and strength correspond well with the results of modelling, including direct observation of corner and edge effects. Three-dimensional analysis of the field shape produced by the square coil, isolating the components H(x) and H(z), is compared with the three-dimensional field solutions from modelling. The finite element modelling is validated and the accuracy and utility of the new system for three-dimensional scanning of general stray fields is confirmed. Full article
(This article belongs to the Special Issue Magnetic Sensors)
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Open AccessArticle A Newly Designed Fiber-Optic Based Earth Pressure Transducer with Adjustable Measurement Range
Sensors 2018, 18(4), 932; https://doi.org/10.3390/s18040932
Received: 3 February 2018 / Revised: 14 March 2018 / Accepted: 15 March 2018 / Published: 21 March 2018
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Abstract
A novel fiber-optic based earth pressure sensor (FPS) with an adjustable measurement range and high sensitivity is developed to measure earth pressures for civil infrastructures. The new FPS combines a cantilever beam with fiber Bragg grating (FBG) sensors and a flexible membrane. Compared
[...] Read more.
A novel fiber-optic based earth pressure sensor (FPS) with an adjustable measurement range and high sensitivity is developed to measure earth pressures for civil infrastructures. The new FPS combines a cantilever beam with fiber Bragg grating (FBG) sensors and a flexible membrane. Compared with a traditional pressure transducer with a dual diaphragm design, the proposed FPS has a larger measurement range and shows high accuracy. The working principles, parameter design, fabrication methods, and laboratory calibration tests are explained in this paper. A theoretical solution is derived to obtain the relationship between the applied pressure and strain of the FBG sensors. In addition, a finite element model is established to analyze the mechanical behavior of the membrane and the cantilever beam and thereby obtain optimal parameters. The cantilever beam is 40 mm long, 15 mm wide, and 1 mm thick. The whole FPS has a diameter of 100 mm and a thickness of 30 mm. The sensitivity of the FPS is 0.104 kPa/με. In addition, automatic temperature compensation can be achieved. The FPS’s sensitivity, physical properties, and response to applied pressure are extensively examined through modeling and experiments. The results show that the proposed FPS has numerous potential applications in soil pressure measurement. Full article
(This article belongs to the Special Issue Recent Advances in Fiber Bragg Grating Based Sensors)
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Open AccessArticle Robust Spacecraft Component Detection in Point Clouds
Sensors 2018, 18(4), 933; https://doi.org/10.3390/s18040933
Received: 10 January 2018 / Revised: 10 March 2018 / Accepted: 13 March 2018 / Published: 21 March 2018
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Abstract
Automatic component detection of spacecraft can assist in on-orbit operation and space situational awareness. Spacecraft are generally composed of solar panels and cuboidal or cylindrical modules. These components can be simply represented by geometric primitives like plane, cuboid and cylinder. Based on this
[...] Read more.
Automatic component detection of spacecraft can assist in on-orbit operation and space situational awareness. Spacecraft are generally composed of solar panels and cuboidal or cylindrical modules. These components can be simply represented by geometric primitives like plane, cuboid and cylinder. Based on this prior, we propose a robust automatic detection scheme to automatically detect such basic components of spacecraft in three-dimensional (3D) point clouds. In the proposed scheme, cylinders are first detected in the iteration of the energy-based geometric model fitting and cylinder parameter estimation. Then, planes are detected by Hough transform and further described as bounded patches with their minimum bounding rectangles. Finally, the cuboids are detected with pair-wise geometry relations from the detected patches. After successive detection of cylinders, planar patches and cuboids, a mid-level geometry representation of the spacecraft can be delivered. We tested the proposed component detection scheme on spacecraft 3D point clouds synthesized by computer-aided design (CAD) models and those recovered by image-based reconstruction, respectively. Experimental results illustrate that the proposed scheme can detect the basic geometric components effectively and has fine robustness against noise and point distribution density. Full article
(This article belongs to the Special Issue Sensor Signal and Information Processing)
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Open AccessArticle The Effect of Vibration Characteristics on the Atomization Rate in a Micro-Tapered Aperture Atomizer
Sensors 2018, 18(4), 934; https://doi.org/10.3390/s18040934
Received: 25 January 2018 / Revised: 28 February 2018 / Accepted: 20 March 2018 / Published: 21 March 2018
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Abstract
Because little is known about the atomization theory of a micro-tapered aperture atomizer, we investigated the vibration characteristics of this type of atomizer. The atomization mechanism of a micro-tapered aperture atomizer was described, and the atomization rate equation was deduced. As observed via
[...] Read more.
Because little is known about the atomization theory of a micro-tapered aperture atomizer, we investigated the vibration characteristics of this type of atomizer. The atomization mechanism of a micro-tapered aperture atomizer was described, and the atomization rate equation was deduced. As observed via microscopy, the angle of the micro-tapered aperture changes with the applied voltage, which proved the existence of a dynamic cone angle. The forward and reverse atomization rates were measured at various voltages, and the influence of the micro-tapered aperture and its variation on the atomization rate was characterized. The resonance frequency of the piezoelectric vibrator was obtained using a laser vibrometer, and the atomization rates were measured at each resonance frequency. From experiments, we found that the atomization rates at the first five resonance frequencies increased as the working frequency increased. At the fifth resonance frequency (121.1 kHz), the atomization rate was maximized (0.561 mL/min), and at the sixth resonance frequency (148.3 kHz), the atomization rate decreased significantly (0.198 mL/min). The experimental results show that the vibration characteristics of the piezoelectric vibrator have a relatively strong impact on the atomization rate. This research is expected to contribute to the manufacture of micro-tapered aperture atomizers. Full article
(This article belongs to the Special Issue Ultrasonic Sensors 2018)
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Open AccessArticle Detection and Compensation of Degeneracy Cases for IMU-Kinect Integrated Continuous SLAM with Plane Features
Sensors 2018, 18(4), 935; https://doi.org/10.3390/s18040935
Received: 24 January 2018 / Revised: 16 March 2018 / Accepted: 20 March 2018 / Published: 22 March 2018
Cited by 1 | PDF Full-text (6871 KB) | HTML Full-text | XML Full-text
Abstract
In a group of general geometric primitives, plane-based features are widely used for indoor localization because of their robustness against noises. However, a lack of linearly independent planes may lead to a non-trivial estimation. This in return can cause a degenerate state from
[...] Read more.
In a group of general geometric primitives, plane-based features are widely used for indoor localization because of their robustness against noises. However, a lack of linearly independent planes may lead to a non-trivial estimation. This in return can cause a degenerate state from which all states cannot be estimated. To solve this problem, this paper first proposed a degeneracy detection method. A compensation method that could fix orientations by projecting an inertial measurement unit’s (IMU) information was then explained. Experiments were conducted using an IMU-Kinect v2 integrated sensor system prone to fall into degenerate cases owing to its narrow field-of-view. Results showed that the proposed framework could enhance map accuracy by successful detection and compensation of degenerated orientations. Full article
(This article belongs to the Section Remote Sensors)
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Open AccessArticle Resonance-Based Time-Frequency Manifold for Feature Extraction of Ship-Radiated Noise
Sensors 2018, 18(4), 936; https://doi.org/10.3390/s18040936
Received: 3 January 2018 / Revised: 12 March 2018 / Accepted: 12 March 2018 / Published: 22 March 2018
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Abstract
In this paper, a novel time-frequency signature using resonance-based sparse signal decomposition (RSSD), phase space reconstruction (PSR), time-frequency distribution (TFD) and manifold learning is proposed for feature extraction of ship-radiated noise, which is called resonance-based time-frequency manifold (RTFM). This is suitable for analyzing
[...] Read more.
In this paper, a novel time-frequency signature using resonance-based sparse signal decomposition (RSSD), phase space reconstruction (PSR), time-frequency distribution (TFD) and manifold learning is proposed for feature extraction of ship-radiated noise, which is called resonance-based time-frequency manifold (RTFM). This is suitable for analyzing signals with oscillatory, non-stationary and non-linear characteristics in a situation of serious noise pollution. Unlike the traditional methods which are sensitive to noise and just consider one side of oscillatory, non-stationary and non-linear characteristics, the proposed RTFM can provide the intact feature signature of all these characteristics in the form of a time-frequency signature by the following steps: first, RSSD is employed on the raw signal to extract the high-oscillatory component and abandon the low-oscillatory component. Second, PSR is performed on the high-oscillatory component to map the one-dimensional signal to the high-dimensional phase space. Third, TFD is employed to reveal non-stationary information in the phase space. Finally, manifold learning is applied to the TFDs to fetch the intrinsic non-linear manifold. A proportional addition of the top two RTFMs is adopted to produce the improved RTFM signature. All of the case studies are validated on real audio recordings of ship-radiated noise. Case studies of ship-radiated noise on different datasets and various degrees of noise pollution manifest the effectiveness and robustness of the proposed method. Full article
(This article belongs to the Special Issue Sensor Signal and Information Processing)
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Open AccessArticle Source Localization in Acoustic Sensor Networks via Constrained Least-Squares Optimization Using AOA and GROA Measurements
Sensors 2018, 18(4), 937; https://doi.org/10.3390/s18040937
Received: 29 January 2018 / Revised: 15 March 2018 / Accepted: 19 March 2018 / Published: 22 March 2018
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Abstract
A constrained least-squares (CLS) 3D source localization method is presented for acoustic sensor networks with sensor position errors. The proposed approach uses angles of arrivals (AOAs) and gain ratios of arrival (GROAs) measured simultaneously at each node to estimate the source position jointly.
[...] Read more.
A constrained least-squares (CLS) 3D source localization method is presented for acoustic sensor networks with sensor position errors. The proposed approach uses angles of arrivals (AOAs) and gain ratios of arrival (GROAs) measured simultaneously at each node to estimate the source position jointly. Compared to AOA-only localization methods, the GROAs can be used in conjunction with AOA measurements so as to get more accurate results by exploiting the geometrical relationship between these two measurements. Compared to time difference of arrival localization methods, the proposed algorithm does not require accurate time synchronization over different nodes. The theoretical mean-square error matrices of the proposed approach are derived and they are exactly equal to the Cramér–Rao bound for Gaussian noise under the small error condition. Simulations validate the performance of the proposed estimator. Full article
(This article belongs to the Section Sensor Networks)
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Open AccessArticle Application of Least-Squares Support Vector Machines for Quantitative Evaluation of Known Contaminant in Water Distribution System Using Online Water Quality Parameters
Sensors 2018, 18(4), 938; https://doi.org/10.3390/s18040938
Received: 27 February 2018 / Revised: 15 March 2018 / Accepted: 19 March 2018 / Published: 22 March 2018
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Abstract
In water-quality, early warning systems and qualitative detection of contaminants are always challenging. There are a number of parameters that need to be measured which are not entirely linearly related to pollutant concentrations. Besides the complex correlations between variable water parameters that need
[...] Read more.
