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Sensors, Volume 17, Issue 7 (July 2017)

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Cover Story Capsule endoscopy is a less invasive way than conventional endoscopy to image the interior of the [...] Read more.
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Open AccessArticle Graphene-Ag Hybrids on Laser-Textured Si Surface for SERS Detection
Sensors 2017, 17(7), 1462; doi:10.3390/s17071462
Received: 7 May 2017 / Revised: 12 June 2017 / Accepted: 14 June 2017 / Published: 22 June 2017
Cited by 1 | PDF Full-text (10770 KB) | HTML Full-text | XML Full-text
Abstract
Surface-enhanced Raman scattering (SERS) has been extensively investigated as an effective approach for trace species detection. Silver nanostructures are high-sensitivity SERS substrates in common use, but their poor chemical stability impedes practical applications. Herein, a stable and sensitive SERS substrate based on the
[...] Read more.
Surface-enhanced Raman scattering (SERS) has been extensively investigated as an effective approach for trace species detection. Silver nanostructures are high-sensitivity SERS substrates in common use, but their poor chemical stability impedes practical applications. Herein, a stable and sensitive SERS substrate based on the hybrid structures of graphene/silver film/laser-textured Si (G/Ag/LTSi) was developed, and a simple, rapid, and low-cost fabrication approach was explored. Abundant nanoparticles were directly created and deposited on the Si surface via laser ablation. These aggregated nanoparticles functioned as hotspots after a 30 nm Ag film coating. A monolayer graphene was transferred to the Ag film surface to prevent the Ag from oxidation. The SERS behavior was investigated by detecting R6G and 4-MBT molecules. The experimental results indicate that the maximum enhancement factor achieved by the G/Ag/LTSi substrate is over 107 and less than 23% SERS signals lost when the substrate was exposed to ambient conditions for 50 days. The covering graphene layer played crucial roles in both the Raman signals enhancement and the Ag nanostructure protection. The stable and sensitive SERS performance of G/Ag/LTSi substrate evince that the present strategy is a useful and convenient route to fabricate large-area graphene-silver plasmonic hybrids for SERS applications. Full article
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Open AccessArticle Novel Concrete Temperature Monitoring Method Based on an Embedded Passive RFID Sensor Tag
Sensors 2017, 17(7), 1463; doi:10.3390/s17071463
Received: 17 April 2017 / Revised: 12 June 2017 / Accepted: 20 June 2017 / Published: 22 June 2017
Cited by 1 | PDF Full-text (7777 KB) | HTML Full-text | XML Full-text
Abstract
This paper firstly introduces the importance of temperature control in concrete measurement, then a passive radio frequency identification (RFID) sensor tag embedded for concrete temperature monitoring is presented. In order to reduce the influences of concrete electromagnetic parameters during the drying process, a
[...] Read more.
This paper firstly introduces the importance of temperature control in concrete measurement, then a passive radio frequency identification (RFID) sensor tag embedded for concrete temperature monitoring is presented. In order to reduce the influences of concrete electromagnetic parameters during the drying process, a T-type antenna is proposed to measure the concrete temperature at the required depth. The proposed RFID sensor tag is based on the EPC generation-2 ultra-high frequency (UHF) communication protocol and operates in passive mode. The temperature sensor can convert the sensor signals to corresponding digital signals without an external reference clock due to the adoption of phase-locked loop (PLL)-based architecture. Laboratory experimentation and on-site testing demonstrate that our sensor tag embedded in concrete can provide reliable communication performance in passive mode. The maximum communicating distance between reader and tag is 7 m at the operating frequency of 915 MHz and the tested results show high consistency with the results tested by a thermocouple. Full article
(This article belongs to the Special Issue Low Power Embedded Sensing: Hardware-Software Design and Applications)
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Open AccessArticle Paper as Active Layer in Inkjet-Printed Capacitive Humidity Sensors
Sensors 2017, 17(7), 1464; doi:10.3390/s17071464
Received: 21 May 2017 / Revised: 19 June 2017 / Accepted: 20 June 2017 / Published: 22 June 2017
Cited by 1 | PDF Full-text (1300 KB) | HTML Full-text | XML Full-text
Abstract
An inkjet-printed relative humidity sensor based on capacitive changes which responds to different humidity levels in the environment is presented in this work. The inkjet-printed silver interdigitated electrodes configuration on the paper substrate allowed for the fabrication of a functional proof-of-concept of the
[...] Read more.
An inkjet-printed relative humidity sensor based on capacitive changes which responds to different humidity levels in the environment is presented in this work. The inkjet-printed silver interdigitated electrodes configuration on the paper substrate allowed for the fabrication of a functional proof-of-concept of the relative humidity sensor, by using the paper itself as a sensing material. The sensor sensitivity in terms of relative humidity changes was calculated to be around 2 pF/RH %. The response time against different temperature steps from 3 to 85 °C was fairly constant (about 4–5 min), and it was considered fast for the aimed application, a smart label. Full article
(This article belongs to the Special Issue Flexible Electronics and Sensors)
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Open AccessArticle Automatic Detection and Classification of Pole-Like Objects for Urban Cartography Using Mobile Laser Scanning Data
Sensors 2017, 17(7), 1465; doi:10.3390/s17071465
Received: 23 May 2017 / Revised: 12 June 2017 / Accepted: 19 June 2017 / Published: 22 June 2017
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Abstract
Mobile laser scanning (MLS) is a modern and powerful technology capable of obtaining massive point clouds of objects in a short period of time. Although this technology is nowadays being widely applied in urban cartography and 3D city modelling, it has some drawbacks
[...] Read more.
Mobile laser scanning (MLS) is a modern and powerful technology capable of obtaining massive point clouds of objects in a short period of time. Although this technology is nowadays being widely applied in urban cartography and 3D city modelling, it has some drawbacks that need to be avoided in order to strengthen it. One of the most important shortcomings of MLS data is concerned with the fact that it provides an unstructured dataset whose processing is very time-consuming. Consequently, there is a growing interest in developing algorithms for the automatic extraction of useful information from MLS point clouds. This work is focused on establishing a methodology and developing an algorithm to detect pole-like objects and classify them into several categories using MLS datasets. The developed procedure starts with the discretization of the point cloud by means of a voxelization, in order to simplify and reduce the processing time in the segmentation process. In turn, a heuristic segmentation algorithm was developed to detect pole-like objects in the MLS point cloud. Finally, two supervised classification algorithms, linear discriminant analysis and support vector machines, were used to distinguish between the different types of poles in the point cloud. The predictors are the principal component eigenvalues obtained from the Cartesian coordinates of the laser points, the range of the Z coordinate, and some shape-related indexes. The performance of the method was tested in an urban area with 123 poles of different categories. Very encouraging results were obtained, since the accuracy rate was over 90%. Full article
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Open AccessArticle A Novel Active Imaging Model to Design Visual Systems: A Case of Inspection System for Specular Surfaces
Sensors 2017, 17(7), 1466; doi:10.3390/s17071466
Received: 29 March 2017 / Revised: 7 June 2017 / Accepted: 20 June 2017 / Published: 22 June 2017
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Abstract
The use of visual information is a very well known input from different kinds of sensors. However, most of the perception problems are individually modeled and tackled. It is necessary to provide a general imaging model that allows us to parametrize different input
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The use of visual information is a very well known input from different kinds of sensors. However, most of the perception problems are individually modeled and tackled. It is necessary to provide a general imaging model that allows us to parametrize different input systems as well as their problems and possible solutions. In this paper, we present an active vision model considering the imaging system as a whole (including camera, lighting system, object to be perceived) in order to propose solutions to automated visual systems that present problems that we perceive. As a concrete case study, we instantiate the model in a real application and still challenging problem: automated visual inspection. It is one of the most used quality control systems to detect defects on manufactured objects. However, it presents problems for specular products. We model these perception problems taking into account environmental conditions and camera parameters that allow a system to properly perceive the specific object characteristics to determine defects on surfaces. The validation of the model has been carried out using simulations providing an efficient way to perform a large set of tests (different environment conditions and camera parameters) as a previous step of experimentation in real manufacturing environments, which more complex in terms of instrumentation and more expensive. Results prove the success of the model application adjusting scale, viewpoint and lighting conditions to detect structural and color defects on specular surfaces. Full article
(This article belongs to the Special Issue State-of-the-Art Sensors Technology in Spain 2017)
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Open AccessArticle A Carrier Estimation Method Based on MLE and KF for Weak GNSS Signals
Sensors 2017, 17(7), 1468; doi:10.3390/s17071468
Received: 10 April 2017 / Revised: 8 June 2017 / Accepted: 16 June 2017 / Published: 22 June 2017
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Abstract
Maximum likelihood estimation (MLE) has been researched for some acquisition and tracking applications of global navigation satellite system (GNSS) receivers and shows high performance. However, all current methods are derived and operated based on the sampling data, which results in a large computation
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Maximum likelihood estimation (MLE) has been researched for some acquisition and tracking applications of global navigation satellite system (GNSS) receivers and shows high performance. However, all current methods are derived and operated based on the sampling data, which results in a large computation burden. This paper proposes a low-complexity MLE carrier tracking loop for weak GNSS signals which processes the coherent integration results instead of the sampling data. First, the cost function of the MLE of signal parameters such as signal amplitude, carrier phase, and Doppler frequency are used to derive a MLE discriminator function. The optimal value of the cost function is searched by an efficient Levenberg–Marquardt (LM) method iteratively. Its performance including Cramér–Rao bound (CRB), dynamic characteristics and computation burden are analyzed by numerical techniques. Second, an adaptive Kalman filter is designed for the MLE discriminator to obtain smooth estimates of carrier phase and frequency. The performance of the proposed loop, in terms of sensitivity, accuracy and bit error rate, is compared with conventional methods by Monte Carlo (MC) simulations both in pedestrian-level and vehicle-level dynamic circumstances. Finally, an optimal loop which combines the proposed method and conventional method is designed to achieve the optimal performance both in weak and strong signal circumstances. Full article
(This article belongs to the Section Remote Sensors)
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Open AccessArticle Fourier Transform Infrared Spectroscopy (FT-IR) and Simple Algorithm Analysis for Rapid and Non-Destructive Assessment of Developmental Cotton Fibers
Sensors 2017, 17(7), 1469; doi:10.3390/s17071469
Received: 15 May 2017 / Revised: 16 June 2017 / Accepted: 20 June 2017 / Published: 22 June 2017
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Abstract
With cotton fiber growth or maturation, cellulose content in cotton fibers markedly increases. Traditional chemical methods have been developed to determine cellulose content, but it is time-consuming and labor-intensive, mostly owing to the slow hydrolysis process of fiber cellulose components. As one approach,
[...] Read more.
With cotton fiber growth or maturation, cellulose content in cotton fibers markedly increases. Traditional chemical methods have been developed to determine cellulose content, but it is time-consuming and labor-intensive, mostly owing to the slow hydrolysis process of fiber cellulose components. As one approach, the attenuated total reflection Fourier transform infrared (ATR FT-IR) spectroscopy technique has also been utilized to monitor cotton cellulose formation, by implementing various spectral interpretation strategies of both multivariate principal component analysis (PCA) and 1-, 2- or 3-band/-variable intensity or intensity ratios. The main objective of this study was to compare the correlations between cellulose content determined by chemical analysis and ATR FT-IR spectral indices acquired by the reported procedures, among developmental Texas Marker-1 (TM-1) and immature fiber (im) mutant cotton fibers. It was observed that the R value, CIIR, and the integrated intensity of the 895 cm−1 band exhibited strong and linear relationships with cellulose content. The results have demonstrated the suitability and utility of ATR FT-IR spectroscopy, combined with a simple algorithm analysis, in assessing cotton fiber cellulose content, maturity, and crystallinity in a manner which is rapid, routine, and non-destructive. Full article
(This article belongs to the Section Chemical Sensors)
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Open AccessArticle A Decision Mixture Model-Based Method for Inshore Ship Detection Using High-Resolution Remote Sensing Images
Sensors 2017, 17(7), 1470; doi:10.3390/s17071470
Received: 12 May 2017 / Revised: 14 June 2017 / Accepted: 19 June 2017 / Published: 22 June 2017
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Abstract
With the rapid development of optical remote sensing satellites, ship detection and identification based on large-scale remote sensing images has become a significant maritime research topic. Compared with traditional ocean-going vessel detection, inshore ship detection has received increasing attention in harbor dynamic surveillance
[...] Read more.
With the rapid development of optical remote sensing satellites, ship detection and identification based on large-scale remote sensing images has become a significant maritime research topic. Compared with traditional ocean-going vessel detection, inshore ship detection has received increasing attention in harbor dynamic surveillance and maritime management. However, because the harbor environment is complex, gray information and texture features between docked ships and their connected dock regions are indistinguishable, most of the popular detection methods are limited by their calculation efficiency and detection accuracy. In this paper, a novel hierarchical method that combines an efficient candidate scanning strategy and an accurate candidate identification mixture model is presented for inshore ship detection in complex harbor areas. First, in the candidate region extraction phase, an omnidirectional intersected two-dimension scanning (OITDS) strategy is designed to rapidly extract candidate regions from the land-water segmented images. In the candidate region identification phase, a decision mixture model (DMM) is proposed to identify real ships from candidate objects. Specifically, to improve the robustness regarding the diversity of ships, a deformable part model (DPM) was employed to train a key part sub-model and a whole ship sub-model. Furthermore, to improve the identification accuracy, a surrounding correlation context sub-model is built. Finally, to increase the accuracy of candidate region identification, these three sub-models are integrated into the proposed DMM. Experiments were performed on numerous large-scale harbor remote sensing images, and the results showed that the proposed method has high detection accuracy and rapid computational efficiency. Full article
(This article belongs to the Section Remote Sensors)
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Open AccessArticle Analysis of Serotonin Molecules on Silver Nanocolloids—A Raman Computational and Experimental Study
Sensors 2017, 17(7), 1471; doi:10.3390/s17071471
Received: 24 May 2017 / Revised: 16 June 2017 / Accepted: 20 June 2017 / Published: 22 June 2017
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Abstract
Combined theoretical and experimental analysis of serotonin by quantum chemical density functional calculations and surface-enhanced Raman spectroscopy, respectively, is presented in this work to better understand phenomena related to this neurotransmitter’s detection and monitoring at very low concentrations specific to physiological levels. In
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Combined theoretical and experimental analysis of serotonin by quantum chemical density functional calculations and surface-enhanced Raman spectroscopy, respectively, is presented in this work to better understand phenomena related to this neurotransmitter’s detection and monitoring at very low concentrations specific to physiological levels. In addition to the successful ultrasensitive analyte detection on silver nanoparticles for concentrations as low as 10−11 molar, the relatively good agreement between the simulated and experimentally determined results indicates the presence of all serotonin molecular forms, such as neutral, ionic, and those oxidized through redox reactions. Obvious structural molecular deformations such as bending of lateral amino chains are observed for both ionic and oxidized forms. Not only does this combined approach reveal more probable adsorption of serotonin into the silver surface through hydroxyl/oxygen sites than through NH/nitrogen sites, but also that it does so predominantly in its neutral (reduced) form, somewhat less so in its ionic forms, and much less in its oxidized forms. If the development of opto-voltammetric biosensors and their effective implementation is envisioned for the future, this study provides some needed scientific background for comprehending changes in the vibrational signatures of this important neurotransmitter. Full article
(This article belongs to the Special Issue Applications of Raman Spectroscopy in Biosensors)
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Open AccessArticle Performance Optimization Design for a High-Speed Weak FBG Interrogation System Based on DFB Laser
Sensors 2017, 17(7), 1472; doi:10.3390/s17071472
Received: 25 April 2017 / Revised: 9 June 2017 / Accepted: 19 June 2017 / Published: 22 June 2017
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Abstract
A performance optimization design for a high-speed fiber Bragg grating (FBG) interrogation system based on a high-speed distributed feedback (DFB) swept laser is proposed. A time-division-multiplexing sensor network with identical weak FBGs is constituted to realize high-capacity sensing. In order to further improve
[...] Read more.
