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Sensors, Volume 17, Issue 3 (March 2017)

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Cover Story Sensing selectivity (data on the foreground) of a concentric-electrode organic electrochemical [...] Read more.
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Open AccessArticle Lightdrum—Portable Light Stage for Accurate BTF Measurement on Site
Sensors 2017, 17(3), 423; doi:10.3390/s17030423
Received: 2 December 2016 / Revised: 11 February 2017 / Accepted: 11 February 2017 / Published: 23 February 2017
Cited by 1 | PDF Full-text (61728 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
We propose a miniaturised light stage for measuring the bidirectional reflectance distribution function (BRDF) and the bidirectional texture function (BTF) of surfaces on site in real world application scenarios. The main principle of our lightweight BTF acquisition gantry is a compact hemispherical skeleton
[...] Read more.
We propose a miniaturised light stage for measuring the bidirectional reflectance distribution function (BRDF) and the bidirectional texture function (BTF) of surfaces on site in real world application scenarios. The main principle of our lightweight BTF acquisition gantry is a compact hemispherical skeleton with cameras along the meridian and with light emitting diode (LED) modules shining light onto a sample surface. The proposed device is portable and achieves a high speed of measurement while maintaining high degree of accuracy. While the positions of the LEDs are fixed on the hemisphere, the cameras allow us to cover the range of the zenith angle from 0 to 75 and by rotating the cameras along the axis of the hemisphere we can cover all possible camera directions. This allows us to take measurements with almost the same quality as existing stationary BTF gantries. Two degrees of freedom can be set arbitrarily for measurements and the other two degrees of freedom are fixed, which provides a tradeoff between accuracy of measurements and practical applicability. Assuming that a measured sample is locally flat and spatially accessible, we can set the correct perpendicular direction against the measured sample by means of an auto-collimator prior to measuring. Further, we have designed and used a marker sticker method to allow for the easy rectification and alignment of acquired images during data processing. We show the results of our approach by images rendered for 36 measured material samples. Full article
(This article belongs to the Section Physical Sensors)
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Open AccessArticle PTZ Camera-Based Displacement Sensor System with Perspective Distortion Correction Unit for Early Detection of Building Destruction
Sensors 2017, 17(3), 430; doi:10.3390/s17030430
Received: 5 December 2016 / Revised: 12 February 2017 / Accepted: 20 February 2017 / Published: 23 February 2017
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Abstract
This paper presents a pan-tilt-zoom (PTZ) camera-based displacement measurement system, specially based on the perspective distortion correction technique for the early detection of building destruction. The proposed PTZ-based vision system rotates the camera to monitor the specific targets from various distances and controls
[...] Read more.
This paper presents a pan-tilt-zoom (PTZ) camera-based displacement measurement system, specially based on the perspective distortion correction technique for the early detection of building destruction. The proposed PTZ-based vision system rotates the camera to monitor the specific targets from various distances and controls the zoom level of the lens for a constant field of view (FOV). The proposed approach adopts perspective distortion correction to expand the measurable range in monitoring the displacement of the target structure. The implemented system successfully obtains the displacement information in structures, which is not easily accessible on the remote site. We manually measured the displacement acquired from markers which is attached on a sample of structures covering a wide geographic region. Our approach using a PTZ-based camera reduces the perspective distortion, so that the improved system could overcome limitations of previous works related to displacement measurement. Evaluation results show that a PTZ-based displacement sensor system with the proposed distortion correction unit is possibly a cost effective and easy-to-install solution for commercialization. Full article
(This article belongs to the Section Remote Sensors)
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Open AccessArticle An Improved Metal-Packaged Strain Sensor Based on A Regenerated Fiber Bragg Grating in Hydrogen-Loaded Boron–Germanium Co-Doped Photosensitive Fiber for High-Temperature Applications
Sensors 2017, 17(3), 431; doi:10.3390/s17030431
Received: 30 November 2016 / Revised: 16 January 2017 / Accepted: 25 January 2017 / Published: 23 February 2017
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Abstract
Local strain measurements are considered as an effective method for structural health monitoring of high-temperature components, which require accurate, reliable and durable sensors. To develop strain sensors that can be used in higher temperature environments, an improved metal-packaged strain sensor based on a
[...] Read more.
Local strain measurements are considered as an effective method for structural health monitoring of high-temperature components, which require accurate, reliable and durable sensors. To develop strain sensors that can be used in higher temperature environments, an improved metal-packaged strain sensor based on a regenerated fiber Bragg grating (RFBG) fabricated in hydrogen (H2)-loaded boron–germanium (B–Ge) co-doped photosensitive fiber is developed using the process of combining magnetron sputtering and electroplating, addressing the limitation of mechanical strength degradation of silica optical fibers after annealing at a high temperature for regeneration. The regeneration characteristics of the RFBGs and the strain characteristics of the sensor are evaluated. Numerical simulation of the sensor is conducted using a three-dimensional finite element model. Anomalous decay behavior of two regeneration regimes is observed for the FBGs written in H2-loaded B–Ge co-doped fiber. The strain sensor exhibits good linearity, stability and repeatability when exposed to constant high temperatures of up to 540 °C. A satisfactory agreement is obtained between the experimental and numerical results in strain sensitivity. The results demonstrate that the improved metal-packaged strain sensors based on RFBGs in H2-loaded B–Ge co-doped fiber provide great potential for high-temperature applications by addressing the issues of mechanical integrity and packaging. Full article
(This article belongs to the Section Physical Sensors)
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Open AccessArticle A RLS-SVM Aided Fusion Methodology for INS during GPS Outages
Sensors 2017, 17(3), 432; doi:10.3390/s17030432
Received: 23 November 2016 / Revised: 15 January 2017 / Accepted: 16 February 2017 / Published: 24 February 2017
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Abstract
In order to maintain a relatively high accuracy of navigation performance during global positioning system (GPS) outages, a novel robust least squares support vector machine (LS-SVM)-aided fusion methodology is explored to provide the pseudo-GPS position information for the inertial navigation system (INS). The
[...] Read more.
In order to maintain a relatively high accuracy of navigation performance during global positioning system (GPS) outages, a novel robust least squares support vector machine (LS-SVM)-aided fusion methodology is explored to provide the pseudo-GPS position information for the inertial navigation system (INS). The relationship between the yaw, specific force, velocity, and the position increment is modeled. Rather than share the same weight in the traditional LS-SVM, the proposed algorithm allocates various weights for different data, which makes the system immune to the outliers. Field test data was collected to evaluate the proposed algorithm. The comparison results indicate that the proposed algorithm can effectively provide position corrections for standalone INS during the 300 s GPS outage, which outperforms the traditional LS-SVM method. Historical information is also involved to better represent the vehicle dynamics. Full article
(This article belongs to the Section Physical Sensors)
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Open AccessArticle Effective Visual Tracking Using Multi-Block and Scale Space Based on Kernelized Correlation Filters
Sensors 2017, 17(3), 433; doi:10.3390/s17030433
Received: 9 December 2016 / Revised: 13 February 2017 / Accepted: 17 February 2017 / Published: 23 February 2017
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Abstract
Accurate scale estimation and occlusion handling is a challenging problem in visual tracking. Recently, correlation filter-based trackers have shown impressive results in terms of accuracy, robustness, and speed. However, the model is not robust to scale variation and occlusion. In this paper, we
[...] Read more.
Accurate scale estimation and occlusion handling is a challenging problem in visual tracking. Recently, correlation filter-based trackers have shown impressive results in terms of accuracy, robustness, and speed. However, the model is not robust to scale variation and occlusion. In this paper, we address the problems associated with scale variation and occlusion by employing a scale space filter and multi-block scheme based on a kernelized correlation filter (KCF) tracker. Furthermore, we develop a more robust algorithm using an appearance update model that approximates the change of state of occlusion and deformation. In particular, an adaptive update scheme is presented to make each process robust. The experimental results demonstrate that the proposed method outperformed 29 state-of-the-art trackers on 100 challenging sequences. Specifically, the results obtained with the proposed scheme were improved by 8% and 18% compared to those of the KCF tracker for 49 occlusion and 64 scale variation sequences, respectively. Therefore, the proposed tracker can be a robust and useful tool for object tracking when occlusion and scale variation are involved. Full article
(This article belongs to the Special Issue Video Analysis and Tracking Using State-of-the-Art Sensors)
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Open AccessArticle A Mobility-Aware Adaptive Duty Cycling Mechanism for Tracking Objects during Tunnel Excavation
Sensors 2017, 17(3), 435; doi:10.3390/s17030435
Received: 31 October 2016 / Revised: 16 February 2017 / Accepted: 17 February 2017 / Published: 23 February 2017
Cited by 1 | PDF Full-text (5028 KB) | HTML Full-text | XML Full-text
Abstract
Tunnel construction workers face many dangers while working under dark conditions, with difficult access and egress, and many potential hazards. To enhance safety at tunnel construction sites, low latency tracking of mobile objects (e.g., heavy-duty equipment) and construction workers is critical for managing
[...] Read more.
Tunnel construction workers face many dangers while working under dark conditions, with difficult access and egress, and many potential hazards. To enhance safety at tunnel construction sites, low latency tracking of mobile objects (e.g., heavy-duty equipment) and construction workers is critical for managing the dangerous construction environment. Wireless Sensor Networks (WSNs) are the basis for a widely used technology for monitoring the environment because of their energy-efficiency and scalability. However, their use involves an inherent point-to-point delay caused by duty cycling mechanisms that can result in a significant rise in the delivery latency for tracking mobile objects. To overcome this issue, we proposed a mobility-aware adaptive duty cycling mechanism for the WSNs based on object mobility. For the evaluation, we tested this mechanism for mobile object tracking at a tunnel excavation site. The evaluation results showed that the proposed mechanism could track mobile objects with low latency while they were moving, and could reduce energy consumption by increasing sleep time while the objects were immobile. Full article
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Open AccessArticle Operational Modal Analysis of Bridge Structures with Data from GNSS/Accelerometer Measurements
Sensors 2017, 17(3), 436; doi:10.3390/s17030436
Received: 3 January 2017 / Revised: 20 February 2017 / Accepted: 20 February 2017 / Published: 23 February 2017
Cited by 2 | PDF Full-text (5023 KB) | HTML Full-text | XML Full-text
Abstract
Real-time dynamic displacement and acceleration responses of the main span section of the Tianjin Fumin Bridge in China under ambient excitation were tested using a Global Navigation Satellite System (GNSS) dynamic deformation monitoring system and an acceleration sensor vibration test system. Considering the
[...] Read more.
Real-time dynamic displacement and acceleration responses of the main span section of the Tianjin Fumin Bridge in China under ambient excitation were tested using a Global Navigation Satellite System (GNSS) dynamic deformation monitoring system and an acceleration sensor vibration test system. Considering the close relationship between the GNSS multipath errors and measurement environment in combination with the noise reduction characteristics of different filtering algorithms, the researchers proposed an AFEC mixed filtering algorithm, which is an combination of autocorrelation function-based empirical mode decomposition (EMD) and Chebyshev mixed filtering to extract the real vibration displacement of the bridge structure after system error correction and filtering de-noising of signals collected by the GNSS. The proposed AFEC mixed filtering algorithm had high accuracy (1 mm) of real displacement at the elevation direction. Next, the traditional random decrement technique (used mainly for stationary random processes) was expanded to non-stationary random processes. Combining the expanded random decrement technique (RDT) and autoregressive moving average model (ARMA), the modal frequency of the bridge structural system was extracted using an expanded ARMA_RDT modal identification method, which was compared with the power spectrum analysis results of the acceleration signal and finite element analysis results. Identification results demonstrated that the proposed algorithm is applicable to analyze the dynamic displacement monitoring data of real bridge structures under ambient excitation and could identify the first five orders of the inherent frequencies of the structural system accurately. The identification error of the inherent frequency was smaller than 6%, indicating the high identification accuracy of the proposed algorithm. Furthermore, the GNSS dynamic deformation monitoring method can be used to monitor dynamic displacement and identify the modal parameters of bridge structures. The GNSS can monitor the working state of bridges effectively and accurately. Research results can provide references to evaluate the bearing capacity, safety performance, and durability of bridge structures during operation. Full article
(This article belongs to the Special Issue Multi-Sensor Integration and Fusion)
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Open AccessArticle The Influence of Land Use on the Grassland Fire Occurrence in the Northeastern Inner Mongolia Autonomous Region, China
Sensors 2017, 17(3), 437; doi:10.3390/s17030437
Received: 1 January 2017 / Revised: 2 February 2017 / Accepted: 18 February 2017 / Published: 23 February 2017
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Abstract
Grassland, as one of the most important ecosystems on Earth, experiences fires that affect the local ecology, economy and society. Notably, grassland fires occur frequently each year in northeastern China. Fire occurrence is a complex problem with multiple causes, such as natural factors,
[...] Read more.
Grassland, as one of the most important ecosystems on Earth, experiences fires that affect the local ecology, economy and society. Notably, grassland fires occur frequently each year in northeastern China. Fire occurrence is a complex problem with multiple causes, such as natural factors, human activities and land use. This paper investigates the disruptive effects of grassland fire in the northeastern Inner Mongolia Autonomous Region of China. In this study, we relied on thermal anomaly detection from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor to identify fire occurrences, and land use data were acquired by Landsat Thematic Mapper/Enhanced Thematic Mapper (TM/ETM). We discussed the relationship between land use and the spatial distribution of grassland fires. The results showed that the impact of land use on grassland fires was significant. Spatially, approximately 80% of grassland fires were clustered within 10 km of cultivated land, and grassland fires generally occurred in areas of intense human activity. The correlation between the spatial distribution of grassland fires and the land use degree in 2000, 2005 and 2010 was high, with R2 values of 0.686, 0.716, 0.633, respectively (p < 0.01). These results highlight the importance of the relationship between land use and grassland fire occurrence in the northeastern Inner Mongolia Autonomous Region. This study provides significance for local fire management and prevention. Full article
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Open AccessArticle ECCE Toolkit: Prototyping Sensor-Based Interaction
Sensors 2017, 17(3), 438; doi:10.3390/s17030438
Received: 20 September 2016 / Revised: 15 February 2017 / Accepted: 15 February 2017 / Published: 23 February 2017
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Abstract
Building and exploring physical user interfaces requires high technical skills and hours of specialized work. The behavior of multiple devices with heterogeneous input/output channels and connectivity has to be programmed in a context where not only the software interface matters, but also the
[...] Read more.
