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Sensors, Volume 19, Issue 6 (March-2 2019)

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Open AccessArticle Distributed Learning Fractal Algorithm for Optimizing a Centralized Control Topology of Wireless Sensor Network Based on the Hilbert Curve L-System
Sensors 2019, 19(6), 1442; https://doi.org/10.3390/s19061442 (registering DOI)
Received: 20 February 2019 / Revised: 14 March 2019 / Accepted: 19 March 2019 / Published: 23 March 2019
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Abstract
Wireless sensor networks (WSNs) consist of a large number of small devices or nodes, called micro controller units (MCUs) and located in homes and/or offices, to be operated through the internet from anywhere, making these devices smarter and more efficient. Quality of service [...] Read more.
Wireless sensor networks (WSNs) consist of a large number of small devices or nodes, called micro controller units (MCUs) and located in homes and/or offices, to be operated through the internet from anywhere, making these devices smarter and more efficient. Quality of service routing is one of the critical challenges in WSNs, especially in surveillance systems. To improve the efficiency of the network, in this article we proposes a distributed learning fractal algorithm (DFLA) to design the control topology of a wireless sensor network (WSN), whose nodes are the MCUs distributed in a physical space and which are connected to share parameters of the sensors such as concentrations of C O 2 , humidity, temperature within the space or adjustment of the intensity of light inside and outside the home or office. For this, we start defining the production rules of the L-systems to generate the Hilbert fractal, since these rules facilitate the generation of this fractal, which is a fill-space curve. Then, we model the optimization of a centralized control topology of WSNs and proposed a DFLA to find the best two nodes where a device can find the highly reliable link between these nodes. Thus, we propose a software defined network (SDN) with strong mobility since it can be reconfigured depending on the amount of nodes, also we employ a target coverage because distributed learning fractal algorithm (DLFA) only consider reliable links among devices. Finally, through laboratory tests and computer simulations, we demonstrate the effectiveness of our approach by means of a fractal routing in WSNs, by using a large amount of WSNs devices (from 16 to 64 sensors) for real time monitoring of different parameters, in order to make efficient WSNs and its application in a forthcoming Smart City. Full article
(This article belongs to the Special Issue Artificial Intelligence and Sensors)
Open AccessArticle Spatial–Temporal Sensing and Utilization in Full Duplex Spectrum-Heterogeneous Cognitive Radio Networks for the Internet of Things
Sensors 2019, 19(6), 1441; https://doi.org/10.3390/s19061441 (registering DOI)
Received: 31 January 2019 / Revised: 17 March 2019 / Accepted: 19 March 2019 / Published: 23 March 2019
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Abstract
The continuous growth of interconnected devices in the Internet of Things (IoT) presents a challenge in terms of network resources. Cognitive radio (CR) is a promising technology that
can address the IoT spectral demands by enabling an opportunistic spectrum access (OSA) scheme. The [...] Read more.
The continuous growth of interconnected devices in the Internet of Things (IoT) presents a challenge in terms of network resources. Cognitive radio (CR) is a promising technology that
can address the IoT spectral demands by enabling an opportunistic spectrum access (OSA) scheme. The application of full duplex (FD) radios in spectrum sensing enables secondary users (SUs) to perform sensing and transmission simultaneously, and improves the utilization of the spectrum. However, random and dense distributions of FD-enabled SU transmitters (FD-SU TXs) with sensing capabilities in small-cell CR-IoT environments poses new challenges, and creates heterogeneous environments with different spectral opportunities. In this paper, we propose a spatial and temporal spectral-hole sensing framework for FD-SU TXs deployed in CR-IoT spectrum-heterogeneous environment. Incorporating the proposed sensing model, we present the analytical formulation and an evaluation of a utilization of spectrum (UoS) scheme for FD-SU TXs present at different spatial
positions. The numerical results are evaluated under different network and sensing parameters to examine the sensitivities of different parameters. It is demonstrated that self-interference, primary user activity level, and the sensing outcomes in spatial and temporal domains have a significant influence on the utilization performance of spectrum. Full article
Open AccessArticle Sensors Information Fusion System with Fault Detection Based on Multi-Manifold Regularization Neighborhood Preserving Embedding
Sensors 2019, 19(6), 1440; https://doi.org/10.3390/s19061440 (registering DOI)
Received: 28 January 2019 / Revised: 8 March 2019 / Accepted: 21 March 2019 / Published: 23 March 2019
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Abstract
Electrical drive systems play an increasingly important role in high-speed trains. The whole system is equipped with sensors that support complicated information fusion, which means the performance around this system ought to be monitored especially during incipient changes. In such situation, it is [...] Read more.
Electrical drive systems play an increasingly important role in high-speed trains. The whole system is equipped with sensors that support complicated information fusion, which means the performance around this system ought to be monitored especially during incipient changes. In such situation, it is crucial to distinguish faulty state from observed normal state because of the dire consequences closed-loop faults might bring. In this research, an optimal neighborhood preserving embedding (NPE) method called multi-manifold regularization NPE (MMRNPE) is proposed to detect various faults in an electrical drive sensor information fusion system. By taking locality preserving embedding into account, the proposed methodology extends the united application of Euclidean distance of both designated points and paired points, which guarantees the access to both local and global sensor information. Meanwhile, this structure fuses several manifolds to extract their own features. In addition, parameters are allocated in diverse manifolds to seek an optimal combination of manifolds while entropy of information with parameters is also selected to avoid the overweight of single manifold. Moreover, an experimental test based on the platform was built to validate the MMRNPE approach and demonstrate the effectiveness of the fault detection. Results and observations show that the proposed MMRNPE offers a better fault detection representation in comparison with NPE. Full article
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Open AccessArticle High-Rise Building 3D Reconstruction with the Wrapped Interferometric Phase
Sensors 2019, 19(6), 1439; https://doi.org/10.3390/s19061439 (registering DOI)
Received: 12 February 2019 / Revised: 14 March 2019 / Accepted: 20 March 2019 / Published: 23 March 2019
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Abstract
The great development of high-resolution SAR system gives more opportunities to observe building structures in detail, especially the advanced interferometric SAR (InSAR), which techniques attract more attention on exploiting useful information on urban infrastructures. Considering that the high-rise buildings in urban areas are [...] Read more.
