Open AccessArticle
Graphene-Ag Hybrids on Laser-Textured Si Surface for SERS Detection
Sensors 2017, 17(7), 1462; doi:10.3390/s17071462 -
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
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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
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 (registering DOI) -
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
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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
Open AccessArticle
Paper as Active Layer in Inkjet-Printed Capacitive Humidity Sensors
Sensors 2017, 17(7), 1464; doi:10.3390/s17071464 (registering DOI) -
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
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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
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Open AccessReview
Bluetooth Low Energy Mesh Networks: A Survey
Sensors 2017, 17(7), 1467; doi:10.3390/s17071467 (registering DOI) -
Abstract
Bluetooth Low Energy (BLE) has gained significant momentum. However, the original design of BLE focused on star topology networking, which limits network coverage range and precludes end-to-end path diversity. In contrast, other competing technologies overcome such constraints by supporting the mesh network topology.
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Bluetooth Low Energy (BLE) has gained significant momentum. However, the original design of BLE focused on star topology networking, which limits network coverage range and precludes end-to-end path diversity. In contrast, other competing technologies overcome such constraints by supporting the mesh network topology. For these reasons, academia, industry, and standards development organizations have been designing solutions to enable BLE mesh networks. Nevertheless, the literature lacks a consolidated view on this emerging area. This paper comprehensively surveys state of the art BLE mesh networking. We first provide a taxonomy of BLE mesh network solutions. We then review the solutions, describing the variety of approaches that leverage existing BLE functionality to enable BLE mesh networks. We identify crucial aspects of BLE mesh network solutions and discuss their advantages and drawbacks. Finally, we highlight currently open issues. Full article
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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 (registering DOI) -
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
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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 (registering DOI) -
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,
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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
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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 (registering DOI) -
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
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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 (registering DOI) -
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
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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
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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 (registering DOI) -
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
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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
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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 (registering DOI) -
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
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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
Block-Diagonal Constrained Low-Rank and Sparse Graph for Discriminant Analysis of Image Data
Sensors 2017, 17(7), 1475; doi:10.3390/s17071475 (registering DOI) -
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
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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
Open AccessLetter
The Resistance–Amplitude–Frequency Effect of In–Liquid Quartz Crystal Microbalance
Sensors 2017, 17(7), 1476; doi:10.3390/s17071476 (registering DOI) -
Abstract
Due to the influence of liquid load, the equivalent resistance of in-liquid quartz crystal microbalance (QCM) increases sharply, and the quality factor and resonant frequency decreases. We found that the change in the resonant frequency of in-liquid QCM consisted of two parts: besides
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Due to the influence of liquid load, the equivalent resistance of in-liquid quartz crystal microbalance (QCM) increases sharply, and the quality factor and resonant frequency decreases. We found that the change in the resonant frequency of in-liquid QCM consisted of two parts: besides the frequency changes due to the mass and viscous load (which could be equivalent to motional inductance), the second part of frequency change was caused by the increase of motional resistance. The theoretical calculation and simulation proved that the increases of QCM motional resistance may indeed cause the decreases of resonant frequency, and revealed that the existence of static capacitance was the root cause of this frequency change. The second part of frequency change (due to the increases of motional resistance) was difficult to measure accurately, and may cause great error for in-liquid QCM applications. A technical method to reduce the interference caused by this effect is presented. The study contributes to the accurate determination of the frequency and amplitude change of in-liquid QCM caused by liquid load, which is significant for the QCM applications in the liquid phase. Full article
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 (registering DOI) -
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
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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 (registering DOI) -
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.