A performance optimization design for a high-speed fiber Bragg grating (FBG) interrogation system based on a high-speed distributed feedback (DFB) swept laser is proposed. A time-division-multiplexing sensor network with identical weak FBGs is constituted to realize high-capacity sensing. In order to further improve the multiplexing capacity, a waveform repairing algorithm is designed to extend the dynamic demodulation range of FBG sensors. It is based on the fact that the spectrum of an FBG keeps stable over a long period of time. Compared with the pre-collected spectra, the distorted spectra waveform are identified and repaired. Experimental results show that all the identical weak FBGs are distinguished and demodulated at the speed of 100 kHz with a linearity of above 0.99, and the range of dynamic demodulation is extended by 40%. Full article
(This article belongs to the Special Issue Fiber Bragg Grating Based Sensors)
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Open AccessArticle Stereo Vision-Based High Dynamic Range Imaging Using Differently-Exposed Image Pair
Sensors 2017, 17(7), 1473; doi:10.3390/s17071473
Received: 4 May 2017 / Revised: 19 June 2017 / Accepted: 19 June 2017 / Published: 22 June 2017
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Abstract
In this paper, a high dynamic range (HDR) imaging method based on the stereo vision system is presented. The proposed method uses differently exposed low dynamic range (LDR) images captured from a stereo camera. The stereo LDR images are first converted to initial
[...] Read more.
In this paper, a high dynamic range (HDR) imaging method based on the stereo vision system is presented. The proposed method uses differently exposed low dynamic range (LDR) images captured from a stereo camera. The stereo LDR images are first converted to initial stereo HDR images using the inverse camera response function estimated from the LDR images. However, due to the limited dynamic range of the stereo LDR camera, the radiance values in under/over-exposed regions of the initial main-view (MV) HDR image can be lost. To restore these radiance values, the proposed stereo matching and hole-filling algorithms are applied to the stereo HDR images. Specifically, the auxiliary-view (AV) HDR image is warped by using the estimated disparity between initial the stereo HDR images and then effective hole-filling is applied to the warped AV HDR image. To reconstruct the final MV HDR, the warped and hole-filled AV HDR image is fused with the initial MV HDR image using the weight map. The experimental results demonstrate objectively and subjectively that the proposed stereo HDR imaging method provides better performance compared to the conventional method. Full article
(This article belongs to the Section Physical Sensors)
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Open AccessArticle A Study for Texture Feature Extraction of High-Resolution Satellite Images Based on a Direction Measure and Gray Level Co-Occurrence Matrix Fusion Algorithm
Sensors 2017, 17(7), 1474; doi:10.3390/s17071474
Received: 10 March 2017 / Revised: 5 June 2017 / Accepted: 5 June 2017 / Published: 22 June 2017
Cited by 2 | PDF Full-text (2657 KB) | HTML Full-text | XML Full-text
Abstract
To address the problem of image texture feature extraction, a direction measure statistic that is based on the directionality of image texture is constructed, and a new method of texture feature extraction, which is based on the direction measure and a gray level
[...] Read more.
To address the problem of image texture feature extraction, a direction measure statistic that is based on the directionality of image texture is constructed, and a new method of texture feature extraction, which is based on the direction measure and a gray level co-occurrence matrix (GLCM) fusion algorithm, is proposed in this paper. This method applies the GLCM to extract the texture feature value of an image and integrates the weight factor that is introduced by the direction measure to obtain the final texture feature of an image. A set of classification experiments for the high-resolution remote sensing images were performed by using support vector machine (SVM) classifier with the direction measure and gray level co-occurrence matrix fusion algorithm. Both qualitative and quantitative approaches were applied to assess the classification results. The experimental results demonstrated that texture feature extraction based on the fusion algorithm achieved a better image recognition, and the accuracy of classification based on this method has been significantly improved. Full article
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Open AccessArticle Block-Diagonal Constrained Low-Rank and Sparse Graph for Discriminant Analysis of Image Data
Sensors 2017, 17(7), 1475; doi:10.3390/s17071475
Received: 29 March 2017 / Revised: 14 June 2017 / Accepted: 19 June 2017 / Published: 22 June 2017
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Abstract
Recently, low-rank and sparse model-based dimensionality reduction (DR) methods have aroused lots of interest. In this paper, we propose an effective supervised DR technique named block-diagonal constrained low-rank and sparse-based embedding (BLSE). BLSE has two steps, i.e., block-diagonal constrained low-rank and sparse representation
[...] Read more.
Recently, low-rank and sparse model-based dimensionality reduction (DR) methods have aroused lots of interest. In this paper, we propose an effective supervised DR technique named block-diagonal constrained low-rank and sparse-based embedding (BLSE). BLSE has two steps, i.e., block-diagonal constrained low-rank and sparse representation (BLSR) and block-diagonal constrained low-rank and sparse graph embedding (BLSGE). Firstly, the BLSR model is developed to reveal the intrinsic intra-class and inter-class adjacent relationships as well as the local neighborhood relations and global structure of data. Particularly, there are mainly three items considered in BLSR. First, a sparse constraint is required to discover the local data structure. Second, a low-rank criterion is incorporated to capture the global structure in data. Third, a block-diagonal regularization is imposed on the representation to promote discrimination between different classes. Based on BLSR, informative and discriminative intra-class and inter-class graphs are constructed. With the graphs, BLSGE seeks a low-dimensional embedding subspace by simultaneously minimizing the intra-class scatter and maximizing the inter-class scatter. Experiments on public benchmark face and object image datasets demonstrate the effectiveness of the proposed approach. Full article
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Open AccessArticle A Comprehensive Study on the Internet of Underwater Things: Applications, Challenges, and Channel Models
Sensors 2017, 17(7), 1477; doi:10.3390/s17071477
Received: 5 May 2017 / Revised: 10 June 2017 / Accepted: 20 June 2017 / Published: 22 June 2017
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Abstract
The Internet of Underwater Things (IoUT) is a novel class of Internet of Things (IoT), and is defined as the network of smart interconnected underwater objects. IoUT is expected to enable various practical applications, such as environmental monitoring, underwater exploration, and disaster prevention.
[...] Read more.
The Internet of Underwater Things (IoUT) is a novel class of Internet of Things (IoT), and is defined as the network of smart interconnected underwater objects. IoUT is expected to enable various practical applications, such as environmental monitoring, underwater exploration, and disaster prevention. With these applications, IoUT is regarded as one of the potential technologies toward developing smart cities. To support the concept of IoUT, Underwater Wireless Sensor Networks (UWSNs) have emerged as a promising network system. UWSNs are different from the traditional Territorial Wireless Sensor Networks (TWSNs), and have several unique properties, such as long propagation delay, narrow bandwidth, and low reliability. These unique properties would be great challenges for IoUT. In this paper, we provide a comprehensive study of IoUT, and the main contributions of this paper are threefold: (1) we introduce and classify the practical underwater applications that can highlight the importance of IoUT; (2) we point out the differences between UWSNs and traditional TWSNs, and these differences are the main challenges for IoUT; and (3) we investigate and evaluate the channel models, which are the technical core for designing reliable communication protocols on IoUT. Full article
(This article belongs to the Special Issue Selected Papers from IEEE ICASI 2017)
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Open AccessArticle Trimethylamine Sensors Based on Au-Modified Hierarchical Porous Single-Crystalline ZnO Nanosheets
Sensors 2017, 17(7), 1478; doi:10.3390/s17071478
Received: 27 May 2017 / Revised: 20 June 2017 / Accepted: 21 June 2017 / Published: 22 June 2017
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Abstract
It is of great significance for dynamic monitoring of foods in storage or during the transportation process through on-line detecting trimethylamine (TMA). Here, TMA were sensitively detected by Au-modified hierarchical porous single-crystalline ZnO nanosheets (HPSCZNs)-based sensors. The HPSCZNs were synthesized through a one-pot
[...] Read more.
It is of great significance for dynamic monitoring of foods in storage or during the transportation process through on-line detecting trimethylamine (TMA). Here, TMA were sensitively detected by Au-modified hierarchical porous single-crystalline ZnO nanosheets (HPSCZNs)-based sensors. The HPSCZNs were synthesized through a one-pot wet-chemical method followed by an annealing treatment. Polyethyleneimine (PEI) was used to modify the surface of the HPSCZNs, and then the PEI-modified samples were mixed with Au nanoparticles (NPs) sol solution. Electrostatic interactions drive Au nanoparticles loading onto the surface of the HPSCZNs. The Au-modified HPSCZNs were characterized by X-ray diffraction (XRD), scanning electron microscopy (SEM), transmission electron microscopy (TEM) and energy dispersive spectrum (EDS), respectively. The results show that Au-modified HPSCZNs-based sensors exhibit a high response to TMA. The linear range is from 10 to 300 ppb; while the detection limit is 10 ppb, which is the lowest value to our knowledge. Full article
(This article belongs to the Section Chemical Sensors)
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Open AccessArticle GADEN: A 3D Gas Dispersion Simulator for Mobile Robot Olfaction in Realistic Environments
Sensors 2017, 17(7), 1479; doi:10.3390/s17071479
Received: 17 May 2017 / Revised: 15 June 2017 / Accepted: 17 June 2017 / Published: 23 June 2017
Cited by 2 | PDF Full-text (4959 KB) | HTML Full-text | XML Full-text
Abstract
This work presents a simulation framework developed under the widely used Robot Operating System (ROS) to enable the validation of robotics systems and gas sensing algorithms under realistic environments. The framework is rooted in the principles of computational fluid dynamics and filament dispersion
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This work presents a simulation framework developed under the widely used Robot Operating System (ROS) to enable the validation of robotics systems and gas sensing algorithms under realistic environments. The framework is rooted in the principles of computational fluid dynamics and filament dispersion theory, modeling wind flow and gas dispersion in 3D real-world scenarios (i.e., accounting for walls, furniture, etc.). Moreover, it integrates the simulation of different environmental sensors, such as metal oxide gas sensors, photo ionization detectors, or anemometers. We illustrate the potential and applicability of the proposed tool by presenting a simulation case in a complex and realistic office-like environment where gas leaks of different chemicals occur simultaneously. Furthermore, we accomplish quantitative and qualitative validation by comparing our simulated results against real-world data recorded inside a wind tunnel where methane was released under different wind flow profiles. Based on these results, we conclude that our simulation framework can provide a good approximation to real world measurements when advective airflows are present in the environment. Full article
(This article belongs to the Section Chemical Sensors)
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Open AccessArticle Adenosine Triphosphate-Encapsulated Liposomes with Plasmonic Nanoparticles for Surface Enhanced Raman Scattering-Based Immunoassays
Sensors 2017, 17(7), 1480; doi:10.3390/s17071480
Received: 22 May 2017 / Revised: 21 June 2017 / Accepted: 21 June 2017 / Published: 23 June 2017
PDF Full-text (2076 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
In this study, we prepared adenosine triphosphate (ATP) encapsulated liposomes, and assessed their applicability for the surface enhanced Raman scattering (SERS)-based assays with gold-silver alloy (Au@Ag)-assembled silica nanoparticles (NPs; SiO2@Au@Ag). The liposomes were prepared by the thin film hydration method from
[...] Read more.
In this study, we prepared adenosine triphosphate (ATP) encapsulated liposomes, and assessed their applicability for the surface enhanced Raman scattering (SERS)-based assays with gold-silver alloy (Au@Ag)-assembled silica nanoparticles (NPs; SiO2@Au@Ag). The liposomes were prepared by the thin film hydration method from a mixture of l-α-phosphatidylcholine, cholesterol, and PE-PEG2000 in chloroform; evaporating the solvent, followed by hydration of the resulting thin film with ATP in phosphate-buffered saline (PBS). Upon lysis of the liposome, the SERS intensity of the SiO2@Au@Ag NPs increased with the logarithm of number of ATP-encapsulated liposomes after lysis in the range of 8 × 106 to 8 × 1010. The detection limit of liposome was calculated to be 1.3 × 10−17 mol. The successful application of ATP-encapsulated liposomes to SiO2@Au@Ag NPs based SERS analysis has opened a new avenue for Raman label chemical (RCL)-encapsulated liposome-enhanced SERS-based immunoassays. Full article
(This article belongs to the Special Issue Novel Approaches to Biosensing with Nanoparticles)
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Open AccessArticle An Operational In Situ Soil Moisture & Soil Temperature Monitoring Network for West Wales, UK: The WSMN Network
Sensors 2017, 17(7), 1481; doi:10.3390/s17071481
Received: 20 February 2017 / Revised: 9 June 2017 / Accepted: 13 June 2017 / Published: 23 June 2017
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Abstract
This paper describes a soil moisture dataset that has been collecting ground measurements of soil moisture, soil temperature and related parameters for west Wales, United Kingdom. Already acquired in situ data have been archived to the autonomous Wales Soil Moisture Network (WSMN) since
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This paper describes a soil moisture dataset that has been collecting ground measurements of soil moisture, soil temperature and related parameters for west Wales, United Kingdom. Already acquired in situ data have been archived to the autonomous Wales Soil Moisture Network (WSMN) since its foundation in July 2011. The sites from which measurements are being collected represent a range of conditions typical of the Welsh environment, with climate ranging from oceanic to temperate and a range of the most typical land use/cover types found in Wales. At present, WSMN consists of a total of nine monitoring sites across the area with a concentration of sites in three sub-areas around the region of Aberystwyth located in Mid-Wales. The dataset of composed of 0–5 (or 0–10) cm soil moisture, soil temperature, precipitation, and other ancillary data. WSMN data are provided openly to the public via the International Soil Moisture Network (ISMN) platform. At present, WSMN is also rapidly expanding thanks to funding obtained recently which allows more monitoring sites to be added to the network to the wider community interested in using its data. Full article
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Open AccessArticle An Efficient User Authentication and User Anonymity Scheme with Provably Security for IoT-Based Medical Care System
Sensors 2017, 17(7), 1482; doi:10.3390/s17071482
Received: 28 May 2017 / Revised: 21 June 2017 / Accepted: 21 June 2017 / Published: 23 June 2017
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Abstract
In recent years, with the increase in degenerative diseases and the aging population in advanced countries, demands for medical care of older or solitary people have increased continually in hospitals and healthcare institutions. Applying wireless sensor networks for the IoT-based telemedicine system enables
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In recent years, with the increase in degenerative diseases and the aging population in advanced countries, demands for medical care of older or solitary people have increased continually in hospitals and healthcare institutions. Applying wireless sensor networks for the IoT-based telemedicine system enables doctors, caregivers or families to monitor patients’ physiological conditions at anytime and anyplace according to the acquired information. However, transmitting physiological data through the Internet concerns the personal privacy of patients. Therefore, before users can access medical care services in IoT-based medical care system, they must be authenticated. Typically, user authentication and data encryption are most critical for securing network communications over a public channel between two or more participants. In 2016, Liu and Chung proposed a bilinear pairing-based password authentication scheme for wireless healthcare sensor networks. They claimed their authentication scheme cannot only secure sensor data transmission, but also resist various well-known security attacks. In this paper, we demonstrate that Liu–Chung’s scheme has some security weaknesses, and we further present an improved secure authentication and data encryption scheme for the IoT-based medical care system, which can provide user anonymity and prevent the security threats of replay and password/sensed data disclosure attacks. Moreover, we modify the authentication process to reduce redundancy in protocol design, and the proposed scheme is more efficient in performance compared with previous related schemes. Finally, the proposed scheme is provably secure in the random oracle model under ECDHP. Full article
(This article belongs to the Special Issue Selected Papers from IEEE ICASI 2017)
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Open AccessArticle Relationship between Remote Sensing Data, Plant Biomass and Soil Nitrogen Dynamics in Intensively Managed Grasslands under Controlled Conditions
Sensors 2017, 17(7), 1483; doi:10.3390/s17071483
Received: 13 April 2017 / Accepted: 18 June 2017 / Published: 23 June 2017
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Abstract
The sustainable use of grasslands in intensive farming systems aims to optimize nitrogen (N) inputs to increase crop yields and decrease harmful losses to the environment at the same time. To achieve this, simple optical sensors may provide a non-destructive, time- and cost-effective