The great development of high-resolution SAR system gives more opportunities to observe building structures in detail, especially the advanced interferometric SAR (InSAR), which techniques attract more attention on exploiting useful information on urban infrastructures. Considering that the high-rise buildings in urban areas are quite common in big cities, it is of great importance to retrieve the three-dimension (3D) information of the urban high-rise buildings in urban remote sensing applications. In this paper, the 3D reconstruction of high-rise buildings using the wrapped InSAR phase image was studied, referring to the geometric modulation in very high resolution (VHR) SAR images, such as serious layover cause by high-rise buildings. Under the assumption of a rectangular shape, the high-rise buildings were detected and building façades were extracted based on the local frequency analysis of the layover fringe patterns. Then 3D information of buildings were finally extracted according to the detected façade geometry. Except for testing on a small urban area from the TanDEM-X data, the experiment carried on the single-pass InSAR wrapped phase in the wide urban scene, which was collected by the Chinese airborne N-SAR system, also demonstrated the possibility and applicability of the approach. Full article
(This article belongs to the Special Issue First Experiences with Chinese Gaofen-3 SAR Sensor)
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Open AccessArticle Proactive Deployment of Aerial Drones for Coverage over Very Uneven Terrains: A Version of the 3D Art Gallery Problem
Sensors 2019, 19(6), 1438; https://doi.org/10.3390/s19061438 (registering DOI)
Received: 10 March 2019 / Revised: 20 March 2019 / Accepted: 20 March 2019 / Published: 23 March 2019
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Abstract
The paper focuses on surveillance and monitoring using aerial drones. The aim is to estimate the minimal number of drones necessary to monitor a given area of a very uneven terrain. The proposed problem may be viewed as a drone version of the [...] Read more.
The paper focuses on surveillance and monitoring using aerial drones. The aim is to estimate the minimal number of drones necessary to monitor a given area of a very uneven terrain. The proposed problem may be viewed as a drone version of the 3D Art Gallery Problem. A computationally simple algorithm to calculate an upper estimate of the minimal number of drones together with their locations is developed. Computer simulations are conducted to demonstrate the effectiveness of the proposed method. Full article
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Open AccessArticle A Real-Time Detection Method for BDS Signal in Space Anomalies
Sensors 2019, 19(6), 1437; https://doi.org/10.3390/s19061437 (registering DOI)
Received: 18 February 2019 / Revised: 17 March 2019 / Accepted: 21 March 2019 / Published: 23 March 2019
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Abstract
Signal In Space (SIS) anomalies in satellite navigation systems can degrade satellite-based navigation and positioning performance. The occurrence of SIS anomalies from the BeiDou navigation satellite System (BDS) may be more frequent than for the Global Positioning System (GPS). In order to guarantee [...] Read more.
Signal In Space (SIS) anomalies in satellite navigation systems can degrade satellite-based navigation and positioning performance. The occurrence of SIS anomalies from the BeiDou navigation satellite System (BDS) may be more frequent than for the Global Positioning System (GPS). In order to guarantee the integrity of BDS users, detecting and excluding SIS anomalies is indispensable. The traditional method through the comparison between the final precision ephemeris and the broadcast ephemeris is limited by the issue of long latency of precision ephemeris release. Through the statistical characteristics analysis of Signal In Space User Range Error (SISURE), we propose a real-time Instantaneous SISURE (IURE) estimation method by using the Kalman filtering-based carrier-smoothed-code to detect and exclude BDS SIS anomalies, in which the threshold for BDS IURE anomaly detection are obtained from the integrity requirement. The experimental results based on 1 Hz data from ground observations show that the proposed method has an estimation accuracy of 1.1 m for BDS IURE. The test results show that the proposed method can effectively detect the SIS anomalies caused by either orbit faults or clock faults. Full article
(This article belongs to the Section Remote Sensors)
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Open AccessArticle LSPR Biosensing Approach for the Detection of Microtubule Nucleation
Sensors 2019, 19(6), 1436; https://doi.org/10.3390/s19061436 (registering DOI)
Received: 27 February 2019 / Revised: 15 March 2019 / Accepted: 18 March 2019 / Published: 23 March 2019
PDF Full-text (1767 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Microtubules are dynamic protein filaments that are involved in a number of cellular processes. Here, we report the development of a novel localized surface plasmon resonance (LSPR) biosensing approach for investigating one aspect of microtubule dynamics that is not well understood, namely, nucleation. [...] Read more.
Microtubules are dynamic protein filaments that are involved in a number of cellular processes. Here, we report the development of a novel localized surface plasmon resonance (LSPR) biosensing approach for investigating one aspect of microtubule dynamics that is not well understood, namely, nucleation. Using a modified Mie theory with radially variable refractive index, we construct a theoretical model to describe the optical response of gold nanoparticles when microtubules form around them. The model predicts that the extinction maximum wavelength is sensitive to a change in the local refractive index induced by microtubule nucleation within a few tens of nanometers from the nanoparticle surface, but insensitive to a change in the refractive index outside this region caused by microtubule elongation. As a proof of concept to demonstrate that LSPR can be used for detecting microtubule nucleation experimentally, we induce spontaneous microtubule formation around gold nanoparticles by immobilizing tubulin subunits on the nanoparticles. We find that, consistent with the theoretical model, there is a redshift in the extinction maximum wavelength upon the formation of short microtubules around the nanoparticles, but no significant change in maximum wavelength when the microtubules are elongated. We also perform kinetic experiments and demonstrate that the maximum wavelength is sensitive to the microtubule nuclei assembly even when microtubules are too small to be detected from an optical density measurement. Full article
(This article belongs to the Section Biosensors)
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Open AccessArticle Organoleptic Analysis of Drinking Water Using an Electronic Tongue Based on Electrochemical Microsensors
Sensors 2019, 19(6), 1435; https://doi.org/10.3390/s19061435 (registering DOI)
Received: 23 February 2019 / Revised: 18 March 2019 / Accepted: 19 March 2019 / Published: 23 March 2019
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Abstract
The standards that establish water’s quality criteria for human consumption include organoleptic analysis. These analyses are performed by taste panels that are not available to all water supply companies with the required frequency. In this work, we propose the use of an electronic [...] Read more.
