Next Issue
Previous Issue

E-Mail Alert

Add your e-mail address to receive forthcoming issues of this journal:

Journal Browser

Journal Browser

Table of Contents

Sensors, Volume 17, Issue 3 (March 2017)

  • Issues are regarded as officially published after their release is announced to the table of contents alert mailing list.
  • You may sign up for e-mail alerts to receive table of contents of newly released issues.
  • PDF is the official format for papers published in both, html and pdf forms. To view the papers in pdf format, click on the "PDF Full-text" link, and use the free Adobe Readerexternal link to open them.
Cover Story Sensing selectivity (data on the foreground) of a concentric-electrode organic electrochemical [...] Read more.
View options order results:
result details:
Displaying articles 1-233
Export citation of selected articles as:

Research

Jump to: Review, Other

Open AccessArticle Joint Resource Allocation of Spectrum Sensing and Energy Harvesting in an Energy-Harvesting-Based Cognitive Sensor Network
Sensors 2017, 17(3), 600; doi:10.3390/s17030600
Received: 11 February 2017 / Revised: 10 March 2017 / Accepted: 13 March 2017 / Published: 16 March 2017
PDF Full-text (639 KB) | HTML Full-text | XML Full-text
Abstract
The cognitive sensor (CS) can transmit data to the control center in the same spectrum that is licensed to the primary user (PU) when the absence of the PU is detected by spectrum sensing. However, the battery energy of the CS is limited
[...] Read more.
The cognitive sensor (CS) can transmit data to the control center in the same spectrum that is licensed to the primary user (PU) when the absence of the PU is detected by spectrum sensing. However, the battery energy of the CS is limited due to its small size, deployment in atrocious environments and long-term working. In this paper, an energy-harvesting-based CS is described, which senses the PU together with collecting the radio frequency energy to supply data transmission. In order to improve the transmission performance of the CS, we have proposed the joint resource allocation of spectrum sensing and energy harvesting in the cases of a single energy-harvesting-based CS and an energy-harvesting-based cognitive sensor network (CSN), respectively. Based on the proposed frame structure, we have formulated the resource allocation as a class of joint optimization problems, which seek to maximize the transmission rate of the CS by jointly optimizing sensing time, harvesting time and the numbers of sensing nodes and harvesting nodes. Using the half searching method and the alternating direction optimization, we have achieved the sub-optimal solution by converting the joint optimization problem into several convex sub-optimization problems. The simulation results have indicated the predominance of the proposed energy-harvesting-based CS and CSN models. Full article
(This article belongs to the Special Issue Energy Harvesting Sensors for Long Term Applications in the IoT Era)
Figures

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
PDF Full-text (1057 KB) | HTML Full-text | XML Full-text
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
[...] Read more.
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
Figures

Figure 1

Open AccessArticle An Approach to Automated Fusion System Design and Adaptation
Sensors 2017, 17(3), 601; doi:10.3390/s17030601
Received: 14 December 2016 / Revised: 9 February 2017 / Accepted: 9 March 2017 / Published: 16 March 2017
PDF Full-text (824 KB) | HTML Full-text | XML Full-text
Abstract
Industrial applications are in transition towards modular and flexible architectures that are capable of self-configuration and -optimisation. This is due to the demand of mass customisation and the increasing complexity of industrial systems. The conversion to modular systems is related to challenges in
[...] Read more.
Industrial applications are in transition towards modular and flexible architectures that are capable of self-configuration and -optimisation. This is due to the demand of mass customisation and the increasing complexity of industrial systems. The conversion to modular systems is related to challenges in all disciplines. Consequently, diverse tasks such as information processing, extensive networking, or system monitoring using sensor and information fusion systems need to be reconsidered. The focus of this contribution is on distributed sensor and information fusion systems for system monitoring, which must reflect the increasing flexibility of fusion systems. This contribution thus proposes an approach, which relies on a network of self-descriptive intelligent sensor nodes, for the automatic design and update of sensor and information fusion systems. This article encompasses the fusion system configuration and adaptation as well as communication aspects. Manual interaction with the flexibly changing system is reduced to a minimum. Full article
(This article belongs to the Special Issue System-Integrated Intelligence and Intelligent Systems)
Figures

Figure 1

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
PDF Full-text (3389 KB) | HTML Full-text | XML Full-text
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
[...] Read more.
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)
Figures

Figure 1

Open AccessArticle The Impact of Satellite Time Group Delay and Inter-Frequency Differential Code Bias Corrections on Multi-GNSS Combined Positioning
Sensors 2017, 17(3), 602; doi:10.3390/s17030602
Received: 19 January 2017 / Revised: 2 March 2017 / Accepted: 3 March 2017 / Published: 16 March 2017
PDF Full-text (5010 KB) | HTML Full-text | XML Full-text
Abstract
We present quad-constellation (namely, GPS, GLONASS, BeiDou and Galileo) time group delay (TGD) and differential code bias (DCB) correction models to fully exploit the code observations of all the four global navigation satellite systems (GNSSs) for navigation and positioning. The relationship between TGDs
[...] Read more.
We present quad-constellation (namely, GPS, GLONASS, BeiDou and Galileo) time group delay (TGD) and differential code bias (DCB) correction models to fully exploit the code observations of all the four global navigation satellite systems (GNSSs) for navigation and positioning. The relationship between TGDs and DCBs for multi-GNSS is clearly figured out, and the equivalence of TGD and DCB correction models combining theory with practice is demonstrated. Meanwhile, the TGD/DCB correction models have been extended to various standard point positioning (SPP) and precise point positioning (PPP) scenarios in a multi-GNSS and multi-frequency context. To evaluate the effectiveness and practicability of broadcast TGDs in the navigation message and DCBs provided by the Multi-GNSS Experiment (MGEX), both single-frequency GNSS ionosphere-corrected SPP and dual-frequency GNSS ionosphere-free SPP/PPP tests are carried out with quad-constellation signals. Furthermore, the author investigates the influence of differential code biases on GNSS positioning estimates. The experiments show that multi-constellation combination SPP performs better after DCB/TGD correction, for example, for GPS-only b1-based SPP, the positioning accuracies can be improved by 25.0%, 30.6% and 26.7%, respectively, in the N, E, and U components, after the differential code biases correction, while GPS/GLONASS/BDS b1-based SPP can be improved by 16.1%, 26.1% and 9.9%. For GPS/BDS/Galileo the 3rd frequency based SPP, the positioning accuracies are improved by 2.0%, 2.0% and 0.4%, respectively, in the N, E, and U components, after Galileo satellites DCB correction. The accuracy of Galileo-only b1-based SPP are improved about 48.6%, 34.7% and 40.6% with DCB correction, respectively, in the N, E, and U components. The estimates of multi-constellation PPP are subject to different degrees of influence. For multi-constellation combination SPP, the accuracy of single-frequency is slightly better than that of dual-frequency combinations. Dual-frequency combinations are more sensitive to the differential code biases, especially for the 2nd and 3rd frequency combination, such as for GPS/BDS SPP, accuracy improvements of 60.9%, 26.5% and 58.8% in the three coordinate components is achieved after DCB parameters correction. For multi-constellation PPP, the convergence time can be reduced significantly with differential code biases correction. And the accuracy of positioning is slightly better with TGD/DCB correction. Full article
(This article belongs to the Section Remote Sensors)
Figures

Figure 1

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
PDF Full-text (5339 KB) | HTML Full-text | XML Full-text
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
[...] Read more.
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)
Figures

Figure 1

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
PDF Full-text (8025 KB) | HTML Full-text | XML Full-text
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
[...] Read more.
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)
Figures

Figure 1

Open AccessArticle An Improved Label-Free Indirect Competitive SPR Immunosensor and Its Comparison with Conventional ELISA for Ractopamine Detection in Swine Urine
Sensors 2017, 17(3), 604; doi:10.3390/s17030604
Received: 13 January 2017 / Revised: 8 February 2017 / Accepted: 22 February 2017 / Published: 16 March 2017
PDF Full-text (1870 KB) | HTML Full-text | XML Full-text
Abstract
Ractopamine (RCT) is banned for use in animals in many countries, and it is urgent to develop efficient methods for specific and sensitive RCT detection. A label-free indirect competitive surface plasmon resonance (SPR) immunosensor was first developed with a primary antibody herein and
[...] Read more.
Ractopamine (RCT) is banned for use in animals in many countries, and it is urgent to develop efficient methods for specific and sensitive RCT detection. A label-free indirect competitive surface plasmon resonance (SPR) immunosensor was first developed with a primary antibody herein and then improved by a secondary antibody for the detection of RCT residue in swine urine. Meanwhile, a pre-incubation process of RCT and the primary antibody was performed to further improve the sensitivity. With all the key parameters optimized, the improved immunosenor can attain a linear range of 0.3–32 ng/mL and a limit of detection (LOD) of 0.09 ng/mL for RCT detection with high specificity. Furthermore, the improved label-free SPR immunosenor was compared thoroughly with a conventional enzyme-linked immunosorbent assay (ELISA). The SPR immunosensor showed advantages over the ELISA in terms of LOD, reagent consumption, analysis time, experiment automation, and so on. The SPR immunosensor can be used as potential method for real-time monitoring and screening of RCT residue in swine urine or other samples. In addition, the design using antibody pairs for biosensor development can be further referred to for other small molecule detection. Full article
Figures

Figure 1

Open AccessArticle Person Recognition System Based on a Combination of Body Images from Visible Light and Thermal Cameras
Sensors 2017, 17(3), 605; doi:10.3390/s17030605
Received: 5 January 2017 / Revised: 3 March 2017 / Accepted: 14 March 2017 / Published: 16 March 2017
PDF Full-text (6431 KB) | HTML Full-text | XML Full-text
Abstract
The human body contains identity information that can be used for the person recognition (verification/recognition) problem. In this paper, we propose a person recognition method using the information extracted from body images. Our research is novel in the following three ways compared to
[...] Read more.
The human body contains identity information that can be used for the person recognition (verification/recognition) problem. In this paper, we propose a person recognition method using the information extracted from body images. Our research is novel in the following three ways compared to previous studies. First, we use the images of human body for recognizing individuals. To overcome the limitations of previous studies on body-based person recognition that use only visible light images for recognition, we use human body images captured by two different kinds of camera, including a visible light camera and a thermal camera. The use of two different kinds of body image helps us to reduce the effects of noise, background, and variation in the appearance of a human body. Second, we apply a state-of-the art method, called convolutional neural network (CNN) among various available methods, for image features extraction in order to overcome the limitations of traditional hand-designed image feature extraction methods. Finally, with the extracted image features from body images, the recognition task is performed by measuring the distance between the input and enrolled samples. The experimental results show that the proposed method is efficient for enhancing recognition accuracy compared to systems that use only visible light or thermal images of the human body. Full article
Figures

Figure 1

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
PDF Full-text (2219 KB) | HTML Full-text | XML Full-text
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
[...] Read more.
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)
Figures

Figure 1

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
PDF Full-text (1303 KB) | HTML Full-text | XML Full-text
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
[...] Read more.
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)
Figures

Figure 1

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
PDF Full-text (2995 KB) | HTML Full-text | XML Full-text
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
[...] Read more.
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)
Figures

Figure 1

Open AccessArticle Non-Destructive Detection of Wire Rope Discontinuities from Residual Magnetic Field Images Using the Hilbert-Huang Transform and Compressed Sensing
Sensors 2017, 17(3), 608; doi:10.3390/s17030608
Received: 2 January 2017 / Revised: 14 March 2017 / Accepted: 15 March 2017 / Published: 16 March 2017
PDF Full-text (3968 KB) | HTML Full-text | XML Full-text
Abstract
Electromagnetic methods are commonly employed to detect wire rope discontinuities. However, determining the residual strength of wire rope based on the quantitative recognition of discontinuities remains problematic. We have designed a prototype device based on the residual magnetic field (RMF) of ferromagnetic materials,
[...] Read more.
Electromagnetic methods are commonly employed to detect wire rope discontinuities. However, determining the residual strength of wire rope based on the quantitative recognition of discontinuities remains problematic. We have designed a prototype device based on the residual magnetic field (RMF) of ferromagnetic materials, which overcomes the disadvantages associated with in-service inspections, such as large volume, inconvenient operation, low precision, and poor portability by providing a relatively small and lightweight device with improved detection precision. A novel filtering system consisting of the Hilbert-Huang transform and compressed sensing wavelet filtering is presented. Digital image processing was applied to achieve the localization and segmentation of defect RMF images. The statistical texture and invariant moment characteristics of the defect images were extracted as the input of a radial basis function neural network. Experimental results show that the RMF device can detect defects in various types of wire rope and prolong the service life of test equipment by reducing the friction between the detection device and the wire rope by accommodating a high lift-off distance. Full article
(This article belongs to the Section Physical Sensors)
Figures