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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
Open AccessArticle
Trimethylamine Sensors Based on Au-Modified Hierarchical Porous Single-Crystalline ZnO Nanosheets
Sensors 2017, 17(7), 1478; doi:10.3390/s17071478 (registering DOI) -
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
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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
Open AccessArticle
Stereo Vision-Based High Dynamic Range Imaging Using Differently-Exposed Image Pair
Sensors 2017, 17(7), 1473; doi:10.3390/s17071473 (registering DOI) -
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
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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
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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 (registering DOI) -
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
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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
Open AccessArticle
Single-Lead Fetal ECG Extraction Based on a Parallel Marginalized Particle Filter
Sensors 2017, 17(6), 1456; doi:10.3390/s17061456 -
Abstract
This paper presents a novel method for extracting the fetal ECG (FECG) from a single-lead abdominal signal. A dynamical model for a modified abdominal signal is proposed, in which both the maternal ECG (MECG) and the FECG are modeled, and then a parallel
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This paper presents a novel method for extracting the fetal ECG (FECG) from a single-lead abdominal signal. A dynamical model for a modified abdominal signal is proposed, in which both the maternal ECG (MECG) and the FECG are modeled, and then a parallel marginalized particle filter (par-MPF) is used for tracking the abdominal signal. Finally, the FECG and MECG are simultaneously separated. Several experiments are conducted using both simulated and clinical signals. The results indicate that the method proposed in this paper effectively extracts the FECG and outperforms other Bayesian filtering algorithms. Full article
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Open AccessArticle
Towards the Internet of Smart Trains: A Review on Industrial IoT-Connected Railways
Sensors 2017, 17(6), 1457; doi:10.3390/s17061457 -
Abstract
Nowadays, the railway industry is in a position where it is able to exploit the opportunities created by the IIoT (Industrial Internet of Things) and enabling communication technologies under the paradigm of Internet of Trains. This review details the evolution of communication technologies
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Nowadays, the railway industry is in a position where it is able to exploit the opportunities created by the IIoT (Industrial Internet of Things) and enabling communication technologies under the paradigm of Internet of Trains. This review details the evolution of communication technologies since the deployment of GSM-R, describing the main alternatives and how railway requirements, specifications and recommendations have evolved over time. The advantages of the latest generation of broadband communication systems (e.g., LTE, 5G, IEEE 802.11ad) and the emergence of Wireless Sensor Networks (WSNs) for the railway environment are also explained together with the strategic roadmap to ensure a smooth migration from GSM-R. Furthermore, this survey focuses on providing a holistic approach, identifying scenarios and architectures where railways could leverage better commercial IIoT capabilities. After reviewing the main industrial developments, short and medium-term IIoT-enabled services for smart railways are evaluated. Then, it is analyzed the latest research on predictive maintenance, smart infrastructure, advanced monitoring of assets, video surveillance systems, railway operations, Passenger and Freight Information Systems (PIS/FIS), train control systems, safety assurance, signaling systems, cyber security and energy efficiency. Overall, it can be stated that the aim of this article is to provide a detailed examination of the state-of-the-art of different technologies and services that will revolutionize the railway industry and will allow for confronting today challenges. Full article
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Open AccessArticle
Compressed-Sensing Reconstruction Based on Block Sparse Bayesian Learning in Bearing-Condition Monitoring
Sensors 2017, 17(6), 1454; doi:10.3390/s17061454 -
Abstract
Remote monitoring of bearing conditions, using wireless sensor network (WSN), is a developing trend in the industrial field. In complicated industrial environments, WSN face three main constraints: low energy, less memory, and low operational capability. Conventional data-compression methods, which concentrate on data compression
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Remote monitoring of bearing conditions, using wireless sensor network (WSN), is a developing trend in the industrial field. In complicated industrial environments, WSN face three main constraints: low energy, less memory, and low operational capability. Conventional data-compression methods, which concentrate on data compression only, cannot overcome these limitations. Aiming at these problems, this paper proposed a compressed data acquisition and reconstruction scheme based on Compressed Sensing (CS) which is a novel signal-processing technique and applied it for bearing conditions monitoring via WSN. The compressed data acquisition is realized by projection transformation and can greatly reduce the data volume, which needs the nodes to process and transmit. The reconstruction of original signals is achieved in the host computer by complicated algorithms. The bearing vibration signals not only exhibit the sparsity property, but also have specific structures. This paper introduced the block sparse Bayesian learning (BSBL) algorithm which works by utilizing the block property and inherent structures of signals to reconstruct CS sparsity coefficients of transform domains and further recover the original signals. By using the BSBL, CS reconstruction can be improved remarkably. Experiments and analyses showed that BSBL method has good performance and is suitable for practical bearing-condition monitoring. Full article
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