The standards that establish water’s quality criteria for human consumption include organoleptic analysis. These analyses are performed by taste panels that are not available to all water supply companies with the required frequency. In this work, we propose the use of an electronic tongue to perform organoleptic tests in drinking water. The aim is to automate the whole process of these tests, making them more economical, simple, and accessible. The system is composed by an array of electrochemical microsensors and chemometric tools for multivariable processing to extract the useful chemical information. The array of sensors is composed of six Ion-Sensitive Field Effect Transistors (ISFET)-based sensors, one conductivity sensor, one redox potential sensor, and two amperometric electrodes, one gold microelectrode for chlorine detection, and one nanocomposite planar electrode for sensing electrochemical oxygen demand. A previous study addressed to classify water samples according to taste/smell descriptors (sweet, acidic, salty, bitter, medicinal, chlorinous, mouldy, and earthy) was performed. A second study comparing the results of two organoleptic tests (hedonic evaluation and ranking test) with the electronic tongue, using Partial Least Squares regression, was conducted. The results show that the proposed electronic tongue is capable of analyzing water samples according to their organoleptic characteristics, which can be used as an alternative method to the taste panel. Full article
(This article belongs to the Special Issue Multivariate Data Analysis for Sensors and Sensor Arrays)
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Open AccessArticle One-Stage Multi-Sensor Data Fusion Convolutional Neural Network for 3D Object Detection
Sensors 2019, 19(6), 1434; https://doi.org/10.3390/s19061434 (registering DOI)
Received: 21 February 2019 / Revised: 17 March 2019 / Accepted: 20 March 2019 / Published: 23 March 2019
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Abstract
Three-dimensional (3D) object detection has important applications in robotics, automatic loading, automatic driving and other scenarios. With the improvement of devices, people can collect multi-sensor/multimodal data from a variety of sensors such as Lidar and cameras. In order to make full use of [...] Read more.
Three-dimensional (3D) object detection has important applications in robotics, automatic loading, automatic driving and other scenarios. With the improvement of devices, people can collect multi-sensor/multimodal data from a variety of sensors such as Lidar and cameras. In order to make full use of various information advantages and improve the performance of object detection, we proposed a Complex-Retina network, a convolution neural network for 3D object detection based on multi-sensor data fusion. Firstly, a unified architecture with two feature extraction networks was designed, and the feature extraction of point clouds and images from different sensors realized synchronously. Then, we set a series of 3D anchors and projected them to the feature maps, which were cropped into 2D anchors with the same size and fused together. Finally, the object classification and 3D bounding box regression were carried out on the multipath of fully connected layers. The proposed network is a one-stage convolution neural network, which achieves the balance between the accuracy and speed of object detection. The experiments on KITTI datasets show that the proposed network is superior to the contrast algorithms in average precision (AP) and time consumption, which shows the effectiveness of the proposed network. Full article
(This article belongs to the Section Remote Sensors)
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Open AccessArticle Device Identification Interoperability in Heterogeneous IoT Platforms
Sensors 2019, 19(6), 1433; https://doi.org/10.3390/s19061433 (registering DOI)
Received: 31 January 2019 / Revised: 18 March 2019 / Accepted: 21 March 2019 / Published: 23 March 2019
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Abstract
With the continuous improvement of Internet of Things (IoT) technologies, various IoT platforms are under development. However, each IoT platform is developed based on its own device identification system. That is, it is challenging to identify each sensor device between heterogeneous IoT platforms [...] Read more.
With the continuous improvement of Internet of Things (IoT) technologies, various IoT platforms are under development. However, each IoT platform is developed based on its own device identification system. That is, it is challenging to identify each sensor device between heterogeneous IoT platforms owing to the resource request format (e.g., device identifier) varying between platforms. Moreover, despite the considerable research focusing on resource interoperability between heterogeneous IoT platforms, little attention is given to sensor device identification systems in diverse IoT platforms. In order to overcome this problem, the current work proposes an IoT device name system (DNS) architecture based on the comparative analysis of heterogeneous IoT platforms (i.e., oneM2M, GS1 ‘Oliot’, IBM ‘Watson IoT’, OCF ‘IoTivity’, FIWARE). The proposed IoT DNS analyzes and translates the identification system of the device and resource request format. In this process, resource requests between heterogeneous IoT platforms can be reconfigured appropriately for the resources and services requested by the user, and as a result, users can use heterogeneous IoT services. Furthermore, in order to illustrate the aim of the proposed architecture, the proposed IoT DNS is implemented and tested on a microcomputer. The experimental results show that a oneM2M-based device successfully performs a resource request to a Watson IoT and FIWARE sensor devices. Full article
(This article belongs to the Special Issue I3S 2018 Selected Papers)
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Open AccessArticle High Temperature Effects during High Energy Laser Strikes on Embedded Fiber Bragg Grating Sensors
Sensors 2019, 19(6), 1432; https://doi.org/10.3390/s19061432 (registering DOI)
Received: 19 February 2019 / Revised: 8 March 2019 / Accepted: 11 March 2019 / Published: 23 March 2019
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Abstract
As the applications of fiber Bragg gratings (FBGs) continue to grow and become more advanced, it becomes necessary to understand their behavior when exposed to high temperatures in unique situations. In these experiments, uniform 1530-nm fiber Bragg gratings and Type K Cr-Al thermocouples [...] Read more.