Figure 1

Open AccessArticle Investigation into the Effect of Atmospheric Particulate Matter (PM2.5 and PM10) Concentrations on GPS Signals
Sensors 2017, 17(3), 508; doi:10.3390/s17030508
Received: 2 January 2017 / Revised: 1 March 2017 / Accepted: 2 March 2017 / Published: 3 March 2017
PDF Full-text (4204 KB) | HTML Full-text | XML Full-text
Abstract
The Global Positioning System (GPS) has been widely used in navigation, surveying, geophysical and geodynamic studies, machine guidance, etc. High-precision GPS applications such as geodetic surveying need millimeter and centimeter level accuracy. Since GPS signals are affected by atmospheric effects, methods of correcting
[...] Read more.
The Global Positioning System (GPS) has been widely used in navigation, surveying, geophysical and geodynamic studies, machine guidance, etc. High-precision GPS applications such as geodetic surveying need millimeter and centimeter level accuracy. Since GPS signals are affected by atmospheric effects, methods of correcting or eliminating ionospheric and tropospheric bias are needed in GPS data processing. Relative positioning can be used to mitigate the atmospheric effect, but its efficiency depends on the baseline lengths. Air pollution is a serious problem globally, especially in developing countries that causes health problems to humans and damage to the ecosystem. Respirable suspended particles are coarse particles with a diameter of 10 micrometers or less, also known as PM10. Moreover, fine particles with a diameter of 2.5 micrometers or less are known as PM2.5. GPS signals travel through the atmosphere before arriving at receivers on the Earth’s surface, and the research question posed in this paper is: are GPS signals affected by the increased concentration of the PM2.5/PM10 particles? There is no standard model of the effect of PM2.5/PM10 particles on GPS signals in GPS data processing, although an approximate generic model of non-gaseous atmospheric constituents (<1 mm) can be found in the literature. This paper investigates the effect of the concentration of PM2.5/PM10 particles on GPS signals and validates the aforementioned approximate model with a carrier-to-noise ratio (CNR)-based empirical method. Both the approximate model and the empirical results show that the atmospheric PM2.5/PM10 particles and their concentrations have a negligible effect on GPS signals and the effect is comparable with the noise level of GPS measurements. Full article
(This article belongs to the Section Remote Sensors)
Figures

Figure 1

Open AccessArticle A Context-Aware S-Health Service System for Drivers
Sensors 2017, 17(3), 609; doi:10.3390/s17030609
Received: 13 January 2017 / Revised: 8 March 2017 / Accepted: 10 March 2017 / Published: 17 March 2017
PDF Full-text (7679 KB) | HTML Full-text | XML Full-text
Abstract
As a stressful and sensitive task, driving can be disturbed by various factors from the health condition of the driver to the environmental variables of the vehicle. Continuous monitoring of driving hazards and providing the most appropriate business services to meet actual needs
[...] Read more.
As a stressful and sensitive task, driving can be disturbed by various factors from the health condition of the driver to the environmental variables of the vehicle. Continuous monitoring of driving hazards and providing the most appropriate business services to meet actual needs can guarantee safe driving and make great use of the existing information resources and business services. However, there is no in-depth research on the perception of a driver’s health status or the provision of customized business services in case of various hazardous situations. In order to constantly monitor the health status of the drivers and react to abnormal situations, this paper proposes a context-aware service system providing a configurable architecture for the design and implementation of the smart health service system for safe driving, which can perceive a driver’s health status and provide helpful services to the driver. With the context-aware technology to construct a smart health services system for safe driving, this is the first time that such a service system has been implemented in practice. Additionally, an assessment model is proposed to mitigate the impact of the acceptable abnormal status and, thus, reduce the unnecessary invocation of the services. With regard to different assessed situations, the business services can be invoked for the driver to adapt to hazardous situations according to the services configuration model, which can take full advantage of the existing information resources and business services. The evaluation results indicate that the alteration of the observed status in a valid time range T can be tolerated and the frequency of the service invocation can be reduced. Full article
(This article belongs to the Special Issue Context Aware Environments and Applications)
Figures

Figure 1

Open AccessArticle Reconstruction of Undersampled Big Dynamic MRI Data Using Non-Convex Low-Rank and Sparsity Constraints
Sensors 2017, 17(3), 509; doi:10.3390/s17030509
Received: 10 November 2016 / Revised: 16 February 2017 / Accepted: 20 February 2017 / Published: 3 March 2017
PDF Full-text (10171 KB) | HTML Full-text | XML Full-text
Abstract
Dynamic magnetic resonance imaging (MRI) has been extensively utilized for enhancing medical living environment visualization, however, in clinical practice it often suffers from long data acquisition times. Dynamic imaging essentially reconstructs the visual image from raw (k,t)-space measurements, commonly
[...] Read more.
Dynamic magnetic resonance imaging (MRI) has been extensively utilized for enhancing medical living environment visualization, however, in clinical practice it often suffers from long data acquisition times. Dynamic imaging essentially reconstructs the visual image from raw (k,t)-space measurements, commonly referred to as big data. The purpose of this work is to accelerate big medical data acquisition in dynamic MRI by developing a non-convex minimization framework. In particular, to overcome the inherent speed limitation, both non-convex low-rank and sparsity constraints were combined to accelerate the dynamic imaging. However, the non-convex constraints make the dynamic reconstruction problem difficult to directly solve through the commonly-used numerical methods. To guarantee solution efficiency and stability, a numerical algorithm based on Alternating Direction Method of Multipliers (ADMM) is proposed to solve the resulting non-convex optimization problem. ADMM decomposes the original complex optimization problem into several simple sub-problems. Each sub-problem has a closed-form solution or could be efficiently solved using existing numerical methods. It has been proven that the quality of images reconstructed from fewer measurements can be significantly improved using non-convex minimization. Numerous experiments have been conducted on two in vivo cardiac datasets to compare the proposed method with several state-of-the-art imaging methods. Experimental results illustrated that the proposed method could guarantee the superior imaging performance in terms of quantitative and visual image quality assessments. Full article
(This article belongs to the Special Issue Multisensory Big Data Analytics for Enhanced Living Environments)
Figures

Figure 1

Open AccessArticle An EM Induction Hi-Speed Rotation Angular Rate Sensor
Sensors 2017, 17(3), 610; doi:10.3390/s17030610
Received: 13 February 2017 / Revised: 22 February 2017 / Accepted: 15 March 2017 / Published: 17 March 2017
PDF Full-text (2223 KB) | HTML Full-text | XML Full-text
Abstract
A hi-speed rotation angular rate sensor based on an electromagnetic induction signal is proposed to provide a possibility of wide range measurement of high angular rates. An angular rate sensor is designed that works on the principle of electromagnetism (EM) induction. In addition
[...] Read more.
A hi-speed rotation angular rate sensor based on an electromagnetic induction signal is proposed to provide a possibility of wide range measurement of high angular rates. An angular rate sensor is designed that works on the principle of electromagnetism (EM) induction. In addition to a zero-phase detection technique, this sensor uses the feedback principle of magnetic induction coils in response to a rotating magnetic field. It solves the challenge of designing an angular rate sensor that is suitable for both low and high rotating rates. The sensor was examined for angular rate measurement accuracy in simulation tests using a rotary table. The results show that it is capable of measuring angular rates ranging from 1 rps to 100 rps, with an error within 1.8‰ of the full scale (FS). The proposed sensor is suitable to measurement applications where the rotation angular rate is widely varied, and it contributes to design technology advancements of real-time sensors measuring angular acceleration, angular rate, and angular displacement of hi-speed rotary objects. Full article
(This article belongs to the Special Issue Magnetic Sensors and Their Applications)
Figures

Figure 1

Open AccessArticle A Novel System for Correction of Relative Angular Displacement between Airborne Platform and UAV in Target Localization
Sensors 2017, 17(3), 510; doi:10.3390/s17030510
Received: 30 December 2016 / Revised: 16 February 2017 / Accepted: 27 February 2017 / Published: 4 March 2017
PDF Full-text (9652 KB) | HTML Full-text | XML Full-text
Abstract
This paper provides a system and method for correction of relative angular displacements between an Unmanned Aerial Vehicle (UAV) and its onboard strap-down photoelectric platform to improve localization accuracy. Because the angular displacements have an influence on the final accuracy, by attaching a
[...] Read more.
This paper provides a system and method for correction of relative angular displacements between an Unmanned Aerial Vehicle (UAV) and its onboard strap-down photoelectric platform to improve localization accuracy. Because the angular displacements have an influence on the final accuracy, by attaching a measuring system to the platform, the texture image of platform base bulkhead can be collected in a real-time manner. Through the image registration, the displacement vector of the platform relative to its bulkhead can be calculated to further determine angular displacements. After being decomposed and superposed on the three attitude angles of the UAV, the angular displacements can reduce the coordinate transformation errors and thus improve the localization accuracy. Even a simple kind of method can improve the localization accuracy by 14.3%. Full article
(This article belongs to the Special Issue UAV-Based Remote Sensing)
Figures

Figure 1

Open AccessArticle A Wellness Mobile Application for Smart Health: Pilot Study Design and Results
Sensors 2017, 17(3), 611; doi:10.3390/s17030611
Received: 15 February 2017 / Revised: 13 March 2017 / Accepted: 15 March 2017 / Published: 17 March 2017
PDF Full-text (3601 KB) | HTML Full-text | XML Full-text
Abstract
Wellness is one of the main factors crucial in the avoidance of illness or disease. Experience has shown that healthy lifestyle programs are an important strategy to prevent the major shared risk factors for many diseases including cardiovascular diseases, strokes, diabetes, obesity, and
[...] Read more.
Wellness is one of the main factors crucial in the avoidance of illness or disease. Experience has shown that healthy lifestyle programs are an important strategy to prevent the major shared risk factors for many diseases including cardiovascular diseases, strokes, diabetes, obesity, and hypertension. Within the ambit of the Smart Health 2.0 project, a Wellness App has been developed which has the aim of providing people with something similar to a personal trainer. This Wellness App is able to gather information about the subject, to classify her/him by evaluating some of her/his specific characteristics (physical parameters and lifestyle) and to make personal recommendations to enhance her/his well-being. The application can also give feedback on the effectiveness of the specified characteristics by monitoring their evolution over time, and can provide a positive incentive to stimulate the subject to achieve her/his wellness goals. In this paper, we present a pilot study conducted in Calabria, a region of Italy, aimed at an evaluation of the validity, usability, and navigability of the app, and of people’s level of satisfaction with it. The preliminary results show an average score of 77.16 for usability and of 76.87 for navigability, with an improvement of the Wellness Index with a significance average of 95% and of the Mediterranean Adequacy Index with a significance average of as high as 99%. Full article
(This article belongs to the Special Issue Advances in Body Sensor Networks: Sensors, Systems, and Applications)
Figures

Figure 1

Open AccessArticle Inertial Motion Capture Costume Design Study
Sensors 2017, 17(3), 612; doi:10.3390/s17030612
Received: 29 November 2016 / Revised: 9 March 2017 / Accepted: 13 March 2017 / Published: 17 March 2017
PDF Full-text (2467 KB) | HTML Full-text | XML Full-text
Abstract
The paper describes a scalable, wearable multi-sensor system for motion capture based on inertial measurement units (IMUs). Such a unit is composed of accelerometer, gyroscope and magnetometer. The final quality of an obtained motion arises from all the individual parts of the described
[...] Read more.
The paper describes a scalable, wearable multi-sensor system for motion capture based on inertial measurement units (IMUs). Such a unit is composed of accelerometer, gyroscope and magnetometer. The final quality of an obtained motion arises from all the individual parts of the described system. The proposed system is a sequence of the following stages: sensor data acquisition, sensor orientation estimation, system calibration, pose estimation and data visualisation. The construction of the system’s architecture with the dataflow programming paradigm makes it easy to add, remove and replace the data processing steps. The modular architecture of the system allows an effortless introduction of a new sensor orientation estimation algorithms. The original contribution of the paper is the design study of the individual components used in the motion capture system. The two key steps of the system design are explored in this paper: the evaluation of sensors and algorithms for the orientation estimation. The three chosen algorithms have been implemented and investigated as part of the experiment. Due to the fact that the selection of the sensor has a significant impact on the final result, the sensor evaluation process is also explained and tested. The experimental results confirmed that the choice of sensor and orientation estimation algorithm affect the quality of the final results. Full article
(This article belongs to the Special Issue Inertial Sensors and Systems 2016)
Figures

Figure 1

Open AccessArticle Robust Scale Adaptive Tracking by Combining Correlation Filters with Sequential Monte Carlo
Sensors 2017, 17(3), 512; doi:10.3390/s17030512
Received: 12 January 2017 / Revised: 25 February 2017 / Accepted: 27 February 2017 / Published: 4 March 2017
PDF Full-text (2182 KB) | HTML Full-text | XML Full-text
Abstract
A robust and efficient object tracking algorithm is required in a variety of computer vision applications. Although various modern trackers have impressive performance, some challenges such as occlusion and target scale variation are still intractable, especially in the complex scenarios. This paper proposes
[...] Read more.
A robust and efficient object tracking algorithm is required in a variety of computer vision applications. Although various modern trackers have impressive performance, some challenges such as occlusion and target scale variation are still intractable, especially in the complex scenarios. This paper proposes a robust scale adaptive tracking algorithm to predict target scale by a sequential Monte Carlo method and determine the target location by the correlation filter simultaneously. By analyzing the response map of the target region, the completeness of the target can be measured by the peak-to-sidelobe rate (PSR), i.e., the lower the PSR, the more likely the target is being occluded. A strict template update strategy is designed to accommodate the appearance change and avoid template corruption. If the occlusion occurs, a retained scheme is allowed and the tracker refrains from drifting away. Additionally, the feature integration is incorporated to guarantee the robustness of the proposed approach. The experimental results show that our method outperforms other state-of-the-art trackers in terms of both the distance precision and overlap precision on the publicly available TB-50 dataset. Full article
(This article belongs to the Section Physical Sensors)
Figures