Building and exploring physical user interfaces requires high technical skills and hours of specialized work. The behavior of multiple devices with heterogeneous input/output channels and connectivity has to be programmed in a context where not only the software interface matters, but also the hardware components are critical (e.g., sensors and actuators). Prototyping physical interaction is hindered by the challenges of: (1) programming interactions among physical sensors/actuators and digital interfaces; (2) implementing functionality for different platforms in different programming languages; and (3) building custom electronic-incorporated objects. We present ECCE (Entities, Components, Couplings and Ecosystems), a toolkit for non-programmers that copes with these issues by abstracting from low-level implementations, thus lowering the complexity of prototyping small-scale, sensor-based physical interfaces to support the design process. A user evaluation provides insights and use cases of the kind of applications that can be developed with the toolkit. Full article
(This article belongs to the Special Issue State-of-the-Art Sensors Technology in Spain 2016)
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Open AccessArticle On the Error State Selection for Stationary SINS Alignment and Calibration Kalman Filters—Part II: Observability/Estimability Analysis
Sensors 2017, 17(3), 439; doi:10.3390/s17030439
Received: 22 November 2016 / Revised: 12 January 2017 / Accepted: 18 February 2017 / Published: 23 February 2017
Cited by 2 | PDF Full-text (876 KB) | HTML Full-text | XML Full-text
Abstract
This paper presents the second part of a study aiming at the error state selection in Kalman filters applied to the stationary self-alignment and calibration (SSAC) problem of strapdown inertial navigation systems (SINS). The observability properties of the system are systematically investigated, and
[...] Read more.
This paper presents the second part of a study aiming at the error state selection in Kalman filters applied to the stationary self-alignment and calibration (SSAC) problem of strapdown inertial navigation systems (SINS). The observability properties of the system are systematically investigated, and the number of unobservable modes is established. Through the analytical manipulation of the full SINS error model, the unobservable modes of the system are determined, and the SSAC error states (except the velocity errors) are proven to be individually unobservable. The estimability of the system is determined through the examination of the major diagonal terms of the covariance matrix and their eigenvalues/eigenvectors. Filter order reduction based on observability analysis is shown to be inadequate, and several misconceptions regarding SSAC observability and estimability deficiencies are removed. As the main contributions of this paper, we demonstrate that, except for the position errors, all error states can be minimally estimated in the SSAC problem and, hence, should not be removed from the filter. Corroborating the conclusions of the first part of this study, a 12-state Kalman filter is found to be the optimal error state selection for SSAC purposes. Results from simulated and experimental tests support the outlined conclusions. Full article
(This article belongs to the Special Issue Inertial Sensors and Systems 2016)
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Open AccessArticle Markov Chain Model-Based Optimal Cluster Heads Selection for Wireless Sensor Networks
Sensors 2017, 17(3), 440; doi:10.3390/s17030440
Received: 29 November 2016 / Revised: 7 February 2017 / Accepted: 17 February 2017 / Published: 23 February 2017
Cited by 3 | PDF Full-text (3991 KB) | HTML Full-text | XML Full-text
Abstract
The longer network lifetime of Wireless Sensor Networks (WSNs) is a goal which is directly related to energy consumption. This energy consumption issue becomes more challenging when the energy load is not properly distributed in the sensing area. The hierarchal clustering architecture is
[...] Read more.
The longer network lifetime of Wireless Sensor Networks (WSNs) is a goal which is directly related to energy consumption. This energy consumption issue becomes more challenging when the energy load is not properly distributed in the sensing area. The hierarchal clustering architecture is the best choice for these kind of issues. In this paper, we introduce a novel clustering protocol called Markov chain model-based optimal cluster heads (MOCHs) selection for WSNs. In our proposed model, we introduce a simple strategy for the optimal number of cluster heads selection to overcome the problem of uneven energy distribution in the network. The attractiveness of our model is that the BS controls the number of cluster heads while the cluster heads control the cluster members in each cluster in such a restricted manner that a uniform and even load is ensured in each cluster. We perform an extensive range of simulation using five quality measures, namely: the lifetime of the network, stable and unstable region in the lifetime of the network, throughput of the network, the number of cluster heads in the network, and the transmission time of the network to analyze the proposed model. We compare MOCHs against Sleep-awake Energy Efficient Distributed (SEED) clustering, Artificial Bee Colony (ABC), Zone Based Routing (ZBR), and Centralized Energy Efficient Clustering (CEEC) using the above-discussed quality metrics and found that the lifetime of the proposed model is almost 1095, 2630, 3599, and 2045 rounds (time steps) greater than SEED, ABC, ZBR, and CEEC, respectively. The obtained results demonstrate that the MOCHs is better than SEED, ABC, ZBR, and CEEC in terms of energy efficiency and the network throughput. Full article
(This article belongs to the Section Sensor Networks)
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Open AccessArticle A Weighted Spatial-Spectral Kernel RX Algorithm and Efficient Implementation on GPUs
Sensors 2017, 17(3), 441; doi:10.3390/s17030441
Received: 22 January 2017 / Revised: 19 February 2017 / Accepted: 20 February 2017 / Published: 23 February 2017
Cited by 1 | PDF Full-text (11830 KB) | HTML Full-text | XML Full-text
Abstract
The kernel RX (KRX) detector proposed by Kwon and Nasrabadi exploits a kernel function to obtain a better detection performance. However, it still has two limits that can be improved. On the one hand, reasonable integration of spatial-spectral information can be used to
[...] Read more.
The kernel RX (KRX) detector proposed by Kwon and Nasrabadi exploits a kernel function to obtain a better detection performance. However, it still has two limits that can be improved. On the one hand, reasonable integration of spatial-spectral information can be used to further improve its detection accuracy. On the other hand, parallel computing can be used to reduce the processing time in available KRX detectors. Accordingly, this paper presents a novel weighted spatial-spectral kernel RX (WSSKRX) detector and its parallel implementation on graphics processing units (GPUs). The WSSKRX utilizes the spatial neighborhood resources to reconstruct the testing pixels by introducing a spectral factor and a spatial window, thereby effectively reducing the interference of background noise. Then, the kernel function is redesigned as a mapping trick in a KRX detector to implement the anomaly detection. In addition, a powerful architecture based on the GPU technique is designed to accelerate WSSKRX. To substantiate the performance of the proposed algorithm, both synthetic and real data are conducted for experiments. Full article
(This article belongs to the Section Remote Sensors)
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Open AccessArticle Design and Elementary Evaluation of a Highly-Automated Fluorescence-Based Instrument System for On-Site Detection of Food-Borne Pathogens
Sensors 2017, 17(3), 442; doi:10.3390/s17030442
Received: 27 December 2016 / Revised: 15 February 2017 / Accepted: 21 February 2017 / Published: 23 February 2017
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Abstract
A simple, highly-automated instrument system used for on-site detection of foodborne pathogens based on fluorescence was designed, fabricated, and preliminarily tested in this paper. A corresponding method has been proved effective in our previous studies. This system utilizes a light-emitting diode (LED) to
[...] Read more.
A simple, highly-automated instrument system used for on-site detection of foodborne pathogens based on fluorescence was designed, fabricated, and preliminarily tested in this paper. A corresponding method has been proved effective in our previous studies. This system utilizes a light-emitting diode (LED) to excite fluorescent labels and a spectrometer to record the fluorescence signal from samples. A rotation stage for positioning and switching samples was innovatively designed for high-throughput detection, ten at most in one single run. We also developed software based on LabVIEW for data receiving, processing, and the control of the whole system. In the test of using a pure quantum dot (QD) solution as a standard sample, detection results from this home-made system were highly-relevant with that from a well-commercialized product and even slightly better reproducibility was found. And in the test of three typical kinds of food-borne pathogens, fluorescence signals recorded by this system are highly proportional to the variation of the sample concentration, with a satisfied limit of detection (LOD) (nearly 102–103 CFU·mL−1 in food samples). Additionally, this instrument system is low-cost and easy-to-use, showing a promising potential for on-site rapid detection of food-borne pathogens. Full article
(This article belongs to the Special Issue Sensors for Toxic and Pathogen Detection)
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Open AccessArticle Measurement of the Length of Installed Rock Bolt Based on Stress Wave Reflection by Using a Giant Magnetostrictive (GMS) Actuator and a PZT Sensor
Sensors 2017, 17(3), 444; doi:10.3390/s17030444
Received: 17 January 2017 / Revised: 11 February 2017 / Accepted: 21 February 2017 / Published: 23 February 2017
Cited by 1 | PDF Full-text (5305 KB) | HTML Full-text | XML Full-text
Abstract
Rock bolts, as a type of reinforcing element, are widely adopted in underground excavations and civil engineering structures. Given the importance of rock bolts, the research outlined in this paper attempts to develop a portable non-destructive evaluation method for assessing the length of
[...] Read more.
Rock bolts, as a type of reinforcing element, are widely adopted in underground excavations and civil engineering structures. Given the importance of rock bolts, the research outlined in this paper attempts to develop a portable non-destructive evaluation method for assessing the length of installed rock bolts for inspection purposes. Traditionally, piezoelectric elements or hammer impacts were used to perform non-destructive evaluation of rock bolts. However, such methods suffered from many major issues, such as the weak energy generated and the requirement for permanent installation for piezoelectric elements, and the inconsistency of wave generation for hammer impact. In this paper, we proposed a portable device for the non-destructive evaluation of rock bolt conditions based on a giant magnetostrictive (GMS) actuator. The GMS actuator generates enough energy to ensure multiple reflections of the stress waves along the rock bolt and a lead zirconate titantate (PZT) sensor is used to detect the reflected waves. A new integrated procedure that involves correlation analysis, wavelet denoising, and Hilbert transform was proposed to process the multiple reflection signals to determine the length of an installed rock bolt. The experimental results from a lab test and field tests showed that, by analyzing the instant phase of the periodic reflections of the stress wave generated by the GMS transducer, the length of an embedded rock bolt can be accurately determined. Full article
(This article belongs to the Section Physical Sensors)
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Open AccessArticle A Topology Control Strategy with Reliability Assurance for Satellite Cluster Networks in Earth Observation
Sensors 2017, 17(3), 445; doi:10.3390/s17030445
Received: 15 December 2016 / Revised: 15 February 2017 / Accepted: 20 February 2017 / Published: 23 February 2017
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Abstract
This article investigates the dynamic topology control problemof satellite cluster networks (SCNs) in Earth observation (EO) missions by applying a novel metric of stability for inter-satellite links (ISLs). The properties of the periodicity and predictability of satellites’ relative position are involved in the
[...] Read more.
This article investigates the dynamic topology control problemof satellite cluster networks (SCNs) in Earth observation (EO) missions by applying a novel metric of stability for inter-satellite links (ISLs). The properties of the periodicity and predictability of satellites’ relative position are involved in the link cost metric which is to give a selection criterion for choosing the most reliable data routing paths. Also, a cooperative work model with reliability is proposed for the situation of emergency EO missions. Based on the link cost metric and the proposed reliability model, a reliability assurance topology control algorithm and its corresponding dynamic topology control (RAT) strategy are established to maximize the stability of data transmission in the SCNs. The SCNs scenario is tested through some numeric simulations of the topology stability of average topology lifetime and average packet loss rate. Simulation results show that the proposed reliable strategy applied in SCNs significantly improves the data transmission performance and prolongs the average topology lifetime. Full article
(This article belongs to the Special Issue Topology Control in Emerging Sensor Networks)
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Open AccessArticle Unmanned Aerial Vehicle Systems for Remote Estimation of Flooded Areas Based on Complex Image Processing
Sensors 2017, 17(3), 446; doi:10.3390/s17030446
Received: 28 December 2016 / Accepted: 20 February 2017 / Published: 23 February 2017
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Abstract
Floods are natural disasters which cause the most economic damage at the global level. Therefore, flood monitoring and damage estimation are very important for the population, authorities and insurance companies. The paper proposes an original solution, based on a hybrid network and complex
[...] Read more.
Floods are natural disasters which cause the most economic damage at the global level. Therefore, flood monitoring and damage estimation are very important for the population, authorities and insurance companies. The paper proposes an original solution, based on a hybrid network and complex image processing, to this problem. As first novelty, a multilevel system, with two components, terrestrial and aerial, was proposed and designed by the authors as support for image acquisition from a delimited region. The terrestrial component contains a Ground Control Station, as a coordinator at distance, which communicates via the internet with more Ground Data Terminals, as a fixed nodes network for data acquisition and communication. The aerial component contains mobile nodes—fixed wing type UAVs. In order to evaluate flood damage, two tasks must be accomplished by the network: area coverage and image processing. The second novelty of the paper consists of texture analysis in a deep neural network, taking into account new criteria for feature selection and patch classification. Color and spatial information extracted from chromatic co-occurrence matrix and mass fractal dimension were used as well. Finally, the experimental results in a real mission demonstrate the validity of the proposed methodologies and the performances of the algorithms. Full article
(This article belongs to the Special Issue UAV-Based Remote Sensing)
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Open AccessArticle Monitoring Citrus Soil Moisture and Nutrients Using an IoT Based System
Sensors 2017, 17(3), 447; doi:10.3390/s17030447
Received: 28 November 2016 / Revised: 17 February 2017 / Accepted: 20 February 2017 / Published: 23 February 2017
Cited by 1 | PDF Full-text (3068 KB) | HTML Full-text | XML Full-text
Abstract
Chongqing mountain citrus orchard is one of the main origins of Chinese citrus. Its planting terrain is complex and soil parent material is diverse. Currently, the citrus fertilization, irrigation and other management processes still have great blindness. They usually use the same pattern
[...] Read more.