As the applications of fiber Bragg gratings (FBGs) continue to grow and become more advanced, it becomes necessary to understand their behavior when exposed to high temperatures in unique situations. In these experiments, uniform 1530-nm fiber Bragg gratings and Type K Cr-Al thermocouples were embedded in three-ply carbon fiber composites. A 100 W high energy laser (HEL) heated the composites to high temperatures over timespans less than one second, and FBG spectral data and thermocouple temperature data were collected during each HEL heating test. The data from three high energy laser tests that represent different levels of damage to the FBG are analyzed to explore the spectral response and thermal decay of embedded FBG sensors when exposed to high temperatures over short timespans. Results are compared to a previously proposed power-law model describing the decay of FBGs in bare fiber when held at constant temperatures over much longer timespans. Full article
(This article belongs to the Special Issue Fiber Optic Sensors for Industrial Applications)
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Open AccessArticle Small-Target Detection between SAR Images Based on Statistical Modeling of Log-Ratio Operator
Sensors 2019, 19(6), 1431; https://doi.org/10.3390/s19061431 (registering DOI)
Received: 2 January 2019 / Revised: 10 March 2019 / Accepted: 18 March 2019 / Published: 23 March 2019
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Abstract
The log-ratio (LR) operator is well suited for change detection in synthetic aperture radar (SAR) amplitude or intensity images. In applying the LR operator to change detection in multi-temporal SAR images, a crucial problem is how to develop precise models for the LR [...] Read more.
The log-ratio (LR) operator is well suited for change detection in synthetic aperture radar (SAR) amplitude or intensity images. In applying the LR operator to change detection in multi-temporal SAR images, a crucial problem is how to develop precise models for the LR statistics. In this study, we first derive analytically the probability density function (PDF) of the LR operator. Subsequently, the PDF of the LR statistics is parameterized by three parameters, i.e., the number of looks, the coherence magnitude, and the true intensity ratio. Then, the maximum-likelihood (ML) estimates of parameters in the LR PDF are also derived. As an example, the proposed statistical model and corresponding ML estimation are used in an operational application, i.e., determining the constant false alarm rate (CFAR) detection thresholds for small target detection between SAR images. The effectiveness of the proposed model and corresponding ML estimation are verified by applying them to measured multi-temporal SAR images, and comparing the results to the well-known generalized Gaussian (GG) distribution; the usefulness of the proposed LR PDF for small target detection is also shown. Full article
(This article belongs to the Section Remote Sensors)
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Open AccessArticle Application of a Multi-Satellite Dynamic Mission Scheduling Model Based on Mission Priority in Emergency Response
Sensors 2019, 19(6), 1430; https://doi.org/10.3390/s19061430 (registering DOI)
Received: 19 December 2018 / Revised: 5 March 2019 / Accepted: 21 March 2019 / Published: 23 March 2019
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Abstract
Emergency observations are missions executed by Earth observation satellites to support urgent ground operations. Emergency observations become more important for meeting the requirements of highly dynamic and highly time-sensitive observation missions, such as disaster monitoring and early warning. Considering the complex scheduling problem [...] Read more.
Emergency observations are missions executed by Earth observation satellites to support urgent ground operations. Emergency observations become more important for meeting the requirements of highly dynamic and highly time-sensitive observation missions, such as disaster monitoring and early warning. Considering the complex scheduling problem of Earth observation satellites under emergency conditions, a multi-satellite dynamic mission scheduling model based on mission priority is proposed in this paper. A calculation model of mission priority is designed for emergency missions based on seven impact factors. In the satellite mission scheduling, the resource constraints of scheduling are analyzed in detail, and the optimization objective function is built to maximize the observation mission priority and mission revenues, and minimize the waiting time for missions that require urgency for execution time. Then, the hybrid genetic tabu search algorithm is used to obtain the initial satellite scheduling plan. In case of the dynamic arrival of new emergency missions before scheduling plan releases, a dynamic scheduling algorithm based on mission priority is proposed to solve the scheduling problem caused by newly arrived missions and to obtain the scheduling plan of newly arrived missions. A simulation experiment was conducted for different numbers of initial missions and newly arrived missions, and the scheduling results were evaluated with a model performance evaluation function. The results show that the execution probability of high-priority missions increased because the mission priority was taken into account in the model. In the case of more satellite resources, when new missions dynamically arrived, the satellite resources can be reasonably allocated to these missions based on the mission priority. Overall, this approach reduces the complexity of the dynamic adjustment and maintains the stability of the initial scheduling plan. Full article
(This article belongs to the Special Issue Satellite and Airborne Remote Sensing for Earth Monitoring)
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Open AccessArticle Finger-Counting-Based Gesture Recognition within Cars Using Impulse Radar with Convolutional Neural Network
Sensors 2019, 19(6), 1429; https://doi.org/10.3390/s19061429 (registering DOI)
Received: 19 February 2019 / Revised: 13 March 2019 / Accepted: 20 March 2019 / Published: 23 March 2019
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Abstract
The diversion of a driver’s attention from driving can be catastrophic. Given that conventional button- and touch-based interfaces may distract the driver, developing novel distraction-free interfaces for the various devices present in cars has becomes necessary. Hand gesture recognition may provide an alternative [...] Read more.
The diversion of a driver’s attention from driving can be catastrophic. Given that conventional button- and touch-based interfaces may distract the driver, developing novel distraction-free interfaces for the various devices present in cars has becomes necessary. Hand gesture recognition may provide an alternative interface inside cars. Given that cars are the targeted application area, we determined the optimal location for the radar sensor, so that the signal reflected from the driver’s hand during gesturing is unaffected by interference from the motion of the driver’s body or other motions within the car. We implemented a Convolutional Neural Network-based technique to recognize the finger-counting-based hand gestures using an Impulse Radio (IR) radar sensor. The accuracy of the proposed method was sufficiently high for real-world applications. Full article
(This article belongs to the Section Intelligent Sensors)
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Open AccessErratum Erratum: Li, D.; Scarano, S.; Lisi, S.; Palladino, P.; Minunni, M. Real-Time Tau Protein Detection by Sandwich-Based Piezoelectric Biosensing: Exploring Tubulin as a Mass Enhancer. Sensors 2018, 18, 946.