Figure 1

Open AccessArticle Phase Error Correction for Approximated Observation-Based Compressed Sensing Radar Imaging
Sensors 2017, 17(3), 613; doi:10.3390/s17030613
Received: 18 December 2016 / Revised: 14 March 2017 / Accepted: 15 March 2017 / Published: 17 March 2017
PDF Full-text (1930 KB) | HTML Full-text | XML Full-text
Abstract
Defocus of the reconstructed image of synthetic aperture radar (SAR) occurs in the presence of the phase error. In this work, a phase error correction method is proposed for compressed sensing (CS) radar imaging based on approximated observation. The proposed method has better
[...] Read more.
Defocus of the reconstructed image of synthetic aperture radar (SAR) occurs in the presence of the phase error. In this work, a phase error correction method is proposed for compressed sensing (CS) radar imaging based on approximated observation. The proposed method has better image focusing ability with much less memory cost, compared to the conventional approaches, due to the inherent low memory requirement of the approximated observation operator. The one-dimensional (1D) phase error correction for approximated observation-based CS-SAR imaging is first carried out and it can be conveniently applied to the cases of random-frequency waveform and linear frequency modulated (LFM) waveform without any a priori knowledge. The approximated observation operators are obtained by calculating the inverse of Omega-K and chirp scaling algorithms for random-frequency and LFM waveforms, respectively. Furthermore, the 1D phase error model is modified by incorporating a priori knowledge and then a weighted 1D phase error model is proposed, which is capable of correcting two-dimensional (2D) phase error in some cases, where the estimation can be simplified to a 1D problem. Simulation and experimental results validate the effectiveness of the proposed method in the presence of 1D phase error or weighted 1D phase error. Full article
(This article belongs to the Section Remote Sensors)
Figures

Figure 1

Open AccessArticle Mid-Infrared Trace Gas Sensor Technology Based on Intracavity Quartz-Enhanced Photoacoustic Spectroscopy
Sensors 2017, 17(3), 513; doi:10.3390/s17030513
Received: 27 January 2017 / Revised: 24 February 2017 / Accepted: 2 March 2017 / Published: 4 March 2017
PDF Full-text (3929 KB) | HTML Full-text | XML Full-text
Abstract
The application of compact inexpensive trace gas sensor technology to a mid-infrared nitric oxide (NO) detectoion using intracavity quartz-enhanced photoacoustic spectroscopy (I-QEPAS) is reported. A minimum detection limit of 4.8 ppbv within a 30 ms integration time was demonstrated by using a room-temperature,
[...] Read more.
The application of compact inexpensive trace gas sensor technology to a mid-infrared nitric oxide (NO) detectoion using intracavity quartz-enhanced photoacoustic spectroscopy (I-QEPAS) is reported. A minimum detection limit of 4.8 ppbv within a 30 ms integration time was demonstrated by using a room-temperature, continuous-wave, distributed-feedback quantum cascade laser (QCL) emitting at 5.263 µm (1900.08 cm−1) and a new compact design of a high-finesse bow-tie optical cavity with an integrated resonant quartz tuning fork (QTF). The optimum configuration of the bow-tie cavity was simulated using custom software. Measurements were performed with a wavelength modulation scheme (WM) using a 2f detection procedure. Full article
(This article belongs to the Special Issue Air Pollution Sensors: A New Class of Tools to Measure Air Quality)
Figures

Figure 1

Open AccessArticle All-Direction Random Routing for Source-Location Privacy Protecting against Parasitic Sensor Networks
Sensors 2017, 17(3), 614; doi:10.3390/s17030614
Received: 21 January 2017 / Revised: 8 March 2017 / Accepted: 15 March 2017 / Published: 17 March 2017
PDF Full-text (5775 KB) | HTML Full-text | XML Full-text
Abstract
Wireless sensor networks are deployed to monitor the surrounding physical environments and they also act as the physical environments of parasitic sensor networks, whose purpose is analyzing the contextual privacy and obtaining valuable information from the original wireless sensor networks. Recently, contextual privacy
[...] Read more.
Wireless sensor networks are deployed to monitor the surrounding physical environments and they also act as the physical environments of parasitic sensor networks, whose purpose is analyzing the contextual privacy and obtaining valuable information from the original wireless sensor networks. Recently, contextual privacy issues associated with wireless communication in open spaces have not been thoroughly addressed and one of the most important challenges is protecting the source locations of the valuable packages. In this paper, we design an all-direction random routing algorithm (ARR) for source-location protecting against parasitic sensor networks. For each package, the routing process of ARR is divided into three stages, i.e., selecting a proper agent node, delivering the package to the agent node from the source node, and sending it to the final destination from the agent node. In ARR, the agent nodes are randomly chosen in all directions by the source nodes using only local decisions, rather than knowing the whole topology of the networks. ARR can control the distributions of the routing paths in a very flexible way and it can guarantee that the routing paths with the same source and destination are totally different from each other. Therefore, it is extremely difficult for the parasitic sensor nodes to trace the packages back to the source nodes. Simulation results illustrate that ARR perfectly confuses the parasitic nodes and obviously outperforms traditional routing-based schemes in protecting source-location privacy, with a marginal increase in the communication overhead and energy consumption. In addition, ARR also requires much less energy than the cloud-based source-location privacy protection schemes. Full article
(This article belongs to the Section Sensor Networks)
Figures

Figure 1

Open AccessArticle Design of an Acoustic Target Intrusion Detection System Based on Small-Aperture Microphone Array
Sensors 2017, 17(3), 514; doi:10.3390/s17030514
Received: 22 December 2016 / Revised: 17 February 2017 / Accepted: 2 March 2017 / Published: 4 March 2017
PDF Full-text (989 KB) | HTML Full-text | XML Full-text
Abstract
Automated surveillance of remote locations in a wireless sensor network is dominated by the detection algorithm because actual intrusions in such locations are a rare event. Therefore, a detection method with low power consumption is crucial for persistent surveillance to ensure longevity of
[...] Read more.
Automated surveillance of remote locations in a wireless sensor network is dominated by the detection algorithm because actual intrusions in such locations are a rare event. Therefore, a detection method with low power consumption is crucial for persistent surveillance to ensure longevity of the sensor networks. A simple and effective two-stage algorithm composed of energy detector (ED) and delay detector (DD) with all its operations in time-domain using small-aperture microphone array (SAMA) is proposed. The algorithm analyzes the quite different velocities between wind noise and sound waves to improve the detection capability of ED in the surveillance area. Experiments in four different fields with three types of vehicles show that the algorithm is robust to wind noise and the probability of detection and false alarm are 96.67% and 2.857%, respectively. Full article
(This article belongs to the Special Issue Integrated Sensor Arrays and Array Signal Processing)
Figures

Figure 1

Open AccessArticle Accurate Compensation of Attitude Angle Error in a Dual-Axis Rotation Inertial Navigation System
Sensors 2017, 17(3), 615; doi:10.3390/s17030615
Received: 17 December 2016 / Revised: 28 February 2017 / Accepted: 10 March 2017 / Published: 17 March 2017
PDF Full-text (3543 KB) | HTML Full-text | XML Full-text
Abstract
In the dual-axis rotation inertial navigation system (INS), besides the gyro error, accelerometer error, rolling misalignment angle error, and the gimbal angle error, the shaft swing angle and the axis non-orthogonal angle also affect the attitude accuracy. Through the analysis of the structure,
[...] Read more.
In the dual-axis rotation inertial navigation system (INS), besides the gyro error, accelerometer error, rolling misalignment angle error, and the gimbal angle error, the shaft swing angle and the axis non-orthogonal angle also affect the attitude accuracy. Through the analysis of the structure, we can see that the shaft swing angle and axis non-orthogonal angle will produce coning errors which cause the fluctuation of the attitude. According to the analysis of the rotation vector, it can be seen that the coning error will generate additional drift velocity along the rotating shaft, which can reduce the navigation precision of the system. In this paper, based on the establishment of the modulation average frame, the vector projection is carried out, and then the attitude conversion matrix and the attitude error matrix mainly including the shaft swing angle and axis non-orthogonal are obtained. Because the attitude angles are given under the static condition, the shaft swing angle and the axis non-orthogonal angle are estimated by the static Kalman filter (KF). This kind of KF method has been widely recognized as the standard optimal estimation tool for estimating the parameters such as coning angles (α1 , α2), initial phase angles (ϕ12), and the non-perpendicular angle (η). In order to carry out the system level verification, a dual axis rotation INS is designed. Through simulation and experiments, the results show that the amplitudes of the attitude angles’ variation are reduced by about 20%–30% when the shaft rotates. The attitude error equation is reasonably simplified and the calibration method is accurate enough. The attitude accuracy is further improved. Full article
(This article belongs to the Special Issue Inertial Sensors and Systems 2016)
Figures

Figure 1

Open AccessArticle A Fast Algorithm for 2D DOA Estimation Using an Omnidirectional Sensor Array
Sensors 2017, 17(3), 515; doi:10.3390/s17030515
Received: 15 December 2016 / Revised: 25 February 2017 / Accepted: 28 February 2017 / Published: 4 March 2017
PDF Full-text (2592 KB) | HTML Full-text | XML Full-text
Abstract
The traditional 2D MUSIC algorithm fixes the azimuth or the elevation, and searches for the other without considering the directions of sources. A spectrum peak diffusion effect phenomenon is observed and may be utilized to detect the approximate directions of sources. Accordingly, a
[...] Read more.
The traditional 2D MUSIC algorithm fixes the azimuth or the elevation, and searches for the other without considering the directions of sources. A spectrum peak diffusion effect phenomenon is observed and may be utilized to detect the approximate directions of sources. Accordingly, a fast 2D MUSIC algorithm, which performs azimuth and elevation simultaneous searches (henceforth referred to as AESS) based on only three rounds of search is proposed. Firstly, AESS searches along a circle to detect the approximate source directions. Then, a subsequent search is launched along several straight lines based on these approximate directions. Finally, the 2D Direction of Arrival (DOA) of each source is derived by searching on several small concentric circles. Unlike the 2D MUSIC algorithm, AESS does not fix any azimuth and elevation parameters. Instead, the adjacent point of each search possesses different azimuth and elevation, i.e., azimuth and elevation are simultaneously searched to ensure that the search path is minimized, and hence the total spectral search over the angular field of view is avoided. Simulation results demonstrate the performance characters of the proposed AESS over some existing algorithms. Full article
(This article belongs to the Section Physical Sensors)
Figures

Figure 1

Open AccessArticle Study on Climate and Grassland Fire in HulunBuir, Inner Mongolia Autonomous Region, China
Sensors 2017, 17(3), 616; doi:10.3390/s17030616
Received: 15 January 2017 / Revised: 12 February 2017 / Accepted: 13 March 2017 / Published: 17 March 2017
PDF Full-text (3966 KB) | HTML Full-text | XML Full-text
Abstract
Grassland fire is one of the most important disturbance factors of the natural ecosystem. Climate factors influence the occurrence and development of grassland fire. An analysis of the climate conditions of fire occurrence can form the basis for a study of the temporal
[...] Read more.
Grassland fire is one of the most important disturbance factors of the natural ecosystem. Climate factors influence the occurrence and development of grassland fire. An analysis of the climate conditions of fire occurrence can form the basis for a study of the temporal and spatial variability of grassland fire. The purpose of this paper is to study the effects of monthly time scale climate factors on the occurrence of grassland fire in HulunBuir, located in the northeast of the Inner Mongolia Autonomous Region in China. Based on the logistic regression method, we used the moderate-resolution imaging spectroradiometer (MODIS) active fire data products named thermal anomalies/fire daily L3 Global 1km (MOD14A1 (Terra) and MYD14A1 (Aqua)) and associated climate data for HulunBuir from 2000 to 2010, and established the model of grassland fire climate index. The results showed that monthly maximum temperature, monthly sunshine hours and monthly average wind speed were all positively correlated with the fire climate index; monthly precipitation, monthly average temperature, monthly average relative humidity, monthly minimum relative humidity and the number of days with monthly precipitation greater than or equal to 5 mm were all negatively correlated with the fire climate index. We used the active fire data from 2011 to 2014 to validate the fire climate index during this time period, and the validation result was good (Pearson’s correlation coefficient was 0.578), which showed that the fire climate index model was suitable for analyzing the occurrence of grassland fire in HulunBuir. Analyses were conducted on the temporal and spatial distribution of the fire climate index from January to December in the years 2011–2014; it could be seen that from March to May and from September to October, the fire climate index was higher, and that the fire climate index of the other months is relatively low. The zones with higher fire climate index are mainly distributed in Xin Barag Youqi, Xin Barag Zuoqi, Zalantun Shi, Oroqen Zizhiqi, and Molidawa Zizhiqi; the zones with medium fire climate index are mainly distributed in Chen Barag Qi, Ewenkizu Zizhiqi, Manzhouli Shi, and Arun Qi; and the zones with lower fire climate index are mainly distributed in Genhe Shi, Ergun city, Yakeshi Shi, and Hailar Shi. The results of this study will contribute to the quantitative assessment and management of early warning and forecasting for mid-to long-term grassland fire risk in HulunBuir. Full article
Figures

