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Sensors, Volume 17, Issue 12 (December 2017)

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Open AccessArticle Single-Shot Detection of Neurotransmitters in Whole-Blood Samples by Means of the Heat-Transfer Method in Combination with Synthetic Receptors
Sensors 2017, 17(12), 2701; doi:10.3390/s17122701
Received: 13 October 2017 / Revised: 17 November 2017 / Accepted: 22 November 2017 / Published: 23 November 2017
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Abstract
Serotonin is an important neurotransmitter that plays a major role in the pathogenesis of a variety of conditions, including psychiatric disorders. The detection of serotonin typically relies on high-performance liquid chromatography (HPLC), an expensive technique that requires sophisticated equipment and trained personnel, and
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Serotonin is an important neurotransmitter that plays a major role in the pathogenesis of a variety of conditions, including psychiatric disorders. The detection of serotonin typically relies on high-performance liquid chromatography (HPLC), an expensive technique that requires sophisticated equipment and trained personnel, and is not suitable for point-of-care applications. In this contribution, we introduce a novel sensor platform that can measure spiked neurotransmitter concentrations in whole blood samples in a fast and low-cost manner by combining synthetic receptors with a thermal readout technique—the heat-transfer method. In addition, the design of a miniaturized version of the sensing platform is presented that aims to bridge the gap between measurements in a laboratory setting and point-of-care measurements. This fully automated and integrated, user-friendly design features a capillary pumping unit that is compatible with point-of-care sampling techniques such as a blood lancet device (sample volume—between 50 µL and 300 µL). Sample pre-treatment is limited to the addition of an anti-coagulant. With this fully integrated setup, it is possible to successfully discriminate serotonin from a competitor neurotransmitter (histamine) in whole blood samples. This is the first demonstration of a point-of-care ready device based on synthetic receptors for the screening of neurotransmitters in complex matrices, illustrating the sensor’s potential application in clinical research and diagnosis of e.g., early stage depression. Full article
(This article belongs to the Special Issue Polymer-Based Sensors for Bioanalytes)
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Open AccessArticle Verification of Non-Invasive Blood Glucose Measurement Method Based on Pulse Wave Signal Detected by FBG Sensor System
Sensors 2017, 17(12), 2702; doi:10.3390/s17122702
Received: 22 September 2017 / Revised: 17 November 2017 / Accepted: 22 November 2017 / Published: 23 November 2017
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Abstract
This paper describes and verifies a non-invasive blood glucose measurement method using a fiber Bragg grating (FBG) sensor system. The FBG sensor is installed on the radial artery, and the strain (pulse wave) that is propagated from the heartbeat is measured. The measured
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This paper describes and verifies a non-invasive blood glucose measurement method using a fiber Bragg grating (FBG) sensor system. The FBG sensor is installed on the radial artery, and the strain (pulse wave) that is propagated from the heartbeat is measured. The measured pulse wave signal was used as a collection of feature vectors for multivariate analysis aiming to determine the blood glucose level. The time axis of the pulse wave signal was normalized by two signal processing methods: the shortest-time-cut process and 1-s-normalization process. The measurement accuracy of the calculated blood glucose level was compared with the accuracy of these signal processing methods. It was impossible to calculate a blood glucose level exceeding 200 mg/dL in the calibration curve that was constructed by the shortest-time-cut process. In the 1-s-normalization process, the measurement accuracy of the blood glucose level was improved, and a blood glucose level exceeding 200 mg/dL could be calculated. By verifying the loading vector of each calibration curve to calculate the blood glucose level with a high measurement accuracy, we found the gradient of the peak of the pulse wave at the acceleration plethysmogram greatly affected. Full article
(This article belongs to the Special Issue Fiber Bragg Grating Based Sensors)
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Open AccessArticle Designing and Testing a UAV Mapping System for Agricultural Field Surveying
Sensors 2017, 17(12), 2703; doi:10.3390/s17122703
Received: 30 September 2017 / Revised: 9 November 2017 / Accepted: 13 November 2017 / Published: 23 November 2017
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Abstract
A Light Detection and Ranging (LiDAR) sensor mounted on an Unmanned Aerial Vehicle (UAV) can map the overflown environment in point clouds. Mapped canopy heights allow for the estimation of crop biomass in agriculture. The work presented in this paper contributes to sensory
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A Light Detection and Ranging (LiDAR) sensor mounted on an Unmanned Aerial Vehicle (UAV) can map the overflown environment in point clouds. Mapped canopy heights allow for the estimation of crop biomass in agriculture. The work presented in this paper contributes to sensory UAV setup design for mapping and textual analysis of agricultural fields. LiDAR data are combined with data from Global Navigation Satellite System (GNSS) and Inertial Measurement Unit (IMU) sensors to conduct environment mapping for point clouds. The proposed method facilitates LiDAR recordings in an experimental winter wheat field. Crop height estimates ranging from 0.35–0.58 m are correlated to the applied nitrogen treatments of 0–300 kg N ha . The LiDAR point clouds are recorded, mapped, and analysed using the functionalities of the Robot Operating System (ROS) and the Point Cloud Library (PCL). Crop volume estimation is based on a voxel grid with a spatial resolution of 0.04 × 0.04 × 0.001 m. Two different flight patterns are evaluated at an altitude of 6 m to determine the impacts of the mapped LiDAR measurements on crop volume estimations. Full article
(This article belongs to the Special Issue UAV or Drones for Remote Sensing Applications)
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Open AccessArticle Statistical Analysis of the Random Telegraph Noise in a 1.1 μm Pixel, 8.3 MP CMOS Image Sensor Using On-Chip Time Constant Extraction Method
Sensors 2017, 17(12), 2704; doi:10.3390/s17122704
Received: 18 October 2017 / Revised: 20 November 2017 / Accepted: 21 November 2017 / Published: 23 November 2017
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Abstract
A study of the random telegraph noise (RTN) of a 1.1 μm pitch, 8.3 Mpixel CMOS image sensor (CIS) fabricated in a 45 nm backside-illumination (BSI) technology is presented in this paper. A noise decomposition scheme is used to pinpoint the noise source.
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A study of the random telegraph noise (RTN) of a 1.1 μm pitch, 8.3 Mpixel CMOS image sensor (CIS) fabricated in a 45 nm backside-illumination (BSI) technology is presented in this paper. A noise decomposition scheme is used to pinpoint the noise source. The long tail of the random noise (RN) distribution is directly linked to the RTN from the pixel source follower (SF). The full 8.3 Mpixels are classified into four categories according to the observed RTN histogram peaks. A theoretical formula describing the RTN as a function of the time difference between the two phases of the correlated double sampling (CDS) is derived and validated by measured data. An on-chip time constant extraction method is developed and applied to the RTN analysis. The effects of readout circuit bandwidth on the settling ratios of the RTN histograms are investigated and successfully accounted for in a simulation using a RTN behavior model. Full article
(This article belongs to the Special Issue Special Issue on the 2017 International Image Sensor Workshop (IISW))
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Open AccessArticle Experimental Investigation on the Detection of Multiple Surface Cracks Using Vibrothermography with a Low-Power Piezoceramic Actuator
Sensors 2017, 17(12), 2705; doi:10.3390/s17122705
Received: 17 October 2017 / Revised: 14 November 2017 / Accepted: 20 November 2017 / Published: 23 November 2017
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Abstract
Vibrothermography often employs a high-power actuator to generate heat on a specimen to reveal damage, however, the high-power actuator brings inconvenience to the application and possibly introduces additional damage to the inspected objects. This study uses a low-power piezoceramic transducer as the actuator
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Vibrothermography often employs a high-power actuator to generate heat on a specimen to reveal damage, however, the high-power actuator brings inconvenience to the application and possibly introduces additional damage to the inspected objects. This study uses a low-power piezoceramic transducer as the actuator of vibrothermography and explores its ability to detect multiple surface cracks in a metal part. Experiments were conducted on a thin aluminum beam with three cracks in different orientations. Detailed analyses of both thermograms and temperature data are presented to validate the proposed vibrothermography method. To further investigate the performance of the proposed vibrothermography method, we experimentally studied the effects of several critical factors, including the amplitude of excitation signal, specimen constraints, relative position between the transducer and cracks (the transducer is mounted on the same or the opposite side with the cracks). The results demonstrate that all cracks can be detected conveniently and simultaneously by using the proposed low-power vibrothermography. We also found that the magnitude of excitation signal and the specimen constraints have a great influence on detection results. Combined with effective data processing methods, such as Fourier transformation employed in this study, the proposed method provides a promising potential to detect multiple cracks on a metal surface in a safe and effective manner. Full article
(This article belongs to the Special Issue Materials and Applications for Sensors and Transducers)
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Open AccessArticle FTUC: A Flooding Tree Uneven Clustering Protocol for a Wireless Sensor Network
Sensors 2017, 17(12), 2706; doi:10.3390/s17122706
Received: 30 October 2017 / Revised: 20 November 2017 / Accepted: 21 November 2017 / Published: 23 November 2017
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Abstract
Clustering is an efficient approach in a wireless sensor network (WSN) to reduce the energy consumption of nodes and to extend the lifetime of the network. Unfortunately, this approach requires that all cluster heads (CHs) transmit their data to the base station (BS),
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Clustering is an efficient approach in a wireless sensor network (WSN) to reduce the energy consumption of nodes and to extend the lifetime of the network. Unfortunately, this approach requires that all cluster heads (CHs) transmit their data to the base station (BS), which gives rise to the long distance communications problem, and in multi-hop routing, the CHs near the BS have to forward data from other nodes that lead those CHs to die prematurely, creating the hot zones problem. Unequal clustering has been proposed to solve these problems. Most of the current algorithms elect CH only by considering their competition radius, leading to unevenly distributed cluster heads. Furthermore, global distances values are needed when calculating the competition radius, which is a tedious task in large networks. To face these problems, we propose a flooding tree uneven clustering protocol (FTUC) suited for large networks. Based on the construction of a tree type sub-network to calculate the minimum and maximum distances values of the network, we then apply the unequal cluster theory. We also introduce referenced position circles to evenly elect cluster heads. Therefore, cluster heads are elected depending on the node’s residual energy and their distance to a referenced circle. FTUC builds the best inter-cluster communications route by evaluating a cluster head cost function to find the best next hop to the BS. The simulation results show that the FTUC algorithm decreases the energy consumption of the nodes and balances the global energy consumption effectively, thus extending the lifetime of the network. Full article
(This article belongs to the Section Sensor Networks)
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Open AccessArticle Wearable Stretch Sensors for Motion Measurement of the Wrist Joint Based on Dielectric Elastomers
Sensors 2017, 17(12), 2708; doi:10.3390/s17122708
Received: 15 September 2017 / Revised: 19 November 2017 / Accepted: 21 November 2017 / Published: 23 November 2017
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Abstract
Motion capture of the human body potentially holds great significance for exoskeleton robots, human-computer interaction, sports analysis, rehabilitation research, and many other areas. Dielectric elastomer sensors (DESs) are excellent candidates for wearable human motion capture systems because of their intrinsic characteristics of softness,
[...] Read more.
Motion capture of the human body potentially holds great significance for exoskeleton robots, human-computer interaction, sports analysis, rehabilitation research, and many other areas. Dielectric elastomer sensors (DESs) are excellent candidates for wearable human motion capture systems because of their intrinsic characteristics of softness, light weight, and compliance. In this paper, DESs were applied to measure all component motions of the wrist joints. Five sensors were mounted to different positions on the wrist, and each one is for one component motion. To find the best position to mount the sensors, the distribution of the muscles is analyzed. Even so, the component motions and the deformation of the sensors are coupled; therefore, a decoupling method was developed. By the decoupling algorithm, all component motions can be measured with a precision of 5°, which meets the requirements of general motion capture systems. Full article
(This article belongs to the Special Issue Wearable and Ambient Sensors for Healthcare and Wellness Applications)
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Open AccessArticle A Polar Initial Alignment Algorithm for Unmanned Underwater Vehicles
Sensors 2017, 17(12), 2709; doi:10.3390/s17122709
Received: 10 October 2017 / Revised: 20 November 2017 / Accepted: 20 November 2017 / Published: 23 November 2017
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Abstract
Due to its highly autonomy, the strapdown inertial navigation system (SINS) is widely used in unmanned underwater vehicles (UUV) navigation. Initial alignment is crucial because the initial alignment results will be used as the initial SINS value, which might affect the subsequent SINS
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Due to its highly autonomy, the strapdown inertial navigation system (SINS) is widely used in unmanned underwater vehicles (UUV) navigation. Initial alignment is crucial because the initial alignment results will be used as the initial SINS value, which might affect the subsequent SINS results. Due to the rapid convergence of Earth meridians, there is a calculation overflow in conventional initial alignment algorithms, making conventional initial algorithms are invalid for polar UUV navigation. To overcome these problems, a polar initial alignment algorithm for UUV is proposed in this paper, which consists of coarse and fine alignment algorithms. Based on the principle of the conical slow drift of gravity, the coarse alignment algorithm is derived under the grid frame. By choosing the velocity and attitude as the measurement, the fine alignment with the Kalman filter (KF) is derived under the grid frame. Simulation and experiment are realized among polar, conventional and transversal initial alignment algorithms for polar UUV navigation. Results demonstrate that the proposed polar initial alignment algorithm can complete the initial alignment of UUV in the polar region rapidly and accurately. Full article
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Open AccessArticle Turning and Radius Deviation Correction for a Hexapod Walking Robot Based on an Ant-Inspired Sensory Strategy
Sensors 2017, 17(12), 2710; doi:10.3390/s17122710
Received: 10 October 2017 / Revised: 11 November 2017 / Accepted: 20 November 2017 / Published: 23 November 2017
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Abstract
In order to find a common approach to plan the turning of a bio-inspired hexapod robot, a locomotion strategy for turning and deviation correction of a hexapod walking robot based on the biological behavior and sensory strategy of ants. A series of experiments
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In order to find a common approach to plan the turning of a bio-inspired hexapod robot, a locomotion strategy for turning and deviation correction of a hexapod walking robot based on the biological behavior and sensory strategy of ants. A series of experiments using ants were carried out where the gait and the movement form of ants was studied. Taking the results of the ant experiments as inspiration by imitating the behavior of ants during turning, an extended turning algorithm based on arbitrary gait was proposed. Furthermore, after the observation of the radius adjustment of ants during turning, a radius correction algorithm based on the arbitrary gait of the hexapod robot was raised. The radius correction surface function was generated by fitting the correction data, which made it possible for the robot to move in an outdoor environment without the positioning system and environment model. The proposed algorithm was verified on the hexapod robot experimental platform. The turning and radius correction experiment of the robot with several gaits were carried out. The results indicated that the robot could follow the ideal radius and maintain stability, and the proposed ant-inspired turning strategy could easily make free turns with an arbitrary gait. Full article
(This article belongs to the Special Issue Smart Sensors for Mechatronic and Robotic Systems)
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Open AccessArticle Off-Nadir Hyperspectral Sensing for Estimation of Vertical Profile of Leaf Chlorophyll Content within Wheat Canopies
Sensors 2017, 17(12), 2711; doi:10.3390/s17122711
Received: 14 September 2017 / Revised: 22 November 2017 / Accepted: 22 November 2017 / Published: 23 November 2017
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Abstract
Monitoring the vertical profile of leaf chlorophyll (Chl) content within winter wheat canopies is of significant importance for revealing the real nutritional status of the crop. Information on the vertical profile of Chl content is not accessible to nadir-viewing remote or proximal sensing.