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The sustainable use of grasslands in intensive farming systems aims to optimize nitrogen (N) inputs to increase crop yields and decrease harmful losses to the environment at the same time. To achieve this, simple optical sensors may provide a non-destructive, time- and cost-effective tool for estimating plant biomass in the field, considering spatial and temporal variability. However, the plant growth and related N uptake is affected by the available N in the soil, and therefore, N mineralization and N losses. These soil N dynamics and N losses are affected by the N input and environmental conditions, and cannot easily be determined non-destructively. Therefore, the question arises: whether a relationship can be depicted between N fertilizer levels, plant biomass and N dynamics as indicated by nitrous oxide (N2O) losses and inorganic N levels. We conducted a standardized greenhouse experiment to explore the potential of spectral measurements for analyzing yield response, N mineralization and N2O emissions in a permanent grassland. Ryegrass was subjected to four mineral fertilizer input levels over 100 days (four harvests) under controlled environmental conditions. The soil temperature and moisture content were automatically monitored, and the emission rates of N2O and carbon dioxide (CO2) were detected frequently. Spectral measurements of the swards were performed directly before harvesting. The normalized difference vegetation index (NDVI) and simple ratio (SR) were moderately correlated with an increasing biomass as affected by fertilization level. Furthermore, we found a non-linear response of increasing N2O emissions to elevated fertilizer levels. Moreover, inorganic N and extractable organic N levels at the end of the experiment tended to increase with the increasing N fertilizer addition. However, microbial biomass C and CO2 efflux showed no significant differences among fertilizer treatments, reflecting no substantial changes in the soil biological pool size and the extent of the C mineralization. Neither the NDVI nor SR, nor the plant biomass, were related to cumulative N2O emissions or inorganic N at harvesting. Our results verify the usefulness of optical sensors for biomass detection, and show the difficulty in linking spectral measurements of plant traits to N processes in the soil, despite that the latter affects the former. Full article
(This article belongs to the Special Issue Precision Agriculture and Remote Sensing Data Fusion)
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Open AccessFeature PaperArticle Probing the Interaction of Dielectric Nanoparticles with Supported Lipid Membrane Coatings on Nanoplasmonic Arrays
Sensors 2017, 17(7), 1484; doi:10.3390/s17071484
Received: 31 May 2017 / Revised: 20 June 2017 / Accepted: 20 June 2017 / Published: 23 June 2017
Cited by 1 | PDF Full-text (2082 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
The integration of supported lipid membranes with surface-based nanoplasmonic arrays provides a powerful sensing approach to investigate biointerfacial phenomena at membrane interfaces. While a growing number of lipid vesicles, protein, and nucleic acid systems have been explored with nanoplasmonic sensors, there has been
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The integration of supported lipid membranes with surface-based nanoplasmonic arrays provides a powerful sensing approach to investigate biointerfacial phenomena at membrane interfaces. While a growing number of lipid vesicles, protein, and nucleic acid systems have been explored with nanoplasmonic sensors, there has been only very limited investigation of the interactions between solution-phase nanomaterials and supported lipid membranes. Herein, we established a surface-based localized surface plasmon resonance (LSPR) sensing platform for probing the interaction of dielectric nanoparticles with supported lipid bilayer (SLB)-coated, plasmonic nanodisk arrays. A key emphasis was placed on controlling membrane functionality by tuning the membrane surface charge vis-à-vis lipid composition. The optical sensing properties of the bare and SLB-coated sensor surfaces were quantitatively compared, and provided an experimental approach to evaluate nanoparticle–membrane interactions across different SLB platforms. While the interaction of negatively-charged silica nanoparticles (SiNPs) with a zwitterionic SLB resulted in monotonic adsorption, a stronger interaction with a positively-charged SLB resulted in adsorption and lipid transfer from the SLB to the SiNP surface, in turn influencing the LSPR measurement responses based on the changing spatial proximity of transferred lipids relative to the sensor surface. Precoating SiNPs with bovine serum albumin (BSA) suppressed lipid transfer, resulting in monotonic adsorption onto both zwitterionic and positively-charged SLBs. Collectively, our findings contribute a quantitative understanding of how supported lipid membrane coatings influence the sensing performance of nanoplasmonic arrays, and demonstrate how the high surface sensitivity of nanoplasmonic sensors is well-suited for detecting the complex interactions between nanoparticles and lipid membranes. Full article
(This article belongs to the Special Issue Micro and Nanofabrication Technologies for Biosensors)
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Open AccessArticle A Novel Wearable Forehead EOG Measurement System for Human Computer Interfaces
Sensors 2017, 17(7), 1485; doi:10.3390/s17071485
Received: 2 May 2017 / Revised: 18 June 2017 / Accepted: 20 June 2017 / Published: 23 June 2017
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Abstract
Amyotrophic lateral sclerosis (ALS) patients whose voluntary muscles are paralyzed commonly communicate with the outside world using eye movement. There have been many efforts to support this method of communication by tracking or detecting eye movement. An electrooculogram (EOG), an electro-physiological signal, is
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Amyotrophic lateral sclerosis (ALS) patients whose voluntary muscles are paralyzed commonly communicate with the outside world using eye movement. There have been many efforts to support this method of communication by tracking or detecting eye movement. An electrooculogram (EOG), an electro-physiological signal, is generated by eye movements and can be measured with electrodes placed around the eye. In this study, we proposed a new practical electrode position on the forehead to measure EOG signals, and we developed a wearable forehead EOG measurement system for use in Human Computer/Machine interfaces (HCIs/HMIs). Four electrodes, including the ground electrode, were placed on the forehead. The two channels were arranged vertically and horizontally, sharing a positive electrode. Additionally, a real-time eye movement classification algorithm was developed based on the characteristics of the forehead EOG. Three applications were employed to evaluate the proposed system: a virtual keyboard using a modified Bremen BCI speller and an automatic sequential row-column scanner, and a drivable power wheelchair. The mean typing speeds of the modified Bremen brain–computer interface (BCI) speller and automatic row-column scanner were 10.81 and 7.74 letters per minute, and the mean classification accuracies were 91.25% and 95.12%, respectively. In the power wheelchair demonstration, the user drove the wheelchair through an 8-shape course without collision with obstacles. Full article
(This article belongs to the Special Issue Biomedical Sensors and Systems 2017)
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Open AccessArticle Context Relevant Prediction Model for COPD Domain Using Bayesian Belief Network
Sensors 2017, 17(7), 1486; doi:10.3390/s17071486
Received: 1 May 2017 / Revised: 13 June 2017 / Accepted: 14 June 2017 / Published: 23 June 2017
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Abstract
In the last three decades, researchers have examined extensively how context-aware systems can assist people, specifically those suffering from incurable diseases, to help them cope with their medical illness. Over the years, a huge number of studies on Chronic Obstructive Pulmonary Disease (COPD)
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In the last three decades, researchers have examined extensively how context-aware systems can assist people, specifically those suffering from incurable diseases, to help them cope with their medical illness. Over the years, a huge number of studies on Chronic Obstructive Pulmonary Disease (COPD) have been published. However, how to derive relevant attributes and early detection of COPD exacerbations remains a challenge. In this research work, we will use an efficient algorithm to select relevant attributes where there is no proper approach in this domain. Such algorithm predicts exacerbations with high accuracy by adding discretization process, and organizes the pertinent attributes in priority order based on their impact to facilitate the emergency medical treatment. In this paper, we propose an extension of our existing Helper Context-Aware Engine System (HCES) for COPD. This project uses Bayesian network algorithm to depict the dependency between the COPD symptoms (attributes) in order to overcome the insufficiency and the independency hypothesis of naïve Bayesian. In addition, the dependency in Bayesian network is realized using TAN algorithm rather than consulting pneumologists. All these combined algorithms (discretization, selection, dependency, and the ordering of the relevant attributes) constitute an effective prediction model, comparing to effective ones. Moreover, an investigation and comparison of different scenarios of these algorithms are also done to verify which sequence of steps of prediction model gives more accurate results. Finally, we designed and validated a computer-aided support application to integrate different steps of this model. The findings of our system HCES has shown promising results using Area Under Receiver Operating Characteristic (AUC = 81.5%). Full article
(This article belongs to the Special Issue Context Aware Environments and Applications)
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Open AccessArticle An Energy-Efficient Multi-Tier Architecture for Fall Detection on Smartphones
Sensors 2017, 17(7), 1487; doi:10.3390/s17071487
Received: 11 April 2017 / Revised: 12 June 2017 / Accepted: 17 June 2017 / Published: 23 June 2017
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Abstract
Automatic detection of fall events is vital to providing fast medical assistance to the causality, particularly when the injury causes loss of consciousness. Optimization of the energy consumption of mobile applications, especially those which run 24/7 in the background, is essential for longer
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Automatic detection of fall events is vital to providing fast medical assistance to the causality, particularly when the injury causes loss of consciousness. Optimization of the energy consumption of mobile applications, especially those which run 24/7 in the background, is essential for longer use of smartphones. In order to improve energy-efficiency without compromising on the fall detection performance, we propose a novel 3-tier architecture that combines simple thresholding methods with machine learning algorithms. The proposed method is implemented on a mobile application, called uSurvive, for Android smartphones. It runs as a background service and monitors the activities of a person in daily life and automatically sends a notification to the appropriate authorities and/or user defined contacts when it detects a fall. The performance of the proposed method was evaluated in terms of fall detection performance and energy consumption. Real life performance tests conducted on two different models of smartphone demonstrate that our 3-tier architecture with feature reduction could save up to 62% of energy compared to machine learning only solutions. In addition to this energy saving, the hybrid method has a 93% of accuracy, which is superior to thresholding methods and better than machine learning only solutions. Full article
(This article belongs to the Section Sensor Networks)
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Open AccessArticle Slab Waveguide and Optical Fibers for Novel Plasmonic Sensor Configurations
Sensors 2017, 17(7), 1488; doi:10.3390/s17071488
Received: 6 May 2017 / Revised: 15 June 2017 / Accepted: 22 June 2017 / Published: 24 June 2017
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Abstract
The use of plasmonic sensor devices often requires replaceable parts and disposable chips for easy, fast and on-site detection analysis. In light of these requests, we propose a novel low-cost surface plasmon resonance sensor platform for possible selective detection of analytes in aqueous
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The use of plasmonic sensor devices often requires replaceable parts and disposable chips for easy, fast and on-site detection analysis. In light of these requests, we propose a novel low-cost surface plasmon resonance sensor platform for possible selective detection of analytes in aqueous solutions. It is based on a Polymethyl methacrylate (PMMA) slab waveguide with a thin gold film on the top surface inserted in a special holder, designed to produce the plasmonic resonance at the gold-dielectric interface. A wide-band light is launched in the PMMA slab waveguide through a trench realized in the holder directly, and illuminated with a PMMA plastic optical fiber (POF) to excite surface Plasmon waves. The output light is then collected by another PMMA POF kept at the end of the slab at an angle of 90° to the trench, and carried to a spectrometer. In this configuration, the trench has been used because a large incident angle is required for surface plasmon resonance excitation. The preliminary results showed that the sensor’s performances make it suitable for bio-chemical applications. The easy replacement of the chip allows for the production of an engineered platform by simplifying the measurement procedures. Full article
(This article belongs to the Special Issue Surface Plasmon Resonance Sensing)
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Open AccessArticle A Direct Georeferencing Method for Terrestrial Laser Scanning Using GNSS Data and the Vertical Deflection from Global Earth Gravity Models
Sensors 2017, 17(7), 1489; doi:10.3390/s17071489
Received: 8 May 2017 / Revised: 14 June 2017 / Accepted: 21 June 2017 / Published: 24 June 2017
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Abstract
Terrestrial laser scanning is an efficient technique in providing highly accurate point clouds for various geoscience applications. The point clouds have to be transformed to a well-defined reference frame, such as the global Geodetic Reference System 1980. The transformation to the geocentric coordinate
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Terrestrial laser scanning is an efficient technique in providing highly accurate point clouds for various geoscience applications. The point clouds have to be transformed to a well-defined reference frame, such as the global Geodetic Reference System 1980. The transformation to the geocentric coordinate frame is based on estimating seven Helmert parameters using several GNSS (Global Navigation Satellite System) referencing points. This paper proposes a method for direct point cloud georeferencing that provides coordinates in the geocentric frame. The proposed method employs the vertical deflection from an external global Earth gravity model and thus demands a minimum number of GNSS measurements. The proposed method can be helpful when the number of georeferencing GNSS points is limited, for instance in city corridors. It needs only two georeferencing points. The validation of the method in a field test reveals that the differences between the classical georefencing and the proposed method amount at maximum to 7 mm with the standard deviation of 8 mm for all of three coordinate components. The proposed method may serve as an alternative for the laser scanning data georeferencing, especially when the number of GNSS points is insufficient for classical methods. Full article
(This article belongs to the Section Remote Sensors)
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Open AccessArticle A Real-Time Contactless Pulse Rate and Motion Status Monitoring System Based on Complexion Tracking
Sensors 2017, 17(7), 1490; doi:10.3390/s17071490
Received: 3 May 2017 / Revised: 12 June 2017 / Accepted: 20 June 2017 / Published: 24 June 2017
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Abstract
Subject movement and a dark environment will increase the difficulty of image-based contactless pulse rate detection. In this paper, we detected the subject’s motion status based on complexion tracking and proposed a motion index (MI) to filter motion artifacts in order to increase
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Subject movement and a dark environment will increase the difficulty of image-based contactless pulse rate detection. In this paper, we detected the subject’s motion status based on complexion tracking and proposed a motion index (MI) to filter motion artifacts in order to increase pulse rate measurement accuracy. Additionally, we integrated the near infrared (NIR) LEDs with the adopted sensor and proposed an effective method to measure the pulse rate in a dark environment. To achieve real-time data processing, the proposed framework is constructed on a Field Programmable Gate Array (FPGA) platform. Next, the instant pulse rate and motion status are transmitted to a smartphone for remote monitoring. The experiment results showed the error of the pulse rate detection to be within −3.44 to +4.53 bpm under sufficient ambient light and −2.96 to + 4.24 bpm for night mode detection, when the moving speed is higher than 14.45 cm/s. These results demonstrate that the proposed method can improve the robustness of image-based contactless pulse rate detection despite subject movement and a dark environment. Full article
(This article belongs to the Special Issue Advanced Physiological Sensing)
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Open AccessArticle Simultaneous Calibration: A Joint Optimization Approach for Multiple Kinect and External Cameras
Sensors 2017, 17(7), 1491; doi:10.3390/s17071491
Received: 5 April 2017 / Revised: 27 May 2017 / Accepted: 20 June 2017 / Published: 24 June 2017
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Abstract
Camera calibration is a crucial problem in many applications, such as 3D reconstruction, structure from motion, object tracking and face alignment. Numerous methods have been proposed to solve the above problem with good performance in the last few decades. However, few methods are
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Camera calibration is a crucial problem in many applications, such as 3D reconstruction, structure from motion, object tracking and face alignment. Numerous methods have been proposed to solve the above problem with good performance in the last few decades. However, few methods are targeted at joint calibration of multi-sensors (more than four devices), which normally is a practical issue in the real-time systems. In this paper, we propose a novel method and a corresponding workflow framework to simultaneously calibrate relative poses of a Kinect and three external cameras. By optimizing the final cost function and adding corresponding weights to the external cameras in different locations, an effective joint calibration of multiple devices is constructed. Furthermore, the method is tested in a practical platform, and experiment results show that the proposed joint calibration method can achieve a satisfactory performance in a project real-time system and its accuracy is higher than the manufacturer’s calibration. Full article
(This article belongs to the Special Issue Imaging Depth Sensors—Sensors, Algorithms and Applications)
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Open AccessArticle Static and Dynamic Accuracy of an Innovative Miniaturized Wearable Platform for Short Range Distance Measurements for Human Movement Applications
Sensors 2017, 17(7), 1492; doi:10.3390/s17071492
Received: 8 May 2017 / Revised: 14 June 2017 / Accepted: 21 June 2017 / Published: 24 June 2017
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Abstract
Magneto-inertial measurement units (MIMU) are a suitable solution to assess human motor performance both indoors and outdoors. However, relevant quantities such as step width and base of support, which play an important role in gait stability, cannot be directly measured using MIMU alone.