Figure 1

Open AccessArticle Humidity Sensing Properties of Paper Substrates and Their Passivation with ZnO Nanoparticles for Sensor Applications
Sensors 2017, 17(3), 516; doi:10.3390/s17030516
Received: 5 December 2016 / Revised: 2 February 2017 / Accepted: 4 February 2017 / Published: 4 March 2017
PDF Full-text (3884 KB) | HTML Full-text | XML Full-text
Abstract
In this paper, we investigated the effect of humidity on paper substrates and propose a simple and low-cost method for their passivation using ZnO nanoparticles. To this end, we built paper-based microdevices based on an interdigitated electrode (IDE) configuration by means of a
[...] Read more.
In this paper, we investigated the effect of humidity on paper substrates and propose a simple and low-cost method for their passivation using ZnO nanoparticles. To this end, we built paper-based microdevices based on an interdigitated electrode (IDE) configuration by means of a mask-less laser patterning method on simple commercial printing papers. Initial resistive measurements indicate that a paper substrate with a porous surface can be used as a cost-effective, sensitive and disposable humidity sensor in the 20% to 70% relative humidity (RH) range. Successive spin-coated layers of ZnO nanoparticles then, control the effect of humidity. Using this approach, the sensors become passive to relative humidity changes, paving the way to the development of ZnO-based gas sensors on paper substrates insensitive to humidity. Full article
(This article belongs to the Special Issue Materials and Applications for Sensors and Transducers)
Figures

Open AccessArticle Conditional Random Field (CRF)-Boosting: Constructing a Robust Online Hybrid Boosting Multiple Object Tracker Facilitated by CRF Learning
Sensors 2017, 17(3), 617; doi:10.3390/s17030617
Received: 6 December 2016 / Revised: 13 March 2017 / Accepted: 14 March 2017 / Published: 17 March 2017
PDF Full-text (6794 KB) | HTML Full-text | XML Full-text
Abstract
Due to the reasonably acceptable performance of state-of-the-art object detectors, tracking-by-detection is a standard strategy for visual multi-object tracking (MOT). In particular, online MOT is more demanding due to its diverse applications in time-critical situations. A main issue of realizing online MOT is
[...] Read more.
Due to the reasonably acceptable performance of state-of-the-art object detectors, tracking-by-detection is a standard strategy for visual multi-object tracking (MOT). In particular, online MOT is more demanding due to its diverse applications in time-critical situations. A main issue of realizing online MOT is how to associate noisy object detection results on a new frame with previously being tracked objects. In this work, we propose a multi-object tracker method called CRF-boosting which utilizes a hybrid data association method based on online hybrid boosting facilitated by a conditional random field (CRF) for establishing online MOT. For data association, learned CRF is used to generate reliable low-level tracklets and then these are used as the input of the hybrid boosting. To do so, while existing data association methods based on boosting algorithms have the necessity of training data having ground truth information to improve robustness, CRF-boosting ensures sufficient robustness without such information due to the synergetic cascaded learning procedure. Further, a hierarchical feature association framework is adopted to further improve MOT accuracy. From experimental results on public datasets, we could conclude that the benefit of proposed hybrid approach compared to the other competitive MOT systems is noticeable. Full article
(This article belongs to the Special Issue Video Analysis and Tracking Using State-of-the-Art Sensors)
Figures

Figure 1

Open AccessArticle PDMAA Hydrogel Coated U-Bend Humidity Sensor Suited for Mass-Production
Sensors 2017, 17(3), 517; doi:10.3390/s17030517
Received: 14 November 2016 / Revised: 28 February 2017 / Accepted: 2 March 2017 / Published: 4 March 2017
PDF Full-text (1856 KB) | HTML Full-text | XML Full-text
Abstract
We present a full-polymer respiratory monitoring device suited for application in environments with strong magnetic fields (e.g., during an MRI measurement). The sensor is based on the well-known evanescent field method and consists of a 1 mm plastic optical fiber with a bent
[...] Read more.
We present a full-polymer respiratory monitoring device suited for application in environments with strong magnetic fields (e.g., during an MRI measurement). The sensor is based on the well-known evanescent field method and consists of a 1 mm plastic optical fiber with a bent region where the cladding is removed and the fiber is coated with poly-dimethylacrylamide (PDMAA). The combination of materials allows for a mass-production of the device by spray-coating and enables integration in disposable medical devices like oxygen masks, which we demonstrate here. We also present results of the application of an autocorrelation-based algorithm for respiratory frequency determination that is relevant for real applications of the device. Full article
(This article belongs to the Special Issue System-Integrated Intelligence and Intelligent Systems)
Figures

Figure 1

Open AccessArticle A Spatially Offset Raman Spectroscopy Method for Non-Destructive Detection of Gelatin-Encapsulated Powders
Sensors 2017, 17(3), 618; doi:10.3390/s17030618
Received: 26 January 2017 / Revised: 9 March 2017 / Accepted: 16 March 2017 / Published: 18 March 2017
PDF Full-text (4327 KB) | HTML Full-text | XML Full-text
Abstract
Non-destructive subsurface detection of encapsulated, coated, or seal-packaged foods and pharmaceuticals can help prevent distribution and consumption of counterfeit or hazardous products. This study used a Spatially Offset Raman Spectroscopy (SORS) method to detect and identify urea, ibuprofen, and acetaminophen powders contained within
[...] Read more.
Non-destructive subsurface detection of encapsulated, coated, or seal-packaged foods and pharmaceuticals can help prevent distribution and consumption of counterfeit or hazardous products. This study used a Spatially Offset Raman Spectroscopy (SORS) method to detect and identify urea, ibuprofen, and acetaminophen powders contained within one or more (up to eight) layers of gelatin capsules to demonstrate subsurface chemical detection and identification. A 785-nm point-scan Raman spectroscopy system was used to acquire spatially offset Raman spectra for an offset range of 0 to 10 mm from the surfaces of 24 encapsulated samples, using a step size of 0.1 mm to obtain 101 spectral measurements per sample. As the offset distance was increased, the spectral contribution from the subsurface powder gradually outweighed that of the surface capsule layers, allowing for detection of the encapsulated powders. Containing mixed contributions from the powder and capsule, the SORS spectra for each sample were resolved into pure component spectra using self-modeling mixture analysis (SMA) and the corresponding components were identified using spectral information divergence values. As demonstrated here for detecting chemicals contained inside thick capsule layers, this SORS measurement technique coupled with SMA has the potential to be a reliable non-destructive method for subsurface inspection and authentication of foods, health supplements, and pharmaceutical products that are prepared or packaged with semi-transparent materials. Full article
(This article belongs to the Special Issue Applications of Raman Spectroscopy in Biosensors)
Figures

Figure 1

Open AccessArticle A Pulsed Thermographic Imaging System for Detection and Identification of Cotton Foreign Matter
Sensors 2017, 17(3), 518; doi:10.3390/s17030518
Received: 29 December 2016 / Revised: 22 February 2017 / Accepted: 2 March 2017 / Published: 4 March 2017
PDF Full-text (3499 KB) | HTML Full-text | XML Full-text
Abstract
Detection of foreign matter in cleaned cotton is instrumental to accurately grading cotton quality, which in turn impacts the marketability of the cotton. Current grading systems return estimates of the amount of foreign matter present, but provide no information about the identity of
[...] Read more.
Detection of foreign matter in cleaned cotton is instrumental to accurately grading cotton quality, which in turn impacts the marketability of the cotton. Current grading systems return estimates of the amount of foreign matter present, but provide no information about the identity of the contaminants. This paper explores the use of pulsed thermographic analysis to detect and identify cotton foreign matter. The design and implementation of a pulsed thermographic analysis system is described. A sample set of 240 foreign matter and cotton lint samples were collected. Hand-crafted waveform features and frequency-domain features were extracted and analyzed for statistical significance. Classification was performed on these features using linear discriminant analysis and support vector machines. Using waveform features and support vector machine classifiers, detection of cotton foreign matter was performed with 99.17% accuracy. Using frequency-domain features and linear discriminant analysis, identification was performed with 90.00% accuracy. These results demonstrate that pulsed thermographic imaging analysis produces data which is of significant utility for the detection and identification of cotton foreign matter. Full article
(This article belongs to the Section Physical Sensors)
Figures

Figure 1

Open AccessArticle Spectroscopic On-Line Monitoring of Cu/W Contacts Erosion in HVCBs Using Optical-Fibre Based Sensor and Chromatic Methodology
Sensors 2017, 17(3), 519; doi:10.3390/s17030519
Received: 20 December 2016 / Revised: 23 February 2017 / Accepted: 24 February 2017 / Published: 6 March 2017
PDF Full-text (3136 KB) | HTML Full-text | XML Full-text
Abstract
Contact erosion is one of the most crucial factors affecting the electrical service lifetime of high-voltage circuit breakers (HVCBs). On-line monitoring the contacts’ erosion degree is increasingly in demand for the sake of condition based maintenance to guarantee the functional operation of HVCBs.
[...] Read more.
Contact erosion is one of the most crucial factors affecting the electrical service lifetime of high-voltage circuit breakers (HVCBs). On-line monitoring the contacts’ erosion degree is increasingly in demand for the sake of condition based maintenance to guarantee the functional operation of HVCBs. A spectroscopic monitoring system has been designed based upon a commercial 245 kV/40 kA S F 6 live tank circuit breaker with copper–tungsten (28 wt % and 72 wt %) arcing contacts at atmospheric S F 6 pressure. Three optical-fibre based sensors are used to capture the time-resolved spectra of arcs. A novel approach using chromatic methods to process the time-resolved spectral signal has been proposed. The processed chromatic parameters have been interpreted to show that the time variation of spectral emission from the contact material and quenching gas are closely correlated to the mass loss and surface degradation of the plug arcing contact. The feasibility of applying this method to online monitoring of contact erosion is indicated. Full article
(This article belongs to the Section Physical Sensors)
Figures

Figure 1

Open AccessArticle Design and Validation of a Low-Cost Portable Device to Quantify Postural Stability
Sensors 2017, 17(3), 619; doi:10.3390/s17030619
Received: 3 February 2017 / Revised: 16 March 2017 / Accepted: 16 March 2017 / Published: 18 March 2017
PDF Full-text (3129 KB) | HTML Full-text | XML Full-text
Abstract
Measurement of the displacement of the center-of-pressure (COP) is an important tool used in biomechanics to assess postural stability and human balance. The goal of this research was to design and validate a low-cost portable device that can offer a quick indication of
[...] Read more.
Measurement of the displacement of the center-of-pressure (COP) is an important tool used in biomechanics to assess postural stability and human balance. The goal of this research was to design and validate a low-cost portable device that can offer a quick indication of the state of postural stability and human balance related conditions. Approximate entropy (ApEn) values reflecting the amount of irregularity hiding in COP oscillations were used to calculate the index. The prototype adopted a portable design using the measurements of the load cells located at the four corners of a low-cost force platform. The test subject was asked to stand on the device in a quiet, normal, upright stance for 30 s with eyes open and subsequently for 30 s with eyes closed. Based on the COP displacement signals, the ApEn values were calculated. The results indicated that the prototype device was capable of capturing the increase in regularity of postural control in the visual-deprivation conditions. It was also able to decipher the subtle postural control differences along anterior–posterior and medial–lateral directions. The data analysis demonstrated that the prototype would enable the quantification of postural stability and thus provide a low-cost portable device to assess many conditions related to postural stability and human balance such as aging and pathologies. Full article
(This article belongs to the Section Physical Sensors)
Figures

Figure 1

Open AccessArticle Development of Fluorescent FRET Probes for “Off-On” Detection of L-Cysteine Based on Gold Nanoparticles and Porous Silicon Nanoparticles in Ethanol Solution
Sensors 2017, 17(3), 520; doi:10.3390/s17030520
Received: 10 January 2017 / Revised: 20 February 2017 / Accepted: 28 February 2017 / Published: 5 March 2017
PDF Full-text (2470 KB) | HTML Full-text | XML Full-text
Abstract
A new type of fluorescence “off-on” probe was designed for L-Cysteine (L-Cys) based on the fluorescence resonance energy transfer (FRET) between negatively charged amino-capped porous silicon nanoparticles (SiNPs) and positively charged citrate-stabilized Au nanoparticles (AuNPs). In this proposed FRET immunosensor, novel water-soluble amino-conjugated
[...] Read more.
A new type of fluorescence “off-on” probe was designed for L-Cysteine (L-Cys) based on the fluorescence resonance energy transfer (FRET) between negatively charged amino-capped porous silicon nanoparticles (SiNPs) and positively charged citrate-stabilized Au nanoparticles (AuNPs). In this proposed FRET immunosensor, novel water-soluble amino-conjugated porous SiNPs in ethanol with excellent photoluminescence properties act as the energy donor. Excellent quenching efficiency between SiNPs-ethanol and citrate-stabilized AuNPs by electrostatic interaction via FRET provides an ideal “off-state” (turn-off). The addition of L-Cys leads to releasing the adsorbed AuNPs from the surface of SiNPs and hence the fluorescence emission of SiNPs-ethanol is restored (turn-on), which means the coordination ability of the thiols with AuNPs is stronger than that of the electrostatic interaction. The fluorescence intensity of SiNPs-AuNPs in ethanol is sensitive to L-Cys, and such a restored fluorescence is proportional to the concentration of L-Cys. The method will broadly benefit the development of a new thiol biosensor based on nanostructured porous materials, and the proposed procedure is also expected to develop a variety of functional nanoparticles to form other novel kinds of nanosensors. Full article
(This article belongs to the Special Issue Micro and Nanofabrication Technologies for Biosensors)
Figures