Sensors 2019, 19(6), 1428; https://doi.org/10.3390/s19061428 (registering DOI)
Received: 8 March 2019 / Accepted: 13 March 2019 / Published: 22 March 2019
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Abstract
The authors wish to make the following erratum to this paper [...] Full article
(This article belongs to the Special Issue Label-Free Biosensors)
Open AccessArticle Thermoelectric Photosensor Based on Ultrathin Single-Crystalline Si Films
Sensors 2019, 19(6), 1427; https://doi.org/10.3390/s19061427 (registering DOI)
Received: 13 February 2019 / Revised: 15 March 2019 / Accepted: 19 March 2019 / Published: 22 March 2019
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Abstract
Ultrathin Si films have a reduced thermal conductivity in comparison to Si bulk due to phonon scattering at the surfaces. Furthermore, the small thickness guarantees a reduced thermal mass (in the µJ/K range), which opens up the possibility of developing thermal sensors with [...] Read more.
Ultrathin Si films have a reduced thermal conductivity in comparison to Si bulk due to phonon scattering at the surfaces. Furthermore, the small thickness guarantees a reduced thermal mass (in the µJ/K range), which opens up the possibility of developing thermal sensors with a high sensitivity. Based on these premises, a thermoelectric (TE) microsensor based on ultrathin suspended Si films was developed and used as a thermal photosensor. The photoresponse of the device was evaluated with an argon laser (λ = 457 nm) with a variable power ranging from 0 to 10 mW in air at atmospheric pressure, with laser diodes at 406 nm, 520 nm and 638 nm wavelengths, and fixed powers in high vacuum conditions. The responsivity per unit area, response time (τ) and detectivity (D*) of the device were determined in air at ambient pressure, being 2.6 × 107 V/Wm2, ~4.3 ms and 2.86 × 10 7   c m H z ( 1 / 2 ) W 1 , respectively. Temperature differences up to 30 K between the central hot region and the Si frame were achieved during open-circuit voltage measurements, with and without laser diodes. During illumination, the photogeneration of carriers caused a slight reduction of the Seebeck coefficient, which did not significantly change the sensitivity of the device. Moreover, the measurements performed with light beam chopped at different frequencies evidenced the quick response of the device. The temperature gradients applied to the thermoelectric Si legs were corrected using finite element modeling (FEM) due to the non-flat temperature profile generated during the experiments. Full article
(This article belongs to the Special Issue Eurosensors 2018 Selected Papers)
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Open AccessArticle Comparison of Kalman Filters for Inertial Integrated Navigation
Sensors 2019, 19(6), 1426; https://doi.org/10.3390/s19061426 (registering DOI)
Received: 25 January 2019 / Revised: 18 March 2019 / Accepted: 20 March 2019 / Published: 22 March 2019
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Abstract
The current research on integrated navigation is mainly focused on filtering or integrated navigation equipment. Studies systematically comparing and analyzing how to choose appropriate integrated filtering methods under different circumstances are lacking. This paper focuses on integrated navigation filters that are used by [...] Read more.
The current research on integrated navigation is mainly focused on filtering or integrated navigation equipment. Studies systematically comparing and analyzing how to choose appropriate integrated filtering methods under different circumstances are lacking. This paper focuses on integrated navigation filters that are used by different filters and attitude parameters for inertial integrated navigation. We researched integrated navigation filters, established algorithms, and examined the relative merits for practical integrated navigation. Some suggestions for the use of filtering algorithms are provided.We completed simulations and car-mounted experiments for low-cost strapdown inertial navigation system(SINS) to assess the performance of the integrated navigation filtering algorithms. Full article
(This article belongs to the Section Physical Sensors)
Open AccessArticle Baseball Player Behavior Classification System Using Long Short-Term Memory with Multimodal Features
Sensors 2019, 19(6), 1425; https://doi.org/10.3390/s19061425 (registering DOI)
Received: 14 February 2019 / Revised: 12 March 2019 / Accepted: 20 March 2019 / Published: 22 March 2019
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Abstract
In this paper, a preliminary baseball player behavior classification system is proposed. By using multiple IoT sensors and cameras, the proposed method accurately recognizes many of baseball players’ behaviors by analyzing signals from heterogeneous sensors. The contribution of this paper is threefold: (i) [...] Read more.
In this paper, a preliminary baseball player behavior classification system is proposed. By using multiple IoT sensors and cameras, the proposed method accurately recognizes many of baseball players’ behaviors by analyzing signals from heterogeneous sensors. The contribution of this paper is threefold: (i) signals from a depth camera and from multiple inertial sensors are obtained and segmented, (ii) the time-variant skeleton vector projection from the depth camera and the statistical features extracted from the inertial sensors are used as features, and (iii) a deep learning-based scheme is proposed for training behavior classifiers. The experimental results demonstrate that the proposed deep learning behavior system achieves an accuracy of greater than 95% compared to the proposed dataset. Full article
(This article belongs to the Special Issue Selected Papers from INNOV 2018)
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Open AccessArticle Performance in Solar Orientation Determination for Regular Pyramid Sun Sensors
Sensors 2019, 19(6), 1424; https://doi.org/10.3390/s19061424 (registering DOI)
Received: 21 January 2019 / Revised: 15 March 2019 / Accepted: 18 March 2019 / Published: 22 March 2019
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Abstract
Non-planar sun sensors can determine solar orientation by existing photodiodes or by reusing solar panels, without increasing the size and mass of spacecraft. However, a limiting factor for the improvement of the accuracy of orientation lies in the lack of a detailed performance [...] Read more.