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Magneto-inertial measurement units (MIMU) are a suitable solution to assess human motor performance both indoors and outdoors. However, relevant quantities such as step width and base of support, which play an important role in gait stability, cannot be directly measured using MIMU alone. To overcome this limitation, we developed a wearable platform specifically designed for human movement analysis applications, which integrates a MIMU and an Infrared Time-of-Flight proximity sensor (IR-ToF), allowing for the estimate of inter-object distance. We proposed a thorough testing protocol for evaluating the IR-ToF sensor performances under experimental conditions resembling those encountered during gait. In particular, we tested the sensor performance for different (i) target colors; (ii) sensor-target distances (up to 200 mm) and (iii) sensor-target angles of incidence (AoI) (up to 60 ). Both static and dynamic conditions were analyzed. A pendulum, simulating the oscillation of a human leg, was used to generate highly repeatable oscillations with a maximum angular velocity of 6 rad/s. Results showed that the IR-ToF proximity sensor was not sensitive to variations of both distance and target color (except for black). Conversely, a relationship between error magnitude and AoI values was found. For AoI equal to 0 , the IR-ToF sensor performed equally well both in static and dynamic acquisitions with a distance mean absolute error <1.5 mm. Errors increased up to 3.6 mm (static) and 11.9 mm (dynamic) for AoI equal to ± 30 , and up to 7.8 mm (static) and 25.6 mm (dynamic) for AoI equal to ± 60 . In addition, the wearable platform was used during a preliminary experiment for the estimation of the inter-foot distance on a single healthy subject while walking. In conclusion, the combination of magneto-inertial unit and IR-ToF technology represents a valuable alternative solution in terms of accuracy, sampling frequency, dimension and power consumption, compared to existing technologies. Full article
(This article belongs to the Special Issue Inertial Sensors for Positioning and Navigation)
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Open AccessArticle A Spaceborne Synthetic Aperture Radar Partial Fixed-Point Imaging System Using a Field- Programmable Gate Array−Application-Specific Integrated Circuit Hybrid Heterogeneous Parallel Acceleration Technique
Sensors 2017, 17(7), 1493; doi:10.3390/s17071493
Received: 21 April 2017 / Revised: 22 June 2017 / Accepted: 23 June 2017 / Published: 24 June 2017
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Abstract
With the development of satellite load technology and very large scale integrated (VLSI) circuit technology, onboard real-time synthetic aperture radar (SAR) imaging systems have become a solution for allowing rapid response to disasters. A key goal of the onboard SAR imaging system design
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With the development of satellite load technology and very large scale integrated (VLSI) circuit technology, onboard real-time synthetic aperture radar (SAR) imaging systems have become a solution for allowing rapid response to disasters. A key goal of the onboard SAR imaging system design is to achieve high real-time processing performance with severe size, weight, and power consumption constraints. In this paper, we analyse the computational burden of the commonly used chirp scaling (CS) SAR imaging algorithm. To reduce the system hardware cost, we propose a partial fixed-point processing scheme. The fast Fourier transform (FFT), which is the most computation-sensitive operation in the CS algorithm, is processed with fixed-point, while other operations are processed with single precision floating-point. With the proposed fixed-point processing error propagation model, the fixed-point processing word length is determined. The fidelity and accuracy relative to conventional ground-based software processors is verified by evaluating both the point target imaging quality and the actual scene imaging quality. As a proof of concept, a field- programmable gate array−application-specific integrated circuit (FPGA-ASIC) hybrid heterogeneous parallel accelerating architecture is designed and realized. The customized fixed-point FFT is implemented using the 130 nm complementary metal oxide semiconductor (CMOS) technology as a co-processor of the Xilinx xc6vlx760t FPGA. A single processing board requires 12 s and consumes 21 W to focus a 50-km swath width, 5-m resolution stripmap SAR raw data with a granularity of 16,384 × 16,384. Full article
(This article belongs to the Section Remote Sensors)
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Open AccessArticle Tunable Fano Resonance in Asymmetric MIM Waveguide Structure
Sensors 2017, 17(7), 1494; doi:10.3390/s17071494
Received: 5 May 2017 / Revised: 14 June 2017 / Accepted: 20 June 2017 / Published: 25 June 2017
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Abstract
A plasmonic waveguide coupled system that uses a metal-insulator-metal (MIM) waveguide with two silver baffles and a coupled ring cavity is proposed in this study. The transmission properties of the plasmonic system were investigated using the finite element method. The simulation results show
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A plasmonic waveguide coupled system that uses a metal-insulator-metal (MIM) waveguide with two silver baffles and a coupled ring cavity is proposed in this study. The transmission properties of the plasmonic system were investigated using the finite element method. The simulation results show a Fano profile in the transmission spectrum, which was caused by the interaction of the broadband resonance of the Fabry-Perot (F-P) cavity and the narrow band resonance of the ring cavity. The Fabry-Perot (F-P) cavity in this case was formed by two silver baffles dividing the MIM waveguide. The maximum sensitivity of 718 nm/RIU and the maximum figure of merit of 4354 were achieved. Furthermore, the effects of the structural parameters of the F-P cavity and the ring cavity on the transmission properties of the plasmonic system were analyzed. The results can provide a guide for designing highly sensitive on-chip sensors based on surface plasmon polaritons. Full article
(This article belongs to the Section Physical Sensors)
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Open AccessArticle A Novel Range Compression Algorithm for Resolution Enhancement in GNSS-SARs
Sensors 2017, 17(7), 1496; doi:10.3390/s17071496
Received: 23 March 2017 / Revised: 22 June 2017 / Accepted: 22 June 2017 / Published: 25 June 2017
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Abstract
In this paper, a novel range compression algorithm for enhancing range resolutions of a passive Global Navigation Satellite System-based Synthetic Aperture Radar (GNSS-SAR) is proposed. In the proposed algorithm, within each azimuth bin, firstly range compression is carried out by correlating a reflected
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In this paper, a novel range compression algorithm for enhancing range resolutions of a passive Global Navigation Satellite System-based Synthetic Aperture Radar (GNSS-SAR) is proposed. In the proposed algorithm, within each azimuth bin, firstly range compression is carried out by correlating a reflected GNSS intermediate frequency (IF) signal with a synchronized direct GNSS base-band signal in the range domain. Thereafter, spectrum equalization is applied to the compressed results for suppressing side lobes to obtain a final range-compressed signal. Both theoretical analysis and simulation results have demonstrated that significant range resolution improvement in GNSS-SAR images can be achieved by the proposed range compression algorithm, compared to the conventional range compression algorithm. Full article
(This article belongs to the Section Remote Sensors)
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Open AccessArticle A Multi-Resolution Mode CMOS Image Sensor with a Novel Two-Step Single-Slope ADC for Intelligent Surveillance Systems
Sensors 2017, 17(7), 1497; doi:10.3390/s17071497
Received: 9 May 2017 / Revised: 8 June 2017 / Accepted: 24 June 2017 / Published: 25 June 2017
Cited by 2 | PDF Full-text (3534 KB) | HTML Full-text | XML Full-text
Abstract
In this paper, we present a multi-resolution mode CMOS image sensor (CIS) for intelligent surveillance system (ISS) applications. A low column fixed-pattern noise (CFPN) comparator is proposed in 8-bit two-step single-slope analog-to-digital converter (TSSS ADC) for the CIS that supports normal, 1/2, 1/4,
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In this paper, we present a multi-resolution mode CMOS image sensor (CIS) for intelligent surveillance system (ISS) applications. A low column fixed-pattern noise (CFPN) comparator is proposed in 8-bit two-step single-slope analog-to-digital converter (TSSS ADC) for the CIS that supports normal, 1/2, 1/4, 1/8, 1/16, 1/32, and 1/64 mode of pixel resolution. We show that the scaled-resolution images enable CIS to reduce total power consumption while images hold steady without events. A prototype sensor of 176 × 144 pixels has been fabricated with a 0.18 μm 1-poly 4-metal CMOS process. The area of 4-shared 4T-active pixel sensor (APS) is 4.4 μm × 4.4 μm and the total chip size is 2.35 mm × 2.35 mm. The maximum power consumption is 10 mW (with full resolution) with supply voltages of 3.3 V (analog) and 1.8 V (digital) and 14 frame/s of frame rates. Full article
(This article belongs to the Special Issue Image Sensors)
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Open AccessArticle Energy Efficiency Optimization for Downlink Cloud RAN with Limited Fronthaul Capacity
Sensors 2017, 17(7), 1498; doi:10.3390/s17071498
Received: 22 March 2017 / Revised: 22 June 2017 / Accepted: 24 June 2017 / Published: 26 June 2017
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Abstract
In the downlink cloud radio access network (C-RAN), fronthaul compression has been developed to combat the performance bottleneck caused by the capacity-limited fronthaul links. Nevertheless, the state-of-arts focusing on fronthaul compression for spectral efficiency improvement become questionable for energy efficiency (EE) maximization, especially
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In the downlink cloud radio access network (C-RAN), fronthaul compression has been developed to combat the performance bottleneck caused by the capacity-limited fronthaul links. Nevertheless, the state-of-arts focusing on fronthaul compression for spectral efficiency improvement become questionable for energy efficiency (EE) maximization, especially for meeting its requirements of large-scale implementation. Therefore, this paper aims to develop a low-complexity algorithm with closed-form solution for the EE maximization problem in a downlink C-RAN with limited fronthaul capacity. To solve such a non-trivial problem, we first derive an optimal solution using branch-and-bound approach to provide a performance benchmark. Then, by transforming the original problem into a parametric subtractive form, we propose a low-complexity two-layer decentralized (TLD) algorithm. Specifically, a bisection search is involved in the outer layer, while in the inner layer we propose an alternating direction method of multipliers algorithm to find a closed-form solution in a parallel manner with convergence guaranteed. Simulations results demonstrate that the TLD algorithm can achieve near optimal solution, and its EE is much higher than the spectral efficiency maximization one. Furthermore, the optimal and TLD algorithms are also extended to counter the channel error. The results show that the robust algorithms can provide robust performance in the case of lacking perfect channel state information. Full article
(This article belongs to the Section Sensor Networks)
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Open AccessArticle Vicarious Calibration of sUAS Microbolometer Temperature Imagery for Estimation of Radiometric Land Surface Temperature
Sensors 2017, 17(7), 1499; doi:10.3390/s17071499
Received: 20 May 2017 / Revised: 20 June 2017 / Accepted: 20 June 2017 / Published: 26 June 2017
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Abstract
In recent years, the availability of lightweight microbolometer thermal cameras compatible with small unmanned aerial systems (sUAS) has allowed their use in diverse scientific and management activities that require sub-meter pixel resolution. Nevertheless, as with sensors already used in temperature remote sensing (e.g.,
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In recent years, the availability of lightweight microbolometer thermal cameras compatible with small unmanned aerial systems (sUAS) has allowed their use in diverse scientific and management activities that require sub-meter pixel resolution. Nevertheless, as with sensors already used in temperature remote sensing (e.g., Landsat satellites), a radiance atmospheric correction is necessary to estimate land surface temperature. This is because atmospheric conditions at any sUAS flight elevation will have an adverse impact on the image accuracy, derived calculations, and study replicability using the microbolometer technology. This study presents a vicarious calibration methodology (sUAS-specific, time-specific, flight-specific, and sensor-specific) for sUAS temperature imagery traceable back to NIST-standards and current atmospheric correction methods. For this methodology, a three-year data collection campaign with a sUAS called “AggieAir”, developed at Utah State University, was performed for vineyards near Lodi, California, for flights conducted at different times (early morning, Landsat overpass, and mid-afternoon”) and seasonal conditions. From the results of this study, it was found that, despite the spectral response of microbolometer cameras (7.0 to 14.0 μm), it was possible to account for the effects of atmospheric and sUAS operational conditions, regardless of time and weather, to acquire accurate surface temperature data. In addition, it was found that the main atmospheric correction parameters (transmissivity and atmospheric radiance) significantly varied over the course of a day. These parameters fluctuated the most in early morning and partially stabilized in Landsat overpass and in mid-afternoon times. In terms of accuracy, estimated atmospheric correction parameters presented adequate statistics (confidence bounds under ±0.1 for transmissivity and ±1.2 W/m2/sr/um for atmospheric radiance, with a range of RMSE below 1.0 W/m2/sr/um) for all sUAS flights. Differences in estimated temperatures between original thermal image and the vicarious calibration procedure reported here were estimated from −5 °C to 10 °C for early morning, and from 0 to 20 °C for Landsat overpass and mid-afternoon times. Full article
(This article belongs to the Special Issue UAV or Drones for Remote Sensing Applications)
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Open AccessArticle SLAE–CPS: Smart Lean Automation Engine Enabled by Cyber-Physical Systems Technologies
Sensors 2017, 17(7), 1500; doi:10.3390/s17071500
Received: 21 May 2017 / Revised: 21 June 2017 / Accepted: 22 June 2017 / Published: 28 June 2017
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Abstract
In the context of Industry 4.0, the demand for the mass production of highly customized products will lead to complex products and an increasing demand for production system flexibility. Simply implementing lean production-based human-centered production or high automation to improve system flexibility is
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In the context of Industry 4.0, the demand for the mass production of highly customized products will lead to complex products and an increasing demand for production system flexibility. Simply implementing lean production-based human-centered production or high automation to improve system flexibility is insufficient. Currently, lean automation (Jidoka) that utilizes cyber-physical systems (CPS) is considered a cost-efficient and effective approach for improving system flexibility under shrinking global economic conditions. Therefore, a smart lean automation engine enabled by CPS technologies (SLAE–CPS), which is based on an analysis of Jidoka functions and the smart capacity of CPS technologies, is proposed in this study to provide an integrated and standardized approach to design and implement a CPS-based smart Jidoka system. A set of comprehensive architecture and standardized key technologies should be presented to achieve the above-mentioned goal. Therefore, a distributed architecture that joins service-oriented architecture, agent, function block (FB), cloud, and Internet of things is proposed to support the flexible configuration, deployment, and performance of SLAE–CPS. Then, several standardized key techniques are proposed under this architecture. The first one is for converting heterogeneous physical data into uniform services for subsequent abnormality analysis and detection. The second one is a set of Jidoka scene rules, which is abstracted based on the analysis of the operator, machine, material, quality, and other factors in different time dimensions. These Jidoka rules can support executive FBs in performing different Jidoka functions. Finally, supported by the integrated and standardized approach of our proposed engine, a case study is conducted to verify the current research results. The proposed SLAE–CPS can serve as an important reference value for combining the benefits of innovative technology and proper methodology. Full article
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Open AccessArticle Spatiotemporal Recurrent Convolutional Networks for Traffic Prediction in Transportation Networks
Sensors 2017, 17(7), 1501; doi:10.3390/s17071501
Received: 4 May 2017 / Revised: 17 June 2017 / Accepted: 21 June 2017 / Published: 26 June 2017
Cited by 4 | PDF Full-text (4360 KB) | HTML Full-text | XML Full-text
Abstract
Predicting large-scale transportation network traffic has become an important and challenging topic in recent decades. Inspired by the domain knowledge of motion prediction, in which the future motion of an object can be predicted based on previous scenes, we propose a network grid
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Predicting large-scale transportation network traffic has become an important and challenging topic in recent decades. Inspired by the domain knowledge of motion prediction, in which the future motion of an object can be predicted based on previous scenes, we propose a network grid representation method that can retain the fine-scale structure of a transportation network. Network-wide traffic speeds are converted into a series of static images and input into a novel deep architecture, namely, spatiotemporal recurrent convolutional networks (SRCNs), for traffic forecasting. The proposed SRCNs inherit the advantages of deep convolutional neural networks (DCNNs) and long short-term memory (LSTM) neural networks. The spatial dependencies of network-wide traffic can be captured by DCNNs, and the temporal dynamics can be learned by LSTMs. An experiment on a Beijing transportation network with 278 links demonstrates that SRCNs outperform other deep learning-based algorithms in both short-term and long-term traffic prediction. Full article
(This article belongs to the Section Sensor Networks)
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Open AccessArticle Statistical Analysis of the Main Configuration Parameters of the Network Dynamic and Adaptive Radio Protocol (DARP)
Sensors 2017, 17(7), 1502; doi:10.3390/s17071502
Received: 18 May 2017 / Revised: 15 June 2017 / Accepted: 16 June 2017 / Published: 26 June 2017
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Abstract
The present work analyses the wireless sensor network protocol (DARP) and the impact of different configuration parameter sets on its performance. Different scenarios have been considered, in order to gain a better understanding of the influence of the configuration on network protocols. The
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The present work analyses the wireless sensor network protocol (DARP) and the impact of different configuration parameter sets on its performance. Different scenarios have been considered, in order to gain a better understanding of the influence of the configuration on network protocols. The developed statistical analysis is based on the method known as Analysis of Variance (ANOVA), which focuses on the effect of the configuration on the performance of DARP. Three main dependent variables were considered: number of control messages sent during the set-up time, energy consumption and convergence time. A total of 20,413 simulations were carried out to ensure greater robustness in the statistical conclusions. The main goal of this work is to discover the most critical configuration parameters for the protocol, with a view to potential applications in Smart City type scenarios. Full article