Figure 1

Open AccessArticle Estimation of Image Sensor Fill Factor Using a Single Arbitrary Image
Sensors 2017, 17(3), 620; doi:10.3390/s17030620
Received: 29 January 2017 / Revised: 12 March 2017 / Accepted: 15 March 2017 / Published: 18 March 2017
PDF Full-text (4078 KB) | HTML Full-text | XML Full-text
Abstract
Achieving a high fill factor is a bottleneck problem for capturing high-quality images. There are hardware and software solutions to overcome this problem. In the solutions, the fill factor is known. However, this is an industrial secrecy by most image sensor manufacturers due
[...] Read more.
Achieving a high fill factor is a bottleneck problem for capturing high-quality images. There are hardware and software solutions to overcome this problem. In the solutions, the fill factor is known. However, this is an industrial secrecy by most image sensor manufacturers due to its direct effect on the assessment of the sensor quality. In this paper, we propose a method to estimate the fill factor of a camera sensor from an arbitrary single image. The virtual response function of the imaging process and sensor irradiance are estimated from the generation of virtual images. Then the global intensity values of the virtual images are obtained, which are the result of fusing the virtual images into a single, high dynamic range radiance map. A non-linear function is inferred from the original and global intensity values of the virtual images. The fill factor is estimated by the conditional minimum of the inferred function. The method is verified using images of two datasets. The results show that our method estimates the fill factor correctly with significant stability and accuracy from one single arbitrary image according to the low standard deviation of the estimated fill factors from each of images and for each camera. Full article
(This article belongs to the Section Physical Sensors)
Figures

Figure 1

Open AccessArticle Implicit Regularization for Reconstructing 3D Building Rooftop Models Using Airborne LiDAR Data
Sensors 2017, 17(3), 621; doi:10.3390/s17030621
Received: 22 January 2017 / Revised: 28 February 2017 / Accepted: 1 March 2017 / Published: 19 March 2017
PDF Full-text (6808 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
With rapid urbanization, highly accurate and semantically rich virtualization of building assets in 3D become more critical for supporting various applications, including urban planning, emergency response and location-based services. Many research efforts have been conducted to automatically reconstruct building models at city-scale from
[...] Read more.
With rapid urbanization, highly accurate and semantically rich virtualization of building assets in 3D become more critical for supporting various applications, including urban planning, emergency response and location-based services. Many research efforts have been conducted to automatically reconstruct building models at city-scale from remotely sensed data. However, developing a fully-automated photogrammetric computer vision system enabling the massive generation of highly accurate building models still remains a challenging task. One the most challenging task for 3D building model reconstruction is to regularize the noises introduced in the boundary of building object retrieved from a raw data with lack of knowledge on its true shape. This paper proposes a data-driven modeling approach to reconstruct 3D rooftop models at city-scale from airborne laser scanning (ALS) data. The focus of the proposed method is to implicitly derive the shape regularity of 3D building rooftops from given noisy information of building boundary in a progressive manner. This study covers a full chain of 3D building modeling from low level processing to realistic 3D building rooftop modeling. In the element clustering step, building-labeled point clouds are clustered into homogeneous groups by applying height similarity and plane similarity. Based on segmented clusters, linear modeling cues including outer boundaries, intersection lines, and step lines are extracted. Topology elements among the modeling cues are recovered by the Binary Space Partitioning (BSP) technique. The regularity of the building rooftop model is achieved by an implicit regularization process in the framework of Minimum Description Length (MDL) combined with Hypothesize and Test (HAT). The parameters governing the MDL optimization are automatically estimated based on Min-Max optimization and Entropy-based weighting method. The performance of the proposed method is tested over the International Society for Photogrammetry and Remote Sensing (ISPRS) benchmark datasets. The results show that the proposed method can robustly produce accurate regularized 3D building rooftop models. Full article
Figures

Open AccessArticle 5 V Compatible Two-Axis PZT Driven MEMS Scanning Mirror with Mechanical Leverage Structure for Miniature LiDAR Application
Sensors 2017, 17(3), 521; doi:10.3390/s17030521
Received: 9 February 2017 / Revised: 1 March 2017 / Accepted: 2 March 2017 / Published: 5 March 2017
PDF Full-text (8540 KB) | HTML Full-text | XML Full-text
Abstract
The MEMS (Micro-Electronical Mechanical System) scanning mirror is an optical MEMS device that can scan laser beams across one or two dimensions. MEMS scanning mirrors can be applied in a variety of applications, such as laser display, bio-medical imaging and Light Detection and
[...] Read more.
The MEMS (Micro-Electronical Mechanical System) scanning mirror is an optical MEMS device that can scan laser beams across one or two dimensions. MEMS scanning mirrors can be applied in a variety of applications, such as laser display, bio-medical imaging and Light Detection and Ranging (LiDAR). These commercial applications have recently created a great demand for low-driving-voltage and low-power MEMS mirrors. However, no reported two-axis MEMS scanning mirror is available for usage in a universal supplying voltage such as 5 V. In this paper, we present an ultra-low voltage driven two-axis MEMS scanning mirror which is 5 V compatible. In order to realize low voltage and low power, a two-axis MEMS scanning mirror with mechanical leverage driven by PZT (Lead zirconate titanate) ceramic is designed, modeled, fabricated and characterized. To further decrease the power of the MEMS scanning mirror, a new method of impedance matching for PZT ceramic driven by a two-frequency mixed signal is established. As experimental results show, this MEMS scanning mirror reaches a two-axis scanning angle of 41.9° × 40.3° at a total driving voltage of 4.2 Vpp and total power of 16 mW. The effective diameter of reflection of the mirror is 2 mm and the operating frequencies of two-axis scanning are 947.51 Hz and 1464.66 Hz, respectively. Full article
(This article belongs to the Special Issue Systems and Software for Low Power Embedded Sensing)
Figures

Figure 1

Open AccessArticle Mobile Health Applications to Promote Active and Healthy Ageing
Sensors 2017, 17(3), 622; doi:10.3390/s17030622
Received: 3 November 2016 / Revised: 14 March 2017 / Accepted: 15 March 2017 / Published: 18 March 2017
Cited by 1 | PDF Full-text (1271 KB) | HTML Full-text | XML Full-text
Abstract
The European population is ageing, and there is a need for health solutions that keep older adults independent longer. With increasing access to mobile technology, such as smartphones and smartwatches, the development and use of mobile health applications is rapidly growing. To meet
[...] Read more.
The European population is ageing, and there is a need for health solutions that keep older adults independent longer. With increasing access to mobile technology, such as smartphones and smartwatches, the development and use of mobile health applications is rapidly growing. To meet the societal challenge of changing demography, mobile health solutions are warranted that support older adults to stay healthy and active and that can prevent or delay functional decline. This paper reviews the literature on mobile technology, in particular wearable technology, such as smartphones, smartwatches, and wristbands, presenting new ideas on how this technology can be used to encourage an active lifestyle, and discusses the way forward in order further to advance development and practice in the field of mobile technology for active, healthy ageing. Full article
(This article belongs to the Special Issue Body Worn Behavior Sensing)
Figures

Figure 1

Open AccessArticle New System of Shrinkage Measurement through Cement Mortars Drying
Sensors 2017, 17(3), 522; doi:10.3390/s17030522
Received: 18 January 2017 / Revised: 1 March 2017 / Accepted: 2 March 2017 / Published: 6 March 2017
PDF Full-text (2082 KB) | HTML Full-text | XML Full-text
Abstract
Cement mortar is used as a conglomerate in the majority of construction work. There are multiple variants of cement according to the type of aggregate used in its fabrication. One of the major problems that occurs while working with this type of material
[...] Read more.
Cement mortar is used as a conglomerate in the majority of construction work. There are multiple variants of cement according to the type of aggregate used in its fabrication. One of the major problems that occurs while working with this type of material is the excessive loss of moisture during cement hydration (setting and hardening), known as shrinkage, which provokes a great number of construction pathologies that are difficult to repair. In this way, the design of a new sensor able to measure the moisture loss of mortars at different age levels is useful to establish long-term predictions concerning mortar mass volume loss. The purpose of this research is the design and fabrication of a new capacitive sensor able to measure the moisture of mortars and to relate it with the shrinkage. Full article
(This article belongs to the Special Issue State-of-the-Art Sensors Technology in Spain 2016)
Figures

Figure 1

Open AccessArticle Improved Wallis Dodging Algorithm for Large-Scale Super-Resolution Reconstruction Remote Sensing Images
Sensors 2017, 17(3), 623; doi:10.3390/s17030623
Received: 5 December 2016 / Revised: 2 March 2017 / Accepted: 16 March 2017 / Published: 18 March 2017
PDF Full-text (9719 KB) | HTML Full-text | XML Full-text
Abstract
A sub-block algorithm is usually applied in the super-resolution (SR) reconstruction of images because of limitations in computer memory. However, the sub-block SR images can hardly achieve a seamless image mosaicking because of the uneven distribution of brightness and contrast among these sub-blocks.
[...] Read more.
A sub-block algorithm is usually applied in the super-resolution (SR) reconstruction of images because of limitations in computer memory. However, the sub-block SR images can hardly achieve a seamless image mosaicking because of the uneven distribution of brightness and contrast among these sub-blocks. An effectively improved weighted Wallis dodging algorithm is proposed, aiming at the characteristic that SR reconstructed images are gray images with the same size and overlapping region. This algorithm can achieve consistency of image brightness and contrast. Meanwhile, a weighted adjustment sequence is presented to avoid the spatial propagation and accumulation of errors and the loss of image information caused by excessive computation. A seam line elimination method can share the partial dislocation in the seam line to the entire overlapping region with a smooth transition effect. Subsequently, the improved method is employed to remove the uneven illumination for 900 SR reconstructed images of ZY-3. Then, the overlapping image mosaic method is adopted to accomplish a seamless image mosaic based on the optimal seam line. Full article
(This article belongs to the Special Issue Sensors and Smart Sensing of Agricultural Land Systems)
Figures

Figure 1

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
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)
Figures

Figure 1

Open AccessArticle A Segment-Based Trajectory Similarity Measure in the Urban Transportation Systems
Sensors 2017, 17(3), 524; doi:10.3390/s17030524
Received: 7 January 2017 / Revised: 1 March 2017 / Accepted: 1 March 2017 / Published: 6 March 2017
PDF Full-text (3991 KB) | HTML Full-text | XML Full-text
Abstract
With the rapid spread of built-in GPS handheld smart devices, the trajectory data from GPS sensors has grown explosively. Trajectory data has spatio-temporal characteristics and rich information. Using trajectory data processing techniques can mine the patterns of human activities and the moving patterns
[...] Read more.
With the rapid spread of built-in GPS handheld smart devices, the trajectory data from GPS sensors has grown explosively. Trajectory data has spatio-temporal characteristics and rich information. Using trajectory data processing techniques can mine the patterns of human activities and the moving patterns of vehicles in the intelligent transportation systems. A trajectory similarity measure is one of the most important issues in trajectory data mining (clustering, classification, frequent pattern mining, etc.). Unfortunately, the main similarity measure algorithms with the trajectory data have been found to be inaccurate, highly sensitive of sampling methods, and have low robustness for the noise data. To solve the above problems, three distances and their corresponding computation methods are proposed in this paper. The point-segment distance can decrease the sensitivity of the point sampling methods. The prediction distance optimizes the temporal distance with the features of trajectory data. The segment-segment distance introduces the trajectory shape factor into the similarity measurement to improve the accuracy. The three kinds of distance are integrated with the traditional dynamic time warping algorithm (DTW) algorithm to propose a new segment–based dynamic time warping algorithm (SDTW). The experimental results show that the SDTW algorithm can exhibit about 57%, 86%, and 31% better accuracy than the longest common subsequence algorithm (LCSS), and edit distance on real sequence algorithm (EDR) , and DTW, respectively, and that the sensitivity to the noise data is lower than that those algorithms. Full article
(This article belongs to the Special Issue Sensors for Transportation)
Figures

Figure 1

Open AccessArticle A Geographic Information-Assisted Temporal Mixture Analysis for Addressing the Issue of Endmember Class and Endmember Spectra Variability
Sensors 2017, 17(3), 624; doi:10.3390/s17030624
Received: 22 January 2017 / Revised: 14 March 2017 / Accepted: 16 March 2017 / Published: 18 March 2017
PDF Full-text (2230 KB) | HTML Full-text | XML Full-text
Abstract
Spectral mixture analysis (SMA) is a common approach for parameterizing biophysical fractions of urban environment and widely applied in many fields. For successful SMA, the selection of endmember class and corresponding spectra has been assumed as the most important step. Thanks to the
[...] Read more.
Spectral mixture analysis (SMA) is a common approach for parameterizing biophysical fractions of urban environment and widely applied in many fields. For successful SMA, the selection of endmember class and corresponding spectra has been assumed as the most important step. Thanks to the spatial heterogeneity of natural and urban landscapes, the variability of endmember class and corresponding spectra has been widely considered as the profound error source in SMA. To address the challenging problems, we proposed a geographic information-assisted temporal mixture analysis (GATMA). Specifically, a logistic regression analysis was applied to analyze the relationship between land use/land covers and surrounding socio-economic factors, and a classification tree method was used to identify the present status of endmember classes throughout the whole study area. Furthermore, an ordinary kriging analysis was employed to generate a spatially varying endmember spectra at all pixels in the remote sensing image. As a consequence, a fully constrained temporal mixture analysis was conducted for examining the fractional land use land covers. Results show that the proposed GATMA achieved a promising accuracy with an RMSE of 6.81%, SE of 1.29% and MAE of 2.6%. In addition, comparative analysis result illustrates that a significant accuracy improvement has been found in the whole study area and both developed and less developed areas, and this demonstrates that the variability of endmember class and endmember spectra is essential for unmixing analysis. Full article
Figures