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Monitoring the vertical profile of leaf chlorophyll (Chl) content within winter wheat canopies is of significant importance for revealing the real nutritional status of the crop. Information on the vertical profile of Chl content is not accessible to nadir-viewing remote or proximal sensing. Off-nadir or multi-angle sensing would provide effective means to detect leaf Chl content in different vertical layers. However, adequate information on the selection of sensitive spectral bands and spectral index formulas for vertical leaf Chl content estimation is not yet available. In this study, all possible two-band and three-band combinations over spectral bands in normalized difference vegetation index (NDVI)-, simple ratio (SR)- and chlorophyll index (CI)-like types of indices at different viewing angles were calculated and assessed for their capability of estimating leaf Chl for three vertical layers of wheat canopies. The vertical profiles of Chl showed top-down declining trends and the patterns of band combinations sensitive to leaf Chl content varied among different vertical layers. Results indicated that the combinations of green band (520 nm) with NIR bands were efficient in estimating upper leaf Chl content, whereas the red edge (695 nm) paired with NIR bands were dominant in quantifying leaf Chl in the lower layers. Correlations between published spectral indices and all NDVI-, SR- and CI-like types of indices and vertical distribution of Chl content showed that reflectance measured from 50°, 30° and 20° backscattering viewing angles were the most promising to obtain information on leaf Chl in the upper-, middle-, and bottom-layer, respectively. Three types of optimized spectral indices improved the accuracy for vertical leaf Chl content estimation. The optimized three-band CI-like index performed the best in the estimation of vertical distribution of leaf Chl content, with R2 of 0.84–0.69, and RMSE of 5.37–5.56 µg/cm2 from the top to the bottom layers, while the optimized SR-like index was recommended for the bottom Chl estimation due to its simple and universal form. We suggest that it is necessary to take into account the penetration characteristic of the light inside the canopy for different Chl absorption regions of the spectrum and the formula used to derive spectral index when estimating the vertical profile of leaf Chl content using off-nadir hyperspectral data. Full article
(This article belongs to the Section Remote Sensors)
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Open AccessArticle Early Detection of the Initiation of Sit-to-Stand Posture Transitions Using Orthosis-Mounted Sensors
Sensors 2017, 17(12), 2712; doi:10.3390/s17122712
Received: 24 August 2017 / Revised: 8 November 2017 / Accepted: 22 November 2017 / Published: 23 November 2017
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Abstract
Assistance during sit-to-stand (SiSt) transitions for frail elderly may be provided by powered orthotic devices. The control of the powered orthosis may be performed by the means of electromyography (EMG), which requires direct contact of measurement electrodes to the skin. The purpose of
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Assistance during sit-to-stand (SiSt) transitions for frail elderly may be provided by powered orthotic devices. The control of the powered orthosis may be performed by the means of electromyography (EMG), which requires direct contact of measurement electrodes to the skin. The purpose of this study was to determine if a non-EMG-based method that uses inertial sensors placed at different positions on the orthosis, and a lightweight pattern recognition algorithm may accurately identify SiSt transitions without false positives. A novel method is proposed to eliminate false positives based on a two-stage design: stage one detects the sitting posture; stage two recognizes the initiation of a SiSt transition from a sitting position. The method was validated using data from 10 participants who performed 34 different activities and posture transitions. Features were obtained from the sensor signals and then combined into lagged epochs. A reduced number of features was selected using a minimum-redundancy-maximum-relevance (mRMR) algorithm and forward feature selection. To obtain a recognition model with low computational complexity, we compared the use of an extreme learning machine (ELM) and multilayer perceptron (MLP) for both stages of the recognition algorithm. Both classifiers were able to accurately identify all posture transitions with no false positives. The average detection time was 0.19 ± 0.33 s for ELM and 0.13 ± 0.32 s for MLP. The MLP classifier exhibited less time complexity in the recognition phase compared to ELM. However, the ELM classifier presented lower computational demands in the training phase. Results demonstrated that the proposed algorithm could potentially be adopted to control a powered orthosis. Full article
(This article belongs to the Special Issue Wearable and Ambient Sensors for Healthcare and Wellness Applications)
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Open AccessArticle A Microwave Microfluidic Sensor Based on a Dual-Mode Resonator for Dual-Sensing Applications
Sensors 2017, 17(12), 2713; doi:10.3390/s17122713
Received: 19 September 2017 / Revised: 16 November 2017 / Accepted: 21 November 2017 / Published: 24 November 2017
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Abstract
In this paper, we propose a novel microwave microfluidic sensor with dual-sensing capability. The sensor is based on a dual-mode resonator that consists of a folded microstrip line loaded with interdigital lines and a stub at the plane of symmetry. Due to the
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In this paper, we propose a novel microwave microfluidic sensor with dual-sensing capability. The sensor is based on a dual-mode resonator that consists of a folded microstrip line loaded with interdigital lines and a stub at the plane of symmetry. Due to the specific configuration, the resonator exhibits two entirely independent resonant modes, which allows simultaneous sensing of two fluids using a resonance shift method. The sensor is designed in a multilayer configuration with the proposed resonator and two separated microfluidic channels—one intertwined with the interdigital lines and the other positioned below the stub. The circuit has been fabricated using low-temperature co-fired ceramics technology and its performance was verified through the measurement of its responses for different fluids in the microfluidic channels. The results confirm the dual-sensing capability with zero mutual influence as well as good overall performance. Besides an excellent potential for dual-sensing applications, the proposed sensor is a good candidate for application in mixing fluids and cell counting. Full article
(This article belongs to the Special Issue Microfluidic Sensors)
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Open AccessArticle Monitoring the Wobbe Index of Natural Gas Using Fiber-Enhanced Raman Spectroscopy
Sensors 2017, 17(12), 2714; doi:10.3390/s17122714
Received: 26 October 2017 / Revised: 13 November 2017 / Accepted: 23 November 2017 / Published: 24 November 2017
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Abstract
The fast and reliable analysis of the natural gas composition requires the simultaneous quantification of numerous gaseous components. To this end, fiber-enhanced Raman spectroscopy is a powerful tool to detect most components in a single measurement using a single laser source. However, practical
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The fast and reliable analysis of the natural gas composition requires the simultaneous quantification of numerous gaseous components. To this end, fiber-enhanced Raman spectroscopy is a powerful tool to detect most components in a single measurement using a single laser source. However, practical issues such as detection limit, gas exchange time and background Raman signals from the fiber material still pose obstacles to utilizing the scheme in real-world settings. This paper compares the performance of two types of hollow-core photonic crystal fiber (PCF), namely photonic bandgap PCF and kagomé-style PCF, and assesses their potential for online determination of the Wobbe index. In contrast to bandgap PCF, kagomé-PCF allows for reliable detection of Raman-scattered photons even below 1200 cm−1, which in turn enables fast and comprehensive assessment of the natural gas quality of arbitrary mixtures. Full article
(This article belongs to the Special Issue Spectroscopy Based Sensors)
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Open AccessArticle Portable Electronic Nose Based on Electrochemical Sensors for Food Quality Assessment
Sensors 2017, 17(12), 2715; doi:10.3390/s17122715
Received: 18 September 2017 / Revised: 20 October 2017 / Accepted: 22 November 2017 / Published: 24 November 2017
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Abstract
The steady increase in global consumption puts a strain on agriculture and might lead to a decrease in food quality. Currently used techniques of food analysis are often labour-intensive and time-consuming and require extensive sample preparation. For that reason, there is a demand
[...] Read more.
The steady increase in global consumption puts a strain on agriculture and might lead to a decrease in food quality. Currently used techniques of food analysis are often labour-intensive and time-consuming and require extensive sample preparation. For that reason, there is a demand for novel methods that could be used for rapid food quality assessment. A technique based on the use of an array of chemical sensors for holistic analysis of the sample’s headspace is called electronic olfaction. In this article, a prototype of a portable, modular electronic nose intended for food analysis is described. Using the SVM method, it was possible to classify samples of poultry meat based on shelf-life with 100% accuracy, and also samples of rapeseed oil based on the degree of thermal degradation with 100% accuracy. The prototype was also used to detect adulterations of extra virgin olive oil with rapeseed oil with 82% overall accuracy. Due to the modular design, the prototype offers the advantages of solutions targeted for analysis of specific food products, at the same time retaining the flexibility of application. Furthermore, its portability allows the device to be used at different stages of the production and distribution process. Full article
(This article belongs to the Special Issue Sensors in Agriculture)
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Open AccessArticle A Functionalized Tetrakis(4-Nitrophenyl)Porphyrin Film Optical Waveguide Sensor for Detection of H2S and Ethanediamine Gases
Sensors 2017, 17(12), 2717; doi:10.3390/s17122717
Received: 8 October 2017 / Revised: 15 November 2017 / Accepted: 22 November 2017 / Published: 24 November 2017
PDF Full-text (3886 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
The detection of hydrogen sulfide (H2S) and ethanediamine, toxic gases that are emitted from industrial processes, is important for health and safety. An optical sensor, based on the absorption spectrum of tetrakis(4-nitrophenyl)porphyrin (TNPP) immobilized in a Nafion membrane (Nf) and deposited
[...] Read more.
The detection of hydrogen sulfide (H2S) and ethanediamine, toxic gases that are emitted from industrial processes, is important for health and safety. An optical sensor, based on the absorption spectrum of tetrakis(4-nitrophenyl)porphyrin (TNPP) immobilized in a Nafion membrane (Nf) and deposited onto an optical waveguide glass slide, has been developed for the detection of these gases. Responses to analytes were compared for sensors modified with TNPP and Nf-TNPP composites. Among them, Nf-TNPP exhibited significant responses to H2S and ethanediamine. The analytical performance characteristics of the Nf-TNPP-modified sensor were investigated and the response mechanism is discussed in detail. The sensor exhibited excellent reproducibilities, reversibilities, and selectivities, with detection limits for H2S and ethanediamine of 1 and 10 ppb, respectively, and it is a promising candidate for use in industrial sensing applications. Full article
(This article belongs to the Special Issue Nanostructured Hybrid Materials Based Opto-Electronics Sensors)
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Open AccessArticle Direction-Of-Arrival Estimation and Tracking Based on a Sequential Implementation of C-SPICE with an Off-Grid Model
Sensors 2017, 17(12), 2718; doi:10.3390/s17122718
Received: 25 September 2017 / Revised: 10 November 2017 / Accepted: 22 November 2017 / Published: 24 November 2017
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Abstract
This paper focuses on the problem of estimating and tracking time-varying direction-of-arrivals (DoAs) with an antenna array. A sequential DoA estimation method is proposed by extending the capon and sparse iterative covariance-based estimation (C-SPICE) method, which is an iterative off-grid method for estimating
[...] Read more.
This paper focuses on the problem of estimating and tracking time-varying direction-of-arrivals (DoAs) with an antenna array. A sequential DoA estimation method is proposed by extending the capon and sparse iterative covariance-based estimation (C-SPICE) method, which is an iterative off-grid method for estimating constant DoAs. Then, a moving average initialization technique is introduced such that the spatial spectrum information estimated in this snapshot can be utilized in the next one. In uniform linear arrays (ULAs), we replace the uniform grid in direction domain with that in a “frequency” domain, to improve estimation accuracy without additional complexity in practical applications. The validity and efficiency of the proposed methods are demonstrated through numerical experiments. Full article
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Open AccessArticle Robust Vehicle Detection in Aerial Images Based on Cascaded Convolutional Neural Networks
Sensors 2017, 17(12), 2720; doi:10.3390/s17122720
Received: 13 October 2017 / Revised: 21 November 2017 / Accepted: 22 November 2017 / Published: 24 November 2017
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Abstract
Vehicle detection in aerial images is an important and challenging task. Traditionally, many target detection models based on sliding-window fashion were developed and achieved acceptable performance, but these models are time-consuming in the detection phase. Recently, with the great success of convolutional neural
[...] Read more.