(This article belongs to the Special Issue Selected Papers from UCAmI 2016)
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Open AccessArticle Temperature Dependence of the Rayleigh Brillouin Spectrum Linewidth in Air and Nitrogen
Sensors 2017, 17(7), 1503; doi:10.3390/s17071503
Received: 9 May 2017 / Revised: 10 June 2017 / Accepted: 19 June 2017 / Published: 26 June 2017
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Abstract
The relation between spontaneous Rayleigh Brillouin (SRB) spectrum linewidth, gas temperature, and pressure are analyzed at the temperature range from 220 to 340 K and the pressure range from 0.1 to 1 bar, covering the stratosphere and troposphere relevant for the Earth’s atmosphere
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The relation between spontaneous Rayleigh Brillouin (SRB) spectrum linewidth, gas temperature, and pressure are analyzed at the temperature range from 220 to 340 K and the pressure range from 0.1 to 1 bar, covering the stratosphere and troposphere relevant for the Earth’s atmosphere and for atmospheric Lidar missions. Based on the analysis, a model retrieving gas temperature from directly measured linewidth is established and the accuracy limitations are estimated. Furthermore, some experimental data of air and nitrogen are used to verify the accuracy of the model. As the results show, the retrieved temperature shows good agreement with the reference temperature, and the absolute difference is less than 3 K, which indicates that this method provides a fruitful tool in satellite retrieval to extract the gaseous properties of atmospheres on-line by directly measuring the SRB spectrum linewidth. Full article
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Open AccessArticle Novel Hybrid Scheduling Technique for Sensor Nodes with Mixed Criticality Tasks
Sensors 2017, 17(7), 1504; doi:10.3390/s17071504
Received: 23 May 2017 / Revised: 8 June 2017 / Accepted: 22 June 2017 / Published: 26 June 2017
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Abstract
Sensor networks become increasingly a key technology for complex control applications. Their potential use in safety- and time-critical domains has raised the need for task scheduling mechanisms specially adapted to sensor node specific requirements, often materialized in predictable jitter-less execution of tasks characterized
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Sensor networks become increasingly a key technology for complex control applications. Their potential use in safety- and time-critical domains has raised the need for task scheduling mechanisms specially adapted to sensor node specific requirements, often materialized in predictable jitter-less execution of tasks characterized by different criticality levels. This paper offers an efficient scheduling solution, named Hybrid Hard Real-Time Scheduling (H2RTS), which combines a static, clock driven method with a dynamic, event driven scheduling technique, in order to provide high execution predictability, while keeping a high node Central Processing Unit (CPU) utilization factor. From the detailed, integrated schedulability analysis of the H2RTS, a set of sufficiency tests are introduced and demonstrated based on the processor demand and linear upper bound metrics. The performance and correct behavior of the proposed hybrid scheduling technique have been extensively evaluated and validated both on a simulator and on a sensor mote equipped with ARM7 microcontroller. Full article
(This article belongs to the Section Sensor Networks)
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Open AccessArticle Development of a Computer Writing System Based on EOG
Sensors 2017, 17(7), 1505; doi:10.3390/s17071505
Received: 8 April 2017 / Revised: 21 June 2017 / Accepted: 22 June 2017 / Published: 26 June 2017
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Abstract
The development of a novel computer writing system based on eye movements is introduced herein. A system of these characteristics requires the consideration of three subsystems: (1) A hardware device for the acquisition and transmission of the signals generated by eye movement to
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The development of a novel computer writing system based on eye movements is introduced herein. A system of these characteristics requires the consideration of three subsystems: (1) A hardware device for the acquisition and transmission of the signals generated by eye movement to the computer; (2) A software application that allows, among other functions, data processing in order to minimize noise and classify signals; and (3) A graphical interface that allows the user to write text easily on the computer screen using eye movements only. This work analyzes these three subsystems and proposes innovative and low cost solutions for each one of them. This computer writing system was tested with 20 users and its efficiency was compared to a traditional virtual keyboard. The results have shown an important reduction in the time spent on writing, which can be very useful, especially for people with severe motor disorders. Full article
(This article belongs to the Special Issue Sensors for Ambient Assisted Living, Ubiquitous and Mobile Health)
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Open AccessArticle Tuning the Sensitivity of the PDR5 Promoter-Based Detection of Diclofenac in Yeast Biosensors
Sensors 2017, 17(7), 1506; doi:10.3390/s17071506
Received: 23 May 2017 / Revised: 20 June 2017 / Accepted: 21 June 2017 / Published: 26 June 2017
PDF Full-text (1282 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
The commonly used drug diclofenac is an important environmental anthropogenic pollutant. Currently, detection of diclofenac is mainly based on chemical and physical methods. Here we describe a yeast biosensor that drives the diclofenac-dependent expression of a recombinant fluorescent protein from the authentic promoter
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The commonly used drug diclofenac is an important environmental anthropogenic pollutant. Currently, detection of diclofenac is mainly based on chemical and physical methods. Here we describe a yeast biosensor that drives the diclofenac-dependent expression of a recombinant fluorescent protein from the authentic promoter of the PDR5 gene. This key component of the pleiotropic drug response encodes a multidrug transporter that is involved in cellular detoxification. We analyse the effects on diclofenac sensitivity of artificial PDR5 promoter derivatives in wild-type and various yeast mutant strains. This approach enabled us to generate sensor strains with elevated drug sensitivity. Full article
(This article belongs to the Special Issue Whole Cell-Based Biosensors and Application)
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Open AccessArticle Novel Gas Sensor Arrays Based on High-Q SAM-Modified Piezotransduced Single-Crystal Silicon Bulk Acoustic Resonators
Sensors 2017, 17(7), 1507; doi:10.3390/s17071507
Received: 4 April 2017 / Revised: 9 June 2017 / Accepted: 14 June 2017 / Published: 26 June 2017
PDF Full-text (4558 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
This paper demonstrates a novel micro-size (120 μm × 200 μm) piezoelectric gas sensor based on a piezotransduced single-crystal silicon bulk acoustic resonator (PSBAR). The PSBARs operate at 102 MHz and possess high Q values (about 2000), ensuring the stability of the measurement.
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This paper demonstrates a novel micro-size (120 μm × 200 μm) piezoelectric gas sensor based on a piezotransduced single-crystal silicon bulk acoustic resonator (PSBAR). The PSBARs operate at 102 MHz and possess high Q values (about 2000), ensuring the stability of the measurement. A corresponding gas sensor array is fabricated by integrating three different self-assembled monolayers (SAMs) modified PSBARs. The limit of detection (LOD) for ethanol vapor is demonstrated to be as low as 25 ppm with a sensitivity of about 1.5 Hz/ppm. Two sets of identification code bars based on the sensitivities and the adsorption energy constants are utilized to successfully discriminate isopropanol (IPA), ethanol, hexane and heptane vapors at low and high gas partial pressures, respectively. The proposed sensor array shows the potential to form a portable electronic nose system for volatile organic compound (VOC) differentiation. Full article
(This article belongs to the Special Issue Acoustic Wave Resonator-Based Sensors)
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Open AccessArticle Does RAIM with Correct Exclusion Produce Unbiased Positions?
Sensors 2017, 17(7), 1508; doi:10.3390/s17071508
Received: 18 May 2017 / Revised: 16 June 2017 / Accepted: 22 June 2017 / Published: 26 June 2017
Cited by 1 | PDF Full-text (4221 KB) | HTML Full-text | XML Full-text
Abstract
As the navigation solution of exclusion-based RAIM follows from a combination of least-squares estimation and a statistically based exclusion-process, the computation of the integrity of the navigation solution has to take the propagated uncertainty of the combined estimation-testing procedure into account. In this
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As the navigation solution of exclusion-based RAIM follows from a combination of least-squares estimation and a statistically based exclusion-process, the computation of the integrity of the navigation solution has to take the propagated uncertainty of the combined estimation-testing procedure into account. In this contribution, we analyse, theoretically as well as empirically, the effect that this combination has on the first statistical moment, i.e., the mean, of the computed navigation solution. It will be shown, although statistical testing is intended to remove biases from the data, that biases will always remain under the alternative hypothesis, even when the correct alternative hypothesis is properly identified. The a posteriori exclusion of a biased satellite range from the position solution will therefore never remove the bias in the position solution completely. Full article
(This article belongs to the Section Remote Sensors)
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Open AccessArticle Ultra-Weak Fiber Bragg Grating Sensing Network Coated with Sensitive Material for Multi-Parameter Measurements
Sensors 2017, 17(7), 1509; doi:10.3390/s17071509
Received: 11 May 2017 / Revised: 16 June 2017 / Accepted: 20 June 2017 / Published: 26 June 2017
Cited by 1 | PDF Full-text (7400 KB) | HTML Full-text | XML Full-text
Abstract
A multi-parameter measurement system based on ultra-weak fiber Bragg grating (UFBG) array with sensitive material was proposed and experimentally demonstrated. The UFBG array interrogation principle is time division multiplex technology with two semiconductor optical amplifiers as timing units. Experimental results showed that the
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A multi-parameter measurement system based on ultra-weak fiber Bragg grating (UFBG) array with sensitive material was proposed and experimentally demonstrated. The UFBG array interrogation principle is time division multiplex technology with two semiconductor optical amplifiers as timing units. Experimental results showed that the performance of the proposed UFBG system is almost equal to that of traditional FBG, while the UFBG array system has obvious superiority with potential multiplexing ability for multi-point and multi-parameter measurement. The system experimented on a 144 UFBG array with the reflectivity of UFBG ~0.04% for the four target parameters: hydrogen, humidity, temperature and salinity. Moreover, a uniform solution was customized to divide the cross-sensitivity between temperature and other target parameters. It is expected that this scheme will be capable of handling thousands of multi-parameter sensors in a single fiber. Full article
(This article belongs to the Special Issue Fiber Bragg Grating Based Sensors)
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Open AccessArticle Research on a Novel Exciting Method for a Sandwich Transducer Operating in Longitudinal-Bending Hybrid Modes
Sensors 2017, 17(7), 1510; doi:10.3390/s17071510
Received: 4 May 2017 / Revised: 6 June 2017 / Accepted: 21 June 2017 / Published: 27 June 2017
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Abstract
A novel exciting method for a sandwich type piezoelectric transducer operating in longitudinal-bending hybrid vibration modes is proposed and discussed, in which the piezoelectric elements for the excitations of the longitudinal and bending vibrations share the same axial location, but correspond to different
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A novel exciting method for a sandwich type piezoelectric transducer operating in longitudinal-bending hybrid vibration modes is proposed and discussed, in which the piezoelectric elements for the excitations of the longitudinal and bending vibrations share the same axial location, but correspond to different partitions. Whole-piece type piezoelectric plates with three separated partitions are used, in which the center partitions generate the first longitudinal vibration, while the upper and lower partitions produce the second bending vibration. Detailed comparisons between the proposed exciting method and the traditional one were accomplished by finite element method (FEM) calculations, which were further verified by experiments. Compared with the traditional exciting method using independent longitudinal ceramics and bending ceramics, the proposed method achieves higher electromechanical coupling factors and larger vibration amplitudes, especially for the bending vibration mode. This novel exciting method for longitudinal-bending hybrid vibrations has not changed the structural dimensions of the sandwich transducer, but markedly improves the mechanical output ability, which makes it very helpful and meaningful in designing new piezoelectric actuators operated in longitudinal-bending hybrid vibration modes. Full article
(This article belongs to the Special Issue Piezoelectric Micro- and Nano-Devices)
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Open AccessArticle Flexible Packaging by Film-Assisted Molding for Microintegration of Inertia Sensors
Sensors 2017, 17(7), 1511; doi:10.3390/s17071511
Received: 8 April 2017 / Revised: 12 June 2017 / Accepted: 21 June 2017 / Published: 27 June 2017
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Abstract
Packaging represents an important part in the microintegration of sensors based on microelectromechanical system (MEMS). Besides miniaturization and integration density, functionality and reliability in combination with flexibility in packaging design at moderate costs and consequently high-mix, low-volume production are the main requirements for
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Packaging represents an important part in the microintegration of sensors based on microelectromechanical system (MEMS). Besides miniaturization and integration density, functionality and reliability in combination with flexibility in packaging design at moderate costs and consequently high-mix, low-volume production are the main requirements for future solutions in packaging. This study investigates possibilities employing printed circuit board (PCB-)based assemblies to provide high flexibility for circuit designs together with film-assisted transfer molding (FAM) to package sensors. The feasibility of FAM in combination with PCB and MEMS as a packaging technology for highly sensitive inertia sensors is being demonstrated. The results prove the technology to be a viable method for damage-free packaging of stress- and pressure-sensitive MEMS. Full article
(This article belongs to the Special Issue MEMS and Nano-Sensors)
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Open AccessArticle Integration of Sensors, Controllers and Instruments Using a Novel OPC Architecture
Sensors 2017, 17(7), 1512; doi:10.3390/s17071512
Received: 26 April 2017 / Revised: 9 June 2017 / Accepted: 23 June 2017 / Published: 27 June 2017
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Abstract
The interconnection between sensors, controllers and instruments through a communication network plays a vital role in the performance and effectiveness of a control system. Since its inception in the 90s, the Object Linking and Embedding for Process Control (OPC) protocol has provided open
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The interconnection between sensors, controllers and instruments through a communication network plays a vital role in the performance and effectiveness of a control system. Since its inception in the 90s, the Object Linking and Embedding for Process Control (OPC) protocol has provided open connectivity for monitoring and automation systems. It has been widely used in several environments such as industrial facilities, building and energy automation, engineering education and many others. This paper presents a novel OPC-based architecture to implement automation systems devoted to R&D and educational activities. The proposal is a novel conceptual framework, structured into four functional layers where the diverse components are categorized aiming to foster the systematic design and implementation of automation systems involving OPC communication. Due to the benefits of OPC, the proposed architecture provides features like open connectivity, reliability, scalability, and flexibility. Furthermore, four successful experimental applications of such an architecture, developed at the University of Extremadura (UEX), are reported. These cases are a proof of concept of the ability of this architecture to support interoperability for different domains. Namely, the automation of energy systems like a smart microgrid and photobioreactor facilities, the implementation of a network-accessible industrial laboratory and the development of an educational hardware-in-the-loop platform are described. All cases include a Programmable Logic Controller (PLC) to automate and control the plant behavior, which exchanges operative data (measurements and signals) with a multiplicity of sensors, instruments and supervisory systems under the structure of the novel OPC architecture. Finally, the main conclusions and open research directions are highlighted. Full article
(This article belongs to the Section Sensor Networks)
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Open AccessArticle Analysis of Public Datasets for Wearable Fall Detection Systems
Sensors 2017, 17(7), 1513; doi:10.3390/s17071513
Received: 18 May 2017 / Revised: 16 June 2017 / Accepted: 20 June 2017 / Published: 27 June 2017
Cited by 1 | PDF Full-text (1869 KB) | HTML Full-text | XML Full-text
Abstract
Due to the boom of wireless handheld devices such as smartwatches and smartphones, wearable Fall Detection Systems (FDSs) have become a major focus of attention among the research community during the last years. The effectiveness of a wearable FDS must be contrasted against
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Due to the boom of wireless handheld devices such as smartwatches and smartphones, wearable Fall Detection Systems (FDSs) have become a major focus of attention among the research community during the last years. The effectiveness of a wearable FDS must be contrasted against a wide variety of measurements obtained from inertial sensors during the occurrence of falls and Activities of Daily Living (ADLs). In this regard, the access to public databases constitutes the basis for an open and systematic assessment of fall detection techniques. This paper reviews and appraises twelve existing available data repositories containing measurements of ADLs and emulated falls envisaged for the evaluation of fall detection algorithms in wearable FDSs. The analysis of the found datasets is performed in a comprehensive way, taking into account the multiple factors involved in the definition of the testbeds deployed for the generation of the mobility samples. The study of the traces brings to light the lack of a common experimental benchmarking procedure and, consequently, the large heterogeneity of the datasets from a number of perspectives (length and number of samples, typology of the emulated falls and ADLs, characteristics of the test subjects, features and positions of the sensors, etc.). Concerning this, the statistical analysis of the samples reveals the impact of the sensor range on the reliability of the traces. In addition, the study evidences the importance of the selection of the ADLs and the need of categorizing the ADLs depending on the intensity of the movements in order to evaluate the capability of a certain detection algorithm to discriminate falls from ADLs. Full article
(This article belongs to the Special Issue Wearable and Ambient Sensors for Healthcare and Wellness Applications)