Chongqing mountain citrus orchard is one of the main origins of Chinese citrus. Its planting terrain is complex and soil parent material is diverse. Currently, the citrus fertilization, irrigation and other management processes still have great blindness. They usually use the same pattern and the same formula rather than considering the orchard terrain features, soil differences, species characteristics and the state of tree growth. With the help of the ZigBee technology, artificial intelligence and decision support technology, this paper has developed the research on the application technology of agricultural Internet of Things for real-time monitoring of citrus soil moisture and nutrients as well as the research on the integration of fertilization and irrigation decision support system. Some achievements were obtained including single-point multi-layer citrus soil temperature and humidity detection wireless sensor nodes and citrus precision fertilization and irrigation management decision support system. They were applied in citrus base in the Three Gorges Reservoir Area. The results showed that the system could help the grower to scientifically fertilize or irrigate, improve the precision operation level of citrus production, reduce the labor cost and reduce the pollution caused by chemical fertilizer. Full article
(This article belongs to the Special Issue Sensors for Agriculture)
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Open AccessArticle Optimal Divergence-Free Hatch Filter for GNSS Single-Frequency Measurement
Sensors 2017, 17(3), 448; doi:10.3390/s17030448
Received: 13 December 2016 / Revised: 20 February 2017 / Accepted: 21 February 2017 / Published: 24 February 2017
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Abstract
The Hatch filter is a code-smoothing technique that uses the variation of the carrier phase. It can effectively reduce the noise of a pseudo-range with a very simple filter construction, but it occasionally causes an ionosphere-induced error for low-lying satellites. Herein, we propose
[...] Read more.
The Hatch filter is a code-smoothing technique that uses the variation of the carrier phase. It can effectively reduce the noise of a pseudo-range with a very simple filter construction, but it occasionally causes an ionosphere-induced error for low-lying satellites. Herein, we propose an optimal single-frequency (SF) divergence-free Hatch filter that uses a satellite-based augmentation system (SBAS) message to reduce the ionospheric divergence and applies the optimal smoothing constant for its smoothing window width. According to the data-processing results, the overall performance of the proposed filter is comparable to that of the dual frequency (DF) divergence-free Hatch filter. Moreover, it can reduce the horizontal error of 57 cm to 37 cm and improve the vertical accuracy of the conventional Hatch filter by 25%. Considering that SF receivers dominate the global navigation satellite system (GNSS) market and that most of these receivers include the SBAS function, the filter suggested in this paper is of great value in that it can make the differential GPS (DGPS) performance of the low-cost SF receivers comparable to that of DF receivers. Full article
(This article belongs to the Section Remote Sensors)
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Open AccessArticle Improved Accuracy of the Asymmetric Second-Order Vegetation Isoline Equation over the RED–NIR Reflectance Space
Sensors 2017, 17(3), 450; doi:10.3390/s17030450
Received: 10 January 2017 / Revised: 16 February 2017 / Accepted: 22 February 2017 / Published: 24 February 2017
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Abstract
The relationship between two reflectances of different bands is often encountered in cross calibration and parameter retrievals from remotely-sensed data. The asymmetric-order vegetation isoline is one such relationship, derived previously, where truncation error was reduced from the first-order approximated isoline by including a
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The relationship between two reflectances of different bands is often encountered in cross calibration and parameter retrievals from remotely-sensed data. The asymmetric-order vegetation isoline is one such relationship, derived previously, where truncation error was reduced from the first-order approximated isoline by including a second-order term. This study introduces a technique for optimizing the magnitude of the second-order term and further improving the isoline equation’s accuracy while maintaining the simplicity of the derived formulation. A single constant factor was introduced into the formulation to adjust the second-order term. This factor was optimized by simulating canopy radiative transfer. Numerical experiments revealed that the errors in the optimized asymmetric isoline were reduced in magnitude to nearly 1/25 of the errors obtained from the first-order vegetation isoline equation, and to nearly one-fifth of the error obtained from the non-optimized asymmetric isoline equation. The errors in the optimized asymmetric isoline were compared with the magnitudes of the signal-to-noise ratio (SNR) estimates reported for four specific sensors aboard four Earth observation satellites. These results indicated that the error in the asymmetric isoline could be reduced to the level of the SNR by adjusting a single factor. Full article
(This article belongs to the Section Remote Sensors)
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Open AccessArticle An Effective 3D Shape Descriptor for Object Recognition with RGB-D Sensors
Sensors 2017, 17(3), 451; doi:10.3390/s17030451
Received: 28 December 2016 / Revised: 20 February 2017 / Accepted: 20 February 2017 / Published: 24 February 2017
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Abstract
RGB-D sensors have been widely used in various areas of computer vision and graphics. A good descriptor will effectively improve the performance of operation. This article further analyzes the recognition performance of shape features extracted from multi-modality source data using RGB-D sensors. A
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RGB-D sensors have been widely used in various areas of computer vision and graphics. A good descriptor will effectively improve the performance of operation. This article further analyzes the recognition performance of shape features extracted from multi-modality source data using RGB-D sensors. A hybrid shape descriptor is proposed as a representation of objects for recognition. We first extracted five 2D shape features from contour-based images and five 3D shape features over point cloud data to capture the global and local shape characteristics of an object. The recognition performance was tested for category recognition and instance recognition. Experimental results show that the proposed shape descriptor outperforms several common global-to-global shape descriptors and is comparable to some partial-to-global shape descriptors that achieved the best accuracies in category and instance recognition. Contribution of partial features and computational complexity were also analyzed. The results indicate that the proposed shape features are strong cues for object recognition and can be combined with other features to boost accuracy. Full article
(This article belongs to the Section Physical Sensors)
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Open AccessArticle eFarm: A Tool for Better Observing Agricultural Land Systems
Sensors 2017, 17(3), 453; doi:10.3390/s17030453
Received: 22 November 2016 / Revised: 14 February 2017 / Accepted: 16 February 2017 / Published: 24 February 2017
Cited by 1 | PDF Full-text (11665 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Currently, observations of an agricultural land system (ALS) largely depend on remotely-sensed images, focusing on its biophysical features. While social surveys capture the socioeconomic features, the information was inadequately integrated with the biophysical features of an ALS and the applications are limited due
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Currently, observations of an agricultural land system (ALS) largely depend on remotely-sensed images, focusing on its biophysical features. While social surveys capture the socioeconomic features, the information was inadequately integrated with the biophysical features of an ALS and the applications are limited due to the issues of cost and efficiency to carry out such detailed and comparable social surveys at a large spatial coverage. In this paper, we introduce a smartphone-based app, called eFarm: a crowdsourcing and human sensing tool to collect the geotagged ALS information at the land parcel level, based on the high resolution remotely-sensed images. We illustrate its main functionalities, including map visualization, data management, and data sensing. Results of the trial test suggest the system works well. We believe the tool is able to acquire the human–land integrated information which is broadly-covered and timely-updated, thus presenting great potential for improving sensing, mapping, and modeling of ALS studies. Full article
(This article belongs to the Special Issue Sensors and Smart Sensing of Agricultural Land Systems)
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Open AccessArticle Holistic Context-Sensitivity for Run-Time Optimization of Flexible Manufacturing Systems
Sensors 2017, 17(3), 455; doi:10.3390/s17030455
Received: 28 December 2016 / Revised: 13 February 2017 / Accepted: 21 February 2017 / Published: 24 February 2017
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Abstract
Highly flexible manufacturing systems require continuous run-time (self-) optimization of processes with respect to diverse parameters, e.g., efficiency, availability, energy consumption etc. A promising approach for achieving (self-) optimization in manufacturing systems is the usage of the context sensitivity approach based on data
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Highly flexible manufacturing systems require continuous run-time (self-) optimization of processes with respect to diverse parameters, e.g., efficiency, availability, energy consumption etc. A promising approach for achieving (self-) optimization in manufacturing systems is the usage of the context sensitivity approach based on data streaming from high amount of sensors and other data sources. Cyber-physical systems play an important role as sources of information to achieve context sensitivity. Cyber-physical systems can be seen as complex intelligent sensors providing data needed to identify the current context under which the manufacturing system is operating. In this paper, it is demonstrated how context sensitivity can be used to realize a holistic solution for (self-) optimization of discrete flexible manufacturing systems, by making use of cyber-physical systems integrated in manufacturing systems/processes. A generic approach for context sensitivity, based on self-learning algorithms, is proposed aiming at a various manufacturing systems. The new solution encompasses run-time context extractor and optimizer. Based on the self-learning module both context extraction and optimizer are continuously learning and improving their performance. The solution is following Service Oriented Architecture principles. The generic solution is developed and then applied to two very different manufacturing processes. Full article
(This article belongs to the Special Issue Context Aware Environments and Applications)
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Open AccessArticle Temperature Mapping of 3D Printed Polymer Plates: Experimental and Numerical Study
Sensors 2017, 17(3), 456; doi:10.3390/s17030456
Received: 22 November 2016 / Revised: 21 February 2017 / Accepted: 22 February 2017 / Published: 24 February 2017
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Abstract
In Fused Deposition Modeling (FDM), which is a common thermoplastic Additive Manufacturing (AM) method, the polymer model material that is in the form of a flexible filament is heated above its glass transition temperature (Tg) to a semi-molten state in the
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In Fused Deposition Modeling (FDM), which is a common thermoplastic Additive Manufacturing (AM) method, the polymer model material that is in the form of a flexible filament is heated above its glass transition temperature (Tg) to a semi-molten state in the head’s liquefier. The heated material is extruded in a rastering configuration onto the building platform where it rapidly cools and solidifies with the adjoining material. The heating and rapid cooling cycles of the work materials exhibited during the FDM process provoke non-uniform thermal gradients and cause stress build-up that consequently result in part distortions, dimensional inaccuracy and even possible part fabrication failure. Within the purpose of optimizing the FDM technique by eliminating the presence of such undesirable effects, real-time monitoring is essential for the evaluation and control of the final parts’ quality. The present work investigates the temperature distributions developed during the FDM building process of multilayered thin plates and on this basis a numerical study is also presented. The recordings of temperature changes were achieved by embedding temperature measuring sensors at various locations into the middle-plane of the printed structures. The experimental results, mapping the temperature variations within the samples, were compared to the corresponding ones obtained by finite element modeling, exhibiting good correlation. Full article
(This article belongs to the Special Issue 3D Printed Sensors)
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Open AccessArticle A Novel Technique for Fetal ECG Extraction Using Single-Channel Abdominal Recording
Sensors 2017, 17(3), 457; doi:10.3390/s17030457
Received: 1 October 2016 / Revised: 16 January 2017 / Accepted: 16 February 2017 / Published: 24 February 2017
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Abstract
Non-invasive fetal electrocardiograms (FECGs) are an alternative method to standard means of fetal monitoring which permit long-term continual monitoring. However, in abdominal recording, the FECG amplitude is weak in the temporal domain and overlaps with the maternal electrocardiogram (MECG) in the spectral domain.