In water-quality, early warning systems and qualitative detection of contaminants are always challenging. There are a number of parameters that need to be measured which are not entirely linearly related to pollutant concentrations. Besides the complex correlations between variable water parameters that need to be analyzed also impairs the accuracy of quantitative detection. In aspects of these problems, the application of least-squares support vector machines (LS-SVM) is used to evaluate the water contamination and various conventional water quality sensors quantitatively. The various contaminations may cause different correlative responses of sensors, and also the degree of response is related to the concentration of the injected contaminant. Therefore to enhance the reliability and accuracy of water contamination detection a new method is proposed. In this method, a new relative response parameter is introduced to calculate the differences between water quality parameters and their baselines. A variety of regression models has been examined, as result of its high performance, the regression model based on genetic algorithm (GA) is combined with LS-SVM. In this paper, the practical application of the proposed method is considered, controlled experiments are designed, and data is collected from the experimental setup. The measured data is applied to analyze the water contamination concentration. The evaluation of results validated that the LS-SVM model can adapt to the local nonlinear variations between water quality parameters and contamination concentration with the excellent generalization ability and accuracy. The validity of the proposed approach in concentration evaluation for potassium ferricyanide is proven to be more than 0.5 mg/L in water distribution systems. Full article
(This article belongs to the Section Physical Sensors)
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Open AccessArticle Visual Odometry and Place Recognition Fusion for Vehicle Position Tracking in Urban Environments
Sensors 2018, 18(4), 939; https://doi.org/10.3390/s18040939
Received: 16 February 2018 / Revised: 17 March 2018 / Accepted: 19 March 2018 / Published: 22 March 2018
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Abstract
In this paper, we address the problem of vehicle localization in urban environments. We rely on visual odometry, calculating the incremental motion, to track the position of the vehicle and on place recognition to correct the accumulated drift of visual odometry, whenever a
[...] Read more.
In this paper, we address the problem of vehicle localization in urban environments. We rely on visual odometry, calculating the incremental motion, to track the position of the vehicle and on place recognition to correct the accumulated drift of visual odometry, whenever a location is recognized. The algorithm used as a place recognition module is SeqSLAM, addressing challenging environments and achieving quite remarkable results. Specifically, we perform the long-term navigation of a vehicle based on the fusion of visual odometry and SeqSLAM. The template library for this latter is created online using navigation information from the visual odometry module. That is, when a location is recognized, the corresponding information is used as an observation of the filter. The fusion is done using the EKF and the UKF, the well-known nonlinear state estimation methods, to assess the superior alternative. The algorithm is evaluated using the KITTI dataset and the results show the reduction of the navigation errors by loop-closure detection. The overall position error of visual odometery with SeqSLAM is 0.22% of the trajectory, which is much smaller than the navigation errors of visual odometery alone 0.45%. In addition, despite the superiority of the UKF in a variety of estimation problems, our results indicate that the UKF performs as efficiently as the EKF at the expense of an additional computational overhead. This leads to the conclusion that the EKF is a better choice for fusing visual odometry and SeqSlam in a long-term navigation context. Full article
(This article belongs to the Section Intelligent Sensors)
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Open AccessArticle Explicit Content Caching at Mobile Edge Networks with Cross-Layer Sensing
Sensors 2018, 18(4), 940; https://doi.org/10.3390/s18040940
Received: 16 February 2018 / Revised: 15 March 2018 / Accepted: 18 March 2018 / Published: 22 March 2018
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Abstract
The deployment density and computational power of small base stations (BSs) are expected to increase significantly in the next generation mobile communication networks. These BSs form the mobile edge network, which is a pervasive and distributed infrastructure that can empower a variety of
[...] Read more.
The deployment density and computational power of small base stations (BSs) are expected to increase significantly in the next generation mobile communication networks. These BSs form the mobile edge network, which is a pervasive and distributed infrastructure that can empower a variety of edge/fog computing applications. This paper proposes a novel edge-computing application called explicit caching, which stores selective contents at BSs and exposes such contents to local users for interactive browsing and download. We formulate the explicit caching problem as a joint content recommendation, caching, and delivery problem, which aims to maximize the expected user quality-of-experience (QoE) with varying degrees of cross-layer sensing capability. Optimal and effective heuristic algorithms are presented to solve the problem. The theoretical performance bounds of the explicit caching system are derived in simplified scenarios. The impacts of cache storage space, BS backhaul capacity, cross-layer information, and user mobility on the system performance are simulated and discussed in realistic scenarios. Results suggest that, compared with conventional implicit caching schemes, explicit caching can better exploit the mobile edge network infrastructure for personalized content dissemination. Full article
(This article belongs to the Special Issue New Paradigms in Data Sensing and Processing for Edge Computing)
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Open AccessArticle A High-Resolution SAR Focusing Experiment Based on GF-3 Staring Data
Sensors 2018, 18(4), 943; https://doi.org/10.3390/s18040943
Received: 30 December 2017 / Revised: 11 March 2018 / Accepted: 20 March 2018 / Published: 22 March 2018
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Abstract
Spotlight synthetic aperture radar (SAR) is a proven technique, which can provide high-resolution images as compared to those produced by traditional stripmap SAR. This paper addresses a high-resolution SAR focusing experiment based on Gaofen-3 satellite (GF-3) staring data with about 55 cm azimuth
[...] Read more.
Spotlight synthetic aperture radar (SAR) is a proven technique, which can provide high-resolution images as compared to those produced by traditional stripmap SAR. This paper addresses a high-resolution SAR focusing experiment based on Gaofen-3 satellite (GF-3) staring data with about 55 cm azimuth resolution and 240 MHz range bandwidth. In staring spotlight (ST) mode, the antenna always illuminates the same scene on the ground, which can extend the synthetic aperture. Based on a two-step processing algorithm, some special aspects such as curved-orbit model error correction, stop-and-go correction, and antenna pattern demodulation must be considered in image focusing. We provide detailed descriptions of all these aspects and put forward corresponding solutions. Using these suggested methods directly in an imaging module without any modification for other data processing software can make the most of the existing ground data processor. Finally, actual data acquired in GF-3 ST mode is used to validate these methodologies, and a well-focused, high-resolution image is obtained as a result of this focusing experiment. Full article
(This article belongs to the Special Issue First Experiences with Chinese Gaofen-3 SAR Sensor)
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Open AccessArticle Aerial Mapping of Forests Affected by Pathogens Using UAVs, Hyperspectral Sensors, and Artificial Intelligence
Sensors 2018, 18(4), 944; https://doi.org/10.3390/s18040944
Received: 22 February 2018 / Revised: 17 March 2018 / Accepted: 21 March 2018 / Published: 22 March 2018
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Abstract
The environmental and economic impacts of exotic fungal species on natural and plantation forests have been historically catastrophic. Recorded surveillance and control actions are challenging because they are costly, time-consuming, and hazardous in remote areas. Prolonged periods of testing and observation of site-based
[...] Read more.
The environmental and economic impacts of exotic fungal species on natural and plantation forests have been historically catastrophic. Recorded surveillance and control actions are challenging because they are costly, time-consuming, and hazardous in remote areas. Prolonged periods of testing and observation of site-based tests have limitations in verifying the rapid proliferation of exotic pathogens and deterioration rates in hosts. Recent remote sensing approaches have offered fast, broad-scale, and affordable surveys as well as additional indicators that can complement on-ground tests. This paper proposes a framework that consolidates site-based insights and remote sensing capabilities to detect and segment deteriorations by fungal pathogens in natural and plantation forests. This approach is illustrated with an experimentation case of myrtle rust (Austropuccinia psidii) on paperbark tea trees (Melaleuca quinquenervia) in New South Wales (NSW), Australia. The method integrates unmanned aerial vehicles (UAVs), hyperspectral image sensors, and data processing algorithms using machine learning. Imagery is acquired using a Headwall Nano-Hyperspec ® camera, orthorectified in Headwall SpectralView ® , and processed in Python programming language using eXtreme Gradient Boosting (XGBoost), Geospatial Data Abstraction Library (GDAL), and Scikit-learn third-party libraries. In total, 11,385 samples were extracted and labelled into five classes: two classes for deterioration status and three classes for background objects. Insights reveal individual detection rates of 95% for healthy trees, 97% for deteriorated trees, and a global multiclass detection rate of 97%. The methodology is versatile to be applied to additional datasets taken with different image sensors, and the processing of large datasets with freeware tools. Full article
(This article belongs to the Special Issue UAV or Drones for Remote Sensing Applications)
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Open AccessArticle Sensor Buoy System for Monitoring Renewable Marine Energy Resources
Sensors 2018, 18(4), 945; https://doi.org/10.3390/s18040945
Received: 13 February 2018 / Revised: 16 March 2018 / Accepted: 20 March 2018 / Published: 22 March 2018
Cited by 1 | PDF Full-text (15475 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
In this paper we present a multi-sensor floating system designed to monitor marine energy parameters, in order to sample wind, wave, and marine current energy resources. For this purpose, a set of dedicated sensors to measure the height and period of the waves,
[...] Read more.
In this paper we present a multi-sensor floating system designed to monitor marine energy parameters, in order to sample wind, wave, and marine current energy resources. For this purpose, a set of dedicated sensors to measure the height and period of the waves, wind, and marine current intensity and direction have been selected and installed in the system. The floating device incorporates wind and marine current turbines for renewable energy self-consumption and to carry out complementary studies on the stability of such a system. The feasibility, safety, sensor communications, and buoy stability of the floating device have been successfully checked in real operating conditions. Full article
(This article belongs to the Section Remote Sensors)
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Open AccessArticle Real-Time Tau Protein Detection by Sandwich-Based Piezoelectric Biosensing: Exploring Tubulin as a Mass Enhancer
Sensors 2018, 18(4), 946; https://doi.org/10.3390/s18040946
Received: 12 January 2018 / Revised: 16 March 2018 / Accepted: 19 March 2018 / Published: 22 March 2018
PDF Full-text (1825 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Human tau protein is one of the most advanced and accepted biomarkers for AD and tauopathies diagnosis in general. In this work, a quartz crystal balance (QCM) immunosensor was developed for the detection of human tau protein in buffer and artificial cerebrospinal fluid
[...] Read more.