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Open AccessArticle A Force-Sensing System on Legs for Biomimetic Hexapod Robots Interacting with Unstructured Terrain
Sensors 2017, 17(7), 1514; doi:10.3390/s17071514
Received: 7 May 2017 / Revised: 14 June 2017 / Accepted: 22 June 2017 / Published: 27 June 2017
Cited by 1 | PDF Full-text (12931 KB) | HTML Full-text | XML Full-text
Abstract
The tiger beetle can maintain its stability by controlling the interaction force between its legs and an unstructured terrain while it runs. The biomimetic hexapod robot mimics a tiger beetle, and a comprehensive force sensing system combined with certain algorithms can provide force
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The tiger beetle can maintain its stability by controlling the interaction force between its legs and an unstructured terrain while it runs. The biomimetic hexapod robot mimics a tiger beetle, and a comprehensive force sensing system combined with certain algorithms can provide force information that can help the robot understand the unstructured terrain that it interacts with. This study introduces a complicated leg force sensing system for a hexapod robot that is the same for all six legs. First, the layout and configuration of sensing system are designed according to the structure and sizes of legs. Second, the joint toque sensors, 3-DOF foot-end force sensor and force information processing module are designed, and the force sensor performance parameters are tested by simulations and experiments. Moreover, a force sensing system is implemented within the robot control architecture. Finally, the experimental evaluation of the leg force sensor system on the hexapod robot is discussed and the performance of the leg force sensor system is verified. Full article
(This article belongs to the Section Physical Sensors)
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Open AccessArticle Exploitation of SPR to Investigate the Importance of Glycan Chains in the Interaction between Lactoferrin and Bacteria
Sensors 2017, 17(7), 1515; doi:10.3390/s17071515
Received: 15 May 2017 / Revised: 21 June 2017 / Accepted: 22 June 2017 / Published: 27 June 2017
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Abstract
Bovine lactoferrin (LF) has been shown to prevent adhesion to and invasion of mammalian cell lines by pathogenic bacteria, with evidence for direct bacterial binding by the milk glycoprotein. However, the glycosylation pattern of LF changes over the lactation cycle. In this study,
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Bovine lactoferrin (LF) has been shown to prevent adhesion to and invasion of mammalian cell lines by pathogenic bacteria, with evidence for direct bacterial binding by the milk glycoprotein. However, the glycosylation pattern of LF changes over the lactation cycle. In this study, we aim to investigate the effect that this variation has on the milk glycoprotein’s ability to interact with pathogens. Surface plasmon resonance technology was employed to compare the binding of LF from colostrum (early lactation) and mature milk (late lactation) to a panel of pathogenic bacteria (Staphylococcus aureus, Escherichia coli, Cronobacter sakazakii, Streptococcus pneumoniae, Pseudomonas aeruginosa, Listeria monocytogenes and Salmonella typhimurium). Novel interactions with LF were identified for C. sakazakii, S. pneumoniae and P. aeruginosa with the highest binding ability observed for mature milk LF in all cases, with the exception of S. typhimurium. The difference in bacterial binding observed may be as a result of the varying glycosylation profiles. This work demonstrates the potential of LF as a functional food ingredient to prevent bacterial infection. Full article
(This article belongs to the Special Issue Sensors for Glycoproteins and Glycated Proteins)
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Open AccessArticle Experimental Demonstration of Long-Range Underwater Acoustic Communication Using a Vertical Sensor Array
Sensors 2017, 17(7), 1516; doi:10.3390/s17071516
Received: 11 May 2017 / Revised: 20 June 2017 / Accepted: 24 June 2017 / Published: 27 June 2017
Cited by 1 | PDF Full-text (3948 KB) | HTML Full-text | XML Full-text
Abstract
This paper proposes a composite channel virtual time reversal mirror (CCVTRM) for vertical sensor array (VSA) processing and applies it to long-range underwater acoustic (UWA) communication in shallow water. Because of weak signal-to-noise ratio (SNR), it is unable to accurately estimate the channel
[...] Read more.
This paper proposes a composite channel virtual time reversal mirror (CCVTRM) for vertical sensor array (VSA) processing and applies it to long-range underwater acoustic (UWA) communication in shallow water. Because of weak signal-to-noise ratio (SNR), it is unable to accurately estimate the channel impulse response of each sensor of the VSA, thus the traditional passive time reversal mirror (PTRM) cannot perform well in long-range UWA communication in shallow water. However, CCVTRM only needs to estimate the composite channel of the VSA to accomplish time reversal mirror (TRM), which can effectively mitigate the inter-symbol interference (ISI) and reduce the bit error rate (BER). In addition, the calculation of CCVTRM is simpler than traditional PTRM. An UWA communication experiment using a VSA of 12 sensors was conducted in the South China Sea. The experiment achieves a very low BER communication at communication rate of 66.7 bit/s over an 80 km range. The results of the sea trial demonstrate that CCVTRM is feasible and can be applied to long-range UWA communication in shallow water. Full article
(This article belongs to the Special Issue Acoustic Sensing and Ultrasonic Drug Delivery)
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Open AccessArticle Algorithms for Lightweight Key Exchange
Sensors 2017, 17(7), 1517; doi:10.3390/s17071517
Received: 20 May 2017 / Revised: 15 June 2017 / Accepted: 23 June 2017 / Published: 27 June 2017
Cited by 1 | PDF Full-text (1003 KB) | HTML Full-text | XML Full-text
Abstract
Public-key cryptography is too slow for general purpose encryption, with most applications limiting its use as much as possible. Some secure protocols, especially those that enable forward secrecy, make a much heavier use of public-key cryptography, increasing the demand for lightweight cryptosystems that
[...] Read more.
Public-key cryptography is too slow for general purpose encryption, with most applications limiting its use as much as possible. Some secure protocols, especially those that enable forward secrecy, make a much heavier use of public-key cryptography, increasing the demand for lightweight cryptosystems that can be implemented in low powered or mobile devices. This performance requirements are even more significant in critical infrastructure and emergency scenarios where peer-to-peer networks are deployed for increased availability and resiliency. We benchmark several public-key key-exchange algorithms, determining those that are better for the requirements of critical infrastructure and emergency applications and propose a security framework based on these algorithms and study its application to decentralized node or sensor networks. Full article
(This article belongs to the Special Issue Selected Papers from UCAmI 2016)
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Open AccessArticle Active Multimodal Sensor System for Target Recognition and Tracking
Sensors 2017, 17(7), 1518; doi:10.3390/s17071518
Received: 26 April 2017 / Revised: 19 June 2017 / Accepted: 23 June 2017 / Published: 28 June 2017
PDF Full-text (7072 KB) | HTML Full-text | XML Full-text
Abstract
High accuracy target recognition and tracking systems using a single sensor or a passive multisensor set are susceptible to external interferences and exhibit environmental dependencies. These difficulties stem mainly from limitations to the available imaging frequency bands, and a general lack of coherent
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High accuracy target recognition and tracking systems using a single sensor or a passive multisensor set are susceptible to external interferences and exhibit environmental dependencies. These difficulties stem mainly from limitations to the available imaging frequency bands, and a general lack of coherent diversity of the available target-related data. This paper proposes an active multimodal sensor system for target recognition and tracking, consisting of a visible, an infrared, and a hyperspectral sensor. The system makes full use of its multisensor information collection abilities; furthermore, it can actively control different sensors to collect additional data, according to the needs of the real-time target recognition and tracking processes. This level of integration between hardware collection control and data processing is experimentally shown to effectively improve the accuracy and robustness of the target recognition and tracking system. Full article
(This article belongs to the Special Issue Multi-Sensor Integration and Fusion)
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Open AccessFeature PaperArticle Nanoporous Gold Nanocomposites as a Versatile Platform for Plasmonic Engineering and Sensing
Sensors 2017, 17(7), 1519; doi:10.3390/s17071519
Received: 1 June 2017 / Revised: 23 June 2017 / Accepted: 24 June 2017 / Published: 28 June 2017
Cited by 3 | PDF Full-text (5704 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Plasmonic metal nanostructures have shown great potential in sensing applications. Among various materials and structures, monolithic nanoporous gold disks (NPGD) have several unique features such as three-dimensional (3D) porous network, large surface area, tunable plasmonic resonance, high-density hot-spots, and excellent architectural integrity and
[...] Read more.
Plasmonic metal nanostructures have shown great potential in sensing applications. Among various materials and structures, monolithic nanoporous gold disks (NPGD) have several unique features such as three-dimensional (3D) porous network, large surface area, tunable plasmonic resonance, high-density hot-spots, and excellent architectural integrity and environmental stability. They exhibit a great potential in surface-enhanced spectroscopy, photothermal conversion, and plasmonic sensing. In this work, interactions between smaller colloidal gold nanoparticles (AuNP) and individual NPGDs are studied. Specifically, colloidal gold nanoparticles with different sizes are loaded onto NPGD substrates to form NPG hybrid nanocomposites with tunable plasmonic resonance peaks in the near-infrared spectral range. Newly formed plasmonic hot-spots due to the coupling between individual nanoparticles and NPG disk have been identified in the nanocomposites, which have been experimentally studied using extinction and surface-enhanced Raman scattering. Numerical modeling and simulations have been employed to further unravel various coupling scenarios between AuNP and NPGDs. Full article
(This article belongs to the Special Issue Micro and Nanofabrication Technologies for Biosensors)
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Open AccessArticle Tuning Selectivity of Fluorescent Carbon Nanotube-Based Neurotransmitter Sensors
Sensors 2017, 17(7), 1521; doi:10.3390/s17071521
Received: 30 April 2017 / Revised: 24 June 2017 / Accepted: 25 June 2017 / Published: 28 June 2017
PDF Full-text (3576 KB) | HTML Full-text | XML Full-text
Abstract
Detection of neurotransmitters is an analytical challenge and essential to understand neuronal networks in the brain and associated diseases. However, most methods do not provide sufficient spatial, temporal, or chemical resolution. Near-infrared (NIR) fluorescent single-walled carbon nanotubes (SWCNTs) have been used as building
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Detection of neurotransmitters is an analytical challenge and essential to understand neuronal networks in the brain and associated diseases. However, most methods do not provide sufficient spatial, temporal, or chemical resolution. Near-infrared (NIR) fluorescent single-walled carbon nanotubes (SWCNTs) have been used as building blocks for sensors/probes that detect catecholamine neurotransmitters, including dopamine. This approach provides a high spatial and temporal resolution, but it is not understood if these sensors are able to distinguish dopamine from similar catecholamine neurotransmitters, such as epinephrine or norepinephrine. In this work, the organic phase (DNA sequence) around SWCNTs was varied to create sensors with different selectivity and sensitivity for catecholamine neurotransmitters. Most DNA-functionalized SWCNTs responded to catecholamine neurotransmitters, but both dissociation constants (Kd) and limits of detection were highly dependent on functionalization (sequence). Kd values span a range of 2.3 nM (SWCNT-(GC)15 + norepinephrine) to 9.4 μM (SWCNT-(AT)15 + dopamine) and limits of detection are mostly in the single-digit nM regime. Additionally, sensors of different SWCNT chirality show different fluorescence increases. Moreover, certain sensors (e.g., SWCNT-(GT)10) distinguish between different catecholamines, such as dopamine and norepinephrine at low concentrations (50 nM). These results show that SWCNTs functionalized with certain DNA sequences are able to discriminate between catecholamine neurotransmitters or to detect them in the presence of interfering substances of similar structure. Such sensors will be useful to measure and study neurotransmitter signaling in complex biological settings. Full article
(This article belongs to the Special Issue Chemiresistive Sensors: Status and the Future)
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Open AccessArticle Towards Mobile Gait Analysis: Concurrent Validity and Test-Retest Reliability of an Inertial Measurement System for the Assessment of Spatio-Temporal Gait Parameters
Sensors 2017, 17(7), 1522; doi:10.3390/s17071522
Received: 30 May 2017 / Revised: 20 June 2017 / Accepted: 23 June 2017 / Published: 28 June 2017
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Abstract
The purpose of this study was to assess the concurrent validity and test–retest reliability of a sensor-based gait analysis system. Eleven healthy subjects and four Parkinson’s disease (PD) patients were asked to complete gait tasks whilst wearing two inertial measurement units at their
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The purpose of this study was to assess the concurrent validity and test–retest reliability of a sensor-based gait analysis system. Eleven healthy subjects and four Parkinson’s disease (PD) patients were asked to complete gait tasks whilst wearing two inertial measurement units at their feet. The extracted spatio-temporal parameters of 1166 strides were compared to those extracted from a reference camera-based motion capture system concerning concurrent validity. Test–retest reliability was assessed for five healthy subjects at three different days in a two week period. The two systems were highly correlated for all gait parameters ( r > 0.93 ). The bias for stride time was 0 ± 16 ms and for stride length was 1.4 ± 6.7 cm. No systematic range dependent errors were observed and no significant changes existed between healthy subjects and PD patients. Test-retest reliability was excellent for all parameters (intraclass correlation (ICC) > 0.81) except for gait velocity (ICC > 0.55). The sensor-based system was able to accurately capture spatio-temporal gait parameters as compared to the reference camera-based system for normal and impaired gait. The system’s high retest reliability renders the use in recurrent clinical measurements and in long-term applications feasible. Full article
(This article belongs to the Special Issue Wearable and Ambient Sensors for Healthcare and Wellness Applications)
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Open AccessArticle Colorization-Based RGB-White Color Interpolation using Color Filter Array with Randomly Sampled Pattern
Sensors 2017, 17(7), 1523; doi:10.3390/s17071523
Received: 26 April 2017 / Revised: 23 June 2017 / Accepted: 24 June 2017 / Published: 28 June 2017
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Abstract
Recently, several RGB-White (RGBW) color filter arrays (CFAs) have been proposed, which have extra white (W) pixels in the filter array that are highly sensitive. Due to the high sensitivity, the W pixels have better SNR (Signal to Noise Ratio) characteristics than other
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Recently, several RGB-White (RGBW) color filter arrays (CFAs) have been proposed, which have extra white (W) pixels in the filter array that are highly sensitive. Due to the high sensitivity, the W pixels have better SNR (Signal to Noise Ratio) characteristics than other color pixels in the filter array, especially, in low light conditions. However, most of the RGBW CFAs are designed so that the acquired RGBW pattern image can be converted into the conventional Bayer pattern image, which is then again converted into the final color image by using conventional demosaicing methods, i.e., color interpolation techniques. In this paper, we propose a new RGBW color filter array based on a totally different color interpolation technique, the colorization algorithm. The colorization algorithm was initially proposed for colorizing a gray image into a color image using a small number of color seeds. Here, we adopt this algorithm as a color interpolation technique, so that the RGBW color filter array can be designed with a very large number of W pixels to make the most of the highly sensitive characteristics of the W channel. The resulting RGBW color filter array has a pattern with a large proportion of W pixels, while the small-numbered RGB pixels are randomly distributed over the array. The colorization algorithm makes it possible to reconstruct the colors from such a small number of RGB values. Due to the large proportion of W pixels, the reconstructed color image has a high SNR value, especially higher than those of conventional CFAs in low light condition. Experimental results show that many important information which are not perceived in color images reconstructed with conventional CFAs are perceived in the images reconstructed with the proposed method. Full article
(This article belongs to the Section Physical Sensors)
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Open AccessArticle Physical Layer Secret-Key Generation Scheme for Transportation Security Sensor Network
Sensors 2017, 17(7), 1524; doi:10.3390/s17071524
Received: 13 April 2017 / Revised: 23 June 2017 / Accepted: 24 June 2017 / Published: 28 June 2017
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Abstract
Wireless Sensor Networks (WSNs) are widely used in different disciplines, including transportation systems, agriculture field environment monitoring, healthcare systems, and industrial monitoring. The security challenge of the wireless communication link between sensor nodes is critical in WSNs. In this paper, we propose a
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Wireless Sensor Networks (WSNs) are widely used in different disciplines, including transportation systems, agriculture field environment monitoring, healthcare systems, and industrial monitoring. The security challenge of the wireless communication link between sensor nodes is critical in WSNs. In this paper, we propose a new physical layer secret-key generation scheme for transportation security sensor network. The scheme is based on the cooperation of all the sensor nodes, thus avoiding the key distribution process, which increases the security of the system. Different passive and active attack models are analyzed in this paper. We also prove that when the cooperative node number is large enough, even when the eavesdropper is equipped with multiple antennas, the secret-key is still secure. Numerical results are performed to show the efficiency of the proposed scheme. Full article
(This article belongs to the Special Issue Sensors for Transportation)
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Open AccessArticle Characterizing Computer Access Using a One-Channel EEG Wireless Sensor
Sensors 2017, 17(7), 1525; doi:10.3390/s17071525
Received: 1 June 2017 / Revised: 22 June 2017 / Accepted: 26 June 2017 / Published: 29 June 2017
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Abstract
This work studies the feasibility of using mental attention to access a computer. Brain activity was measured with an electrode placed at the Fp1 position and the reference on the left ear; seven normally developed people and three subjects with cerebral palsy (CP)