Vehicle detection in aerial images is an important and challenging task. Traditionally, many target detection models based on sliding-window fashion were developed and achieved acceptable performance, but these models are time-consuming in the detection phase. Recently, with the great success of convolutional neural networks (CNNs) in computer vision, many state-of-the-art detectors have been designed based on deep CNNs. However, these CNN-based detectors are inefficient when applied in aerial image data due to the fact that the existing CNN-based models struggle with small-size object detection and precise localization. To improve the detection accuracy without decreasing speed, we propose a CNN-based detection model combining two independent convolutional neural networks, where the first network is applied to generate a set of vehicle-like regions from multi-feature maps of different hierarchies and scales. Because the multi-feature maps combine the advantage of the deep and shallow convolutional layer, the first network performs well on locating the small targets in aerial image data. Then, the generated candidate regions are fed into the second network for feature extraction and decision making. Comprehensive experiments are conducted on the Vehicle Detection in Aerial Imagery (VEDAI) dataset and Munich vehicle dataset. The proposed cascaded detection model yields high performance, not only in detection accuracy but also in detection speed. Full article
(This article belongs to the Special Issue UAV or Drones for Remote Sensing Applications)
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Open AccessArticle A Method for Measurement of Nonlinearity of Laser Interferometer Based on Optical Frequency Tuning
Sensors 2017, 17(12), 2721; doi:10.3390/s17122721
Received: 11 October 2017 / Revised: 15 November 2017 / Accepted: 21 November 2017 / Published: 24 November 2017
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Abstract
A method for measuring the nonlinearity of laser interferometer using optical frequency tuning technique is presented in this paper. The basic principle of this method is to make the fractional part of an interference fringe change by tuning the laser frequency and determining
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A method for measuring the nonlinearity of laser interferometer using optical frequency tuning technique is presented in this paper. The basic principle of this method is to make the fractional part of an interference fringe change by tuning the laser frequency and determining the nonlinearity of interferometer by comparing the fractional fringe change measured by the interferometer to that calculated from the laser frequency change. An experimental interferometric system with a wavelength tunable laser source is set up and the nonlinearity of the interferometer is measured. Since it does not require the precise displacement mechanism to produce the optical path difference change, this method is more convenient to use and may achieve a higher accuracy than the conventional measurement methods. The nonlinearity of the arbitrary interferometric phase can be measured by changing the laser frequency with this method. Experiments results have shown that the repeatability of nonlinearity measurement is less than 0.2 nm. This method can be applied to interferometry-based high precision dimensional measurements, such as coordinate measurement and displacement sensor calibration. Full article
(This article belongs to the Section Physical Sensors)
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Open AccessArticle Optimization of a Focusable and Rotatable Shear-Wave Periodic Permanent Magnet Electromagnetic Acoustic Transducers for Plates Inspection
Sensors 2017, 17(12), 2722; doi:10.3390/s17122722
Received: 4 October 2017 / Revised: 5 November 2017 / Accepted: 9 November 2017 / Published: 24 November 2017
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Abstract
Due to the symmetry of conventional periodic-permanent-magnet electromagnetic acoustic transducers (PPM EMATs), two shear (SH) waves can be generated and propagated simultaneously in opposite directions, which makes the signal recognition and interpretation complicatedly. Thus, this work presents a new SH wave PPM EMAT
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Due to the symmetry of conventional periodic-permanent-magnet electromagnetic acoustic transducers (PPM EMATs), two shear (SH) waves can be generated and propagated simultaneously in opposite directions, which makes the signal recognition and interpretation complicatedly. Thus, this work presents a new SH wave PPM EMAT design, rotating the parallel line sources to realize the wave beam focusing in a single-direction. The theoretical model of distributed line sources was deduced firstly, and the effects of some parameters, such as the inner coil width, adjacent line sources spacing and the angle between parallel line sources, on SH wave focusing and directivity were studied mainly with the help of 3D FEM. Employing the proposed PPM EMATs, some experiments are carried out to verify the reliability of FEM simulation. The results indicate that rotating the parallel line sources can strength the wave on the closing side of line sources, decreasing the inner coil width and the adjacent line sources spacing can improve the amplitude and directivity of signals excited by transducers. Compared with traditional PPM EMATs, both the capacity of unidirectional excitation and directivity of the proposed PPM EMATs are improved significantly. Full article
(This article belongs to the Section Physical Sensors)
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Open AccessArticle LSPR Coupling and Distribution of Interparticle Distances between Nanoparticles in Hydrogel on Optical Fiber End Face
Sensors 2017, 17(12), 2723; doi:10.3390/s17122723
Received: 30 September 2017 / Revised: 16 November 2017 / Accepted: 20 November 2017 / Published: 25 November 2017
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Abstract
We report on a new localized surface plasmon resonance (LSPR)-based optical fiber (OF) architecture with a potential in sensor applications. The LSPR-OF system is fabricated by immobilizing gold nanoparticles (GNPs) in a hydrogel droplet polymerized on the fiber end face. This design has
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We report on a new localized surface plasmon resonance (LSPR)-based optical fiber (OF) architecture with a potential in sensor applications. The LSPR-OF system is fabricated by immobilizing gold nanoparticles (GNPs) in a hydrogel droplet polymerized on the fiber end face. This design has several advantages over earlier designs. It dramatically increase the number nanoparticles (NP) available for sensing, it offers precise control over the NP density, and the NPs are positioned in a true 3D aqueous environment. The OF-hydrogel design is also compatible with low-cost manufacturing. The LSPR-OF platform can measure volumetric changes in a stimuli-responsive hydrogel or measure binding to receptors on the NP surface. It can also be used as a two-parameter sensor by utilizing both effects. We present results from proof-of-concept experiments exploring the properties of LSPR and interparticle distances of the GNP-hydrogel OF design by characterizing the distribution of distances between NPs in the hydrogel, the refractive index of the hydrogel and the LSPR attributes of peak position, amplitude and linewidth for hydrogel deswelling controlled with pH solutions. Full article
(This article belongs to the Special Issue Surface Plasmon Resonance Sensing)
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Open AccessArticle GNSS/Electronic Compass/Road Segment Information Fusion for Vehicle-to-Vehicle Collision Avoidance Application
Sensors 2017, 17(12), 2724; doi:10.3390/s17122724
Received: 24 September 2017 / Revised: 13 November 2017 / Accepted: 15 November 2017 / Published: 25 November 2017
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Abstract
The increasing number of vehicles in modern cities brings the problem of increasing crashes. One of the applications or services of Intelligent Transportation Systems (ITS) conceived to improve safety and reduce congestion is collision avoidance. This safety critical application requires sub-meter level vehicle
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The increasing number of vehicles in modern cities brings the problem of increasing crashes. One of the applications or services of Intelligent Transportation Systems (ITS) conceived to improve safety and reduce congestion is collision avoidance. This safety critical application requires sub-meter level vehicle state estimation accuracy with very high integrity, continuity and availability, to detect an impending collision and issue a warning or intervene in the case that the warning is not heeded. Because of the challenging city environment, to date there is no approved method capable of delivering this high level of performance in vehicle state estimation. In particular, the current Global Navigation Satellite System (GNSS) based collision avoidance systems have the major limitation that the real-time accuracy of dynamic state estimation deteriorates during abrupt acceleration and deceleration situations, compromising the integrity of collision avoidance. Therefore, to provide the Required Navigation Performance (RNP) for collision avoidance, this paper proposes a novel Particle Filter (PF) based model for the integration or fusion of real-time kinematic (RTK) GNSS position solutions with electronic compass and road segment data used in conjunction with an Autoregressive (AR) motion model. The real-time vehicle state estimates are used together with distance based collision avoidance algorithms to predict potential collisions. The algorithms are tested by simulation and in the field representing a low density urban environment. The results show that the proposed algorithm meets the horizontal positioning accuracy requirement for collision avoidance and is superior to positioning accuracy of GNSS only, traditional Constant Velocity (CV) and Constant Acceleration (CA) based motion models, with a significant improvement in the prediction accuracy of potential collision. Full article
(This article belongs to the Special Issue Sensors for Transportation)
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Open AccessArticle Adaptive Spatial Filter Based on Similarity Indices to Preserve the Neural Information on EEG Signals during On-Line Processing
Sensors 2017, 17(12), 2725; doi:10.3390/s17122725
Received: 27 September 2017 / Revised: 13 November 2017 / Accepted: 19 November 2017 / Published: 25 November 2017
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Abstract
This work presents a new on-line adaptive filter, which is based on a similarity analysis between standard electrode locations, in order to reduce artifacts and common interferences throughout electroencephalography (EEG) signals, but preserving the useful information. Standard deviation and Concordance Correlation Coefficient (CCC)
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This work presents a new on-line adaptive filter, which is based on a similarity analysis between standard electrode locations, in order to reduce artifacts and common interferences throughout electroencephalography (EEG) signals, but preserving the useful information. Standard deviation and Concordance Correlation Coefficient (CCC) between target electrodes and its correspondent neighbor electrodes are analyzed on sliding windows to select those neighbors that are highly correlated. Afterwards, a model based on CCC is applied to provide higher values of weight to those correlated electrodes with lower similarity to the target electrode. The approach was applied to brain computer-interfaces (BCIs) based on Canonical Correlation Analysis (CCA) to recognize 40 targets of steady-state visual evoked potential (SSVEP), providing an accuracy (ACC) of 86.44 ± 2.81%. In addition, also using this approach, features of low frequency were selected in the pre-processing stage of another BCI to recognize gait planning. In this case, the recognition was significantly ( p < 0.01 ) improved for most of the subjects ( A C C 74.79 % ) , when compared with other BCIs based on Common Spatial Pattern, Filter Bank-Common Spatial Pattern, and Riemannian Geometry. Full article
(This article belongs to the Special Issue Biomedical Sensors and Systems 2017)
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Open AccessArticle Dimension Reduction Aided Hyperspectral Image Classification with a Small-sized Training Dataset: Experimental Comparisons
Sensors 2017, 17(12), 2726; doi:10.3390/s17122726
Received: 17 October 2017 / Revised: 14 November 2017 / Accepted: 20 November 2017 / Published: 25 November 2017
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Abstract
Hyperspectral images (HSI) provide rich information which may not be captured by other sensing technologies and therefore gradually find a wide range of applications. However, they also generate a large amount of irrelevant or redundant data for a specific task. This causes a
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Hyperspectral images (HSI) provide rich information which may not be captured by other sensing technologies and therefore gradually find a wide range of applications. However, they also generate a large amount of irrelevant or redundant data for a specific task. This causes a number of issues including significantly increased computation time, complexity and scale of prediction models mapping the data to semantics (e.g., classification), and the need of a large amount of labelled data for training. Particularly, it is generally difficult and expensive for experts to acquire sufficient training samples in many applications. This paper addresses these issues by exploring a number of classical dimension reduction algorithms in machine learning communities for HSI classification. To reduce the size of training dataset, feature selection (e.g., mutual information, minimal redundancy maximal relevance) and feature extraction (e.g., Principal Component Analysis (PCA), Kernel PCA) are adopted to augment a baseline classification method, Support Vector Machine (SVM). The proposed algorithms are evaluated using a real HSI dataset. It is shown that PCA yields the most promising performance in reducing the number of features or spectral bands. It is observed that while significantly reducing the computational complexity, the proposed method can achieve better classification results over the classic SVM on a small training dataset, which makes it suitable for real-time applications or when only limited training data are available. Furthermore, it can also achieve performances similar to the classic SVM on large datasets but with much less computing time. Full article
(This article belongs to the Special Issue Analysis of Multispectral and Hyperspectral Data)
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Open AccessArticle Economic Feasibility of Wireless Sensor Network-Based Service Provision in a Duopoly Setting with a Monopolist Operator
Sensors 2017, 17(12), 2727; doi:10.3390/s17122727
Received: 20 October 2017 / Revised: 17 November 2017 / Accepted: 22 November 2017 / Published: 25 November 2017
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Abstract
We analyze the feasibility of providing Wireless Sensor Network-data-based services in an Internet of Things scenario from an economical point of view. The scenario has two competing service providers with their own private sensor networks, a network operator and final users. The scenario
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We analyze the feasibility of providing Wireless Sensor Network-data-based services in an Internet of Things scenario from an economical point of view. The scenario has two competing service providers with their own private sensor networks, a network operator and final users. The scenario is analyzed as two games using game theory. In the first game, sensors decide to subscribe or not to the network operator to upload the collected sensing-data, based on a utility function related to the mean service time and the price charged by the operator. In the second game, users decide to subscribe or not to the sensor-data-based service of the service providers based on a Logit discrete choice model related to the quality of the data collected and the subscription price. The sinks and users subscription stages are analyzed using population games and discrete choice models, while network operator and service providers pricing stages are analyzed using optimization and Nash equilibrium concepts respectively. The model is shown feasible from an economic point of view for all the actors if there are enough interested final users and opens the possibility of developing more efficient models with different types of services. Full article
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Open AccessArticle New Possibilities of Substance Identification Based on THz Time Domain Spectroscopy Using a Cascade Mechanism of High Energy Level Excitation
Sensors 2017, 17(12), 2728; doi:10.3390/s17122728
Received: 9 October 2017 / Revised: 17 November 2017 / Accepted: 21 November 2017 / Published: 25 November 2017
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Abstract
Using an experiment with thin paper layers and computer simulation, we demonstrate the principal limitations of standard Time Domain Spectroscopy (TDS) based on using a broadband THz pulse for the detection and identification of a substance placed inside a disordered structure. We demonstrate
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Using an experiment with thin paper layers and computer simulation, we demonstrate the principal limitations of standard Time Domain Spectroscopy (TDS) based on using a broadband THz pulse for the detection and identification of a substance placed inside a disordered structure. We demonstrate the spectrum broadening of both transmitted and reflected pulses due to the cascade mechanism of the high energy level excitation considering, for example, a three-energy level medium. The pulse spectrum in the range of high frequencies remains undisturbed in the presence of a disordered structure. To avoid false absorption frequencies detection, we apply the spectral dynamics analysis method (SDA-method) together with certain integral correlation criteria (ICC). Full article
(This article belongs to the Section Physical Sensors)
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Open AccessArticle Autonomous Shepherding Behaviors of Multiple Target Steering Robots
Sensors 2017, 17(12), 2729; doi:10.3390/s17122729
Received: 14 August 2017 / Revised: 20 November 2017 / Accepted: 21 November 2017 / Published: 25 November 2017
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Abstract
This paper presents a distributed coordination methodology for multi-robot systems, based on nearest-neighbor interactions. Among many interesting tasks that may be performed using swarm robots, we propose a biologically-inspired control law for a shepherding task, whereby a group of external agents drives another
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This paper presents a distributed coordination methodology for multi-robot systems, based on nearest-neighbor interactions. Among many interesting tasks that may be performed using swarm robots, we propose a biologically-inspired control law for a shepherding task, whereby a group of external agents drives another group of agents to a desired location. First, we generated sheep-like robots that act like a flock. We assume that each agent is capable of measuring the relative location and velocity to each of its neighbors within a limited sensing area. Then, we designed a control strategy for shepherd-like robots that have information regarding where to go and a steering ability to control the flock, according to the robots’ position relative to the flock. We define several independent behavior rules; each agent calculates to what extent it will move by summarizing each rule. The flocking sheep agents detect the steering agents and try to avoid them; this tendency leads to movement of the flock. Each steering agent only needs to focus on guiding the nearest flocking agent to the desired location. Without centralized coordination, multiple steering agents produce an arc formation to control the flock effectively. In addition, we propose a new rule for collecting behavior, whereby a scattered flock or multiple flocks are consolidated. From simulation results with multiple robots, we show that each robot performs actions for the shepherding behavior, and only a few steering agents are needed to control the whole flock. The results are displayed in maps that trace the paths of the flock and steering robots. Performance is evaluated via time cost and path accuracy to demonstrate the effectiveness of this approach. Full article
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Open AccessArticle A 2.5D Map-Based Mobile Robot Localization via Cooperation of Aerial and Ground Robots
Sensors 2017, 17(12), 2730; doi:10.3390/s17122730
Received: 23 October 2017 / Revised: 22 November 2017 / Accepted: 22 November 2017 / Published: 25 November 2017
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Abstract
Recently, there has been increasing interest in studying the task coordination of aerial and ground robots. When a robot begins navigation in an unknown area, it has no information about the surrounding environment. Accordingly, for robots to perform tasks based on location information,