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Non-invasive fetal electrocardiograms (FECGs) are an alternative method to standard means of fetal monitoring which permit long-term continual monitoring. However, in abdominal recording, the FECG amplitude is weak in the temporal domain and overlaps with the maternal electrocardiogram (MECG) in the spectral domain. Research in the area of non-invasive separations of FECG from abdominal electrocardiograms (AECGs) is in its infancy and several studies are currently focusing on this area. An adaptive noise canceller (ANC) is commonly used for cancelling interference in cases where the reference signal only correlates with an interference signal, and not with a signal of interest. However, results from some existing studies suggest that propagation of electrocardiogram (ECG) signals from the maternal heart to the abdomen is nonlinear, hence the adaptive filter approach may fail if the thoracic and abdominal MECG lack strict waveform similarity. In this study, singular value decomposition (SVD) and smooth window (SW) techniques are combined to build a reference signal in an ANC. This is to avoid the limitation that thoracic MECGs recorded separately must be similar to abdominal MECGs in waveform. Validation of the proposed method with r01 and r07 signals from a public dataset, and a self-recorded private dataset showed that the proposed method achieved F1 scores of 99.61%, 99.28% and 98.58%, respectively for the detection of fetal QRS. Compared with four other single-channel methods, the proposed method also achieved higher accuracy values of 99.22%, 98.57% and 97.21%, respectively. The findings from this study suggest that the proposed method could potentially aid accurate extraction of FECG from MECG recordings in both clinical and commercial applications. Full article
(This article belongs to the Special Issue Wearable Biomedical Sensors)
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Open AccessArticle Surface EMG-Based Inter-Session Gesture Recognition Enhanced by Deep Domain Adaptation
Sensors 2017, 17(3), 458; doi:10.3390/s17030458
Received: 29 December 2016 / Revised: 3 February 2017 / Accepted: 21 February 2017 / Published: 24 February 2017
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Abstract
High-density surface electromyography (HD-sEMG) is to record muscles’ electrical activity from a restricted area of the skin by using two dimensional arrays of closely spaced electrodes. This technique allows the analysis and modelling of sEMG signals in both the temporal and spatial domains,
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High-density surface electromyography (HD-sEMG) is to record muscles’ electrical activity from a restricted area of the skin by using two dimensional arrays of closely spaced electrodes. This technique allows the analysis and modelling of sEMG signals in both the temporal and spatial domains, leading to new possibilities for studying next-generation muscle-computer interfaces (MCIs). sEMG-based gesture recognition has usually been investigated in an intra-session scenario, and the absence of a standard benchmark database limits the use of HD-sEMG in real-world MCI. To address these problems, we present a benchmark database of HD-sEMG recordings of hand gestures performed by 23 participants, based on an 8 × 16 electrode array, and propose a deep-learning-based domain adaptation framework to enhance sEMG-based inter-session gesture recognition. Experiments on NinaPro, CSL-HDEMG and our CapgMyo dataset validate that our approach outperforms state-of-the-arts methods on intra-session and effectively improved inter-session gesture recognition. Full article
(This article belongs to the Section Physical Sensors)
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Open AccessArticle Bismuth Infusion of ABS Enables Additive Manufacturing of Complex Radiological Phantoms and Shielding Equipment
Sensors 2017, 17(3), 459; doi:10.3390/s17030459
Received: 18 November 2016 / Revised: 10 February 2017 / Accepted: 15 February 2017 / Published: 24 February 2017
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Abstract
Radiopacity is a critical property of materials that are used for a range of radiological applications, including the development of phantom devices that emulate the radiodensity of native tissues and the production of protective equipment for personnel handling radioactive materials. Three-dimensional (3D) printing
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Radiopacity is a critical property of materials that are used for a range of radiological applications, including the development of phantom devices that emulate the radiodensity of native tissues and the production of protective equipment for personnel handling radioactive materials. Three-dimensional (3D) printing is a fabrication platform that is well suited to creating complex anatomical replicas or custom labware to accomplish these radiological purposes. We created and tested multiple ABS (Acrylonitrile butadiene styrene) filaments infused with varied concentrations of bismuth (1.2–2.7 g/cm3), a radiopaque metal that is compatible with plastic infusion, to address the poor gamma radiation attenuation of many mainstream 3D printing materials. X-ray computed tomography (CT) experiments of these filaments indicated that a density of 1.2 g/cm3 of bismuth-infused ABS emulates bone radiopacity during X-ray CT imaging on preclinical and clinical scanners. ABS-bismuth filaments along with ABS were 3D printed to create an embedded human nasocranial anatomical phantom that mimicked radiological properties of native bone and soft tissue. Increasing the bismuth content in the filaments to 2.7 g/cm3 created a stable material that could attenuate 50% of 99mTechnetium gamma emission when printed with a 2.0 mm wall thickness. A shielded test tube rack was printed to attenuate source radiation as a protective measure for lab personnel. We demonstrated the utility of novel filaments to serve multiple radiological purposes, including the creation of anthropomorphic phantoms and safety labware, by tuning the level of radiation attenuation through material customization. Full article
(This article belongs to the Special Issue 3D Printed Sensors)
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Open AccessArticle Unmanned Aerial Vehicle Based Wireless Sensor Network for Marine-Coastal Environment Monitoring
Sensors 2017, 17(3), 460; doi:10.3390/s17030460
Received: 31 December 2016 / Revised: 12 February 2017 / Accepted: 20 February 2017 / Published: 24 February 2017
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Abstract
Marine environments are delicate ecosystems which directly influence local climates, flora, fauna, and human activities. Their monitorization plays a key role in their preservation, which is most commonly done through the use of environmental sensing buoy networks. These devices transmit data by means
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Marine environments are delicate ecosystems which directly influence local climates, flora, fauna, and human activities. Their monitorization plays a key role in their preservation, which is most commonly done through the use of environmental sensing buoy networks. These devices transmit data by means of satellite communications or close-range base stations, which present several limitations and elevated infrastructure costs. Unmanned Aerial Vehicles (UAV) are another alternative for remote environmental monitoring which provide new types of data and ease of use. These aircraft are mainly used in video capture related applications, in its various light spectrums, and do not provide the same data as sensing buoys, nor can they be used for such extended periods of time. The aim of this research is to provide a flexible, easy to deploy and cost-effective Wireless Sensor Network (WSN) for monitoring marine environments. This proposal uses a UAV as a mobile data collector, low-power long-range communications and sensing buoys as part of a single WSN. A complete description of the design, development, and implementation of the various parts of this system is presented, as well as its validation in a real-world scenario. Full article
(This article belongs to the Section Remote Sensors)
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Open AccessArticle Phase Compensation Sensor for Ranging Consistency in Inter-Satellite Links of Navigation Constellation
Sensors 2017, 17(3), 461; doi:10.3390/s17030461
Received: 8 December 2016 / Revised: 6 February 2017 / Accepted: 20 February 2017 / Published: 24 February 2017
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Abstract
Theperformanceoftheglobalnavigationsatellitesystem(GNSS)canbeenhancedsignificantly by introducing the inter-satellite links (ISL) of a navigation constellation. In particular, the improvement of the position, velocity, and time accuracy, and the realization of autonomous functions require the ISL distance measurement data as the original input. For building a high-performance ISL,
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Theperformanceoftheglobalnavigationsatellitesystem(GNSS)canbeenhancedsignificantly by introducing the inter-satellite links (ISL) of a navigation constellation. In particular, the improvement of the position, velocity, and time accuracy, and the realization of autonomous functions require the ISL distance measurement data as the original input. For building a high-performance ISL, the ranging consistency between navigation satellites becomes a crucial problem to be addressed. Considering the frequency aging drift and the relativistic effect of the navigation satellite, the frequency and phase adjustment (FPA) instructions for the 10.23 MHz must be injected from the ground station to ensure the time synchronization of the navigation constellation. Moreover, the uncertainty of the initial phase each time the onboard clock equipment boots also results in a pseudo-range offset. In this Ref., we focus on the influence of the frequency and phase characteristics of the onboard clock equipment on the ranging consistency of the ISL and propose a phase compensation sensor design method for the phase offset. The simulation and experimental results show that the proposed method not only realized a phase compensation for the pseudo-range jitter, but, when the 1 PPS (1 pulse per second) falls in the 10.23 MHz skip area, also overcomes the problem of compensating the ambiguous phase by directly tracking the 10.23 MHz to ensure consistency in the ranging. Full article
(This article belongs to the Section Remote Sensors)
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Open AccessArticle Testing of Piezo-Actuated Glass Micro-Membranes by Optical Low-Coherence Reflectometry
Sensors 2017, 17(3), 462; doi:10.3390/s17030462
Received: 13 January 2017 / Revised: 20 February 2017 / Accepted: 20 February 2017 / Published: 25 February 2017
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Abstract
In this work, we have applied optical low-coherence reflectometry (OLCR), implemented with infra-red light propagating in fiberoptic paths, to perform static and dynamic analyses on piezo-actuated glass micro-membranes. The actuator was fabricated by means of thin-film piezoelectric MEMS technology and was employed for
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In this work, we have applied optical low-coherence reflectometry (OLCR), implemented with infra-red light propagating in fiberoptic paths, to perform static and dynamic analyses on piezo-actuated glass micro-membranes. The actuator was fabricated by means of thin-film piezoelectric MEMS technology and was employed for modifying the micro-membrane curvature, in view of its application in micro-optic devices, such as variable focus micro-lenses. We are here showing that OLCR incorporating a near-infrared superluminescent light emitting diode as the read-out source is suitable for measuring various parameters such as the micro-membrane optical path-length, the membrane displacement as a function of the applied voltage (yielding the piezo-actuator hysteresis) as well as the resonance curve of the fundamental vibration mode. The use of an optical source with short coherence-time allows performing interferometric measurements without spurious resonance effects due to multiple parallel interfaces of highly planar slabs, furthermore selecting the plane/layer to be monitored. We demonstrate that the same compact and flexible setup can be successfully employed to perform spot optical measurements for static and dynamic characterization of piezo-MEMS in real time. Full article
(This article belongs to the collection Modeling, Testing and Reliability Issues in MEMS Engineering)
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Open AccessArticle Hybrid ARQ Scheme with Autonomous Retransmission for Multicasting in Wireless Sensor Networks
Sensors 2017, 17(3), 463; doi:10.3390/s17030463
Received: 5 January 2017 / Revised: 22 February 2017 / Accepted: 22 February 2017 / Published: 25 February 2017
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Abstract
A new hybrid automatic repeat request (HARQ) scheme for multicast service for wireless sensor networks is proposed in this study. In the proposed algorithm, the HARQ operation is combined with an autonomous retransmission method that ensure a data packet is transmitted irrespective of
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A new hybrid automatic repeat request (HARQ) scheme for multicast service for wireless sensor networks is proposed in this study. In the proposed algorithm, the HARQ operation is combined with an autonomous retransmission method that ensure a data packet is transmitted irrespective of whether or not the packet is successfully decoded at the receivers. The optimal number of autonomous retransmissions is determined to ensure maximum spectral efficiency, and a practical method that adjusts the number of autonomous retransmissions for realistic conditions is developed. Simulation results show that the proposed method achieves higher spectral efficiency than existing HARQ techniques. Full article
(This article belongs to the Section Sensor Networks)
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Open AccessArticle Toward Exposing Timing-Based Probing Attacks in Web Applications
Sensors 2017, 17(3), 464; doi:10.3390/s17030464
Received: 31 October 2016 / Revised: 23 January 2017 / Accepted: 16 February 2017 / Published: 25 February 2017
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Abstract
Web applications have become the foundation of many types of systems, ranging from cloud services to Internet of Things (IoT) systems. Due to the large amount of sensitive data processed by web applications, user privacy emerges as a major concern in web security.
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Web applications have become the foundation of many types of systems, ranging from cloud services to Internet of Things (IoT) systems. Due to the large amount of sensitive data processed by web applications, user privacy emerges as a major concern in web security. Existing protection mechanisms in modern browsers, e.g., the same origin policy, prevent the users’ browsing information on one website from being directly accessed by another website. However, web applications executed in the same browser share the same runtime environment. Such shared states provide side channels for malicious websites to indirectly figure out the information of other origins. Timing is a classic side channel and the root cause of many recent attacks, which rely on the variations in the time taken by the systems to process different inputs. In this paper, we propose an approach to expose the timing-based probing attacks in web applications. It monitors the browser behaviors and identifies anomalous timing behaviors to detect browser probing attacks. We have prototyped our system in the Google Chrome browser and evaluated the effectiveness of our approach by using known probing techniques. We have applied our approach on a large number of top Alexa sites and reported the suspicious behavior patterns with corresponding analysis results. Our theoretical analysis illustrates that the effectiveness of the timing-based probing attacks is dramatically limited by our approach. Full article
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Open AccessArticle VineSens: An Eco-Smart Decision-Support Viticulture System
Sensors 2017, 17(3), 465; doi:10.3390/s17030465
Received: 23 December 2016 / Revised: 20 February 2017 / Accepted: 22 February 2017 / Published: 25 February 2017
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Abstract
This article presents VineSens, a hardware and software platform for supporting the decision-making of the vine grower. VineSens is based on a wireless sensor network system composed by autonomous and self-powered nodes that are deployed throughout a vineyard. Such nodes include sensors that
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This article presents VineSens, a hardware and software platform for supporting the decision-making of the vine grower. VineSens is based on a wireless sensor network system composed by autonomous and self-powered nodes that are deployed throughout a vineyard. Such nodes include sensors that allow us to obtain detailed knowledge on different viticulture processes. Thanks to the use of epidemiological models, VineSens is able to propose a custom control plan to prevent diseases like one of the most feared by vine growers: downy mildew. VineSens generates alerts that warn farmers about the measures that have to be taken and stores the historical weather data collected from different spots of the vineyard. Such data can then be accessed through a user-friendly web-based interface that can be accessed through the Internet by using desktop or mobile devices. VineSens was deployed at the beginning in 2016 in a vineyard in the Ribeira Sacra area (Galicia, Spain) and, since then, its hardware and software have been tested to prevent the development of downy mildew, showing during its first season that the system can led to substantial savings, to decrease the amount of phytosanitary products applied, and, as a consequence, to obtain a more ecologically sustainable and healthy wine. Full article
(This article belongs to the Special Issue Precision Agriculture and Remote Sensing Data Fusion)
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Open AccessArticle Quantifying Variation in Gait Features from Wearable Inertial Sensors Using Mixed Effects Models
Sensors 2017, 17(3), 466; doi:10.3390/s17030466
Received: 12 January 2017 / Revised: 17 February 2017 / Accepted: 21 February 2017 / Published: 25 February 2017
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Abstract
The emerging technology of wearable inertial sensors has shown its advantages in collecting continuous longitudinal gait data outside laboratories. This freedom also presents challenges in collecting high-fidelity gait data. In the free-living environment, without constant supervision from researchers, sensor-based gait features are susceptible
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The emerging technology of wearable inertial sensors has shown its advantages in collecting continuous longitudinal gait data outside laboratories. This freedom also presents challenges in collecting high-fidelity gait data. In the free-living environment, without constant supervision from researchers, sensor-based gait features are susceptible to variation from confounding factors such as gait speed and mounting uncertainty, which are challenging to control or estimate. This paper is one of the first attempts in the field to tackle such challenges using statistical modeling. By accepting the uncertainties and variation associated with wearable sensor-based gait data, we shift our efforts from detecting and correcting those variations to modeling them statistically. From gait data collected on one healthy, non-elderly subject during 48 full-factorial trials, we identified four major sources of variation, and quantified their impact on one gait outcome—range per cycle—using a random effects model and a fixed effects model. The methodology developed in this paper lays the groundwork for a statistical framework to account for sources of variation in wearable gait data, thus facilitating informative statistical inference for free-living gait analysis. Full article
(This article belongs to the Special Issue Wearable and Ambient Sensors for Healthcare and Wellness Applications)
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Open AccessArticle SERS Taper-Fiber Nanoprobe Modified by Gold Nanoparticles Wrapped with Ultrathin Alumina Film by Atomic Layer Deposition
Sensors 2017, 17(3), 467; doi:10.3390/s17030467
Received: 6 January 2017 / Revised: 13 February 2017 / Accepted: 15 February 2017 / Published: 25 February 2017
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Abstract