Human tau protein is one of the most advanced and accepted biomarkers for AD and tauopathies diagnosis in general. In this work, a quartz crystal balance (QCM) immunosensor was developed for the detection of human tau protein in buffer and artificial cerebrospinal fluid (aCSF), through both direct and sandwich assays. Starting from a conventional immuno-based sandwich strategy, two monoclonal antibodies recognizing different epitopes of tau protein were used, achieving a detection limit for the direct assay in nanomolar range both in HBES-EP and aCSF. Afterward, for exploring alternative specific receptors as secondary recognition elements for tau protein biosensing, we tested tubulin and compared its behavior to a conventional secondary antibody in the sandwich assay. Tau–tubulin binding has shown an extended working range coupled to a signal improvement in comparison with the conventional secondary antibody-based approach, showing a dose–response trend at lower tau concentration than is usually investigated and closer to the physiological levels in the reference matrix for protein tau biomarker. Our results open up new and encouraging perspectives for the use of tubulin as an alternative receptor for tau protein with interesting features due to the possibility of taking advantage of its polymerization and reversible binding to this key hallmark of Alzheimer’s disease. Full article
(This article belongs to the Special Issue Label-Free Biosensors)
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Open AccessArticle An Equivalent Circuit of Longitudinal Vibration for a Piezoelectric Structure with Losses
Sensors 2018, 18(4), 947; https://doi.org/10.3390/s18040947
Received: 2 February 2018 / Revised: 14 March 2018 / Accepted: 20 March 2018 / Published: 22 March 2018
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Abstract
Equivalent circuits of piezoelectric structures such as bimorphs and unimorphs conventionally focus on the bending vibration modes. However, the longitudinal vibration modes are rarely considered even though they also play a remarkable role in piezoelectric devices. Losses, especially elastic loss in the metal
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Equivalent circuits of piezoelectric structures such as bimorphs and unimorphs conventionally focus on the bending vibration modes. However, the longitudinal vibration modes are rarely considered even though they also play a remarkable role in piezoelectric devices. Losses, especially elastic loss in the metal substrate, are also generally neglected, which leads to discrepancies compared with experiments. In this paper, a novel equivalent circuit with four kinds of losses is proposed for a beamlike piezoelectric structure under the longitudinal vibration mode. This structure consists of a slender beam as the metal substrate, and a piezoelectric patch which covers a partial length of the beam. In this approach, first, complex numbers are used to deal with four kinds of losses—elastic loss in the metal substrate, and piezoelectric, dielectric, and elastic losses in the piezoelectric patch. Next in this approach, based on Mason’s model, a new equivalent circuit is developed. Using MATLAB, impedance curves of this structure are simulated by the equivalent circuit method. Experiments are conducted and good agreements are revealed between experiments and equivalent circuit results. It is indicated that the introduction of four losses in an equivalent circuit can increase the result accuracy considerably. Full article
(This article belongs to the Special Issue Piezoelectric Micro- and Nano-Devices)
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Open AccessArticle Remarkably Enhanced Room-Temperature Hydrogen Sensing of SnO2 Nanoflowers via Vacuum Annealing Treatment
Sensors 2018, 18(4), 949; https://doi.org/10.3390/s18040949
Received: 22 January 2018 / Revised: 7 March 2018 / Accepted: 12 March 2018 / Published: 23 March 2018
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Abstract
In this work, SnO2 nanoflowers synthesized by a hydrothermal method were employed as hydrogen sensing materials. The as-synthesized SnO2 nanoflowers consisted of cuboid-like SnO2 nanorods with tetragonal structures. A great increase in the relative content of surface-adsorbed oxygen was observed
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In this work, SnO2 nanoflowers synthesized by a hydrothermal method were employed as hydrogen sensing materials. The as-synthesized SnO2 nanoflowers consisted of cuboid-like SnO2 nanorods with tetragonal structures. A great increase in the relative content of surface-adsorbed oxygen was observed after the vacuum annealing treatment, and this increase could have been due to the increase in surface oxygen vacancies serving as preferential adsorption sites for oxygen species. Annealing treatment resulted in an 8% increase in the specific surface area of the samples. Moreover, the conductivity of the sensors decreased after the annealing treatment, which should be attributed to the increase in electron scattering around the defects and the compensated donor behavior of the oxygen vacancies due to the surface oxygen adsorption. The hydrogen sensors of the annealed samples, compared to those of the unannealed samples, exhibited a much higher sensitivity and faster response rate. The sensor response factor and response rate increased from 27.1% to 80.2% and 0.34%/s to 1.15%/s, respectively. This remarkable enhancement in sensing performance induced by the annealing treatment could be attributed to the larger specific surface areas and higher amount of surface-adsorbed oxygen, which provides a greater reaction space for hydrogen. Moreover, the sensors with annealed SnO2 nanoflowers also exhibited high selectivity towards hydrogen against CH4, CO, and ethanol. Full article
(This article belongs to the Section Chemical Sensors)
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Open AccessArticle Localization of an Underwater Control Network Based on Quasi-Stable Adjustment
Sensors 2018, 18(4), 950; https://doi.org/10.3390/s18040950
Received: 19 January 2018 / Revised: 14 March 2018 / Accepted: 16 March 2018 / Published: 23 March 2018
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Abstract
There exists a common problem in the localization of underwater control networks that the precision of the absolute coordinates of known points obtained by marine absolute measurement is poor, and it seriously affects the precision of the whole network in traditional constraint adjustment.
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There exists a common problem in the localization of underwater control networks that the precision of the absolute coordinates of known points obtained by marine absolute measurement is poor, and it seriously affects the precision of the whole network in traditional constraint adjustment. Therefore, considering that the precision of underwater baselines is good, we use it to carry out quasi-stable adjustment to amend known points before constraint adjustment so that the points fit the network shape better. In addition, we add unconstrained adjustment for quality control of underwater baselines, the observations of quasi-stable adjustment and constrained adjustment, to eliminate the unqualified baselines and improve the results’ accuracy of the two adjustments. Finally, the modified method is applied to a practical LBL (Long Baseline) experiment and obtains a mean point location precision of 0.08 m, which improves by 38% compared with the traditional method. Full article
(This article belongs to the Special Issue Dependable Monitoring in Wireless Sensor Networks)
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Open AccessArticle Micro-Doppler Effect Removal in ISAR Imaging by Promoting Joint Sparsity in Time-Frequency Domain
Sensors 2018, 18(4), 951; https://doi.org/10.3390/s18040951
Received: 1 February 2018 / Revised: 18 March 2018 / Accepted: 19 March 2018 / Published: 23 March 2018
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Abstract
For micromotion scatterers with small rotating radii, the micro-Doppler (m-D) effect interferes with cross-range compression in inverse synthetic aperture radar (ISAR) imaging and leads to a blurred main body image. In this paper, a novel method is proposed to remove the m-D effect
[...] Read more.
For micromotion scatterers with small rotating radii, the micro-Doppler (m-D) effect interferes with cross-range compression in inverse synthetic aperture radar (ISAR) imaging and leads to a blurred main body image. In this paper, a novel method is proposed to remove the m-D effect by promoting the joint sparsity in the time-frequency domain. Firstly, to obtain the time-frequency representations of the limited measurements, the short-time Fourier transform (STFT) was modelled by an underdetermined equation. Then, a new objective function was used to measure the joint sparsity of the STFT entries so that the joint sparse recovery problem could be formulated as a constrained minimization problem. Similar to the smoothed l 0 (SL0) algorithm, a steepest descend approach was used to minimize the new objective function, where the projection step was tailored to make it suitable for m-D effect removal. Finally, we utilized the recovered STFT entries to obtain the main body echoes, based on which cross-range compression could be realized without m-D interference. After all contaminated range cells were processed by the proposed method, a clear main body image could be achieved. Experiments using both the point-scattering model and electromagnetic (EM) computation validated the performance of the proposed method. Full article
(This article belongs to the Special Issue Automatic Target Recognition of High Resolution SAR/ISAR Images)
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Open AccessArticle Competitive Deep-Belief Networks for Underwater Acoustic Target Recognition
Sensors 2018, 18(4), 952; https://doi.org/10.3390/s18040952
Received: 10 January 2018 / Revised: 19 March 2018 / Accepted: 19 March 2018 / Published: 23 March 2018
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Abstract
Underwater acoustic target recognition based on ship-radiated noise belongs to the small-sample-size recognition problems. A competitive deep-belief network is proposed to learn features with more discriminative information from labeled and unlabeled samples. The proposed model consists of four stages: (1) A standard restricted
[...] Read more.