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This work studies the feasibility of using mental attention to access a computer. Brain activity was measured with an electrode placed at the Fp1 position and the reference on the left ear; seven normally developed people and three subjects with cerebral palsy (CP) took part in the experimentation. They were asked to keep their attention high and low for as long as possible during several trials. We recorded attention levels and power bands conveyed by the sensor, but only the first was used for feedback purposes. All of the information was statistically analyzed to find the most significant parameters and a classifier based on linear discriminant analysis (LDA) was also set up. In addition, 60% of the participants were potential users of this technology with an accuracy of over 70%. Including power bands in the classifier did not improve the accuracy in discriminating between the two attentional states. For most people, the best results were obtained by using only the attention indicator in classification. Tiredness was higher in the group with disabilities (2.7 in a scale of 3) than in the other (1.5 in the same scale); and modulating the attention to access a communication board requires that it does not contain many pictograms (between 4 and 7) on screen and has a scanning period of a relatively high t s c a n 10 s. The information transfer rate (ITR) is similar to the one obtained by other brain computer interfaces (BCI), like those based on sensorimotor rhythms (SMR) or slow cortical potentials (SCP), and makes it suitable as an eye-gaze independent BCI. Full article
(This article belongs to the Section Physical Sensors)
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Open AccessArticle Multisensor Parallel Largest Ellipsoid Distributed Data Fusion with Unknown Cross-Covariances
Sensors 2017, 17(7), 1526; doi:10.3390/s17071526
Received: 26 April 2017 / Revised: 20 June 2017 / Accepted: 23 June 2017 / Published: 29 June 2017
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Abstract
As the largest ellipsoid (LE) data fusion algorithm can only be applied to two-sensor system, in this contribution, parallel fusion structure is proposed to introduce the LE algorithm into a multisensor system with unknown cross-covariances, and three parallel fusion structures based on different
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As the largest ellipsoid (LE) data fusion algorithm can only be applied to two-sensor system, in this contribution, parallel fusion structure is proposed to introduce the LE algorithm into a multisensor system with unknown cross-covariances, and three parallel fusion structures based on different estimate pairing methods are presented and analyzed. In order to assess the influence of fusion structure on fusion performance, two fusion performance assessment parameters are defined as Fusion Distance and Fusion Index. Moreover, the formula for calculating the upper bounds of actual fused error covariances of the presented multisensor LE fusers is also provided. Demonstrated with simulation examples, the Fusion Index indicates fuser’s actual fused accuracy and its sensitivity to the sensor orders, as well as its robustness to the accuracy of newly added sensors. Compared to the LE fuser with sequential structure, the LE fusers with proposed parallel structures not only significantly improve their properties in these aspects, but also embrace better performances in consistency and computation efficiency. The presented multisensor LE fusers generally have better accuracies than that of covariance intersection (CI) fusion algorithm and are consistent when the local estimates are weakly correlated. Full article
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Open AccessArticle Data Aggregation Based on Overlapping Rate of Sensing Area in Wireless Sensor Networks
Sensors 2017, 17(7), 1527; doi:10.3390/s17071527
Received: 26 April 2017 / Revised: 9 June 2017 / Accepted: 20 June 2017 / Published: 29 June 2017
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Abstract
Wireless sensor networks are required in smart applications to provide accurate control, where the high density of sensors brings in a large quantity of redundant data. In order to reduce the waste of limited network resources, data aggregation is utilized to avoid redundancy
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Wireless sensor networks are required in smart applications to provide accurate control, where the high density of sensors brings in a large quantity of redundant data. In order to reduce the waste of limited network resources, data aggregation is utilized to avoid redundancy forwarding. However, most of aggregation schemes reduce information accuracy and prolong end-to-end delay when eliminating transmission overhead. In this paper, we propose a data aggregation scheme based on overlapping rate of sensing area, namely AggOR, aiming for energy-efficient data collection in wireless sensor networks with high information accuracy. According to aggregation rules, gathering nodes are selected from candidate parent nodes and appropriate neighbor nodes considering a preset threshold of overlapping rate of sensing area. Therefore, the collected data in a gathering area are highly correlated, and a large amount of redundant data could be cleaned. Meanwhile, AggOR keeps the original entropy by only deleting the duplicated data. Experiment results show that compared with others, AggOR has a high data accuracy and a short end-to-end delay with a similar network lifetime. Full article
(This article belongs to the Special Issue Smart Industrial Wireless Sensor Networks)
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Open AccessArticle Online Recognition of Daily Activities by Color-Depth Sensing and Knowledge Models
Sensors 2017, 17(7), 1528; doi:10.3390/s17071528
Received: 29 April 2017 / Revised: 21 June 2017 / Accepted: 23 June 2017 / Published: 29 June 2017
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Abstract
Visual activity recognition plays a fundamental role in several research fields as a way to extract semantic meaning of images and videos. Prior work has mostly focused on classification tasks, where a label is given for a video clip. However, real life scenarios
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Visual activity recognition plays a fundamental role in several research fields as a way to extract semantic meaning of images and videos. Prior work has mostly focused on classification tasks, where a label is given for a video clip. However, real life scenarios require a method to browse a continuous video flow, automatically identify relevant temporal segments and classify them accordingly to target activities. This paper proposes a knowledge-driven event recognition framework to address this problem. The novelty of the method lies in the combination of a constraint-based ontology language for event modeling with robust algorithms to detect, track and re-identify people using color-depth sensing (Kinect® sensor). This combination enables to model and recognize longer and more complex events and to incorporate domain knowledge and 3D information into the same models. Moreover, the ontology-driven approach enables human understanding of system decisions and facilitates knowledge transfer across different scenes. The proposed framework is evaluated with real-world recordings of seniors carrying out unscripted, daily activities at hospital observation rooms and nursing homes. Results demonstrated that the proposed framework outperforms state-of-the-art methods in a variety of activities and datasets, and it is robust to variable and low-frame rate recordings. Further work will investigate how to extend the proposed framework with uncertainty management techniques to handle strong occlusion and ambiguous semantics, and how to exploit it to further support medicine on the timely diagnosis of cognitive disorders, such as Alzheimer’s disease. Full article
(This article belongs to the Special Issue Sensors for Ambient Assisted Living, Ubiquitous and Mobile Health)
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Open AccessArticle Surface Acoustic Wave Sensor with Pd/ZnO Bilayer Structure for Room Temperature Hydrogen Detection
Sensors 2017, 17(7), 1529; doi:10.3390/s17071529
Received: 6 June 2017 / Revised: 20 June 2017 / Accepted: 28 June 2017 / Published: 29 June 2017
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Abstract
A Surface Acoustic Wave (SAW) hydrogen sensor with a Pd/ZnO bilayer structure for room temperature sensing operation has been obtained by Pulsed Laser Deposition (PLD). The sensor structure combines a Pd layer with optimized porosity for maximizing mass effects, with the large acoustoelectric
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A Surface Acoustic Wave (SAW) hydrogen sensor with a Pd/ZnO bilayer structure for room temperature sensing operation has been obtained by Pulsed Laser Deposition (PLD). The sensor structure combines a Pd layer with optimized porosity for maximizing mass effects, with the large acoustoelectric effect at the Pd/ZnO interface. The large acoustoelectric effect is due to the fact that ZnO has a surface conductivity which is highly sensitive to chemisorbed gases. The sensitivity of the sensor was determined for hydrogen concentrations between 0.2% and 2%. The limit of detection (LOD) of the bilayer sensor was about 4.5 times better than the single ZnO films and almost twice better than single Pd films. Full article
(This article belongs to the Special Issue Surface Acoustic Wave and Bulk Acoustic Wave Sensors)
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Open AccessArticle Substrate Oxide Layer Thickness Optimization for a Dual-Width Plasmonic Grating for Surface-Enhanced Raman Spectroscopy (SERS) Biosensor Applications
Sensors 2017, 17(7), 1530; doi:10.3390/s17071530
Received: 2 June 2017 / Revised: 22 June 2017 / Accepted: 27 June 2017 / Published: 30 June 2017
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Abstract
This work investigates a new design for a plasmonic SERS biosensor via computational electromagnetic models. It utilizes a dual-width plasmonic grating design, which has two different metallic widths per grating period. These types of plasmonic gratings have shown larger optical enhancement than standard
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This work investigates a new design for a plasmonic SERS biosensor via computational electromagnetic models. It utilizes a dual-width plasmonic grating design, which has two different metallic widths per grating period. These types of plasmonic gratings have shown larger optical enhancement than standard single-width gratings. The new structures have additional increased enhancement when the spacing between the metal decreases to sub-10 nm dimensions. This work integrates an oxide layer to improve the enhancement even further by carefully studying the effects of the substrate oxide thickness on the enhancement and reports ideal substrate parameters. The combined effects of varying the substrate and the grating geometry are studied to fully optimize the device’s enhancement for SERS biosensing and other plasmonic applications. The work reports the ideal widths and substrate thickness for both a standard and a dual-width plasmonic grating SERS biosensor. The ideal geometry, comprising a dual-width grating structure atop an optimal SiO2 layer thickness, improves the enhancement by 800%, as compared to non-optimized structures with a single-width grating and a non-optimal oxide thickness. Full article
(This article belongs to the Special Issue Applications of Raman Spectroscopy in Biosensors)
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Open AccessArticle Temperature Measurement and Numerical Prediction in Machining Inconel 718
Sensors 2017, 17(7), 1531; doi:10.3390/s17071531
Received: 22 May 2017 / Revised: 24 June 2017 / Accepted: 24 June 2017 / Published: 30 June 2017
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Abstract
Thermal issues are critical when machining Ni-based superalloy components designed for high temperature applications. The low thermal conductivity and extreme strain hardening of this family of materials results in elevated temperatures around the cutting area. This elevated temperature could lead to machining-induced damage
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Thermal issues are critical when machining Ni-based superalloy components designed for high temperature applications. The low thermal conductivity and extreme strain hardening of this family of materials results in elevated temperatures around the cutting area. This elevated temperature could lead to machining-induced damage such as phase changes and residual stresses, resulting in reduced service life of the component. Measurement of temperature during machining is crucial in order to control the cutting process, avoiding workpiece damage. On the other hand, the development of predictive tools based on numerical models helps in the definition of machining processes and the obtainment of difficult to measure parameters such as the penetration of the heated layer. However, the validation of numerical models strongly depends on the accurate measurement of physical parameters such as temperature, ensuring the calibration of the model. This paper focuses on the measurement and prediction of temperature during the machining of Ni-based superalloys. The temperature sensor was based on a fiber-optic two-color pyrometer developed for localized temperature measurements in turning of Inconel 718. The sensor is capable of measuring temperature in the range of 250 to 1200 °C. Temperature evolution is recorded in a lathe at different feed rates and cutting speeds. Measurements were used to calibrate a simplified numerical model for prediction of temperature fields during turning. Full article
(This article belongs to the Section Physical Sensors)
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Open AccessArticle GNSS Spoofing Detection and Mitigation Based on Maximum Likelihood Estimation
Sensors 2017, 17(7), 1532; doi:10.3390/s17071532
Received: 19 April 2017 / Revised: 27 June 2017 / Accepted: 28 June 2017 / Published: 30 June 2017
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Abstract
Spoofing attacks are threatening the global navigation satellite system (GNSS). The maximum likelihood estimation (MLE)-based positioning technique is a direct positioning method originally developed for multipath rejection and weak signal processing. We find this method also has a potential ability for GNSS anti-spoofing
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Spoofing attacks are threatening the global navigation satellite system (GNSS). The maximum likelihood estimation (MLE)-based positioning technique is a direct positioning method originally developed for multipath rejection and weak signal processing. We find this method also has a potential ability for GNSS anti-spoofing since a spoofing attack that misleads the positioning and timing result will cause distortion to the MLE cost function. Based on the method, an estimation-cancellation approach is presented to detect spoofing attacks and recover the navigation solution. A statistic is derived for spoofing detection with the principle of the generalized likelihood ratio test (GLRT). Then, the MLE cost function is decomposed to further validate whether the navigation solution obtained by MLE-based positioning is formed by consistent signals. Both formulae and simulations are provided to evaluate the anti-spoofing performance. Experiments with recordings in real GNSS spoofing scenarios are also performed to validate the practicability of the approach. Results show that the method works even when the code phase differences between the spoofing and authentic signals are much less than one code chip, which can improve the availability of GNSS service greatly under spoofing attacks. Full article
(This article belongs to the Section Remote Sensors)
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Open AccessArticle Dimension-Reduced Analog—Digital Mixed Measurement Method of Inductive Proximity Sensor
Sensors 2017, 17(7), 1533; doi:10.3390/s17071533
Received: 3 May 2017 / Revised: 24 June 2017 / Accepted: 27 June 2017 / Published: 30 June 2017
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Abstract
Inductive proximity sensors (IPSs) present a unique no-contact advantage. They are widely preferred for displacement measurement in various industrial fields (e.g., aviation and aerospace), and they are improved continuously. When the inductance and resistance components of the IPS sensing core are separated, the
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Inductive proximity sensors (IPSs) present a unique no-contact advantage. They are widely preferred for displacement measurement in various industrial fields (e.g., aviation and aerospace), and they are improved continuously. When the inductance and resistance components of the IPS sensing core are separated, the influence of temperature drift on measurement can be eliminated. The complexity of online computation of component separation can be reduced using a two-dimensional look-up table method. However, this method exhibits disadvantages, such as large capacity of the look-up table, dependency on precision measurement of sensing core parameter, and nonlinear distribution of measurement resolution. This study aims to overcome these disadvantages by examining the nonlinear relationship between the response of the sensing core and the ambient temperature, and proposes a dimension-reduced measurement method. The proposed method extracts the characteristics of the response curves at two temperatures and calculates the characteristics of the response curves at any temperature using a linear approximation. The look-up table capacity is less than 0.37% of the two-dimensional look-up table capacity (condensed) under the same condition; dimension reduction enables the construction of a complete look-up table directly by calibration procedures and avoids precise measurement on sensing core parameters; the calibration procedures establish uniform mapping of the distribution of measurement resolution. The experiment shows that, when the measurement ranges are 0–6, 0–5, and 0–4 mm, the maximum measurement errors are 0.140, 0.065, and 0.040 mm, respectively, under temperature ranging from 20 C to 110 C. This study extends the measurement range from 0–5 mm to 0–7 mm and improves the measurement accuracy over 0.1 mm (50% at 5 mm) compared with the two-dimensional look-up table method. Therefore, the proposed method not only inherits the advantages of the original method but also achieves the above-mentioned expected capacity improvements effectively. Full article
(This article belongs to the Section Physical Sensors)
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Open AccessArticle A Study of Deep CNN-Based Classification of Open and Closed Eyes Using a Visible Light Camera Sensor
Sensors 2017, 17(7), 1534; doi:10.3390/s17071534
Received: 2 June 2017 / Revised: 26 June 2017 / Accepted: 28 June 2017 / Published: 30 June 2017
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Abstract
The necessity for the classification of open and closed eyes is increasing in various fields, including analysis of eye fatigue in 3D TVs, analysis of the psychological states of test subjects, and eye status tracking-based driver drowsiness detection. Previous studies have used various
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The necessity for the classification of open and closed eyes is increasing in various fields, including analysis of eye fatigue in 3D TVs, analysis of the psychological states of test subjects, and eye status tracking-based driver drowsiness detection. Previous studies have used various methods to distinguish between open and closed eyes, such as classifiers based on the features obtained from image binarization, edge operators, or texture analysis. However, when it comes to eye images with different lighting conditions and resolutions, it can be difficult to find an optimal threshold for image binarization or optimal filters for edge and texture extraction. In order to address this issue, we propose a method to classify open and closed eye images with different conditions, acquired by a visible light camera, using a deep residual convolutional neural network. After conducting performance analysis on both self-collected and open databases, we have determined that the classification accuracy of the proposed method is superior to that of existing methods. Full article
(This article belongs to the Special Issue Video Analysis and Tracking Using State-of-the-Art Sensors)
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Open AccessArticle An Automatic Car Counting System Using OverFeat Framework
Sensors 2017, 17(7), 1535; doi:10.3390/s17071535
Received: 26 May 2017 / Revised: 27 June 2017 / Accepted: 28 June 2017 / Published: 30 June 2017
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Abstract
Automatic car counting is an important component in the automated traffic system. Car counting is very important to understand the traffic load and optimize the traffic signals. In this paper, we implemented the Gaussian Background Subtraction Method and OverFeat Framework to count cars.