A taper-fiber SERS nanoprobe modified by gold nanoparticles (Au-NPs) with ultrathin alumina layers was fabricated and its ability to perform remote Raman detection was demonstrated. The taper-fiber nanoprobe (TFNP) with a nanoscale tip size under 80 nm was made by heated pulling combined
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A taper-fiber SERS nanoprobe modified by gold nanoparticles (Au-NPs) with ultrathin alumina layers was fabricated and its ability to perform remote Raman detection was demonstrated. The taper-fiber nanoprobe (TFNP) with a nanoscale tip size under 80 nm was made by heated pulling combined with the chemical etching method. The Au-NPs were deposited on the TFNP surface with the electrostatic self-assembly technology, and then the TFNP was wrapped with ultrathin alumina layers by the atomic layer deposition (ALD) technique. The results told us that with the increasing thickness of the alumina film, the Raman signals decreased. With approximately 1 nm alumina film, the remote detection limit for R6G aqueous solution reached 10−6 mol/L. Full article
(This article belongs to the Section Chemical Sensors)
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Open AccessArticle Kriging with Unknown Variance Components for Regional Ionospheric Reconstruction
Sensors 2017, 17(3), 468; doi:10.3390/s17030468
Received: 1 December 2016 / Revised: 9 February 2017 / Accepted: 21 February 2017 / Published: 27 February 2017
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Abstract
Ionospheric delay effect is a critical issue that limits the accuracy of precise Global Navigation Satellite System (GNSS) positioning and navigation for single-frequency users, especially in mid- and low-latitude regions where variations in the ionosphere are larger. Kriging spatial interpolation techniques have been
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Ionospheric delay effect is a critical issue that limits the accuracy of precise Global Navigation Satellite System (GNSS) positioning and navigation for single-frequency users, especially in mid- and low-latitude regions where variations in the ionosphere are larger. Kriging spatial interpolation techniques have been recently introduced to model the spatial correlation and variability of ionosphere, which intrinsically assume that the ionosphere field is stochastically stationary but does not take the random observational errors into account. In this paper, by treating the spatial statistical information on ionosphere as prior knowledge and based on Total Electron Content (TEC) semivariogram analysis, we use Kriging techniques to spatially interpolate TEC values. By assuming that the stochastic models of both the ionospheric signals and measurement errors are only known up to some unknown factors, we propose a new Kriging spatial interpolation method with unknown variance components for both the signals of ionosphere and TEC measurements. Variance component estimation has been integrated with Kriging to reconstruct regional ionospheric delays. The method has been applied to data from the Crustal Movement Observation Network of China (CMONOC) and compared with the ordinary Kriging and polynomial interpolations with spherical cap harmonic functions, polynomial functions and low-degree spherical harmonic functions. The statistics of results indicate that the daily ionospheric variations during the experimental period characterized by the proposed approach have good agreement with the other methods, ranging from 10 to 80 TEC Unit (TECU, 1 TECU = 1 × 1016 electrons/m2) with an overall mean of 28.2 TECU. The proposed method can produce more appropriate estimations whose general TEC level is as smooth as the ordinary Kriging but with a smaller standard deviation around 3 TECU than others. The residual results show that the interpolation precision of the new proposed method is better than the ordinary Kriging and polynomial interpolation by about 1.2 TECU and 0.7 TECU, respectively. The root mean squared error of the proposed new Kriging with variance components is within 1.5 TECU and is smaller than those from other methods under comparison by about 1 TECU. When compared with ionospheric grid points, the mean squared error of the proposed method is within 6 TECU and smaller than Kriging, indicating that the proposed method can produce more accurate ionospheric delays and better estimation accuracy over China regional area. Full article
(This article belongs to the Section Remote Sensors)
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Open AccessArticle A Wireless Sensor System for Real-Time Monitoring and Fault Detection of Motor Arrays
Sensors 2017, 17(3), 469; doi:10.3390/s17030469
Received: 27 December 2016 / Revised: 16 February 2017 / Accepted: 21 February 2017 / Published: 25 February 2017
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Abstract
This paper presents a wireless fault detection system for industrial motors that combines vibration, motor current and temperature analysis, thus improving the detection of mechanical faults. The design also considers the time of detection and further possible actions, which are also important for
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This paper presents a wireless fault detection system for industrial motors that combines vibration, motor current and temperature analysis, thus improving the detection of mechanical faults. The design also considers the time of detection and further possible actions, which are also important for the early detection of possible malfunctions, and thus for avoiding irreversible damage to the motor. The remote motor condition monitoring is implemented through a wireless sensor network (WSN) based on the IEEE 802.15.4 standard. The deployed network uses the beacon-enabled mode to synchronize several sensor nodes with the coordinator node, and the guaranteed time slot mechanism provides data monitoring with a predetermined latency. A graphic user interface offers remote access to motor conditions and real-time monitoring of several parameters. The developed wireless sensor node exhibits very low power consumption since it has been optimized both in terms of hardware and software. The result is a low cost, highly reliable and compact design, achieving a high degree of autonomy of more than two years with just one 3.3 V/2600 mAh battery. Laboratory and field tests confirm the feasibility of the wireless system. Full article
(This article belongs to the Special Issue State-of-the-Art Sensors Technology in Spain 2016)
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Open AccessArticle Computationally Efficient 2D DOA Estimation with Uniform Rectangular Array in Low-Grazing Angle
Sensors 2017, 17(3), 470; doi:10.3390/s17030470
Received: 13 December 2016 / Revised: 7 February 2017 / Accepted: 22 February 2017 / Published: 26 February 2017
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Abstract
In this paper, we propose a computationally efficient spatial differencing matrix set (SDMS) method for two-dimensional direction of arrival (2D DOA) estimation with uniform rectangular arrays (URAs) in a low-grazing angle (LGA) condition. By rearranging the auto-correlation and cross-correlation matrices in turn among
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In this paper, we propose a computationally efficient spatial differencing matrix set (SDMS) method for two-dimensional direction of arrival (2D DOA) estimation with uniform rectangular arrays (URAs) in a low-grazing angle (LGA) condition. By rearranging the auto-correlation and cross-correlation matrices in turn among different subarrays, the SDMS method can estimate the two parameters independently with one-dimensional (1D) subspace-based estimation techniques, where we only perform difference for auto-correlation matrices and the cross-correlation matrices are kept completely. Then, the pair-matching of two parameters is achieved by extracting the diagonal elements of URA. Thus, the proposed method can decrease the computational complexity, suppress the effect of additive noise and also have little information loss. Simulation results show that, in LGA, compared to other methods, the proposed methods can achieve performance improvement in the white or colored noise conditions. Full article
(This article belongs to the Section Physical Sensors)
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Open AccessArticle Coordinated Target Tracking via a Hybrid Optimization Approach
Sensors 2017, 17(3), 472; doi:10.3390/s17030472
Received: 30 December 2016 / Revised: 15 February 2017 / Accepted: 23 February 2017 / Published: 27 February 2017
Cited by 2 | PDF Full-text (3258 KB) | HTML Full-text | XML Full-text
Abstract
Recent advances in computer science and electronics have greatly expanded the capabilities of unmanned aerial vehicles (UAV) in both defense and civil applications, such as moving ground object tracking. Due to the uncertainties of the application environments and objects’ motion, it is difficult
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Recent advances in computer science and electronics have greatly expanded the capabilities of unmanned aerial vehicles (UAV) in both defense and civil applications, such as moving ground object tracking. Due to the uncertainties of the application environments and objects’ motion, it is difficult to maintain the tracked object always within the sensor coverage area by using a single UAV. Hence, it is necessary to deploy a group of UAVs to improve the robustness of the tracking. This paper investigates the problem of tracking ground moving objects with a group of UAVs using gimbaled sensors under flight dynamic and collision-free constraints. The optimal cooperative tracking path planning problem is solved using an evolutionary optimization technique based on the framework of chemical reaction optimization (CRO). The efficiency of the proposed method was demonstrated through a series of comparative simulations. The results show that the cooperative tracking paths determined by the newly developed method allows for longer sensor coverage time under flight dynamic restrictions and safety conditions. Full article
(This article belongs to the Special Issue UAV-Based Remote Sensing)
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Open AccessArticle Sensing Properties of a Novel Temperature Sensor Based on Field Assisted Thermal Emission
Sensors 2017, 17(3), 473; doi:10.3390/s17030473
Received: 20 January 2017 / Revised: 20 February 2017 / Accepted: 22 February 2017 / Published: 27 February 2017
Cited by 1 | PDF Full-text (2925 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
The existing temperature sensors using carbon nanotubes (CNTs) are limited by low sensitivity, complicated processes, or dependence on microscopy to observe the experimental results. Here we report the fabrication and successful testing of an ionization temperature sensor featuring non-self-sustaining discharge. The sharp tips
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The existing temperature sensors using carbon nanotubes (CNTs) are limited by low sensitivity, complicated processes, or dependence on microscopy to observe the experimental results. Here we report the fabrication and successful testing of an ionization temperature sensor featuring non-self-sustaining discharge. The sharp tips of nanotubes generate high electric fields at relatively low voltages, lowering the work function of electrons emitted by CNTs, and thereby enabling the safe operation of such sensors. Due to the temperature effect on the electron emission of CNTs, the collecting current exhibited an exponential increase with temperature rising from 20 °C to 100 °C. Additionally, a higher temperature coefficient of 0.04 K−1 was obtained at 24 V voltage applied on the extracting electrode, higher than the values of other reported CNT-based temperature sensors. The triple-electrode ionization temperature sensor is easy to fabricate and converts the temperature change directly into an electrical signal. It shows a high temperature coefficient and good application potential. Full article
(This article belongs to the Special Issue Materials and Applications for Sensors and Transducers)
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Open AccessArticle Coalition Game-Based Secure and Effective Clustering Communication in Vehicular Cyber-Physical System (VCPS)
Sensors 2017, 17(3), 475; doi:10.3390/s17030475
Received: 18 December 2016 / Revised: 18 February 2017 / Accepted: 23 February 2017 / Published: 27 February 2017
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Abstract
In this paper, we address the low efficiency of cluster-based communication for the crossroad scenario in the Vehicular Cyber-Physical System (VCPS), which is due to the overload of the cluster head resulting from a large number of transmission bandwidth requirements. After formulating the
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In this paper, we address the low efficiency of cluster-based communication for the crossroad scenario in the Vehicular Cyber-Physical System (VCPS), which is due to the overload of the cluster head resulting from a large number of transmission bandwidth requirements. After formulating the issue as a coalition formation game, a coalition-based clustering strategy is proposed, which could converge into a Nash-stable partition to accomplish the clustering formation process. In the proposed strategy, the coalition utility is formulated by the relative velocity, relative position and the bandwidth availability ratio of vehicles among the cluster. Employing the coalition utility, the vehicles are denoted as the nodes that make the decision whether to switch to a new coalition or stay in the current coalition. Based on this, we can make full use of the bandwidth provided by cluster head under the requirement of clustering stability. Nevertheless, there exist selfish nodes duringtheclusteringformation,soastointendtobenefitfromnetworks. Thisbehaviormaydegrade the communication quality and even destroy the cluster. Thus, we also present a reputation-based incentive and penalty mechanism to stop the selfish nodes from entering clusters. Numerical simulation results show that our strategy, CG-SECC, takes on a better performance for the tradeoff between the stability and efficiency of clustering communication. Besides, a case study demonstrates that the proposed incentive and penalty mechanism can play an important role in discovering and removing malicious nodes. Full article
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Open AccessArticle Training Classifiers with Shadow Features for Sensor-Based Human Activity Recognition
Sensors 2017, 17(3), 476; doi:10.3390/s17030476
Received: 16 October 2016 / Revised: 20 December 2016 / Accepted: 22 December 2016 / Published: 27 February 2017
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Abstract
In this paper, a novel training/testing process for building/using a classification model based on human activity recognition (HAR) is proposed. Traditionally, HAR has been accomplished by a classifier that learns the activities of a person by training with skeletal data obtained from a
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In this paper, a novel training/testing process for building/using a classification model based on human activity recognition (HAR) is proposed. Traditionally, HAR has been accomplished by a classifier that learns the activities of a person by training with skeletal data obtained from a motion sensor, such as Microsoft Kinect. These skeletal data are the spatial coordinates (x, y, z) of different parts of the human body. The numeric information forms time series, temporal records of movement sequences that can be used for training a classifier. In addition to the spatial features that describe current positions in the skeletal data, new features called ‘shadow features’ are used to improve the supervised learning efficacy of the classifier. Shadow features are inferred from the dynamics of body movements, and thereby modelling the underlying momentum of the performed activities. They provide extra dimensions of information for characterising activities in the classification process, and thereby significantly improve the classification accuracy. Two cases of HAR are tested using a classification model trained with shadow features: one is by using wearable sensor and the other is by a Kinect-based remote sensor. Our experiments can demonstrate the advantages of the new method, which will have an impact on human activity detection research. Full article
(This article belongs to the Section Physical Sensors)
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Open AccessArticle A Fast Channel Assignment Scheme for Emergency Handling in Wireless Body Area Networks
Sensors 2017, 17(3), 477; doi:10.3390/s17030477
Received: 2 December 2016 / Revised: 21 February 2017 / Accepted: 24 February 2017 / Published: 27 February 2017
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Abstract
Ubiquitous healthcare is a promising technology that has attracted significant attention in recent years; this has led to the realization of wireless body area networks (WBANs). For designing a robust WBAN system, the WBAN has to solve the drawbacks of wireless technology. Also,
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Ubiquitous healthcare is a promising technology that has attracted significant attention in recent years; this has led to the realization of wireless body area networks (WBANs). For designing a robust WBAN system, the WBAN has to solve the drawbacks of wireless technology. Also, a WBAN has to support immediate, reliable data transmission for medical services during emergencies. Hence, this study proposes a new MAC superframe structure that can handle emergencies by delivering strongly correlated regular data to a caretaker, within a certain time threshold. Simulation results demonstrate that the proposed MAC protocol achieves low latency and high throughput. Full article
(This article belongs to the Special Issue Advances in Body Sensor Networks: Sensors, Systems, and Applications)
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Open AccessArticle Wearable Device-Based Gait Recognition Using Angle Embedded Gait Dynamic Images and a Convolutional Neural Network
Sensors 2017, 17(3), 478; doi:10.3390/s17030478
Received: 7 December 2016 / Revised: 17 February 2017 / Accepted: 22 February 2017 / Published: 28 February 2017
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Abstract
The widespread installation of inertial sensors in smartphones and other wearable devices provides a valuable opportunity to identify people by analyzing their gait patterns, for either cooperative or non-cooperative circumstances. However, it is still a challenging task to reliably extract discriminative features for
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The widespread installation of inertial sensors in smartphones and other wearable devices provides a valuable opportunity to identify people by analyzing their gait patterns, for either cooperative or non-cooperative circumstances. However, it is still a challenging task to reliably extract discriminative features for gait recognition with noisy and complex data sequences collected from casually worn wearable devices like smartphones. To cope with this problem, we propose a novel image-based gait recognition approach using the Convolutional Neural Network (CNN) without the need to manually extract discriminative features. The CNN’s input image, which is encoded straightforwardly from the inertial sensor data sequences, is called Angle Embedded Gait Dynamic Image (AE-GDI). AE-GDI is a new two-dimensional representation of gait dynamics, which is invariant to rotation and translation. The performance of the proposed approach in gait authentication and gait labeling is evaluated using two datasets: (1) the McGill University dataset, which is collected under realistic conditions; and (2) the Osaka University dataset with the largest number of subjects. Experimental results show that the proposed approach achieves competitive recognition accuracy over existing approaches and provides an effective parametric solution for identification among a large number of subjects by gait patterns. Full article
(This article belongs to the Special Issue Wearable Biomedical Sensors)
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Open AccessArticle Error-Based Observer of a Charge Couple Device Tracking Loop for Fast Steering Mirror
Sensors 2017, 17(3), 479; doi:10.3390/s17030479
Received: 13 January 2017 / Revised: 19 February 2017 / Accepted: 24 February 2017 / Published: 28 February 2017
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Abstract
The charge couple device (CCD) tracking loop of a fast steering mirror (FSM) is usually used to stabilize line of sight (LOS). High closed-loop bandwidth facilitates good performance. However, low-rate sample and time delay of the CCD greatly limit the high control bandwidth.