Underwater acoustic target recognition based on ship-radiated noise belongs to the small-sample-size recognition problems. A competitive deep-belief network is proposed to learn features with more discriminative information from labeled and unlabeled samples. The proposed model consists of four stages: (1) A standard restricted Boltzmann machine is pretrained using a large number of unlabeled data to initialize its parameters; (2) the hidden units are grouped according to categories, which provides an initial clustering model for competitive learning; (3) competitive training and back-propagation algorithms are used to update the parameters to accomplish the task of clustering; (4) by applying layer-wise training and supervised fine-tuning, a deep neural network is built to obtain features. Experimental results show that the proposed method can achieve classification accuracy of 90.89%, which is 8.95% higher than the accuracy obtained by the compared methods. In addition, the highest accuracy of our method is obtained with fewer features than other methods. Full article
(This article belongs to the Section Intelligent Sensors)
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Open AccessArticle Stretchable, Flexible, Scalable Smart Skin Sensors for Robotic Position and Force Estimation
Sensors 2018, 18(4), 953; https://doi.org/10.3390/s18040953
Received: 19 February 2018 / Revised: 15 March 2018 / Accepted: 21 March 2018 / Published: 23 March 2018
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Abstract
The design and validation of a continuously stretchable and flexible skin sensor for collaborative robotic applications is outlined. The skin consists of a PDMS skin doped with Carbon Nanotubes and the addition of conductive fabric, connected by only five wires to a simple
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The design and validation of a continuously stretchable and flexible skin sensor for collaborative robotic applications is outlined. The skin consists of a PDMS skin doped with Carbon Nanotubes and the addition of conductive fabric, connected by only five wires to a simple microcontroller. The accuracy is characterized in position as well as force, and the skin is also tested under uniaxial stretch. There are also two examples of practical implementations in collaborative robotic applications. The stationary position estimate has an RMSE of 7.02 mm, and the sensor error stays within 2.5 ± 1.5 mm even under stretch. The skin consistently provides an emergency stop command at only 0.5 N of force and is shown to maintain a collaboration force of 10 N in a collaborative control experiment. Full article
(This article belongs to the Section Intelligent Sensors)
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Open AccessArticle A Framework to Design the Computational Load Distribution of Wireless Sensor Networks in Power Consumption Constrained Environments
Sensors 2018, 18(4), 954; https://doi.org/10.3390/s18040954
Received: 26 January 2018 / Revised: 10 March 2018 / Accepted: 20 March 2018 / Published: 23 March 2018
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Abstract
In this paper, we present a work based on the computational load distribution among the homogeneous nodes and the Hub/Sink of Wireless Sensor Networks (WSNs). The main contribution of the paper is an early decision support framework helping WSN designers to take decisions
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In this paper, we present a work based on the computational load distribution among the homogeneous nodes and the Hub/Sink of Wireless Sensor Networks (WSNs). The main contribution of the paper is an early decision support framework helping WSN designers to take decisions about computational load distribution for those WSNs where power consumption is a key issue (when we refer to “framework” in this work, we are considering it as a support tool to make decisions where the executive judgment can be included along with the set of mathematical tools of the WSN designer; this work shows the need to include the load distribution as an integral component of the WSN system for making early decisions regarding energy consumption). The framework takes advantage of the idea that balancing sensors nodes and Hub/Sink computational load can lead to improved energy consumption for the whole or at least the battery-powered nodes of the WSN. The approach is not trivial and it takes into account related issues such as the required data distribution, nodes, and Hub/Sink connectivity and availability due to their connectivity features and duty-cycle. For a practical demonstration, the proposed framework is applied to an agriculture case study, a sector very relevant in our region. In this kind of rural context, distances, low costs due to vegetable selling prices and the lack of continuous power supplies may lead to viable or inviable sensing solutions for the farmers. The proposed framework systematize and facilitates WSN designers the required complex calculations taking into account the most relevant variables regarding power consumption, avoiding full/partial/prototype implementations, and measurements of different computational load distribution potential solutions for a specific WSN. Full article
(This article belongs to the Special Issue Sensors in Agriculture)
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Open AccessArticle Supporting Beacon and Event-Driven Messages in Vehicular Platoons through Token-Based Strategies
Sensors 2018, 18(4), 955; https://doi.org/10.3390/s18040955
Received: 14 February 2018 / Revised: 17 March 2018 / Accepted: 22 March 2018 / Published: 23 March 2018
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Abstract
Timely and reliable inter-vehicle communications is a critical requirement to support traffic safety applications, such as vehicle platooning. Furthermore, low-delay communications allow the platoon to react quickly to unexpected events. In this scope, having a predictable and highly effective medium access control (MAC)
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Timely and reliable inter-vehicle communications is a critical requirement to support traffic safety applications, such as vehicle platooning. Furthermore, low-delay communications allow the platoon to react quickly to unexpected events. In this scope, having a predictable and highly effective medium access control (MAC) method is of utmost importance. However, the currently available IEEE 802.11p technology is unable to adequately address these challenges. In this paper, we propose a MAC method especially adapted to platoons, able to transmit beacons within the required time constraints, but with a higher reliability level than IEEE 802.11p, while concurrently enabling efficient dissemination of event-driven messages. The protocol circulates the token within the platoon not in a round-robin fashion, but based on beacon data age, i.e., the time that has passed since the previous collection of status information, thereby automatically offering repeated beacon transmission opportunities for increased reliability. In addition, we propose three different methods for supporting event-driven messages co-existing with beacons. Analysis and simulation results in single and multi-hop scenarios showed that, by providing non-competitive channel access and frequent retransmission opportunities, our protocol can offer beacon delivery within one beacon generation interval while fulfilling the requirements on low-delay dissemination of event-driven messages for traffic safety applications. Full article
(This article belongs to the Special Issue Smart Vehicular Mobile Sensing)
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Open AccessArticle Gas Sensing Properties of p-Co3O4/n-TiO2 Nanotube Heterostructures
Sensors 2018, 18(4), 956; https://doi.org/10.3390/s18040956
Received: 20 February 2018 / Revised: 16 March 2018 / Accepted: 21 March 2018 / Published: 23 March 2018
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Abstract
In this paper, we fabricated p-Co3O4/n-TiO2 heterostructures and investigated their gas sensing properties. The structural and morphological characterization were performed by scanning electron microscopy (SEM), X-ray diffraction (XRD), and X-ray photoelectron spectroscopy analysis (XPS). The electrical properties of
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In this paper, we fabricated p-Co3O4/n-TiO2 heterostructures and investigated their gas sensing properties. The structural and morphological characterization were performed by scanning electron microscopy (SEM), X-ray diffraction (XRD), and X-ray photoelectron spectroscopy analysis (XPS). The electrical properties of the heterostructure were studied within the temperature range from 293 K to 423 K. Changes in electrical properties and sensing behavior against reducing and oxidizing gases were attributed to the formation of p–n heterojunctions at the Co3O4 and TiO2 interface. In comparison with sensing performed with pristine TiO2 nanotubes (NTs), a significant improvement in H2 sensing at 200 °C was observed, while the sensing response against NO2 decreased for the heterostructures. Additionally, a response against toluene gas, in contrast to pristine TiO2 NTs, appeared in the Co3O4/TiO2 heterostructure samples. Full article
(This article belongs to the Special Issue I3S 2017 Selected Papers)
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Open AccessArticle Convolutional Neural Network-Based Classification of Driver’s Emotion during Aggressive and Smooth Driving Using Multi-Modal Camera Sensors
Sensors 2018, 18(4), 957; https://doi.org/10.3390/s18040957
Received: 20 February 2018 / Revised: 17 March 2018 / Accepted: 21 March 2018 / Published: 23 March 2018
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Abstract
Because aggressive driving often causes large-scale loss of life and property, techniques for advance detection of adverse driver emotional states have become important for the prevention of aggressive driving behaviors. Previous studies have primarily focused on systems for detecting aggressive driver emotion via
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Because aggressive driving often causes large-scale loss of life and property, techniques for advance detection of adverse driver emotional states have become important for the prevention of aggressive driving behaviors. Previous studies have primarily focused on systems for detecting aggressive driver emotion via smart-phone accelerometers and gyro-sensors, or they focused on methods of detecting physiological signals using electroencephalography (EEG) or electrocardiogram (ECG) sensors. Because EEG and ECG sensors cause discomfort to drivers and can be detached from the driver’s body, it becomes difficult to focus on bio-signals to determine their emotional state. Gyro-sensors and accelerometers depend on the performance of GPS receivers and cannot be used in areas where GPS signals are blocked. Moreover, if driving on a mountain road with many quick turns, a driver’s emotional state can easily be misrecognized as that of an aggressive driver. To resolve these problems, we propose a convolutional neural network (CNN)-based method of detecting emotion to identify aggressive driving using input images of the driver’s face, obtained using near-infrared (NIR) light and thermal camera sensors. In this research, we conducted an experiment using our own database, which provides a high classification accuracy for detecting driver emotion leading to either aggressive or smooth (i.e., relaxed) driving. Our proposed method demonstrates better performance than existing methods. Full article
(This article belongs to the Special Issue Advances in Infrared Imaging: Sensing, Exploitation and Applications)
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Open AccessArticle A Highly Thermostable In2O3/ITO Thin Film Thermocouple Prepared via Screen Printing for High Temperature Measurements
Sensors 2018, 18(4), 958; https://doi.org/10.3390/s18040958
Received: 1 February 2018 / Revised: 15 March 2018 / Accepted: 16 March 2018 / Published: 23 March 2018
PDF Full-text (10191 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
An In2O3/ITO thin film thermocouple was prepared via screen printing. Glass additives were added to improve the sintering process and to increase the density of the In2O3/ITO films. The surface and cross-sectional images indicate that
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An In2O3/ITO thin film thermocouple was prepared via screen printing. Glass additives were added to improve the sintering process and to increase the density of the In2O3/ITO films. The surface and cross-sectional images indicate that both the grain size and densification of the ITO and In2O3 films increased with the increase in annealing time. The thermoelectric voltage of the In2O3/ITO thermocouple was 53.5 mV at 1270 °C at the hot junction. The average Seebeck coefficient of the thermocouple was calculated as 44.5 μV/°C. The drift rate of the In2O3/ITO thermocouple was 5.44 °C/h at a measuring time of 10 h at 1270 °C. Full article
(This article belongs to the Special Issue Sensors and Materials for Harsh Environments)
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Open AccessArticle Sensor-Based Optimization Model for Air Quality Improvement in Home IoT
Sensors 2018, 18(4), 959; https://doi.org/10.3390/s18040959
Received: 31 January 2018 / Revised: 19 March 2018 / Accepted: 19 March 2018 / Published: 23 March 2018
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Abstract
We introduce current home Internet of Things (IoT) technology and present research on its various forms and applications in real life. In addition, we describe IoT marketing strategies as well as specific modeling techniques for improving air quality, a key home IoT service.
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We introduce current home Internet of Things (IoT) technology and present research on its various forms and applications in real life. In addition, we describe IoT marketing strategies as well as specific modeling techniques for improving air quality, a key home IoT service. To this end, we summarize the latest research on sensor-based home IoT, studies on indoor air quality, and technical studies on random data generation. In addition, we develop an air quality improvement model that can be readily applied to the market by acquiring initial analytical data and building infrastructures using spectrum/density analysis and the natural cubic spline method. Accordingly, we generate related data based on user behavioral values. We integrate the logic into the existing home IoT system to enable users to easily access the system through the Web or mobile applications. We expect that the present introduction of a practical marketing application method will contribute to enhancing the expansion of the home IoT market. Full article
(This article belongs to the Special Issue Sensor Networks for Collaborative and Secure Internet of Things)
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Open AccessArticle Convolutional Neural Network-Based Shadow Detection in Images Using Visible Light Camera Sensor
Sensors 2018, 18(4), 960; https://doi.org/10.3390/s18040960
Received: 22 January 2018 / Revised: 22 March 2018 / Accepted: 22 March 2018 / Published: 23 March 2018
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Abstract
Recent developments in intelligence surveillance camera systems have enabled more research on the detection, tracking, and recognition of humans. Such systems typically use visible light cameras and images, in which shadows make it difficult to detect and recognize the exact human area. Near-infrared
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Recent developments in intelligence surveillance camera systems have enabled more research on the detection, tracking, and recognition of humans. Such systems typically use visible light cameras and images, in which shadows make it difficult to detect and recognize the exact human area. Near-infrared (NIR) light cameras and thermal cameras are used to mitigate this problem. However, such instruments require a separate NIR illuminator, or are prohibitively expensive. Existing research on shadow detection in images captured by visible light cameras have utilized object and shadow color features for detection. Unfortunately, various environmental factors such as illumination change and brightness of background cause detection to be a difficult task. To overcome this problem, we propose a convolutional neural network-based shadow detection method. Experimental results with a database built from various outdoor surveillance camera environments, and from the context-aware vision using image-based active recognition (CAVIAR) open database, show that our method outperforms previous works. Full article
(This article belongs to the Special Issue Sensors Signal Processing and Visual Computing)
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Open AccessArticle A Multi-Hop Clustering Mechanism for Scalable IoT Networks
Sensors 2018, 18(4), 961; https://doi.org/10.3390/s18040961
Received: 4 January 2018 / Accepted: 22 February 2018 / Published: 23 March 2018
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Abstract
It is expected that up to 26 billion Internet of Things (IoT) equipped with sensors and wireless communication capabilities will be connected to the Internet by 2020 for various purposes. With a large scale IoT network, having each node connected to the Internet
[...] Read more.