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Recently, there has been increasing interest in studying the task coordination of aerial and ground robots. When a robot begins navigation in an unknown area, it has no information about the surrounding environment. Accordingly, for robots to perform tasks based on location information, they need a simultaneous localization and mapping (SLAM) process that uses sensor information to draw a map of the environment, while simultaneously estimating the current location of the robot on the map. This paper aims to present a localization method based in cooperation between aerial and ground robots in an indoor environment. The proposed method allows a ground robot to reach accurate destination by using a 2.5D elevation map built by a low-cost RGB-D (Red Green and Blue-Depth) sensor and 2D Laser sensor attached onto an aerial robot. A 2.5D elevation map is formed by projecting height information of an obstacle using depth information obtained by the RGB-D sensor onto a grid map, which is generated by using the 2D Laser sensor and scan matching. Experimental results demonstrate the effectiveness of the proposed method for its accuracy in location recognition and computing speed. Full article
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Open AccessArticle Optimal Power Allocation Strategy in a Joint Bistatic Radar and Communication System Based on Low Probability of Intercept
Sensors 2017, 17(12), 2731; doi:10.3390/s17122731
Received: 18 October 2017 / Revised: 20 November 2017 / Accepted: 22 November 2017 / Published: 25 November 2017
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Abstract
In this paper, we investigate a low probability of intercept (LPI)-based optimal power allocation strategy for a joint bistatic radar and communication system, which is composed of a dedicated transmitter, a radar receiver, and a communication receiver. The joint system is capable of
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In this paper, we investigate a low probability of intercept (LPI)-based optimal power allocation strategy for a joint bistatic radar and communication system, which is composed of a dedicated transmitter, a radar receiver, and a communication receiver. The joint system is capable of fulfilling the requirements of both radar and communications simultaneously. First, assuming that the signal-to-noise ratio (SNR) corresponding to the target surveillance path is much weaker than that corresponding to the line of sight path at radar receiver, the analytically closed-form expression for the probability of false alarm is calculated, whereas the closed-form expression for the probability of detection is not analytically tractable and is approximated due to the fact that the received signals are not zero-mean Gaussian under target presence hypothesis. Then, an LPI-based optimal power allocation strategy is presented to minimize the total transmission power for information signal and radar waveform, which is constrained by a specified information rate for the communication receiver and the desired probabilities of detection and false alarm for the radar receiver. The well-known bisection search method is employed to solve the resulting constrained optimization problem. Finally, numerical simulations are provided to reveal the effects of several system parameters on the power allocation results. It is also demonstrated that the LPI performance of the joint bistatic radar and communication system can be markedly improved by utilizing the proposed scheme. Full article
(This article belongs to the Special Issue Advances on Resources Management for Multi-Platform Infrastructures)
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Open AccessArticle Pre-Clinical Tests of an Integrated CMOS Biomolecular Sensor for Cardiac Diseases Diagnosis
Sensors 2017, 17(12), 2733; doi:10.3390/s17122733
Received: 26 October 2017 / Revised: 22 November 2017 / Accepted: 24 November 2017 / Published: 26 November 2017
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Abstract
Coronary artery disease and its related complications pose great threats to human health. In this work, we aim to clinically evaluate a CMOS field-effect biomolecular sensor for cardiac biomarkers, cardiac-specific troponin-I (cTnI), N-terminal prohormone brain natriuretic peptide (NT-proBNP), and interleukin-6 (IL-6). The
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Coronary artery disease and its related complications pose great threats to human health. In this work, we aim to clinically evaluate a CMOS field-effect biomolecular sensor for cardiac biomarkers, cardiac-specific troponin-I (cTnI), N-terminal prohormone brain natriuretic peptide (NT-proBNP), and interleukin-6 (IL-6). The CMOS biosensor is implemented via a standard commercialized 0.35 μm CMOS process. To validate the sensing characteristics, in buffer conditions, the developed CMOS biosensor has identified the detection limits of IL-6, cTnI, and NT-proBNP as being 45 pM, 32 pM, and 32 pM, respectively. In clinical serum conditions, furthermore, the developed CMOS biosensor performs a good correlation with an enzyme-linked immuno-sorbent assay (ELISA) obtained from a hospital central laboratory. Based on this work, the CMOS field-effect biosensor poses good potential for accomplishing the needs of a point-of-care testing (POCT) system for heart disease diagnosis. Full article
(This article belongs to the Special Issue Sensors for Health Monitoring and Disease Diagnosis)
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Open AccessFeature PaperArticle Screen-Printed Graphite Electrodes as Low-Cost Devices for Oxygen Gas Detection in Room-Temperature Ionic Liquids
Sensors 2017, 17(12), 2734; doi:10.3390/s17122734
Received: 10 October 2017 / Revised: 20 November 2017 / Accepted: 21 November 2017 / Published: 26 November 2017
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Abstract
Screen-printed graphite electrodes (SPGEs) have been used for the first time as platforms to detect oxygen gas in room-temperature ionic liquids (RTILs). Up until now, carbon-based SPEs have shown inferior behaviour compared to platinum and gold SPEs for gas sensing with RTIL solvents.
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Screen-printed graphite electrodes (SPGEs) have been used for the first time as platforms to detect oxygen gas in room-temperature ionic liquids (RTILs). Up until now, carbon-based SPEs have shown inferior behaviour compared to platinum and gold SPEs for gas sensing with RTIL solvents. The electrochemical reduction of oxygen (O2) in a range of RTILs has therefore been explored on home-made SPGEs, and is compared to the behaviour on commercially-available carbon SPEs (C-SPEs). Six common RTILs are initially employed for O2 detection using cyclic voltammetry (CV), and two RTILs ([C2mim][NTf2] and [C4mim][PF6]) chosen for further detailed analytical studies. Long-term chronoamperometry (LTCA) was also performed to test the ability of the sensor surface for real-time gas monitoring. Both CV and LTCA gave linear calibration graphs—for CV in the 10–100% vol. range, and for LTCA in the 0.1–20% vol. range—on the SPGE. The responses on the SPGE were far superior to the commercial C-SPEs; more instability in the electrochemical responses were observed on the C-SPEs, together with some breaking-up or dissolution of the electrode surface materials. This study highlights that not all screen-printed ink formulations are compatible with RTIL solvents for longer-term electrochemical experiments, and that the choice of RTIL is also important. Overall, the low-cost SPGEs appear to be promising platforms for the detection of O2, particularly in [C4mim][PF6]. Full article
(This article belongs to the collection Gas Sensors)
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Open AccessArticle IMU-Based Gait Recognition Using Convolutional Neural Networks and Multi-Sensor Fusion
Sensors 2017, 17(12), 2735; doi:10.3390/s17122735
Received: 17 September 2017 / Revised: 10 November 2017 / Accepted: 23 November 2017 / Published: 27 November 2017
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Abstract
The wide spread usage of wearable sensors such as in smart watches has provided continuous access to valuable user generated data such as human motion that could be used to identify an individual based on his/her motion patterns such as, gait. Several methods
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The wide spread usage of wearable sensors such as in smart watches has provided continuous access to valuable user generated data such as human motion that could be used to identify an individual based on his/her motion patterns such as, gait. Several methods have been suggested to extract various heuristic and high-level features from gait motion data to identify discriminative gait signatures and distinguish the target individual from others. However, the manual and hand crafted feature extraction is error prone and subjective. Furthermore, the motion data collected from inertial sensors have complex structure and the detachment between manual feature extraction module and the predictive learning models might limit the generalization capabilities. In this paper, we propose a novel approach for human gait identification using time-frequency (TF) expansion of human gait cycles in order to capture joint 2 dimensional (2D) spectral and temporal patterns of gait cycles. Then, we design a deep convolutional neural network (DCNN) learning to extract discriminative features from the 2D expanded gait cycles and jointly optimize the identification model and the spectro-temporal features in a discriminative fashion. We collect raw motion data from five inertial sensors placed at the chest, lower-back, right hand wrist, right knee, and right ankle of each human subject synchronously in order to investigate the impact of sensor location on the gait identification performance. We then present two methods for early (input level) and late (decision score level) multi-sensor fusion to improve the gait identification generalization performance. We specifically propose the minimum error score fusion (MESF) method that discriminatively learns the linear fusion weights of individual DCNN scores at the decision level by minimizing the error rate on the training data in an iterative manner. 10 subjects participated in this study and hence, the problem is a 10-class identification task. Based on our experimental results, 91% subject identification accuracy was achieved using the best individual IMU and 2DTF-DCNN. We then investigated our proposed early and late sensor fusion approaches, which improved the gait identification accuracy of the system to 93.36% and 97.06%, respectively. Full article
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Open AccessArticle Analysis of Sources of Large Positioning Errors in Deterministic Fingerprinting
Sensors 2017, 17(12), 2736; doi:10.3390/s17122736
Received: 31 October 2017 / Revised: 22 November 2017 / Accepted: 23 November 2017 / Published: 27 November 2017
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Abstract
Wi-Fi fingerprinting is widely used for indoor positioning and indoor navigation due to the ubiquity of wireless networks, high proliferation of Wi-Fi-enabled mobile devices, and its reasonable positioning accuracy. The assumption is that the position can be estimated based on the received signal
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Wi-Fi fingerprinting is widely used for indoor positioning and indoor navigation due to the ubiquity of wireless networks, high proliferation of Wi-Fi-enabled mobile devices, and its reasonable positioning accuracy. The assumption is that the position can be estimated based on the received signal strength intensity from multiple wireless access points at a given point. The positioning accuracy, within a few meters, enables the use of Wi-Fi fingerprinting in many different applications. However, it has been detected that the positioning error might be very large in a few cases, which might prevent its use in applications with high accuracy positioning requirements. Hybrid methods are the new trend in indoor positioning since they benefit from multiple diverse technologies (Wi-Fi, Bluetooth, and Inertial Sensors, among many others) and, therefore, they can provide a more robust positioning accuracy. In order to have an optimal combination of technologies, it is crucial to identify when large errors occur and prevent the use of extremely bad positioning estimations in hybrid algorithms. This paper investigates why large positioning errors occur in Wi-Fi fingerprinting and how to detect them by using the received signal strength intensities. Full article
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Open AccessArticle A Strategic Bargaining Game for a Spectrum Sharing Scheme in Cognitive Radio-Based Heterogeneous Wireless Sensor Networks
Sensors 2017, 17(12), 2737; doi:10.3390/s17122737
Received: 12 September 2017 / Revised: 2 November 2017 / Accepted: 23 November 2017 / Published: 27 November 2017
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Abstract
In Wireless Sensor Networks (WSNs), unlicensed users, that is, sensor nodes, have excessively exploited the unlicensed radio spectrum. Through Cognitive Radio (CR), licensed radio spectra, which are owned by licensed users, can be partly or entirely shared with unlicensed users. This paper proposes
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In Wireless Sensor Networks (WSNs), unlicensed users, that is, sensor nodes, have excessively exploited the unlicensed radio spectrum. Through Cognitive Radio (CR), licensed radio spectra, which are owned by licensed users, can be partly or entirely shared with unlicensed users. This paper proposes a strategic bargaining spectrum-sharing scheme, considering a CR-based heterogeneous WSN (HWSN). The sensors of HWSNs are discrepant and exist in different wireless environments, which leads to various signal-to-noise ratios (SNRs) for the same or different licensed users. Unlicensed users bargain with licensed users regarding the spectrum price. In each round of bargaining, licensed users are allowed to adaptively adjust their spectrum price to the best for maximizing their profits. . Then, each unlicensed user makes their best response and informs licensed users of “bargaining” and “warning”. Through finite rounds of bargaining, this scheme can obtain a Nash bargaining solution (NBS), which makes all licensed and unlicensed users reach an agreement. The simulation results demonstrate that the proposed scheme can quickly find a NBS and all players in the game prefer to be honest. The proposed scheme outperforms existing schemes, within a certain range, in terms of fairness and trade success probability. Full article
(This article belongs to the Special Issue Cognitive Radio Sensing and Sensor Networks)
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Open AccessArticle On-Tree Mango Fruit Size Estimation Using RGB-D Images
Sensors 2017, 17(12), 2738; doi:10.3390/s17122738
Received: 17 October 2017 / Revised: 15 November 2017 / Accepted: 25 November 2017 / Published: 28 November 2017
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Abstract
In-field mango fruit sizing is useful for estimation of fruit maturation and size distribution, informing the decision to harvest, harvest resourcing (e.g., tray insert sizes), and marketing. In-field machine vision imaging has been used for fruit count, but assessment of fruit size from
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In-field mango fruit sizing is useful for estimation of fruit maturation and size distribution, informing the decision to harvest, harvest resourcing (e.g., tray insert sizes), and marketing. In-field machine vision imaging has been used for fruit count, but assessment of fruit size from images also requires estimation of camera-to-fruit distance. Low cost examples of three technologies for assessment of camera to fruit distance were assessed: a RGB-D (depth) camera, a stereo vision camera and a Time of Flight (ToF) laser rangefinder. The RGB-D camera was recommended on cost and performance, although it functioned poorly in direct sunlight. The RGB-D camera was calibrated, and depth information matched to the RGB image. To detect fruit, a cascade detection with histogram of oriented gradients (HOG) feature was used, then Otsu’s method, followed by color thresholding was applied in the CIE L*a*b* color space to remove background objects (leaves, branches etc.). A one-dimensional (1D) filter was developed to remove the fruit pedicles, and an ellipse fitting method employed to identify well-separated fruit. Finally, fruit lineal dimensions were calculated using the RGB-D depth information, fruit image size and the thin lens formula. A Root Mean Square Error (RMSE) = 4.9 and 4.3 mm was achieved for estimated fruit length and width, respectively, relative to manual measurement, for which repeated human measures were characterized by a standard deviation of 1.2 mm. In conclusion, the RGB-D method for rapid in-field mango fruit size estimation is practical in terms of cost and ease of use, but cannot be used in direct intense sunshine. We believe this work represents the first practical implementation of machine vision fruit sizing in field, with practicality gauged in terms of cost and simplicity of operation. Full article
(This article belongs to the Special Issue Sensors in Agriculture and Forestry)
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Open AccessArticle Colorectal Cancer and Colitis Diagnosis Using Fourier Transform Infrared Spectroscopy and an Improved K-Nearest-Neighbour Classifier
Sensors 2017, 17(12), 2739; doi:10.3390/s17122739
Received: 2 August 2017 / Revised: 31 October 2017 / Accepted: 2 November 2017 / Published: 27 November 2017
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Abstract
Combining Fourier transform infrared spectroscopy (FTIR) with endoscopy, it is expected that noninvasive, rapid detection of colorectal cancer can be performed in vivo in the future. In this study, Fourier transform infrared spectra were collected from 88 endoscopic biopsy colorectal tissue samples (41
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Combining Fourier transform infrared spectroscopy (FTIR) with endoscopy, it is expected that noninvasive, rapid detection of colorectal cancer can be performed in vivo in the future. In this study, Fourier transform infrared spectra were collected from 88 endoscopic biopsy colorectal tissue samples (41 colitis and 47 cancers). A new method, viz., entropy weight local-hyperplane k-nearest-neighbor (EWHK), which is an improved version of K-local hyperplane distance nearest-neighbor (HKNN), is proposed for tissue classification. In order to avoid limiting high dimensions and small values of the nearest neighbor, the new EWHK method calculates feature weights based on information entropy. The average results of the random classification showed that the EWHK classifier for differentiating cancer from colitis samples produced a sensitivity of 81.38% and a specificity of 92.69%. Full article
(This article belongs to the Section Biosensors)
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Open AccessArticle Mn-Doped CaBi4Ti4O15/Pb(Zr,Ti)O3 Ultrasonic Transducers for Continuous Monitoring at Elevated Temperatures
Sensors 2017, 17(12), 2740; doi:10.3390/s17122740
Received: 1 November 2017 / Revised: 16 November 2017 / Accepted: 24 November 2017 / Published: 27 November 2017
PDF Full-text (2418 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Continuous ultrasonic in-situ monitoring for industrial applications is difficult owing to the high operating temperatures in industrial fields. It is expected that ultrasonic transducers consisting of a CaBi4Ti4O15(CBT)/Pb(Zr,Ti)O3(PZT) sol-gel composite could be one solution for
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Continuous ultrasonic in-situ monitoring for industrial applications is difficult owing to the high operating temperatures in industrial fields. It is expected that ultrasonic transducers consisting of a CaBi4Ti4O15(CBT)/Pb(Zr,Ti)O3(PZT) sol-gel composite could be one solution for ultrasonic nondestructive testing (NDT) above 500 °C because no couplant is required and CBT has a high Curie temperature. To verify the high temperature durability, CBT/PZT sol-gel composite films were fabricated on titanium substrates by spray coating, and the CBT/PZT samples were tested in a furnace at various temperatures. Reflected echoes with a high signal-to-noise ratio were observed up to 600 °C. A thermal cycle test was conducted from room temperature to 600 °C, and no significant deterioration was found after the second thermal cycle. To investigate the long-term high-temperature durability, a CBT/PZT ultrasonic transducer was tested in the furnace at 600 °C for 36 h. Ultrasonic responses were recorded every 3 h, and the sensitivity and signal-to-noise ratio were stable throughout the experiment. Full article
(This article belongs to the Special Issue Materials and Applications for Sensors and Transducers)
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Open AccessArticle Adapting Local Features for Face Detection in Thermal Image
Sensors 2017, 17(12), 2741; doi:10.3390/s17122741
Received: 19 October 2017 / Revised: 20 November 2017 / Accepted: 23 November 2017 / Published: 27 November 2017
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Abstract
A thermal camera captures the temperature distribution of a scene as a thermal image. In thermal images, facial appearances of different people under different lighting conditions are similar. This is because facial temperature distribution is generally constant and not affected by lighting condition.