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The charge couple device (CCD) tracking loop of a fast steering mirror (FSM) is usually used to stabilize line of sight (LOS). High closed-loop bandwidth facilitates good performance. However, low-rate sample and time delay of the CCD greatly limit the high control bandwidth. This paper proposes an error-based observer (EBO) to improve the low-frequency performance of the CCD tracking system. The basic idea is by combining LOS error from the CCD and the controller output to produce the high-gain observer, forwarding into the originally closed-loop control system. This proposed EBO can improve the system both in target tracking and disturbance suppression due to LOS error from the CCD’s sensing of the two signals. From a practical engineering view, the closed-loop stability and robustness of the EBO system are investigated on the condition of gain margin and phase margin of the open-loop transfer function. Two simulations of CCD experiments are provided to verify the benefits of the proposed algorithm. Full article
(This article belongs to the Section Physical Sensors)
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Open AccessArticle Rapid and Sensitive Lateral Flow Immunoassay Method for Procalcitonin (PCT) Based on Time-Resolved Immunochromatography
Sensors 2017, 17(3), 480; doi:10.3390/s17030480
Received: 10 January 2017 / Revised: 24 February 2017 / Accepted: 24 February 2017 / Published: 28 February 2017
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Abstract
Procalcitonin (PCT) is a current, frequently-used marker for severe bacterial infection. The aim of this study was to develop a cost-effective detection kit for rapid quantitative and on-site detection of PCT. To develop the new PCT quantitative detecting kit, a double-antibody sandwich immunofluorescent
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Procalcitonin (PCT) is a current, frequently-used marker for severe bacterial infection. The aim of this study was to develop a cost-effective detection kit for rapid quantitative and on-site detection of PCT. To develop the new PCT quantitative detecting kit, a double-antibody sandwich immunofluorescent assay was employed based on time-resolved immunofluorescent assay (TRFIA) combined with lateral flow immunoassay (LFIA). The performance of the new developed kit was evaluated in the aspects of linearity, precision, accuracy, and specificity. Two-hundred thirty-four serum samples were enrolled to carry out the comparison test. The new PCT quantitative detecting kit exhibited a higher sensitivity (0.08 ng/mL). The inter-assay coefficient of variation (CV) and the intra-assay CV were 5.4%–7.7% and 5.7%–13.4%, respectively. The recovery rates ranged from 93% to 105%. Furthermore, a high correlation (n = 234, r = 0.977, p < 0.0001) and consistency (Kappa = 0.875) were obtained when compared with the PCT kit from Roche Elecsys BRAHMS. Thus, the new quantitative method for detecting PCT has been successfully established. The results indicated that the newly-developed system based on TRFIA combined with LFIA was suitable for rapid and on-site detection for PCT, which might be a useful platform for other biomarkers in point-of-care tests. Full article
(This article belongs to the Section Biosensors)
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Open AccessArticle Spectral Analysis of Acceleration Data for Detection of Generalized Tonic-Clonic Seizures
Sensors 2017, 17(3), 481; doi:10.3390/s17030481
Received: 21 November 2016 / Revised: 6 February 2017 / Accepted: 22 February 2017 / Published: 28 February 2017
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Abstract
Generalized tonic-clonic seizures (GTCSs) can be underestimated and can also increase mortality rates. The monitoring devices used to detect GTCS events in daily life are very helpful for early intervention and precise estimation of seizure events. Several studies have introduced methods for GTCS
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Generalized tonic-clonic seizures (GTCSs) can be underestimated and can also increase mortality rates. The monitoring devices used to detect GTCS events in daily life are very helpful for early intervention and precise estimation of seizure events. Several studies have introduced methods for GTCS detection using an accelerometer (ACM), electromyography, or electroencephalography. However, these studies need to be improved with respect to accuracy and user convenience. This study proposes the use of an ACM banded to the wrist and spectral analysis of ACM data to detect GTCS in daily life. The spectral weight function dependent on GTCS was used to compute a GTCS-correlated score that can effectively discriminate between GTCS and normal movement. Compared to the performance of the previous temporal method, which used a standard deviation method, the spectral analysis method resulted in better sensitivity and fewer false positive alerts. Finally, the spectral analysis method can be implemented in a GTCS monitoring device using an ACM and can provide early alerts to caregivers to prevent risks associated with GTCS. Full article
(This article belongs to the Special Issue Sensing Technology for Healthcare System)
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Open AccessArticle Modeling Vehicle Collision Angle in Traffic Crashes Based on Three-Dimensional Laser Scanning Data
Sensors 2017, 17(3), 482; doi:10.3390/s17030482
Received: 16 November 2016 / Revised: 6 February 2017 / Accepted: 22 February 2017 / Published: 28 February 2017
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Abstract
In road traffic accidents, the analysis of a vehicle’s collision angle plays a key role in identifying a traffic accident’s form and cause. However, because accurate estimation of vehicle collision angle involves many factors, it is difficult to accurately determine it in cases
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In road traffic accidents, the analysis of a vehicle’s collision angle plays a key role in identifying a traffic accident’s form and cause. However, because accurate estimation of vehicle collision angle involves many factors, it is difficult to accurately determine it in cases in which less physical evidence is available and there is a lack of monitoring. This paper establishes the mathematical relation model between collision angle, deformation, and normal vector in the collision region according to the equations of particle deformation and force in Hooke’s law of classical mechanics. At the same time, the surface reconstruction method suitable for a normal vector solution is studied. Finally, the estimation model of vehicle collision angle is presented. In order to verify the correctness of the model, verification of multi-angle collision experiments and sensitivity analysis of laser scanning precision for the angle have been carried out using three-dimensional (3D) data obtained by a 3D laser scanner in the collision deformation zone. Under the conditions with which the model has been defined, validation results show that the collision angle is a result of the weighted synthesis of the normal vector of the collision point and the weight value is the deformation of the collision point corresponding to normal vectors. These conclusions prove the applicability of the model. The collision angle model proposed in this paper can be used as the theoretical basis for traffic accident identification and cause analysis. It can also be used as a theoretical reference for the study of the impact deformation of elastic materials. Full article
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Open AccessArticle The Theoretical Highest Frame Rate of Silicon Image Sensors
Sensors 2017, 17(3), 483; doi:10.3390/s17030483
Received: 14 January 2017 / Revised: 19 February 2017 / Accepted: 24 February 2017 / Published: 28 February 2017
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Abstract
The frame rate of the digital high-speed video camera was 2000 frames per second (fps) in 1989, and has been exponentially increasing. A simulation study showed that a silicon image sensor made with a 130 nm process technology can achieve about 1010
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The frame rate of the digital high-speed video camera was 2000 frames per second (fps) in 1989, and has been exponentially increasing. A simulation study showed that a silicon image sensor made with a 130 nm process technology can achieve about 1010 fps. The frame rate seems to approach the upper bound. Rayleigh proposed an expression on the theoretical spatial resolution limit when the resolution of lenses approached the limit. In this paper, the temporal resolution limit of silicon image sensors was theoretically analyzed. It is revealed that the limit is mainly governed by mixing of charges with different travel times caused by the distribution of penetration depth of light. The derived expression of the limit is extremely simple, yet accurate. For example, the limit for green light of 550 nm incident to silicon image sensors at 300 K is 11.1 picoseconds. Therefore, the theoretical highest frame rate is 90.1 Gfps (about 1011 fps) Full article
(This article belongs to the Section Physical Sensors)
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Open AccessArticle Dynamic Involvement of Real World Objects in the IoT: A Consensus-Based Cooperation Approach
Sensors 2017, 17(3), 484; doi:10.3390/s17030484
Received: 16 November 2016 / Revised: 15 February 2017 / Accepted: 22 February 2017 / Published: 1 March 2017
Cited by 2 | PDF Full-text (524 KB) | HTML Full-text | XML Full-text
Abstract
A significant role in the Internet of Things (IoT) will be taken by mobile and low-cost unstable devices, which autonomously self-organize and introduce highly dynamic and heterogeneous scenarios for the deployment of distributed applications. This entails the devices to cooperate to dynamically find
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A significant role in the Internet of Things (IoT) will be taken by mobile and low-cost unstable devices, which autonomously self-organize and introduce highly dynamic and heterogeneous scenarios for the deployment of distributed applications. This entails the devices to cooperate to dynamically find the suitable combination of their involvement so as to improve the system reliability while following the changes in their status. Focusing on the above scenario, we propose a distributed algorithm for resources allocation that is run by devices that can perform the same task required by the applications, allowing for a flexible and dynamic binding of the requested services with the physical IoT devices. It is based on a consensus approach, which maximizes the lifetime of groups of nodes involved and ensures the fulfillment of the requested Quality of Information (QoI) requirements. Experiments have been conducted with real devices, showing an improvement of device lifetime of more than 20 % , with respect to a uniform distribution of tasks. Full article
(This article belongs to the Special Issue Next Generation Wireless Technologies for Internet of Things)
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Open AccessArticle A Doppler Radar System for Sensing Physiological Parameters in Walking and Standing Positions
Sensors 2017, 17(3), 485; doi:10.3390/s17030485
Received: 30 January 2017 / Revised: 21 February 2017 / Accepted: 24 February 2017 / Published: 1 March 2017
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Abstract
Doppler radar can be implemented for sensing physiological parameters wirelessly at a distance. Detecting respiration rate, an important human body parameter, is essential in a range of applications like emergency and military healthcare environments, and Doppler radar records actual chest motion. One challenge
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Doppler radar can be implemented for sensing physiological parameters wirelessly at a distance. Detecting respiration rate, an important human body parameter, is essential in a range of applications like emergency and military healthcare environments, and Doppler radar records actual chest motion. One challenge in using Doppler radar is being able to monitor several patients simultaneously and in different situations like standing, walking, or lying. This paper presents a complete transmitter-receiver Doppler radar system, which uses a 4 GHz continuous wave radar signal transmission and receiving system, to extract base-band data from a phase-shifted signal. This work reports experimental evaluations of the system for one and two subjects in various standing and walking positions. It provides a detailed signal analysis of various breathing rates of these two subjects simultaneously. These results will be useful in future medical monitoring applications. Full article
(This article belongs to the Special Issue Advances in Body Sensor Networks: Sensors, Systems, and Applications)
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Open AccessArticle Design of a Fatigue Detection System for High-Speed Trains Based on Driver Vigilance Using a Wireless Wearable EEG
Sensors 2017, 17(3), 486; doi:10.3390/s17030486
Received: 25 December 2016 / Revised: 23 February 2017 / Accepted: 27 February 2017 / Published: 1 March 2017
Cited by 3 | PDF Full-text (8648 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
The vigilance of the driver is important for railway safety, despite not being included in the safety management system (SMS) for high-speed train safety. In this paper, a novel fatigue detection system for high-speed train safety based on monitoring train driver vigilance using
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The vigilance of the driver is important for railway safety, despite not being included in the safety management system (SMS) for high-speed train safety. In this paper, a novel fatigue detection system for high-speed train safety based on monitoring train driver vigilance using a wireless wearable electroencephalograph (EEG) is presented. This system is designed to detect whether the driver is drowsiness. The proposed system consists of three main parts: (1) a wireless wearable EEG collection; (2) train driver vigilance detection; and (3) early warning device for train driver. In the first part, an 8-channel wireless wearable brain-computer interface (BCI) device acquires the locomotive driver’s brain EEG signal comfortably under high-speed train-driving conditions. The recorded data are transmitted to a personal computer (PC) via Bluetooth. In the second step, a support vector machine (SVM) classification algorithm is implemented to determine the vigilance level using the Fast Fourier transform (FFT) to extract the EEG power spectrum density (PSD). In addition, an early warning device begins to work if fatigue is detected. The simulation and test results demonstrate the feasibility of the proposed fatigue detection system for high-speed train safety. Full article
(This article belongs to the Special Issue Sensors for Transportation)
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Open AccessArticle Parameter Selection and Performance Comparison of Particle Swarm Optimization in Sensor Networks Localization
Sensors 2017, 17(3), 487; doi:10.3390/s17030487
Received: 24 December 2016 / Revised: 28 February 2017 / Accepted: 28 February 2017 / Published: 1 March 2017
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Abstract
Localization is a key technology in wireless sensor networks. Faced with the challenges of the sensors’ memory, computational constraints, and limited energy, particle swarm optimization has been widely applied in the localization of wireless sensor networks, demonstrating better performance than other optimization methods.