It is expected that up to 26 billion Internet of Things (IoT) equipped with sensors and wireless communication capabilities will be connected to the Internet by 2020 for various purposes. With a large scale IoT network, having each node connected to the Internet with an individual connection may face serious scalability issues. The scalability problem of the IoT network may be alleviated by grouping the nodes of the IoT network into clusters and having a representative node in each cluster connect to the Internet on behalf of the other nodes in the cluster instead of having a per-node Internet connection and communication. In this paper, we propose a multi-hop clustering mechanism for IoT networks to minimize the number of required Internet connections. Specifically, the objective of proposed mechanism is to select the minimum number of coordinators, which take the role of a representative node for the cluster, i.e., having the Internet connection on behalf of the rest of the nodes in the cluster and to map a partition of the IoT nodes onto the selected set of coordinators to minimize the total distance between the nodes and their respective coordinator under a certain constraint in terms of maximum hop count between the IoT nodes and their respective coordinator. Since this problem can be mapped into a set cover problem which is known as NP-hard, we pursue a heuristic approach to solve the problem and analyze the complexity of the proposed solution. Through a set of experiments with varying parameters, the proposed scheme shows 63–87.3% reduction of the Internet connections depending on the number of the IoT nodes while that of the optimal solution is 65.6–89.9% in a small scale network. Moreover, it is shown that the performance characteristics of the proposed mechanism coincide with expected performance characteristics of the optimal solution in a large-scale network. Full article
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Open AccessArticle Sensor Fusion to Estimate the Depth and Width of the Weld Bead in Real Time in GMAW Processes
Sensors 2018, 18(4), 962; https://doi.org/10.3390/s18040962
Received: 4 December 2017 / Revised: 19 February 2018 / Accepted: 5 March 2018 / Published: 23 March 2018
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Abstract
The arc welding process is widely used in industry but its automatic control is limited by the difficulty in measuring the weld bead geometry and closing the control loop on the arc, which has adverse environmental conditions. To address this problem, this work
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The arc welding process is widely used in industry but its automatic control is limited by the difficulty in measuring the weld bead geometry and closing the control loop on the arc, which has adverse environmental conditions. To address this problem, this work proposes a system to capture the welding variables and send stimuli to the Gas Metal Arc Welding (GMAW) conventional process with a constant voltage power source, which allows weld bead geometry estimation with an open-loop control. Dynamic models of depth and width estimators of the weld bead are implemented based on the fusion of thermographic data, welding current and welding voltage in a multilayer perceptron neural network. The estimators were trained and validated off-line with data from a novel algorithm developed to extract the features of the infrared image, a laser profilometer was implemented to measure the bead dimensions and an image processing algorithm that measures depth by making a longitudinal cut in the weld bead. These estimators are optimized for embedded devices and real-time processing and were implemented on a Field-Programmable Gate Array (FPGA) device. Experiments to collect data, train and validate the estimators are presented and discussed. The results show that the proposed method is useful in industrial and research environments. Full article
(This article belongs to the Section Physical Sensors)
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Open AccessArticle MFAM: Multiple Frequency Adaptive Model-Based Indoor Localization Method
Sensors 2018, 18(4), 963; https://doi.org/10.3390/s18040963
Received: 20 February 2018 / Revised: 20 March 2018 / Accepted: 21 March 2018 / Published: 24 March 2018
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Abstract
This paper presents MFAM (Multiple Frequency Adaptive Model-based localization method), a novel model-based indoor localization method that is capable of using multiple wireless signal frequencies simultaneously. It utilizes indoor architectural model and physical properties of wireless signal propagation through objects and space. The
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This paper presents MFAM (Multiple Frequency Adaptive Model-based localization method), a novel model-based indoor localization method that is capable of using multiple wireless signal frequencies simultaneously. It utilizes indoor architectural model and physical properties of wireless signal propagation through objects and space. The motivation for developing multiple frequency localization method lies in the future Wi-Fi standards (e.g., 802.11ah) and the growing number of various wireless signals present in the buildings (e.g., Wi-Fi, Bluetooth, ZigBee, etc.). Current indoor localization methods mostly rely on a single wireless signal type and often require many devices to achieve the necessary accuracy. MFAM utilizes multiple wireless signal types and improves the localization accuracy over the usage of a single frequency. It continuously monitors signal propagation through space and adapts the model according to the changes indoors. Using multiple signal sources lowers the required number of access points for a specific signal type while utilizing signals, already present in the indoors. Due to the unavailability of the 802.11ah hardware, we have evaluated proposed method with similar signals; we have used 2.4 GHz Wi-Fi and 868 MHz HomeMatic home automation signals. We have performed the evaluation in a modern two-bedroom apartment and measured mean localization error 2.0 to 2.3 m and median error of 2.0 to 2.2 m. Based on our evaluation results, using two different signals improves the localization accuracy by 18% in comparison to 2.4 GHz Wi-Fi-only approach. Additional signals would improve the accuracy even further. We have shown that MFAM provides better accuracy than competing methods, while having several advantages for real-world usage. Full article
(This article belongs to the Section Sensor Networks)
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Open AccessArticle Green, Hydrothermal Synthesis of Fluorescent Carbon Nanodots from Gardenia, Enabling the Detection of Metronidazole in Pharmaceuticals and Rabbit Plasma
Sensors 2018, 18(4), 964; https://doi.org/10.3390/s18040964
Received: 25 February 2018 / Revised: 15 March 2018 / Accepted: 16 March 2018 / Published: 24 March 2018
Cited by 1 | PDF Full-text (5010 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Strong fluorescent carbon nanodots (FCNs) were synthesized with a green approach using gardenia as a carbon source through a one-step hydrothermal method. FCNs were characterized by their UV-vis absorption spectra, photoluminescence (PL), Fourier transform infrared spectroscopy (FTIR) as well as X-ray photoelectron spectroscopy
[...] Read more.
Strong fluorescent carbon nanodots (FCNs) were synthesized with a green approach using gardenia as a carbon source through a one-step hydrothermal method. FCNs were characterized by their UV-vis absorption spectra, photoluminescence (PL), Fourier transform infrared spectroscopy (FTIR) as well as X-ray photoelectron spectroscopy (XPS). We further explored the use of as-synthesized FCNs as an effective probe for the detection of metronidazole (MNZ), which is based on MNZ-induced fluorescence quenching of FCNs. The proposed method displayed a wide linear range from 0.8 to 225.0 µM with a correlation coefficient of 0.9992 and a limit of detection as low as 279 nM. It was successfully applied to the determination of MNZ in commercial tablets and rabbit plasma with excellent sensitivity and selectivity, which indicates its potential applications in clinical analysis and biologically related studies. Full article
(This article belongs to the Special Issue Optical Chemical Nanosensors)
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Open AccessArticle Efficient Proximity Computation Techniques Using ZIP Code Data for Smart Cities
Sensors 2018, 18(4), 965; https://doi.org/10.3390/s18040965
Received: 15 February 2018 / Revised: 16 March 2018 / Accepted: 21 March 2018 / Published: 24 March 2018
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Abstract
In this paper, we are interested in computing ZIP code proximity from two perspectives, proximity between two ZIP codes (Ad-Hoc) and neighborhood proximity (Top-K). Such a computation can be used for ZIP code-based target marketing as one of the
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In this paper, we are interested in computing ZIP code proximity from two perspectives, proximity between two ZIP codes (Ad-Hoc) and neighborhood proximity (Top-K). Such a computation can be used for ZIP code-based target marketing as one of the smart city applications. A naïve approach to this computation is the usage of the distance between ZIP codes. We redefine a distance metric combining the centroid distance with the intersecting road network between ZIP codes by using a weighted sum method. Furthermore, we prove that the results of our combined approach conform to the characteristics of distance measurement. We have proposed a general and heuristic approach for computing Ad-Hoc proximity, while for computing Top-K proximity, we have proposed a general approach only. Our experimental results indicate that our approaches are verifiable and effective in reducing the execution time and search space. Full article
(This article belongs to the Special Issue Smart Cities)
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Open AccessArticle Building Change Detection from Bi-Temporal Dense-Matching Point Clouds and Aerial Images
Sensors 2018, 18(4), 966; https://doi.org/10.3390/s18040966
Received: 10 February 2018 / Revised: 21 March 2018 / Accepted: 22 March 2018 / Published: 24 March 2018
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Abstract
In this work, a novel building change detection method from bi-temporal dense-matching point clouds and aerial images is proposed to address two major problems, namely, the robust acquisition of the changed objects above ground and the automatic classification of changed objects into buildings
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In this work, a novel building change detection method from bi-temporal dense-matching point clouds and aerial images is proposed to address two major problems, namely, the robust acquisition of the changed objects above ground and the automatic classification of changed objects into buildings or non-buildings. For the acquisition of changed objects above ground, the change detection problem is converted into a binary classification, in which the changed area above ground is regarded as the foreground and the other area as the background. For the gridded points of each period, the graph cuts algorithm is adopted to classify the points into foreground and background, followed by the region-growing algorithm to form candidate changed building objects. A novel structural feature that was extracted from aerial images is constructed to classify the candidate changed building objects into buildings and non-buildings. The changed building objects are further classified as “newly built”, “taller”, “demolished”, and “lower” by combining the classification and the digital surface models of two periods. Finally, three typical areas from a large dataset are used to validate the proposed method. Numerous experiments demonstrate the effectiveness of the proposed algorithm. Full article
(This article belongs to the Section Remote Sensors)
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Open AccessArticle Optimize the Coverage Probability of Prediction Interval for Anomaly Detection of Sensor-Based Monitoring Series
Sensors 2018, 18(4), 967; https://doi.org/10.3390/s18040967
Received: 6 February 2018 / Revised: 18 March 2018 / Accepted: 21 March 2018 / Published: 24 March 2018
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Abstract
Effective anomaly detection of sensing data is essential for identifying potential system failures. Because they require no prior knowledge or accumulated labels, and provide uncertainty presentation, the probability prediction methods (e.g., Gaussian process regression (GPR) and relevance vector machine (RVM)) are especially adaptable
[...] Read more.