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A thermal camera captures the temperature distribution of a scene as a thermal image. In thermal images, facial appearances of different people under different lighting conditions are similar. This is because facial temperature distribution is generally constant and not affected by lighting condition. This similarity in face appearances is advantageous for face detection. To detect faces in thermal images, cascade classifiers with Haar-like features are generally used. However, there are few studies exploring the local features for face detection in thermal images. In this paper, we introduce two approaches relying on local features for face detection in thermal images. First, we create new feature types by extending Multi-Block LBP. We consider a margin around the reference and the generally constant distribution of facial temperature. In this way, we make the features more robust to image noise and more effective for face detection in thermal images. Second, we propose an AdaBoost-based training method to get cascade classifiers with multiple types of local features. These feature types have different advantages. In this way we enhance the description power of local features. We did a hold-out validation experiment and a field experiment. In the hold-out validation experiment, we captured a dataset from 20 participants, comprising 14 males and 6 females. For each participant, we captured 420 images with 10 variations in camera distance, 21 poses, and 2 appearances (participant with/without glasses). We compared the performance of cascade classifiers trained by different sets of the features. The experiment results showed that the proposed approaches effectively improve the performance of face detection in thermal images. In the field experiment, we compared the face detection performance in realistic scenes using thermal and RGB images, and gave discussion based on the results. Full article
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Open AccessArticle Dynamic Obstacle Avoidance for Unmanned Underwater Vehicles Based on an Improved Velocity Obstacle Method
Sensors 2017, 17(12), 2742; doi:10.3390/s17122742
Received: 25 October 2017 / Revised: 18 November 2017 / Accepted: 21 November 2017 / Published: 27 November 2017
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Abstract
In view of a dynamic obstacle environment with motion uncertainty, we present a dynamic collision avoidance method based on the collision risk assessment and improved velocity obstacle method. First, through the fusion optimization of forward-looking sonar data, the redundancy of the data is
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In view of a dynamic obstacle environment with motion uncertainty, we present a dynamic collision avoidance method based on the collision risk assessment and improved velocity obstacle method. First, through the fusion optimization of forward-looking sonar data, the redundancy of the data is reduced and the position, size and velocity information of the obstacles are obtained, which can provide an accurate decision-making basis for next-step collision avoidance. Second, according to minimum meeting time and the minimum distance between the obstacle and unmanned underwater vehicle (UUV), this paper establishes the collision risk assessment model, and screens key obstacles to avoid collision. Finally, the optimization objective function is established based on the improved velocity obstacle method, and a UUV motion characteristic is used to calculate the reachable velocity sets. The optimal collision speed of UUV is searched in velocity space. The corresponding heading and speed commands are calculated, and outputted to the motion control module. The above is the complete dynamic obstacle avoidance process. The simulation results show that the proposed method can obtain a better collision avoidance effect in the dynamic environment, and has good adaptability to the unknown dynamic environment. Full article
(This article belongs to the Special Issue Smart Sensors for Mechatronic and Robotic Systems)
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Open AccessArticle Effect of Composition and Thickness on the Perpendicular Magnetic Anisotropy of (Co/Pd) Multilayers
Sensors 2017, 17(12), 2743; doi:10.3390/s17122743
Received: 2 November 2017 / Revised: 22 November 2017 / Accepted: 22 November 2017 / Published: 28 November 2017
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Abstract
Magnetic materials with perpendicular magnetic anisotropy (PMA) have wide-ranging applications in magnetic recording and sensing devices. Multilayers comprised of ferromagnetic and non-magnetic metals (FM–NM) are interesting materials, as their magnetic anisotropy depends strongly on composition and growth parameters. In this context, (Co/Pd) multilayers
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Magnetic materials with perpendicular magnetic anisotropy (PMA) have wide-ranging applications in magnetic recording and sensing devices. Multilayers comprised of ferromagnetic and non-magnetic metals (FM–NM) are interesting materials, as their magnetic anisotropy depends strongly on composition and growth parameters. In this context, (Co/Pd) multilayers have gained huge interest recently due to their robustness and tunable PMA. Here, we report a systematic study of the effect of composition on the magnetic anisotropy of (Co/Pd) multilayers grown by Direct Current (DC) magnetron sputtering. Four different series of (Co/Pd)×10 multilayers with different thicknesses of Co and Pd were examined. Vibrating sample magnetometery was used to determine the magnetic anisotropy of these films. X-ray diffraction and transmission electron microscopy experiments were performed to understand the structural morphology of the films. Our results showed that (Co/Pd)×10 multilayers exhibit PMA when the Co to Pd ratio is less than or equal to 1 and the thickness of Co layers is not more than 5 Å. Maximum effective anisotropy energy is shown by the films with a Co to Pd ratio of 1/3. Full article
(This article belongs to the Special Issue Magnetic Sensors and Their Applications)
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Open AccessArticle All-Fiber Laser Curvature Sensor Using an In-Fiber Modal Interferometer Based on a Double Clad Fiber and a Multimode Fiber Structure
Sensors 2017, 17(12), 2744; doi:10.3390/s17122744
Received: 28 September 2017 / Revised: 31 October 2017 / Accepted: 7 November 2017 / Published: 28 November 2017
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Abstract
An all-fiber curvature laser sensor by using a novel modal interference in-fiber structure is proposed and experimentally demonstrated. The in-fiber device, fabricated by fusion splicing of multimode fiber and double-clad fiber segments, is used as wavelength filter as well as the sensing element.
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An all-fiber curvature laser sensor by using a novel modal interference in-fiber structure is proposed and experimentally demonstrated. The in-fiber device, fabricated by fusion splicing of multimode fiber and double-clad fiber segments, is used as wavelength filter as well as the sensing element. By including a multimode fiber in an ordinary modal interference structure based on a double-clad fiber, the fringe visibility of the filter transmission spectrum is significantly increased. By using the modal interferometer as a curvature sensitive wavelength filter within a ring cavity erbium-doped fiber laser, the spectral quality factor Q is considerably increased. The results demonstrate the reliability of the proposed curvature laser sensor with advantages of robustness, ease of fabrication, low cost, repeatability on the fabrication process and simple operation. Full article
(This article belongs to the Special Issue Optical Fiber Sensors 2017)
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Open AccessArticle Development of a High Precision Displacement Measurement System by Fusing a Low Cost RTK-GPS Sensor and a Force Feedback Accelerometer for Infrastructure Monitoring
Sensors 2017, 17(12), 2745; doi:10.3390/s17122745
Received: 21 September 2017 / Revised: 22 November 2017 / Accepted: 24 November 2017 / Published: 28 November 2017
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Abstract
A displacement measurement system fusing a low cost real-time kinematic global positioning system (RTK-GPS) receiver and a force feedback accelerometer is proposed for infrastructure monitoring. The proposed system is composed of a sensor module, a base module and a computation module. The sensor
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A displacement measurement system fusing a low cost real-time kinematic global positioning system (RTK-GPS) receiver and a force feedback accelerometer is proposed for infrastructure monitoring. The proposed system is composed of a sensor module, a base module and a computation module. The sensor module consists of a RTK-GPS rover and a force feedback accelerometer, and is installed on a target structure like conventional RTK-GPS sensors. The base module is placed on a rigid ground away from the target structure similar to conventional RTK-GPS bases, and transmits observation messages to the sensor module. Then, the initial acceleration, velocity and displacement responses measured by the sensor module are transmitted to the computation module located at a central monitoring facility. Finally, high precision and high sampling rate displacement, velocity, and acceleration are estimated by fusing the acceleration from the accelerometer, the velocity from the GPS rover, and the displacement from RTK-GPS. Note that the proposed displacement measurement system can measure 3-axis acceleration, velocity as well as displacement in real time. In terms of displacement, the proposed measurement system can estimate dynamic and pseudo-static displacement with a root-mean-square error of 2 mm and a sampling rate of up to 100 Hz. The performance of the proposed system is validated under sinusoidal, random and steady-state vibrations. Field tests were performed on the Yeongjong Grand Bridge and Yi Sun-sin Bridge in Korea, and the Xihoumen Bridge in China to compare the performance of the proposed system with a commercial RTK-GPS sensor and other data fusion techniques. Full article
(This article belongs to the Special Issue Sensors and Sensor Networks for Structural Health Monitoring)
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Open AccessArticle Radio-Frequency-Controlled Urea Dosing for NH3-SCR Catalysts: NH3 Storage Influence to Catalyst Performance under Transient Conditions
Sensors 2017, 17(12), 2746; doi:10.3390/s17122746
Received: 24 October 2017 / Accepted: 23 November 2017 / Published: 28 November 2017
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Abstract
Current developments in exhaust gas aftertreatment led to a huge mistrust in diesel driven passenger cars due to their NOx emissions being too high. The selective catalytic reduction (SCR) with ammonia (NH3) as reducing agent is the only approach today
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Current developments in exhaust gas aftertreatment led to a huge mistrust in diesel driven passenger cars due to their NOx emissions being too high. The selective catalytic reduction (SCR) with ammonia (NH3) as reducing agent is the only approach today with the capability to meet upcoming emission limits. Therefore, the radio-frequency-based (RF) catalyst state determination to monitor the NH3 loading on SCR catalysts has a huge potential in emission reduction. Recent work on this topic proved the basic capability of this technique under realistic conditions on an engine test bench. In these studies, an RF system calibration for the serial type SCR catalyst Cu-SSZ-13 was developed and different approaches for a temperature dependent NH3 storage were determined. This paper continues this work and uses a fully calibrated RF-SCR system under transient conditions to compare different directly measured and controlled NH3 storage levels, and NH3 target curves. It could be clearly demonstrated that the right NH3 target curve, together with a direct control on the desired level by the RF system, is able to operate the SCR system with the maximum possible NOx conversion efficiency and without NH3 slip. Full article
(This article belongs to the Section Chemical Sensors)
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Open AccessArticle A Quartz Crystal Microbalance Immunosensor for Stem Cell Selection and Extraction
Sensors 2017, 17(12), 2747; doi:10.3390/s17122747
Received: 23 October 2017 / Revised: 21 November 2017 / Accepted: 24 November 2017 / Published: 28 November 2017
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Abstract
A cost-effective immunosensor for the detection and isolation of dental pulp stem cells (DPSCs) based on a quartz crystal microbalance (QCM) has been developed. The recognition mechanism relies on anti-CD34 antibodies, DPSC-specific monoclonal antibodies that are anchored on the surface of the quartz
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A cost-effective immunosensor for the detection and isolation of dental pulp stem cells (DPSCs) based on a quartz crystal microbalance (QCM) has been developed. The recognition mechanism relies on anti-CD34 antibodies, DPSC-specific monoclonal antibodies that are anchored on the surface of the quartz crystals. Due to its high specificity, real time detection, and low cost, the proposed technology has a promising potential in the field of cell biology, for the simultaneous detection and sorting of stem cells from heterogeneous cell samples. The QCM surface was properly tailored through a biotinylated self-assembled monolayer (SAM). The biotin–avidin interaction was used to immobilize the biotinylated anti-CD34 antibody on the gold-coated quartz crystal. After antibody immobilization, a cellular pellet, with a mixed cell population, was analyzed; the results indicated that the developed QCM immunosensor is highly specific, being able to detect and sort only CD34+ cells. Our study suggests that the proposed technology can detect and efficiently sort any kind of cell from samples with high complexity, being simple, selective, and providing for more convenient and time-saving operations. Full article
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Open AccessArticle Tactile Perception of Roughness and Hardness to Discriminate Materials by Friction-Induced Vibration
Sensors 2017, 17(12), 2748; doi:10.3390/s17122748
Received: 22 September 2017 / Revised: 3 November 2017 / Accepted: 22 November 2017 / Published: 28 November 2017
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Abstract
The human fingertip is an exquisitely powerful bio-tactile sensor in perceiving different materials based on various highly-sensitive mechanoreceptors distributed all over the skin. The tactile perception of surface roughness and material hardness can be estimated by skin vibrations generated during a fingertip stroking
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The human fingertip is an exquisitely powerful bio-tactile sensor in perceiving different materials based on various highly-sensitive mechanoreceptors distributed all over the skin. The tactile perception of surface roughness and material hardness can be estimated by skin vibrations generated during a fingertip stroking of a surface instead of being maintained in a static position. Moreover, reciprocating sliding with increasing velocities and pressures are two common behaviors in humans to discriminate different materials, but the question remains as to what the correlation of the sliding velocity and normal load on the tactile perceptions of surface roughness and hardness is for material discrimination. In order to investigate this correlation, a finger-inspired crossed-I beam structure tactile tester has been designed to mimic the anthropic tactile discrimination behaviors. A novel method of characterizing the fast Fourier transform integral (FFT) slope of the vibration acceleration signal generated from fingertip rubbing on surfaces at increasing sliding velocity and normal load, respectively, are defined as kv and kw, and is proposed to discriminate the surface roughness and hardness of different materials. Over eight types of materials were tested, and they proved the capability and advantages of this high tactile-discriminating method. Our study may find applications in investigating humanoid robot perceptual abilities. Full article
(This article belongs to the Special Issue Tactile Sensors and Sensing)
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Open AccessArticle Mathematical Model for Localised and Surface Heat Flux of the Human Body Obtained from Measurements Performed with a Calorimetry Minisensor
Sensors 2017, 17(12), 2749; doi:10.3390/s17122749
Received: 10 October 2017 / Revised: 18 November 2017 / Accepted: 21 November 2017 / Published: 28 November 2017
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Abstract
The accuracy of the direct and local measurements of the heat power dissipated by the surface of the human body, using a calorimetry minisensor, is directly related to the calibration rigor of the sensor and the correct interpretation of the experimental results. For
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The accuracy of the direct and local measurements of the heat power dissipated by the surface of the human body, using a calorimetry minisensor, is directly related to the calibration rigor of the sensor and the correct interpretation of the experimental results. For this, it is necessary to know the characteristics of the body’s local heat dissipation. When the sensor is placed on the surface of the human body, the body reacts until a steady state is reached. We propose a mathematical model that represents the rate of heat flow at a given location on the surface of a human body by the sum of a series of exponentials: W(t) = A0 + ∑Aiexp(−t/τi). In this way, transient and steady states of heat dissipation can be interpreted. This hypothesis has been tested by simulating the operation of the sensor. At the steady state, the power detected in the measurement area (4 cm2) varies depending on the sensor’s thermostat temperature, as well as the physical state of the subject. For instance, for a thermostat temperature of 24 °C, this power can vary between 100–250 mW in a healthy adult. In the transient state, two exponentials are sufficient to represent this dissipation, with 3 and 70 s being the mean values of its time constants. Full article