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Localization is a key technology in wireless sensor networks. Faced with the challenges of the sensors’ memory, computational constraints, and limited energy, particle swarm optimization has been widely applied in the localization of wireless sensor networks, demonstrating better performance than other optimization methods. In particle swarm optimization-based localization algorithms, the variants and parameters should be chosen elaborately to achieve the best performance. However, there is a lack of guidance on how to choose these variants and parameters. Further, there is no comprehensive performance comparison among particle swarm optimization algorithms. The main contribution of this paper is three-fold. First, it surveys the popular particle swarm optimization variants and particle swarm optimization-based localization algorithms for wireless sensor networks. Secondly, it presents parameter selection of nine particle swarm optimization variants and six types of swarm topologies by extensive simulations. Thirdly, it comprehensively compares the performance of these algorithms. The results show that the particle swarm optimization with constriction coefficient using ring topology outperforms other variants and swarm topologies, and it performs better than the second-order cone programming algorithm. Full article
(This article belongs to the Section Sensor Networks)
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Open AccessArticle A Guided Wave Sensor Enabling Simultaneous Wavenumber-Frequency Analysis for Both Lamb and Shear-Horizontal Waves
Sensors 2017, 17(3), 488; doi:10.3390/s17030488
Received: 9 January 2017 / Revised: 21 February 2017 / Accepted: 24 February 2017 / Published: 1 March 2017
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Abstract
Guided waves in plate-like structures have been widely investigated for structural health monitoring. Lamb waves and shear horizontal (SH) waves, two commonly used types of waves in plates, provide different benefits for the detection of various types of defects and material degradation. However,
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Guided waves in plate-like structures have been widely investigated for structural health monitoring. Lamb waves and shear horizontal (SH) waves, two commonly used types of waves in plates, provide different benefits for the detection of various types of defects and material degradation. However, there are few sensors that can detect both Lamb and SH waves and also resolve their modal content, namely the wavenumber-frequency spectrum. A sensor that can detect both waves is desirable to take full advantage of both types of waves in order to improve sensitivity to different discontinuity geometries. We demonstrate that polyvinylidene difluoride (PVDF) film provides the basis for a multi-element array sensor that detects both Lamb and SH waves and also measures their modal content, i.e., the wavenumber-frequency spectrum. Full article
(This article belongs to the Special Issue Ultrasonic Sensors)
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Open AccessArticle A Location-Based Interactive Model of Internet of Things and Cloud (IoT-Cloud) for Mobile Cloud Computing Applications
Sensors 2017, 17(3), 489; doi:10.3390/s17030489
Received: 2 January 2017 / Revised: 18 February 2017 / Accepted: 24 February 2017 / Published: 1 March 2017
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Abstract
This paper presents a location-based interactive model of Internet of Things (IoT) and cloud integration (IoT-cloud) for mobile cloud computing applications, in comparison with the periodic sensing model. In the latter, sensing collections are performed without awareness of sensing demands. Sensors are required
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This paper presents a location-based interactive model of Internet of Things (IoT) and cloud integration (IoT-cloud) for mobile cloud computing applications, in comparison with the periodic sensing model. In the latter, sensing collections are performed without awareness of sensing demands. Sensors are required to report their sensing data periodically regardless of whether or not there are demands for their sensing services. This leads to unnecessary energy loss due to redundant transmission. In the proposed model, IoT-cloud provides sensing services on demand based on interest and location of mobile users. By taking advantages of the cloud as a coordinator, sensing scheduling of sensors is controlled by the cloud, which knows when and where mobile users request for sensing services. Therefore, when there is no demand, sensors are put into an inactive mode to save energy. Through extensive analysis and experimental results, we show that the location-based model achieves a significant improvement in terms of network lifetime compared to the periodic model. Full article
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Open AccessArticle A Study on Optimal Strategy in Relative Radiometric Calibration for Optical Sensors
Sensors 2017, 17(3), 490; doi:10.3390/s17030490
Received: 22 November 2016 / Revised: 22 February 2017 / Accepted: 24 February 2017 / Published: 2 March 2017
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Abstract
Based on the analysis of three main factors involved in the relative radiometric calibration for optical sensors, namely: the number of radiance level; the number of measurements at each level; and the radiance level grouping method, an optimal strategy is presented in this
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Based on the analysis of three main factors involved in the relative radiometric calibration for optical sensors, namely: the number of radiance level; the number of measurements at each level; and the radiance level grouping method, an optimal strategy is presented in this paper for relative radiometric calibration. First, the maximization to the possible extent of either the number of the radiance level or the number of measurements at each level can improve the precision of the calibration results, where the recommended number of measurements is no less than 20. Second, when the number of the radiance level is divisible by four, dividing all the levels evenly into four groups by intensity gradient order and conducting averages for each group could achieve calibration results with the highest precision, which is higher than the result of no grouping or any other grouping method with the mean square error being 2 2 M n / I T (where M n is the mean square error of noise in the calibration data, I is the number of the radiance level, and T is the number of measurements for each level. In this case, the first two factors had an equivalent effect and showed their strongest effect on the precision. Third, when the calibration data were not evenly divided, the number of measurements demonstrated a stronger effect than the number of the radiance level. These cognitions are helping to achieve more precise relative radiometric calibration of optical sensors. Full article
(This article belongs to the Section Physical Sensors)
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Open AccessArticle Dynamic Context-Aware Event Recognition Based on Markov Logic Networks
Sensors 2017, 17(3), 491; doi:10.3390/s17030491
Received: 6 January 2017 / Revised: 23 February 2017 / Accepted: 25 February 2017 / Published: 2 March 2017
PDF Full-text (4971 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Event recognition in smart spaces is an important and challenging task. Most existing approaches for event recognition purely employ either logical methods that do not handle uncertainty, or probabilistic methods that can hardly manage the representation of structured information. To overcome these limitations,
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Event recognition in smart spaces is an important and challenging task. Most existing approaches for event recognition purely employ either logical methods that do not handle uncertainty, or probabilistic methods that can hardly manage the representation of structured information. To overcome these limitations, especially in the situation where the uncertainty of sensing data is dynamically changing over the time, we propose a multi-level information fusion model for sensing data and contextual information, and also present a corresponding method to handle uncertainty for event recognition based on Markov logic networks (MLNs) which combine the expressivity of first order logic (FOL) and the uncertainty disposal of probabilistic graphical models (PGMs). Then we put forward an algorithm for updating formula weights in MLNs to deal with data dynamics. Experiments on two datasets from different scenarios are conducted to evaluate the proposed approach. The results show that our approach (i) provides an effective way to recognize events by using the fusion of uncertain data and contextual information based on MLNs and (ii) outperforms the original MLNs-based method in dealing with dynamic data. Full article
(This article belongs to the Special Issue Sensors for Ambient Assisted Living, Ubiquitous and Mobile Health)
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Open AccessArticle Performance Analysis of Different Backoff Algorithms for WBAN-Based Emerging Sensor Networks
Sensors 2017, 17(3), 492; doi:10.3390/s17030492
Received: 15 November 2016 / Revised: 20 February 2017 / Accepted: 24 February 2017 / Published: 2 March 2017
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Abstract
The Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) procedure of IEEE 802.15.6 Medium Access Control (MAC) protocols for the Wireless Body Area Network (WBAN) use an Alternative Binary Exponential Backoff (ABEB) procedure. The backoff algorithm plays an important role to avoid collision
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The Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) procedure of IEEE 802.15.6 Medium Access Control (MAC) protocols for the Wireless Body Area Network (WBAN) use an Alternative Binary Exponential Backoff (ABEB) procedure. The backoff algorithm plays an important role to avoid collision in wireless networks. The Binary Exponential Backoff (BEB) algorithm used in different standards does not obtain the optimum performance due to enormous Contention Window (CW) gaps induced from packet collisions. Therefore, The IEEE 802.15.6 CSMA/CA has developed the ABEB procedure to avoid the large CW gaps upon each collision. However, the ABEB algorithm may lead to a high collision rate (as the CW size is incremented on every alternative collision) and poor utilization of the channel due to the gap between the subsequent CW. To minimize the gap between subsequent CW sizes, we adopted the Prioritized Fibonacci Backoff (PFB) procedure. This procedure leads to a smooth and gradual increase in the CW size, after each collision, which eventually decreases the waiting time, and the contending node can access the channel promptly with little delay; while ABEB leads to irregular and fluctuated CW values, which eventually increase collision and waiting time before a re-transmission attempt. We analytically approach this problem by employing a Markov chain to design the PFB scheme for the CSMA/CA procedure of the IEEE 80.15.6 standard. The performance of the PFB algorithm is compared against the ABEB function of WBAN CSMA/CA. The results show that the PFB procedure adopted for IEEE 802.15.6 CSMA/CA outperforms the ABEB procedure. Full article
(This article belongs to the Special Issue Advances in Body Sensor Networks: Sensors, Systems, and Applications)
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Open AccessArticle A Cubesat Payload for Exoplanet Detection
Sensors 2017, 17(3), 493; doi:10.3390/s17030493
Received: 5 January 2017 / Revised: 18 February 2017 / Accepted: 23 February 2017 / Published: 2 March 2017
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Abstract
The search for undiscovered planets outside the solar system is a scientific topic that is rapidly spreading into the astrophysical and engineering communities. In this framework, the design of an innovative payload to detect exoplanets from a nano-sized space platform, like a 3U
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The search for undiscovered planets outside the solar system is a scientific topic that is rapidly spreading into the astrophysical and engineering communities. In this framework, the design of an innovative payload to detect exoplanets from a nano-sized space platform, like a 3U cubesat, is presented. The selected detection method is photometric transit, and the payload aims to detect flux decrements down to ~0.01% with a precision of 12 ppm. The payload design is also aimed at false positive recognition. The solution consists of a four-facets pyramid on the top of the payload, to allow for measurement redundancy and low-resolution spectral dispersion of the star images. The innovative concept is the use of a small and cheap platform for a relevant astronomical mission. The faintest observable target star has V-magnitude equal to 3.38. Despite missions aimed at ultra-precise photometry from microsatellites (e.g., MOST, BRITE), the transit of exoplanets orbiting very bright stars has not yet been surveyed photometrically from space, since any observation from a small/medium sized (30 cm optical aperture) telescope would saturate the detector. This cubesat mission can provide these missing measurements. This work is set up as a demonstrative project to verify the feasibility of the payload concept. Full article
(This article belongs to the Section Physical Sensors)
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Open AccessArticle An Adaptive Damping Network Designed for Strapdown Fiber Optic Gyrocompass System for Ships
Sensors 2017, 17(3), 494; doi:10.3390/s17030494
Received: 6 December 2016 / Revised: 28 February 2017 / Accepted: 28 February 2017 / Published: 2 March 2017
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Abstract
The strapdown fiber optic gyrocompass (strapdown FOGC) system for ships primarily works on external horizontal damping and undamping statuses. When there are large sea condition changes, the system will switch frequently between the external horizontal damping status and the undamping status. This means
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The strapdown fiber optic gyrocompass (strapdown FOGC) system for ships primarily works on external horizontal damping and undamping statuses. When there are large sea condition changes, the system will switch frequently between the external horizontal damping status and the undamping status. This means that the system is always in an adjustment status and influences the dynamic accuracy of the system. Aiming at the limitations of the conventional damping method, a new design idea is proposed, where the adaptive control method is used to design the horizontal damping network of the strapdown FOGC system. According to the size of acceleration, the parameters of the damping network are changed to make the system error caused by the ship’s maneuvering to a minimum. Furthermore, the jump in damping coefficient was transformed into gradual change to make a smooth system status switch. The adaptive damping network was applied for strapdown FOGC under the static and dynamic condition, and its performance was compared with the conventional damping, and undamping means. Experimental results showed that the adaptive damping network was effective in improving the dynamic performance of the strapdown FOGC. Full article
(This article belongs to the Section Physical Sensors)
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Open AccessArticle Online Detection of Driver Fatigue Using Steering Wheel Angles for Real Driving Conditions
Sensors 2017, 17(3), 495; doi:10.3390/s17030495
Received: 1 January 2017 / Revised: 8 February 2017 / Accepted: 28 February 2017 / Published: 2 March 2017
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Abstract
This paper presents a drowsiness on-line detection system for monitoring driver fatigue level under real driving conditions, based on the data of steering wheel angles (SWA) collected from sensors mounted on the steering lever. The proposed system firstly extracts approximate entropy (ApEn)featuresfromfixedslidingwindowsonreal-timesteeringwheelanglestimeseries. Afterthat,
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This paper presents a drowsiness on-line detection system for monitoring driver fatigue level under real driving conditions, based on the data of steering wheel angles (SWA) collected from sensors mounted on the steering lever. The proposed system firstly extracts approximate entropy (ApEn)featuresfromfixedslidingwindowsonreal-timesteeringwheelanglestimeseries. Afterthat, this system linearizes the ApEn features series through an adaptive piecewise linear fitting using a given deviation. Then, the detection system calculates the warping distance between the linear features series of the sample data. Finally, this system uses the warping distance to determine the drowsiness state of the driver according to a designed binary decision classifier. The experimental data were collected from 14.68 h driving under real road conditions, including two fatigue levels: “wake” and “drowsy”. The results show that the proposed system is capable of working online with an average 78.01% accuracy, 29.35% false detections of the “awake” state, and 15.15% false detections of the “drowsy” state. The results also confirm that the proposed method based on SWA signal is valuable for applications in preventing traffic accidents caused by driver fatigue. Full article
(This article belongs to the Special Issue Sensors for Transportation)
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Open AccessArticle Development of a Double-Gauss Lens Based Setup for Optoacoustic Applications
Sensors 2017, 17(3), 496; doi:10.3390/s17030496
Received: 19 November 2016 / Revised: 23 January 2017 / Accepted: 10 February 2017 / Published: 3 March 2017
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Abstract
In optoacoustic (photoacoustic) systems, different echo signal intensities such as amplitudes, center frequencies, and bandwidths need to be compensated by utilizing variable gain or time-gain compensation amplifiers. However, such electronic components can increase system complexities and signal noise levels. In this paper, we
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In optoacoustic (photoacoustic) systems, different echo signal intensities such as amplitudes, center frequencies, and bandwidths need to be compensated by utilizing variable gain or time-gain compensation amplifiers. However, such electronic components can increase system complexities and signal noise levels. In this paper, we introduce a double-Gauss lens to generate a large field of view with uniform light intensity due to the low chromatic aberrations of the lens, thus obtaining uniform echo signal intensities across the field of view of the optoacoustic system. In order to validate the uniformity of the echo signal intensities in the system, an in-house transducer was placed at various positions above a tissue sample and echo signals were measured and compared with each other. The custom designed double-Gauss lens demonstrated negligible light intensity variation (±1.5%) across the illumination field of view (~2 cm diameter). When the transducer was used to measure echo signal from an eye of a bigeye tuna within a range of ±1 cm, the peak-to-peak amplitude, center frequency, and their −6 dB bandwidth variations were less than 2 mV, 1 MHz, and 6%, respectively. The custom designed double-Gauss lens can provide uniform light beam across a wide area while generating insignificant echo signal variations, and thus can lower the burden of the receiving electronics or signal processing in the optoacoustic system. Full article