Figure 1

Open AccessArticle Weighted Kernel Entropy Component Analysis for Fault Diagnosis of Rolling Bearings
Sensors 2017, 17(3), 625; doi:10.3390/s17030625
Received: 17 February 2017 / Revised: 13 March 2017 / Accepted: 15 March 2017 / Published: 18 March 2017
PDF Full-text (3314 KB) | HTML Full-text | XML Full-text
Abstract
This paper presents a supervised feature extraction method called weighted kernel entropy component analysis (WKECA) for fault diagnosis of rolling bearings. The method is developed based on kernel entropy component analysis (KECA) which attempts to preserve the Renyi entropy of the data set
[...] Read more.
This paper presents a supervised feature extraction method called weighted kernel entropy component analysis (WKECA) for fault diagnosis of rolling bearings. The method is developed based on kernel entropy component analysis (KECA) which attempts to preserve the Renyi entropy of the data set after dimension reduction. It makes full use of the labeled information and introduces a weight strategy in the feature extraction. The class-related weights are introduced to denote differences among the samples from different patterns, and genetic algorithm (GA) is implemented to seek out appropriate weights for optimizing the classification results. The features based on wavelet packet decomposition are derived from the original signals. Then the intrinsic geometric features extracted by WKECA are fed into the support vector machine (SVM) classifier to recognize different operating conditions of bearings, and we obtain the overall accuracy (97%) for the experimental samples. The experimental results demonstrated the feasibility and effectiveness of the proposed method. Full article
(This article belongs to the Section Physical Sensors)
Figures

Figure 1

Open AccessArticle Off the Shelf Cloud Robotics for the Smart Home: Empowering a Wireless Robot through Cloud Computing
Sensors 2017, 17(3), 525; doi:10.3390/s17030525
Received: 25 November 2016 / Revised: 24 February 2017 / Accepted: 24 February 2017 / Published: 6 March 2017
PDF Full-text (1225 KB) | HTML Full-text | XML Full-text
Abstract
In this paper, we explore the possibilities offered by the integration of home automation systems and service robots. In particular, we examine how advanced computationally expensive services can be provided by using a cloud computing approach to overcome the limitations of the hardware
[...] Read more.
In this paper, we explore the possibilities offered by the integration of home automation systems and service robots. In particular, we examine how advanced computationally expensive services can be provided by using a cloud computing approach to overcome the limitations of the hardware available at the user’s home. To this end, we integrate two wireless low-cost, off-the-shelf systems in this work, namely, the service robot Rovio and the home automation system Z-wave. Cloud computing is used to enhance the capabilities of these systems so that advanced sensing and interaction services based on image processing and voice recognition can be offered. Full article
(This article belongs to the Special Issue Sensors for Home Automation and Security)
Figures

Figure 1

Open AccessArticle A Colorimetric Sensor for the Highly Selective Detection of Sulfide and 1,4-Dithiothreitol Based on the In Situ Formation of Silver Nanoparticles Using Dopamine
Sensors 2017, 17(3), 626; doi:10.3390/s17030626
Received: 18 February 2017 / Revised: 8 March 2017 / Accepted: 16 March 2017 / Published: 20 March 2017
PDF Full-text (4195 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Hydrogen sulfide (H2S) has attracted attention in biochemical research because it plays an important role in biosystems and has emerged as the third endogenous gaseous signaling compound along with nitric oxide (NO) and carbon monoxide (CO). Since H2S is
[...] Read more.
Hydrogen sulfide (H2S) has attracted attention in biochemical research because it plays an important role in biosystems and has emerged as the third endogenous gaseous signaling compound along with nitric oxide (NO) and carbon monoxide (CO). Since H2S is a kind of gaseous molecule, conventional approaches for H2S detection are mostly based on the detection of sulfide (S2−) for indirectly reflecting H2S levels. Hence, there is a need for an accurate and reliable assay capable of determining sulfide in physiological systems. We report here a colorimetric, economic, and green method for sulfide anion detection using in situ formation of silver nanoparticles (AgNPs) using dopamine as a reducing and protecting agent. The changes in the AgNPs absorption response depend linearly on the concentration of Na2S in the range from 2 to 15 μM, with a detection limit of 0.03 μM. Meanwhile, the morphological changes in AgNPs in the presence of S2− and thiol compounds were characterized by transmission electron microscopy (TEM). The as-synthetized AgNPs demonstrate high selectivity, free from interference, especially by other thiol compounds such as cysteine and glutathione. Furthermore, the colorimetric sensor developed was applied to the analysis of sulfide in fetal bovine serum and spiked serum samples with good recovery. Full article
(This article belongs to the Section Chemical Sensors)
Figures

Figure 1

Open AccessArticle The Novel Design of a Single-Sided MRI Probe for Assessing Burn Depth
Sensors 2017, 17(3), 526; doi:10.3390/s17030526
Received: 3 January 2017 / Revised: 2 March 2017 / Accepted: 3 March 2017 / Published: 6 March 2017
PDF Full-text (3951 KB) | HTML Full-text | XML Full-text
Abstract
Burn depth assessment in clinics is still inaccurate because of the lack of feasible and practical testing devices and methods. Therefore, this process often depends on subjective judgment of burn surgeons. In this study, a new unilateral magnetic resonance imaging (UMRI) sensor equipped
[...] Read more.
Burn depth assessment in clinics is still inaccurate because of the lack of feasible and practical testing devices and methods. Therefore, this process often depends on subjective judgment of burn surgeons. In this study, a new unilateral magnetic resonance imaging (UMRI) sensor equipped with a 2D gradient coil system was established, and we attempted to assess burns using unilateral nuclear magnetic resonance devices. A reduced Halbach magnet was utilized to generate a magnetic field that was relatively homogeneous on a target plane with a suitable field of view for 2D spatial localization. A uniplanar gradient coil system was designed by utilizing the mainstream target field method, and a uniplanar RF (radio frequency) coil was designed by using a timeharmonic inverse method for the UMRI sensor. A 2D image of the cross sections of a simple burn model was obtained by a fast 2D pure-phase encoding imaging method. The design details of the novel single-sided MRI probe and imaging tests are also presented. Full article
(This article belongs to the Section Physical Sensors)
Figures

Figure 1

Open AccessArticle Diagnosis of Breast Cancer Tissues Using 785 nm Miniature Raman Spectrometer and Pattern Regression
Sensors 2017, 17(3), 627; doi:10.3390/s17030627
Received: 25 January 2017 / Revised: 13 March 2017 / Accepted: 16 March 2017 / Published: 19 March 2017
PDF Full-text (762 KB) | HTML Full-text | XML Full-text
Abstract
For achieving the development of a portable, low-cost and in vivo cancer diagnosis instrument, a laser 785 nm miniature Raman spectrometer was used to acquire the Raman spectra for breast cancer detection in this paper. However, because of the low spectral signal-to-noise ratio,
[...] Read more.
For achieving the development of a portable, low-cost and in vivo cancer diagnosis instrument, a laser 785 nm miniature Raman spectrometer was used to acquire the Raman spectra for breast cancer detection in this paper. However, because of the low spectral signal-to-noise ratio, it is difficult to achieve high discrimination accuracy by using the miniature Raman spectrometer. Therefore, a pattern recognition method of the adaptive net analyte signal (NAS) weight k-local hyperplane (ANWKH) is proposed to increase the classification accuracy. ANWKH is an extension and improvement of K-local hyperplane distance nearest-neighbor (HKNN), and combines the advantages of the adaptive weight k-local hyperplane (AWKH) and the net analyte signal (NAS). In this algorithm, NAS was first used to eliminate the influence caused by other non-target factors. Then, the distance between the test set samples and hyperplane was calculated with consideration of the feature weights. The HKNN only works well for small values of the nearest-neighbor. However, the accuracy decreases with increasing values of the nearest-neighbor. The method presented in this paper can resolve the basic shortcoming by using the feature weights. The original spectra are projected into the vertical subspace without the objective factors. NAS was employed to obtain the spectra without irrelevant information. NAS can improve the classification accuracy, sensitivity, and specificity of breast cancer early diagnosis. Experimental results of Raman spectra detection in vitro of breast tissues showed that the proposed algorithm can obtain high classification accuracy, sensitivity, and specificity. This paper demonstrates that the ANWKH algorithm is feasible for early clinical diagnosis of breast cancer in the future. Full article
(This article belongs to the Special Issue Applications of Raman Spectroscopy in Biosensors)
Figures

Figure 1

Open AccessArticle Design and Optimization of a Stationary Electrode in a Vertically-Driven MEMS Inertial Switch for Extending Contact Duration
Sensors 2017, 17(3), 527; doi:10.3390/s17030527
Received: 17 November 2016 / Revised: 28 February 2017 / Accepted: 1 March 2017 / Published: 7 March 2017
PDF Full-text (9474 KB) | HTML Full-text | XML Full-text
Abstract
A novel micro-electro-mechanical systems (MEMS) inertial microswitch with a flexible contact-enhanced structure to extend the contact duration has been proposed in the present work. In order to investigate the stiffness k of the stationary electrodes, the stationary electrodes with different shapes, thickness h
[...] Read more.
A novel micro-electro-mechanical systems (MEMS) inertial microswitch with a flexible contact-enhanced structure to extend the contact duration has been proposed in the present work. In order to investigate the stiffness k of the stationary electrodes, the stationary electrodes with different shapes, thickness h, width b, and length l were designed, analyzed, and simulated using ANSYS software. Both the analytical and the simulated results indicate that the stiffness k increases with thickness h and width b, while decreasing with an increase of length l, and it is related to the shape. The inertial micro-switches with different kinds of stationary electrodes were simulated using ANSYS software and fabricated using surface micromachining technology. The dynamic simulation indicates that the contact time will decrease with the increase of thickness h and width b, but increase with the length l, and it is related to the shape. As a result, the contact time decreases with the stiffness k of the stationary electrode. Furthermore, the simulated results reveal that the stiffness k changes more rapidly with h and l compared to b. However, overlarge dimension of the whole microswitch is contradicted with small footprint area expectation in the structure design. Therefore, it is unreasonable to extend the contact duration by increasing the length l excessively. Thus, the best and most convenient way to prolong the contact time is to reduce the thickness h of the stationary electrode while keeping the plane geometric structure of the inertial micro-switch unchanged. Finally, the fabricated micro-switches with different shapes of stationary electrodes have been evaluated by a standard dropping hammer system. The test maximum contact time under 288 g acceleration can reach 125 µs. It is shown that the test results are in accordance with the simulated results. The conclusions obtained in this work can provide guidance for the future design and fabrication of inertial microswitches. Full article
(This article belongs to the Special Issue MEMS and Nano-Sensors)
Figures

Figure 1

Open AccessArticle Comparison of Two Types of Overoxidized PEDOT Films and Their Application in Sensor Fabrication
Sensors 2017, 17(3), 628; doi:10.3390/s17030628
Received: 18 January 2017 / Revised: 16 March 2017 / Accepted: 17 March 2017 / Published: 19 March 2017
PDF Full-text (3066 KB) | HTML Full-text | XML Full-text
Abstract
Poly(3,4-ethylenedioxythiophene) (PEDOT) films were prepared by electro-oxidation on Au microelectrodes in an aqueous solution. Electrolyte solutions and polymerization parameters were optimized prior to overoxidation. The effect of overoxidation time has been optimized by cyclic voltammetry (CV) and electrochemical impedance spectroscopy (EIS), which results
[...] Read more.
Poly(3,4-ethylenedioxythiophene) (PEDOT) films were prepared by electro-oxidation on Au microelectrodes in an aqueous solution. Electrolyte solutions and polymerization parameters were optimized prior to overoxidation. The effect of overoxidation time has been optimized by cyclic voltammetry (CV) and electrochemical impedance spectroscopy (EIS), which results in the film overoxidized for 45 s at 1.35 V presenting a strong adsorption. The other one-step overoxidation film prepared by direct CV ranging from −0.6 V to 1.35 V was polymerized for comparison. Scanning electron microscope (SEM) analysis and Fourier transform infrared (FTIR) spectroscopy were used for monitoring morphological changes and the evolution of functional groups. Both of them indicate increased abundant oxygen functional groups and roughness, yet the products exhibit dendritic morphology and piles of spherical protrusions, respectively. Moreover, double-step overoxidized film showed better electrochemical performance toward lead ion sensing. These characterizations highlight some novel properties that may be beneficial for specific sensing applications. Full article
Figures