Effective anomaly detection of sensing data is essential for identifying potential system failures. Because they require no prior knowledge or accumulated labels, and provide uncertainty presentation, the probability prediction methods (e.g., Gaussian process regression (GPR) and relevance vector machine (RVM)) are especially adaptable to perform anomaly detection for sensing series. Generally, one key parameter of prediction models is coverage probability (CP), which controls the judging threshold of the testing sample and is generally set to a default value (e.g., 90% or 95%). There are few criteria to determine the optimal CP for anomaly detection. Therefore, this paper designs a graphic indicator of the receiver operating characteristic curve of prediction interval (ROC-PI) based on the definition of the ROC curve which can depict the trade-off between the PI width and PI coverage probability across a series of cut-off points. Furthermore, the Youden index is modified to assess the performance of different CPs, by the minimization of which the optimal CP is derived by the simulated annealing (SA) algorithm. Experiments conducted on two simulation datasets demonstrate the validity of the proposed method. Especially, an actual case study on sensing series from an on-orbit satellite illustrates its significant performance in practical application. Full article
(This article belongs to the Section Remote Sensors)
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Open AccessArticle Analysis of Known Linear Distributed Average Consensus Algorithms on Cycles and Paths
Sensors 2018, 18(4), 968; https://doi.org/10.3390/s18040968
Received: 22 February 2018 / Revised: 20 March 2018 / Accepted: 22 March 2018 / Published: 24 March 2018
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Abstract
In this paper, we compare six known linear distributed average consensus algorithms on a sensor network in terms of convergence time (and therefore, in terms of the number of transmissions required). The selected network topologies for the analysis (comparison) are the cycle and
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In this paper, we compare six known linear distributed average consensus algorithms on a sensor network in terms of convergence time (and therefore, in terms of the number of transmissions required). The selected network topologies for the analysis (comparison) are the cycle and the path. Specifically, in the present paper, we compute closed-form expressions for the convergence time of four known deterministic algorithms and closed-form bounds for the convergence time of two known randomized algorithms on cycles and paths. Moreover, we also compute a closed-form expression for the convergence time of the fastest deterministic algorithm considered on grids. Full article
(This article belongs to the Special Issue Smart Communication Protocols and Algorithms for Sensor Networks)
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Open AccessArticle Green Grape Detection and Picking-Point Calculation in a Night-Time Natural Environment Using a Charge-Coupled Device (CCD) Vision Sensor with Artificial Illumination
Sensors 2018, 18(4), 969; https://doi.org/10.3390/s18040969
Received: 8 January 2018 / Revised: 1 March 2018 / Accepted: 19 March 2018 / Published: 25 March 2018
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Abstract
Night-time fruit-picking technology is important to picking robots. This paper proposes a method of night-time detection and picking-point positioning for green grape-picking robots to solve the difficult problem of green grape detection and picking in night-time conditions with artificial lighting systems. Taking a
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Night-time fruit-picking technology is important to picking robots. This paper proposes a method of night-time detection and picking-point positioning for green grape-picking robots to solve the difficult problem of green grape detection and picking in night-time conditions with artificial lighting systems. Taking a representative green grape named Centennial Seedless as the research object, daytime and night-time grape images were captured by a custom-designed visual system. Detection was conducted employing the following steps: (1) The RGB (red, green and blue). Color model was determined for night-time green grape detection through analysis of color features of grape images under daytime natural light and night-time artificial lighting. The R component of the RGB color model was rotated and the image resolution was compressed; (2) The improved Chan–Vese (C–V) level set model and morphological processing method were used to remove the background of the image, leaving out the grape fruit; (3) Based on the character of grape vertical suspension, combining the principle of the minimum circumscribed rectangle of fruit and the Hough straight line detection method, straight-line fitting for the fruit stem was conducted and the picking point was calculated using the stem with an angle of fitting line and vertical line less than 15°. The visual detection experiment results showed that the accuracy of grape fruit detection was 91.67% and the average running time of the proposed algorithm was 0.46 s. The picking-point calculation experiment results showed that the highest accuracy for the picking-point calculation was 92.5%, while the lowest was 80%. The results demonstrate that the proposed method of night-time green grape detection and picking-point calculation can provide technical support to the grape-picking robots. Full article
(This article belongs to the Special Issue Charge-Coupled Device (CCD) Sensors)
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Open AccessFeature PaperArticle A Novel Laser and Video-Based Displacement Transducer to Monitor Bridge Deflections
Sensors 2018, 18(4), 970; https://doi.org/10.3390/s18040970
Received: 22 February 2018 / Revised: 19 March 2018 / Accepted: 23 March 2018 / Published: 25 March 2018
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Abstract
The measurement of static vertical deflections on bridges continues to be a first-level technological challenge. These data are of great interest, especially for the case of long-term bridge monitoring; in fact, they are perhaps more valuable than any other measurable parameter. This is
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The measurement of static vertical deflections on bridges continues to be a first-level technological challenge. These data are of great interest, especially for the case of long-term bridge monitoring; in fact, they are perhaps more valuable than any other measurable parameter. This is because material degradation processes and changes of the mechanical properties of the structure due to aging (for example creep and shrinkage in concrete bridges) have a direct impact on the exhibited static vertical deflections. This paper introduces and evaluates an approach to monitor displacements and rotations of structures using a novel laser and video-based displacement transducer (LVBDT). The proposed system combines the use of laser beams, LED lights, and a digital video camera, and was especially designed to capture static and slow-varying displacements. Contrary to other video-based approaches, the camera is located on the bridge, hence allowing to capture displacements at one location. Subsequently, the sensing approach and the procedure to estimate displacements and the rotations are described. Additionally, laboratory and in-service field testing carried out to validate the system are presented and discussed. The results demonstrate that the proposed sensing approach is robust, accurate, and reliable, and also inexpensive, which are essential for field implementation. Full article
(This article belongs to the Special Issue I3S 2017 Selected Papers)
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Open AccessArticle Nondestructive Estimation of Muscle Contributions to STS Training with Different Loadings Based on Wearable Sensor System
Sensors 2018, 18(4), 971; https://doi.org/10.3390/s18040971
Received: 26 January 2018 / Revised: 20 March 2018 / Accepted: 21 March 2018 / Published: 25 March 2018
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Abstract
Partial body weight support or loading sit-to-stand (STS) rehabilitation can be useful for persons with lower limb dysfunction to achieve movement again based on the internal residual muscle force and external assistance. To explicate how the muscles contribute to the kinetics and kinematics
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Partial body weight support or loading sit-to-stand (STS) rehabilitation can be useful for persons with lower limb dysfunction to achieve movement again based on the internal residual muscle force and external assistance. To explicate how the muscles contribute to the kinetics and kinematics of STS performance by non-invasive in vitro detection and to nondestructively estimate the muscle contributions to STS training with different loadings, a wearable sensor system was developed with ground reaction force (GRF) platforms, motion capture inertial sensors and electromyography (EMG) sensors. To estimate the internal moments of hip, knee and ankle joints and quantify the contributions of individual muscle and gravity to STS movement, the inverse dynamics analysis on a simplified STS biomechanical model with external loading is proposed. The functional roles of the lower limb individual muscles (rectus femoris (RF), gluteus maximus (GM), vastus lateralis (VL), tibialis anterior (TA) and gastrocnemius (GAST)) during STS motion and the mechanism of the muscles’ synergies to perform STS-specific subtasks were analyzed. The muscle contributions to the biomechanical STS subtasks of vertical propulsion, anteroposterior (AP) braking and propulsion for body balance in the sagittal plane were quantified by experimental studies with EMG, kinematic and kinetic data. Full article
(This article belongs to the Special Issue Sensors for Gait, Posture, and Health Monitoring)
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Open AccessArticle Correction of Visual Perception Based on Neuro-Fuzzy Learning for the Humanoid Robot TEO
Sensors 2018, 18(4), 972; https://doi.org/10.3390/s18040972
Received: 8 February 2018 / Revised: 19 March 2018 / Accepted: 23 March 2018 / Published: 25 March 2018
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Abstract
New applications related to robotic manipulation or transportation tasks, with or without physical grasping, are continuously being developed. To perform these activities, the robot takes advantage of different kinds of perceptions. One of the key perceptions in robotics is vision. However, some problems
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New applications related to robotic manipulation or transportation tasks, with or without physical grasping, are continuously being developed. To perform these activities, the robot takes advantage of different kinds of perceptions. One of the key perceptions in robotics is vision. However, some problems related to image processing makes the application of visual information within robot control algorithms difficult. Camera-based systems have inherent errors that affect the quality and reliability of the information obtained. The need of correcting image distortion slows down image parameter computing, which decreases performance of control algorithms. In this paper, a new approach to correcting several sources of visual distortions on images in only one computing step is proposed. The goal of this system/algorithm is the computation of the tilt angle of an object transported by a robot, minimizing image inherent errors and increasing computing speed. After capturing the image, the computer system extracts the angle using a Fuzzy filter that corrects at the same time all possible distortions, obtaining the real angle in only one processing step. This filter has been developed by the means of Neuro-Fuzzy learning techniques, using datasets with information obtained from real experiments. In this way, the computing time has been decreased and the performance of the application has been improved. The resulting algorithm has been tried out experimentally in robot transportation tasks in the humanoid robot TEO (Task Environment Operator) from the University Carlos III of Madrid. Full article
(This article belongs to the Section Physical Sensors)
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Open AccessArticle A Portable Wireless Communication Platform Based on a Multi-Material Fiber Sensor for Real-Time Breath Detection
Sensors 2018, 18(4), 973; https://doi.org/10.3390/s18040973
Received: 6 February 2018 / Revised: 20 March 2018 / Accepted: 23 March 2018 / Published: 25 March 2018
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Abstract
In this paper, we present a new mobile wireless communication platform for real-time monitoring of an individual’s breathing rate. The platform takes the form of a wearable stretching T-shirt featuring a sensor and a detection base station. The sensor is formed by a
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In this paper, we present a new mobile wireless communication platform for real-time monitoring of an individual’s breathing rate. The platform takes the form of a wearable stretching T-shirt featuring a sensor and a detection base station. The sensor is formed by a spiral-shaped antenna made from a multi-material fiber connected to a compact transmitter. Based on the resonance frequency of the antenna at approximately 2.4 GHz, the breathing sensor relies on its Bluetooth transmitter. The contactless and non-invasive sensor is designed without compromising the user’s comfort. The sensing mechanism of the system is based on the detection of the signal amplitude transmitted wirelessly by the sensor, which is found to be sensitive to strain. We demonstrate the capability of the platform to detect the breathing rates of four male volunteers who are not in movement. The breathing pattern is obtained through the received signal strength indicator (RSSI) which is filtered and analyzed with home-made algorithms in the portable system. Numerical simulations of human breath are performed to support the experimental detection, and both results are in a good agreement. Slow, fast, regular, irregular, and shallow breathing types are successfully recorded within a frequency interval of 0.16–1.2 Hz, leading to a breathing rate varying from 10 to 72 breaths per minute. Full article