Non-planar sun sensors can determine solar orientation by existing photodiodes or by reusing solar panels, without increasing the size and mass of spacecraft. However, a limiting factor for the improvement of the accuracy of orientation lies in the lack of a detailed performance assessment on interference suppression. In this paper, a new method that determines solar orientation in the frequency domain is developed for regular pyramid sun sensors, which are formed by regular pyramid arrays. Furthermore, two formulations are established to evaluate the errors of the solar azimuth and elevation angle in solar orientation determination based on the newly proposed frequency-domain method. With these formulations of performance evaluation, we discover the mathematical relationship between the interference spectrum, array geometry, solar irradiance, solar azimuth or elevation angle, and the error in solar orientation determination for the first time. This reveals that the internal interference from the detection system can be completely suppressed in solar orientation determination, and the constant interference can be eliminated in the estimation of solar azimuth angle. Simulation and field experiments validated the effectiveness of the new orientation method, error formulations and performance of each interference source. Full article
(This article belongs to the Section Physical Sensors)
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Open AccessReview EEG-Based Brain-Computer Interfaces Using Motor-Imagery: Techniques and Challenges
Sensors 2019, 19(6), 1423; https://doi.org/10.3390/s19061423 (registering DOI)
Received: 30 January 2019 / Revised: 10 March 2019 / Accepted: 19 March 2019 / Published: 22 March 2019
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Abstract
Electroencephalography (EEG)-based brain-computer interfaces (BCIs), particularly those using motor-imagery (MI) data, have the potential to become groundbreaking technologies in both clinical and entertainment settings. MI data is generated when a subject imagines the movement of a limb. This paper reviews state-of-the-art signal processing [...] Read more.
Electroencephalography (EEG)-based brain-computer interfaces (BCIs), particularly those using motor-imagery (MI) data, have the potential to become groundbreaking technologies in both clinical and entertainment settings. MI data is generated when a subject imagines the movement of a limb. This paper reviews state-of-the-art signal processing techniques for MI EEG-based BCIs, with a particular focus on the feature extraction, feature selection and classification techniques used. It also summarizes the main applications of EEG-based BCIs, particularly those based on MI data, and finally presents a detailed discussion of the most prevalent challenges impeding the development and commercialization of EEG-based BCIs. Full article
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Open AccessArticle Energy-Efficient Nonuniform Content Edge Pre-Caching to Improve Quality of Service in Fog Radio Access Networks
Sensors 2019, 19(6), 1422; https://doi.org/10.3390/s19061422 (registering DOI)
Received: 9 February 2019 / Revised: 3 March 2019 / Accepted: 15 March 2019 / Published: 22 March 2019
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Abstract
The fog radio access network (F-RAN) equipped with enhanced remote radio heads (eRRHs), which can pre-store some requested files in the edge cache and support mobile edge computing (MEC). To guarantee the quality-of-service (QoS) and energy efficiency of F-RAN, a proper content caching [...] Read more.
The fog radio access network (F-RAN) equipped with enhanced remote radio heads (eRRHs), which can pre-store some requested files in the edge cache and support mobile edge computing (MEC). To guarantee the quality-of-service (QoS) and energy efficiency of F-RAN, a proper content caching strategy is necessary to avoid coarse content storing locally in the cache or frequent fetching from a centralized baseband signal processing unit (BBU) pool via backhauls. In this paper we investigate the relationships among eRRH/terminal activities and content requesting in F-RANs, and propose an edge content caching strategy for eRRHs by mining out mobile network behavior information. Especially, to attain the inference for appropriate content caching, we establish a pre-mapping containing content preference information and geographical influence by an efficient non-uniformed accelerated matrix completion algorithm. The energy consumption analysis is given in order to discuss the energy saving properties of the proposed edge content caching strategy. Simulation results demonstrate our theoretical analysis on the inference validity of the pre-mapping construction method in static and dynamic cases, and show the energy efficiency achieved by the proposed edge content pre-caching strategy. Full article
(This article belongs to the Special Issue Advanced Technologies on Green Radio Networks)
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Open AccessArticle R-DEHM: CSI-Based Robust Duration Estimation of Human Motion with WiFi
Sensors 2019, 19(6), 1421; https://doi.org/10.3390/s19061421 (registering DOI)
Received: 27 February 2019 / Revised: 13 March 2019 / Accepted: 15 March 2019 / Published: 22 March 2019
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Abstract
As wireless sensing has developed, wireless behavior recognition has become a promising research area, in which human motion duration is one of the basic and significant parameters to measure human behavior. At present, however, there is no consideration of the duration estimation of [...] Read more.
As wireless sensing has developed, wireless behavior recognition has become a promising research area, in which human motion duration is one of the basic and significant parameters to measure human behavior. At present, however, there is no consideration of the duration estimation of human motion leveraging wireless signals. In this paper, we propose a novel system for robust duration estimation of human motion (R-DEHM) with WiFi in the area of interest. To achieve this, we first collect channel statement information (CSI) measurements on commodity WiFi devices and extract robust features from the CSI amplitude. Then, the back propagation neural network (BPNN) algorithm is introduced for detection by seeking a cutting line of the features for different states, i.e., moving human presence and absence. Instead of directly estimating the duration of human motion, we transform the complex and continuous duration estimation problem into a simple and discrete human motion detection by segmenting the CSI sequences. Furthermore, R-DEHM is implemented and evaluated in detail. The results of our experiments show that R-DEHM achieves the human motion detection and duration estimation with the average detection rate for human motion more than 94% and the average error rate for duration estimation less than 8%, respectively. Full article
(This article belongs to the Section Sensor Networks)
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Open AccessArticle A Water Quality Prediction Method Based on the Deep LSTM Network Considering Correlation in Smart Mariculture
Sensors 2019, 19(6), 1420; https://doi.org/10.3390/s19061420 (registering DOI)
Received: 17 January 2019 / Revised: 16 March 2019 / Accepted: 19 March 2019 / Published: 22 March 2019
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Abstract
An accurate prediction of cage-cultured water quality is a hot topic in smart mariculture. Since the mariculturing environment is always open to its surroundings, the changes in water quality parameters are normally nonlinear, dynamic, changeable, and complex. However, traditional forecasting methods have lots [...] Read more.