(This article belongs to the Special Issue Acoustic Sensing and Ultrasonic Drug Delivery)
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Open AccessArticle Supportive Noninvasive Tool for the Diagnosis of Breast Cancer Using a Thermographic Camera as Sensor
Sensors 2017, 17(3), 497; doi:10.3390/s17030497
Received: 16 December 2016 / Revised: 15 February 2017 / Accepted: 22 February 2017 / Published: 3 March 2017
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Abstract
Breast cancer is the leading disease in incidence and mortality among women in developing countries. The opportune diagnosis of this disease strengthens the survival index. Mammography application is limited by age and periodicity. Temperature is a physical magnitude that can be measured by
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Breast cancer is the leading disease in incidence and mortality among women in developing countries. The opportune diagnosis of this disease strengthens the survival index. Mammography application is limited by age and periodicity. Temperature is a physical magnitude that can be measured by using multiple sensing techniques. IR (infrared) thermography using commercial cameras is gaining relevance in industrial and medical applications because it is a non-invasive and non-intrusive technology. Asymmetrical temperature in certain human body zones is associated with cancer. In this paper, an IR thermographic sensor is applied for breast cancer detection. This work includes an automatic breast segmentation methodology, to spot the hottest regions in thermograms using the morphological watershed operator to help the experts locate the tumor. A protocol for thermogram acquisition considering the required time to achieve a thermal stabilization is also proposed. Breast thermograms are evaluated as thermal matrices, instead of gray scale or false color images, increasing the certainty of the provided diagnosis. The proposed tool was validated using the Database for Mastology Research and tested in a voluntary group of 454 women of different ages and cancer stages with good results, leading to the possibility of being used as a supportive tool to detect breast cancer and angiogenesis cases. Full article
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Open AccessArticle ISAR Imaging of Ship Targets Based on an Integrated Cubic Phase Bilinear Autocorrelation Function
Sensors 2017, 17(3), 498; doi:10.3390/s17030498
Received: 20 January 2017 / Revised: 23 February 2017 / Accepted: 24 February 2017 / Published: 3 March 2017
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Abstract
For inverse synthetic aperture radar (ISAR) imaging of a ship target moving with ocean waves, the image constructed with the standard range-Doppler (RD) technique is blurred and the range-instantaneous-Doppler (RID) technique has to be used to improve the image quality. In this paper,
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For inverse synthetic aperture radar (ISAR) imaging of a ship target moving with ocean waves, the image constructed with the standard range-Doppler (RD) technique is blurred and the range-instantaneous-Doppler (RID) technique has to be used to improve the image quality. In this paper, azimuth echoes in a range cell of the ship target are modeled as noisy multicomponent cubic phase signals (CPSs) after the motion compensation and a RID ISAR imaging algorithm is proposed based on the integrated cubic phase bilinear autocorrelation function (ICPBAF). The ICPBAF is bilinear and based on the two-dimensionally coherent energy accumulation. Compared to five other estimation algorithms, the ICPBAF can acquire higher cross term suppression and anti-noise performance with a reasonable computational cost. Through simulations and analyses with the synthetic model and real radar data, we verify the effectiveness of the ICPBAF and corresponding RID ISAR imaging algorithm. Full article
(This article belongs to the Section Remote Sensors)
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Open AccessArticle Analytical and Experimental Performance Evaluation of BLE Neighbor Discovery Process Including Non-Idealities of Real Chipsets
Sensors 2017, 17(3), 499; doi:10.3390/s17030499
Received: 23 December 2016 / Revised: 16 February 2017 / Accepted: 27 February 2017 / Published: 3 March 2017
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Abstract
The purpose of this paper is to evaluate from a real perspective the performance of Bluetooth Low Energy (BLE) as a technology that enables fast and reliable discovery of a large number of users/devices in a short period of time. The BLE standard
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The purpose of this paper is to evaluate from a real perspective the performance of Bluetooth Low Energy (BLE) as a technology that enables fast and reliable discovery of a large number of users/devices in a short period of time. The BLE standard specifies a wide range of configurable parameter values that determine the discovery process and need to be set according to the particular application requirements. Many previous works have been addressed to investigate the discovery process through analytical and simulation models, according to the ideal specification of the standard. However, measurements show that additional scanning gaps appear in the scanning process, which reduce the discovery capabilities. These gaps have been identified in all of the analyzed devices and respond to both regular patterns and variable events associated with the decoding process. We have demonstrated that these non-idealities, which are not taken into account in other studies, have a severe impact on the discovery process performance. Extensive performance evaluation for a varying number of devices and feasible parameter combinations has been done by comparing simulations and experimental measurements. This work also includes a simple mathematical model that closely matches both the standard implementation and the different chipset peculiarities for any possible parameter value specified in the standard and for any number of simultaneous advertising devices under scanner coverage. Full article
(This article belongs to the Section Sensor Networks)
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Open AccessArticle PAVS: A New Privacy-Preserving Data Aggregation Scheme for Vehicle Sensing Systems
Sensors 2017, 17(3), 500; doi:10.3390/s17030500
Received: 30 December 2016 / Revised: 20 February 2017 / Accepted: 27 February 2017 / Published: 3 March 2017
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Abstract
Air pollution has become one of the most pressing environmental issues in recent years. According to a World Health Organization (WHO) report, air pollution has led to the deaths of millions of people worldwide. Accordingly, expensive and complex air-monitoring instruments have been exploited
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Air pollution has become one of the most pressing environmental issues in recent years. According to a World Health Organization (WHO) report, air pollution has led to the deaths of millions of people worldwide. Accordingly, expensive and complex air-monitoring instruments have been exploited to measure air pollution. Comparatively, a vehicle sensing system (VSS), as it can be effectively used for many purposes and can bring huge financial benefits in reducing high maintenance and repair costs, has received considerable attention. However, the privacy issues of VSS including vehicles’ location privacy have not been well addressed. Therefore, in this paper, we propose a new privacy-preserving data aggregation scheme, called PAVS, for VSS. Specifically, PAVS combines privacy-preserving classification and privacy-preserving statistics on both the mean E(·) and variance Var(·), which makes VSS more promising, as, with minimal privacy leakage, more vehicles are willing to participate in sensing. Detailed analysis shows that the proposed PAVS can achieve the properties of privacy preservation, data accuracy and scalability. In addition, the performance evaluations via extensive simulations also demonstrate its efficiency. Full article
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Open AccessArticle Image-Based Multi-Target Tracking through Multi-Bernoulli Filtering with Interactive Likelihoods
Sensors 2017, 17(3), 501; doi:10.3390/s17030501
Received: 3 January 2017 / Revised: 15 February 2017 / Accepted: 27 February 2017 / Published: 3 March 2017
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Abstract
We develop an interactive likelihood (ILH) for sequential Monte Carlo (SMC) methods for image-based multiple target tracking applications. The purpose of the ILH is to improve tracking accuracy by reducing the need for data association. In addition, we integrate a recently developed deep
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We develop an interactive likelihood (ILH) for sequential Monte Carlo (SMC) methods for image-based multiple target tracking applications. The purpose of the ILH is to improve tracking accuracy by reducing the need for data association. In addition, we integrate a recently developed deep neural network for pedestrian detection along with the ILH with a multi-Bernoulli filter. We evaluate the performance of the multi-Bernoulli filter with the ILH and the pedestrian detector in a number of publicly available datasets (2003 PETS INMOVE, Australian Rules Football League (AFL) and TUD-Stadtmitte) using standard, well-known multi-target tracking metrics (optimal sub-pattern assignment (OSPA) and classification of events, activities and relationships for multi-object trackers (CLEAR MOT)). In all datasets, the ILH term increases the tracking accuracy of the multi-Bernoulli filter. Full article
(This article belongs to the Section Physical Sensors)
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Open AccessArticle Development of an Unmanned Aerial Vehicle-Borne Crop-Growth Monitoring System
Sensors 2017, 17(3), 502; doi:10.3390/s17030502
Received: 10 December 2016 / Revised: 10 February 2017 / Accepted: 24 February 2017 / Published: 3 March 2017
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Abstract
In view of the demand for a low-cost, high-throughput method for the continuous acquisition of crop growth information, this study describes a crop-growth monitoring system which uses an unmanned aerial vehicle (UAV) as an operating platform. The system is capable of real-time online
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In view of the demand for a low-cost, high-throughput method for the continuous acquisition of crop growth information, this study describes a crop-growth monitoring system which uses an unmanned aerial vehicle (UAV) as an operating platform. The system is capable of real-time online acquisition of various major indexes, e.g., the normalized difference vegetation index (NDVI) of the crop canopy, ratio vegetation index (RVI), leaf nitrogen accumulation (LNA), leaf area index (LAI), and leaf dry weight (LDW). By carrying out three-dimensional numerical simulations based on computational fluid dynamics, spatial distributions were obtained for the UAV down-wash flow fields on the surface of the crop canopy. Based on the flow-field characteristics and geometrical dimensions, a UAV-borne crop-growth sensor was designed. Our field experiments show that the monitoring system has good dynamic stability and measurement accuracy over the range of operating altitudes of the sensor. The linear fitting determination coefficients (R2) for the output RVI value with respect to LNA, LAI, and LDW are 0.63, 0.69, and 0.66, respectively, and the Root-mean-square errors (RMSEs) are 1.42, 1.02 and 3.09, respectively. The equivalent figures for the output NDVI value are 0.60, 0.65, and 0.62 (LNA, LAI, and LDW, respectively) and the RMSEs are 1.44, 1.01 and 3.01, respectively. Full article
(This article belongs to the Special Issue UAV-Based Remote Sensing)
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Open AccessArticle A Scheme to Smooth Aggregated Traffic from Sensors with Periodic Reports
Sensors 2017, 17(3), 503; doi:10.3390/s17030503
Received: 20 December 2016 / Revised: 18 February 2017 / Accepted: 1 March 2017 / Published: 3 March 2017
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Abstract
The possibility of smoothing aggregated traffic from sensors with varying reporting periods and frame sizes to be carried on an access link is investigated. A straightforward optimization would take O(pn) time, whereas our heuristic scheme takes O(np) time where n, p denote the
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The possibility of smoothing aggregated traffic from sensors with varying reporting periods and frame sizes to be carried on an access link is investigated. A straightforward optimization would take O(pn) time, whereas our heuristic scheme takes O(np) time where n, p denote the number of sensors and size of periods, respectively. Our heuristic scheme performs local optimization sensor by sensor, starting with the smallest to largest periods. This is based on an observation that sensors with large offsets have more choices in offsets to avoid traffic peaks than the sensors with smaller periods. A MATLAB simulation shows that our scheme excels the known scheme by M. Grenier et al. in a similar situation (aggregating periodic traffic in a controller area network) for almost all possible permutations. The performance of our scheme is very close to the straightforward optimization, which compares all possible permutations. We expect that our scheme would greatly contribute in smoothing the traffic from an ever-increasing number of IoT sensors to the gateway, reducing the burden on the access link to the Internet. Full article
(This article belongs to the Section Sensor Networks)
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Open AccessArticle Study on the Deformation Measurement of the Cast-In-Place Large-Diameter Pile Using Fiber Bragg Grating Sensors
Sensors 2017, 17(3), 505; doi:10.3390/s17030505
Received: 14 December 2016 / Revised: 12 February 2017 / Accepted: 15 February 2017 / Published: 3 March 2017
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Abstract
Compared with conventional piles such as the circle pile, the cast-in-place large-diameter pile (PCC pile) has many advantages: the lateral area of PCC pile is larger and the bearing capacity of PCC pile is higher. It is more cost-effective than other piles such
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Compared with conventional piles such as the circle pile, the cast-in-place large-diameter pile (PCC pile) has many advantages: the lateral area of PCC pile is larger and the bearing capacity of PCC pile is higher. It is more cost-effective than other piles such as square pile under the same condition. The deformation of the PCC pile is very important for its application. In order to obtain the deformation of the PCC pile, a new type of quasi-distributed optical fiber sensing technology named a fiber Bragg grating (FBG) is used to monitor the deformation of the PCC pile. The PCC model pile is made, the packaging process of the PCC model pile and the layout of fiber sensors are designed, and the strains of the PCC model pile based on FBG sensors are monitored. The strain of the PCC pile is analyzed by the static load test. The results show that FBG technology is successfully applied for monitoring the deformation of the PCC pile, the monitoring data is more useful for the PCC pile. It will provide a reference for the engineering applications. Full article
(This article belongs to the Special Issue Recent Advances in Fiber Bragg Grating Sensing)
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Open AccessArticle SVM-Based Spectral Analysis for Heart Rate from Multi-Channel WPPG Sensor Signals
Sensors 2017, 17(3), 506; doi:10.3390/s17030506
Received: 31 December 2016 / Revised: 16 February 2017 / Accepted: 28 February 2017 / Published: 3 March 2017
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Abstract
Although wrist-type photoplethysmographic (hereafter referred to as WPPG) sensor signals can measure heart rate quite conveniently, the subjects’ hand movements can cause strong motion artifacts, and then the motion artifacts will heavily contaminate WPPG signals. Hence, it is challenging for us to accurately
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Although wrist-type photoplethysmographic (hereafter referred to as WPPG) sensor signals can measure heart rate quite conveniently, the subjects’ hand movements can cause strong motion artifacts, and then the motion artifacts will heavily contaminate WPPG signals. Hence, it is challenging for us to accurately estimate heart rate from WPPG signals during intense physical activities. The WWPG method has attracted more attention thanks to the popularity of wrist-worn wearable devices. In this paper, a mixed approach called Mix-SVM is proposed, it can use multi-channel WPPG sensor signals and simultaneous acceleration signals to measurement heart rate. Firstly, we combine the principle component analysis and adaptive filter to remove a part of the motion artifacts. Due to the strong relativity between motion artifacts and acceleration signals, the further denoising problem is regarded as a sparse signals reconstruction problem. Then, we use a spectrum subtraction method to eliminate motion artifacts effectively. Finally, the spectral peak corresponding to heart rate is sought by an SVM-based spectral analysis method. Through the public PPG database in the 2015 IEEE Signal Processing Cup, we acquire the experimental results, i.e., the average absolute error was 1.01 beat per minute, and the Pearson correlation was 0.9972. These results also confirm that the proposed Mix-SVM approach has potential for multi-channel WPPG-based heart rate estimation in the presence of intense physical exercise. Full article
(This article belongs to the Section Physical Sensors)
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Open AccessArticle A Unified Model for BDS Wide Area and Local Area Augmentation Positioning Based on Raw Observations
Sensors 2017, 17(3), 507; doi:10.3390/s17030507
Received: 7 January 2017 / Revised: 26 February 2017 / Accepted: 1 March 2017 / Published: 3 March 2017
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Abstract
In this study, a unified model for BeiDou Navigation Satellite System (BDS) wide area and local area augmentation positioning based on raw observations has been proposed. Applying this model, both the Real-Time Kinematic (RTK) and Precise Point Positioning (PPP) service can be realized
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In this study, a unified model for BeiDou Navigation Satellite System (BDS) wide area and local area augmentation positioning based on raw observations has been proposed. Applying this model, both the Real-Time Kinematic (RTK) and Precise Point Positioning (PPP) service can be realized by performing different corrections at the user end. This algorithm was assessed and validated with the BDS data collected at four regional stations from Day of Year (DOY) 080 to 083 of 2016. When the users are located within the local reference network, the fast and high precision RTK service can be achieved using the regional observation corrections, revealing a convergence time of about several seconds and a precision of about 2–3 cm. For the users out of the regional reference network, the global broadcast State-Space Represented (SSR) corrections can be utilized to realize the global PPP service which shows a convergence time of about 25 min for achieving an accuracy of 10 cm. With this unified model, it can not only integrate the Network RTK (NRTK) and PPP into a seamless positioning service, but also recover the ionosphere Vertical Total Electronic Content (VTEC) and Differential Code Bias (DCB) values that are useful for the ionosphere monitoring and modeling. Full article
(This article belongs to the Special Issue Multi-Sensor Integration and Fusion)