(This article belongs to the Special Issue Biomedical Sensors and Systems 2017)
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Open AccessCommunication Room-Temperature H2 Gas Sensing Characterization of Graphene-Doped Porous Silicon via a Facile Solution Dropping Method
Sensors 2017, 17(12), 2750; doi:10.3390/s17122750
Received: 13 October 2017 / Revised: 6 November 2017 / Accepted: 23 November 2017 / Published: 28 November 2017
PDF Full-text (6575 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
In this study, a graphene-doped porous silicon (G-doped/p-Si) substrate for low ppm H2 gas detection by an inexpensive synthesis route was proposed as a potential noble graphene-based gas sensor material, and to understand the sensing mechanism. The G-doped/p-Si gas sensor was synthesized
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In this study, a graphene-doped porous silicon (G-doped/p-Si) substrate for low ppm H2 gas detection by an inexpensive synthesis route was proposed as a potential noble graphene-based gas sensor material, and to understand the sensing mechanism. The G-doped/p-Si gas sensor was synthesized by a simple capillary force-assisted solution dropping method on p-Si substrates, whose porosity was generated through an electrochemical etching process. G-doped/p-Si was fabricated with various graphene concentrations and exploited as a H2 sensor that was operated at room temperature. The sensing mechanism of the sensor with/without graphene decoration on p-Si was proposed to elucidate the synergetic gas sensing effect that is generated from the interface between the graphene and p-type silicon. Full article
(This article belongs to the Special Issue Graphene Based Sensors and Electronics)
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Open AccessArticle Knee Impedance Modulation to Control an Active Orthosis Using Insole Sensors
Sensors 2017, 17(12), 2751; doi:10.3390/s17122751
Received: 10 October 2017 / Revised: 19 November 2017 / Accepted: 22 November 2017 / Published: 28 November 2017
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Abstract
Robotic devices for rehabilitation and gait assistance have greatly advanced with the objective of improving both the mobility and quality of life of people with motion impairments. To encourage active participation of the user, the use of admittance control strategy is one of
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Robotic devices for rehabilitation and gait assistance have greatly advanced with the objective of improving both the mobility and quality of life of people with motion impairments. To encourage active participation of the user, the use of admittance control strategy is one of the most appropriate approaches, which requires methods for online adjustment of impedance components. Such approach is cited by the literature as a challenge to guaranteeing a suitable dynamic performance. This work proposes a method for online knee impedance modulation, which generates variable gains through the gait cycle according to the users’ anthropometric data and gait sub-phases recognized with footswitch signals. This approach was evaluated in an active knee orthosis with three variable gain patterns to obtain a suitable condition to implement a stance controller: two different gain patterns to support the knee in stance phase, and a third pattern for gait without knee support. The knee angle and torque were measured during the experimental protocol to compare both temporospatial parameters and kinematics data with other studies of gait with knee exoskeletons. The users rated scores related to their satisfaction with both the device and controller through QUEST questionnaires. Experimental results showed that the admittance controller proposed here offered knee support in 50% of the gait cycle, and the walking speed was not significantly different between the three gain patterns (p = 0.067). A positive effect of the controller on users regarding safety during gait was found with a score of 4 in a scale of 5. Therefore, the approach demonstrates good performance to adjust impedance components providing knee support in stance phase. Full article
(This article belongs to the Special Issue Assistance Robotics and Biosensors)
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Open AccessArticle Effect of Shot Noise on Simultaneous Sensing in Frequency Division Multiplexed Diffuse Optical Tomographic Imaging Process
Sensors 2017, 17(12), 2752; doi:10.3390/s17122752
Received: 30 September 2017 / Revised: 17 November 2017 / Accepted: 23 November 2017 / Published: 28 November 2017
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Abstract
Diffuse optical tomography (DOT) has been studied for use in the detection of breast cancer, cerebral oxygenation, and cognitive brain signals. As optical imaging studies have increased significantly, acquiring imaging data in real time has become increasingly important. We have developed frequency-division multiplexing
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Diffuse optical tomography (DOT) has been studied for use in the detection of breast cancer, cerebral oxygenation, and cognitive brain signals. As optical imaging studies have increased significantly, acquiring imaging data in real time has become increasingly important. We have developed frequency-division multiplexing (FDM) DOT systems to analyze their performance with respect to acquisition time and imaging quality, in comparison with the conventional time-division multiplexing (TDM) DOT. A large tomographic area of a cylindrical phantom 60 mm in diameter could be successfully reconstructed using both TDM DOT and FDM DOT systems. In our experiment with 6 source-detector (S-D) pairs, the TDM DOT and FDM DOT systems required 6.18 and 1 s, respectively, to obtain a single tomographic data set. While the absorption coefficient of the reconstruction image was underestimated in the case of the FDM DOT, we experimentally confirmed that the abnormal region can be clearly distinguished from the background phantom using both methods. Full article
(This article belongs to the Special Issue Novel Sensors for Bioimaging)
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Open AccessArticle A Novel Adaptive H∞ Filtering Method with Delay Compensation for the Transfer Alignment of Strapdown Inertial Navigation Systems
Sensors 2017, 17(12), 2753; doi:10.3390/s17122753
Received: 9 September 2017 / Revised: 8 November 2017 / Accepted: 23 November 2017 / Published: 28 November 2017
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Abstract
Transfer alignment is always a key technology in a strapdown inertial navigation system (SINS) because of its rapidity and accuracy. In this paper a transfer alignment model is established, which contains the SINS error model and the measurement model. The time delay in
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Transfer alignment is always a key technology in a strapdown inertial navigation system (SINS) because of its rapidity and accuracy. In this paper a transfer alignment model is established, which contains the SINS error model and the measurement model. The time delay in the process of transfer alignment is analyzed, and an H∞ filtering method with delay compensation is presented. Then the H∞ filtering theory and the robust mechanism of H∞ filter are deduced and analyzed in detail. In order to improve the transfer alignment accuracy in SINS with time delay, an adaptive H∞ filtering method with delay compensation is proposed. Since the robustness factor plays an important role in the filtering process and has effect on the filtering accuracy, the adaptive H∞ filter with delay compensation can adjust the value of robustness factor adaptively according to the dynamic external environment. The vehicle transfer alignment experiment indicates that by using the adaptive H∞ filtering method with delay compensation, the transfer alignment accuracy and the pure inertial navigation accuracy can be dramatically improved, which demonstrates the superiority of the proposed filtering method. Full article
(This article belongs to the Special Issue Inertial Sensors for Positioning and Navigation)
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Open AccessArticle ECG Signal De-noising and Baseline Wander Correction Based on CEEMDAN and Wavelet Threshold
Sensors 2017, 17(12), 2754; doi:10.3390/s17122754
Received: 13 October 2017 / Revised: 21 November 2017 / Accepted: 23 November 2017 / Published: 28 November 2017
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Abstract
A novel electrocardiogram (ECG) signal de-noising and baseline wander correction method based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and wavelet threshold is proposed. Although CEEMDAN is based on empirical mode decomposition (EMD), it represents a significant improvement of the
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A novel electrocardiogram (ECG) signal de-noising and baseline wander correction method based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and wavelet threshold is proposed. Although CEEMDAN is based on empirical mode decomposition (EMD), it represents a significant improvement of the original EMD by overcoming the mode-mixing problem. However, there has been no previous study on using CEEMDAN to de-noise ECG signals, to the authors’ best knowledge. In the proposed method, the original noisy ECG signal is decomposed into a series of intrinsic mode functions (IMFs) sorted from high to low frequency by CEEMDAN. Each IMF is then analyzed by the autocorrelation method to find out the first few high frequency IMFs containing random noise, and these IMFs should be de-noised by the wavelet threshold. The zero-crossing rate (ZCR) of all IMFs, including final residue, are computed, and the IMFs with ZCR less than a certain value are removed. Finally, the remaining IMFs are reconstructed to obtain the clean ECG signal. The proposed algorithm is validated through experiments using the MIT–BIH ECG databases, and the results show that the random noise in the ECG signal can be effectively suppressed, and at the same time the baseline wander can be corrected efficiently. Full article
(This article belongs to the Special Issue Sensor Signal and Information Processing)
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Open AccessArticle Comprehensive Detection of Gas Plumes from Multibeam Water Column Images with Minimisation of Noise Interferences
Sensors 2017, 17(12), 2755; doi:10.3390/s17122755
Received: 11 October 2017 / Revised: 27 November 2017 / Accepted: 27 November 2017 / Published: 29 November 2017
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Abstract
Multibeam echosounder systems (MBES) can record backscatter strengths of gas plumes in the water column (WC) images that may be an indicator of possible occurrence of gas at certain depths. Manual or automatic detection is generally adopted in finding gas plumes, but frequently
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Multibeam echosounder systems (MBES) can record backscatter strengths of gas plumes in the water column (WC) images that may be an indicator of possible occurrence of gas at certain depths. Manual or automatic detection is generally adopted in finding gas plumes, but frequently results in low efficiency and high false detection rates because of WC images that are polluted by noise. To improve the efficiency and reliability of the detection, a comprehensive detection method is proposed in this paper. In the proposed method, the characteristics of WC background noise are first analyzed and given. Then, the mean standard deviation threshold segmentations are respectively used for the denoising of time-angle and depth-angle images, an intersection operation is performed for the two segmented images to further weaken noise in the WC data, and the gas plumes in the WC data are detected from the intersection image by the morphological constraint. The proposed method was tested by conducting shallow-water and deepwater experiments. In these experiments, the detections were conducted automatically and higher correct detection rates than the traditional methods were achieved. The performance of the proposed method is analyzed and discussed. Full article
(This article belongs to the Section Remote Sensors)
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Open AccessArticle First Results of Using a UVTron Flame Sensor to Detect Alpha-Induced Air Fluorescence in the UVC Wavelength Range
Sensors 2017, 17(12), 2756; doi:10.3390/s17122756
Received: 6 October 2017 / Revised: 17 November 2017 / Accepted: 23 November 2017 / Published: 29 November 2017
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Abstract
In this work, a robust stand-off alpha detection method using the secondary effects of alpha radiation has been sought. Alpha particles ionise the surrounding atmosphere as they travel. Fluorescence photons produced as a consequence of this can be used to detect the source
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In this work, a robust stand-off alpha detection method using the secondary effects of alpha radiation has been sought. Alpha particles ionise the surrounding atmosphere as they travel. Fluorescence photons produced as a consequence of this can be used to detect the source of the alpha emissions. This paper details experiments carried out to detect this fluorescence, with the focus on photons in the ultraviolet C (UVC) wavelength range (180–280 nm). A detector, UVTron R9533 (Hamamatsu, 325-6, Sunayama-cho, Naka-ku, Hamamatsu City, Shizuoka Pref., 430-8587, Japan), designed to detect the UVC emissions from flames for fire alarm purposes, was tested in various gas atmospheres with a 210Po alpha source to determine if this could provide an avenue for stand-off alpha detection. The results of the experiments show that this detector is capable of detecting alpha-induced air fluorescence in normal indoor lighting conditions, as the interference from daylight and artificial lighting is less influential on this detection system which operates below the UVA and UVB wavelength ranges (280–315 nm and 315–380 nm respectively). Assuming a standard 1 r 2 drop off in signal, the limit of detection in this configuration can be calculated to be approximately 240 mm, well beyond the range of alpha-particles in air, which indicates that this approach could have potential for stand-off alpha detection. The gas atmospheres tested produced an increase in the detector count, with xenon having the greatest effect with a measured 52% increase in the detector response in comparison to the detector response in an air atmosphere. This type of alpha detection system could be operated at a distance, where it would potentially provide a more cost effective, safer, and faster solution in comparison with traditional alpha detection methods to detect and characterise alpha contamination in nuclear decommissioning and security applications. Full article
(This article belongs to the Special Issue Sensors and Materials for Harsh Environments)
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Open AccessArticle Depth-Based Detection of Standing-Pigs in Moving Noise Environments
Sensors 2017, 17(12), 2757; doi:10.3390/s17122757
Received: 30 October 2017 / Revised: 24 November 2017 / Accepted: 27 November 2017 / Published: 29 November 2017
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Abstract
In a surveillance camera environment, the detection of standing-pigs in real-time is an important issue towards the final goal of 24-h tracking of individual pigs. In this study, we focus on depth-based detection of standing-pigs with “moving noises”, which appear every night in
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In a surveillance camera environment, the detection of standing-pigs in real-time is an important issue towards the final goal of 24-h tracking of individual pigs. In this study, we focus on depth-based detection of standing-pigs with “moving noises”, which appear every night in a commercial pig farm, but have not been reported yet. We first apply a spatiotemporal interpolation technique to remove the moving noises occurring in the depth images. Then, we detect the standing-pigs by utilizing the undefined depth values around them. Our experimental results show that this method is effective for detecting standing-pigs at night, in terms of both cost-effectiveness (using a low-cost Kinect depth sensor) and accuracy (i.e., 94.47%), even with severe moving noises occluding up to half of an input depth image. Furthermore, without any time-consuming technique, the proposed method can be executed in real-time. Full article
(This article belongs to the Special Issue Imaging Depth Sensors—Sensors, Algorithms and Applications)
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Open AccessArticle An Efficient Audio Coding Scheme for Quantitative and Qualitative Large Scale Acoustic Monitoring Using the Sensor Grid Approach
Sensors 2017, 17(12), 2758; doi:10.3390/s17122758
Received: 17 October 2017 / Revised: 20 November 2017 / Accepted: 24 November 2017 / Published: 29 November 2017
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Abstract
The spreading of urban areas and the growth of human population worldwide raise societal and environmental concerns. To better address these concerns, the monitoring of the acoustic environment in urban as well as rural or wilderness areas is an important matter. Building on