An accurate prediction of cage-cultured water quality is a hot topic in smart mariculture. Since the mariculturing environment is always open to its surroundings, the changes in water quality parameters are normally nonlinear, dynamic, changeable, and complex. However, traditional forecasting methods have lots of problems, such as low accuracy, poor generalization, and high time complexity. In order to solve these shortcomings, a novel water quality prediction method based on the deep LSTM (long short-term memory) learning network is proposed to predict pH and water temperature. Firstly, linear interpolation, smoothing, and moving average filtering techniques are used to repair, correct, and de-noise water quality data, respectively. Secondly, Pearson’s correlation coefficient is used to obtain the correlation priors between pH, water temperature, and other water quality parameters. Finally, a water quality prediction model based on LSTM is constructed using the preprocessed data and its correlation information. Experimental results show that, in the short-term prediction, the prediction accuracy of pH and water temperature can reach 98.56% and 98.97%, and the time cost of the predictions is 0.273 s and 0.257 s, respectively. In the long-term prediction, the prediction accuracy of pH and water temperature can reach 95.76% and 96.88%, respectively. Full article
(This article belongs to the Section Internet of Things)
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Open AccessArticle Near-Infrared Imaging of Artificial Enamel Caries Lesions with a Scanning Fiber Endoscope
Sensors 2019, 19(6), 1419; https://doi.org/10.3390/s19061419 (registering DOI)
Received: 20 February 2019 / Revised: 12 March 2019 / Accepted: 20 March 2019 / Published: 22 March 2019
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Abstract
Several studies have shown that near-infrared imaging has great potential for the detection of dental caries lesions. A miniature scanning fiber endoscope (SFE) operating at near-infrared (NIR) wavelengths was developed and used in this study to test whether the device could be used [...] Read more.
Several studies have shown that near-infrared imaging has great potential for the detection of dental caries lesions. A miniature scanning fiber endoscope (SFE) operating at near-infrared (NIR) wavelengths was developed and used in this study to test whether the device could be used to discriminate demineralized enamel from sound enamel. Varying depths of artificial enamel caries lesions were prepared on 20 bovine blocks with smooth enamel surfaces. Samples were imaged with a SFE operating in the reflectance mode at 1310-nm and 1460-nm in both wet and dry conditions. The measurements acquired by the SFE operating at 1460-nm show significant difference between the sound and the demineralized enamel. There was a moderate positive correlation between the SFE measurements and micro-CT measurements, and the NIR SFE was able to detect the presence of demineralization with high sensitivity (0.96) and specificity (0.85). This study demonstrates that the NIR SFE can be used to detect early demineralization from sound enamel. In addition, the NIR SFE can differentiate varying severities of demineralization. With its very small form factor and maneuverability, the NIR SFE should allow clinicians to easily image teeth from multiple viewing angles in real-time. Full article
(This article belongs to the Special Issue Sensors in Dentistry)
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Open AccessArticle Three-Stage Single-Chambered Microbial Fuel Cell Biosensor Inoculated with Exiguobacterium aestuarii YC211 for Continuous Chromium (VI) Measurement
Sensors 2019, 19(6), 1418; https://doi.org/10.3390/s19061418 (registering DOI)
Received: 2 March 2019 / Revised: 17 March 2019 / Accepted: 19 March 2019 / Published: 22 March 2019
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Abstract
Chromium (VI) [Cr(VI)] compounds display high toxic, mutagenic, and carcinogenic potential. Biological analysis techniques (e.g., such as enzyme-based or cell-based sensors) have been developed to measure Cr(VI); however, these biological elements are sensitive to the environment, limited to measuring trace Cr(VI), and require [...] Read more.
Chromium (VI) [Cr(VI)] compounds display high toxic, mutagenic, and carcinogenic potential. Biological analysis techniques (e.g., such as enzyme-based or cell-based sensors) have been developed to measure Cr(VI); however, these biological elements are sensitive to the environment, limited to measuring trace Cr(VI), and require deployment offsite. In this study, a three-stage single-chambered microbial fuel cell (SCMFC) biosensor inoculated with Exiguobacterium aestuarii YC211 was developed for in situ, real-time, and continuous Cr(VI) measurement. A negative linear relationship was observed between the Cr(VI) concentration (5–30 mg/L) and the voltage output using an SCMFC at 2-min liquid retention time. The theoretical Cr(VI) measurement range of the system could be extended to 5–90 mg/L by connecting three separate SCMFCs in series. The three-stage SCMFC biosensor could accurately measure Cr(VI) concentrations in actual tannery wastewater with low deviations (<7%). After treating the wastewater with the SCMFC, the original inoculated E. aestuarii remained dominant (>92.5%), according to the next-generation sequencing analysis. The stable bacterial community present in the SCMFC favored the reliable performance of the SCMFC biosensor. Thus, the three-stage SCMFC biosensor has potential as an early warning device with wide dynamic range for in situ, real-time, and continuous Cr(VI) measurement of tannery wastewater. Full article
(This article belongs to the Special Issue Sensors for Water Monitoring)
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Open AccessArticle Toward Sensor-Based Sleep Monitoring with Electrodermal Activity Measures
Sensors 2019, 19(6), 1417; https://doi.org/10.3390/s19061417 (registering DOI)
Received: 29 January 2019 / Revised: 14 March 2019 / Accepted: 19 March 2019 / Published: 22 March 2019
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Abstract
We use self-report and electrodermal activity (EDA) wearable sensor data from 77 nights of sleep of six participants to test the efficacy of EDA data for sleep monitoring. We used factor analysis to find latent factors in the EDA data, and used causal [...] Read more.
We use self-report and electrodermal activity (EDA) wearable sensor data from 77 nights of sleep of six participants to test the efficacy of EDA data for sleep monitoring. We used factor analysis to find latent factors in the EDA data, and used causal model search to find the most probable graphical model accounting for self-reported sleep efficiency (SE), sleep quality (SQ), and the latent factors in the EDA data. Structural equation modeling was used to confirm fit of the extracted graph to the data. Based on the generated graph, logistic regression and naïve Bayes models were used to test the efficacy of the EDA data in predicting SE and SQ. Six EDA features extracted from the total signal over a night’s sleep could be explained by two latent factors, EDA Magnitude and EDA Storms. EDA Magnitude performed as a strong predictor for SE to aid detection of substantial changes in time asleep. The performance of EDA Magnitude and SE in classifying SQ demonstrates promise for using a wearable sensor for sleep monitoring. However, our data suggest that obtaining a more accurate sensor-based measure of SE will be necessary before smaller changes in SQ can be detected from EDA sensor data alone. Full article
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Open AccessArticle A Method for Estimating Time-Dependent Corrosion Depth of Carbon and Weathering Steel Using an Atmospheric Corrosion Monitor Sensor
Sensors 2019, 19(6), 1416; https://doi.org/10.3390/s19061416 (registering DOI)
Received: 13 January 2019 / Revised: 20 March 2019 / Accepted: 20 March 2019 / Published: 22 March 2019
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Abstract
In this study, a time-dependent corrosion depth estimation method using atmospheric corrosion monitor (ACM) sensor data to evaluate time-dependent corrosion behaviors is proposed. For the time-dependent corrosion depth estimation of uncoated carbon steel and weathering steel, acceleration corrosion tests were conducted in salt-spray [...] Read more.