Figure 1

Open AccessArticle Improving the Accuracy of Urban Environmental Quality Assessment Using Geographically-Weighted Regression Techniques
Sensors 2017, 17(3), 528; doi:10.3390/s17030528
Received: 25 December 2016 / Revised: 15 February 2017 / Accepted: 25 February 2017 / Published: 7 March 2017
PDF Full-text (35754 KB) | HTML Full-text | XML Full-text
Abstract
Urban Environmental Quality (UEQ) can be treated as a generic indicator that objectively represents the physical and socio-economic condition of the urban and built environment. The value of UEQ illustrates a sense of satisfaction to its population through assessing different environmental, urban and
[...] Read more.
Urban Environmental Quality (UEQ) can be treated as a generic indicator that objectively represents the physical and socio-economic condition of the urban and built environment. The value of UEQ illustrates a sense of satisfaction to its population through assessing different environmental, urban and socio-economic parameters. This paper elucidates the use of the Geographic Information System (GIS), Principal Component Analysis (PCA) and Geographically-Weighted Regression (GWR) techniques to integrate various parameters and estimate the UEQ of two major cities in Ontario, Canada. Remote sensing, GIS and census data were first obtained to derive various environmental, urban and socio-economic parameters. The aforementioned techniques were used to integrate all of these environmental, urban and socio-economic parameters. Three key indicators, including family income, higher level of education and land value, were used as a reference to validate the outcomes derived from the integration techniques. The results were evaluated by assessing the relationship between the extracted UEQ results and the reference layers. Initial findings showed that the GWR with the spatial lag model represents an improved precision and accuracy by up to 20% with respect to those derived by using GIS overlay and PCA techniques for the City of Toronto and the City of Ottawa. The findings of the research can help the authorities and decision makers to understand the empirical relationships among environmental factors, urban morphology and real estate and decide for more environmental justice. Full article
Figures

Figure 1

Open AccessArticle Cooperative Opportunistic Pressure Based Routing for Underwater Wireless Sensor Networks
Sensors 2017, 17(3), 629; doi:10.3390/s17030629
Received: 25 December 2016 / Revised: 21 February 2017 / Accepted: 11 March 2017 / Published: 19 March 2017
PDF Full-text (1134 KB) | HTML Full-text | XML Full-text
Abstract
In this paper, three opportunistic pressure based routing techniques for underwater wireless sensor networks (UWSNs) are proposed. The first one is the cooperative opportunistic pressure based routing protocol (Co-Hydrocast), second technique is the improved Hydrocast (improved-Hydrocast), and third one is the cooperative improved
[...] Read more.
In this paper, three opportunistic pressure based routing techniques for underwater wireless sensor networks (UWSNs) are proposed. The first one is the cooperative opportunistic pressure based routing protocol (Co-Hydrocast), second technique is the improved Hydrocast (improved-Hydrocast), and third one is the cooperative improved Hydrocast (Co-improved Hydrocast). In order to minimize lengthy routing paths between the source and the destination and to avoid void holes at the sparse networks, sensor nodes are deployed at different strategic locations. The deployment of sensor nodes at strategic locations assure the maximum monitoring of the network field. To conserve the energy consumption and minimize the number of hops, greedy algorithm is used to transmit data packets from the source to the destination. Moreover, the opportunistic routing is also exploited to avoid void regions by making backward transmissions to find reliable path towards the destination in the network. The relay cooperation mechanism is used for reliable data packet delivery, when signal to noise ratio (SNR) of the received signal is not within the predefined threshold then the maximal ratio combining (MRC) is used as a diversity technique to improve the SNR of the received signals at the destination. Extensive simulations validate that our schemes perform better in terms of packet delivery ratio and energy consumption than the existing technique; Hydrocast. Full article
Figures

Figure 1

Open AccessArticle A Comprehensive Analysis on Wearable Acceleration Sensors in Human Activity Recognition
Sensors 2017, 17(3), 529; doi:10.3390/s17030529
Received: 15 November 2016 / Revised: 22 February 2017 / Accepted: 28 February 2017 / Published: 7 March 2017
PDF Full-text (7520 KB) | HTML Full-text | XML Full-text
Abstract
Sensor-based motion recognition integrates the emerging area of wearable sensors with novel machine learning techniques to make sense of low-level sensor data and provide rich contextual information in a real-life application. Although Human Activity Recognition (HAR) problem has been drawing the attention of
[...] Read more.
Sensor-based motion recognition integrates the emerging area of wearable sensors with novel machine learning techniques to make sense of low-level sensor data and provide rich contextual information in a real-life application. Although Human Activity Recognition (HAR) problem has been drawing the attention of researchers, it is still a subject of much debate due to the diverse nature of human activities and their tracking methods. Finding the best predictive model in this problem while considering different sources of heterogeneities can be very difficult to analyze theoretically, which stresses the need of an experimental study. Therefore, in this paper, we first create the most complete dataset, focusing on accelerometer sensors, with various sources of heterogeneities. We then conduct an extensive analysis on feature representations and classification techniques (the most comprehensive comparison yet with 293 classifiers) for activity recognition. Principal component analysis is applied to reduce the feature vector dimension while keeping essential information. The average classification accuracy of eight sensor positions is reported to be 96.44% ± 1.62% with 10-fold evaluation, whereas accuracy of 79.92% ± 9.68% is reached in the subject-independent evaluation. This study presents significant evidence that we can build predictive models for HAR problem under more realistic conditions, and still achieve highly accurate results. Full article
(This article belongs to the Special Issue State-of-the-Art Sensors Technology in Canada 2017)
Figures

Figure 1

Open AccessArticle Electromagnetic Vortex-Based Radar Imaging Using a Single Receiving Antenna: Theory and Experimental Results
Sensors 2017, 17(3), 630; doi:10.3390/s17030630
Received: 13 February 2017 / Revised: 10 March 2017 / Accepted: 16 March 2017 / Published: 19 March 2017
PDF Full-text (3358 KB) | HTML Full-text | XML Full-text
Abstract
Radar imaging based on electromagnetic vortex can achieve azimuth resolution without relative motion. The present paper investigates this imaging technique with the use of a single receiving antenna through theoretical analysis and experimental results. Compared with the use of multiple receiving antennas, the
[...] Read more.
Radar imaging based on electromagnetic vortex can achieve azimuth resolution without relative motion. The present paper investigates this imaging technique with the use of a single receiving antenna through theoretical analysis and experimental results. Compared with the use of multiple receiving antennas, the echoes from a single receiver cannot be used directly for image reconstruction using Fourier method. The reason is revealed by using the point spread function. An additional phase is compensated for each mode before imaging process based on the array parameters and the elevation of the targets. A proof-of-concept imaging system based on a circular phased array is created, and imaging experiments of corner-reflector targets are performed in an anechoic chamber. The azimuthal image is reconstructed by the use of Fourier transform and spectral estimation methods. The azimuth resolution of the two methods is analyzed and compared through experimental data. The experimental results verify the principle of azimuth resolution and the proposed phase compensation method. Full article
(This article belongs to the Section Remote Sensors)
Figures

Figure 1

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
PDF Full-text (3038 KB) | HTML Full-text | XML Full-text
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)
Figures

Figure 1

Open AccessCommunication Real-Time Sensing of O-Phenylenediamine Oxidation on Gold Nanoparticles
Sensors 2017, 17(3), 530; doi:10.3390/s17030530
Received: 14 December 2016 / Revised: 20 February 2017 / Accepted: 21 February 2017 / Published: 7 March 2017
PDF Full-text (1889 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Real-time monitoring of chemical reactions is still challenging as well as important to study reaction mechanisms and reaction kinetics. Herein, we demonstrated the real-time monitoring of o-phenylenediamine (OPD) oxidation on the surface of gold nanoparticles by surface-enhanced Raman spectroscopy (SERS). The oxidation mechanism
[...] Read more.
Real-time monitoring of chemical reactions is still challenging as well as important to study reaction mechanisms and reaction kinetics. Herein, we demonstrated the real-time monitoring of o-phenylenediamine (OPD) oxidation on the surface of gold nanoparticles by surface-enhanced Raman spectroscopy (SERS). The oxidation mechanism and the reaction kinetics were investigated on the basis of the SERS spectrum variation and the related density functionalized theory calculation. It was shown that the oxidation of OPD in the presence of copper ions was a two-step process of the deprotonation of the amino group on the aromatic rings and the rearrangement of the electron cloud to a π-conjugated system, which may open a new door to comprehensively understand the reaction process. Full article
(This article belongs to the Special Issue Nanobiosensing for Sensors)
Figures

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
PDF Full-text (6364 KB) | HTML Full-text | XML Full-text
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)
Figures

Figure 1

Open AccessArticle On the Prediction of Flickr Image Popularity by Analyzing Heterogeneous Social Sensory Data
Sensors 2017, 17(3), 631; doi:10.3390/s17030631
Received: 20 November 2016 / Revised: 15 March 2017 / Accepted: 16 March 2017 / Published: 19 March 2017
PDF Full-text (1867 KB) | HTML Full-text | XML Full-text
Abstract
The increase in the popularity of social media has shattered the gap between the physical and virtual worlds. The content generated by people or social sensors on social media provides information about users and their living surroundings, which allows us to access a
[...] Read more.
The increase in the popularity of social media has shattered the gap between the physical and virtual worlds. The content generated by people or social sensors on social media provides information about users and their living surroundings, which allows us to access a user’s preferences, opinions, and interactions. This provides an opportunity for us to understand human behavior and enhance the services provided for both the real and virtual worlds. In this paper, we will focus on the popularity prediction of social images on Flickr, a popular social photo-sharing site, and promote the research on utilizing social sensory data in the context of assisting people to improve their life on the Web. Social data are different from the data collected from physical sensors; in the fact that they exhibit special characteristics that pose new challenges. In addition to their huge quantity, social data are noisy, unstructured, and heterogeneous. Moreover, they involve human semantics and contextual data that require analysis and interpretation based on human behavior. Accordingly, we address the problem of popularity prediction for an image by exploiting three main factors that are important for making an image popular. In particular, we investigate the impact of the image’s visual content, where the semantic and sentiment information extracted from the image show an impact on its popularity, as well as the textual information associated with the image, which has a fundamental role in boosting the visibility of the image in the keyword search results. Additionally, we explore social context, such as an image owner’s popularity and how it positively influences the image popularity. With a comprehensive study on the effect of the three aspects, we further propose to jointly consider the heterogeneous social sensory data. Experimental results obtained from real-world data demonstrate that the three factors utilized complement each other in obtaining promising results in the prediction of image popularity on social photo-sharing site. Full article
(This article belongs to the Special Issue Multisensory Big Data Analytics for Enhanced Living Environments)
Figures

Figure 1

Open AccessArticle Development of Portable Digital Radiography System with a Device for Monitoring X-ray Source-Detector Angle and Its Application in Chest Imaging
Sensors 2017, 17(3), 531; doi:10.3390/s17030531
Received: 15 December 2016 / Revised: 28 February 2017 / Accepted: 2 March 2017 / Published: 7 March 2017
PDF Full-text (7719 KB) | HTML Full-text | XML Full-text
Abstract
This study developed a device measuring the X-ray source-detector angle (SDA) and evaluated the imaging performance for diagnosing chest images. The SDA device consisted of Arduino, an accelerometer and gyro sensor, and a Bluetooth module. The SDA values were compared with the values
[...] Read more.
This study developed a device measuring the X-ray source-detector angle (SDA) and evaluated the imaging performance for diagnosing chest images. The SDA device consisted of Arduino, an accelerometer and gyro sensor, and a Bluetooth module. The SDA values were compared with the values of a digital angle meter. The performance of the portable digital radiography (PDR) was evaluated using the signal-to-noise (SNR), contrast-to-noise ratio (CNR), spatial resolution, distortion and entrance surface dose (ESD). According to different angle degrees, five anatomical landmarks were assessed using a five-point scale. The mean SNR and CNR were 182.47 and 141.43. The spatial resolution and ESD were 3.17 lp/mm (157 μm) and 0.266 mGy. The angle values of the SDA device were not significantly difference as compared to those of the digital angle meter. In chest imaging, the SNR and CNR values were not significantly different according to the different angle degrees. The visibility scores of the border of the heart, the fifth rib and the scapula showed significant differences according to different angles (p < 0.05), whereas the scores of the clavicle and first rib were not significant. It is noticeable that the increase in the SDA degree was consistent with the increases of the distortion and visibility score. The proposed PDR with a SDA device would be useful for application in the clinical radiography setting according to the standard radiography guidelines. Full article
(This article belongs to the Special Issue Sensors for Ambient Assisted Living, Ubiquitous and Mobile Health)
Figures

Figure 1

Open AccessArticle Novel Approach for the Recognition and Prediction of Multi-Function Radar Behaviours Based on Predictive State Representations
Sensors 2017, 17(3), 632; doi:10.3390/s17030632
Received: 28 November 2016 / Revised: 10 March 2017 / Accepted: 16 March 2017 / Published: 19 March 2017
PDF Full-text (1358 KB) | HTML Full-text | XML Full-text
Abstract
The extensive applications of multi-function radars (MFRs) have presented a great challenge to the technologies of radar countermeasures (RCMs) and electronic intelligence (ELINT). The recently proposed cognitive electronic warfare (CEW) provides a good solution, whose crux is to perceive present and future MFR
[...] Read more.
The extensive applications of multi-function radars (MFRs) have presented a great challenge to the technologies of radar countermeasures (RCMs) and electronic intelligence (ELINT). The recently proposed cognitive electronic warfare (CEW) provides a good solution, whose crux is to perceive present and future MFR behaviours, including the operating modes, waveform parameters, scheduling schemes, etc. Due to the variety and complexity of MFR waveforms, the existing approaches have the drawbacks of inefficiency and weak practicability in prediction. A novel method for MFR behaviour recognition and prediction is proposed based on predictive state representation (PSR). With the proposed approach, operating modes of MFR are recognized by accumulating the predictive states, instead of using fixed transition probabilities that are unavailable in the battlefield. It helps to reduce the dependence of MFR on prior information. And MFR signals can be quickly predicted by iteratively using the predicted observation, avoiding the very large computation brought by the uncertainty of future observations. Simulations with a hypothetical MFR signal sequence in a typical scenario are presented, showing that the proposed methods perform well and efficiently, which attests to their validity. Full article
(This article belongs to the Special Issue Non-Contact Sensing)
Figures