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The spreading of urban areas and the growth of human population worldwide raise societal and environmental concerns. To better address these concerns, the monitoring of the acoustic environment in urban as well as rural or wilderness areas is an important matter. Building on the recent development of low cost hardware acoustic sensors, we propose in this paper to consider a sensor grid approach to tackle this issue. In this kind of approach, the crucial question is the nature of the data that are transmitted from the sensors to the processing and archival servers. To this end, we propose an efficient audio coding scheme based on third octave band spectral representation that allows: (1) the estimation of standard acoustic indicators; and (2) the recognition of acoustic events at state-of-the-art performance rate. The former is useful to provide quantitative information about the acoustic environment, while the latter is useful to gather qualitative information and build perceptually motivated indicators using for example the emergence of a given sound source. The coding scheme is also demonstrated to transmit spectrally encoded data that, reverted to the time domain using state-of-the-art techniques, are not intelligible, thus protecting the privacy of citizens. Full article
(This article belongs to the Special Issue Smart Communication Protocols and Algorithms for Sensor Networks)
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Open AccessArticle A New Low-Temperature Electrochemical Hydrocarbon and NOx Sensor
Sensors 2017, 17(12), 2759; doi:10.3390/s17122759
Received: 22 October 2017 / Revised: 14 November 2017 / Accepted: 27 November 2017 / Published: 29 November 2017
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Abstract
In this article, a new investigation on a low-temperature electrochemical hydrocarbon and NOx sensor is presented. Based on the mixed-potential-based sensing scheme, the sensor is constructed using platinum and metal oxide electrodes, along with an Yttria-Stabilized Zirconia (YSZ)/Strontium Titanate (SrTiO3)
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In this article, a new investigation on a low-temperature electrochemical hydrocarbon and NOx sensor is presented. Based on the mixed-potential-based sensing scheme, the sensor is constructed using platinum and metal oxide electrodes, along with an Yttria-Stabilized Zirconia (YSZ)/Strontium Titanate (SrTiO3) thin-film electrolyte. Unlike traditional mixed-potential sensors which operate at higher temperatures (>400 °C), this potentiometric sensor operates at 200 °C with dominant hydrocarbon (HC) and NOx response in the open-circuit and biased modes, respectively. The possible low-temperature operation of the sensor is speculated to be primarily due to the enhanced oxygen ion conductivity of the electrolyte, which may be attributed to the space charge effect, epitaxial strain, and atomic reconstruction at the interface of the YSZ/STO thin film. The response and recovery time for the NOx sensor are found to be 7 s and 8 s, respectively. The sensor exhibited stable response even after 120 days of testing, with an 11.4% decrease in HC response and a 3.3% decrease in NOx response. Full article
(This article belongs to the Special Issue Air Pollution Sensors: A New Class of Tools to Measure Air Quality)
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Open AccessArticle Highly Sensitive FPW-Based Microsystem for Rapid Detection of Tetrahydrocannabinol in Human Urine
Sensors 2017, 17(12), 2760; doi:10.3390/s17122760
Received: 13 October 2017 / Revised: 21 November 2017 / Accepted: 23 November 2017 / Published: 29 November 2017
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Abstract
This paper presents a highly sensitive flexural plate-wave (FPW)-based microsystem for rapid detection of tetrahydrocannabinol (THC) in human urine. First, a circular-type interdigital transducer (IDT) was integrated with a circular-type silicon-grooved reflective grating structure (RGS) to reduce insertion loss. Then, with lower insertion
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This paper presents a highly sensitive flexural plate-wave (FPW)-based microsystem for rapid detection of tetrahydrocannabinol (THC) in human urine. First, a circular-type interdigital transducer (IDT) was integrated with a circular-type silicon-grooved reflective grating structure (RGS) to reduce insertion loss. Then, with lower insertion loss (−38.758 dB), the FPW device was used to develop a novel THC biosensor, and the results reveal that this FPW-THC biosensor has low detection limit (1.5625 ng/mL) and high mass-sensitivity (126.67 cm2/g). Finally, this biosensor was integrated with field-programmable gate array (FPGA) board and discrete components for prototyping a FPW readout system, whose maximum error was 12.378 kHz to ensure that the linearity of detection up to R-square is equal to 0.9992. Full article
(This article belongs to the Special Issue Bio-MEMS for Precision Medicine)
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Open AccessArticle A Real-Time Robust Method to Detect BeiDou GEO/IGSO Orbital Maneuvers
Sensors 2017, 17(12), 2761; doi:10.3390/s17122761
Received: 4 November 2017 / Revised: 26 November 2017 / Accepted: 27 November 2017 / Published: 29 November 2017
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Abstract
The frequent maneuvering of BeiDou Geostationary Orbit (GEO) and Inclined Geosynchronous Orbit (IGSO) satellites affects the availability of real-time orbit, and decreases the accuracy and performance of positioning, navigation and time (PNT) services. BeiDou satellite maneuver information cannot be obtained by common users.
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The frequent maneuvering of BeiDou Geostationary Orbit (GEO) and Inclined Geosynchronous Orbit (IGSO) satellites affects the availability of real-time orbit, and decreases the accuracy and performance of positioning, navigation and time (PNT) services. BeiDou satellite maneuver information cannot be obtained by common users. BeiDou broadcast ephemeris is the only indicator of the health status of satellites, which are broadcast on an hourly basis, easily leading to ineffective observations. Sometimes, identification errors of satellite abnormity also appear in the broadcast ephemeris. This study presents a real-time robust detection method for a satellite orbital maneuver with high frequency and high reliability. By using the broadcast ephemeris and pseudo-range observations, the time discrimination factor and the satellite identification factor were defined and used for the real-time detection of start time and the pseudo-random noise code (PRN) of satellites was used for orbital maneuvers. Data from a Multi-GNSS Experiment (MGEX) was collected and analyzed. The results show that the start time and the PRN of the satellite orbital maneuver could be detected accurately in real time. In addition, abnormal start times and satellite abnormities caused by non-maneuver factors also could be detected using the proposed method. The new method not only improves the utilization of observations for users with the data effective for about 92 min, but also promotes the reliability of real-time PNT services. Full article
(This article belongs to the Section Remote Sensors)
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Open AccessArticle GelSight: High-Resolution Robot Tactile Sensors for Estimating Geometry and Force
Sensors 2017, 17(12), 2762; doi:10.3390/s17122762
Received: 9 October 2017 / Revised: 6 November 2017 / Accepted: 13 November 2017 / Published: 29 November 2017
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Abstract
Tactile sensing is an important perception mode for robots, but the existing tactile technologies have multiple limitations. What kind of tactile information robots need, and how to use the information, remain open questions. We believe a soft sensor surface and high-resolution sensing of
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Tactile sensing is an important perception mode for robots, but the existing tactile technologies have multiple limitations. What kind of tactile information robots need, and how to use the information, remain open questions. We believe a soft sensor surface and high-resolution sensing of geometry should be important components of a competent tactile sensor. In this paper, we discuss the development of a vision-based optical tactile sensor, GelSight. Unlike the traditional tactile sensors which measure contact force, GelSight basically measures geometry, with very high spatial resolution. The sensor has a contact surface of soft elastomer, and it directly measures its deformation, both vertical and lateral, which corresponds to the exact object shape and the tension on the contact surface. The contact force, and slip can be inferred from the sensor’s deformation as well. Particularly, we focus on the hardware and software that support GelSight’s application on robot hands. This paper reviews the development of GelSight, with the emphasis in the sensing principle and sensor design. We introduce the design of the sensor’s optical system, the algorithm for shape, force and slip measurement, and the hardware designs and fabrication of different sensor versions. We also show the experimental evaluation on the GelSight’s performance on geometry and force measurement. With the high-resolution measurement of shape and contact force, the sensor has successfully assisted multiple robotic tasks, including material perception or recognition and in-hand localization for robot manipulation. Full article
(This article belongs to the Special Issue Tactile Sensors and Sensing)
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Open AccessArticle Symbiotic Sensing for Energy-Intensive Tasks in Large-Scale Mobile Sensing Applications
Sensors 2017, 17(12), 2763; doi:10.3390/s17122763
Received: 4 October 2017 / Revised: 24 November 2017 / Accepted: 26 November 2017 / Published: 29 November 2017
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Abstract
Energy consumption is a critical performance and user experience metric when developing mobile sensing applications, especially with the significantly growing number of sensing applications in recent years. As proposed a decade ago when mobile applications were still not popular and most mobile operating
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Energy consumption is a critical performance and user experience metric when developing mobile sensing applications, especially with the significantly growing number of sensing applications in recent years. As proposed a decade ago when mobile applications were still not popular and most mobile operating systems were single-tasking, conventional sensing paradigms such as opportunistic sensing and participatory sensing do not explore the relationship among concurrent applications for energy-intensive tasks. In this paper, inspired by social relationships among living creatures in nature, we propose a symbiotic sensing paradigm that can conserve energy, while maintaining equivalent performance to existing paradigms. The key idea is that sensing applications should cooperatively perform common tasks to avoid acquiring the same resources multiple times. By doing so, this sensing paradigm executes sensing tasks with very little extra resource consumption and, consequently, extends battery life. To evaluate and compare the symbiotic sensing paradigm with the existing ones, we develop mathematical models in terms of the completion probability and estimated energy consumption. The quantitative evaluation results using various parameters obtained from real datasets indicate that symbiotic sensing performs better than opportunistic sensing and participatory sensing in large-scale sensing applications, such as road condition monitoring, air pollution monitoring, and city noise monitoring. Full article
(This article belongs to the Special Issue Ubiquitous Massive Sensing Using Smartphones)
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Open AccessArticle An Advanced Hybrid Technique of DCS and JSRC for Telemonitoring of Multi-Sensor Gait Pattern
Sensors 2017, 17(12), 2764; doi:10.3390/s17122764
Received: 25 October 2017 / Revised: 21 November 2017 / Accepted: 28 November 2017 / Published: 29 November 2017
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Abstract
The jointly quantitative analysis of multi-sensor gait data for the best gait-classification performance has been a challenging endeavor in wireless body area networks (WBANs)-based gait telemonitoring applications. In this study, based on the joint sparsity of data, we proposed an advanced hybrid technique
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The jointly quantitative analysis of multi-sensor gait data for the best gait-classification performance has been a challenging endeavor in wireless body area networks (WBANs)-based gait telemonitoring applications. In this study, based on the joint sparsity of data, we proposed an advanced hybrid technique of distributed compressed sensing (DCS) and joint sparse representation classification (JSRC) for multi-sensor gait classification. Firstly, the DCS technique is utilized to simultaneously compress multi-sensor gait data for capturing spatio-temporal correlation information about gait while the energy efficiency of the sensors is available. Then, the jointly compressed gait data are directly used to develop a novel neighboring sample-based JSRC model by defining the sparse representation coefficients-inducing criterion (SRCC), in order to yield the best classification performance as well as a lower computational time cost. The multi-sensor gait data were selected from an open wearable action recognition database (WARD) to validate the feasibility of our proposed method. The results showed that when the comparison ratio and the number of neighboring samples are selected as 70% and 40%, respectively, the best accuracy (95%) can be reached while the lowest computational time spends only 60 ms. Moreover, the best accuracy and the computational time can increase by 5% and decrease by 40 ms, respectively, when compared with the traditional JSRC techniques. Our proposed hybrid technique can take advantage of the joint sparsity of data for jointly processing multi-sensor gait data, which greatly contributes to the best gait-classification performance. This has great potential for energy-efficient telemonitoring of multi-sensor gait. Full article
(This article belongs to the Special Issue Sensors for Gait and Posture)
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Open AccessArticle Gold Nanoparticles Used as Protein Scavengers Enhance Surface Plasmon Resonance Signal
Sensors 2017, 17(12), 2765; doi:10.3390/s17122765
Received: 21 October 2017 / Revised: 20 November 2017 / Accepted: 22 November 2017 / Published: 29 November 2017
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Abstract
Although several researchers had reported on methodologies for surface plasmon resonance (SPR) signal amplification based on the use of nanoparticles (NPs), the majority addressed the sandwich technique and low protein concentration. In this work, a different approach for SPR signal enhancement based on
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Although several researchers had reported on methodologies for surface plasmon resonance (SPR) signal amplification based on the use of nanoparticles (NPs), the majority addressed the sandwich technique and low protein concentration. In this work, a different approach for SPR signal enhancement based on the use of gold NPs was evaluated. The method was used in the detection of two lectins, peanut agglutinin (PNA) and concanavalin A (ConA). Gold NPs were functionalized with antibodies anti-PNA and anti-ConA, and these NPs were used as protein scavengers in a solution. After being incubated with solutions of PNA or ConA, the gold NPs coupled with the collected lectins were injected on the sensor containing the immobilized antibodies. The signal amplification provided by this method was compared to the signal amplification provided by the direct coupling of PNA and ConA to gold NPs. Furthermore, both methods, direct coupling and gold NPs as protein scavengers, were compared to the direct detection of PNA and ConA in solution. Compared to the analysis of free protein, the direct coupling of PNA and ConA to gold NPs resulted in a signal amplification of 10–40-fold and a 13-fold decrease of the limit of detection (LOD), whereas the use of gold NPs as protein scavengers resulted in an SPR signal 40–50-times higher and an LOD 64-times lower. Full article
(This article belongs to the Special Issue Surface Plasmon Resonance Sensing)
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Open AccessFeature PaperArticle A Dependable Localization Algorithm for Survivable Belt-Type Sensor Networks
Sensors 2017, 17(12), 2767; doi:10.3390/s17122767
Received: 17 September 2017 / Revised: 20 November 2017 / Accepted: 22 November 2017 / Published: 29 November 2017
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Abstract
As the key element, sensor networks are widely investigated by the Internet of Things (IoT) community. When massive numbers of devices are well connected, malicious attackers may deliberately propagate fake position information to confuse the ordinary users and lower the network survivability in