In this study, a time-dependent corrosion depth estimation method using atmospheric corrosion monitor (ACM) sensor data to evaluate time-dependent corrosion behaviors is proposed. For the time-dependent corrosion depth estimation of uncoated carbon steel and weathering steel, acceleration corrosion tests were conducted in salt-spray corrosion environments and evaluated with a corrosion damage estimation method using ACM sensing data and corrosion loss data of the tested steel specimens. To estimate the time-dependent corrosion depth using corrosion current by an ACM sensor, the relationship between the mean corrosion depth calculated from the weight loss method and the corrosion current was evaluated. The mean corrosion depth was estimated by calculating the corrosion current and evaluating the relationship between the mean corrosion depth and corrosion current during the expected period. From the test and estimation results, the corrosion current demonstrated a good linear correlation with the mean corrosion depth of carbon steel and weathering. The calculated mean corrosion depth is nearly the same as that of the tested specimen, which can be well used to estimate corrosion rate for the uncoated carbon steel and weathering steel. Full article
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Open AccessArticle The Life of a New York City Noise Sensor Network
Sensors 2019, 19(6), 1415; https://doi.org/10.3390/s19061415 (registering DOI)
Received: 8 February 2019 / Revised: 28 February 2019 / Accepted: 18 March 2019 / Published: 22 March 2019
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Abstract
Noise pollution is one of the topmost quality of life issues for urban residents in the United States. Continued exposure to high levels of noise has proven effects on health, including acute effects such as sleep disruption, and long-term effects such as hypertension, [...] Read more.
Noise pollution is one of the topmost quality of life issues for urban residents in the United States. Continued exposure to high levels of noise has proven effects on health, including acute effects such as sleep disruption, and long-term effects such as hypertension, heart disease, and hearing loss. To investigate and ultimately aid in the mitigation of urban noise, a network of 55 sensor nodes has been deployed across New York City for over two years, collecting sound pressure level (SPL) and audio data. This network has cumulatively amassed over 75 years of calibrated, high-resolution SPL measurements and 35 years of audio data. In addition, high frequency telemetry data have been collected that provides an indication of a sensors’ health. These telemetry data were analyzed over an 18-month period across 31 of the sensors. It has been used to develop a prototype model for pre-failure detection which has the ability to identify sensors in a prefail state 69.1% of the time. The entire network infrastructure is outlined, including the operation of the sensors, followed by an analysis of its data yield and the development of the fault detection approach and the future system integration plans for this. Full article
(This article belongs to the Section Intelligent Sensors)
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Open AccessArticle ProLo: Localization via Projection for Three-Dimensional Mobile Underwater Sensor Networks
Sensors 2019, 19(6), 1414; https://doi.org/10.3390/s19061414 (registering DOI)
Received: 23 February 2019 / Revised: 16 March 2019 / Accepted: 18 March 2019 / Published: 22 March 2019
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Abstract
We study the problem of three-dimensional localization of the underwater mobile sensor networks using only range measurements without GPS devices. This problem is challenging because sensor nodes often drift with unknown water currents. Consequently, the moving direction and speed of a sensor node [...] Read more.
We study the problem of three-dimensional localization of the underwater mobile sensor networks using only range measurements without GPS devices. This problem is challenging because sensor nodes often drift with unknown water currents. Consequently, the moving direction and speed of a sensor node cannot be predicted. Moreover, the motion devices of the sensor nodes are not accurate in underwater environments. Therefore, we propose an adaptive localization scheme, ProLo, taking these uncertainties into consideration. This scheme applies the rigidity theory and maintains a virtual rigid structure through projection. We have proved the correctness of this three-dimensional localization scheme and also validated it using simulation. The results demonstrate that ProLo is promising for real mobile underwater sensor networks with various noises and errors. Full article
(This article belongs to the Special Issue Underwater Sensor Networks: Applications, Advances and Challenges)
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Open AccessArticle A Novel Underdetermined Blind Source Separation Method and Its Application to Source Contribution Quantitative Estimation
Sensors 2019, 19(6), 1413; https://doi.org/10.3390/s19061413 (registering DOI)
Received: 22 February 2019 / Revised: 16 March 2019 / Accepted: 20 March 2019 / Published: 22 March 2019
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Abstract
To identify the major vibration and radiation noise, a source contribution quantitative estimation method is proposed based on underdetermined blind source separation. First, the single source points (SSPs) are identified by directly searching the identical normalized time-frequency vectors of mixed signals, which can [...] Read more.
To identify the major vibration and radiation noise, a source contribution quantitative estimation method is proposed based on underdetermined blind source separation. First, the single source points (SSPs) are identified by directly searching the identical normalized time-frequency vectors of mixed signals, which can improve the efficiency and accuracy in identifying SSPs. Then, the mixing matrix is obtained by hierarchical clustering, and source signals can also be recovered by the least square method. Second, the optimal combination coefficients between source signals and mixed signals can be calculated based on minimum redundant error energy. Therefore, mixed signals can be optimally linearly combined by source signals via the coefficients. Third, the energy elimination method is used to quantitatively estimate source contributions. Finally, the effectiveness of the proposed method is verified via numerical case studies and experiments with a cylindrical structure, and the results show that source signals can be effectively recovered, and source contributions can be quantitatively estimated by the proposed method. Full article
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