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Automatic car counting is an important component in the automated traffic system. Car counting is very important to understand the traffic load and optimize the traffic signals. In this paper, we implemented the Gaussian Background Subtraction Method and OverFeat Framework to count cars. OverFeat Framework is a combination of Convolution Neural Network (CNN) and one machine learning classifier (like Support Vector Machines (SVM) or Logistic Regression). With this study, we showed another possible application area for the OverFeat Framework. The advantages and shortcomings of the Background Subtraction Method and OverFeat Framework were analyzed using six individual traffic videos with different perspectives, such as camera angles, weather conditions and time of the day. In addition, we compared the two algorithms above with manual counting and a commercial software called Placemeter. The OverFeat Framework showed significant potential in the field of car counting with the average accuracy of 96.55% in our experiment. Full article
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Open AccessArticle A Steel Ball Surface Quality Inspection Method Based on a Circumferential Eddy Current Array Sensor
Sensors 2017, 17(7), 1536; doi:10.3390/s17071536
Received: 17 April 2017 / Revised: 14 June 2017 / Accepted: 26 June 2017 / Published: 1 July 2017
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Abstract
To efficiently inspect surface defects on steel ball bearings, a new method based on a circumferential eddy current array (CECA) sensor was proposed here. The best probe configuration, in terms of the coil quality factor (Q-factor), magnetic field intensity, and induced eddy current
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To efficiently inspect surface defects on steel ball bearings, a new method based on a circumferential eddy current array (CECA) sensor was proposed here. The best probe configuration, in terms of the coil quality factor (Q-factor), magnetic field intensity, and induced eddy current density on the surface of a sample steel ball, was determined using 3-, 4-, 5-, and 6-coil probes, for analysis and comparison. The optimal lift-off from the measured steel ball, the number of probe coils, and the frequency of excitation current suitable for steel ball inspection were obtained. Using the resulting CECA sensor to inspect 46,126 steel balls showed a miss rate of ~0.02%. The sensor was inspected for surface defects as small as 0.05 mm in width and 0.1 mm in depth. Full article
(This article belongs to the Section Physical Sensors)
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Open AccessArticle Thiolate-Capped CdSe/ZnS Core-Shell Quantum Dots for the Sensitive Detection of Glucose
Sensors 2017, 17(7), 1537; doi:10.3390/s17071537
Received: 18 April 2017 / Revised: 23 May 2017 / Accepted: 30 May 2017 / Published: 1 July 2017
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Abstract
A semiconducting water-soluble core-shell quantum dots (QDs) system capped with thiolated ligand was used in this study for the sensitive detection of glucose in aqueous samples. The QDs selected are of CdSe-coated ZnS and were prepared in house based on a hot injection
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A semiconducting water-soluble core-shell quantum dots (QDs) system capped with thiolated ligand was used in this study for the sensitive detection of glucose in aqueous samples. The QDs selected are of CdSe-coated ZnS and were prepared in house based on a hot injection technique. The formation of ZnS shell at the outer surface of CdSe core was made via a specific process namely, SILAR (successive ionic layer adsorption and reaction). The distribution, morphology, and optical characteristics of the prepared core-shell QDs were assessed by transmission electron microscopy (TEM) and spectrofluorescence, respectively. From the analysis, the results show that the mean particle size of prepared QDs is in the range of 10–12 nm and that the optimum emission condition was displayed at 620 nm. Further, the prepared CdSe/ZnS core shell QDs were modified by means of a room temperature ligand-exchange method that involves six organic ligands, L-cysteine, L-histidine, thio-glycolic acid (TGA or mercapto-acetic acid, MAA), mercapto-propionic acid (MPA), mercapto-succinic acid (MSA), and mercapto-undecanoic acid (MUA). This process was chosen in order to maintain a very dense water solubilizing environment around the QDs surface. From the analysis, the results show that the CdSe/ZnS capped with TGA (CdSe/ZnS-TGA) exhibited the strongest fluorescence emission as compared to others; hence, it was tested further for the glucose detection after their treatment with glucose oxidase (GOx) and horseradish peroxidase (HRP) enzymes. Here in this study, the glucose detection is based on the fluorescence quenching effect of the QDs, which is correlated to the oxidative reactions occurred between the conjugated enzymes and glucose. From the analysis of results, it can be inferred that the resultant GOx:HRP/CdSe/ZnS-TGA QDs system can be a suitable platform for the fluorescence-based determination of glucose in the real samples. Full article
(This article belongs to the Special Issue Fluorescent Probes and Sensors)
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Open AccessArticle Detection of Quinoline in G. boninense-Infected Plants Using Functionalized Multi-Walled Carbon Nanotubes: A Field Study
Sensors 2017, 17(7), 1538; doi:10.3390/s17071538
Received: 25 April 2017 / Revised: 1 June 2017 / Accepted: 5 June 2017 / Published: 1 July 2017
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Abstract
Carbon nanotubes (CNTs) reinforced with gold nanoparticles (AuNPs) and chitosan nanoparticles (CTSNPs) were anchored on a screen-printed electrode to fabricate a multi-walled structure for the detection of quinoline. The surface morphology of the nanocomposites and the modified electrode was examined by an ultra-high
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Carbon nanotubes (CNTs) reinforced with gold nanoparticles (AuNPs) and chitosan nanoparticles (CTSNPs) were anchored on a screen-printed electrode to fabricate a multi-walled structure for the detection of quinoline. The surface morphology of the nanocomposites and the modified electrode was examined by an ultra-high resolution field emission scanning electron microscope (FESEM), and Fourier-transform infrared (FT-IR) spectroscopy was used to confirm the presence of specific functional groups on the multi-walled carbon nanotubes MWCNTs. Cyclic voltammetry (CV) and linear sweep voltammetry (LSV) were used to monitor the layer-by-layer assembly of ultra-thin films of nanocomposites on the surface of the electrode and other electrochemical characterizations. Under optimized conditions, the novel sensor displayed outstanding electrochemical reactivity towards the electro-oxidation of quinoline. The linear range was fixed between 0.0004 and 1.0 μM, with a limit of detection (LOD) of 3.75 nM. The fabricated electrode exhibited high stability with excellent sensitivity and selectivity, specifically attributable to the salient characteristics of AuNPs, CTSNPs, and MWCNTs and the synergistic inter-relationship between them. The newly developed electrode was tested in the field. The Ipa increased with an increase in the amount of quinoline solution added, and the peak potential deviated minimally, depicting the real capability of the newly fabricated electrode. Full article
(This article belongs to the Special Issue Advanced Sensors Based on Carbon Electrodes)
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Open AccessArticle Development of a PrGO-Modified Electrode for Uric Acid Determination in the Presence of Ascorbic Acid by an Electrochemical Technique
Sensors 2017, 17(7), 1539; doi:10.3390/s17071539
Received: 27 April 2017 / Revised: 30 May 2017 / Accepted: 1 June 2017 / Published: 1 July 2017
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Abstract
An attractive electrochemical sensor of poly(3,4-ethylenedioxythiophene)/reduced graphene oxide electrode (PrGO) was developed for an electrochemical technique for uric acid (UA) detection in the presence of ascorbic acid (AA). PrGO composite film showed an improved electrocatalytic activity towards UA oxidation in pH 6.0 (0.1
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An attractive electrochemical sensor of poly(3,4-ethylenedioxythiophene)/reduced graphene oxide electrode (PrGO) was developed for an electrochemical technique for uric acid (UA) detection in the presence of ascorbic acid (AA). PrGO composite film showed an improved electrocatalytic activity towards UA oxidation in pH 6.0 (0.1 M PBS). The PrGO composite exhibited a high current signal and low charge transfer resistance (Rct) compared to a reduced graphene oxide (rGO) electrode or a bare glassy carbon electrode (GCE). The limit of detection and sensitivity of PrGO for the detection of UA are 0.19 μM (S/N = 3) and 0.01 μA/μM, respectively, in the range of 1–300 μM of UA. Full article
(This article belongs to the Special Issue Sensors for Health Monitoring and Disease Diagnosis)
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Open AccessArticle In-Situ Real-Time Focus Detection during Laser Processing Using Double-Hole Masks and Advanced Image Sensor Software
Sensors 2017, 17(7), 1540; doi:10.3390/s17071540
Received: 10 May 2017 / Revised: 21 June 2017 / Accepted: 28 June 2017 / Published: 1 July 2017
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Abstract
In modern high-intensity ultrafast laser processing, detecting the focal position of the working laser beam, at which the intensity is the highest and the beam diameter is the lowest, and immediately locating the target sample at that point are challenging tasks. A system
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In modern high-intensity ultrafast laser processing, detecting the focal position of the working laser beam, at which the intensity is the highest and the beam diameter is the lowest, and immediately locating the target sample at that point are challenging tasks. A system that allows in-situ real-time focus determination and fabrication using a high-power laser has been in high demand among both engineers and scientists. Conventional techniques require the complicated mathematical theory of wave optics, employing interference as well as diffraction phenomena to detect the focal position; however, these methods are ineffective and expensive for industrial application. Moreover, these techniques could not perform detection and fabrication simultaneously. In this paper, we propose an optical design capable of detecting the focal point and fabricating complex patterns on a planar sample surface simultaneously. In-situ real-time focus detection is performed using a bandpass filter, which only allows for the detection of laser transmission. The technique enables rapid, non-destructive, and precise detection of the focal point. Furthermore, it is sufficiently simple for application in both science and industry for mass production, and it is expected to contribute to the next generation of laser equipment, which can be used to fabricate micro-patterns with high complexity. Full article
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Open AccessArticle Hybrid Optical Unobtrusive Blood Pressure Measurements
Sensors 2017, 17(7), 1541; doi:10.3390/s17071541
Received: 21 April 2017 / Revised: 15 June 2017 / Accepted: 28 June 2017 / Published: 1 July 2017
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Abstract
Blood pressure (BP) is critical in diagnosing certain cardiovascular diseases such as hypertension. Some previous studies have proved that BP can be estimated by pulse transit time (PTT) calculated by a pair of photoplethysmography (PPG) signals at two body sites. Currently, contact PPG
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Blood pressure (BP) is critical in diagnosing certain cardiovascular diseases such as hypertension. Some previous studies have proved that BP can be estimated by pulse transit time (PTT) calculated by a pair of photoplethysmography (PPG) signals at two body sites. Currently, contact PPG (cPPG) and imaging PPG (iPPG) are two feasible ways to obtain PPG signals. In this study, we proposed a hybrid system (called the ICPPG system) employing both methods that can be implemented on a wearable device, facilitating the measurement of BP in an inconspicuous way. The feasibility of the ICPPG system was validated on a dataset with 29 subjects. It has been proved that the ICPPG system is able to estimate PTT values. Moreover, the PTT measured by the new system shows a correlation on average with BP variations for most subjects, which could facilitate a new generation of BP measurement using wearable and mobile devices. Full article
(This article belongs to the Section Physical Sensors)
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Open AccessArticle A Satellite-Based Imaging Instrumentation Concept for Hyperspectral Thermal Remote Sensing
Sensors 2017, 17(7), 1542; doi:10.3390/s17071542
Received: 28 April 2017 / Revised: 26 June 2017 / Accepted: 28 June 2017 / Published: 1 July 2017
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Abstract
This paper describes the concept of the hyperspectral Earth-observing thermal infrared (TIR) satellite mission HiTeSEM (High-resolution Temperature and Spectral Emissivity Mapping). The scientific goal is to measure specific key variables from the biosphere, hydrosphere, pedosphere, and geosphere related to two global problems of
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This paper describes the concept of the hyperspectral Earth-observing thermal infrared (TIR) satellite mission HiTeSEM (High-resolution Temperature and Spectral Emissivity Mapping). The scientific goal is to measure specific key variables from the biosphere, hydrosphere, pedosphere, and geosphere related to two global problems of significant societal relevance: food security and human health. The key variables comprise land and sea surface radiation temperature and emissivity, surface moisture, thermal inertia, evapotranspiration, soil minerals and grain size components, soil organic carbon, plant physiological variables, and heat fluxes. The retrieval of this information requires a TIR imaging system with adequate spatial and spectral resolutions and with day-night following observation capability. Another challenge is the monitoring of temporally high dynamic features like energy fluxes, which require adequate revisit time. The suggested solution is a sensor pointing concept to allow high revisit times for selected target regions (1–5 days at off-nadir). At the same time, global observations in the nadir direction are guaranteed with a lower temporal repeat cycle (>1 month). To account for the demand of a high spatial resolution for complex targets, it is suggested to combine in one optic (1) a hyperspectral TIR system with ~75 bands at 7.2–12.5 µm (instrument NEDT 0.05 K–0.1 K) and a ground sampling distance (GSD) of 60 m, and (2) a panchromatic high-resolution TIR-imager with two channels (8.0–10.25 µm and 10.25–12.5 µm) and a GSD of 20 m. The identified science case requires a good correlation of the instrument orbit with Sentinel-2 (maximum delay of 1–3 days) to combine data from the visible and near infrared (VNIR), the shortwave infrared (SWIR) and TIR spectral regions and to refine parameter retrieval. Full article
(This article belongs to the Special Issue Analysis of Multispectral and Hyperspectral Data)
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