(This article belongs to the Special Issue Wearable Smart Devices)
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Open AccessArticle An Indoor Positioning-Based Mobile Payment System Using Bluetooth Low Energy Technology
Sensors 2018, 18(4), 974; https://doi.org/10.3390/s18040974
Received: 24 February 2018 / Revised: 13 March 2018 / Accepted: 22 March 2018 / Published: 25 March 2018
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Abstract
The development of information technology has paved the way for faster and more convenient payment process flows and new methodology for the design and implementation of next generation payment systems. The growth of smartphone usage nowadays has fostered a new and popular mobile
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The development of information technology has paved the way for faster and more convenient payment process flows and new methodology for the design and implementation of next generation payment systems. The growth of smartphone usage nowadays has fostered a new and popular mobile payment environment. Most of the current generation smartphones support Bluetooth Low Energy (BLE) technology to communicate with nearby BLE-enabled devices. It is plausible to construct an Over-the-Air BLE-based mobile payment system as one of the payment methods for people living in modern societies. In this paper, a secure indoor positioning-based mobile payment authentication protocol with BLE technology and the corresponding mobile payment system design are proposed. The proposed protocol consists of three phases: initialization phase, session key construction phase, and authentication phase. When a customer moves toward the POS counter area, the proposed mobile payment system will automatically detect the position of the customer to confirm whether the customer is ready for the checkout process. Once the system has identified the customer is standing within the payment-enabled area, the payment system will invoke authentication process between POS and the customer’s smartphone through BLE communication channel to generate a secure session key and establish an authenticated communication session to perform the payment transaction accordingly. A prototype is implemented to assess the performance of the proposed design for mobile payment system. In addition, security analysis is conducted to evaluate the security strength of the proposed protocol. Full article
(This article belongs to the Special Issue Mobile Sensing Applications)
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Open AccessArticle Non-Invasive Methodology to Estimate Polyphenol Content in Extra Virgin Olive Oil Based on Stepwise Multilinear Regression
Sensors 2018, 18(4), 975; https://doi.org/10.3390/s18040975
Received: 30 January 2018 / Revised: 20 March 2018 / Accepted: 20 March 2018 / Published: 25 March 2018
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Abstract
Normally the olive oil quality is assessed by chemical analysis according to international standards. These norms define chemical and organoleptic markers, and depending on the markers, the olive oil can be labelled as lampante, virgin, or extra virgin olive oil (EVOO), the last
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Normally the olive oil quality is assessed by chemical analysis according to international standards. These norms define chemical and organoleptic markers, and depending on the markers, the olive oil can be labelled as lampante, virgin, or extra virgin olive oil (EVOO), the last being an indicator of top quality. The polyphenol content is related to EVOO organoleptic features, and different scientific works have studied the positive influence that these compounds have on human health. The works carried out in this paper are focused on studying relations between the polyphenol content in olive oil samples and its spectral response in the near infrared spectra. In this context, several acquisition parameters have been assessed to optimize the measurement process within the virgin olive oil production process. The best regression model reached a mean error value of 156.14 mg/kg in leave one out cross validation, and the higher regression coefficient was 0.81 through holdout validation. Full article
(This article belongs to the Special Issue Spectroscopy Based Sensors)
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Open AccessArticle Ultra-Long-Distance Hybrid BOTDA/Ф-OTDR
Sensors 2018, 18(4), 976; https://doi.org/10.3390/s18040976
Received: 18 February 2018 / Revised: 22 March 2018 / Accepted: 23 March 2018 / Published: 25 March 2018
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Abstract
In the distributed optical fiber sensing (DOFS) domain, simultaneous measurement of vibration and temperature/strain based on Rayleigh scattering and Brillouin scattering in fiber could have wide applications. However, there are certain challenges for the case of ultra-long sensing range, including the interplay of
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In the distributed optical fiber sensing (DOFS) domain, simultaneous measurement of vibration and temperature/strain based on Rayleigh scattering and Brillouin scattering in fiber could have wide applications. However, there are certain challenges for the case of ultra-long sensing range, including the interplay of different scattering mechanisms, the interaction of two types of sensing signals, and the competition of pump power. In this paper, a hybrid DOFS system, which can simultaneously measure temperature/strain and vibration over 150 km, is elaborately designed via integrating the Brillouin optical time-domain analyzer (BOTDA) and phase-sensitive optical time-domain reflectometry (Ф-OTDR). Distributed Raman and Brillouin amplifications, frequency division multiplexing (FDM), wavelength division multiplexing (WDM), and time division multiplexing (TDM) are delicately fused to accommodate ultra-long-distance BOTDA and Ф-OTDR. Consequently, the sensing range of the hybrid system is 150.62 km, and the spatial resolution of BOTDA and Ф-OTDR are 9 m and 30 m, respectively. The measurement uncertainty of the BOTDA is ± 0.82 MHz. To the best of our knowledge, this is the first time that such hybrid DOFS is realized with a hundred-kilometer length scale. Full article
(This article belongs to the Section Physical Sensors)
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Open AccessArticle Multi-Aperture-Based Probabilistic Noise Reduction of Random Telegraph Signal Noise and Photon Shot Noise in Semi-Photon-Counting Complementary-Metal-Oxide-Semiconductor Image Sensor
Sensors 2018, 18(4), 977; https://doi.org/10.3390/s18040977
Received: 16 February 2018 / Revised: 21 March 2018 / Accepted: 23 March 2018 / Published: 26 March 2018
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Abstract
A probabilistic method to remove the random telegraph signal (RTS) noise and to increase the signal level is proposed, and was verified by simulation based on measured real sensor noise. Although semi-photon-counting-level (SPCL) ultra-low noise complementary-metal-oxide-semiconductor (CMOS) image sensors (CISs) with high conversion
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A probabilistic method to remove the random telegraph signal (RTS) noise and to increase the signal level is proposed, and was verified by simulation based on measured real sensor noise. Although semi-photon-counting-level (SPCL) ultra-low noise complementary-metal-oxide-semiconductor (CMOS) image sensors (CISs) with high conversion gain pixels have emerged, they still suffer from huge RTS noise, which is inherent to the CISs. The proposed method utilizes a multi-aperture (MA) camera that is composed of multiple sets of an SPCL CIS and a moderately fast and compact imaging lens to emulate a very fast single lens. Due to the redundancy of the MA camera, the RTS noise is removed by the maximum likelihood estimation where noise characteristics are modeled by the probability density distribution. In the proposed method, the photon shot noise is also relatively reduced because of the averaging effect, where the pixel values of all the multiple apertures are considered. An extremely low-light condition that the maximum number of electrons per aperture was the only 2 e was simulated. PSNRs of a test image for simple averaging, selective averaging (our previous method), and the proposed method were 11.92 dB, 11.61 dB, and 13.14 dB, respectively. The selective averaging, which can remove RTS noise, was worse than the simple averaging because it ignores the pixels with RTS noise and photon shot noise was less improved. The simulation results showed that the proposed method provided the best noise reduction performance. Full article
(This article belongs to the Section Physical Sensors)
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Open AccessArticle Research on Strong Clutter Suppression for Gaofen-3 Dual-Channel SAR/GMTI
Sensors 2018, 18(4), 978; https://doi.org/10.3390/s18040978
Received: 23 January 2018 / Revised: 27 February 2018 / Accepted: 22 March 2018 / Published: 26 March 2018
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Abstract
In spaceborne synthetic aperture radar (SAR), moving targets are almost buried in ground clutter due to the wide clutter Doppler spectrum and the restricted pulse repetition frequency (PRF), which increases the difficulty of moving target detection. Clutter suppression is one of the key
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In spaceborne synthetic aperture radar (SAR), moving targets are almost buried in ground clutter due to the wide clutter Doppler spectrum and the restricted pulse repetition frequency (PRF), which increases the difficulty of moving target detection. Clutter suppression is one of the key issues in the spaceborne SAR moving target indicator operation. In this paper, we describe the clutter suppression principle and analyze the influence of amplitude and phase error on clutter suppression. In the following, a novel dual-channel SAR clutter suppression algorithm is proposed, which is suitable for the Gaofen-3(GF-3) SAR sensor. The proposed algorithm consists of three technique steps, namely adaptive two-dimensional (2D) channel calibration, refined amplitude error correction and refined phase error correction. After channel error is corrected by these procedures, the clutter component, especially a strong clutter component, can be well suppressed. The validity of the proposed algorithm is verified by GF-3 SAR real data which demonstrates the ground moving-target indication (GMTI) capability of GF-3 SAR sensor. Full article
(This article belongs to the Special Issue First Experiences with Chinese Gaofen-3 SAR Sensor)
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Open AccessArticle Development of Implantable Wireless Sensor Nodes for Animal Husbandry and MedTech Innovation
Sensors 2018, 18(4), 979; https://doi.org/10.3390/s18040979
Received: 26 February 2018 / Revised: 22 March 2018 / Accepted: 22 March 2018 / Published: 26 March 2018
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Abstract
In this paper, we report the development, evaluation, and application of ultra-small low-power wireless sensor nodes for advancing animal husbandry, as well as for innovation of medical technologies. A radio frequency identification (RFID) chip with hybrid interface and neglectable power consumption was introduced
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In this paper, we report the development, evaluation, and application of ultra-small low-power wireless sensor nodes for advancing animal husbandry, as well as for innovation of medical technologies. A radio frequency identification (RFID) chip with hybrid interface and neglectable power consumption was introduced to enable switching of ON/OFF and measurement mode after implantation. A wireless power transmission system with a maximum efficiency of 70% and an access distance of up to 5 cm was developed to allow the sensor node to survive for a duration of several weeks from a few minutes’ remote charge. The results of field tests using laboratory mice and a cow indicated the high accuracy of the collected biological data and bio-compatibility of the package. As a result of extensive application of the above technologies, a fully solid wireless pH sensor and a surgical navigation system using artificial magnetic field and a 3D MEMS magnetic sensor are introduced in this paper, and the preliminary experimental results are presented and discussed. Full article
(This article belongs to the Section Physical Sensors)
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Open AccessArticle Embedded Distributed Optical Fiber Sensors in Reinforced Concrete Structures—A Case Study
Sensors 2018, 18(4), 980; https://doi.org/10.3390/s18040980
Received: 13 January 2018 / Revised: 7 March 2018 / Accepted: 22 March 2018 / Published: 26 March 2018
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Abstract
When using distributed optical fiber sensors (DOFS) on reinforced concrete structures, a compromise must be achieved between the protection requirements and robustness of the sensor deployment and the accuracy of the measurements both in the uncracked and cracked stages and under loading, unloading