Figure 1

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
PDF Full-text (1911 KB) | HTML Full-text | XML Full-text
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)
Figures

Figure 1

Open AccessArticle Use of Wearable Sensors and Biometric Variables in an Artificial Pancreas System
Sensors 2017, 17(3), 532; doi:10.3390/s17030532
Received: 25 October 2016 / Revised: 2 March 2017 / Accepted: 3 March 2017 / Published: 7 March 2017
PDF Full-text (1657 KB) | HTML Full-text | XML Full-text
Abstract
An artificial pancreas (AP) computes the optimal insulin dose to be infused through an insulin pump in people with Type 1 Diabetes (T1D) based on information received from a continuous glucose monitoring (CGM) sensor. It has been recognized that exercise is a major
[...] Read more.
An artificial pancreas (AP) computes the optimal insulin dose to be infused through an insulin pump in people with Type 1 Diabetes (T1D) based on information received from a continuous glucose monitoring (CGM) sensor. It has been recognized that exercise is a major challenge in the development of an AP system. The use of biometric physiological variables in an AP system may be beneficial for prevention of exercise-induced challenges and better glucose regulation. The goal of the present study is to find a correlation between biometric variables such as heart rate (HR), heat flux (HF), skin temperature (ST), near-body temperature (NBT), galvanic skin response (GSR), and energy expenditure (EE), 2D acceleration-mean of absolute difference (MAD) and changes in glucose concentrations during exercise via partial least squares (PLS) regression and variable importance in projection (VIP) in order to determine which variables would be most useful to include in a future artificial pancreas. PLS and VIP analyses were performed on data sets that included seven different types of exercises. Data were collected from 26 clinical experiments. Clinical results indicate ST to be the most consistently important (important for six out of seven tested exercises) variable over all different exercises tested. EE and HR are also found to be important variables over several types of exercise. We also found that the importance of GSR and NBT observed in our experiments might be related to stress and the effect of changes in environmental temperature on glucose concentrations. The use of the biometric measurements in an AP system may provide better control of glucose concentration. Full article
(This article belongs to the Special Issue Glucose Sensors: Revolution in Diabetes Management 2016)
Figures

Figure 1

Open AccessArticle An Assessment of Iterative Reconstruction Methods for Sparse Ultrasound Imaging
Sensors 2017, 17(3), 533; doi:10.3390/s17030533
Received: 9 January 2017 / Revised: 21 February 2017 / Accepted: 28 February 2017 / Published: 8 March 2017
PDF Full-text (5509 KB) | HTML Full-text | XML Full-text
Abstract
Ultrasonic image reconstruction using inverse problems has recently appeared as an alternative to enhance ultrasound imaging over beamforming methods. This approach depends on the accuracy of the acquisition model used to represent transducers, reflectivity, and medium physics. Iterative methods, well known in general
[...] Read more.
Ultrasonic image reconstruction using inverse problems has recently appeared as an alternative to enhance ultrasound imaging over beamforming methods. This approach depends on the accuracy of the acquisition model used to represent transducers, reflectivity, and medium physics. Iterative methods, well known in general sparse signal reconstruction, are also suited for imaging. In this paper, a discrete acquisition model is assessed by solving a linear system of equations by an 1 -regularized least-squares minimization, where the solution sparsity may be adjusted as desired. The paper surveys 11 variants of four well-known algorithms for sparse reconstruction, and assesses their optimization parameters with the goal of finding the best approach for iterative ultrasound imaging. The strategy for the model evaluation consists of using two distinct datasets. We first generate data from a synthetic phantom that mimics real targets inside a professional ultrasound phantom device. This dataset is contaminated with Gaussian noise with an estimated SNR, and all methods are assessed by their resulting images and performances. The model and methods are then assessed with real data collected by a research ultrasound platform when scanning the same phantom device, and results are compared with beamforming. A distinct real dataset is finally used to further validate the proposed modeling. Although high computational effort is required by iterative methods, results show that the discrete model may lead to images closer to ground-truth than traditional beamforming. However, computing capabilities of current platforms need to evolve before frame rates currently delivered by ultrasound equipments are achievable. Full article
(This article belongs to the Section Physical Sensors)
Figures

Figure 1

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
PDF Full-text (6784 KB) | HTML Full-text | XML Full-text
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)
Figures

Figure 1

Open AccessArticle A Vehicle Steering Recognition System Based on Low-Cost Smartphone Sensors
Sensors 2017, 17(3), 633; doi:10.3390/s17030633
Received: 10 January 2017 / Revised: 11 March 2017 / Accepted: 16 March 2017 / Published: 20 March 2017
PDF Full-text (8561 KB) | HTML Full-text | XML Full-text
Abstract
Recognizing how a vehicle is steered and then alerting drivers in real time is of utmost importance to the vehicle and driver’s safety, since fatal accidents are often caused by dangerous vehicle maneuvers, such as rapid turns, fast lane-changes, etc. Existing solutions using
[...] Read more.
Recognizing how a vehicle is steered and then alerting drivers in real time is of utmost importance to the vehicle and driver’s safety, since fatal accidents are often caused by dangerous vehicle maneuvers, such as rapid turns, fast lane-changes, etc. Existing solutions using video or in-vehicle sensors have been employed to identify dangerous vehicle maneuvers, but these methods are subject to the effects of the environmental elements or the hardware is very costly. In the mobile computing era, smartphones have become key tools to develop innovative mobile context-aware systems. In this paper, we present a recognition system for dangerous vehicle steering based on the low-cost sensors found in a smartphone: i.e., the gyroscope and the accelerometer. To identify vehicle steering maneuvers, we focus on the vehicle’s angular velocity, which is characterized by gyroscope data from a smartphone mounted in the vehicle. Three steering maneuvers including turns, lane-changes and U-turns are defined, and a vehicle angular velocity matching algorithm based on Fast Dynamic Time Warping (FastDTW) is adopted to recognize the vehicle steering. The results of extensive experiments show that the average accuracy rate of the presented recognition reaches 95%, which implies that the proposed smartphone-based method is suitable for recognizing dangerous vehicle steering maneuvers. Full article
Figures

Figure 1

Open AccessArticle Fully Printed Flexible Single-Chip RFID Tag with Light Detection Capabilities
Sensors 2017, 17(3), 534; doi:10.3390/s17030534
Received: 23 January 2017 / Revised: 5 March 2017 / Accepted: 6 March 2017 / Published: 8 March 2017
PDF Full-text (2141 KB) | HTML Full-text | XML Full-text
Abstract
A printed passive radiofrequency identification (RFID) tag in the ultra-high frequency band for light and temperature monitoring is presented. The whole tag has been manufactured by printing techniques on a flexible substrate. Antenna and interconnects are realized with silver nanoparticles via inkjet printing.
[...] Read more.
A printed passive radiofrequency identification (RFID) tag in the ultra-high frequency band for light and temperature monitoring is presented. The whole tag has been manufactured by printing techniques on a flexible substrate. Antenna and interconnects are realized with silver nanoparticles via inkjet printing. A sprayed photodetector performs the light monitoring, whereas temperature measurement comes from an in-built sensor in the silicon RFID chip. One of the advantages of this system is the digital read-out and transmission of the sensors information on the RFID tag that ensures reliability. Furthermore, the use of printing techniques allows large-scale manufacturing and the direct fabrication of the tag on the desired surface. This work proves for the first time the feasibility of the embedment of large-scale organic photodetectors onto inkjet printed RFID tags. Here, we solve the problem of integration of different manufacturing techniques to develop an optimal final sensor system. Full article
(This article belongs to the Special Issue Flexible Electronics and Sensors)
Figures

Figure 1

Open AccessArticle Detrimental Effect Elimination of Laser Frequency Instability in Brillouin Optical Time Domain Reflectometer by Using Self-Heterodyne Detection
Sensors 2017, 17(3), 634; doi:10.3390/s17030634
Received: 21 December 2016 / Revised: 27 February 2017 / Accepted: 17 March 2017 / Published: 20 March 2017
PDF Full-text (3057 KB) | HTML Full-text | XML Full-text
Abstract
A useful method for eliminating the detrimental effect of laser frequency instability on Brillouin signals by employing the self-heterodyne detection of Rayleigh and Brillouin scattering is presented. From the analysis of Brillouin scattering spectra from fibers with different lengths measured by heterodyne detection,
[...] Read more.
A useful method for eliminating the detrimental effect of laser frequency instability on Brillouin signals by employing the self-heterodyne detection of Rayleigh and Brillouin scattering is presented. From the analysis of Brillouin scattering spectra from fibers with different lengths measured by heterodyne detection, the maximum usable pulse width immune to laser frequency instability is obtained to be about 4 µs in a self-heterodyne detection Brillouin optical time domain reflectometer (BOTDR) system using a broad-band laser with low frequency stability. Applying the self-heterodyne detection of Rayleigh and Brillouin scattering in BOTDR system, we successfully demonstrate that the detrimental effect of laser frequency instability on Brillouin signals can be eliminated effectively. Employing the broad-band laser modulated by a 130-ns wide pulse driven electro-optic modulator, the observed maximum errors in temperatures measured by the local heterodyne and self-heterodyne detection BOTDR systems are 7.9 °C and 1.2 °C, respectively. Full article
(This article belongs to the Section Physical Sensors)
Figures

Figure 1

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
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
Figures

Figure 1

Open AccessArticle The Shock Pulse Index and Its Application in the Fault Diagnosis of Rolling Element Bearings
Sensors 2017, 17(3), 535; doi:10.3390/s17030535
Received: 18 January 2017 / Revised: 1 March 2017 / Accepted: 3 March 2017 / Published: 8 March 2017
PDF Full-text (20278 KB) | HTML Full-text | XML Full-text
Abstract
The properties of the time domain parameters of vibration signals have been extensively studied for the fault diagnosis of rolling element bearings (REBs). Parameters like kurtosis and Envelope Harmonic-to-Noise Ratio are the most widely applied in this field and some important progress has
[...] Read more.
The properties of the time domain parameters of vibration signals have been extensively studied for the fault diagnosis of rolling element bearings (REBs). Parameters like kurtosis and Envelope Harmonic-to-Noise Ratio are the most widely applied in this field and some important progress has been made. However, since only one-sided information is contained in these parameters, problems still exist in practice when the signals collected are of complicated structure and/or contaminated by strong background noises. A new parameter, named Shock Pulse Index (SPI), is proposed in this paper. It integrates the mutual advantages of both the parameters mentioned above and can help effectively identify fault-related impulse components under conditions of interference of strong background noises, unrelated harmonic components and random impulses. The SPI optimizes the parameters of Maximum Correlated Kurtosis Deconvolution (MCKD), which is used to filter the signals under consideration. Finally, the transient information of interest contained in the filtered signal can be highlighted through demodulation with the Teager Energy Operator (TEO). Fault-related impulse components can therefore be extracted accurately. Simulations show the SPI can correctly indicate the fault impulses under the influence of strong background noises, other harmonic components and aperiodic impulse and experiment analyses verify the effectiveness and correctness of the proposed method. Full article
(This article belongs to the Section Physical Sensors)
Figures

Figure 1

Open AccessArticle Precise Orbit Solution for Swarm Using Space-Borne GPS Data and Optimized Pseudo-Stochastic Pulses
Sensors 2017, 17(3), 635; doi:10.3390/s17030635
Received: 25 January 2017 / Revised: 16 March 2017 / Accepted: 17 March 2017 / Published: 20 March 2017
PDF Full-text (3199 KB) | HTML Full-text | XML Full-text
Abstract
Swarm is a European Space Agency (ESA) project that was launched on 22 November 2013, which consists of three Swarm satellites. Swarm precise orbits are essential to the success of the above project. This study investigates how well Swarm zero-differenced (ZD) reduced-dynamic orbit
[...] Read more.
Swarm is a European Space Agency (ESA) project that was launched on 22 November 2013, which consists of three Swarm satellites. Swarm precise orbits are essential to the success of the above project. This study investigates how well Swarm zero-differenced (ZD) reduced-dynamic orbit solutions can be determined using space-borne GPS data and optimized pseudo-stochastic pulses under high ionospheric activity. We choose Swarm space-borne GPS data from 1–25 October 2014, and Swarm reduced-dynamic orbits are obtained. Orbit quality is assessed by GPS phase observation residuals and compared with Precise Science Orbits (PSOs) released by ESA. Results show that pseudo-stochastic pulses with a time interval of 6 min and a priori standard deviation (STD) of 10−2 mm/s in radial (R), along-track (T) and cross-track (N) directions are optimized to Swarm ZD reduced-dynamic precise orbit determination (POD). During high ionospheric activity, the mean Root Mean Square (RMS) of Swarm GPS phase residuals is at 9–11 mm, Swarm orbit solutions are also compared with Swarm PSOs released by ESA and the accuracy of Swarm orbits can reach 2–4 cm in R, T and N directions. Independent Satellite Laser Ranging (SLR) validation indicates that Swarm reduced-dynamic orbits have an accuracy of 2–4 cm. Swarm-B orbit quality is better than those of Swarm-A and Swarm-C. The Swarm orbits can be applied to the geomagnetic, geoelectric and gravity field recovery. Full article
Figures

Figure 1