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As the key element, sensor networks are widely investigated by the Internet of Things (IoT) community. When massive numbers of devices are well connected, malicious attackers may deliberately propagate fake position information to confuse the ordinary users and lower the network survivability in belt-type situation. However, most existing positioning solutions only focus on the algorithm accuracy and do not consider any security aspects. In this paper, we propose a comprehensive scheme for node localization protection, which aims to improve the energy-efficient, reliability and accuracy. To handle the unbalanced resource consumption, a node deployment mechanism is presented to satisfy the energy balancing strategy in resource-constrained scenarios. According to cooperation localization theory and network connection property, the parameter estimation model is established. To achieve reliable estimations and eliminate large errors, an improved localization algorithm is created based on modified average hop distances. In order to further improve the algorithms, the node positioning accuracy is enhanced by using the steepest descent method. The experimental simulations illustrate the performance of new scheme can meet the previous targets. The results also demonstrate that it improves the belt-type sensor networks’ survivability, in terms of anti-interference, network energy saving, etc. Full article
(This article belongs to the Special Issue Next Generation Wireless Technologies for Internet of Things)
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Open AccessArticle Assessment of Embedded Conjugated Polymer Sensor Arrays for Potential Load Transmission Measurement in Orthopaedic Implants
Sensors 2017, 17(12), 2768; doi:10.3390/s17122768
Received: 1 August 2017 / Revised: 23 November 2017 / Accepted: 25 November 2017 / Published: 29 November 2017
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Abstract
Load transfer through orthopaedic joint implants is poorly understood. The longer-term outcomes of these implants are just starting to be studied, making it imperative to monitor contact loads across the entire joint implant interface to elucidate the force transmission and distribution mechanisms exhibited
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Load transfer through orthopaedic joint implants is poorly understood. The longer-term outcomes of these implants are just starting to be studied, making it imperative to monitor contact loads across the entire joint implant interface to elucidate the force transmission and distribution mechanisms exhibited by these implants in service. This study proposes and demonstrates the design, implementation, and characterization of a 3D-printed smart polymer sensor array using conductive polyaniline (PANI) structures embedded within a polymeric parent phase. The piezoresistive characteristics of PANI were investigated to characterize the sensing behaviour inherent to these embedded pressure sensor arrays, including the experimental determination of the stable response of PANI to continuous loading, stability throughout the course of loading and unloading cycles, and finally sensor repeatability and linearity in response to incremental loading cycles. This specially developed multi-material additive manufacturing process for PANI is shown be an attractive approach for the fabrication of implant components having embedded smart-polymer sensors, which could ultimately be employed for the measurement and analysis of joint loads in orthopaedic implants for in vitro testing. Full article
(This article belongs to the Special Issue Force and Pressure Based Sensing Medical Application)
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Open AccessArticle An Adaptive S-Method to Analyze Micro-Doppler Signals for Human Activity Classification
Sensors 2017, 17(12), 2769; doi:10.3390/s17122769
Received: 19 October 2017 / Revised: 20 November 2017 / Accepted: 27 November 2017 / Published: 29 November 2017
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Abstract
In this paper, we propose the multiwindow Adaptive S-method (AS-method) distribution approach used in the time-frequency analysis for radar signals. Based on the results of orthogonal Hermite functions that have good time-frequency resolution, we vary the length of window to suppress the oscillating
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In this paper, we propose the multiwindow Adaptive S-method (AS-method) distribution approach used in the time-frequency analysis for radar signals. Based on the results of orthogonal Hermite functions that have good time-frequency resolution, we vary the length of window to suppress the oscillating component caused by cross-terms. This method can bring a better compromise in the auto-terms concentration and cross-terms suppressing, which contributes to the multi-component signal separation. Finally, the effective micro signal is extracted by threshold segmentation and envelope extraction. To verify the proposed method, six states of motion are separated by a classifier of a support vector machine (SVM) trained to the extracted features. The trained SVM can detect a human subject with an accuracy of 95.4% for two cases without interference. Full article
(This article belongs to the Section Physical Sensors)
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Open AccessArticle Health Diagnosis of Major Transportation Infrastructures in Shanghai Metropolis Using High-Resolution Persistent Scatterer Interferometry
Sensors 2017, 17(12), 2770; doi:10.3390/s17122770
Received: 17 October 2017 / Revised: 26 November 2017 / Accepted: 28 November 2017 / Published: 29 November 2017
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Abstract
Since the Persistent Scatterer Synthetic Aperture Radar (SAR) Interferometry (PSI) technology allows the detection of ground subsidence with millimeter accuracy, it is becoming one of the most powerful and economical means for health diagnosis of major transportation infrastructures. However, structures of different types
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Since the Persistent Scatterer Synthetic Aperture Radar (SAR) Interferometry (PSI) technology allows the detection of ground subsidence with millimeter accuracy, it is becoming one of the most powerful and economical means for health diagnosis of major transportation infrastructures. However, structures of different types may suffer from various levels of localized subsidence due to the different structural characteristics and subsidence mechanisms. Moreover, in the complex urban scenery, some segments of these infrastructures may be sheltered by surrounding buildings in SAR images, obscuring the desirable signals. Therefore, the subsidence characteristics on different types of structures should be discussed separately and the accuracy of persistent scatterers (PSs) should be optimized. In this study, the PSI-based subsidence mapping over the entire transportation network of Shanghai (more than 10,000 km) is illustrated, achieving the city-wide monitoring specifically along the elevated roads, ground highways and underground subways. The precise geolocation and structural characteristics of infrastructures were combined to effectively guide more accurate identification and separation of PSs along the structures. The experimental results from two neighboring TerraSAR-X stacks from 2013 to 2016 were integrated by joint estimating the measurements in the overlapping area, performing large-scale subsidence mapping and were validated by leveling data, showing highly consistent in terms of subsidence velocities and time-series displacements. Spatial-temporal subsidence patterns on each type of infrastructures are strongly dependent on the operational durations and structural characteristics, as well as the variation of the foundation soil layers. Full article
(This article belongs to the Special Issue Sensors for Deformation Monitoring of Large Civil Infrastructures)
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Open AccessArticle Silver Nanoparticle Modified Electrode Covered by Graphene Oxide for the Enhanced Electrochemical Detection of Dopamine
Sensors 2017, 17(12), 2771; doi:10.3390/s17122771
Received: 13 October 2017 / Revised: 23 November 2017 / Accepted: 26 November 2017 / Published: 29 November 2017
PDF Full-text (2459 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Several neurological disorders such as Alzheimer’s disease and Parkinson’s disease have become a serious impediment to aging people nowadays. One of the efficient methods used to monitor these neurological disorders is the detection of neurotransmitters such as dopamine. Metal materials, such as gold
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Several neurological disorders such as Alzheimer’s disease and Parkinson’s disease have become a serious impediment to aging people nowadays. One of the efficient methods used to monitor these neurological disorders is the detection of neurotransmitters such as dopamine. Metal materials, such as gold and platinum, are widely used in this electrochemical detection method; however, low sensitivity and linearity at low dopamine concentrations limit the use of these materials. To overcome these limitations, a silver nanoparticle (SNP) modified electrode covered by graphene oxide for the detection of dopamine was newly developed in this study. For the first time, the surface of an indium tin oxide (ITO) electrode was modified using SNPs and graphene oxide sequentially through the electrochemical deposition method. The developed biosensor provided electrochemical signal enhancement at low dopamine concentrations in comparison with previous biosensors. Therefore, our newly developed SNP modified electrode covered by graphene oxide can be used to monitor neurological diseases through electrochemical signal enhancement at low dopamine concentrations. Full article
(This article belongs to the Special Issue Carbon Materials Based Sensors and the Application)
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Open AccessArticle Development of Spectral Disease Indices for ‘Flavescence Dorée’ Grapevine Disease Identification
Sensors 2017, 17(12), 2772; doi:10.3390/s17122772
Received: 19 October 2017 / Revised: 24 November 2017 / Accepted: 26 November 2017 / Published: 29 November 2017
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Abstract
Spectral measurements are employed in many precision agriculture applications, due to their ability to monitor the vegetation’s health state. Spectral vegetation indices are one of the main techniques currently used in remote sensing activities, since they are related to biophysical and biochemical crop
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Spectral measurements are employed in many precision agriculture applications, due to their ability to monitor the vegetation’s health state. Spectral vegetation indices are one of the main techniques currently used in remote sensing activities, since they are related to biophysical and biochemical crop variables. Moreover, they have been evaluated in some studies as potentially beneficial for detecting or differentiating crop diseases. Flavescence Dorée (FD) is an infectious, incurable disease of the grapevine that can produce severe yield losses and, hence, compromise the stability of the vineyards. The aim of this study was to develop specific spectral disease indices (SDIs) for the detection of FD disease in grapevines. Spectral signatures of healthy and diseased grapevine leaves were measured with a non-imaging spectro-radiometer at two infection severity levels. The most discriminating wavelengths were selected by a genetic algorithm (GA) feature selection tool, the Spectral Disease Indices (SDIs) are designed by exhaustively testing all possible combinations of wavelengths chosen. The best weighted combination of a single wavelength and a normalized difference is chosen to create the index. The SDIs are tested for their ability to differentiate healthy from diseased vine leaves and they are compared to some common set of Spectral Vegetation Indices (SVIs). It was demonstrated that using vegetation indices was, in general, better than using complete spectral data and that SDIs specifically designed for FD performed better than traditional SVIs in most of cases. The precision of the classification is higher than 90%. This study demonstrates that SDIs have the potential to improve disease detection, identification and monitoring in precision agriculture applications. Full article
(This article belongs to the Special Issue Sensors in Agriculture)
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Open AccessArticle Study of Optical Fiber Sensors for Cryogenic Temperature Measurements
Sensors 2017, 17(12), 2773; doi:10.3390/s17122773
Received: 30 October 2017 / Revised: 27 November 2017 / Accepted: 28 November 2017 / Published: 30 November 2017
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Abstract
In this work, the performance of five different fiber optic sensors at cryogenic temperatures has been analyzed. A photonic crystal fiber Fabry-Pérot interferometer, two Sagnac interferometers, a commercial fiber Bragg grating (FBG), and a π-phase shifted fiber Bragg grating interrogated in a random
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In this work, the performance of five different fiber optic sensors at cryogenic temperatures has been analyzed. A photonic crystal fiber Fabry-Pérot interferometer, two Sagnac interferometers, a commercial fiber Bragg grating (FBG), and a π-phase shifted fiber Bragg grating interrogated in a random distributed feedback fiber laser have been studied. Their sensitivities and resolutions as sensors for cryogenic temperatures have been compared regarding their advantages and disadvantages. Additionally, the results have been compared with the given by a commercial optical backscatter reflectometer that allowed for distributed temperature measurements of a single mode fiber. Full article
(This article belongs to the Section Physical Sensors)
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Open AccessArticle Laser Scanning Confocal Thermoreflectance Microscope for the Backside Thermal Imaging of Microelectronic Devices
Sensors 2017, 17(12), 2774; doi:10.3390/s17122774
Received: 20 October 2017 / Revised: 24 November 2017 / Accepted: 27 November 2017 / Published: 30 November 2017
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Abstract
In this paper, we report on a confocal thermoreflectance imaging system that can examine the thermal characteristics of microelectronic devices by penetrating the backside of a device through the substrate. In this system, the local reflectivity variations due to heat generation in the
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In this paper, we report on a confocal thermoreflectance imaging system that can examine the thermal characteristics of microelectronic devices by penetrating the backside of a device through the substrate. In this system, the local reflectivity variations due to heat generation in the device are measured point by point by a laser scanning confocal microscope capable of eliminating out-of-focus reflections and the thermoreflectance is extracted via Fourier-domain signal processing. In comparison to the conventional widefield thermoreflectance microscope, the proposed laser scanning confocal thermoreflectance microscope improves the thermoreflectance sensitivity by ~23 times and the spatial resolution by ~25% in backside thermoreflectance measurements. Full article
(This article belongs to the Section Physical Sensors)
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Open AccessArticle Modeling of Sensor Placement Strategy for Shape Sensing and Structural Health Monitoring of a Wing-Shaped Sandwich Panel Using Inverse Finite Element Method
Sensors 2017, 17(12), 2775; doi:10.3390/s17122775
Received: 10 October 2017 / Revised: 25 November 2017 / Accepted: 26 November 2017 / Published: 30 November 2017
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Abstract
This paper investigated the effect of sensor density and alignment for three-dimensional shape sensing of an airplane-wing-shaped thick panel subjected to three different loading conditions, i.e., bending, torsion, and membrane loads. For shape sensing analysis of the panel, the Inverse Finite Element Method
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This paper investigated the effect of sensor density and alignment for three-dimensional shape sensing of an airplane-wing-shaped thick panel subjected to three different loading conditions, i.e., bending, torsion, and membrane loads. For shape sensing analysis of the panel, the Inverse Finite Element Method (iFEM) was used together with the Refined Zigzag Theory (RZT), in order to enable accurate predictions for transverse deflection and through-the-thickness variation of interfacial displacements. In this study, the iFEM-RZT algorithm is implemented by utilizing a novel three-node C°-continuous inverse-shell element, known as i3-RZT. The discrete strain data is generated numerically through performing a high-fidelity finite element analysis on the wing-shaped panel. This numerical strain data represents experimental strain readings obtained from surface patched strain gauges or embedded fiber Bragg grating (FBG) sensors. Three different sensor placement configurations with varying density and alignment of strain data were examined and their corresponding displacement contours were compared with those of reference solutions. The results indicate that a sparse distribution of FBG sensors (uniaxial strain measurements), aligned in only the longitudinal direction, is sufficient for predicting accurate full-field membrane and bending responses (deformed shapes) of the panel, including a true zigzag representation of interfacial displacements. On the other hand, a sparse deployment of strain rosettes (triaxial strain measurements) is essentially enough to produce torsion shapes that are as accurate as those of predicted by a dense sensor placement configuration. Hence, the potential applicability and practical aspects of i3-RZT/iFEM methodology is proven for three-dimensional shape-sensing of future aerospace structures. Full article
(This article belongs to the Special Issue Sensor Technologies for Health Monitoring of Composite Structures)
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