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When using distributed optical fiber sensors (DOFS) on reinforced concrete structures, a compromise must be achieved between the protection requirements and robustness of the sensor deployment and the accuracy of the measurements both in the uncracked and cracked stages and under loading, unloading and reloading processes. With this in mind the authors have carried out an experiment where polyimide-coated DOFS were installed on two concrete beams, both embedded in the rebar elements and also bonded to the concrete surface. The specimens were subjected to a three-point load test where after cracking, they are unloaded and reloaded again to assess the capability of the sensor when applied to a real loading scenarios in concrete structures. Rayleigh Optical Frequency Domain Reflectometry (OFDR) was used as the most suitable technique for crack detection in reinforced concrete elements. To verify the reliability and accuracy of the DOFS measurements, additional strain gauges were also installed at three locations along the rebar. The results show the feasibility of using a thin coated polyimide DOFS directly bonded on the reinforcing bar without the need of indention or mechanization. A proposal for a Spectral Shift Quality (SSQ) threshold is also obtained and proposed for future works when using polyimide-coated DOFS bonded to rebars with cyanoacrylate adhesive. Full article
(This article belongs to the Special Issue Optical Fiber Sensors 2017)
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Open AccessArticle A Resource Service Model in the Industrial IoT System Based on Transparent Computing
Sensors 2018, 18(4), 981; https://doi.org/10.3390/s18040981
Received: 4 February 2018 / Revised: 14 March 2018 / Accepted: 21 March 2018 / Published: 26 March 2018
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Abstract
The Internet of Things (IoT) has received a lot of attention, especially in industrial scenarios. One of the typical applications is the intelligent mine, which actually constructs the Six-Hedge underground systems with IoT platforms. Based on a case study of the Six Systems
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The Internet of Things (IoT) has received a lot of attention, especially in industrial scenarios. One of the typical applications is the intelligent mine, which actually constructs the Six-Hedge underground systems with IoT platforms. Based on a case study of the Six Systems in the underground metal mine, this paper summarizes the main challenges of industrial IoT from the aspects of heterogeneity in devices and resources, security, reliability, deployment and maintenance costs. Then, a novel resource service model for the industrial IoT applications based on Transparent Computing (TC) is presented, which supports centralized management of all resources including operating system (OS), programs and data on the server-side for the IoT devices, thus offering an effective, reliable, secure and cross-OS IoT service and reducing the costs of IoT system deployment and maintenance. The model has five layers: sensing layer, aggregation layer, network layer, service and storage layer and interface and management layer. We also present a detailed analysis on the system architecture and key technologies of the model. Finally, the efficiency of the model is shown by an experiment prototype system. Full article
(This article belongs to the Section Internet of Things)
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Open AccessArticle Integrated Optoelectronic Position Sensor for Scanning Micromirrors
Sensors 2018, 18(4), 982; https://doi.org/10.3390/s18040982
Received: 3 February 2018 / Revised: 10 March 2018 / Accepted: 13 March 2018 / Published: 26 March 2018
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Abstract
Scanning micromirrors have been used in a wide range of areas, but many of them do not have position sensing built in, which significantly limits their application space. This paper reports an integrated optoelectronic position sensor (iOE-PS) that can measure the linear displacement
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Scanning micromirrors have been used in a wide range of areas, but many of them do not have position sensing built in, which significantly limits their application space. This paper reports an integrated optoelectronic position sensor (iOE-PS) that can measure the linear displacement and tilting angle of electrothermal MEMS (Micro-electromechanical Systems) scanning mirrors. The iOE-PS integrates a laser diode and its driving circuits, a quadrant photo-detector (QPD) and its readout circuits, and a band-gap reference all on a single chip, and it has been fabricated in a standard 0.5 μm CMOS (Complementary Metal Oxide Semiconductor) process. The footprint of the iOE-PS chip is 5 mm × 5 mm. Each quadrant of the QPD has a photosensitive area of 500 µm × 500 µm and the spacing between adjacent quadrants is 500 μm. The iOE-PS chip is simply packaged underneath of an electrothermally-actuated MEMS mirror. Experimental results show that the iOE-PS has a linear response when the MEMS mirror plate moves vertically between 2.0 mm and 3.0 mm over the iOE-PS chip or scans from −5 to +5°. Such MEMS scanning mirrors integrated with the iOE-PS can greatly reduce the complexity and cost of the MEMS mirrors-enabled modules and systems. Full article
(This article belongs to the Section Physical Sensors)
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Open AccessArticle An Experimental Study of a Micro-Projection Enabled Optical Terminal for Short-Range Bidirectional Multi-Wavelength Visible Light Communications
Sensors 2018, 18(4), 983; https://doi.org/10.3390/s18040983
Received: 28 February 2018 / Revised: 19 March 2018 / Accepted: 23 March 2018 / Published: 26 March 2018
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Abstract
A micro-projection enabled short-range communication (SRC) approach using red-, green- and blue-based light-emitting diodes (RGB-LEDs) has experimentally demonstrated recently that micro-projection and high-speed data transmission can be performed simultaneously. In this research, a reconfigurable design of a polarization modulated image system based on
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A micro-projection enabled short-range communication (SRC) approach using red-, green- and blue-based light-emitting diodes (RGB-LEDs) has experimentally demonstrated recently that micro-projection and high-speed data transmission can be performed simultaneously. In this research, a reconfigurable design of a polarization modulated image system based on the use of a Liquid Crystal on Silicon based Spatial Light Modulator (LCoS-based SLM) serving as a portable optical terminal capable of micro-projection and bidirectional multi-wavelength communications is proposed and experimentally demonstrated. For the proof of concept, the system performance was evaluated through a bidirectional communication link at a transmission distance over 0.65 m. In order to make the proposed communication system architecture compatible with the data modulation format of future possible wireless communication system, baseband modulation scheme, i.e., Non-Return-to-Zero On-Off-Keying (NRZ_OOK), M-ary Phase Shift Keying (M-PSK) and M-ary Quadrature Amplitude Modulation (M-QAM) were used to investigate the system transmission performance. The experimental results shown that an acceptable BER (satisfying the limitation of Forward Error Correction, FEC standard) and crosstalk can all be achieved in the bidirectional multi-wavelength communication scenario. Full article
(This article belongs to the Special Issue Visible Light Communication Networks)
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Open AccessArticle Analytical Method to Estimate the Complex Permittivity of Oil Samples
Sensors 2018, 18(4), 984; https://doi.org/10.3390/s18040984
Received: 2 February 2018 / Revised: 15 March 2018 / Accepted: 23 March 2018 / Published: 26 March 2018
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Abstract
In this paper, an analytical method to estimate the complex dielectric constant of liquids is presented. The method is based on the measurement of the transmission coefficient in an embedded microstrip line loaded with a complementary split ring resonator (CSRR), which is etched
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In this paper, an analytical method to estimate the complex dielectric constant of liquids is presented. The method is based on the measurement of the transmission coefficient in an embedded microstrip line loaded with a complementary split ring resonator (CSRR), which is etched in the ground plane. From this response, the dielectric constant and loss tangent of the liquid under test (LUT) can be extracted, provided that the CSRR is surrounded by such LUT, and the liquid level extends beyond the region where the electromagnetic fields generated by the CSRR are present. For that purpose, a liquid container acting as a pool is added to the structure. The main advantage of this method, which is validated from the measurement of the complex dielectric constant of olive and castor oil, is that reference samples for calibration are not required. Full article
(This article belongs to the Section Physical Sensors)
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Open AccessArticle Design and Processing of a Novel Chaos-Based Stepped Frequency Synthesized Wideband Radar Signal
Sensors 2018, 18(4), 985; https://doi.org/10.3390/s18040985
Received: 10 February 2018 / Revised: 21 March 2018 / Accepted: 23 March 2018 / Published: 26 March 2018
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Abstract
The linear stepped frequency and linear frequency shift keying (FSK) signal has been widely used in radar systems. However, such linear modulation signals suffer from the range–Doppler coupling that degrades radar multi-target resolution. Moreover, the fixed frequency-hopping or frequency-coded sequence can be easily
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The linear stepped frequency and linear frequency shift keying (FSK) signal has been widely used in radar systems. However, such linear modulation signals suffer from the range–Doppler coupling that degrades radar multi-target resolution. Moreover, the fixed frequency-hopping or frequency-coded sequence can be easily predicted by the interception receiver in the electronic countermeasures (ECM) environments, which limits radar anti-jamming performance. In addition, the single FSK modulation reduces the radar low probability of intercept (LPI) performance, for it cannot achieve a large time–bandwidth product. To solve such problems, we propose a novel chaos-based stepped frequency (CSF) synthesized wideband signal in this paper. The signal introduces chaotic frequency hopping between the coherent stepped frequency pulses, and adopts a chaotic frequency shift keying (CFSK) and phase shift keying (PSK) composited coded modulation in a subpulse, called CSF-CFSK/PSK. Correspondingly, the processing method for the signal has been proposed. According to our theoretical analyses and the simulations, the proposed signal and processing method achieve better multi-target resolution and LPI performance. Furthermore, flexible modulation is able to increase the robustness against identification of the interception receiver and improve the anti-jamming performance of the radar. Full article
(This article belongs to the Section Remote Sensors)
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Open AccessArticle Coupling Vibration Analysis of Trapped-Energy Rectangular Quartz Resonators by Variational Formulation of Mindlin’s Theory
Sensors 2018, 18(4), 986; https://doi.org/10.3390/s18040986
Received: 21 February 2018 / Revised: 15 March 2018 / Accepted: 21 March 2018 / Published: 26 March 2018
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Abstract
Mindlin’s two-dimensional theory has been derived and applied to research on quartz resonators for a long time. However, most works have focused on vibrations varying only in two directions, including thickness direction, while the effect of other directions like the length or width
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Mindlin’s two-dimensional theory has been derived and applied to research on quartz resonators for a long time. However, most works have focused on vibrations varying only in two directions, including thickness direction, while the effect of other directions like the length or width direction is normally neglected. Besides, researchers often model quartz resonators as fully electroded plates because of the resulting simplicity. Since a real device is finite in all directions and is only centrally electroded, results obtained in such works cannot offer quantitative information on vibrations with enough accuracy. In this paper, a theoretical analysis of a rectangular trapped-energy resonator of AT-cut quartz is studied using the Ritz method, associated with the variational formulation of Mindlin’s first-order equations. Frequency spectra and mode shapes of a real-scaled trapped-energy resonator, which is finite in all directions, are obtained with the consideration of mode couplings among thickness-shear mode, thickness-twist mode, and flexural mode. Results show the existence of an energy-trapping and coupling phenomenon and are helpful for thorough and accurate understanding of quartz resonator vibrations. Detailed discussions on the effects of structural parameters on mode couplings and energy trapping are provided, and the results can helpfully guide the selection of aspect ratio, length/thickness ratio, and electrode inertia in device design. Full article
(This article belongs to the Section Physical Sensors)
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